diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/_ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/_ new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/_ @@ -0,0 +1 @@ + diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_bonus.ipynb b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_bonus.ipynb new file mode 100644 index 000000000..252ddcb5e --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_bonus.ipynb @@ -0,0 +1,97 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best features : 3, 8\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "data = np.loadtxt('data.txt', delimiter='\\t', skiprows=1)\n", + "\n", + "max1a = 0\n", + "max1b = 0\n", + "max2a = 0\n", + "max2b = 0\n", + "\n", + "max1 = 0\n", + "max2 = 0\n", + "\n", + "for a in range(1, 11):\n", + " for b in range(a+1, 11):\n", + " \n", + " x1=[]\n", + " \n", + " x2=[]\n", + " \n", + " for k in range(0, data.shape[0]):\n", + " if data[k][0] == np.float64(1):\n", + " x1.append([data[k][a], data[k][b]])\n", + " \n", + " else:\n", + " x2.append([data[k][a], data[k][b]])\n", + " \n", + " x1 = StandardScaler().fit_transform(x1)\n", + " pca = PCA(n_components=1)\n", + " principalComponents1 = pca.fit_transform(x1)\n", + " #print(\"{}, {}\".format(a, b))\n", + " #print(pca.explained_variance_ratio_)\n", + " #print(x1)\n", + " #print(principalComponents1)\n", + " \n", + " if(pca.explained_variance_ratio_ > max1):\n", + " max1a = a\n", + " max1b = b\n", + " max1 = pca.explained_variance_ratio_\n", + " \n", + " x2 = StandardScaler().fit_transform(x2)\n", + " pca = PCA(n_components=1)\n", + " principalComponents1 = pca.fit_transform(x2)\n", + " #print(\"{}, {}\".format(a, b))\n", + " #print(pca.explained_variance_ratio_)\n", + " #print(x1)\n", + " #print(principalComponents1)\n", + " \n", + " if(pca.explained_variance_ratio_ > max2):\n", + " max2a = a\n", + " max2b = b\n", + " max2 = pca.explained_variance_ratio_\n", + " \n", + "print(\"Best features : {}, {}\".format(max1a, max1b))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_ques1.ipynb b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_ques1.ipynb new file mode 100644 index 000000000..f7a7e751d --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_ques1.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 2. 3.201 5.77470148 ... -0.08273688 -0.31659793\n", + " 8.9705947 ]\n", + " [ 1. 1.066 0.51969346 ... -0.05726641 0.12953615\n", + " 6.40681803]\n", + " [ 2. 1.395 6.18460515 ... -0.21614335 0.6549017\n", + " 17.42562827]\n", + " ...\n", + " [ 1. 0.854 0.97744937 ... -0.14189873 0.35539943\n", + " 0.06023684]\n", + " [ 1. 0.03 0.09371179 ... -0.13902791 0.49674919\n", + " 0.07933982]\n", + " [ 2. 3.881 5.77286718 ... 0.84925859 -0.92115962\n", + " 17.13040574]]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "data = np.loadtxt('data.txt', delimiter='\\t', skiprows=1)\n", + "\n", + "print(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_ques2.ipynb b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_ques2.ipynb new file mode 100644 index 000000000..abb12730d --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_ques2.ipynb @@ -0,0 +1,624 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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qS2sYbRDLCIX4bywYd99sk48z8Zt05XlPza4hnFITRCGvBBt5QHuYsMSD3j7u44D03n5fbaNVHkHrRTcxkOcdwmiYcp1dbnclwjpdJaeBEOpRl60g2iCW0YRXz/sAPArgJID9AK4B0Afwicyd828BfJ9NXs7Eb9KV52EWMrHFpK7Jr0uwiRfp2Ng183icFzB+APr8/GQYhLEOiKXq/vuuTBXCaHCtQ8U6l1lg2EqxZatW9TX4UhOF0B2ixF8/urWBy2bDlcAAtgSh6lB9HBgzDJs2CgNL49Esy7KRCwuEcJp5A6uUuvTpTUj8sn5K5L5vUFcP5Wq0BkQdf/3oFvGbJH7JaLMhCKMBMBOXbDf6jrkL+tA/qOBsbKgJPnT8eeOWZHHZey4zOTSh41d16zLRvEPqEtGrpz50i/htN1wRqTuI5Aejy19G3rbzzti9LhZHh1OvkyQ1UEpj5pcV75ry6inOqI6ToIx0ZWo522yn7dWjEyjKLNxCWARG1ItuEb8l8yb9rXLpa7hDKpYlwx3qTT4G1RGwpF8taHwMx/YYFMlbw1JS6VJcZZRR6FZchZSeMzyofZwmZPtspwZd/cvYCKrmFaFGKCuObhG/pY5/0D8iH/S9fUo2GL3QzKsnwSbpmy2++P7cYUVVlnhAe9LJZuKmvnyPgeWBJ7YrFBmUHbcCAVeaMzxYI21Jf9rk52LPVzVDWRtBWya9NiEkG0O3iN9SRWK1aUc2KgTJ3nZWT4Y7Jgh8rGMsnORkfsv4l/PzPMBuPXlrWEr7fK4rBdOGNgumrCS0l7xZ1BS5JFvyqyrZuZLEcFhe42XbDmXPFpoVVH2nIXkVdYv4VaJNoUcrX5BK4hcIOVl1rXNY4mS4I4tjL1f7TIQHgGaPgTg5uUr8vX3SippIcjDQZFqYFGWoJLSXEKNsJdzJtl2q1cArogxJ1GEjKKauesH4eKchuMrm6BbxM6dvqmC9Gtt8NWAebvjKhF++UsdfSEbffw1cVhrKckSjcBnJ3eJaacc1XagZJZUlIUfGKyPpp+mUVXV8SHbTJAndQrjqc8wKfLzTKPF7Sj5i9ah05UPcvGw4zXXtzCmpqEYEDLt97aslJ3NxspIZoIs6fg1sudKGJEcd12ppIK/LNHWfLhLu2DvEKas6+SDtaZKEqv2V7dBBI6+Pdxp1/J5SaeIX3qJRcpaNuEIgN9GzRhWqoayRTarjX1xkHg45WXWt2qvH0qhado+CtuOWGCXT9HYwTWbzOMFKtdugfP4upD1tkpC1f0gSatPw1RbRq6dB4k/6W0eEqRrgwNI4oYqklamKZKsFWaiGykGuil8Oh/rD4j2GJi6lui87SqY0KmSGUDH1Vz+u7Bc2Ep4v0m6aJEKSUJvGrLVF54g/SZgXV5xQDnopj+IoJ/2ty5lkxK9aLYyFaugfqdY5XEQxQB6xUwIrA2+SpCol1zN0y4ySKY0slSlixYoCwVeQ+PNyQpDsqmJWnsMHZqktOkf8Kh99Uxr0jyxnkomLWs+afj9NhV7i1Hlcla+2IilrtDE4lW5ZHQ5HZY2ps2wnMtdRMiW9gotht+KG4IiIYNE54leRtVrlI+HTjD2U9oH+EfkO3w23T3oL5QbZXm8yqparu0VGlDacazQkz81Vyt/9xUzHjcXVsFv1OWdJSiyDrj9/qOgc8avIuoeTRgIYIZPElSd09bdKM7EyJIvkT6QOy6CIpCUNHyGRVKWLCdlZsoWUYJM2/9IDPUCJv6pJwqf2qm0EKvGarvT8eZ5taoOQ0Tnil+qscZR1Er80MmHWCxNsyoKdpXsAkuEOJZNYbbrq9Yx1TfpbJ0dWtgnNhT+ThNVeQYqUbjST568lOsOoTYY7sr0UQl1q0vHbkP7iwsm0nVX1tSB1n54gbTIsmrZ0lJnL29YGoaNzxM9Jwsn8lnHCm9+i1f3P956w6mBS8hJJ09Z1NIMyZpBobyjAWWOiCMUoXWksLiqNnjk/KuurGbXSQU3HlvdOLL86LxKfKvrkKHJH/4jchVYoUKeF07WF9l0oYDuBhCIRm1ZVZbR30bXUL7pH/MzSEWKUUjRkm2ep1N9nvV2mGkpXAQK5ChJ/GbW3jpCkRJAkE2ojqQorI2LdAHSOcZSNWptB7Vttos3LokI2toIqgdNE2PQD3TNNe0IwtU0Zsg4p3MEsoJvEr0CqBtCESdaMIJOxNNlwOw9oDwOnMnvCqQnVzyKOcrLhdnOeA/0zqCYwJVEmyZjBWBd2QkcwRoOxYtTaDGpV3rUcHWBRIVtbgQ/PIJt+oGufaatIdG1T9kCXUCT+UFZVVRGJvwCtOkYjUun097LwCqrrx6Tc4Q55zCBDZyvwuN1AERjdFHZC1fmVk4LC2O0i8eukyCqDT/osFhUyrRCLt1X1DDKRdxlvJV8QJ/2839lI/S5tEYKOP4Q6+EI3iX84XO6hBTdKpUGVNst7cCZSKScM2uO0d2AkVAqeQ7KYQSbJo9TSOMu0SqC5Yr2Gw1RNNmH3MOn4LQ2lVUhMWa7iwB2Zgdd0jrLSqD5wmwxM97h4Kxn7gQNcJsBiciXNpqXtUFYdPtA94lcdv5iTv8z4i03GXqw8GGXD7U7S2AC70p6kOf8uSVKvk7GBjKUxT9AyhuEcviQbpcto7pVUuNbkGlmGxHT5ageyJcvoCNfajdaD1KjKt85jFHUrS+v+7qEe08Is2Rm6R/y6npoP8g0bJt+yBXurPGFUJCzV8ZtcKok0+S3Hi9e6ghahMHZXla58S0hKEtOcI1Akw/n55Q3VZSaSIlxVUHVKjbJ3Ns2JpkxqE2mGIvH7GJvdI/6qPbVEGq56t+TrJd6Au5z96Hkw0Ow+FjqhavNXcaTVxAxlJXRTnspNZwXVkavqo+xAdiWDslJjlcFeh4qkrHG7adKsghB0/L7q0D3ir7o2LZGcDMa6tJge7K7bZTwiEFtGMvh/ltVHu27gsS0nSTjbMCcPRV1VEl1cOOmkc5eRm24glpEaqxyrWKyvL/K3dWcdDsu1U6iYFTtD94hfoeNXhkbwkLQ7dnXiab8/1stk4RiUHUBgQG2QNU1YiGR+y4QtwWaw1q731ojN5ST9pbHNfKYHlNU3r5KJDGyf1WbVYjvY65BUTXXLSV/2TG13hWwSvuwMQRE/gP8C4MsAvgTgfQBW6q4v7dWzevVYqykNsz7IX6Pjl5G0bmSaBtuEtJoowioLBlbl79lkUIZwdHWUDXZnA6RG7HF1awRYf+iOW/FGdit6AskOMLddtdgO9jp001Umv4jymDmJH8C5AHYBOC37/OcAtujuqbRzV+i11qEUbFJBSs/VJUZetxCH1KS2pAyXrOwomZppsPqQ9Pd8k5kr4SSJ247VUrYATYO6SvzSCd7AqOodykvuoSkcD78pM9jr8kZpSoLv8sph5nT8GfHvA/B9AFYA+AiAF+vuqbSBSxC9rIKnWbGI+g005SVjCqOgMxTbbDKzrSORm5eLkdgUDZok+sPJxNSfOyxf1WkK1rkw9umQ9kFs35+t/ty2D9Ul8TdF+k0bWJvGzHn1AHgdgKMADgLYrrjmOgA7Aexcu3at+xPnEHqQVuKfm7PT/2dKzToHRJlOr5X4Nc++TP5LTuXpSEvWOL535CZJGhbAap5WnWesmbx1KpgFnFD3DU3bFCVv06Ywmf7c1CY+ybJJ8g3FpbLtCIb4AZwJ4JMAzgYwD+CDAK7W3VP6zN2CZ8gQN0v13EPcnB2evjTxm2yAy0IzWA0Ih9kiSXh8J2y+o1hxn9YFUlFn2aCynciUA1MRoVNl/ygbg8dV1TPoH7F+QJu8pepBg8S/atVyFfp94zk7pSdEXwJJk+Q7S5uomkRIxP8zAG4XPv97AO/Q3VMqHr8s/g2O8gbclem1l7iHk7wBd/EijqkHoDDAlw2h7ue0Oseh14mdivsmJotC2ASd+mKs7hbsoZQGFfF6klXXyo3LhZDMtnA17sq2Nage0UoFU1QPFnT8tmooXxNhHWiSfKPE7wchEf/lmUfPIgAC8B4AW3X3OBN/kmQRMuUDdvw7w1GM2QCXeQRNXrukqo46lHOhJ48IyeRuqp1l1KymIuzcD5uwlMYKEsvVTTTFYjSsKVWllRzJzhK/UIxJhWEl8RtWECY1ju0EVQa+pP4myTfq+P0gGOJP64LfBPDVzJ3zvQCepLvemfgH+l2vToRhqSMHUrVSDnHwKaVs7Bob6SZ1jS+GKBKDcN66ulzVBFUkF1dGLvkcugmsbCC4/BFNOn4bAirjbuqDVH0SZtPk22WvHl8IivhdkzPxE1kRtSkt0rHRoSmmiSQlyk3M7OCfjVNjI91koPXKELw8sEyTXpGgtYSg+rGGKGIyYkiSVJcuFrF6tb0qR9TB57F+Vq1aPpNeDPKqIybX+U9GqmWIz7eUHsm33egW8Q8GCtWMXq0jXtfHwTGJVz2RLE2oLaz9s2nP2EgyuWQqGaIEbCansXJtJqiBkLmMkWsWH3W6dfFgEJ0rarF6qhWEaWVhO/mLqUj6ZZorGkUjRHSL+LNRI+qUdXFvgHQj0+ggdYmB0rjrV2AWm2W+7KxZFSH1cWBZN97bZ2UQNUlqLp4r+VnFeV7KicJELjWLj6Zn0qlyVO9MpaazMZLLVGq2B+eUldynrZePK4Kw0S3iZ+aiC4tOVTMW+VEVxx+GOD+CO4Zq8IkHdMsGSJLwRMyceTzOCzjhJPnZSIumySlVc21K3UCLZwI47NidJkzPJE5MSVLeAGtbhgy2gdiqRPecll6+aRtA29DEJNk94mce65EqVU0PJ8cJvKxVzlYHLkIiEhYPh0n3F0hItrdP2YNspD6tbl/I0kUtMlaNBnq5rcSfV89GFVNF4i9CtdKQbdKqIrlPq+ld6tj1lUFTk2T3iD9Jxtip1gBtgDTCprajW+ob1CuVU/KjGoc7WHmQPOmLd5E88wEufb5EcrqZKRqmB2aw1fEz26m6yur4VY/iSpShS9O2q5I2PEuOuiaoplxjvRA/gN9xud5XKkX8kpauFJJ5ONTrBnK3DxXTWNRPllQrleKEkH5esju8JYNNJy/TYZ1OBcsrIjBDgo3cx8HRBCaLbKl6jn5/0qtHdr9uQiu2h6qdXG3Yruqb0KVk277R5H4AF9Q5QTVldHcmfgBvK6SbAXwn/6y6r47U+Alc+ahz9dErbMMcG8iWE0+CjRObv8rsUTDNQyqUGQzamEjSGwbCruhTLIsWurAgn3tXrZqM2VPlLIGqqhRdvrbqtWmh6sRSddUYmqdRnRNUayT+LIJmkoVU+LksHcz/V91XR5raCVyye3LGcfXNy1OGJGFeXFEw0qpCBRdG0pjaqLdPSoyOc5ATXAlCGwVVlj82GXdFuybTgCor3Znu05Gcya4gnhNc90TgS7qta9XYBOqcoFqj4wdwOoCbAPwpgKdl3z2sur7O1KjEn+vuy96fQRUPf2yTlBg7AVieiPK/mQFYFY5Cl6YpXSkPpOkfkV/f2+eV9G2ft44NUqbfXRaOdRLDNMm4LTr+utukVV49AC4DcDeA1wPYbbq+jlSK+Ovw1SuTct9+nRQsUx5rArQlG26fUP+Y0qC3b7yn1dgLZW6pujNuiyGhfaS6pEmTVOjDaB7Cc/hG6PYK5vZMUC6oZNzNgqn9IoDE5nrfqdQGrqKx1ZRM1r6KDKTd+TsodC4Lv0RRcpysniK8tOh2YutXWAJFH3mTcbbKgkqWmpaUq6g/pkXEbVG/TBttmKBc0C13zlCkfWHkJrRZq8ceIyuXnUgs3Q4wdg7BhB1BZf/IFdEVUEZqkm1qKqbhUO2q2eu1UzduYzaqK0zzLEq3EZPoFvGXIOhKrp66fGnzKN8+DkgPfBlJW7k6xmUnkgplVymm7cUGuEqSNgQo6seLc7ppNVEHfEmFYj6q11VnfH7X55g1abgLiMRvIH3j5q5+f0LkVE0WomtiUbeflqPR96t2BynEMuVg9KE/KSECuuqOTdWsy9MkNITu8hhXCO1EVR3/aQB+yObaOlIdqh6bAG4jjxtRN57lrZoshrjF6JpoLC+PCZz/kNsrCiymHYy2ugRTKrqjGNjUVeI37Qx2IZYkmdy81RaCUrVbYUN4Y88RbQLtRBWvnn8L4GsAdmWfLwbwYdN9PsoDU3YAACAASURBVFMp467mJG6b07SATAKX6RKShAfYLSd1esKCT0+5hY9QOHgrB2P/CCf9rZVVV8uTo9upXC6SoS9CSRL9cYehE5Ss3ebnzZvTfK5udHmFviIJBaGtNqsQ/z0AngzgXuG7B0z3+UylY/UoJH/bQ1pUfuc8GGh2z5pdEwe9fdVsCtnoV0vLDhNLrtMvGHyt1F8KNnXp/DLCnp93HzAmlVEbCKrYbqaza3yqX0x5RYnfjBDVYVWI/7PZX5H4v2i6z2cqTfwKVYdNyAPC0vI5tEUCI/UJX6a4/8BSGlBNo4YZ4uaxA+GHuFk64tRkpzAei5vFir2y0F7asAse2VS2OFOFlygjkbaZoExStk8yttl4FhqphYYQJ8cqxH87gE0AvgjgmVnMnj8y3ecz+QrSZiK1YlJ29MGAh7hZargd4matGmnkpSF67wg7c4cL75QQ99Ik+RMp5jb1ioOwpBfFBWbVbjjz2KNtB0tZiTQnyjYSlKltfKpfbPIKTY0RGkJUh1Uh/kUA2wB8Pku/DWCl6T6fqRTxa0TABBu1BCly8cSgy9Q0RXInnBqRcxpdctJtM6+SdNBko0q1YujhpHT0i4OxN6dfybjwtJJ0igbvirAdLDYSqUrH72lf2tQxTfVLiNJq2xBiG5YifgA9AHfrrplG8i3xM6Al/pTUFTHtM528alIoTjAjt87J2GvLvCmMcHW9lhQ3L+ehPWVMEzJBhiThibAQIx1/r+dN3LMdLLYSqcuO4bowLYPrNHX8qvoo1aGenrFNCFEdVkXi/wSAJ5uuqzOV1vFD7WuvOtlqDk+k9ygCoeV5ySeFpckvAXWAtkFWV4H9VBI/4Ynl5+gfmexMg4Ha7jB3qtyAxCa5AdpjBDRbHX+I0pQM0x7805pk8t9NHsKuz1qmvUKeKEKrWxXi/xCAvZmufxSf33Sfz1T26EW1r/3NUlXMAk6MbcJSSbwqgu3jkHQ0KCeKnD8FcXaImyfqBSzxCnxPPziI1J44qs5n6KVFl9Ahbh6fCPpb1VZYw2gWTRwTk+/cpPdqiNKUDD4mqNDII4ftnkAvKkVFHrZnFjOH247TRBXi/zlZMt3nM5Ui/uHQ+vQqYIn7ODBBbEPcIo15k2Ajz+PxiXzFiUNMynNzB1ldC72/6NWzGo+ZB0eWx8QKx/LUq+IISpLJCJvS4G+yIxUNo9l1b5npSMOQ4HKylwwuxDZt2EYBcTFmuhhEk0R9fVH72BZBoW4EFbIBwFMAvB/AVwF8BcCVuuvLHsTiclIV4YlJYqNjnGCT9AYlmQvujiojL1BQZ+h6NCxWDHkeHndOWUt32OWskLfNW1KtqaHsJGPzbDoJVdV0Iai0mpb4TeXHfQeTqCLx7wLwcDGZ7jPk+R4AP5/9vwDgKbrryx7EYuu2qe3Ec3ulP5jcHU27gyeCb2kqoTzYZFDIw4WtMtXQhA4/I2dr6S4/T0CEYdSViR83KmIKYn8VadF2NeNKbCFsQKuq45e9Ope2tuk3Pl1d27DCNKEK8feFdC6AXwLwP0z3afJ7cjaZkO09ZSV+ldulE+GIfut5Wljgwdxe9UShMbTKOmCS8JhKaUzl1NsnjdkmHRwOPVV5IPqqa63qP3pemcRvGM2lJX7fbiyKtqoqLYpZuxC5KW5RCCjr1aN7dbbd1qbf+NrcNiuqIq+qHgD3lLkvu/diAJ8D8G4A9wK4DcAqyXXXAdgJYOfatWvdn3g4ZMakztu0wUpKbMUv+31OhjvkhuMNX7HqoKL/uWkT1vz8ePAxqZuiY09VrSJyu8IFuG+iHtY6foNfpayqlB2y3scBXsCJiXr1++lk5YUVDW3lcyOOD1WGuAHNxvMmRCnVl9HbJYS3aTjo2mpWVEVVJP5LhbQewH8CcL/pPk1+6wE8AeDy7PNbAfyW7p7S7pxzc1J1hk1kzhGxqWLoJEl6CHouqdMeq8icab7HjLpI7f0yPnfsqeZls3w/QW/ulN6rx3ICGht02aa4/HrlBjhhk1x+3ejdDhxIrqR9o8ygd5mPpRMiLW9AM+VlW1YTk4OvyVT0BjMZwXXPaWqrEHfhlkEV4r9bSB8HcCsqhGgG8FQIZ/cC+FEAf627p+wGLptAY3Jd/JJewhR6SZIwD1YfMk4io3xxIDUYZyh7XsoECTn21DITjlXHL8OakrrrPLLyybv43hRBTK3KEx/O9zLfhWirSKE2TS97Nut2q4A6JOgqE5iPtmwDqhD/D0i+O890nyHPHfnkAeBNAG7UXV82ZINVoDFoXCANa8ukv9XSLXFpfAOU0HvKEjCwNP68jj3V1aXStuMPcYs8wJxuxpDUXWeLGWCXlQ1CSdYWbeWqUvGxg1UGF3uBzdxfxeuo6nOEpDM3tZWP+oagdqtC/F+QfFdax5/df3Gmv/8igA8COFN3fVmJ3yrQmKnXJ4nyWicDqNizcpLJVgxlCLiHk+PPW6Knistm1eQylh0d03be1KyiCDCnI1VJtFLVzun8/dka6aVHF1Yc1VW9W2zh6iFkM/fbrjDrkGzrJELXvG1XR2XrG8pE50z8AP5vAFcB+CcArxDSFgBfVt1XRyqr41eHXdi9/JYFUS3pb+VB/8jki1b0EhvyyVVLUtdJUV00kEiPOCUh0jydWn4GjeXPpvNmdvAJwt6Auwp13jR5swBVYLseTo4KVg6I4Y6xiibDHWqfduziQW+fFYEBisFWYVTbrtKqkqdNOeJ+EBuysa17m3TZZUi2bmIORVVUhvhfBuBdAA5nf/P0NgA/rLqvjlQ6ZMNwhzrQWOG0D21HSBInPXQPJyeMyUpbg64naMoYW0UoeqxL5x4Ol4lbdwaADmoiWVZLqQaE7Iz34ZCZZJ5E2MTDC+62Jn7fg62OHaxlyykeWmOjoiq7z6AJ2MzPZUm2zhVIKMbhKqoe7a7aaaSyxM/MnGy4XX3SlaAHUHWePh1S3m91SpWBvBNsVHe+ft+6DFkvtx4QsiVHMXJar2e0ACol/p6NWqnQ7pkHqGg8F9tfpwpSPXPd0pxv4qlrZSGW2++bj3dsCraCSygkK6K1Ev/oAmAlgF8E8A4Ad+TJdJ/PVIX4lWyUp6wXqaUrmcQ5Tv6pS+fS8uAt+JypVUKSIxKL4Zrn58fURH0c4D4OWEXMtBoQKjcPU7tJgq7JDjoHmDdsKGfHEA++Kf7ouhHPJ6HZSs26cwBsSM22HBXB2Uq0IRghZbAlz1BIVkRrdfyjC4D/DeC3Ml3/zwH4GIC3mu7zmSoRvyUbuEij0k1dOcMxT4i3rsc0jnVYIS9XlZHVgChc5HQWcJaRnKCWeA5PjALduZL0WBGFGSzBRnX4aoN6xBcZiGSpPLRHU5bpPF2XcmQG7ExmGLuuzFnGTcJWkg+FZIsIYUKtQvz3Zn+/mP2dR3YO77RSrRJ/NtpU5CXteIJX0ARRXrBtIiMVYcMm+Bpz2mP6fbXKiPbIdfyK3cXJcMfyRcLoslYrFSqqnGCyCVJ3ML1p7wPReAG6GEiLi8uGcV2ePmDrZinToqlIX/ruC2WqThkr7uZWlSH1cnJ41mkSmK0knw0PZVt0GVWI/3PZ378DcCGAs1AxSJtrqkT8cpcV6Qgdiz8/t9cYgdOFKGWStNLraCDUX5iRtAfAyDAYyCV4sQCLlYlyhZPlo5TMsgnSZOPQ7XZetWq5DXSSfjEsr87eUBW2Kph+313FZVqR6CYNUcrVlVH1WaclTZdVh4Ug7YeCKsT/8wDOBPB8pJE5DwD4T6b7fKZKxM8sD3JeJGPaM07YmW5dR+zORFlghWS4w9xpbYh5oHhum7Wy1cQi2fdgEXRNO0EunEzj/GS/p2Gx5fkkidxDq/hINlJ4nl8V2KoFV6+2u86lbraqLF/E37T+vKwBPBSvpKZRmvhDSJWJP4cgLllJ64pYP/nvpTaIFXqmcRltUsXopBud/r63L/XLHwxS20Svp55Y5vaqt6ZmKyWb0BiD3r7x24UJWbtTd2BHtrZullUlwrJhNkypqIaR9Q1TO+Rzui9VT4geMyJCr1/TqCLxfz/SYxf/Jvt8AYBrTPf5TN6IX+gl1tK6SqnqkkeVnqkj74GBwARpXhmTCAfGj5uUEbgsAqck/1G9Vh8aSfNatrVYzeRN5Ztsq0iELo4AtslWhSEL0S17riSxO8u47LOGIlGHXr+mUYX4/wbAv8sjcgJYAeAB030+kzfiF3qJlbQuc3IukLCUKBe2WK/JZVLdmJ/1k7476b7pIrJmmemIVZTOlSsccSRleSqv7fXMwWuSZKItVe/EVuJ3SVUkQt86fpkxUkdoRWOmbvKoapQNXYceev2aRhXi/3z2917hu/tM9/lM3ohf6CVGaX1+Pj312zBqladY6X4XQjXIXOg1c01K0htud3504zxkWqUUolcZVWW60SfkIbbNBtw1Qf7iYR22XldiKuNqaQPTJqg8nLLMq8eGjG3NM7bEXmUSCMEtUYfQ69ckqhD/p5CevvWF7PMVAD5tus9n8kb8zCNJU01cm4ySvm1SlrHhdqMUrk9LVh3cxgd8RCgmu0TOlJkoatyNrFNHZasF6UE2c38kj5eUP48wUai8rsRJw/r0sorwfUi6qwpDR36ySbMQK7CViIRvRhXivxTAZwA8lv39OoCLTPf5TF6Jn3mMmCekcebKeoU8X5VEOqA9zCi3+9SWWOSOTGoJWSvxS/wES+9GZmZtyOz+Ef27M/j054HtCvZnLSn6II8qumaVuq/KAS62exrqmgjrRlTx2KFMkLa1wv8rADwr8+OfV91TV/Iu8WeMqIzDX5H0TadwmfzbXZKMWIRHlJT9xMQEsLjiRPrsNvqIbOlQaTfyYKC2sZh074URb9ynYJ9VJfIo612iq4PtpKQidxeDeNuModGoa4cyxP8F4f+/UF03jeSV+LMeo/RyWXlEH6bAkGzIXOffPj/PvIB/sS5SpvM1TToTrpWFkMha5hMI12k3MpbGlOID7LYauFLyMz2opeXWJ3mUzctHHXx4PLXN/TF0N85Q1FBliP9e2f9NJK/En/UYWy8XVVJ5tJjUN1L/9oI+PMGmiaBsSrXRIH0sW0+TkUqnYKyduFC1711gqiFunjhtSxtWQmy/uat5EYWQ2a67Mm1ZUzEKfZJHWR2/jzr48Hhqm6QcssQfkhqqqsQ/cQrXNJMP4h+N/YyoVZLpBEEWRyM0O1GzkA9KqSo/LLzEaRkJNk6eLSDcZrXBKZP2x0aI7kZZbzV49Qxx8+T3dEwexqK/VSsVGQe3zQjTXOOLPFTGU110TutnrFC+sh+UNEKHIsXmdQmFXIsIaVIqQ/ynAHwXwBEAT2T/55+/q7qvjlSV+KUDw2hYLZyTC4x037rQCSbJe9DbZx5Bil6dDHcobzMv99MTtcQRUvSSka5yJP77DKhVNUWvnnyHsKpimpFqHVa65OwRwianJPETRVPmNlrVdlDM3wfR+pw8QpqIRISkhup0yAal8cvy6MRchbG8WtBE7aT0CEf1NYqAakWoerXwvXhUpFUQUuwa5SUdyDIVl6ASSua3jNrBJnLpiBkEu4pqT4PLe3OSnDSj0BfpVhnoviYfVd6+iLGulUkoUrpPtFriDylVOoEr0ZOhLtqheoJQ7DAV1ENWkTfLPIw2BIPhOQQiUnbOooorq7AsHo/0/t4+6WSVzG9xO34y0cQA6m+1ZwvNKPQ1QKuYGkIiCR3qtEWE9qxVEdIE10nitzF4jrwX+0fYZieoKi3gxOSxjBqdfCkII0fpTqmR/EXduFXICjECp4W3ku750vaV1Am7JtmjMMFJ1VEuimnFKPS1JLdxyczzLV6jastQvFNy1Ol9FNqz+kAoaqhOEr+tt8NII7H6UGni7+PAxJdjpNU/Uv7lJ8nE0kTnB6+VOLIflXYKmcTOOrXYklXn1sbsL7KH7YuT2R8cdmn5lEBlRdgIHnWFlPANH1JsVyT+kNBJ4nfdwCJTLdjurjWFPEiwKT003EUCkBB+nkyx+ZPhjuws4MzAmp+6pdnHUEpiN+20ze8fKNpdOD2s6Hll3E+hcUlN5rcoQz+IzVvnktxF8KirDj5RVYoNSQXSFXSS+F38m4k4Nfhho+A3v8Sr8Bgv4IR54hD81IvqCZmL4/x8yunKQWQQF7XErRthhTwmdi4rRmGSpDt8x7JcccJ60EqrRMdGE5KtsXnCYyiZfNEuk1qdS3IbwUPU9TetFpgGuvSsIaCTxO/ixjkYMPNgwEPcPHHNPE5wf+WR0Yaq4kQg+qmr48foCUB38paO/EdSbf/IctgFnf7A5P6jYUjRqyeVyDeNs5fF+1ANehtjs5LQC+6izieV1QTTK4zSbkTdCI74AfQA3AvgI6Zrq3r1jMimt0++wSg7gDwZ7lBPDEWJnvYwYWniyMYq8XfG1CY24mK+u9bGNQkw5qmNqmliMdsdSwoYbQDDIQ96++Tt1ts39kXpOECeIRU8aHkSiqQfHmZtRRIi8V8P4E/rJv4xqIKzZf7kKj32iICKX0qk52oRN5cmdPHa1Ou5hY8eDJT5KqXpPI6P7cRScqTYGP7Uk8PSGMNOU+I3EcWsEUmI8NXGKu2qKnpJGxAU8QNYA+ATAH5sqsRvIDAdacu8dmSpasTNRRxLVTaieOgj6U80sY6xI04UxpO6HGBj+NNODoWNbYsLJ7V5ieWWJY06jJVxonCDz3ego4e2quVCI/73A7gMwAtUxA/gOgA7Aexcu3atn1YwGEx1pF3001cl88aqJamdYIzIxE1UVci/15MyyJjHT0baOr9+G2P1yBAr06dYspmN9OwSo95UZFXS8O2eGL1e3OHzHZiGWhvdToMhfgA/BeAd2f9K4hdTHSdwKUl7QR5PfoKQDeRvCmlgc81Yj3NxUdIwhsq7RnWaVR8HrI3Vo5AQxgLLs5lPibgqafjekBT93N3h8x3YmLHahpCI/3cB7AewG8A3ABwHkOjuqesELlkarnq3+sWbjicUeogyZIMY1kGlXilOMKpdWaqkUUqqOreM4Bdx1Hi84UT7FMudApuVnQxMESxNefl+NF19otQvh+9NeNoAiyXybBrBEP9Y4U1I/Mxal0adusck8YsqEanbpyQWv/bA8mKPE/f/y1KvZ2QInfeMTG/vYqyWbuaqeZ9+lQWFzSJKl5dv1cws6pjrhu93oHKSa2v7R+Ifbw1l0hHdyOiqIP2JTVp4nPs4oN6JygVptX+Ek/kt5h5Xobfb+MuLz9SjJ5S8bVW8conRr/gSDc+Th7/WwHYRpZP0fKqeZlHinAbqMIjPipE9SOK3Td6JX8UWREqXzn6f5ccsGU70Ksanz0/UkvrKM9v3ONl1Fvcqd9BKdsgWT8gS04YNWVFYWo65r7LIFmMfA95iD2v9/y0mQ7HJVM86Td2uxgTVSh1zRLPoNvEXCVF2SkW2AUkpTA93yO+54AJm6FYKp7RePt6WkEliHdh9Yn6QPJvKRjEmfdquPFQbzDyIsMYVjEMZoRhXQ6lHRPvRXeJXkVN2mpbU3VEmOGtWCTqJ3ybUs5cBrSLXhYXluue2DYudRmSoN5G8TSYOck+4Vj2/9PWKdhKHMkJxpwylHl3DrKh3RHSP+F0MoUWLjswrxiLcgevBKB75z71QA5OYokAMBpNtotz9q7KNmGY8F///wr6EsrNqKIM/lHp0BabJtq3vo1vEb2u1yyV/mQ5aTP2+VTycBBu5B/U+AC2J5vUu27uKUrdsV60l8arU8hMDojCxKu0c/SPuImzhHY7FR5I1zZTF5LYSwbTRlnbSqdfavALrFvG7bHayOaw2TytWjH+WrAJcY/VYhVIWkSScrLp2mdjn9qY6+mxisnYRzevv2Hxjg7dQZ92zj0UPtWEAoRLWYZYdWKYKIfkigraQYlm0iTB12sg221y6Rfw+Y9wUkxhEX2Ik1sfqWeL+qsflcfhteleS8BBvnyDYRRzlZMPt2vKlexAUPddpI5HAXqromaUGvVAJ30HXqhKSDyJoEymWRZsIU1fXNh8Z2S3id5H45+bsr5X12oL/XYKNPId/kd6qjVps0buS/lZ1uITePubh0O4sXQPLmCR+FWw0bNaDXqiE7zDLIYRqcKnDcLi8MO31KkW/niraRJi6ibhNE1gR3SJ+4S2O67t3j6s85ufdVD2qXlvoGaowB9qOoupdghFav5o4lWUh98YZk/gt1CAuj1+8VzdxWA964R36lvirEpIPIrCtw3Aov260jyJgNVHbCFOlemvz6qxbxM+c6sIlZ+imm5WyzUYFg61yo5VoIJVJ/A4HocvqORohKjbIeplOh677rYx7Y1XXey+DPmubBJt4kY7JmsQ1K+MBZbZ5TevgcVu5xNN+uLFnrDqxtJkwi2irPaZ7xM8Wg0sgWpM75iKOpuEUxB2yCrIeYLcdsdh6H2U3qw+KUfncL5V2b6w6aH0Pet/GWMXcWqo+/T7zqlXLedkc3GHbPjZdQyzXB3y+u7YS5qygk8RvXE4LYq3NASqD1YesWCTpb9UeLD6Ciy2CKC26EDY6lfQtwzs7jF6brQ02eYQw6HVaNB+nNpWNSGHTPq6aSB9om4omQo1OEr+xAwvMZuOGSThld74tUXrYCe2ZON5xbHS7eB9llZaRhVIt46DXFzN2OcFKkUU9RF+ygKo6fV2xZQ3htuWJK4lpEb+uvZqczLtadhV0kviVS1bJObJWEr/MJVI14m3EJtU11qEvNRKneGKYiYEKmWg3Yrm09cJJN999Ux19RyQdlCsWWF4B+Q7uJitvbm7Z+azXY37Sk+Tl+VL1qNqr329OZ9+kvaDNtopOEj+zZKaWBVuDpY7f4uhFBvSMIIpNKpLXxBEqQjlI8zOCbXqopXF67DB4y3qMTZZ1BKu3YG9ZUFWbjcMmTdzion4BWIaILbdzTEz28/P+iEhFdLpnrVsKblL91GbVV2eJfwKa3mvl1SMTe2Sj3UVsyllJNXo060xtWGLb0Vi4Wbf6WaRj0iy19bAZLaa1dEl9jYzEskCsTvfoXrdP4rd9TFfVg4/rTZrJqlKwro5N7glo036EIrpJ/DrXizIp79kqxXoxqpOL2KQjRM06UymN0B770S2ZAHWrH1lVrSR+1WgRnnFs30X/yPIjlBS7ytzmaHPX/uaKOqRLVRdyWFhq6+ajniZ1SpT4y6F7xO8ituVvsUjoq1axPL6CUIZu5LiITSqWMPS6JOFJH/dcLaXrmYb20R4Gr+fuyXpYzhramDwlFa1lpDVXm7tPYiijljLBgymJme2GVF27qaOOvxy6R/wuYhsgfYu1WPJdWcImlAM2yaNx6kahqX16Pfv9CHk9xPayPUaSefS7cYduiReia25VdrZdR1wA+gra5qqWskEJ5zFtHevwZLKZoKNXjzu6R/wuvV2ijE2GOyrvFpXClSWqeAfpRqGufbKeXbkNbEdL5qzuOyZPXgWVmkP1GlxUIyIR6s65sUFdKgVX1ZUNfEvBbVanhIzuEb+r2CZiOLSKD+MkBRTtDToVUvE+0wgrMwotR9pUJJ2sbN8xeXLInsFGtWB67rLkp8q7LiOiaiVRta199o02q1NCRveI31bHX9ySmunhTdKntKOq/Nar9mrdCCsrclrUaWrLWxsdv2dMO8JmDl2z1yn1Ft+l6thp2+5TB9qqTgkZ3SN+ZrmULRN3JO4DJunTyovFNKL7/Wo9vcYJZaoSmI1Xj2f4INkyk4fJ5jBNqVfsmr4NyhFhoJvEL4NpxGcjQCp9Cj7sTn7rtvaGukI81tBM3jFlcc+VZMuoi2QwTRYuzeCryaJ+fXYRiT+HaeQJo2Di7Fph16qVxJ/n62Jdq1vkbD7rYGBLnGUMxCr4IlmfqwMLO39ESxEM8QN4OoC7ATwI4MsAXme6Z6oSv86nTmAKaSAzld+6hb1hbJIZWA62JiT+3r7OMUEZl1AVfBG2z1dvkkui2scvprm4DYn4zwFwafb/6QC+DuAC3T1eid/WS0bmt1e4L5nfwoP+kWW/9bmrx/MVA6gU8xQ2ipU2ataoFJZmnU9sJctoq/HO9+rHRzv4rJONH0RU+/jBtO04wRD/RAWADwH4cd01VYl/YqDl0Tll3jeqEakLtZAk8t91QdmFHmDlxqiqW40HsiZJKuFPbAwrrpAsWKzN7noh6sB910lns6gyybmU3TaBoAym3ZeCJH4A6wDsBXCG5LfrAOwEsHPt2rWlH9yacHQXJol6ROTXqX7TRerKSNu4ccmnktkVpiijluW7dviQyCDESauuOk2bmEJs2zoxbdtZcMQPYDWAewC8wnRtFYnfuiPrLrQwzk4YgkXpWNaLXSR+Vfk2h8dWZdAy7SJhCZcOryWDgp1lpGor8WguCGkiylFHnaZNxCGupupEpyV+APMAPgrgepvrqxC/1u1SHC06Yje4Y0p19Jk+PJ0QdjNhaXxwFryHtDp+l/ATIpvq7BJY4kFv3/LB864uLUnixObWHT5JeNDbJ7+2f2TM339am726hmlOcl3wHhPRWR0/AALwJwBusr2nksSvOKB85HaZt7pKeu71jBK/SmLv44CanAo9XuvVoyo/P5ZpomD5mQAJNvI8Hh+7dB6Pmw22KiZwEF+sOnx2kVL1JeyRqCu8Q1cQygqmaxI/cxhePZT+Nj0Q0Y8A2AHgAQBL2de/xsx3qu5Zv34979y5072w7dux/TV/i+tO3oLjWDX6ehHHcCuuxWa8L/1iMAD27FHnkyTAq1+d9kkJ5nAKjDnJL4x0nhvHYADsxjp5mb0esLQErF0LbNsGbN4MbN8OvOY1wMmTk9cCwKlT498vLAB33DFR57NwAIdx9kSRfRzEIfyrrGK7pc8oxfbtwHXXAcePL3+3uAjcemtab8nlN9wA7N07/ngjrFsH7NmDddiFPVg3cf8Au7Eb5wFQtzlR2nwRaji+tiDqcvLkSezfvx8nTpyopR7HjgHf/nY6lHo94MwzgVWrzPeFgpUrV2LNmjWYn58fcY+KsQAAELJJREFU+56I7mHm9RM3yGaD0FJpiT8TJ7T693xdaRI9Skj82nj2Nj50okis8ipSSf0SHbyqPsCSUDFH+BRfdLumFzmNgxQl/soITcq26UIPP/wwHzx4kJeWlryXf+gQ8z33MH/+88vpnnvS79uApaUlPnjwID/88MMTvyEUVU+ZVJr4bXXjqk1Wkhg+sqTS8fdxQD/AxB5vMtSW0fMXnslI/FV8Afv96nF2lLumd6cuuMLzRB1/ebRRr/7ggw/WQvrMzPffP076ebr//lqKqwVLS0v84IMPTnzfTeK3CZUgBloH1BEuDRL6BFH1Xu1GTg6hJMaSacIQJpf+3GHppX0cKM+aScI8Py9/1oWTblnq2lh0rW3Aq2eWEJrEbwMZqfmCjPTz1CZE4s9h8r8fDNx84U27XMbYNI28mWBTtglqSU9OptHowZc/SdI9ZeKlCziRqlDKsmZWb2+qF52hPWRmahHa6DtfJ/HPgsTPHIl/HKbDzcuKP6rdumJygc1olChDk4Qz98zMfmEgce8eBdlKxevpWW3URbQMoXj12KJO4rfV8a9atco6zze+8Y184403OtVDlf9rXvMaPvvss/lZz3qW9n4X4pe5oswW3vrW1E1AxOJi6lICqL159u7V57t5M7B6tfr33OPGBrm7y/Hjy/cNBpOuDZs3p143S0vA7t3Yjs247jpgz+HVYMxhD9bhusffhu1Qu2YUsqjuxbF2bfoH8vbKfi6Vp5/MImTw3g9Cw/btqZfY3Fz6d/t25aX9fjrcFhbSzwsL6ed+fyo1NWLLli246667/GYqmw1CS5WDtKnEG90mJBu1gsngalu3kuvuIHS1PnX8Yp5t00VEOMF1xeEk8dfUf2QS+Yc//GF+7nOfyxdffDFv2LCBv/GNbzBzKvFfffXVfMUVV/AP/uAP8q233jq6581vfjOvX7+en/3sZ/Nv/MZvaPPPsWvXLq8Sf+OkbpO8RucUoWLO3COm7P0u7FuBvYPRiPjy6inm2SZdRIQ1yvCyE/HXJBHJiPlb3/rWyNvone98J19//fXMnBL/RRddxMePH+eDBw/ymjVr+JFHHuGPfvSjfO211/LS0hKfOnWKX/rSl/KnP/1pZf45fBP/Cr/rh5ZBpc5htlv7btsm31i1sLCsSipbB5OqCanmQ6apmrpGZPNmYPNmbAYEJZNGDeaQZ8TsIddqijh+PP3eyyuvMKZcsX//fvzsz/4sHn30UXzve9/DeeedN/rtZS97GU477TScdtppeOELX4jPfe5z+Pu//3t87GMfwyWXXAIAOHr0KP7xH/8Rz3ve87zXTYfZ1/HroGJIIq1OcITNm4F3vWtcGdjvp7tmbXtwBX32tm1680XEbMBBXd0K1M7LU7QRbd26Fa997WvxwAMP4I//+I/HdhYTje/aJyIwM97whjfgvvvuw3333YeHHnoI11xzjfd6mdAN4leNHBVDMqfihw02bwYOHVpeUB46BGzebD9YK7D35s2p/XcwSOcqmT04ot3IQxrs2ZN2rz170s9Nkb+PSah2Xp6iRPTYY4/h3HPPBQC85z3vGfvtQx/6EE6cOIHDhw/jU5/6FJ7znOfgJS95Ce644w4cPXoUAPDII4/gwIED3utlhEz/E1qqpOMvKBQTbOQB7Un96vtHJsM3eFCUO+swLfXZZdXes64un+XnC8KAn8GXzbR2HX9eiOdOQUR87rnnjtJb3vIW/uAHP8jnnXceX3rppfz617+en//85zNzquN/9atfLTXu3nTTTXzhhRfyhRdeyFdccQU/9NBDzKzW8b/qVa/ipz71qbxixQo+99xz+bbbbpNeF427Ikzhj2Xn5FYcWcrB2j9SOk/XwSLuNSsagWfJQcbULm2fFIIx4LPfSahWr56OIhK/CGHkKHeX5iGaxVSBIbRnAJTM12XQ2cR/m5VNsLp2mQWv0JAk/iYnoUj8ZsQNXCIExeFeyJWIE9/3+5UU5UodJvba2w4KcDGIybwmbPNrG3TtovMeaQvqVle76OzjvrrZwewTvzBylLtLxe8XF4G3vrWSEWvbtjTmv4hFHMM2/FppxnUZdDZFzMpg1bXLFL36akOdBnxXw3H0IpshyJYBoSVfO3cTbOJFOja+9F84mca3KcS/qaoiSPpb5WcAlFyju9TJFEeubeoOHXTtEpKaJESUaZ+mbCZR1WNG1PFrYNNxvRBGDQpm20EnKzrXzzZl4KyTMHQROaah42+rATkkw7EJkfjNiMRfEd4GRIOMEBIZNWlkrbsd2mxAbtOKKBK/GZH4K6INAyIkYjehDe1ZFm1+tjZNWiEQfxNhmffu3csveMEL+Pzzz+cLLriAb7rpJuX90aunIkI3YoW2m9OEWTCyqtDmZ5vlnd+zEuZixYoVeMtb3oIHH3wQn/3sZ/H2t78dDz74YOV8I/FLEPqAaJub4iy7Abb92WYxLv80BaO/+qu/wuWXX45LLrkEL3rRi/DNb35z9Nv999+PK6+8Es985jPxzne+c/T9jTfeiOc85zm46KKL8MY3vlGb/znnnINLL70UAHD66afj/PPPxyOPPFK94rJlQGiptnj8IaBE3VTeOiEa5ZjbpVJwxSw/W0hwUfXUpX5rMiwzcxqa+elPfzo/9thj0t9jWGYR+fSfi8j59A80L96UqNv27ekqhHnyt1ClzPxRbrghVYGsXZuqzZpufh+Y5WdrK6apfptWWOajR4/iqquuwk033YQzzjijcr1nX9UTsl6kRN1uuEFO+kTh2CBkmJZKoQnd7iyqS9qMaarfphGW+eTJk7jqqquwefNmvOIVr/BS79kn/pCtbyXqVvXsmFlG24zeEfVgms4ZdYdlZmZcc801OP/883H99dd7q3cjxE9EP0FEXyOih4joV2stLGTrW4m6qX4aDDzUp+UIeXEXMT3U5Zxx/PhxrFmzZpR+//d/H29605vwMz/zM7jssstw1llnjV1/0UUX4YUvfCGuuOIK/Pqv/zqe9rSn4cUvfjE2bdqEK6+8Es9+9rPxyle+EkeOHFGW+ZnPfAbvfe978clPfhIXX3wxLr74Ytx5553VHgSYvnEXQA/APwH4AQALAO4HcIHuHp/x+IOyvpWoW8iP0zTatBM1wg0h+PGHjtD9+J8L4CFmfpiZvwfgzwC8rLbSQvbNLFG3kB+naYS8uIuICAlNePWcC2Cf8Hk/gMuLFxHRdQCuA4C1VUduyAd3l6hbyI/TJLZtG3eSAsLaeBcREQqCNe4y863MvJ6Z15999tlNVyeiBYirodkGy9zZIgC4t00TEv8jAJ4ufF6TfRcRURlxNTSbWLlyJQ4fPox+vz/hJtl1MDMOHz6MlStXWt/TBPF/HsAzieg8pIT/KgCbGqhHRERES7BmzRrs378fBw8ebLoqQWLlypVYs2aN9fVTJ35mfoKIXgvgo0g9fO5g5i9Pux4RERHtwfz8/Niu2IhqaCRkAzPfCcCDM2pEREREhCuCNe5GRERERNSDSPwRERERHQO1wUWKiA4C2FPy9rMAHPJYHZ8IuW5A2PWLdSuPkOsX61YOqroNmHnCH74VxF8FRLSTmdc3XQ8ZQq4bEHb9Yt3KI+T6xbqVg2vdoqonIiIiomOIxB8RERHRMXSB+G9tugIahFw3IOz6xbqVR8j1i3UrB6e6zbyOPyIiIiJiHF2Q+CMiIiIiBETij4iIiOgYZpr4p3rEowOI6A4iOkBEX2q6LkUQ0dOJ6G4iepCIvkxEr2u6TiKIaCURfY6I7s/q95tN16kIIuoR0b1E9JGm6yKCiHYT0QNEdB8R7Wy6PiKI6ClE9H4i+ioRfYWIrmy6TjmI6IeyNsvTd4nol5quVw4i+i/ZWPgSEb2PiIxhOmdWx09EPQBfB/DjSA97+TyAjcz8YKMVA0BEzwNwFMCfMPOFTddHBBGdA+AcZv4CEZ0O4B4ALw+h3QCA0pi8q5j5KBHNA/h7AK9j5s82XLURiOh6AOsBnMHMP9V0fXIQ0W4A65k5uE1IRPQeADuY+TYiWgCwyMzfabpeRWS88giAy5m57KZSn/U5F+kYuICZHyeiPwdwJzO/W3ffLEv80z3i0QHM/HcAvtV0PWRg5keZ+QvZ/0cAfAXpqWlBIDtK9Gj2cT5LwUgvRLQGwEsB3NZ0XdoCInoygOcBuB0AmPl7IZJ+hg0A/ikE0hewAsBpRLQCwCKAfzbdMMvELzviMRgCawOIaB2ASwD8Q7M1GUemSrkPwAEAH2fmkOp3E4BfAbDUdEUkYAAfI6J7sqNNQ8F5AA4CeFemIruNiFY1XSkFXgXgfU1XIgczPwLgfwLYC+BRAI8x88dM980y8UdUABGtBvAXAH6Jmb/bdH1EMPMpZr4Y6eltzyWiINRlRPRTAA4w8z1N10WBH2HmSwH8GwC/mKkcQ8AKAJcC+ENmvgTAMQDB2ORyZCqonwbwv5uuSw4iOhOpJuM8AE8DsIqIrjbdN8vEH494LIlMd/4XALYz8wearo8KmTrgbgA/0XRdMvxrAD+d6dL/DMCPEVHSbJWWkUmHYOYDAP4SqTo0BOwHsF9Yub0f6UQQGv4NgC8w8zebroiAFwHYxcwHmfkkgA8A+GHTTbNM/KMjHrOZ+lUAPtxwnYJHZjy9HcBXmPn3m65PEUR0NhE9Jfv/NKTG+682W6sUzPwGZl7DzOuQ9rdPMrNR+poGiGhVZqxHpkZ5MYAgvMqY+RsA9hHRD2VfbQAQhDNBARsRkJonw14AVxDRYjZ2NyC1y2nRyAlc00DIRzwS0fsAvADAWUS0H8Abmfn2Zms1wr8G8GoAD2R6dAD4tezUtBBwDoD3ZN4VcwD+nJmDcpsMFN8P4C+zg8pXAPhTZr6r2SqNYSuA7ZmQ9jCA1zRcnzFkk+WPA/iPTddFBDP/AxG9H8AXADwB4F5YhG+YWXfOiIiIiAg5ZlnVExEREREhQST+iIiIiI4hEn9ERERExxCJPyIiIqJjiMQfERER0TFE4o/oBIjoVCHC4roSebyciC7wX7tR/ncR0XdCi+oZMXuYWT/+iIgCHs/CPFTBywF8BA6bi4hoBTM/YXn5jUiDbAXlKx4xe4gSf0RnQUSXEdGns6BlH81CUoOIriWiz2cx//8i2xX5w0jjtNyYrRieQUSfIqL12T1nZaEaQERbiOjDRPRJAJ/Ids3ekZ0jcC8RSaPEMvMnAByZysNHdBqR+CO6gtMENc9fZvGIbgbwSma+DMAdALZl136AmZ/DzP8P0u3v1zDz/0Ea8uOXmfliZv4nQ3mXZnk/H8ANSMM3PBfAC5FOHqFGn4zoAKKqJ6IrGFP1ZBE9LwTw8SyMQQ9pWFsAuJCIfhvAUwCsRhr2wxUfZ+b8zIUXIw3e9vrs80oAa2ERUyUiog5E4o/oKgjAl5lZdsTfu5GeOnY/EW1BGldJhiewvGouHnd3rFDWVcz8tdK1jYjwiKjqiegqvgbg7PxsVyKaJ6JnZb+dDuDRTB20WbjnSPZbjt0ALsv+f6WmrI8C2JpFTwQRXVK9+hER5RGJP6KTyI7jfCWA3yOi+wHch+U45r+O9NSxz2A85POfAfjlzED7DKQnHw2J6F4AZ2mK+y2kR0R+kYi+nH2eABHtQHrIxwYi2k9ELyn9gBERGsTonBEREREdQ5T4IyIiIjqGSPwRERERHUMk/oiIiIiOIRJ/RERERMcQiT8iIiKiY4jEHxEREdExROKPiIiI6Bj+f5EOIjfroQMUAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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rJC6mstI2YeqHYQiiqPJZ5nboo2MjmjZBZJnfOZQ+gWwS66HUvVXuYq9aelXHcuvGqolxNTEDV8KfNAqPBODc4nrT2wC8XvH7RgB3AvhOkV4uKTemRjFuetjBGTbY7Qf1mdSUx0o36+rtKv9XrThFHzKs5+H0blnTi5Vp3fjmssV3NBIfbNjowtONDXaMj41nI5p0avqcQ6npspHwlf2oE+e6PKKy2LrEMLgMb+xxNY2J66tPPgrHBGC6uLnu8QBWAtgO4MRano0A3utadiwfRYYNk1Iy7s2jYpjdHLFNzAZbbKJ0I16lnAzreQb3Tyx6bdOLMq1HaUhXlISCVBrTqCpfa7PWj4MNDTbCDbaAtyqBU7nSXIly/XnXWI/yGenJu10Mr2nq+hD+FPXkkACcBeCqyv+XALiklqcbRlG8Sa2UjB1L+VxCPmLrl7YZ7OI4LvIOaJ/yZ63poSjTqlFIV5SNytUa0qhGUWuzto/Tu6M1IiTQTiqluz7XhsTrE9PQlrnGNnVDmWRfmEVfGcX5AC6v/H9hnSkUjOKHAG4A8HEAx0jK9mIUVZGqmLVaKRnz489V9XzdxbdVESjWLDH5KOorTxjX6UJgmHnMRGeNmrL1O8vMDVBQrMZV+Uqb9fNhYTFybNFMSbucDxa0EWybOUei4KoYvk2TaIOIuSjnbTGvKmIS9r6an5YzoxgAeFjx+bcAfMVQ3iYAWwFsXbt2rdsIaVaoVoIc7NeXZZPaI8ySqgl/emqeR3NXsJI5qKiEpm6JyUI7doVWpQolDX0Hi2l6WjtGjUhnikKNCtxoy8SR5a6L35VYlq9bojBWp0AMv0RsSJjk3Jy5H5I6XALfbHl9511fHdp9ZRRW01Mt/zSAuyVlO2sUmjfndSmOyQ/gE09Xgy4YaJFZ2CiLYnZLTBbG45ti3GJjopJtiVsKrbLahmy0JVqYpAq+W0rKdkijo6URSKq2N2kyMZndQjfouchnkryu5VXHzLQ8u0RfGcUKALcDOK7izD6plueoyudfB/BNSdnOjMKwQsek5KFgYurMQeXdCoHim87pN42DdgqhWfmDqbuMj1m3hcQQSU1UMmbMqI7KSbjlcKgtQjIENiLrqlGo6pKExNangpTodWkyCWXELs9L8saMzXDtS1PoJaPI24XnAri1iH66tPjuzQCeX3z+YwA3F0zkGgC/KCk3lkbhvRrqfouqnqyry2BaqULfzAX3PmT5Md3AgnHyWpsVQyRtWh+3UTkJlTYwPlvzfaVU11T6McrmSrti46EhEVExECqLuDwvySstT8r8+xAp1VtG0VRyZhQmp7DPW8mySdGuFMtN1EDAkLQaxdS8ex8MkV1OBCCGSNq0uGqj5BK7j2FAfPmQSgJVmV9UW2BMzbQRKOm7lTCvNkwmy1WjME2r+hW5OrSlySVGIYFEpJKyc9vVp1km3/Vbg9ZHMfJopyGSB1jI94u4GIFtddvE0tHIfG5WCGwiYASt0jQEMaxzkoCDskwTgXIhMpL6TMwulvSrIpa2KDDb8234KGL4JNpyfidGEQIfdm5aVbbVLpg9Y1FP0+qrpUUwaBQD7HWnKjZIjheRntXgCh/bUIhW6Vh9jLKqZQZaOBdhe2X1IWpS+q1rW65TpYuopyzTj6H03bscahAyzolRhMBnhZtWlsBh2hoyzVWi9ZvtXOwUTXhrY4yJVExsyBAck4BKtIVY9dlkmnr5MQLgQtrVtUNYhdFIPVahWp3kpkUXJEYRAtdoHJMIYdqMF/qWfYlcVrtKFDvM15+a6m/KWxvLCN6GR7CF6qXaQoz6dK/V5Wjx6iuMNQZ92PvhgpB+u74DX2aZGEUITCLV7GwuLpj0YWkK1W+bFB8lM8/VW+s6Noc4XM0ibYapVl+Zz5lM1ekdq93LSaOIAdX8iM0sE6MIgU0KDtklpZrdNkNsdU9GCZOI6XoAje9Kdp21Ou9kUz6KHsNn2NtWjqTKoMkcYotl0PVB1de2mWUfEZtZJkYRiiwLZwa6VD9dTGqaGY3cpHPpKvKlQD6zVkcBIlDALq1MrnUvB+lYMs2q/hFV/20yleqgvVLWcKnnoYLYzDIxihjwdcSaUn12u9bhagdokvL0SMRzakqWRQ3J9RmG5WBvt23eC2GIqiXRp5iPPiNFPQWkWMeMW3VdidlJl0dlFophxrK1xaPrQeMWmN+nPWIJPcvM937WHf31611D6g58ptqFNqTqGFqPhAHYzkPqIxM9VJAYhRR16bIuEtZXpeqksvouIFUeXfmxtBadpmE82a9lpUBQmW97xBK6abwHA85mNvJKHBjnISsejK4d+PazifelYzyx6pL4KgI3ySd4IjEKCWzijil6pyTMksgl045sVRtsIbWqZJOUNWjNVp5lop3pvu0RP2cZxwH2Kn+aw93ad+3b5kY1JyFszCCm9mKqyyYvPdSc1m0hMQoJbLOzfgxoU9FBqtWo2rGjS6XW4BFk3YqtXBJFFtge0evRxBdWTwvWH5a4kO81Ubxzl6kRSnhjv69Y8QhSuGgvETfJJ2iQGIUENkJcXS1t7DeoQ8IsqhqDBxVpRaOwMeQIGgWzgIApClfdP6JtJnZoGyNRNmMQw9jvK0aEcxPHdRwKzGE59CcxCglsW02rb9aUzwahbX44ZCYs8HB6N2fYYDg2VrNXwoOKtOKjsDG7StSRpD0TC3C0xbwiDbYN00m6k2neSEVtbY9hXtExG9+zv1ynTCwHd+T4h96hRwGBRiRGIYGJUdSdwCaiLYFhtisnVf3spQbFPWXTdFFgPitW4rCvtNNUjWishPtUMqxn070cE8OO+bweDVW0EdFYDlvJOULSV+U6ZUJMg+X4uJyBtFwIbh2t+f4CkRiFBC5nOplWdx2OBFU7qUpTh8tsiyF+qVanage1VJSVxEgKV5F4rMqyNA+4mJzGiqVd2jG1EdFYIaC6cgYDf2IsnTK+Pg3fPRJdEdyufUltaVG9ZRQAzgXwXQC3AXi94veHAfjr4vdvAThWUm5UZ7YqpFQ6Y3VE1nCIvnZSlaYO6YqPBQlFq858SXuqM99GMQyrQzxW5YrUPOBmcqrWs+A8bOUUCSGYkjGwpRjE1UfCD2GQPnEgXfSxjlBfW1taVC8ZBYDp4grUx1fuzD6xlueVAN5ffH4RgL+WlB0tPNYUriLJ62hmMT2ilJLb0LldKZErBdJ1WCAGx9Io9Jc3mS2SrpKzaouNj9QvGQNJqrcjZuSSDiEmNxPBjU1QbWG6rn4Y37a1qUX1lVGcBeCqyv+XALiklucqAGcVn1cA2AeAbGUHbbiTznpJXimRrbx1Z7t7KEwxij5UyDU20xT+Y1kdtrHKsJ6HtCsPChgWjm7Fih0O9hur813o1aG1XbRTzxtyc1toamrvBLNsSulOUTG9B125rhc1ScfUtoEylkuvzeNd+soozgdweeX/CwG8t5bnJgBrKv9/D8DhmvI2AdgKYOvatWvjj6IPpIS29tbHop6wU+3ItomzkvOLdCvPtptctZnPQMytC6SeyWF1ZqMtPJzenR+xMb2bs3M+zDwccoYNPEv3jXeN7lv8nYny4zkKJhHLIaxDiLXS5v6pt831rghdu5owe0gZm+veE5NMFstUJFl+sccsaRSRGUU1Rb8z27csFQVyfes+we3SXdk6imI6bLAcG0G4jfeiEa6ObLSl0BgqFy4VFWiLKBzQJkWmvvchxtSQvkaTJU5ar4uSpksmnh1KpKSMzaUeG3F3Kcs2VqY5HHvMko+iT6Yn08ryMdq62AJ8vX+uhtz6M1nmRjlUVM1CQb0XjWB1ZBlPagyl2Wk4ZNKEupaO7uH0bm0Xq//HuiJDGithIlKudnHVq5EqubZ8MRHDvGJbdi5l2ZaQ6d03YSp6SEc9FYT/dgDHVZzZJ9Xy/HbNmf03krKdGYVtVbhQBle7vsTQHSO4vT5bffwPjmJR0KLxZUKFI3tIu4y/mxzYDQyFWNFzJdCuREQix5TTK3S7kBQ+AoXODyA4QsyK5eJ8jo1eMoq8XXgugFsLk9KlxXdvBvD84vMqAH9bhMd+G8DjJeU6MwqJXl7ugLZ5Gl10fMnskfobSkg1Ch9bhKMo0+SiMYbGTk8r90ZUHd2+IbHV4XCFxMRiU/TqfpNQJ7sp6smFYYXAtR+m/LFMNb5SfJumotjoLaNoKkXXKEypdP6Ws0on0vjEP0q23dYRS3SNQB2aXDT65i8s+iuqB/wt+jCKjCpGEpvH1yHVsGzTzWZGimGiilW+tH7AfgiztF2hppq2n2/LtGRDYhQ2hMYYSrxfrsHqWWYPE9XNMIkWomMoMSmjoYmhhdhe2Uoc0B97UmEWw+ndY5K1ZBO6L7OTEl6pYzbGjl8TI4/N6EejJWYwPc18zjl+5dusqyHEtm2NQFpfG8wkMQoJ6sQ1NOkO65PCRC1KM1DojK73eW4uTypG15WYY+hnVRpVpQH2Lv0jpPg623eMRarizTMzaqIgccyGSvyS52P1fTSSLx1b+yXKcNPM3BWuwQX1d9AG80qMQooQE5RuNfvCFv7iMaONi15HnQLvkA6GoJ/617DQDMX3RJZN8irdXVISx2woAWlzM5fL9e62+qUGgCbNgy4wvSdJfW05yBOjkI9UvBT6FnWzo9QmHGe0laj0NVxD0E/Ta+gTpENc1ZSa3AjY5iuPtXRUPg1fhqNCE2NiKlNSX1sMPTEKKVzEnjJNT8vOZqjObN84xuoWXccZbc3epnjpAkE/dRZDyxXhrUMyxLrXLp02LmjTHi9dWqa+6tobY8OerY6QMTG9d0l9SaNoKHWiUaiYgElH9oljDIiNtBKpvmoUAsoptf3ripeEiqryuhIPyRDHfg02n4vLuVIh0PkozjlHrj3pxmYwaOZAwFhjEhqllXwUDaXOfBTSmR2LCDtQOe3Bd4P9S2XFcI434QMQ2GJ8qpbYumdnmbPRFs4Gmyf3ZXgMj22IbdKna+Bck1FcLsiy8TiJqanx86tCTTA2maop15Sk7D4vrSoSo5BC6iWTEn9b2KzErCOdIZbZqCR0uJezwebJugA3M5mg/igwUBOfhSSVC4a0S7tBz5XX29oZU2p2kXuaVBxDGaStP6YAwyanpUvZHcdRiJAYhQuqb1QVKmpLVfEmdHW6zESbSEak3oBWZ1a+K6sN05WGmmTYED0ef+yVYl575EdsF05MO7zLBsImXVEmAh8aJlpPbdr3+2qt9UViFCFYvVq+2gC16KdLMYmvgiosMgbi/Ahu1Qa08s5MKo7dnrpDuZPZOvvbcIZrxkN3wJ+tyVKJe4C9PI2D0YmCTspUfe8zvH3RKEwMy/XojerYSM51anJa9jX+wxeJUYTANsvr/0s37UlCclxmYo0q2M46YmDMYG3Nb5v9juKVlyquoSbak2IjxOPP4ACvxAEjkfOBq+LmI732xUchcdVlmduRZsyy5WE7Sd/VwirpV9IolkmKyihMs8FF9FOtTJtIaVpZddSogtamPr17qb7KKtLmxw7Z7HegfEF2Y8WYhSxYWzzAYPX9akIzNe9GWGoV2W7VUz3uEzZri3pqw14uNRm5zgdJNJHLKTVVBtWWk7pPSIwiBLFEv6mpydWqKtd2u5yp7gqDEdnUK0xNmx/z4tk/cdvcaIvTEPlKYk0u2CjmBUUDfXweguCv3iLLzMeW+8wH23v3DWJ00bqWg5NaisQoQuEyG3RijPT0VtPOJJM+XtPdRVE6lTYYNQoJk3Ag1k0dk9DEgo3C1BSFhERRLVeTh26O6KZ7aFCgizNfkvo+vqFIjKJtSEJUfGaxSaypMSedz2HMtDLYzxk2GPOPhc8a4EK8lhOhi6KtaAINVPsyJIcMm6ZO36Ei7E3NB1+NIoRxLWckRtE2QoLCp6bMs9WB8tbDYUdzV0wSvRUHxpjFWPjszEaxwdZEvOqPdGnb9dE8nJ+pP6ARHLLB5gn/iGRcdFOnjMxebtD5MELPo5T4RlxSHwWZmOgdowDwGABfBPBvxd9Ha/LNA/hOkT4rLb9zRiENCleZqFRnR9nERqF2YtydXba5HgYipOo26a0uLbd1dEQVrTCoLJt8f7bzwApIJWtTzMRyI2Ymv0uM91OPpqoyVV2dXe1e7xp9ZBRvB/D64vPrAbxNk97rLkwAACAASURBVO9en/I7ZxRSimQ6zU5HcVUXFkt07MHAzzdg8qVUqLxEetM6YFsKzWnF5KV7d3Nz3lqZ6v2Yxni5QCrxx3g/punUh8iwPqCPjOK7AI4qPh8F4LuafMuTUTDLZpqJMphWjqouUxxgQZFdJNbFptc33hnKt0X1Kuse7G8t2L+VDVIu762GQ9XPo4N0riwn5rec0UdG8dPKZ6r+X8v3IICtAL4J4AWWMjcVebeuXbs2+iA2ApPT2/XsbJ2OPT29ePqaRNFR5qlv1LNQKBdmQZiXZw6kgratKVGkxgBG4WIaOxRi+KXxHCFh0w81rSAEnTAKAF8CcJMinVdnDAB+oinj6OLv4wHsBHC8pO7ONIpYYbSjkVn8DdgFZGuiVlItN94JxD7T5jDnciOKlkLFKwyBl2O4TqHlTAglAoXvO2mTkS7391CijxqFyPRUe+YKAOdLyu+EUcTamDc3Jw/VcAmJEYplWv6EefkBOzy5eJQRPSsPckYvljOK6pZbz5VpO2El2HSj4kbSyzEiYLkQLZ0CPDMTJ8ihLdPcoaDZlegjo3hHzZn9dkWeRwN4WPH58CJC6kRJ+Z0wCteZGWs3UL38QEO8tRsBK2OMiA325+G3qspMPorAlWkbdsKCqBxjJ10PLYqEpolWLCZkc2LHYHAx/FGS/h4KvqISURgFgOMA/AaAX5Q+YyhrAODLBfH/EoDHFN+vA3B58flpAG4EsL34+zJp+VEYheuqsG0kqJcTczdQxN1LImITg2KYoqlMYSeB/bMN+5B2Gftj7HrH4mWTRCtm15o0OdnqkI6FtL9NB0hIl1qMJenFKAB8uvL5PAA7AHy0MBtt1D3XhxTMKHxWhfTUWJtkLC1HV7bCxpPNbOThYL94EiknXcwYwizT98G2wgJXpkmaXXTaa6iJdVp0KF6GDKkEMQhvOVWk0zlk2EIZm7S/fWDOsZi4L6O4vvL56wCOKz4fDmC77rk+pGBG4fP2XQi8ydYuDS43lV0pNxts5tmV43cpKCeRLdDcN4RV4qxwWWE2TUSAJcVrvrhnonb/hoayWqdFK/G36v6EDKkEIV3zndKhwxYiZUv726QS2Taz8mUU11U+f7v22/W65/qQghmFz2E6LqKSbQXoPH0eZYsmkWm2Z5n9WBFdwS7hT9IVZlQJHFeo4wqzEo+ONAqTKceHaKkIbEjXfK2sXdr5XfrbVACBlFnFkk98GcU8gHsA7AfwQCVKaSWAG3TP9SE1plGYDtNxWQ2u+nrA6hJNIl0dg4H7gf6S86x0ycV0JT2b2lVTMlDWJh39ITDxXh8mIT39Xtq1mOdfNgGpYt92NNOy0Ci0DwCPAnCW63Ntpig+CulhOlViXn8m1g5j4UrLsGHpUL/BfrkkGCsCq15wkwZpCQeUrHaLOFj9eTAwv84sy6O5Fg9VHGxuhbLEVGRMZflKzqYyR6NJnh9TKrfBpkw3oSnEaJtPPht6Fx7bdIoS9aQjaDZCVBKwUIdvFSaJv/gtw4bJY6tXHtRKgmPHWevu03ZN0js3tIc+RRiTKnU05Jl4LaMtE+9J55pRxfl3KYHGrLsJN0sfpHMdOow/UELl0ut11NNyTlEYRQghEu7CFcO20oZD40U4En/yxDEdPhFY9X6b7BimWS2Z9RLqo6F6GTaI+u9yXWnXBCeW9NtUP7qWznXoKP5Aia4ZamIUPgggRAzEf7umlUZkvlqz9qyWAFbv0y61IRcfhWp1ZVl+70LNJGbsp8uBRybqo6F6w+nd6v7XjhNxua60TwQnBLGJVV8ZRIlYjLFJw0FbwkYwowDwcABPkuTtQ4q2M9uTEDHgFK4pqs/0m0mjUJzO6nRfcz0Cq7xIQjijlYSnvDlPNT4xjeQaqkdYUPe/dkChy3WlXS9y1Wvqmrh3LSFLEKONsfrZtbARxCgA/FqxyW5H8f9pcLhEqIvU2s7sLNMzivINFye3iuozedVMMzHLOJvZqPRRZIPNE+0y3o8dQIBVz2kJKHYon8mwgQfYy8ACAws8wN5Fk1A2s1F9A5+trbX3aGxT5YtssNlJuYmlCLlCp/jVXUaScnzapXuua+YpRej7iNXPrscrlFFsA/DI2ga8G23PdZla3Zlts+ObQmqr9ZnCPSUzSGfiUYgp2vuxSxu9dD+DYHVppaSq9F5xHs/gwCTBwwEe4b1Bd3rXmy7y0YxGTmazWK4VV5gUWymREcgiyr6ZnutaQm4LsfrZtQYWyii+WfytMoqH5j4K1apTvd16KqOTpLH89dkWMhM1fVm8r7m+KzmyGCOV3nl2lger79cOQ76DWlOODSWVAxYZcjbYPH6sST3qKWTjgM94DP3LNLnKpMTKZvHTDYXuOZ3cE3Fq9QYx32mXPp1QRvFhABsA3ADgBADvAfB+23NdpkZ3ZuuidEwro55qEUvGvMOhk09gAjYxpWGxTyS9I9dyoPEd5MngV7D5dnx2ckda/fWm2Qi6D6GIoVGYpoFpKFy34PTNRxEDXWsCsRDKKGYBvBXAtUV6C4BVtue6TI1pFKZZYPNX6Faw7UiL0SjcAG1xhMcgiKY6s8FmHq7ep9deoPeblGmaHlQ3c9WPrKHDovdQh8thP5qxVREQ0z5OX4Kj81G4xFP4MAMb81OlFk9dbxV9j+6SwJtRAJgGcI0pTx9TIz4KCYGZm5OvmJLgmHR3k24fa69GE+KQrsxy/4SiP7pILIB55YoHeTR3hdpHobn4yGpa0xH+EkK/kGnsdEXo9huG8OzRSDFujrKEq3lJx9xsaTlK2w8FhGoUXwbwSFu+PqWoUU82Ql+Fz5EVIaYhn3DR4TA/6mPqjiUiOndRnGvFStgonqLPQ9qlfGQKDy5ecJRh/XjUk4b4W531EiosYaCWftosmPXhbsAV5aQY+jis689JLbCHmp/iUEAoo/gMgDsKX8W7y2R7rssU9YY7lxUo1cN15w0BSyutXKkmjcJFEyhWu5aIeoaaKiE9h6lSVjbaYvZl6CiQ4ntj+K90vCT9tfTTlXj7EHubPAPon3WB9NVLNYxDLfIpJqrnX01PyyPsQxHKKF6iSrbnLGX+VwA3A1gAsM6Q79xiD8dtKK5OlaSojMI1QF4SAeUSN6mLvtE5uEuTVH1lF/mteyh0Y6CqTzcOnuJtlrFZYxCOj3ZDYTUkN4ax3NJPV6tejPwqPto2JBqGKRDwoQyVCbE6rZocp94d4QHgyQCeBOCrOkZR+Ee+B+DxxdHm29H0ndkm3Vs6q1XHYYaKk6r6TXYNw+VARiKqOYLDSI2kIcNSw7TrOCjej/aIkqpGEcP2oRubChOqNq3c1G7bZyGdalIFNjZc2qgaoliHKjeBrp3SNtNdk+MUqlHsAHB7PdmekyQLozgLwFWV/y8BcImkXC9GEcOpKxHxdDq3q4HaRCUMs81Zo7BRI137fFecj1hdq0cUkhvL9iHUtpqIGZC4xGL7Anz6oVFuG2+rK/oQ5iph/E2NUyijGFTS0QB+F8Cbbc9JkoVRnA/g8sr/FwJ4r6GsTQC2Ati6du1a91GK4Q2UiHixDNSu4bhFcvZR2KhRE7M2xCBetHdxQ13DGwqZWfTuJP59V75qm25NELkYy8RFJmpTwo/Rt1BIggGa8u9ENz0B2CbI8yUANynSeZU80RhFNXlpFDE2ntmIquk4D1Uw/MyMeWWsXq2ux2QYHgzU0UM6m72JGnVtL5BQyoZ2WI9BMHdswWu++yd0+zSaIqq2XeCSeqUEuW0J39S3tmDyUTTNuEI1itMraR2AVwDYbntOknplempLo9AhyyYNt/VA+LrBW8UQVq7UE0eD70K7CgV2+Chin08ZUttL02JpoEYRMvXatqlLprjEFCVhAG1L+Lr6JMe1xYTJzdlnH8U1lfRFAB9EpCPHLYxiReEPOa7izD5JUq4Xo9Cxcl1smmqF+jh+S0hsE5K4w6mppbbU2+drGjPF60lXvYmi+folbH0pV7mwGd4QtN+UZTkdniedhjZiLnkPrvtQYvTNtHO+C7QpCIQyiscrvjvO9pylzF8HsAfAzwH8uNQcADwOwOcr+Z4L4NYi+ulSafmNaxSmVe8aSlrCRi2khNFUl0QCVx1R4XBExphZa1hkcyzDeexraawN07vHFB/pxjHnRSl4WJfFxz3VZWROltlt6TGYnEnCd1laLvDtT9fvJAZCGcV1iu+sPoouU+M+Ct0Mnp5emimu133aQkGkTMJEZXw0ChsVqxHoCUf5LCvvxBgrI8bYF/m1bTAoIeXrcXURhaBuRaxbHQG126gPkTnM7cQ4mHwwTdTH7Bd00Jd3EgovRgHgFwH8l0Ka/41K2gjgZt1zfUhRNQrVziCJZO6zU2rFCn0ZLqfT6oisTRpXtdlGxCvtEu+IribT2KtWp6XfQ+xU/mQavpJY615/bKhew8yM+qgwx1NDWoOFXy+2KZRY1gmzy3T3rc9kLHDZ+9qVucoXvoziPAAfBXBX8bdM7wbwNN1zfUjR9lGodgbNzOR+AAmxdg2FLSmTSgtxYRKmuqv+BqI8csq0G8xBoxDtiK6n0Ui7ArPRFnOIqyKZDhc0DZXp99gwKaS2V9mVP6NOsG1xESoiG8M00wajdDUR6lIffUwmhJqezrLl6VuKtjPbdnuddKYYxKKJUFXdjW0mjSfkiBEi5nPOcRejFKvHeG+3bozKMyZqY2Q9+0mTbMeV6whZm4xCopDqCE4XGoVuClQPAzZpbK7Hkvm0xZfxuDAw1/f2kNAoFjMAqwD8NoA/B/CRMtme6zJFO+vJdWaoZorB0Krd/KaarDadWDLbXUUik2G22q7CuG/yD7hSYi1BNJmxijbMQH9T3lJayB3doy3M3K7pKUSjaMoebnrFNt+ORLPQ9ddHw4ilnbiOZQxZrc8IZRR/C+APC1/FSwBcDeDPbM91maIxChfCqgvFMJRhlMBVK6EeUeVysJ1NbNb1SYJKm3QRR1oqqKlDa2Kpm7EUGQfYa+3aIsMp3lOW2bexxIJJQg+NNI7ZnrJck7nLVfbQpS4Iq0+0Wais1meEMorri783FH9nUNyj3dcUjVG4iksqL55BKxHb9E1mICL7OcS+Yp9UnJYYznX7VFasUK4qJ42iVr/NTzFhwiooQ5uLXVdXFwTHRjBNv9uUbpOzV0qgdQgdKx9/j0+dy4WJhDKKbxd//xHAyQAOR6RDAZtK0Y8ZL1eKzylshlUmOuW0Wq4psNw0+3zFPimjkIZ96I4cUVCILGOeXXnQTODrZRSfTX6KaRycLGO5eR0jw0YwTZK0aWqZwkddCXQdutgTl/u3TCbAWMR8OYXOhjKKlwN4NICzi53SewG8wvZcl8k76kkVIO3CJFSz3TBTstEW+U1sRPatqjoonhPdFidZuaoNCIDabuMowmWDzaIb7cZ8KbOzWj/FSvzc7Za7hwgkJhiTBuRqLpP4Ynzb7EKQTQwsFjHvSzizBEGMYjkmZ0YhDY2VJNUMMMTbSa/3tEZhmYi6ItJKxKAks9nk4ZPm1dXjs1+lGOsMG3hA+xhYWGxONtrSuXjXRzNEqNTr0qe6m833NUjlNtsUzrI4jMu1nX1UYkM1iscivwb1C8X/JwJ4me25LpMzo+jKIyed7aWnUyW5S2Z1jRKIN8ZJ7mB0WQmxwkx8w2XKNnREqftshnAdFh8fi06C97ls0GXJ2tAkMX/IaBQAvgDghShOjC0O67vR9lyXyZlRhIbBlsQr1mxX7QY3rQwJtamsYLETPUSjMIWOuIifBWUZ07wG+2VD3TFTkL7CklD2TdPQwTdqKybRlPo9JFfBNknM+ywc1BHKKK4t/l5f+e47tue6TJ1oFD7ih8ssMjEzR5FMHFEk9VHoDuSJFLuZDTbr92e4tKulFaqr2jR9+no1qAq++0BiSe5Vpis52UZSXpNTZbkIAaGM4qvIb7e7rvj/TABfsz3XZYrio9DNaN1vvuKHdBbZmJnDzFYuDJWPYmpKLvnrnP7SdhnGwUvia1jnN702V0LqMqX6QHR8d5bHeCU6oh563lIfxrVrhDKK0wH8M4C7i7+3Avgl23NdJu+oJ9uq1cX6tSH+SZiZw4rLRlt4SLvk5yiZjgUJDWexjKmXJGp6KJAq2KZADEumqqmqU2b7tFHNNi1jLB2T+W65mHhK9I05+R4KuLbyeQWAk4p9FDO6Z/qSvPdR6Ahd3dApfcOxZ4LNV+Giw/uY2+oEX6qJVRmtS1uK+qJqFBEoiq09pt9djg9TNdV5HBqAy2vXBKV5O82lTLUPhNeEPvoufBnFdZXPn9Dl62PyZhSut9yZ0ORMCNXhbdqTlBG5MhtX/0tRn9dQNmWjsDfX2F7Vb6pIbJcdzbFDLSUE1xRWGhKUVq9D5/7qmllKoRvLPkZD+TKK61WfYyQA/xXAzQAWoLkKtci3E8CNAL5j6kQ9Be3MNl376YK+hlK4iIO2ttdWrWhPiKr/Bulf5bgcDvbnp+z6aHQRPKqSV2sLE63/5tLUJomLSwhr03sEdOPs6wZrG6Zl2sf9FTE0iolb7kISgCcDeBIMd2YX+XYCONy1/KhHePgi4kxQEh1fPds3wku1GitliTfx6fZXKPaIZNMXTh7jsfIgZzMb/SlFBAbeltlA8qpi1+sShd20VGyLHem7ick0PoeSRjEP4B4A+wE8WHwu/79H95xLOqQZRaSZEJ0o2cJsgXGNqmyzzv5QNE68iU/Xf4WdxenGPBezW4QBbcMerjNTuZxn5AqbFlPXmppkmH0kpi6wxVMcEj6KNpKAUewAcB2AbQA2WcraBGArgK1r1651H6XYK380iqIjR18sposXfGZvMW6iTXymshSrCg435mXYIH99y8jr2RZDkiqaqqPMmmpfH4mpC2xrt2/TsBNGAeBLAG5SpPMqeWyM4uji7y8A2A7gGZK6o+yjCJmROi9c1d8hnCXRbZkmRhHAlbSPTt0xyYwEBWRYr2U+dY0iw3peiZ+P5WnqLgkJ+kYATHB1WXURYdWHsfRpx3JjdMtWo6jlfROAiyV5o+3M9l0VElFCOIOiaxQmzhPAlZRdcvEn1ArQmZ0I85xNXzj25QB3anlf23AhDlnmfw9VLLi4rPpM5JpEaOxIHxidBMuSUQCYA3BY5fPXAZwrKTfaWU/SIyxcwlVMwfQK6h9dKonpYav1PRttGR+KwWbv8vSXD83nvpOKob48IVaV2oZ0CDX++3rXGicsksiq5UDkXOFCwLv2lbTFbHrHKAD8OoA9AH4O4McAriq+fxyAzxefH1+Ym7YXobSXSst3ZhSSC5N1MY0usfqSFanAWNXS0FAdTL4T3T6SclVk2VJbsMBD2jUe1VTnYAEMWPtKsHfs3WSZeUjFiLQapV2WSvJNS/G2diwXx7ELXIWvLkNZ2zRf9Y5RNJ2iMwpXhiDdUqsSJ22O45BZY/OdWKhGHgJ733j1mmtFjeUJqM9g9eTFQ2OMouACpiaLTU8RV6O0yy7HfDRJrE0+ivJE2OViOpHCdVp2qVG0WXdiFDbYRAbfvQeup8DZCJRk1pgkY9vzFurldI9FWZdP5FeWyaKoLE0WE7WIq1HKc1ymVNOSazXqqRoRbTs2vCuEKn+uGkIMOcK3zW1qM4lR2BBIQK2rXPX9YOB+iF7I2RGS5y3Uy0a8tTuzy3qlK2Q4tDOlQl3Q3Ts+mLtf/v4jr0YJUdD5KCLxqyjo2javgivRVr0Ln36FMKcQRpM0igZTtKtQS6+ij2ZgYhblLDE9U22b1JBsm1U+0ViVZCLe1p3ZLjObyFzezMziKsvmLlLnm7tIXl9HFLEe9TQ353c6rI6I2Yib7fc+HjMhmcJln3Sn7batKYVMr+SjaDB5HzNummGhSRXKYjutVhLk7qIxSGadgTGZiLdVA3ChLkX9Sg2lTtF0+QDZu67ZWMbKmrojv2e7RbhKrrpXaiOGkqnQBf/UmcEkU1y6J6SqWbThewlluA/ZqKemU/ARHr4+CSlBL2HKL2lH3fkd6sOoQUWAl75bGAuBtfoUXKiLixhlG0Np2aMRj1Z+aKIfs7i3dWYhRZaZZQ3TVJBOlTYlb5tj3WY2ki7btjUiXbtUNx53icQoXCH1SQjyjRHb6d3jkyHUN6I6TyHSys5GW/Smn6mpCdHUqFH4tEHK0CShzVXotKXBZv1O8OndUZoaE1LpWTe9XKZUW32TWFhNU7yL03YlkL6rrgMFEqNwhVQEUO1JqDEJ7V3PdQO1ara4BLnbdHYXZBkPaZee8NfbORpxhg1qxjLY3OzsV3mFKz6MsXyG8dQxujwtaDfBtS11l5AomzpG0CciWoVULtIxL4lG0RUxrk4/237crpAYhStcVr+BWWil7MF+tYhRPcNBx0hU7YlNrYZDWXhqzY4xYaoabHYedi/p1XaHiECk0+8ENw99V5FBJmKj81FICLHv9SuhUUESIq8aU5trsYxJqTLPmJqRa79t/ewyUCAxCh+4zID6bC1WpIjYqlaCjrBNTalnemxqRSTbM1HO6gjhMUpet/KgfRd6iFe2kl8XZmsjWm1GBlWnmck3oYt6cmWCLu2SyD26yCxfs4wtWLGspymtz6fcPmt0iVG0hdrMcbpPoUphYu+vcGl/RTuwXkTk4hm11Duc3m0fK5eda9W6NeOTYT0Pp3czYYEHc/dPnEBrSratJzEXvE25lBIpKbOox0jYZCZTuaV2U5f0y9N9Tc/aNADdsz4xHrpx9+m3qVzbWCUfRYupM0ZRmwVaH4XusDwpdbLU60WtFOKR8WrTWKav4lmx9lXvk0lEM4xPhvU8S+PHkczgfh5gL5sOGaw3w7XrMcJf60QxVlmqPkj6Z5OSS0W4nirKt3iqV2Ezv0lCal3Gqm6ai1VuORYp6qnl1BmjUMycMWI72L/kyPYJWdER/hi6tUnUKR33Jgrna6Qu6hVrX5rT9SaZ2gYjIzM560d4j5FZ6Lae2Lru85pi27Ql5qvqa7dJ7VkWth81RMaRxnrElPzL/Rq+5dbfQR/CYkskRhEbdVtAIQ5kg81qCVx12F91tkhWlI2ihM6+po3tuvYV9aq0L8I8j/Ae8yrMMnPElaZ+0jCC8t2ZiE9AjIAzYWnSpm2TV4jM9c/MhO9LjaCMGtuvyxfiS/DVJvuOxChiQhWOCXA2fSHPrjgwSaxmNvqHQrjYFULRpLHdtKIq9Y7wHvWGt5Lh1s1dBeE3aiOu3S0YfRM804cXN23TNmkEJo1CmmZn86NJTERXpay6aGk601Z16saMTqq+r1jaQR+0jMQoYkIzg4yhsDb0QTQxtCF4EpuYUKVeI8E3hMoY/Rsu3bUdRzLUlyUZHx9erJOaV6+ONz1M08/XSlon+qYDEFWmPOly0JUdehVulukZe+zIpD4sf2buH6MA8A4A/wrgBgCfAvAoTb5zAXwXwG0AXi8tv1FGoZk9wVJoVbqu1tGml0tB8ayTWEIlJWdQDQ17N+pjWKO4vkw6y/I8hHkeYC8PsHfx88qpB8SEyoWo+RAE011TsVCdfvVoI1c/hIqQ1pRH4zMuDFWXN8Y1uG2MO3M7kXMS9JFRPAfAiuLz2wC8TZFnGsD3ipvuVhY33Z0oKb9XGsXQoeymxKMAGCexlPIJV4J4wdRWrzK6bOVB2ZANh8rnZ3BAdCWp6yL30c5CCIlLfa6axcwM84oVblNVYn5zMdFJ8/pqxW2YhNrci2NC7xjFWAPya1GvVHx/VnlFavH/JQAukZTZmY9i5cEw6UMqcrUI4ySWUjAJQymCAbRHnlShCXcdYufSmVrSg/xMPo6h9elWFrlvHa4ajO111onmaCQ7PcWlDmkel7x9Me3okDQKQQLwdwAuUHx/PoDLK/9fCOC9kjI7i3qqSx/nfFh/tES9DJPHrwvxooB2Eq/e50bBTKJZZSUrw4lVZdkM5xJKUNhUQsyGbSxy3zpcn3NlSLF8LqE+Ct+N+aHvKKYjuw+MrBNGAeBLAG5SpPMqeS4tfBSkeN6JUQDYBGArgK1r165taizlGI3Us3P1arUoZkuhcZCeMzrLWB3NhfV5X2K01WElL3YFC7nmgA3uO9kXO5avznyTnV83mljkKsndp46mCX+IpmObjro8qu9t5TWh9cV+7ynqSU/YNwL4BoBZze/9Mz25vE2T90+6d6JMIT6KCDM6oxfrrziNsVqEK1nbFWxwpwQFVcywnmdwv37IBe885iLX9dG239HQRTHhj22qig3fqdxEO/tiLoqJ3jGKIprpFgBHGPKsAHA7gOMqzuyTJOU3wihcZ6kLIzCl0KgnW1iIREQztS8GlQx1dk/vtj+v6ZPOPzEYcCc2gZgESGrmqWsvMZzfTcB3bJpoZ18c0DHRR0ZxG4DdAL5TpPcX3z8OwOcr+Z4L4NYi+ulSafmNMArXWRp6z3Ys8cSkvahsGi7bbcsrW0MhXMnaxYkF8wE6qvKLwoz+iQ7ExhAC5GqWiUFAY0QTDQbqE1/r5cYemxB0oVE0bZ7qHaNoOjXCKFxnqc5HIU0u5xiYZo9JIwhlZr6XF6iaPthvPVLcuDjrwQFFymY28nDqDvXBhraIpw7Exjal5q7MJ5JYBNVy0J2g24W5R9cHydHqLnVUmWldfoutvSVGEQM+q8o08+fmJqOeJIH7VUhtCyHMQJdWrgwaTh/CZnomy7g4Mnz8fm/bUenGkNwOKGmbdviuzCc2a6YuDQb9iA4qoZFNFn1KIW2VMtOYUzExihiIKbLFWo1S6qATxUI1ioAVGiI5q8wrquM4tNFM2DFWmVby6yhusU6AJG6qmOdINS2hu8ZyVPvSh+igKnRjKDl/yqfcJpl6YhSx4DpLTYbV+ozxWQEu21J1oTQhPgpb+KmhPzGlWf2i0p8QG8201wDaMiP5anVNxS6ETLeu4Mr0pPNbWm7SKAKSN6OITRSyTL2RbsxeMlyaQS4rltmNOlTrqh7mYzu603S1mmbWZ6MtPKRd436BWn+0TZceolhps+7IcB2jGE7vibQclgAAGvJJREFUDn63PjKDJH+W2beFiLUq4Z7DtqOcfH0UpmitrjQLV6YXU6NIPorA5MUoQlaBdOUC5kgc11ll0hRUq8i3jw5exCzjiZvjFv0Cg8Fiu7LBZr9j2RV90F1CNKB94ceqyJpgLFea3zYlStOL0U8zbI54xg7drUc9lX3ULZnqs33xVbi4AEN9FKr7wGMiMQoJdKugQtyUb0e3um1GSonI4BP3Z/KitRBSo62i9AtUUoYN6o18pvYoKlBdazpL93E22tKIkmiS+FX1SYfdNiWGw+78Csxmc0jM8bW9ry7HQAWdHGUjHTa0rTUlRiGB1CgoPUjGlCz1jZ13NHScIKZVFOIYGI3051YJhnHi7mtfBqmpIMN6q7krFBIlsN4800kt9W6apmDZlS43enVhDlGhb5vdYprkujSnJUYhgQvBrxqLXZlE+bxGDFGGdLpMOtMq6plGYR1fl/fkc96TI3ydsNJumrpWDrWPWyqmtN92yKYKfdMomMPHug/mtMQoJMgyebSPzljsklTRRbYNYBKYVpHvbHRYmcoq6D7O5i7Sj6VLe3R9ML2rSLCZXlxev9RHoXLi+vo7YvlnTMpp5CHXtqFrohobfWB+iVFIkGXyE11jHM8BKI2YwWq1bRX5iD6mRinKU1bh6niv9scUleXqCAiAqzJje/WqoVc5eVUxCbY8bRCepuswTdWuzTSx0QdzWmIUEuhmve/uIEmqzoJi5ke7KS/mKjI5+l3Df1yYgm6fh456Nixmqk9lWeDR3BU8HOx3fv2hkVKmPG0QniZt84ei1mBC0ig6SNGd2eUsjqVJ1GdBZVUE+yiagG7Vxjx8R1WHhEmHaksOMPlfspmNE6G4wORVodKhCvVXtEV4pAqf6fmmp9ZyQB8YY2IUEpg8leVslxCu0qbgQuBqdQdFPTWFqnnHxjDjbq+2p5aohy2iK5u7SEs0XYcqNAKqK8LjWq/ra/fViKTMq0tzV9fmtMQoJLA5p01iTpWZlJAwHhsVAewztc3Z5Rr24tK2CtUbY5TV/RWxqYcjRBFdmj66Svi2aSHRGnymRuh0cu2nq2U3lrLqGgDQB4m/aSRGIYWNcLvY5CVOZVeiKzmXqcnZKxH/Sh9CScClbSvKlpz4KqIegRRPbDevt01DyVwJjW16NKE1xCjP1TcSy/1lgpR59cGU1yUSo3CFY5SPFqa8Up27XGEuOnpTs9cWE6k6NF/atoJKaZ352CE/lD+Q4tkky+Fg/4S2IzEXSqKV6vl9zn3yRQxiaCL8qnZaxzpC36TMy5SvD1FJTcPEKCj/vV0Q0TsA/BqAB5DfXvdSZv6pIt9OAPsBzAN4kJnXScpft24db9261b+Bxx4L7Nql/m04BN76VuDFLx7//sorgUsvBe64A1i7Vp2niqmpfK7ZMD0N/MVfABdeKMtfIsvM9ftANy7DIbBzp3ncShABCwvq3668ElMXrAdjSvEjI8sIL/7nVwIf/CAwP5+PzaZNwJ//uVs7LRA9fvjhwF134Uqsx6vxZ7gLhwOgxbyz9DN88BXX4cV//nRVN7FpE/Czny19Nzubd0s1rep5ifKpoJuKvtBNSdMrq0PV3pmZvIwHHlj6rtpf16UjwcGDB7Fnzx4cOHAAe/bk06WO6WlgzZql/035AFkZywGrVq3CmjVrMDMzM/Y9EW3T0lgdB2kyAXgOgBXF57cBeJsm304Ah7uWH6xR2PT+eoimKoyTyHwDnIuGYPOP6J5pwttmktQlBmeLeGoKM51deTA/NNDWz0DxT/R4luWRTjUz2VhXaZfyHbhK7lWLqOv+RFN5Te29qJffRQTT7bffznfeeScvLCzwvn3M27YxX3vtUtq2jXnfvvFnTPmkZfQdCwsLfOedd/Ltt98+8Rv6bHoC8OsArtT81j6jcInuqVIQ3fcuRuiZGf1hgiqjre3uiCZWYog5TUDVsoyVYaaLXVIdBeJibBZAbNO27J0gzCvr9OVjMQh5Fw7bLsw2t9xyCy8sLCz+v28f8/btOYHfvj3/f+fOccK/c6c6n6mM5YiFhQW+5ZZbJr7vO6P4OwAXaH7bAeA6ANsAbLKUswnAVgBb165d6zeCUgezSzKt4iybvMrMxIx0HlbTM23CtBfCwchsji5WHC5Y72eDPooqbArUAHuV78CX4McguKbLDpsKouvCEawihFXUmUSVWTwU0BtGAeBLAG5SpPMqeS4F8Ckg95Uoyji6+PsLALYDeIakbm+NIiSW34dYmwiry6ryXYlNUIVIZeq6RJifjIBqKepJ2sYxRqFom04esV15GkpwzQy4uYC5LkJLbYxCxSTK9FBAbxiFLQHYCOAbAGaF+d8E4GJJXm9G0dRxHa6UJtZBeb7PdL3zp2ielmdWzU8rV3YWzG5qY8nUqm2rRz1JLj8MPdaiWo7EktqUlK9Sng91RjE3NyfO+8Y3vpHf8Y53RCn/pS99KR9xxBF80kknaZ9dFowCwLkAbgFwhCHPHIDDKp+/DuBcSfnRNYq5OfMqm51lPucc8wp0tV00vVMqVgB7g0zFSIDLf6pnWngipAtGolu51tVFeTS9AtfobFdLalPWyra1CldG8b03Z3zgyCEvRJzHXTGKr33ta7xt27ZDglHcBmA3gO8U6f3F948D8Pni8+MLc9N2ADcDuFRaftCGO9UJsra9AeUKdt1DEGJLcKVu9fwu1EPXpoZXv3Z46g7twcC7jtAumJRCn20zrq/Ap20x6whtS1P1ufgovvfmjB9cFX8eqwj5Zz/7WT7jjDP4tNNO43POOYd/9KMfMXPOKC644AI+88wz+QlPeAJ/8IMfXHzm7W9/O69bt45POeUUfsMb3mAsv8SOHTuWP6NoOgVFPZm8faaZLlmVIU7Xut3CZUe2izgrbbupz6F3QJbNHm2R79L2RAy7vyQyOoZV0+buqg+5a52hu7pNr7ztyCcbo2BeYhYHjhyGTQINVIT83//93xejsT70oQ/xa17zGmbOGcUv/dIv8c9+9jO+8847ec2aNfz973+fr7rqKr7ooot4YWGB5+fn+XnPex5/7Wtf05ZfIjEKQQpiFLbT2HQry3cPgUQzkNoQdNt1dcxP5QtxCXqXUqKAsxfE5z55IgYBC3F8h76Csn7V1Fy92v5qbLvDpf23Mcu+aRRjaIiLqQj5DTfcwM9+9rP55JNP5ic+8Yn8K7/yK7xvH/MrXvFGftnLLlsMu73wwgv5U5/6FL/2ta/l4XDIp556Kp966ql8/PHH8+WXX64tv0RiFIIUxChMM7rhPQTObVJNbFfDdIjH1MW2UaUIUrOZlBEFmJ7aImC6YVXd2+RqDgsxa5mGX/qaJOa3vvkoxqDpwINrhkH7JlSE/Oyzz+bPfOYzzMx8zTXX8NOedjZv28Z80UVv5Je//A2LG/le+MIL+dOf/jS/5jWv4fe///3i8kskRiFIwRvufGZ0pD0ESkgJ5nDoRjVMB/BIKIQLUyqls9iMaGYmaGzbJGCujuhqXtNFgKFmrdDjsmwxGT79Dxk7ZkdGoejswsNn+fa3ZEE7sVWE/LTTTuOtW7cyM/PGjRt53bqz+dprc0Zxwgmn8j/90/38xS/u4yOPPGbR9HTGGWfw/v15YMSePXv4xz/+sbb8EolRCFKUIzyq/gCpfj4aLfkypqfNR3i4QHJ8h+2SAhWBVfg5stEWt8UsNXPZfDlSZ7nuhrsAdBUNHMKPq0S7CUe5y2sy1R/DB+HDzJ0YRVlJ5WXs+qNMGTq7fbu8SCLio48+ejG9853v5E9/+tN83HHH8emnn84XX3wxn376EqN47nMv5FNOOZOPOeYJ/Hu/t+TMfte73sUnn3wyn3zyyXzmmWfybbfdxsx6RvGiF72IjzzySF6xYgUfffTRi6aqKhKjiMEoSrg6m5sQTXWRWNPTbpclA+P5FQQ9w3qepfvCulAbhwzreUi7mLCQNxMb3ChKV1S84abEUKyqu6ljxyrYzPZ1Wco05ULhYx50ZhQ1tLUhrzRthTAkHyRGEZNRxBCrQo3dpsgiFTRaRYb1PJzevUTkFAQ7yn3dZRuGQ86wgWdRYzy4b8IhPdG27niBFjbCrmMiuu9dpostvqJkFnVTlaq9Uke5zVUntTjG2A/pwrTK9oUyirYIeFeHDSZGwREZhUs0RFPxfz7lKpjERJgpTRJswnzULugOzRtip7ltNi2mTS2jqMvERE2Oah1zMRHXOmymJZ0fQGU1lWoypnyupq6mZKWSIaoix669NoxRtEnAuzhsMDEKfohoFKZya89oiRztkuXz7IKW8WB+UbS11TnBE0Zb2vU+F3WZmKjJNKTrm+636WljM7Rt0OXTMQGpb0SVz4VJVNsX4TWM9UvFJMp09dVhjIL50DktVoXEKPgQ9FG42jxqz+gJ9sKYLcJLujfAeFtdQV1NBFjZdYUmFIUhV6EQm00MzccfYPpd1yTfPZ+xh8a1vzHqV01zk2bzhS+EM4pDGYlRcERGwWwXv6q/z80t3ScRM+rJZAAXHAo0nN6tX8C11Sa50lPc7MFm9c7qweZFamMiwFrCVz/Co6S+MaAR301M1EejMLmedNPNJjO0sfvZ1ezU5F4JE8OKoVEcykiMgiMzChMkNoGSMjSxWoQipJHAaFZbhg1yN4CBkWUzG8d3Vs9szH8vNBkTAdYSPtWdFLHEZgMl1DHRLJuMNF650uyj0EX+2k5mMcktbWgULtpE0yfE6vpLFO6j6BOaMIElRsEtMgoX8aoJ0cq0amvQEhhFH5zCZH1NY5WQ3zECPNi/FBmkc4bXfCtRx9ZGCRV1qSKYy32AJsJe/y30ytCmrJ9V+GhCpvaGxCTowoJHo/CopxiIcXqsyamuKv+OO+7gZz7zmfzkJz+ZTzzxRH7Xu96lrC8xihiMQjqDmzLWSus3iVTSVadYbUPaJW++D+WQhPxm6jupZ1cezB3aTUU9mTYFaOqKJcnbdjj77OiOMTR162oMGSgWU9P115VRNDFuMRiFKUxXVf4PfvAD3rZtGzMz33PPPXzCCSfwzTffPJEvMYpQRqGLt1OJh5Id03UCrkLFFCOVYhef01EXFypV6xdhQd18LEw+63owoCk2tDo+FdPUmOlqsNnUdOcInolMumPmDZQjlm/AxPdDCaovJNZVkytON+5Nm8lcGEVTmliMY8avvZb5Va96Oz/5yev4CU84ZfE8qGuvlTGi5z//+Xz11VdPfJ8YBQfeR6Fb9arbZFRGZVNSrQIdcZKuIFemJBiDIXaqmzC9ezK/i/lNNYaqPgqZiUs46ES+wqk+dmTJ9G51RJVle3EsoqczpTRJUG2QvF5dW0zvx8RcY0j3LoyiKaYV45jx973vKn7BCy7ib397gb/1rXl++tOfxx/4wNe0GkUVO3bs4GOOOYbvvvvuid8So+CW78yumljKHU6qFR56AquO8Eec5dloS2F2mp8IW83vgdigeMgickqT9PCiSr+kXddaurBXdt+FhenGlEjrRNJ1OsSGRGH0mZom62OMsXRhFE1Fi0mPGWfOGcVll122mK88ZvyVr3wtH3XUkE844VQ+4YRTec2a4/myyy7X+ihK7N+/n08//XT+xCc+ofx92TAKAH8I4IbihrurATxOk+8lAP6tSC+RlN36ndm2sNkYfg4fsU2XX9GuLOMJB3bOLCr3QJjaYDDJie6UqLbXNCaVfNIFri9ObWKbCL8VMN0mbNzM7UQy+dQvaYvp/YxG6t90d2i49teFUaxZo65zzRq3OuuQHDN+9tlnMzPz6173Rv6t33rDov+hesz4n/zJ+5VRTzpG8cADD/BznvMcfuc736lt23JiFI+ofH5VeR1qLc9jANxe/H108fnRtrKjaxS2k898xR5TnS5luxjqNUzFumfBpX+VepThr3WpXawCDETZpMXpGMVY+G2bDgEF2ohkcq1f2hYfjUKXXKV7F0bxR3/EvGrVeH2rVuXfh0ByzPjZZ5/N+/Yxb9o0fsz4Yx97DN94o/sx4wsLC3zhhRfyq1/9amPblg2jGGsEcAmA9ym+Xw/gA5X/PwBgva28IB+FLt5OYmLxEfNsPoqWxFPjnoXymFLXfg2H5t3ZOkojpI6hPooB9qrbNr27nXOkhGhKW/Gp3+XUfR8fRayl5cIorr2W+c1vZj7yyLxdRx6Z/x96UqzkmPGzzz6bt29XHzNeHkDocsz4li1bGACfcsopi7fife5zn5vIt6wYBYC3AtgN4CYARyh+vxjA71f+vwzAxZqyNgHYCmDr2rVrzW/QBEngeyyxp1puVWNpaqeSwRag5SG0K6gtRgYUHKbkGPU02D9m/op9ZEnCJFyjnrrwUXR11HeJto40r6JXjALAlwomUE/n1fJdAuAPFM+LGUU1Nb7hrmvDsS8M7VZKf3RfvmehmSq7QY1yOV/UlBAFJm2j7ainro76LtEFo+oVo5AmAGsB3KT4vl3TkxRdG459YWl3U5u1luNQJTSPJk1qrhvuujwptgtGtWwYBYATKp83A/i4Is9jAOwoHNmPLj4/xlZ2K0d4dG049kUH7V6uQ5WwfHHLLbcs7ldYDmiTUS0sLDgzCsp/bx9E9AkATwKwAGAXgFcw8/eJaF3x+eVFvt8E8HvFY29l5o/ayl63bh1v3bq1oZYnJCT0HTt27MBhhx2GwWAAIuq6Ob0BM+Ouu+7C/v37cdxxx439RkTbmHmd6rnOGEWTSIwiIeGhjYMHD2LPnj04cOBA103pHVatWoU1a9ZgZmZm7HsTo1jRSssSEhISWsTMzMyExJzgj6muG5CQkJCQ0G8kRpGQkJCQYERiFAkJCQkJRhySzmwiuhN5JJUPDgewL2JzYqGv7QL627a+tgvob9v62i6gv23ra7sAt7YNmfkI1Q+HJKMIARFt1Xn+u0Rf2wX0t219bRfQ37b1tV1Af9vW13YB8dqWTE8JCQkJCUYkRpGQkJCQYERiFJP4YNcN0KCv7QL627a+tgvob9v62i6gv23ra7uASG1LPoqEhISEBCOSRpGQkJCQYERiFAkJCQkJRiRGUYCIziWi7xLRbUT0+q7bU4KIPkJEe4nopq7bUgURHUNE1xDRLUR0MxG9uus2lSCiVUT0bSLaXrTtD7puUxVENE1E1xPR33fdliqIaCcR3UhE3yGi3pyqSUSPIqKPE9G/EtG/ENFZXbcJAIjoScVYlekeIvrdrtsFAET034u5fxMRfYyIVgWVl3wU+cIFcCuAZwPYA+Ba5Bck3dJpwwAQ0TMA3AvgfzHzyV23pwQRHQXgKGa+jogOA7ANwAt6MmYEYI6Z7yWiGQD/BODVzPzNjpsGACCi1wBYB+ARzPyfu25PCSLaCWAdM/dq8xgR/QWALcx8ORGtBDDLzD/tul1VFDTk+wB+mZl9N/vGasvRyOf8icx8PxH9DYDPM/MVvmUmjSLHGQBuY+bbmfkBAH8F4LyO2wQAYOZ/BPDvXbejDmb+ITNfV3zeD+BfABzdbatyFPew3Fv8O1OkXkhERLQGwPMAXN51W5YDiOiRAJ4B4MMAwMwP9I1JFDgHwPe6ZhIVrADwcCJaAWAWwA9CCkuMIsfRAHZX/t+DnhC95QAiOhbAUwB8q9uWLKEw73wHwF4AX2TmvrTtXQD+B/ILu/oGBnA1EW0jok1dN6bAcQDuBPDRwlx3ORHNdd0oBV4E4GNdNwIAmPn7AP4EwB0Afgjgbma+OqTMxCgSgkBEqwF8AsDvMvM9XbenBDPPM/NpANYAOIOIOjfbEdF/BrCXmbd13RYNns7MpwP4VQC/XZg9u8YKAKcDeB8zPwXAfQB640MEgMIc9nwAf9t1WwCAiB6N3CJyHIDHAZgjogtCykyMIsf3ARxT+X9N8V2CAYX9/xMArmTmT3bdHhUKM8U1AM7tui0A/iOA5xe+gL8C8J+IKOu2SUsoJFEw814An0Juku0aewDsqWiEH0fOOPqEXwVwHTP/uOuGFHgWgB3MfCczHwTwSQBPCykwMYoc1wI4gYiOK6SDFwH4bMdt6jUKh/GHAfwLM///XbenCiI6gogeVXx+OPIghX/ttlUAM1/CzGuY+Vjkc+wrzBwk6cUCEc0VQQkoTDvPAdB5pB0z/wjAbiJ6UvHVOQA6D5ioYT16YnYqcAeAM4lotlin5yD3IXojXYUKgJkfJKLfAXAVgGkAH2HmmztuFgCAiD4G4JkADieiPQDeyMwf7rZVAHLp+EIANxa+AAD4PWb+fIdtKnEUgL8oIlGmAPwNM/cqFLWHeCyAT+V0BSsA/CUz/0O3TVrEZgBXFkLc7QBe2nF7FlEw1WcD+K2u21KCmb9FRB8HcB2ABwFcj8CjPFJ4bEJCQkKCEcn0lJCQkJBgRGIUCQkJCQlGJEaRkJCQkGBEYhQJCQkJCUYkRpGQkJCQYERiFAkJNRDRfO1U0GM9yngBEZ0Yv3UAEZ1GRN8oTge9gYj+nybqSUgokcJjExJqIKJ7mXl1YBlXAPh7Zv64wzMrmPlBQb4nIj/78N+I6HHIT+59ck8Py0s4BJA0ioQEAYjoqUT0teLAvKuKY9ZBRBcR0bXF3RefKHbDPg352T/vKDSS44noq0S0rnjm8OIYDxDRRiL6LBF9BcCXix3SHynu07ieiCZOMWbmW5n534rPP0B+8OER7YxEwkMRiVEkJEzi4RWz06eKM63eA+B8Zn4qgI8AeGuR95PM/B+Y+VTkxyS8jJm/jvwImNcx82nM/D1LfacXZZ8N4FLkR3ucAeD/Rs5stKelEtEZAFYCsNWRkOCNdIRHQsIk7i9OngUAFCfPngzgi8URF9PIj28GgJOJ6C0AHgVgNfJjYFzxRWYu7xx5DvKDAy8u/l8FYC0UZ/UUWs3/BvASZu7jseUJhwgSo0hIsIMA3MzMqis4r0B+s992ItqI/FwuFR7EkgZfv5byvlpd/4WZv2tsENEjAHwOwKV9ubkv4dBFMj0lJNjxXQBHlHc1E9EMEZ1U/HYYgB8W5qkXV57ZX/xWYieApxafzzfUdRWAzcWpnyCip9QzFIfjfQr59bhiZ3lCgi8So0hIsKC4Hvd8AG8jou0AvoOl8/0vQ36z3z9j/CjzvwLwusIhfTzyG8dGRHQ9gMMN1f0h8qtbbyCim4v/63gh8utBN1Z8Kacp8iUkREEKj01ISEhIMCJpFAkJCQkJRiRGkZCQkJBgRGIUCQkJCQlGJEaRkJCQkGBEYhQJCQkJCUYkRpGQkJCQYERiFAkJCQkJRvwfbaUqmZqYaC8AAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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0FkpxCGe+ZxM12NKisKIjLsh6UzuRk9xgIzpV+WYZPcsXYangtsu4v2gvh4P5KNmhtxB911vICglFCmyXNHETfaJL9I4LvKBoGE78sDYRwuQ+xfbK+UeEjB2bJFxI7HMo4SVSQ6dCW8YobNZapeuXbTGiLE/xvsyZ/VC7VkG2gj6zA20ZkyDXiSE2xqm1w05o4ununZoSN6/Xa8hymNAUWi8oGoQz7dp2MJbIgCoUL2Mwghlo5CXKa7m69qjSP/Par0F7rOdzqv5oh9R97MLHbWoZJMF9aXptj3kwd4N6z6+kwRYCX9T3xsKlToapsHBsm+eUpgldlOcFRYNw4t8tMxhdVGxh0xt5iRItV7jiTe1KE6aYWsxHm/x+2UOmLigdvzQREsCQA+zTb9NNpN5FWLGyXDkWbIWLwr3lBKl3YbptSCMxlHEI4JSEFxQNwknGSBMTXQaTKGG/r/cSpbVc1d4hkvqrHn4j7cL0FhcyYRG33ySoLRRIuVPrbDKQZnFQugtvv29QWCKpZIJZJtVsx1y/L18d7kK5TtFjthFhbQZNtqvGLSXMAl5QNAgnCkdTroMKdMj5lcEhMgbtqTof5ZldS8Z9Y9KlSoEUM2cTQy19Bdgnt6ZUhaXqKywWTISESoDYWLFEbk6ykyDEppGlIlvRnumzoHqdmfpl3RFsExPQWICkPnhB0SCcuDAdBCPLVWxOh9y1IxAQNhZF3BblWoYqpKdXIRvsaaTrUq1AignWBf7zzyqDyenCcoeJK2nWjSubmEO/b5dGbIEwZEFMQr5rrOkwt2yeuKuCxYlNufKComFUjvGVZfqug4saOgrViVYu28QoUnXWEaNIdq8dWTwGUkfXpVqBlOOaJsJitAeWZNW1CkpZ4NJtEobcp72VhLltG6J+GZom9+XJtTWY5F1lIvXHMGbhBcU4oispeGWyr0T3m5aTyzzKZD1ZpqYuz+dhQfsVLTQrA2mMIm1Z5WgysSrKKqdKBufYPSla1Cfa4dV2GCsXhJZEiRCM+n6dP3EMYxZeUHiMD0pm36i4VVX+qGN2hZiALD4TL8Qw3FuwFB9XtrUG96TCC1a6uobDbdJ2KWn3FsV4XxMvKLpiadQF56pfNY+LFbMzcUkodmfVKacmr15Lbw3jR+jmU+9qYl2erdsx38SyYT9pV6ka2EKMwsVr9YJiUlBHwLpr0LUxPyMEE3Xksopv0R30pkIp7dZg8ylbZmbz6k2ODXEJm/RfGwFdhvHJ+skmTFap0emD6BuCK7bgBcWkoA6bvIuQ+TNEsz3HlEVB8Onp6KS7MhOplDWi8y0ptkSRTXjTV6/LgK3DGLXZvFA7XCsSqeonpwK0Q0qbK7bgBcWkYIIX+zDneITokB5V++P/Zbn9QSDI0DJgSDYMelTc1H3RsaOyFdSKGSzjk6avXkZvENTH12wsCmWdDpivziPktP0dcQO7YgteUEwKXGetdGOcj2jRZg/p1FTdauq0aq7Y7TVNk/DYitzKaxHt0zgs3tJ7eqvyuFoZpK++t5ApyKl2X+HdyepSttXB+FZ5hOpqf9vwFkWJa6IFhUNzt3HLWSOVtOsRDLmdtByThVKpcsTMb3k/pnSH2WjUwdzh0ktktIJ0dpb7waKVoHBijIZhdIIf9jCwVNjh1nhc6SLgBoXIxnWt7W8ZPkZR4poUQSHlq47MAFMN1QkMRrLRlhvJzFaUI61KtvWChGsYC66+ft8rk8tE+8u8+t6C0NoKg23C9lcJ6Nu+23B6a5QubDuMDDLGTIWFi6yncYLPerK8JkFQNKHtGzHmup3YJpZAmjEn6SuaGSGcNCbcPJWNJJUlAsFlm/UjLndo1y7Zeg0iYftrG1MuObCJD8ui3HQ/BEH5hIaVgs4KCgDnArgLwN0A3ir4fQ2ALwH4JoBbAbxcV+YkCIomtB8bjbkyDKJtQkY2czSyBCQrrq20JwNt1eTIUlH/CGMUkkwrqWYfn0Uh42LGMZwSgfJKcJ1goZXUZuXK3ont1h8rCZ0UFAB6AO4B8GwAMwB2AViXu+c6AIP4/3UA7tWVW7egaCIA3ERyk47xZDJ2+hXTKw0ln4m7LQy28exMdjdRI81Q1OCko+PKtLJEEBNQCS9jzV62mWIZi6tp7qeKHlfZ9qWittQpV1OXskYU6KqgOBvATanPlwG4LHfPnwP4X6n7/0VXbp2Cooz5XmaMNDXIZT5v8VGgS7wKj9kz6KSivHo9M2P2cK7TK21trXkZ2liqYUqtNRkGR9AqXYVtMiCNu0h7pKpsUlVcIdeZTPLGs0bKo6uC4nwA16c+bwFwTe6eZwL4FoD7AfwAwIskZV0MYAeAHWvWrKmjD5nZnoGXHSOtjK1UpaYnilkx6Onp7IPT06Wkpm5r6yrKW2taaJUYTt20mSDd6ak8VKMdgFUNq/AyO9NfnSFEj3EWFJcAeEv8/9kA7gAwpSq3TovCVkupMkaM54hLszYuy/REMWMNrUpHxJ2euMJk5xIkTa+6R1Aryp9BxWOjmBosfsy8dpIc7VpR9e9Mf3XGtNGjq4LCxPV0O4ATU593A/hxVbl1CgpZIFJ2upbSXeDCVVDTbLDJ4jFSjKpMln5f4gpLNRkHnaVANu5OThOe337VBW1NNyj1ElQKxygbiQ6IFyYG2yqT0onQgLcoKguKVTHjPykVzD4ld89nAGyN//9PAL4DgFTldklQaAOQVZl6TYMwDM2ySY3PR67iXgjlB+SMjl2NmcoYKW8R6lZ721CrDVyYRpnKwWJ9NDaJzpg2enRSUER04eUAvh1nP10ef/dOAOfF/68D8NVYiNwC4GW6MrvkejJKaazC1GvkjINBsfhpHOYA+1Iugk1mhVUMWOZX+o6aiaXM/WOkvEXQEFxZI47LF7p26lSxY8JDbCocbGS6OLGzwj2G1bvphGmjR2cFRR1XnYKiDCMajRHZoTamM6KGFELjKmW0V/XpyBhZzu0gbWZvYTz9+AkUgt5JW2L/fyGgnCgrDSwsyL92EyHRqnA3YOpjN84M4QWFI1QaIFUj241ssm9Zf9V6VIwsVbRN9WOivEVQjAknOkC/Lw8oi/bQaoDbmQiL1piu4UAbO8vVEF5QOERpRmTB7Qp1yPYpqphCaIU66lExsn791bcOxZhw4lUMQ3k6cX4rEofcTvWuRE3uzIppnQTQZAV23V2mgxcUXUFZs1a23fa4j0wVIxvzphlDMiZcaa39Yw+YWxSGHV9VEAi36+qCJqCSzgZBem9RjNHVaUFhAG2m1CSNTGbpttgBHeiAitkeXHn7wmCbPEZRgtvp6CrlWuqK018lnVO/GS0kHEN4QTFGMN5u2/XIbEmjC8Piom2AeQZHCuc+dELrbBA6zd2oK2QL2oBS26nqLB3TrKaMPHLt9E+deRrSZu4fe8A8O0kmsHINE+2FNu7wgmKMIJ0zwWJ9TNKZ+lqOkUt3VE1bUamzPDOTNFgsuDIMdiIfa1i9riprWATQxU5MM5syHi6Xad6DQYaZW2v+sj6Z1Ah2Cl5QjBFascJdTIIKhBtbUZLJr7smwS2QhtXrcjygTOK9uq3aa7UoUntNOY0ldMU9ViO8oBgzNO5hcaHRVZjsNnEZmw0LJ1Txs39dDgeUCb9MV2d0YJBLJpwqwzpRQtdPE+769ILCQw0XGl0FYaPN9Eqd+GOzYWFZmddJpJhUv7cgbKNsKxnL4rU80JZfGt3vigmXtShWgMWggxcUHmq4mCQVhU2GTwSLHM5dlOWA8eLCFWlR5N5PiI08jcOFNpoe8aEpfrx5ZNkYxQqIQejgBYWHHlU1OpfcRlbWYCBM99RdXWF6pbtYwMQC7KvG1wwslH5voTE3iwuDYlQGhtzHvRxio3nW09jtKOkeXlB0HZPi+3TVDo12F4bJ+oulgiuKiHnDhu51ZyU5KmBiyhXXJr6gFDFGq7drlLYudIzKZXiLwguKTmOi7H5H0Gl3qT7Lp8p2tdsq8SHBw8o9nJJ9wGTSMlee8X5QlkzTVG+QpkdbVFeZz4dh+eN6JwReUHQZXpMpQtcnbfdZDesPtPXllIlweivPzhzN6hfpBIB8hWnlQ7B4zGj1toUbxlT/CUNxv1hWV91zJFr5aXpc74TAC4ouw/tGixBxGaJIS2Zur8/CUKz+VljRHASGMkcgnMKQxSuuZVdK0OZXaw9wNfd7C1HxvQVxWRaC2FSWqxboNWpRtK18dABeUHQZXRygXYiZiE5OShhyG32mW0mmqVv0+PR0qV00sjBdCp0SpOFgXmxBDOblxFoSZirLVVt+NBqj8AqbFxSdRtdiFF2hR7f1RNM06hhyiV1XXfjmpdaXomAjOVtRWZDV0ZtayhRpe7ywritKk9xFha1heEHRdVSclE4NgK5MGJOAdl1Wj6hs3W53JfrHWIm1XTGsOdCqCeU5DLkQQ0HuSNvp6cz6uNHVSgy5KwpSi/CCYoLhfHzXwUXKMPW2BJasQ2Wqb4UON9bsy7xgRZ87sWQMEAbbRnGQHvJCQ35VWWHOXEGH6ILLtUV4QTHBcM5PqxZoqd0qy2lDw5O0P5y7iPu0txg4DoLSNBk1scL7EPE9UXJPbVp8Sumw2Xqlqk6ywg2D0vCCYoLh3ACoMtNK+Mu15TWt4QnoFaaP0qHl4G8ZxG0LsSnKNsJQ3MSSL9jWMKqqxQuREnI2W69UsWy64jkdR3hBMcGoZWKUZdAlMnA6B5vFbf2SddgI45Iv2OZV1PY6cgsj88J2God5Bkecav8+eak8vKCYYHTK1DY93qzLKp6gQ/NB2PRVCjbMv8QLVi1ia/x1pJSOMNjG/WAxs3Yj2rNqyMCQg6mHKo9bb1GUR2cFBYBzAdwF4G4Ab5Xc8xoAdwC4HcCHdGWuNEHB3KEYXG6WSo/g7LrTONehvSmxf73XK1m+rdpr8YJ1yz1SBwW2pliEg3kOsL8ggGdnjlamo1OK05ihk4ICQA/APQCeDWAGwC4A63L3nAzgmwCeEn/+cV25K1FQdAFhyAVtUbioK9g2drNWpYmXQo1qr8rl1IWjxyuuWzSuoxOK05ihq4LibAA3pT5fBuCy3D1XAvh1m3K9oMihgVkjjGFLslzKJk+1OdmN+bop0TWqva5WOruGakG9iVHlUT+6KijOB3B96vMWANfk7rkxFhZfBfA1AOdKyroYwA4AO9asWVNHH44nwrCRHTFdx7AbcR9UdOdUPs6zJknYRR+9zoroCp2dR83a0zgLin8A8AkA0wBOArAA4DhVuSvOolANnoZyIV3HsGtndiUDxMo5WjPRHTBWSsNUkWibzk6jgRfbVUFh4nq6FsCvpj5/AcBPqcpdUYJCN3hUs9IhZIxAtdO1CrWnONbB1GskuiPGSmmYKBIV1i2uDDRgKnZVUKwCsDu2FJJg9im5e84F8Ffx/8fHFkWgKndFCQrd4GlIUMgYmersnCrNqoyyTF3FgVsIUI+Lm0ZlUXRBkI0FGlgg0klBEdGFlwP4dpz9dHn83TsBnBf/TwD+JE6P/RaAC3RlrihBoRs8DS7DdanF1m5ll+G8OqJaCFCPS+B3MBDTnxwv4mGAlWpR1HWtJEGR3nQts04hGTyijX1cntpVo4+jVvdJGaZuMlFdEx2X53xleMMYd4uoE1ipMYq6rpUiKMKwuI3zLA5yOL01O3jq4rhdjJrawLZfmlbrNdtflOrqloIX424RdQYrMeuprmulCAqplhYstkxAv5n6m0bT7VWtcu+XFBItCfaVNlSs0KHMAy8oJhCta2mtE9Awmma0rvu3RW497sZnbehYx6gExRQ8xhJr1th931kCtm8H1q4Fpqaiv9u3l6dJV1aVujZvBq67Duj3AaLo73XXRd+7oi8NWT8+9anl2nDffXbfO4SLrptIXH458Oij2e8efTT6vmuQSZBxvVaKRSGMUTjYVM2OgIrakEuNqsWspDQJUi9CmcUQ+funp4sr7U3b4P0/3UPHrHJ411O9aMXNGIYcTm/NZj3lA9k1ETgqDkPu9xY4xKZy5bpkXrqyNL9X7SKhHKBDy31T5vzRPFFVzjDtmJsjTVZHXPTNo2PCu7SgAPCzAH4i/v+nAVwK4BWqZ9q+6hYU+YFd9qTPylAMsgyNwWIkQBwR6JTfuNSodGUpfnfRJunrwM5AMXYAACAASURBVB7xD2XaWrW/OsaVOyq7mkPHOqCUoADwXgD/AuDrAH4//v9tAP4JwHtkz7V91SkoRO+1ykmflSCpOMSm4tjDweU1FhUJdKoEdcSicEGGlIdjSS0obCrpmAZaFRPWnHLokPAuKyhuR7QyehbADwDMxt9PA7hN9lzbV52CwnRzs7JKsQti+r0FM822JIFO3aodiVG4aFMpi8J2n5Mq/VXnepqS5Tbiou8QI+46ygqK2+K/x8SC4gnx5x6AO2TPtX3VKSg6ddKnhGmQ5NjOgmbbBYsiaYfLvT9UZUl+d9Em4evIW3JBUN1vWaa/6nJxVCy3douiY64dU7Ql28oKincDmAdwM4D3APh7AJcD+ByAa2XPtX21YVGU3SW1MgQjykiz7UqMoiMIB/Pi0/gG83blpIP8tDcrJESdZMApnTANWT1V9/yqyOlrH0tj6Ntqc35VCWafDeCs+P/nxMHs1wCYUj3X5tV0jKLKLqmN0ThzNDqC1HXWUwfa6wT9vvh87yqM1KSTNL4XZ0yjrmPvdL4jgz6odSzV4duqefC3KdtUgoKi3ycH69ev5x07dtRW/vbt0XqY++6L1kRdcUX3Fg6JaAS6T3drmJqK5mMK27ERl+NduA99rOlTPf21di2wd2/x+34fuPde3c/V6ylVmEG5/X40wC6+OLugbHa2sZV2R48exf033IAjJ5wQvd80ej1g9Wr7Qg8dAh56KDtWiIAgAObmqhEcQ/aagKhbXeCYY47B6tWrMT09nfmeiHYy83rhQzIJMq7XSllwZwNXmqkLZaqT1khOjXO2CZ8OmhfjTCEOQ7lFUVW7ltHfsttn9+7dvH/3bh7u2MF8883L186dzAcOlCt0165sWcm1a5czuuuuYjgc8v79+3n37t2F3+C38FjZ0O0UYLKzxPbtkYK4d2804/fujT4n97ooozUkJleMy/EuPIqshuh8Z4XE7Hv00UjDBaK/SUXbt7vbpmXz5kjrdVJYrtzU3hzbg21Y+4TvYWrLZqzd+2Vsx8biMw1sGQIAR44cQbB2LWjtWmBmJvpyZiaiVdYXOjz2mN33JSAygKamou9dgIgQBAGOHDli96BMgqQvAE9AvPCu65e3KIpQaaam1oZKQXRRhnOoTJfUb2GwjfvBYiY2QVhyrnwXaMt3WP6anY2C7K4CmzVHSY2yvhq0KO644w73hTZgUTBHBk9S1a5d5Q0gFUT9gypbeAD4rwDuArAn/nw6gE/pnmvr8oKiCBWDNmXeKmHjogynUDHF1G9CFxMOcoB95gJNIZCkP8UdJgyg5yp06qqr0XdYd7adLWoRFAcORK4rQ1fW3NyccdG/+7u/y+95z3usyJGV/6u/+qv8tKc9jU855RTps3UIip0Angzgm6nvvqV7rq1rJQkK03mv4pumzFslDFyU4RSSisJgG/d7CyPGLBMIAfbxLB3SK9+KjlUq8ERSIZURFl3bsr3MokUstRKQqkVQMFup+20Jiq985Su8c+fOxgXF1+K/aUFxq+65tq6VIihsPQkyoWLKvF3ELRvLERdwLRFjhmxxIhkK4bLbgvT70uNNgeGyddG1fP+y7W0B1oKihiwLESP/1Kc+xWeeeSaffvrpvGHDBv7ud7/LzJGguPDCC/mss87i5z73uXzdddeNnrnyyit5/fr1/PznP5/f/va3K8tPsGfPnsYFxQcBbAJwK4CTAVyNFbrgrktwNTFNmHdaGPR6y/UY7I4hrM8gdFBtrgo6R86YK/ShwpRSWllhKI2DZKwLywV/taPmjRVHcLD+wkpQ1KTBiBj597//fR4Oh8zM/IEPfIAvueQSZo4ExQte8AJ+9NFHef/+/bx69Wp+4IEH+KabbuKLLrqIh8MhLy0t8Ste8Qr+yle+Ii0/QRuCYhbAFYhWaN8M4A8AHKN7rq1rpQgKW39/WQZtOofyZdguQnTOaHKFyRnzsHydFTTsfrDoTmA1BU2jBoNlRaLXiz5bw2AgmIwVK0FRkzkkYuS33norv/SlL+VTTz2Vn/e85/Ev/uIvMnMkKN72treN7tuyZQt/4hOf4Le85S3c7/f5tNNO49NOO42f85zn8PXXXy8tP0GjggLRvk5fUt3TtWulCArZ2O71isy5ChOWMTTVHCpTn/O5mpNcwbGHheXPYTHKeipjxZSNUUgeNRX6raFCe1VFZhSKYJt2IJiMFStBUSbLwiBWIWLk55xzDn/yk59kZuYvfelLfM455zBzJCjSbqUtW7bwjTfeyJdccglfe+21QhI6IyiiZ/EFAE/W3deVayUIijCUn2EjmqilmbDCRaKaQ2XqqzsjStZfVbc7KpX1lPt9bCwKZvusp768nDDYpg/oCwaCyVip1aIwzH4SMfLTTz+dd+zYwczMW7duzQiK0047jQ8fPswHDhzgE088ceR6OvPMM3lxcZGZme+//37+3ve+Jy0/QRuC4pMA7otjFe9PLt1zbV2TLihkmujUlHysl2bCiqCra6ZfdzC0Y6dOZtBYkL9GWGU9xQ2Wji3R1ux1WhS2L8BwPQUR8QknnDC6rrrqKr7xxhv5pJNO4jPOOIPf+MZLef36c/jmm5lf//rf5de8ZoswmP3e976XTz31VD711FP5rLPO4rvvvpuZ5YLiggsu4Gc84xm8atUqPuGEE0auqjTqEBSvFV2650wuAOfGazTuBvBWxX2vBsAA1uvKnHRBodJAZYywNBNWpXE6diPVzSy7lpWTRw1JN43Cah1FfLPUWs1viV93jCIp1PQFiIREchnCckmGczgXFHVdcfzjHgDPBjADYBeAdYL7ngjgnwF8zQsKuzMxEkZYmgnLFoYF25SPOfNXD+adcc+mtfZxZ/y2EPavbGV2PIilFkWw2EzWU9kl0A5WaDe0yFuKOiyKPQB25y/dcwblng3gptTnywBcJrjvvQBeAeDLXlDINbcg0AdPrRlXBe5amVHWwNmbYt6T4EpKw7TfMveJVprnTNw6N1/UCooqKr0Dc8CBUVIJdQiKIHWdAOA3AbxT95xBuecDuD71eQuAa3L3nAHg4/H/UkEB4GIAOwDsWLNmjbvebAoWHEzFhGphhA5y2kuh674iBXSkO++vGiVgVWtUZ+JmrNVg0RnpWkGRUukP3HwP77r5CN9885B33fwjM35fcUOmibMohA8BO8s8lytDKSgATMXCYS1rBEX6GjuLosRMLG0d1MBMatOeuxx91sDFJozGqNl8qZIxZ2ziBkF0ORybWkGREhI7b3688ViBziipe2PAOiyKM1LXegCvB7BL95xBuUrXE6L9pQ4AuDe+jgD4jk5YjJ2gsJ2JZRh+jcykNsV/Qi0K582quZ8qyWuTsVrT2DS1KCJLwkyzd828ZeU1Eei2FRQm51Fclbr+MBYYrzF4ToebAZxMRCcR0QyACwB8KvmRmR9m5uOZeS0zr0UUzD6Pmes7vq4NyPbnF31f9kAH3YEUFWBDvhWuuCI6ES2N2dnC2RHGMDkwwxFUpDvvr9peQIRKZ2Js3hydnjccRn9FJ9vVODaViA9+eAwzwp/zR0w89FA03ZLvH3ss+vzQQ+VJCALgBS8A1q+P/ibHZDzwQNRlaQyH0fdtwURQvI6ZXxJfL2XmiwFUPqmDmR8H8BsAbgJwJ4C/ZebbieidRHRe1fLHBjYzseykqpGZODtcB8gy88svB1772tGhOOj3yx+jmROw2/e+GGu3/AymiGuRGaPzfIKDIAzRx7247glvwmY4PIxI92CVA4lScC2vC5CNwb176xXoQQD0+5jBUeHPMzn5UYZ5H3vsscbkvOMd78Af//EfAzA/H0lU/sLCAl7ykpdg3bp1OOWUU/C+973PmAYlZKZGcgH4huC7yjGKuq6xcz3ZmN5l/QA1uieceQ7q9LWn2t/EMadhyIXDkJKKnB5GlFRWc4pVrdliuoVBJdtiuo7C1M1TJkup7DbjpoFuUfnf+c53eOfOnczM/Mgjj/DJJ5/Mt99+e+E+ZzEKAD+JaKHbPQD+e+raCuB22XNtX2MnKJjNZ2JZhi9jJrY791UkX4k6fe0pAVtmpbkNtOsJ+v1y6aaK+8Z60YZsq4GKL8dmwd2BA8zvehfzM54RdeHq1cUuLJOlVHab8ZNOei5ffvl1ozre9KYred269bxunfk24wnOO+88/tznPlf43qWgeCWAvwTwUPw3ud4P4MWy59q+xlJQmKKK9phnJoNBt5L968xySgkh1fbeLqBdoaxpT3r/p3yXjPNaDCXSjXY0BmwEhcm0KhNgrrLN+LOetZo///kH+Oqrb+JXv/oi3r/fbptx5mi/pxNPPJEffvjhwm91ZD2drbunS9dYCwrTLBEX2mMdGnwV2uq0KFKcQGZRJKmrVaHc80jTnpqUayu0apw4HAM2gsK0WlWWkuj7NrcZX1xc5DPOOIM//vGPC3+vQ1AcA+CNAP4MwF8kl+65tq6xFRRNL+d1rcFXpb/u9sccMMQmlp1sp9uLqpJ3EHu07dG5610ZWDK0vqLcIQE2gqLKVFBZGm1tM/7YY4/xy172Mr7qqqukdNchKD4K4PfjWMVrAXwOwPt0z7V1ja2gaHrdgOv6XJTXgDobhvZM2IZ/SWMUwTbtqnadkKjboujE0hVHY6AOi0IEVeyijW3Gh8Mhb9myhd/85jcr6a5DUHwz/ntr/Hca8TnaXbzGVlA0vRLZtfqo4nQdCrCqtHYZY6hrTaSJq6lJ7X6MF8MX4DpGIYMqG0q3zfill16aERRbtlTfZnx+fp4B8POf//yRu+rTn/60Uf9UFRRfj//+M4BTARwPB5sC1nWNraBoQ51zqcHXlOboGip5JiOvLgZq6mpqStZ2wqJwBNttxstOhbb3bCqLOlZmX0dETwHwNkQrp+8AcKXBcx42qH1lkwAmK2dNIaI/jSZW2xpAtg4tCOTNr2tNm2q9Y7LG8K//OmLXmddT0yrzNoZgV1B2KsQLvDOYmoq+nyjIJMi4XmNrUTCPdz48cy1pjnWQaOtmqCvIW0qDd5kirYmZtDkEq9JhfXBRBdS9gV8dqMP19HREx6B+Jv68DtG2Hq0LBdE11oJiUtCUD6MkNym7r6JrBlpKaAXbsodIpRbyOa+sJbggtUlBMY6oQ1B8BtEmgLviz6sAfEv3XFuXFxQV4YIjNsGUxojxqWDT3WHI4mNpsbHVbVxcwwWpd9xxx2hhm0cWw+GwFkFxc/z3m6nvbtE919blBUUFuGS+dfswxojxZVChX5RrNHLtLlSDTeKHO+AOzMNF8sDu3bt5//79XljkMBwOef/+/bx79+7CbypBQdHvchDRlxHt+fR5Zj6DiM4C8G5mPsdhqMQZ1q9fzzt2TNZO5I1h7dpo1848+v0owtclTE1F/CMPouI2n11Bsottegfg2VnjXXGlTcYQw/DDozKE1dCjuI5/HZvx4ezDmne7fXuUg3DffVHw/oorquU8mMDFMDx69Cjuv/9+HDlyxCVpE4FjjjkGq1evxvT0dOZ7ItrJzOuFD8kkSHIhOn/iqwAejv9+G8ALdM+1dXmLogJsVDmZZqzQmJ0aGeNoUVSkWfp4sGh2X7LflKG1KDQw6RCHg/myPWCECfEqatGVxIEEKLkp4JrU/6sAnIJoHcW07JkuXF5QVIApI1PtRiuZ4c4n/2AgpnUwyNLZhah1goo+FdM+VO43ZbEwQzocaG/tXK1rTNQ1uigMywqKb6T+/7jsvq5dXlBUgOnolXGQXk8qaJwbALoCu5QHa0qzAUwYqNaiMKxPKXC6bLm1AUvJ1kWDuKyg+Kbo/65fXlBUhMmAN92YKKUxO1/drCuwzEyse/Y2pEZK95tKUmnznS5550qB08EguHNU2YtF8167uF2KC4uicMpdV6+VIChaN8vHwaIoMxObmL0NvbwwZO73FpiwxAH2cYB9y2svgm3ZG1XuQjokFjgdsChq7Uob5l9icMseCYL25nZZQbEE4BEAiwAej/9PPj8ie67ta9IFRSd8m12IUegK7KpF0SQXCEMOp7cW117MHF2uWtPmcDDPfdqbXeDXtjOdG5gHNmOhhIIhon96mnlmpr25XUpQjOs16YKiM77NtrOedAV2LUbRkoTvB4vq8WLC5BpcAW/crn7N88CG+ZckJt8/QdDu3PaCYoLg3DvSuh+rRtSQ9VS6u1qS8NrxUhNddcvF2r2ENv3iqLFtxy28oJggVJnXBSY3mO+AH6tmOBSEZY2Ufp+LezM1xAW046Umjl63XKxd7tr2i4Nx1ra3wAuKCULZeS1dPJVnXE2OzLrhmAnaTmRt9lEDfW3UBTVYlXVrx4148hq2ttuOP3ZWUAA4F8BdAO4G8FbB75cgOv/iVgBfANDXlTl2gqLEYCwzfrW59ZLZPNaeKccqmor5ifpJ2+fG5kj5zg/DrO87CKKcg7reqardruXiWI9NCdpsUycFBYAeonO4nw1gBsAuAOty97wEwGz8/wDA3+jKHStB0aAKoVw8JZnNbWs4leFYrVWlNIr6ScYsRwvWagys5wVEctWZWSMieWzHzgpEVwXF2QBuSn2+DMBlivtfCOCrunLHSlA06JSUVkV7pbO5bZ9pZegakFZ/ez0OsTFeezDkfr+oecuyf2XZKoplJdVpV0DHsOt6pzpLwguJbqOrguJ8ANenPm8BcI3i/msA/I7kt4sB7ACwY82aNc47sDY0mOYgVVAH81Jb1wl5bdrSKq0891uIjYX1BiKNWOS2kS9UX+LZmaPltOoKna9i2FIrp3o2bOtZOx7VMPaCAsCFAL4G4Md05U68RVGB8do+Wtmi0LlPJASZ0ml0n8hJL3Ck97GntOatikWE01u5HyxmaDSiu0Ln2+6wki62isdr7C3QFY6uCgoj1xOAXwBwJ4AfNyl3rARFmRS8BoMGlatTcQ5J4eFg3qhOY9pkN+ZoIixZa97KKtLZTSlOWZlug85XWRSiGAXR8qa7VdOvxzqmtcLRVUGxCsBuACelgtmn5O55YRzwPtm03LESFMx2an4LKlulrBmVL0LSln5vwaiJxl0huzEXQKhiUST9JDzLOiddrF5hSetRFqNIjKnBgJkwzDL0eFuPqu6jScxEWinopKCI6MLL44OQ7gFwefzdOwGcF///TwC+B+CW+PqUrsyxExQ2aMEJbBIYlWqNKq4oaYtMs8830bgrVH6YEjEKJeMzkAJNvUIVw5Zu6xEsevfRCkZnBUUd10QLihZmsWlgdERCmkMFgTwXU1JwMPWQURMrWxT5xQ4GWU9a7djA99IFRiwVxlgSNmF6OnqV3kqYbHhBMSlowQlsGhglktAn4zKCe8PprTzde7xQ9sxMDTEKh32W0d6DxWgbb8VeUW378WVutj72FNqjkvUekwUvKGpE4z7Zhiu0sihs1eVcW2Qukbk5cZONu6LGPivD+K3JcUx/GGwrbj2Og9lzKmJ0wQLyaAZeUNSELmiHdSMczAt890Nxm1Xmh0GnmFovLvrYFe+VMtLeghvGXocvKIzOqcgE36e3CsvxayNWDrygqAkrQtvq9yPffYqpDHB15MvP8ymV+WHA3W0WivV7C6UZsEsBb7Q1ShXJZtIpqfJdW1krYox7MLMXFLVhRWhbNo3UpUhJuEs6pmwcE8FSaQbskvmpFts54aymHdLv12LhrgSr2SOCFxQ1QcVwBoPlVP1eb3lB09ihTNxByt2LwkXEiCLeOOR+b4ED7FMz4hIMuKyAFynhQs8QDmfPqMZGuWDVafWmZhZRbdp/VTdd19ZWdI2ersALipog07Y2bBBPWBNh0blBXEaltOBY0lvjzQpF6xsyq55LmG9lGKqqGzJZQnSAZ3CkSG8+UGzaryYLWWLiu2jhds0i6Ro9XYIXFDVCxNhlu4b2evqyOjmIbaWXRUNMfPz5GElm1fPcXKnm1CX7VIvZShWYENzvc4hN3J+6r9gPMfFdjCd0jaau0dMleEHRMFSKnwoTNYirBkvzPn7VZSJJc/SEg3kr2WeqrSsFX7oiS/VfKNwSSyUVyO6aoqEKsbRhOXfR6uoKvKBoGGUtipU4iKUMUHREq4rjWFeS46AawWZsUcjuSwSfZmW6rC2mt3fNdSmjOz/WmxJoE6WMOYYXFA1jMBAPRl2MomuDuCmmE4Ysdy2ZXDpJquvYnCAJsZH7tHe0lYcsaA0sb7SXbotO8IXBtmjrcYkLSYQySkQXhIY8WaGdcd4Vq6sL7yYPLyhaQJmsp64M4lZosVlEYcthdFw2tVYEWCrshZQOWotOs5MaJwLBJw3Op1xINt2jSj7r0lhKM8Wy8r4uelz1SSapIZCviezSu0nDC4oxQlc0jcatG9PsnhpMtRCbtDvHJu226hfBzdJ9lkTPa7pHxVy6Zp2m0WXaykI3fNPvqqvt94LCwxqqIKQrIVYQiuljWWWBHskMUwpYDZeVnYEh0natXECCelU7t+o61kaJ6HK8q6sadRWYGMTJti6mW+k3DS8oxhF5P0feGV4zTAa+zeTOM7nBQMMsLCyMEJv0jEfBZfOH+Ki0PeP011S9YbBtFIPpTYmZRCbLywHXrFNrdTE0u2I5u4LJAvok5busVVk3vKAYN4RhtPFbfiSJ9tuukQTDdV6F50xWLxsFNPOFiQIEMD8VTwadUMwc822x86ppPwqzvCpyjbq09g4MzU7CyKKIlQFhnKoDFpUXFOMG1ahrUO1I82mplkTZ+0XMScLfteUJCRJUILMIbI7vlDHzgrZLJF4AKKlM9ip7vVj+ybK8HPghjLR2S9W+9aHZUVNEG6PIZ74lY6hDzfCCYtxgypkbhIkro0rikjGzETAKF26WwcAwt9+yMm2soM3IZgmzo9Wh2dXgRnrlfHxCYibrqbcgVgba9jXl4AXFuKF1ta2IMCyedJZ3N5hudJpmLC7mvJR/pIPjGrXNmF8bS5SIMK1bzJD51aJIx40uWEgSN1rqkXaGZhfThUwXc3ZRwOXgBcW4QecIbsH8FpE0PZ2tWjaPg0A8T6zPpNbQlwmWb7gzXjSXcg8pGLpRJoos2CJKz43vNfJHa95nbXwmdqMJYy6SsluNUTSdymUyz0yFl6ysDrnSvKAYR8hSS+rgGgaD1WQ+qEhrcj6EIfMsHSoyP2wUT+DZWbNMFF0npBuZSu/NaOy9Beu216JIhyFzrydvt+KEvtYS8pq0KEznWRXh1TFLwwuKSYLryWI4WE3ng3OBUKJAaRdhT5HglPtFq/mrOsE0TayE9utckU7RqlzX0QHmJaO7dtpM51mV+dgxV5oXFJME11zDcLC2MqZtXD0paHdwldxc8NXbBLJNI/klOqx038uEbKpA7cFQLTOvApoyTW00o7LCq2OrIjsrKACcC+AuAHcDeKvg9x8D8Dfx7/8GYK2uzIkXFK45tuFgbcVKlrU10eAtH+vT3uJzNv2p6gSTSH7JDiu1yaQBrSE28jQOF8qdwZFilo6OeXXI1+4EtuOiTNu9RWEkJHoA7gHwbAAzAHYBWJe75w0Aro3/vwDA3+jKnXhBYZN1YwKLwdo4L9AdZiCBkEfSIQ4H86M2AEkYYch92ptljKr+NNDSM9dowUS/dIeV4icG1o8sPhFgn11lHfO1O0ETbepYv3VVUJwN4KbU58sAXJa75yYAZ8f/rwJwAACpyp1oQVHSFWNdZlcmucqdo9FwRfxcFUaYpUMcYlN5hl5jP5byUBjEU4ziEybtqEkztlZMXGsyTWhGHbLEuioozgdwferzFgDX5O65DcDq1Od7AByvKneiBUVdpmrZwVr3IFe5dBz6+qt246gbMIwXV1UQOBZ0l7YoYqKlazyCxbzZpW6Pgx0kC+nNur3A8s+ZWIYdYspdxMQLCgAXA9gBYMeaNWtq6MKOoEvBr6YsEYeuNl0YoUw3Vu0GE96lrcPUfMoRprzFpmEmgXxFp8gMZZ1wFJKY3zcrnbrcVcu5I+iqoJho11MtykuXgl9N57Q76Mw6LIoq3WDDu6RdICgknN4an6Cnt3Ck5VYN8lt0imnCWF6YK9Og8w90ae50FF0VFKsA7AZwUiqYfUrunjfmgtl/qyu3C4KiNuWlS1qRS+umoiAwfVwZo9B1o6SSKt2gZHSm/ZArxNnOpLYNS/ePCZc3qErH15Vp0PkHumSNdxSdFBQRXXg5gG/HLqXL4+/eCeC8+P9jAHw0To/9OoBn68rsgqCoVXnpip/VVSMrCj+jx1N9Npi7obDbrDYfQFFJlW7QMjqTfsg97OysgyoNs3xWlQWteq9aiyL9QEsWRVemqwk6KyjquLogKFaE8pJinpmFasGi3WSoOIG1j+eYfClGqqikipwzcp3o0lJzg83Z6WlVYkOWnSK7XbcXmNhCHHKAfcUzyFuwxrvkADCBFxQNY2zdobbqTxiKD/KxmQwVpartNt6lGKmmEpusp3QXB3OHeQY/yvZdPhirIkww0JxYFC7SsC3HUqZf0lt0px9N3RQG20anDYpej3AMNqzejxsf8ILCBg4GU5uaRGnySxJdeTIICtBaKKlGarfxznERK0YahupTl7QpOPoso2kc5gD7srvcmnakgEOKYhTWS21a5HDSbhzMR0F67GFgSSrwjchtSGCMm2fBCwpTOOTwrsdi5TRKXQElmYPVZDBI4xQGY2eOSrN8RPcDqR1NywZ7w1C8n7bsIYP+M3I15TsxKcMiLXWAqwuMtEkrL+k+K+N0MB8LffFphQEdEL5n1SUdg1XnuGHjvEXR4auSoOjomxVqotNF81xKfrConxwlmYMqEJmhbzAvpyFFvFbjl1ggAR0Q8/KUJppo7YOpa6P0UdU81+Rshhs+mOUV2KTtP6MsHVPBlAwMgTCr7H4qMw9iN+Syxj/Ukj96dDBvIATEAkR1CcmtOsctBI3o9eTPcOkSvKAwRUdtRZM8c1Uau5QRpSdHyQlkmkIfbZGhOQ6SSB9DkLwj6b5FQWSRZGiZOao//U6RsxliI8/gSObrGfxI277Ep164OsmqvwAAFk9JREFUJbYoEpcbsMQ9HGXk3VG9XpFOgWusckDbVusOQw6nt2qZvWwoydyH2ctOUEjJrTrHLeZJGOpPhewSvKAwQBhyfN6twFfcskVhmmeeOitHyIiUk0NltqQLFzDVMGTuTRn4jEV05FZQlbEoVMxRSgvtzX5h6kqCfGvuAPvlZUqYaRK8lrnQ0vcI6RQ8YGNRyLwoy66gRGgN5UNA8d5MeLHu3RExH3vMY8p7RBa2EFUtCgtB01EHhRReUGggVKCSydmBfDYTiyLNmwq8KtgmvjnWUItZOxvV0knQJyaMWmjZ5ALCQma66og0RpHQI9PWy9ISDua5j3uFioNcux3KrZT4JRbOvKDNUVBew2iFKbNhKLxZerzpYD7zzsTB9SM80zuqpCXhi6MAucISNGGQJhbF9DQXrLioTsWpgQYxMdl4lsKC+3fUQSGFFxQaSN99b8GdkCgR3VbFHYT0Tt0XMbh8NQr/UDi9teiayadoGkwME42yj3v1E3Qw4BCbssx0emv2PkFfyjI6jRhvbgbL8vM34LPMSkGheM0arqHVqtOCLeE0ioysgkASKD22wlXal70FqZVlwovNYhTR9ueFNsk4r0ogqOaibp6mytVl53mLosNXGUFRu+QvocWY+v4LzF3mBA1DoW9K6qaQuasknSNaT1Ggb+pCvX+gwuxKC1alQaSJl8iF85AHuFrLFIWvWdOuUhZFCa4eBttG1mOZALHsmsKPBBr/cESuLiaUuLoigSmmyyjWphhHITZGdciGn+k8Dc3WDzlMomwEXlBoULvkL1GBmSUxtIupCLin8ZkEOtpjt1GiYQXYJ14foOtUydqAPvYYG2OqvtNmYIlJGF09HOUQG3kqt1BO21UarqEStNIYhSU3V8VBXFxzeKSoYYeStSgKjmmVvSdLIc69RKO0aIt5anqr6zT5OuEFhQa1SX6d70hhspgEsI2C1GkIaJFpsglDFHMugy1ObdscP5t3mQxwtXCCq7Z3MLIQpVFc+TkN0TXkPvbwsXjYmIEShsI6M27CYDFK2xVlPdHmZS6UbqjM9RQEVu/a5VV4pyqzWKI0KOejymxU7O2kavuoWyUDJ8SmwlAZt/iDCbygMIBzyW/iO5Jp11pmpXCfaMoVbUmdj1GM6khrslNTuVmlgYHKlQmi016hUFClehryCG235PsnxEbWu2XM3Tb93oLJq4jSdpPEA5N9llT5l4IK5HEQdy6ozIvV3azgqsr5KHGjZl5yrv26GJAs6SPERp6lQ4V7ZTK6q/EHE3hB0QZ0E0WllcfMSsw0h5FZP5gXrxzWJWpLAsHSeVc2oC8TlPGSaWHw2TLFNX8lywykGqlu7UTqnW3AZx0x0HiTOtNAZ29BvW3IaMm5/H2OkNvYr4xFka5Ox/+DQPHeXXHVXPnFoP0mYd+YZFaJXFt92itt6zjFH0zgBUUbUKbc9Jd90/l5PneRYhLk/NR5/2+eiTgiN0eyORT+aVcZN4KixYktmrhEvhNCbOQ5PMxQBn1VgkS9MrnSKm3DVVvJSmmVG0/VBtVrlS4kM03TK8tVU+VL04DzO8eymfwi4sLAya8wT99r64XoerzCC4o2YOh6ka7f0E00xzauyfwuNbclBdtYD6YLDpVdY+KPiu8xDfomoQAi5rk5VjLdTFUK16I228zw/YchC5noAFdnEgPkfTsUMtx0+VZpwPmOK8slDSykPvYIB6vOIjJMnio1/WrNgHIkgbygcADrd2EwMqSD0IRZOI6amXoMrOWT5ZYbos3sBgO5a8y4a0yij3HmVhRIVtdhsZA7W5XCtWisJBi8f+XYil9iGGraiINGa1iMKk4GT1XOmCpfm7FXJliea2KkAKjfvSXZ1eZTvv8HA2cSyAuKiiitDWgmVSX3g2jfHwftrMifipDMkDDYJrSmRhovljJdZmNVlLUowrC4L1T2GkaB936x642y1PpZOqSuRSDSumPpKLwvHbQVjDHl2IrvMRFuacEinAj5vTMcMi4hUjRo1wCVCJbrFKbMinQLaPUUE01UGNiTFFzC4+AFRUU40wZSUAaQTd0PNUTPdMyjlAYk2UIzDJn7U/eJGeXUVCaOI9tLyniX0tQkK6yojYPcRoveBAJa9S4LdJlIlFSgJZy6UGx5zGxVPisds/H6BtNQArC0zM1SrjmpgNPlLktejbW13u9ziE3FjKS0VVZighoJT/ti1TzEVBM1f2mlPA5eUFSE65xpldZScD9MT5tplo4gOgEzQ1tuvyCjxvZ6OdrvXS5HJEhSkyUczCv7ajB3Q5HJKNRF4YrauM9VcZMZHFl+L0GQaZ6MPuFaMBNJnHZPanaclZUh4z0ihV99RetGktRRI5dZr2clJKwMkNy7DQfz0ZbxsoQPS5jI8TLzXtlOU020slmthhcUFeHaopCV1+txMYUzzhqST9BNmtrMIcpmSbJ+SgumIJDQfmh5HitUclnQd7QgMJ9+EgTFRqSYhsp3r7YohtlFcDHtyncp4lODgXpyh2EmW8l6O4vkCoJooVhvIeMus1FK82NtgGuk8RvpAk0Fw1Za1aJhJnO9DAbOArp1WRQJ+VYJACmJFIYs393aZM2NAbygqAjXGQtWFkp8s9QfK1jMZYXU6A2mHhLzHOzTEKmAivZgUZd6ow9YihLaFbNb5bsf4GopYy7wv/hMC5uzH5STHWCemzPOuDJ2T45eYpRpZJtBlr3UfZO3LDJWZH/kTRsxY+sF/P0+D3D18rbnOMoDXL2sLJSBwEJRDSfl5oailGwT4aXRRJXZkSVcfTJ4QeEALnOgrSyU+GY5wxyWJyQ3ApXbZ6cZjk1HaJi90t8NuUUxSoG02UE1VKyoxR6rzf6iZ+41PvtBOdnT7dXESQCDc7YVXK6u9Suj4RHv8Gp6rrW0bwVzYYBrBGM02qgxn5TQ73Nq2/xNmbGa+Z32FmIs6a1VgsDsnAuj96tZZJt/V1or2OXu1szsBUVbkEgX6aAS5a3HN2sZkqUky2u36pXIy4IipAulW4CHYeJTXz7spk97FQf97FP7u6enhRreLA5yOHcRh3MXSYWMSDNPhXsyVxJ/sF2JnQi6wgp6Yt6w7v7CwT+yPkh/odtmg7DEq5A9xMcmrTakzQV6p6aSHVrcrESvWoaMn6rcXokJotO+peOp4kFlxqnuqfhWcUKK529T+0p5QdEGNFpCwlSNgnCyIGx6ozQL35g4ACuf4Akzk20rMpj5gDK1dBqHC1tQz+KgVICMJle83Fdk0qtOi2PYbVeRtM+WySV0ioWsaVnDDJMqu3GfjSsqbWkFc4cFcan2rt7UklTH0R4YxWqGHWKjVNgUzroItlm5D0xT3UNs1J7Xnh/vTe0r1TlBAeCpAD4P4D/iv08R3HM6gH8FcDuAWwH8iknZbQuKZbNW4hZIv13LKLlU6ZCVI3ET2QU0I9+9mokN+ZhV6kVqmYkYLHIYbDPb4lykgSlSWZOgqtXK7zgFVBajEV3pI0yr7lGVThawiZPkyzB2QaUu/R5Ibs+t0NU1mLpWypjlKcjDURadPO6xpIn95NKsVcfPCmBiUcjiT/n9tERLVRT5Gc7QRUFxJYC3xv+/FcC7Bfc8D8DJ8f/PAvAggON0ZbcpKERadWERWSpjRhvNE6kcImkhKEd1wpltQNMkfVTHTEbMP7VGQLbhWkE7zvcBqY/eVFkroquHaNfWcDCv0a4jphkFUf+UeWbG6dbdtnTL3lP+/UvPBoE6LhXRUlUI2l2ZBX45DDbcqRxnSTxB9o5L0TL6IKYpM/c1Li3VWEmmhaXO5xRdFBR3AXhm/P8zAdxl8MyuRHCorjYFhSxQKNqWQpk/LbyZmTdsKP4+Pb28BXh8KXPd+/1SKZK2ZzAoJ11sJYQbPmi2jUViVaRmko5Bi+IfqitJRQ3mDhtaCEnqqmttu1p5iSDQnjaosYYIj9fQNn07M9Zkr5ddBh1nPanoEmrfOMhlBJ7w+FkFCjpcaoNPhn5/M1UCoNUK7pLooqD4Yep/Sn+W3H8mgDsBTEl+vxjADgA71qxZU7nDyr4LGxdEv8/q1W35m0VCQnLptjYQrWjVM4Ulg8kmLkPI/OOVX7qsp+QKZ7Zm7hPvhJqaWJbprnZ9UedVte6hofasuq8qDdFYCbCvEJsChjylEEL54P5orDAzExklHQTYt7x+JM56klmvqitvUVjzhZyZoVNuknKFtPSL5THg1AfViqAA8E8AbhNcr8wLBgA/UJTzzNgCOcuk3qoWRZV3YeOGIAxtl8gaXya+/xAbuU97o8kULGonn6npTjGTyCxOEzF/i13+ZBbSck598TFbi6IrV/2afP1XehV7IXBeEBzF9yYcK8zK2FS2D4tuzhCbrMZDYU3Ihg+W3+8ttbOCioZE+FRewV0SXbQojFxPAJ4E4BsAzjctu6qgqPIuVOceF8rrLVSbjYpLu1lavmGaCWhrulsvBqvQHpkQqeLrb/daMqa9ehC97KUXZKIxYOLylK44Z47iawZtLsQW4ooTobXsNhS3zWo9jwFfyJ9zEr1f+R5lVVZwV0EXBcV7csHsKwX3zAD4AoDftCm7qqCo9C7CaJtq7QrWWY4WAdU0k622sCYamfSiQ20C7EtNMDMSjHe/NSxQZyGJ3FeumWhTTNkkxpDQkyRJNGmBTOFxI5deYQzMzZl5WUVKRmJRsDwOmFyFcR6P7/yN2tRs0zFowhcEEjLExsg9ZuPeXoEWRRALgf+IXVRPjb9fD+D6+P8LARwFcEvqOl1XdpsWBTNH2TyKlMPRHkA2EWWbwxhkA1FwHvCoYTmNS7iAbd0VgriGpEidRWG5O52VhaR5JjPJYzdZkQnktL2ZozyYujaljZbP/lJ2Sz5rKR5H+R1yEyGhb6v5Wg6T+6ZxeHmdiiatNvNu4kGvG/Kzq46IlZlUQFu23ZPMEkiP7/QlVaZos3g8VbEoXMUWJjFG0dbVZowiXYb2OGubhGn7LT+LRKsaJl6Bx6NJHk/UvEk8GIjTgUNsZF61SjSbs+pTukDRZn5AtP9Rb4vcQpKoqTqtXOZHTwLlo/hNQm4q4yrEppT7YDk42w8WJTkHeUYfPRdgv3wrDpPFmSqmFw8b2ca8o/tiIVhcSLmcClyINel29Z05KlywJmfyqdvSp1Tls564OGwy78dyfBcUo+mt0vM0hCu6bfiCq2yllZT1VOflIj3WxbvI7yskPP1RVJGs8jxTTQpPJpXJpjSqhpVsdIGJJduQlClP0fbM+c/xmQq6PkzvnprpnnjBHxNFR5jl0ouN6FW0L/NTsBhtha6j3WZToby0zrU1P2zS43BuTlCNqn8VbU3n/SfDUNd1NfI5+/FtMtcE7a1zLUObUAkKin6fHKxfv5537NjRNhkeHh4eYwUi2snM60W/TTVNjIeHh4fHeMELCg8PDw8PJbyg8PDw8PBQwgsKDw8PDw8lvKDw8PDw8FBi4rKeiGg/gL2Wjx0P4EAN5LSBSWoL4NvTZUxSW4DJak+ZtvSZ+WmiHyZOUJQBEe2QpYWNGyapLYBvT5cxSW0BJqs9rtviXU8eHh4eHkp4QeHh4eHhoYQXFBGua5sAh5iktgC+PV3GJLUFmKz2OG2Lj1F4eHh4eCjhLQoPDw8PDyW8oPDw8PDwUGJFCwoiOpeI7iKiu4norW3TUwVEdCIRfYmI7iCi24nozW3TVBVE1COibxLRP7RNS1UQ0XFE9DEi+nciupOIzm6bpiogov8Zj7PbiOjDRHRM2zSZgoj+goj2EdFtqe+eSkSfJ6L/iP8+pU0abSBpz3visXYrEX2CiI6rUseKFRRE1APwpwB+CcA6ABuJaF27VFXC4wDewszrAJwF4I1j3h4AeDOAO9smwhHeB+CzzPyTAE7DGLeLiE4A8CYA65n5VAA9ABe0S5UVbgBwbu67twL4AjOfjOj0zXFSHG9AsT2fB3AqM78AwLcBXFalghUrKACcCeBuZt7NzI8B+AiAV7ZMU2kw84PM/I34/0VEjOiEdqkqDyJaDeAVAK5vm5aqIKInA/hZAB8EAGZ+jJl/2C5VlbEKwBOIaBWAWQDfaZkeYzDzPwP4fu7rVwL4q/j/vwLwqkaJqgBRe5j5c8z8ePzxawBWV6ljJQuKEwAspD7fjzFmrGkQ0VoALwTwb+1SUgnvBfDbAIZtE+IAJwHYD+AvY1fa9UQ01zZRZcHMDwD4YwD3AXgQwMPM/Ll2qaqMpzPzg/H/3wXw9DaJcYxfA/CZKgWsZEExkSCiYwF8HMBvMvMjbdNTBkT0XwDsY+adbdPiCKsAnAHg/2PmFwI4hPFybWQQ++9fiUgAPgvAHBFd2C5V7hAfCzoR6waI6HJEbuntVcpZyYLiAQAnpj6vjr8bWxDRNCIhsZ2Z/65teirgpwGcR0T3InIJ/jwRhe2SVAn3A7ifmRML72OIBMe44hcA7GHm/cx8FMDfAXhxyzRVxfeI6JkAEP/d1zI9lUFEWwH8FwCbueKCuZUsKG4GcDIRnUREM4iCcZ9qmabSICJC5AO/k5n/pG16qoCZL2Pm1cy8FtF7+SIzj63GyszfBbBARD8Rf7UBwB0tklQV9wE4i4hm43G3AWMcnI/xKQCvjf9/LYBPtkhLZRDRuYhct+cx86NVy1uxgiIO9PwGgJsQDfK/Zebb26WqEn4awBZE2vct8fXytonyGGEbgO1EdCuA0wG8q2V6SiO2jD4G4BsAvoWIj4zN9hdE9GEA/wrgJ4jofiJ6HYA/AvBSIvoPRBbTH7VJow0k7bkGwBMBfD7mBddWqsNv4eHh4eHhocKKtSg8PDw8PMzgBYWHh4eHhxJeUHh4eHh4KOEFhYeHh4eHEl5QeHh4eHgo4QWFh0cORLSUSjG+Jd4SxbaMV9W9KSMRPSlOh7ymzno8PFa1TYCHRwdxmJlPr1jGqwD8AywW1hHRqtRGbib4fQD/bEuYh4ctvEXh4WEAInoREX2FiHYS0U2p7R4uIqKbiWgXEX08Xq38YgDnAXhPbJE8h4i+TETr42eOj7cnARFtJaJPEdEXAXyBiObi8wW+Hm8gKNzRmIhehGjjunHfjM9jDOAFhYdHEU9IuZ0+Ee+hdTWA85n5RQD+AsAV8b1/x8w/xczJGROvY+Z/QbQlxG8x8+nMfI+mvjPiss8BcDmiLUvOBPASRMIms9MsEU0BuArApY7a6+GhhHc9eXgUkXE9EdGpAE5FtB0CEB3Uk2xJfSoR/QGA4wAci2hLGFt8npmT8wRehmhDxEQIHANgDbJ7Kb0BwD8y8/0xPR4etcILCg8PPQjA7cwsOr70BgCvYuZd8W6dPycp43EsW/D5Y0MP5ep6NTPfpaDnbAA/Q0RvQCScZojoIDOP7dblHt2Gdz15eOhxF4CnJedcE9E0EZ0S//ZEAA/G7qnNqWcW498S3AvgRfH/5yvqugnAtnhXVhDRC/M3MPNmZl4T7657KYD/3wsJjzrhBYWHhwbxUbnnA3g3Ee0CcAuWz194G6KTBL8K4N9Tj30EwG/FAennIDoRbkBE3wRwvKK63wcwDeBWIro9/uzh0Sr87rEeHh4eHkp4i8LDw8PDQwkvKDw8PDw8lPCCwsPDw8NDCS8oPDw8PDyU8ILCw8PDw0MJLyg8PDw8PJTwgsLDw8PDQ4n/C1i8Z/SjDkkeAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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4+uijxdFHHy2++tWvSusn6yMAG4UHzW2c6NskZgxqtJUZFNHvDy7SPPX7Tdesu5hrferCGIToxroIjZCMgY3PHUZX7gbusmG8reA+1YPDlVQD2xg6jK64qHbZMN4GyGwJdfVpsezFi9PENo3RBzOGDiPmrjGkYbPthvE2Q+WTf+qp8fu0XPb996dJRD4boEKqGWHIELxvfPRPdaeu2Rjq0m/G0jPHMN7NRZ1vCOjGOHafqspuwqZx1113iV27donp6el6CuwQpqenxa5du8Rdd9019Bs8bQx853Ng1Hlfb6yyVPcZ9/upay6jPjR5H7eq7LrrAaS+/Dt27Bg4J8CYxYIFC7BkyRJMTEwMfO9753Pj0oBN6pLEULe3SIxdI5G8DUTV82bJwQ26XXvs/muTxMDwAzwlBrYxOMBG7163t0iMg3axDJttC1feBaiuEAXi95+ubIDtRCMNH25Sd2qDxGCrdx8F//JYB4RGoW+aQC5lNbFrL0p4vV6aWNrrDsA2hriw1bvXaWOIifXrU7fXbdtSSWHt2ur1b1JfPgrg/mO4wtfGwKokS9iqiNoYYM8HXVJRzRX49F9Vt2OOxzRH4SNm1J3aoEpiNUh1zLX4PqHh2n9V+5vHq/sAx0qKC9tF4uJ102YPnVh1a3ObuwCX/qu6meHNUPfBjKEGmBalyw6rzbuxuuvGzCIOqrodx3RbZtQDX8bAxueAcDkY1uZDZHXWbVSM9W1E1XFs8xxl2IGNzy2AyxmGNkfHrLNuIQIBzlUDqandVWNUcYyrOQwfMaPu1BZVkgkuOlnVs71e82qVOnXLVdUVbVbJ5YihKoth86qr7oz6gLbZGAA8HcBVAG4BcDOAt2ffnwfgHgCbsnSqKa+uMIaqNoaJCSHmz2+eyNUZRG/UDaR8WDAsmFG5oY2M4SAAy7P/9wdwO4AjMsZwrkteXWEMQlTzSur12rPYQy5AHXGsSjjbbiCNRcDb3u4Y6IJ02Da0jjEMFQRcCuClo84YqmBUF7uJOFZhQm3fOfuOqalP2t7uGJhrbQ6xOWs1YwCwDMA2AE/MGMMWADcCWAfgAMU7awBsBLBx6dKl7j3SQYzqxA/N8Mrxe9qgflPBZ0xtdsZzcfc8qhsnGUKNb2sZA4BFAK4H8Ors89MAjCP1iFoLYJ0pj7kiMYzqYg/J8FS2mbYGd/MZU9v+SpJB9WOv1662h8aobpxkCNXWVjIGABMAvgngHYrflwH4iSmfucIYhBhN41pIhtdF4uA6prY741HdSJRRjC5b7ptRbK8Q4aSj1jEGAATgPwBcWPr+oML/fwHgs6a85hJjGBWUieHUVBiG57NgusZsbZlfF5mkK2TML58DXRhLX4ysxADghQBEZkuYcU0F8CkAN2XfX1ZkFKrEjKFbiLmTdV0wIetSF4OxrfNc0LnPBeYnw8jbGEIkZgzdQszF7LpgQtUlRKRSF6Zi8/xcIJqy9o0a81Nh5L2SqiZmDN1CbHWPy7OhdtUuRFimRoshQY26jSFJ1OM3SswvJpgxMILDd8fSpLqnal1UqGIQjkncumY/cYFq7IhGq50xwYyBERRViHVT6p7Q7fCpo+q5uaoOqQIVQwWarll34MsYRjq66lyNuhkCVaKeul5vGjOaa6irVm0jjbrUma801UPVP/1+vfWYk/DhJnUnH4lhVPWvdakO6vR46YoRVdX3xe/Hx9X9NmpzMTZGdQ3XCbAqaRBdITYuqHOh1Nl/XSYAsrqX0+RkuHMccw2jbEOpA8wYShhF/eQoE+upqdnd9vh4+rkLUI3J+DgTM0bz8GUMI2tjUOknibpra6jzZrVQuvkcOnvP+vXAJz8J7NuXft63L/0cc5xC2Z9UfZ+3hcHoJHy4Sd3J18Ywaj7QXVWPqVw4c6mg7nbVEbtJlndTapE6y2XVT7sAViXJOkWeuuom2FVdvMkfve7QDrGjvermnWrsYhHUOudMV+fnKIMZgwRd3WHr0MUdmc7e0+/XP04x74ewYRDlNnbhgF/byhpFxFjbzBgk4B1MO6BTt+RSQ53jFJOAuRxwy9sfsz51SmNzIahfLMRaA76MYWSNz0B4AyrDD2vXpv0vw9Kl9YxT0di8Zw8wf/7g77LDaj6QHYTTYenSuE4FKieMGIfr6ixr1FDlQGkU+HCTutNcDYnRRbWREPJ6T001d8ir7lvfihfL6FLe/i6EBGlbWaOGWNIWWJWkRhcJbFcXma7eTY1DU7pv20tmYo81eyW1H7HmKDMGBbpKYLtqyGtjvZvUfdsSSiaocxttszE0TvRtUhXG0EZCZYOuGvLaWO+uzoFRATM9O7TJK2mkjc9AvaeFQ6Krhrw21ts2MmoM+JywHqWowOvXA2vWAFu3pux469b0cx1t6lo/rloFbNkCTE+nfxt1kvHhJnWnuSgxdFUF1tZ6N7Fr9emLtvafL9pk3+lyP/oCrEqSow0TxJcodVUEb3u9i55AeeC+GPX0IYpd3cio0JRqcdT60RfMGDRoklC1gTGFRNuJvgm6EBahx8WFKJpcXGMT0ljjqiPQMedSG21dTYAZgyWSJPVfzydKrxeXuLV15+KzKLvA5EztKo597HGxHXubeEsx50uocZX1vSrvqSl5m0Otx7auu7rROsYA4OkArgJwC4CbAbw9+/5AAFcAuCP7e4Apr1CMIUnSg03lyTJ/fjziFjMuT7/vdwGMLyFo+2IztStJ9MTXtJv3UQXa9LPpMFxs5htiXF3Pr+jaHKK9XdjE1IE2MoaDACzP/t8fwO0AjgBwAYB3Zd+/C8D5prxCMQbdZIxF3OqO5FmFwJsul2m7eG7qa5vTyDa7eRcCY3MBkSnIYGxiFmJcXee5KeBgqFPfXVZ7hkDrGMNQQcClAF4K4DYAB4lZ5nGb6d1QjEE3GWMRt5A7F9sAbaZFZRMF1GVnG1tfbAsTgTO127XNJlSVGFx37L7976LyUpXhylxMc7ktm42uo9WMAcAyANsAPBHAg4Xvqfi59M4aABsBbFy6dGmQTvKVGKoSvVBE0zass2lR+TIYF31xE2J7FYkhhpRUxcbg0n++brH5nOz1UnWq7n1TGa7MzST9tkU92XUEZwwA5gH4MwDfAHBjlr4O4C0AJqwLABYBuB7Aq7PPD5Z+f8CUR5M2hjbpKkNJDDYqKRXxc9EX1724bWwMrmNZpW0+Xkkxd/zFslyDCprK8GVOMmeAuWgLiIUYjOEzAP4FwAkAlmTphOy7z1llDkwA+CaAdxS+a0yVJIS7V1JbiJ4Q4WwMeV45Icp14L5tbJPtwURgXQmw78YgSdz6tQpjCKXG0Y23TRlz7bxOFxCDMdzu81vhGQLwHwAuLH3/gZLx+QJTXk2G3Y5B9KoshBBeSbI8q0hFbWKeMRCCmej6te7+95nToz7Go4oYjOFaAK8BMFb4bgzAawH80Jgx8EIAIlNBbcrSqQB6AL6duat+C8CBpryaZAxVjY+2ft1N75KqMqu629TmXabO66ustw/hKefa/z5zuq3zlqFHDMawDMDnAOzKXE1vB7Az++5Qn8J8U5OMoYoqQfae6oBVmWh0DXUS6rYTKVu1i0kt6CKVuvR/lTndVmbMkCOqV1K2y+/5FBAiNX2Dm8+CsDUUt5W4tRk2htCQdobQ9dM9U5eahon83ECt7qoAXurznm9qmjH4wNa1tA5iMGpEQLcjj+GZ5AqdW28+DnVuEkZt/Bn2qJsxbPN5zzd1kTGodoS9nl6FENqTpy1ql5DESbcj9z3L4MOQdW2SOQnYuAgX54mvI4GpXJZM5w5i2BguU6QvA3jYpzDf1EXGoCPIrq6MVdAGb5LQzEmXn+/pZ1eGHMrgq0uy+6Fd66RqL3sTzQ3EYAwPAHgFgBeV0osB/MKnMN/UJcZQPlGqOjRU106+DWcMYjAn1W69LokhlItoPhaqjYLL3HBhPhxyYm4gBmP4OoCTFb9d41OYb+oKY3Al9nXofuuQGEztiHk4SlaXmDYGk4up76EyG7uDacxc7FosMcwN1GpjqDt1hTG0QW1Thg2hrEKQbQitTzgFIJyePZRXko2LqWqsTf1ks9v3jYFVZhhsY3BDl433zBhaAN2OrcnJpJrYIdRZNsywClFsExEzEW+XwHU2qkXXDYaNN1SV0/FdJY5V0BbnDV8wY2gBukLgcoSQcGxtGEUVTPme5Tpi84dA7HsTin3ku8uPQcC7ThyroC4vtlhgxtACmHZ9bSFwOXwM0+XJrTrJLWur62lwWxVKXahTVWhDROoiNG1UkdaFprzYQiH2yef9APyuTwEhUlcYgxDpQDdJ4FyIheuCl03uiQlzLH9TeaazHW0hQG3aOddZlzZ4ttUB2dppyostFKIxBgB/lIXKvjv7/FwAl/kU5pu6xBiEaG5nuXDhcJkqYpEk7rHwdYTdhhmZTiy3JTa/y8G1WPp60zMuc6xqneeCxKCzz4RgwE0x15iM4XoATwJwQ+G7m3wK801dYwx17eZsDJayBezrBeQ6uX3UTk0bOWOPnU3+Ns+42HaqtqdNklIs6JifbE66ztNRlBiuzf4WGcONPoX5pq4xBiGaPaMgSzbvmSap6y61itopJGKq11xhk3+oZ0K2p2mGHRuut+353FY3UjYGAJcAOCO7V+GZAC4C8K8+hfmmLjCGJhaO7YGm8XG790xibZIME3bVtahV1U6h4LogY4v8Nvn7hu2WtWuu2AeqwoWB+jLbkfJKAjAJYC2AH2XpbwEs8CnMN7WdMTS1G6hbYkiS4TuzJybk7WwLQXKVcnQxrEIs7JDSgE195oJ9IARc1nBb5rYNojAGAOMArvLJOGRqO2NoavGFtDEQCbFiRRiDp4nA1okquvgigQhlhAxlY5DlKxu7uWAfCAVbxt8lZhtTYvg2gCf5ZB4qtZ0xNLmD0LnUAWpVz9SUWRXlY/A0Edi6CVJVXXx+s15IYhD6jIKJ+I+6faBudInZxmQMlwLYltka/jFPPoX5prYzhpCifxWUXT51Xka2aqhQRtGmri4NpYtvs/qgSzvYUUFXmG1MxvBGWfIpzDe1nTHEUg+oygoxIW0N164GzzYS0BC6eBvi2xSxaGOf+6ArxLZL4JAYDSPkgSRdGaFEWB+Joa52xoJODy87Y5Gr4VS/hwznXQW+UlqbCLHK7pXPHWYSfogpMdwN4K5y8inMN3WBMZgQYlcXWs9tMlzHMK42RYxU9dJduTkxof69rKZr0j3Xx67TNj25aaPSVh1+2xGTMfQK6RAAfw7g/RbvrQOwE8BPCt+dB+AeAJuydKpNJUeBMYQg6qFVBmUiHSI8syzftu+qdQTJ1rvKVjUXq72unmBtk+xCXFTEGEatqiQA11s8cxKA5RLGcK5reV1iDDGJYtsWsyuarL/L7Wa2ySbYmorZxGAOLhuHttklbPqvazaTNsCXMYzBACJaXkjHEdFbAMwzvSeEuAbAL03PNY3164Fly4CxsfTv+vXV8lqzBti6NZ3KW7emn9evB1atAi6+GOj3AaL078UXp9/bYu1aYHJy8LvJyfT7LmDbNvvvQ44LACxdKv9+fFz/nu734hifeurw2Kiwb1/6zlvfWk8bZd+7PFsHZHO7jKbqNidh4hwAriqkKwBcDMsQ3ACWYVhi2II0vMY6AAdo3l0DYCOAjUuXLo3CTUOrNurwdS/uTssX3oTIPyZsjaQxVE46G4Nu9zw1NRwGRDXG5T61uWeijjZ2wcaQ1ymfI6H7Zq4CEW0Mh0m+O9Qq82HG8DSkp6nHkIbZWGeTTyxVUmjVhk5d4QIbA26Vk7G9XnPB7GyMpDoGEiPctYw55EwhSYbDgNiqOZLEXYVVVa3mejCuLV5JZbS5bl1CTMbwY8l3RhuDkDAG29/KKRZjqKJnlU1cFUHL7xtQvVdGCJ/6cl19QmfYttsFNkZSG2Jqw8hcDvyp2uXr1pvDhSkU5woTRUYIBGcMAJ4F4DQAPwXw6kJaDeBmq8yHJYaDCv//BYDP2uTTNonBRy2RL/AmTuG6GEZNYRrquLSkKjHO6yrb6atChLjW1bYPXPo+Z16qucXMguGKGIzhlQD+DcD92d88/SOAFxgzBj4D4F4AewHsAHAWgE8BuCmzMVxWZBS6FIsx+BI6HUMxEVwbAhdaYvDxyIf001QAACAASURBVJH1QyjVmykfWwlHJ9npxsKlvr5qLZ2+PA9YKJt7KruEr86dpY+5jZiqpBN9Mg6ZYrqr+iwc3Y5XR/SqRPqsYmNw3bWqCGgoF0eb+hfHxSdSq44ZuqhrfO05Nqd4ZXVwYeK+ki0zh7mDmIxhAYD/BeCfM0+idbZG41CpbecYTIelVLs7HdNwPWzmamS02YGrCL7JT7/Xc+/DqvU3EThTfV3yM9XV1hvJRlJxVfv55NWVcy+M6ojJGP4TwN9ktoY3ArgcwEd8CvNNbWMMroQ2N3rqbBOhdnY2bq4uO1Kbtrrq7X3gKtnpbAxVCLesHNu5YOvUoJI2ZPNK1ydtO8TGqB8xGcMN2d8bs78TyO6Briu1jTEIYafqKC/G3P3R1pvJVo9dlDJMDMaWOeTEPoQhuCmovJJ06hpXBufCbG37yGZcbe7PZomBEZMxXJf9vQbAkQAWg4PoDcAlhLWM8Ph4vrjsLIuEwHaHm6uHfMJztx06Yu4qqdUVI8lHXSXzkmMbw9xCTMbwpwAOAPAipJFVdwJ4i09hvqkuxuDrwVF11+izK/dVC5XbaSL0XZYYVDAxxxBeS6q+DwWTmki1cZiaClsPRrsRjTG0IdXBGKp4cFTVM/u4Z9blgmpTty7uQpPEbYx0+ej6J5YkFdqlua2I6W47F1x5Y0oMT0N6refXs89HADjLpzDfFIMxhPQkKeeni5Gjys/VPTOUC6qOIRZtDMW4TLaHrdq+8EIRT5vT3KFh2siMguE5prutbd5tn8MmxGQMXwfwJwA2Z5/nAbjJpzDfFJoxhPYkkWFqSp7f2NgswXDxnS+rAVReN+PjesakkljKk7/qouyCD33IOobU57ucsVA9p2J6Pq7FTSGm1GOTt2yNTUyEn8MxmU9MxvCj7O8Nhe82+RTmm0IzhhieJGUkiTkqp45w2BAanZRTdVE1/X4MqBhg1UUZUp8filnp3HXbxJx1cJF6XMfRJm/V+grJXGNvoGIyhquR3t724+zzCQC+41OYbwrNGEJ7kri4oNoSShvCampHlR1sVVVE21QZMRdgSCYYMq8q6tE2qFBs+yLkAchi3rq1VUZox5VQG6iYjGE5gO8BeCj7ezuAo3wK8011SQw+9/OqdvY2TEFGKE1Mpfi8q51BF13Uto/aJjHYLshY9bExYrsQjZAM1TevtqgBZfWYmEjnsc0mTDe2Nm20ZQxV+iv2BipGEL2lhf/nAXh2do5hwqegKqkOG4OvuK4aWJtDb+XJa2P78DmT4NPOLtgYXMrQLUDf3Z6N26trP7RB+miTGrDs1CE71GdizDZ5y8bdVpVUpb86JzGgcA8DgC/4ZB4q1eGV5EOwTDt2VzdPm/x0XhO2zMH2wpuqfeT6fvl5k/eTy6LSSYm2hNvWk62Yh4s6JH82pBHbhzm3TQ2YQ9WXsTzCZHZCmY2mSn91zsZQMjbf4JN5qNTWk886YlwWc/PJq7uO0yY/HXxcWNviKWQj/ZTvJVA9Z3tWRBfm2kaPrUsubqMq47XtuJv61ZW5t0liKEI35jHdWk39V7W/OuWVVJIYhm5xqzO1lTGoJoQq9IVvfi4TzCeKatMLXgh7pmYjGanaI1uAtrs9X082G3uWzY63TmOw7S62bgO1bn00aSxvi01GhhiMYR+AXwHYDeDx7P/88698CvNNbWUMId0UVfm5TjCTTtaGCIZAKPdB1+TaX7bM2LZ+ZeOobQA83bg0QXhM49dUndpKgNvgxSUDh8RoCKEnROj8pqbMhvAYnkIh709w2an72EFMdU0SdR8Wd/4q42iu/gLsnRKK49IG1Y6tfSV2neq2e3UdzBhahLZMPlu9fej6hXIfjCkplMsuE+583HT1cglz7dq+NoW3cKm7rk5Nr4s2SxyxwIyhRugmeJsmn86LI+birOI/X+zXRYvcmEOV3aqrcXp83M07xUYiUo1L0xKDr32liDasi6b7sQkwY6gJpglel4eCzXOxdpqmsl37QJWfq92hSrtcVVmysnSGZlN+OiLZtI3Btk/KwRerHEJzkS5sn/VZD01LOVXBjMESVQfaNMHr8GmuqhePsbPOy04SOSHUnQ1Q5Ven37orE5KVJWtLHjTRlJdpHjbtlaRieOU6qcbThcG6MEKXZ32YU9NSTlUwY7BAiIE2Ef4qEoPtu6bnXPTirvDRo+tCcbjmlxtzQy9Y3W7fpayyV5iOsJYZapHI2oY2jwEb6cmV+LowcxfDtst6c13/o6B6ah1jALAuu+3tJ4XvDgRwBYA7sr8H2OQVijGEGGgfohw6borpIJyLXtwVPnp0Xf+a2qzaKcfwBlONm29ZJgKbuzU35SQgRZIIwj5lPXzVNfm7pjYlif592/JUErrLWDZt9A+BNjKGk7IAfEXGcAGAd2X/vwvA+TZ5hWIMIQbaVo0TkpDYSgymFGJC6+ro079N78rKO/z8DELxf1/GYxoPkztq1T5xnodJIpKJ1WIce73LN7XFdKJb935VicEFsVSxdaN1jCGtE5aVGMNtAA7K/j8IwG02+bRJYhAinr63io3Bh7D4tMPHJqDr39B6XJs2Fesq28WGUFUlidlukTNMW/tG6A3M0Du9c8Qk9kjLtm2/zdz0kSCB6jYGW8RUxdZtzO4KY3iw8D8VP0veXQNgI4CNS5cuDdJJXTAmuXol2TIFmVTj2xc69Y5PnqEWi600ZyJcqp2iiyrOZmxiSgw+TLqPu+Xtxl5npqhrk48EqbscJzSx1dlJQjOc2PSnc4wh+/yATT4xvJKKBEAX2K7t0C0k3WKJKYK30WiaG8B91XCuC9kkBZQNz7Lb1qoQEJ1aTzVGKtsCYZ/bQGRogwTpg1i2hSbUpl1hDI2qknLEFBXrhu9CGgXDWhkmYmwTn6i4UVClqrp2GcPUeTD5MFgfL6t+b7e8/N5u63LLNhtZiJDQEmRdEkNVAt7EmusKY/hAyfh8gU0+dd/53LVdczEe0vj4cBA/WX2aNvrGgI00YHOuYOFCPRMJeSYlR2iioSpf5wqaJEJMzh80PE/Ot1cjycqU3bhWfL48L32YQh02hhAbRpYYUsL/GQD3AtgLYAeAs5DeHf3tzF31WwAOtMnLhzHoJpitUdA13/z3KpPKJv/i7yZDqao+Mc4CNA1fo7wsTUyomYjtQnYhcjEcI2SeVb7uwTZwaYOKibhKGLZlNi2FCJGuOVldfaMx26B1jCFkcmUMJuLsKzHYEP0qC9yUv+x31UI3GTaLkkNXj/vLkCRuEUx1SaZ2yb837Xx96h3CE8p3fuqMu7ZwkXpcbD0+XkzFMttgtxCCJYbgyZUxmAbA18ZgM7BVVAKm/F0Wk8kVsg22hFiMycaY68IcFi5Uz5OQRKdqf9jMT1XfyK6sdIUL4XMJQVL1HExb1KZsYwicXBmD7S6i7J1kWow2nh6qyW0zCU31diFoNhJDk4jt2pokg/r0XK3iwxx0Upmv542qDVWYgy3hcbnONJa+P5TEYFOmiQnV5ZXIEkPgFFpi8IWLp0cx2e7GTPW2VZHY2Bhi6lht3q3blbHMKEIkInN4EhcbVFXpw7ZPY6pfXBi3jT2IKNXBV2GmLkwopoqpCZUWM4YCYg2Aq6dHMdkYmHSGYtPkLu+Oi0HYXMM7VCXANu/WGT4jpFG6mMbH5WomXdt1bai6obHt+7aoX2xuFwT8jNJFuI5/zB183XY9ZgwlxNRfl/O10Zfm6ibX/GXeQzKmYNKpT0yE33lWedenDNPuvNhnxc+2koKOOLvoxGUMJK+Lcn5gWn3AzDEUhmneV1G/hNCHh5LeXAh4sV9s1uqogBlDg7AVVX12Iqa8JyfNu9YiA7FBFaJg+67OdU9F3GIQ7Zl+zPz1VURzxYrqZWjnBm1VhqSIsYOV2WBCedeZyg0lvfkScF+vxC6CGUOdKFGuZGqDtb7UFTY6bJfFZIMmJQYZodfp4Cvt5LFXEPaJPu4WycTqGcroI7VVS9NiCheJBCuHgtjF0kH7uEbHPOTlk3wJuK9XYhfBjKEuKFZMMrUhyk7EhtCGZgxN2hhMfVcm2r5EZRJ7RIKVVgNkKieExNLH3UIAIsFK0cfdswwrEpGydW0NrY419ZXMkaOqjUEGH6/ELoIZQ12wWFFTU+pdrytsCG1v0aNWxMflEFNTXkk64iuDbT4DQQVx9zBT0BRiynvFCrmh3+WgHWGfdk5VgmRAmjrfohuvomRYnj91G21HBcwYKsBp0lmuqJATWZtXkl6uMh+Plao0PfA5xAGmkEimNohJeth+R91X5GOhs57EHpH0zjEbKySFqGwhoeo2kwdtlVPJyh0t31koA+b14xJhVZ/orn9l+IMZgyec1SYeCvgqPthGZPUpqyCmcNHs595uvzuEHSrn1I6s06dwkfYaSVsaKbMJ9Hu7Z9UxuXTgeGDAxeNMVaGkd47oLTRLdFMrbo1Djft96dzo0X3Sfp5acesQw3biURYTQTpeEZpeFaMgpTBj8IQznXfkJLrHgxj4LKzTXuU4vOTLXFVeOIDIGMa0P1NTDWx+24rFqrf2NuvtHtQllRTiycRqMX/e4/o8+nEIUYIzJLeyTQ+V3+ulUlyftrruewqFuU80lUNBzMByNohlfLctO9Q8YMbgCR9dazK1QfTHt6c7sPHtIpnaoHxWx3iCuARaZKKjkcrJ51A553Zkna6WFqZndvnJxOrhUNA2C1THMC1XuI20MDl/b+rRpHlIxwDL1QpNiPrj2+2YW18I0e9XO0vhMaF1LshN7tBjueuaEJohMWPwhOsEcB04HeMJYgC0qJAVgZssifQGA63NgSFlOwwSQ+6ho31GMT7lMrSU0LA1s8qid46xc23UZePYqyyjCkgiHSjHiqjaWQqPCW0SeJtC24zzvn3BjMETroTedeBUz/d6aq8V50lQkcAVJ/1APyhcOm2NqiZ1nNRvv1QmNETV2CdGq7R+4K3UHBZc1yQxpI4D1U89y2CtDuuLGXvE0JjQw9XUdx4SQx1EWIemJIbQDIkZQwUU6aoprpDrwMmIi8wvW0GbzBW2UEK6eMcMLYTC7j2vnA2xMRqMM3UcsC/bLU+narkSI1LtpMfHDX2UN1zFfS25srGrC50xdAYhV4dhpZiEzANrWvSwU0zhImU78yr5qhJs+WOSiAGGPdMO2qpVlRoL09ilbCSyplBJpVPBSMASg0Oq6xyDdDKUXB1tBs7kdaGKE5PbRZ0rmXMrzSRMEr/4NIR9QxPcpD4yrQXlopvaMPSDSWIwrkFVYboGuMBS+hkgtmPbRIIzZr4fNg4PJ289c5KIpHeOtYeai/1M2R8WXkkmZtWYe3XJo6zf2+3uyVfBSBD6ljdmDAGgJPq4e2ZwTeNuMy+UUgem/StpMQlNYrvtTqXqrkb7fomwmPztrdagjFgpKpH0znHf7CWJ0sg7IHFJkq1h2qV/B9pd6qBkYrWS2LnQtCqeMzYSZ4gb5ZwRwvJbcXGwxOCQ6mIMaoK9b2B0dItCObDj281SB201T0IbS7JiFqlfnRZTK24ditCqisgqO5w2tH40naRTx5XVeqrb03Qhm31dK709oAx9qzTkw84wXewfm/6dQWmimWIx2RKlqvTT62xIHTB1gE2fVzQSsI3BIbVCYrAcHS1zKUodZcKaqx1MVE2z3RpQV/SH562ufUnvnCG7h1Sct9FDlyhHgpWiT1sFYVqrSjNdeARMz9w1EUQb5CCdmGDaBRP2iSlcNPSDl8RgS5lLk9HkcWRLlHQOFTawkRgasS+Ydiw2fc4Sw+gxBqWNId/tWYyOkbnkUgfOkBoqZ55Rbb8UClrpbnD+3tQ+khG+qRW3iuHDTdMzp6Sl9e7tHqSfEgPxUN+UDLLlek3gsaEQHpOTdjaQfm+32WhZkM5cUGW3ZmfgnxaQGaZLmwRj0Dhb6lF6znRGwTZb3Y7fpttNfdVYhFNVB4yNaSZb39w4hwaF0GYVwYxBAh89aJIIfTgFi/e1zMWwCgd24ovukxdZ1JFnq9TqTIDqVGtWnoqYlX3hpW6sRepZoByqevWwc8AgmiSWKgbs059dy+qm06WrJoiVxKDxLMiNlaY2DPThxIRIFp491BfauWvLwUqdapIYqm6KZXRShbLK0OWGwWhIEvONV6Y+z/PxNcBUf30AnWIMALYAuAnAJpuK+97gVonz6kan7OJTigCWJGLWs0MmDSgqqNz16+qc1VO5G8zsIwlWClkohPwZW4ImYzhDFKFAOUz1muk/oSc4xXJVz41jb8oUbO41sLUx0MOzajILsSCZWG19qExqmLaZpC76BtP8mhye2uVpL+OFLnSyU3B13WtE52WPLjKGxbbP+zAGq7XjK1Jku4oEK0UPOzOCOy16ix6dzcKGM5W8Y6qcOtXaDwwukbn7pI3bpJSwy9qVtd1GkkmnodyoPdB9meuwSSqz6keVxLbw7MweUmDqefssOJeLzUAaatsw4M4SrcQArbNDledmgjOkjgaLFjlXvRuwEVtV876FYMZQgumoPcm8RRx2awlWigkMR80cMNjaMp7sZZ2HiikbHbHUESuZr71KslAS9kWLhiulIywyVVRmUS4Srh52ih52mm9ZK9k9TLr0JJlVn+k8hoYGwIJYuHgZKV1ZFdtu5RgXQ4pbvWQRrjZ7J934DFdR5ijQATpphuU4N6vzskfXGMPdAH4M4HoAaxTPrAGwEcDGpUuXOneIanyNYR9MWx6DTt8miyFk4qsqzzKxMfqWlwieTbC6YjLteqWEXUMVVPXSDozLQiwRP53EYLQB6VKA8BdWZSomkLfHiqtCv7D5UaofyX7f0ym01jLuh64xhkOyv08FsBnASbrnQ9kYlIe4ijs3k5I0WzS6naGznjVT2spUPqpytJ43tm6Kih2ruh6GHXa/rycWPsY9K8onBqhU0jtHeR7B6DWmSpbXsZlPMmdqR+yU9+Pk7DWx5T60sjmbbGMObq5BNz8NwYt5tdIy7odOMYaBCgDnAThX90woryQlIS/qek0zPiNuQRdNyb1zVs2xRSiDqxXORejyUxGryUmhjQya4IzZvuvtNoaWnilHpjbKVR1lo71tsuW0JeYg80oyHmKUJZMfaolpJFipjX000DclaTWZ2qCk3UaJwUT4Hd1cdZufSvSxJlEjtOtnF9EZxgBgIYD9C/9/H8ApundCnWMw7hZz+diEJBHJwrPNNobsWeMi0KgojLt9GRdSeNwMEUqdj2gp32Rqg+iPbdOqg7R11UUOVDCZGQY5vt08LJZUIJbEILslzcaYP1NuFhVQR7uNTTQRfhc318lJtatxlXAVNVJrb9XbCKFLjOGwTH20GcDNAN5jeicUY9Dql4mcI1UliciubczUA2P3a08AKxeB0v9y3ByaurSoZ/X5ebTSM/S7sqkpieHFHH56aLdLZOeaqiGmg9FIDe6m5bEoBoorMq5eb4AxJ1MbJAfuHh0wcg9ci2phnFaNkSlq6lDf6NyOi4bzfmGfMbVh9gtlIaSfZ4rNRdI7x3kcjKiRWocOL9FFdIYx+KSQJ59dCKdxsx9KdFflI/HUUZ6LsKiOuVPkDVU2Y3z7gJ7OxZahY3i6U9iyuiaJMN7rUOyQ4oGyHnYOncIuG1xNxmndIT6Tl1K5b5xcljU2G6nE5TFBgmt9aqTWVvN2xPVKzBgCw2oN+Yru+TO6U0R5QbpocaUKxdqMWa3lft/q4h0jAYT6FDZhnzRWhPLEsko91OvNEFRbLyKdqknn9aVy91T1jfKiHFn4a4W9RitxledZOQa37QXbvhyjRonBStJtmdEhNCNmxuAK311yv5CFKt6RSXQ3qG4G6qhyrZLUOcpmLNGElO4X+jErSCndlGwMOuLv6kXlqsKasSX1eg7nDvYppTYdc+lhp8TWoPFKgkLFJpsjikKtpQ6Ta+ZMIwZP9leyE9RpEU5Kd1GMbTPH+WoQMbqGGYMLLEbARGSTRJgjpDr5zPaH6+m4uwq+Gcvqr9yBSi7WmWmfzM2vwIx19xe4SB5aQji2Td8hSSL6Y9usGMPQWZLSwUD1ocB9elWgyWVON5CK50x2CuOEMTGIqhOtDq8kW6Yn7ZhmEEOYYsbgAosRMD2i/L18p4K1zywNP54RkWHCcoa0WVJ+V4qw6rQIVW60uc66wkyW1pUeTolmZnS3NQAnvXMkNGBa9Og+kYyfWSpkcpZJZUxP5l2mYwozzSxIMGN4XPrMOPaqM877ypaIFQlYkiijfqqY3dCwuIR/KPZfywmsEMKN6bVEYogh8TNjcIFukmQwCRVqf3j9LWxK7xmkcXrKdwNP4NHhENX55ewKQ+yMcV0mOlvemZgkQr/L1XaC3UyWetjY7vKKZWXtnvUQK/QV9ohk4dnDOvTCQzo7QGq/UAcfzD+oryA1nI/Ix6LYGaYbiHSMZP587VmIAbhKDHmqdENSTbBlei2yMbDE4JiqMAap1Kq7IL7wniaAqtcgJokYPpFrEfxNRbCGVv/ExGyldQsjd0RXiPSmeprFJk0n6DrH8nTxUMrHRKWeGt9eGIBhgqpVvSRJdtBQki9tnbGbWNlFiuOTt1Umxfl6u42PzzC+BGdkEX6nh4vQqYNsU1VleGx1kqp9Jfdl23Kb0n6xjUGTqpx8lna0zi9d915F+5uSjmbEw+maR91O1CbJzi9khEtL5Mq73BAzWbMDNqqUCkxIa4TWDIDWWKvytipJbUq7SFFaKaiwjH22YsXgMytWzP6mY/o+E9c15W3xpZSWfVCJGKvKsPW6cq9uELBXkkPyZQwmQiynBJYb4bLHQ3bBig6mcAxOEoPGfdIqaYiLlsDKdrdVZ7Kiw41G6LK7rkliULRZ696ZvWO082TMYYiJFdU/unAgxcmluvAgP4CpkxhMeYeQFKpSK4sFFoQYy1xzPTLtgq1dBWYMZSSJmxtjYYKoLlqZUZ17zloTo1Jegym75lET58iYdFcVwqAWiQEFwdbWQ7LCZKeaJ7Fn9gyAhigmWCm/8c2WKuj6u9eTxlNSMhrd+Aihnn+qd4qudK5zxVP1YhpvKRMt2KaiHHdQMWZDplVMaXVKGzIwYygiGw1rf/jihE8S9RWY/Sx/z1krnSQlN8yBBUNbRTK1Qb7jCKES0BBJKYGlVbPqkCpqhPzd3KVVUQ9rt8ti9lMbZm/PG98ukhWXDJanardq7Ewr2yQJSJiALI7SgGpKNz4yY3VRtaNqm+98ieBpJA21kQdbzBDcQ0fXr4ZMqzCpKAzOAcwYishGw9ofvrS4tDplIcwnmjWEUuaJM6CSoq2DcX50RLdMZG2D1FmoE3QH1ZLxMwd/K1ygI2+kxiahSmNjaqNv3zD+RSJp451iDMQkUUlY5u8USr232zyGRu8Gx9CsNnMlMJQn1Xu7Z59RVNe7Orr2+2zo8nM8hg2SM4MLrHdixlBEYTRc/OGLo68S9ZNEqO9zLo66BZPI89NKES5yp63LYwVpQ8ls892eLO+8P2y9jhYuVJdl6g7XtpmYb8X8XexGVg4Fqmh6RWYl83jyPbMQQedhQyy9VTAqwqprv6UBuryhs6mgE4OLoHdixlCEyhBosxg0BFV2J3FOxJXlGQbWykBu2iap1DNlBklbZ90X892Oqh9Uh6d06rkk8Xc5LVKHQh7l+ymMa8RkRyiPUTY+1hs1x523i6fZOPbKz7eUDxfanPcozzvbeo+PV1cRGt7V2c5twodp6+AqMfnGEFfRGFk0X1taH0HvxIyhAKURcv7q2TANJuJUHsmpKaXtQRYPR+r3X6yjQbof2j2qVkdpMcgknXTnXWJo8/cOeN0oF40loZu5OMiBaCoXgc0zKirh4nmUM3XJyemBxVukUI7tUTFSE8Mobjikak3be6oV80ReqOfu1NE1NFpVXG0sLvGdSu1IcIadmnpSfSPfECIcfWbGUIAV4zXdJFZWB/X72iia0vJmvI3OGJgYU1MWqmSVO2phMpfVWlKjJvaIHt0nL2PRffod9sKzMyljdkek1Ptr3Getpbe8bTZSB5H8/gxFe3SSjvYcQ+m8h6skqrurIc9nfEwdcsM1oKCWoKjUT5W26ZoFpwkWaav11Na/XDebAGeubVMwFGXYkdK4JFiZrlGbIlliiMsYTLZhwnSqljDtuooDQqTRF6tDJsiuuzQl4+X0CxeKhFZZGzW1IR0UPvOqnerUEVcNSx+a+lo7ABR1/So/flkySFB50veNqn+mh5iCS3C/AeJQtlcVxlInhbm4XA+VU/DymekbHWF03VX7GLUlRM5qo2xTtxguQIoNpM24yOZLNuTya6TZxtCMxFBOxkVdnJkab6XeInkQtj7uVqqfVETK1h7iYtTUSjT9vnTy6y6fcdk165jpwLvlxVs++atKMh2xROpw66+sSuPbrfKoetjQS5rBloEvpHMzVxfmfRKSsAZ0f7Uq1uahQIR1hn9q1qPNXLCdc0P3orNXUhzGkCTKi63cFnU5zPDQwZwtIll4dqpvlBmle+coD8vJ07T1AnMxavawS7/Tlbi66nbY1oZ8i3oO1KO4lXIxYpfPEkgYnWr3pqzXpBjc2WvaUjU8idb+ofxtsG7Gw4A2h7tcdNy+7q8SRi6l5+WowKr8TOoyk7RjYf+QXqgks0uVxsVljQ6FmwkEZgwlmEwIlRc1kRDz5s1OlLLXTza+Luunh53WD9saNfODacZd/sTEgCeSbrfjskO22TUV85PV09vDrJSK+ajvT5ieVcOUmKUqCqvLuNnUTeuVpGh/JaaVw0Vi8HF/BZTRfQfodG93ejZmoCGK8go2QK2KrGxbUUgWVl6CwKwnW2+3cly8XJWrqL0kYMZQgu28rRxzyDBRk945YnLeY6XHhgnTBB6duYvAlpDIVAdT8z8ejIjahJsefm/wVjuZQVyVnzwkiCTsuIVe35R0jFWVd0zGkLff1bA9Y8DGXuljxvlNNEtIVeqhRYuGia3O59TUVhPxczFm6+IflSVJVT6Fepnimc2kYPo5LgAADARJREFUPH9Ne12k1JlxCnzSnBlDCTY79RAERp7x4ETNFzCkxkT9NY+2hKGPu9OQFQHboSKEKmJj8sAxeW+57LCqMvQEK60u4CmmWKokXd+5GPWld1HYzKuiekdl2ynv9HUuqibbg4n4Gb1HaHADpmqThw3E+v5wy/wTrMzWkVqlbHJtrwJmDCUkiRi6U2AMv85u2poW49grpnCR88QxJk0AtFjGy1jJlViZ2mfKr9aw41BfrpNLDeXdu84g79qvtnmr5obq+fGxff7Sokn/WiZaRcJcPG0tuQxJm08Zdai0pANPIplY7eV5ZhzzhWfPuJb36D7RWyBRQc11GwOAUwDcBuBOAO8yPe8bXTUZe/3MAuxhZxSVhEuKueOMlQaIWG93ekiwQvt06pI6JQZdeaoDi1O4SHoN6Hw8NmycVLRTxRx1TMqrrwv2r7SQSXvDm7Hz+7OqlCx8yVBZ+e++7q+qMxBle0GoNlmMnXeamBg+uCQ7AOhxV4QJnWEMAMYB/BTAYQDmA9gM4AjdO16MoTRh2rBbb0MdKiWDTrVq++q0MajKm8QerQrNRr2mk4yUO31HG4FVXy9aNHx4LdRcmJjQ2xPy3b2Ll5Aqxlax/uVnVEQ3AsMYqJdL/jYeYRHOMAghRJcYw4kAvln4/G4A79a948UYSoPQht267wGp1iSD4SZE+2J6JdmWV/XAmY5oq9Vl+yrbGIaeL1xVq1oX0ZKrEdVGfaSzJ8hOcMtsAL2eXMpxTbbnOHShVErnpIzt90CXGMPpAD5R+HwmgH+SPLcGwEYAG5cuXerTIwOpFbv1zEtJ5+LW6mRxviAmEa8r+R04m51HOuahe7+KV5Ly+TJi7qQHGtR3W682xNM1lpBKWqlql5BJQ7Kw96YAfr5nSBwwcoyhmEKokpx2s+PjYQLC6RaK7wGhcsoPhdW12F3CVfgkw+1ydSS/A2dmW0lOvGuTGmUSg8vpT99UdIG1RRWJwZUJVV17jofmGgvnIYToEmOoR5WUJEMc3GqHlU9qnSGsnJ7wBP3vMl2hzwJdsUKtr4294IttsGUOCxcOiu29ntydsaxLnpqqHr7bN2Xleh84mzdPJAefqyX+yvcXLrS/bMkmyYIM5nPFZjMh098Xf1ONkapc03o1Ec9QeniTGqhsuK/atrzMkHGqLNElxjAPwF0ADi0Yn5+te8eLMQihdqcrcvJyKAXZQJRFRlkELJtnZPmWF+jYWDr5isRxfNxuQqraIyO0xXsb8t+K9bZpQynyqPQglKqett4XslOrsndldZmaGuyPMqOSMS6dt03xwqEVK4bHrjB/khWXiD626DchuXSkmkflOuRtKs8zWb6288XUt7o1ZLt+bGEzL0LFEpKpePJ1ZqIbsRA4TpIQ/oyB0nfrBRGdCuBCpB5K64QQa3XPH3fccWLjxo211I3BYDBGBUR0vRDiONf35sWojAlCiK8B+FoTZTMYDAZDj7GmK8BgMBiMdoEZA4PBYDAGwIyBwWAwGANgxsBgMBiMATTileQKItoFYKvn64sB3BewOqHB9asGrp8/2lw3gOtXFYsBLBRCPMX1xU4whiogoo0+7lp1getXDVw/f7S5bgDXryqq1I9VSQwGg8EYADMGBoPBYAxgLjCGi5uugAFcv2rg+vmjzXUDuH5V4V2/kbcxMBgMBsMNc0FiYDAYDIYDmDEwGAwGYwAjxxiI6ANE9N9EdCMRfZGInqx47hQiuo2I7iSid9VYv9cQ0c1ENE1ESlcyItpCRDcR0SYiqi20rEP9muq/A4noCiK6I/t7gOK5fVnfbSKiyyLXSdsXRPQEIvpc9vsPiWhZzPp41G81Ee0q9Nef1li3dUS0k4h+ovidiOgfs7rfSETL66qbZf1eTEQPFfrur2uu39OJ6CoiuiVbt2+XPOPehz6xutucALwMwLzs//MBnC95ZhzATwEchtk7IY6oqX6HA/hdAFcDOE7z3BYAixvoP2P9Gu6/CwC8K/v/XbLxzX7bU1N9jH0B4K0A/jX7/3UAPlfjeNrUbzUMtyhGrN9JAJYD+Ini91MBfB0AATgBwA9bVr8XA/hKE32XlX8QgOXZ//sDuF0yvs59OHISgxDiciHE49nHawEskTz2PAB3CiHuEkL8BsBnAbyypvrdKoS4rY6yfGBZv8b6Lyvnk9n/nwTwqprKVcGmL4p1/jyAFURELapfYxBCXAPgl5pHXgngP0SKawE8mYgOqqd2VvVrFEKIe4UQP87+3w3gVgCHlB5z7sORYwwlvBkppyzjEADbC593YLgzm4YAcDkRXU9Ea5quTAlN9t/ThBD3Zv//HMDTFM8tIKKNRHQtEcVkHjZ9MfNMtml5CEAvYp2kZWdQjdVpmZrh80T09HqqZoUurNUTiWgzEX2diJ7dVCUyFeUxAH5Y+sm5Dxu5qKcqiOhbAH5L8tN7hBCXZs+8B8DjANbXWbesbGP9LPBCIcQ9RPRUAFcQ0X9nu5e21C8adPUrfhBCCCJS+Vv3s/47DMCVRHSTEOKnoes6IvgygM8IIX5NRH+GVLr5/Ybr1BX8GOlc25PdTPklAM+suxJEtAjAFwD8uRDiV1Xz6yRjEEK8RPc7Ea0G8IcAVohMyVbCPQCKu6Il2Xe11M8yj3uyvzuJ6ItIVQJBGEOA+jXWf0T0CyI6SAhxbyYO71TkkfffXUR0NdKdVAzGYNMX+TM7iGgegCcBuD9CXWQw1k8IUazLJ5DacdqCqHOtKopEWAjxNSL6ZyJaLISoLbgeEU0gZQrrhRD/JXnEuQ9HTpVERKcAeCeAPxZCPKJ47EcAnklEhxLRfKQGwaieKy4gooVEtH/+P1KDutQroiE02X+XAXhj9v8bAQxJOER0ABE9Ift/MYDfA3BLpPrY9EWxzqcDuFKxYWmkfiV98x8j1VO3BZcBeEPmWXMCgIcKqsTGQUS/lduLiOh5SGlqXUwfWdmXALhVCPFhxWPufdiUNT1WAnAnUn3apizl3iAHA/ha4blTkVrwf4pUhVJX/f4nUh3frwH8AsA3y/VD6kGyOUs3t61+DfdfD8C3AdwB4FsADsy+Pw7AJ7L/XwDgpqz/bgJwVuQ6DfUFgPcj3ZwAwAIA/5nNzesAHFZXf1nW7++zebYZwFUAnlVj3T4D4F4Ae7N5dxaAtwB4S/Y7AfhoVveboPHka6h+byv03bUAXlBz/V6I1B55Y4HmnVq1DzkkBoPBYDAGMHKqJAaDwWBUAzMGBoPBYAyAGQODwWAwBsCMgcFgMBgDYMbAYDAYjAEwY2DMCdBgtNVNPhFOiehVRHRE+NrN5H8+Ef0kS6+NVQ6DYUInTz4zGB54VAjx3Ip5vArAV+BwWI6I5onZoI66516BNIrncwE8AcDVRPR1ESC8AYPhCpYYGHMWRHQsEX0nC1T4zfwEMBGdTUQ/ygKjfYGIJonoBUhPBX8gkzh+m4iupuzOCiJaTERbsv9XE9FlRHQlgG9nJ9nXEdF1RHQDEcmimx4B4BohxONCiIeRHlg6pY5+YDDKYMbAmCvYr6BG+mIWX+YiAKcLIY4FsA7A2uzZ/xJCHC+EOBppeIizhBDfRxpa4C+FEM8V5oB8y7O8X4Q0+N+VQojnATgZKXNZWHp+M4BTMia0OHuuTVFOGXMIrEpizBUMqJKI6EgARyKNXAukF9rk8WOOJKK/BfBkAIsAfNOjvCuEEHkc/5cB+GMiOjf7vADAUhRiEgkhLiei4wF8H8AuAD8AsM+jXAajMpgxMOYqCMDNQogTJb/9O4BXCSE2Z5F6X6zI43HMSt0LSr89XCrrNGG4AEkIsRaZ1EJEn0Ya34jBqB2sSmLMVdwG4ClEdCKQhi4uXLKyP4B7M3XTqsI7u7PfcmwBcGz2/+masr4J4JxCFM5jyg8Q0TgR9bL/jwJwFIDLXRvFYIQAMwbGnIRIr7k8HcD5RLQZaVTKF2Q/vxfpLVjfA/Dfhdc+C+AvMwPybwP4IIApIroBwGJNcX8DYALAjUR0c/a5jAkAG4joFgAXA3i9jTcTgxEDHF2VwWAwGANgiYHBYDAYA2DGwGAwGIwBMGNgMBgMxgCYMTAYDAZjAMwYGAwGgzEAZgwMBoPBGAAzBgaDwWAM4P8D9E9cF55zhjMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "data = np.loadtxt('data.txt', delimiter='\\t', skiprows=1)\n", + "#count = 1\n", + "\n", + "for a in range(1, 11):\n", + " for b in range(a+1, 11):\n", + "\n", + " dict1 = {}\n", + " dict2 = {}\n", + "\n", + " for k in range(0, data.shape[0]):\n", + " if data[k][0] == np.float64(1):\n", + " dict1[data[k][a]] = data[k][b]\n", + " \n", + " else:\n", + " dict2[data[k][a]] = data[k][b]\n", + " \n", + " x1 = []\n", + " y1 = []\n", + "\n", + " for i in sorted(dict1):\n", + " x1.append(i)\n", + " y1.append(dict1[i])\n", + "\n", + " x2 = []\n", + " y2 = []\n", + "\n", + " for i in sorted(dict2):\n", + " x2.append(i)\n", + " y2.append(dict2[i])\n", + "\n", + " plt.scatter(x1, y1, color = 'r', label = \"Label 1\")\n", + " plt.scatter(x2, y2, color = 'b', label = \"Label 2\")\n", + "\n", + " plt.title('Feature {} v/s Feature {}'.format(b, a))\n", + " plt.ylabel('Feature {}'.format(b))\n", + " plt.xlabel('Feature {}'.format(a))\n", + " plt.legend()\n", + " plt.show()\n", + " #print(count)\n", + " #count+=1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_ques3.ipynb b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_ques3.ipynb new file mode 100644 index 000000000..cc2d6fb20 --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_ques3.ipynb @@ -0,0 +1,96 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first plot between Feature 3 and Feature 8 classifies the two labels perfectly" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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oPC/OS4bPOixsghhvzeqFMpL3xUExDqR7Tqd2Vg/5hkV7y72lKMusbKZOncratWsdFgNEBGvXrmXq1KnD+j1fcDdOdK1bQM2UP7F66xvIdWHeua3HAPeSSh9fourMSmvBggWsWrWKNWvWlLuUMWfq1KksWLBgWL/joBhHera8kcbp99G56V1kC4vtVNPUegwc5047G58mT568y1XPNjplbXqSdIqkFZKelvS5QbbvJ+kXkh6W9JikD5ajzkrSsfG4PPssqmlqCo+GMrOcyhYUkiYB1wAfAGqBsyTVDtjt74DbIuII4EwgXdoqK1O653Qapv2a/IbO+qI8M8uunGcURwNPR8QzEbEFuBU4bcA+AfTNaLU3sLqE9VW0jo3H0TztJsR2sgeGaG31mYWZDa2cQVEDPN/v/qrksf6uBJokrQJ+Clwy2BNJWixpuaTl7rzaKb3xk/Q2vD+PPcW5TVuLXo+ZVaaxPjz2LOCmiFgAfBC4WdJuNUfEtRFRHxH18+bNK3mRY1pHBw3znyBXM9R2qqmbvao0NZlZRSlnUPQA+/a7vyB5rL/zgdsAIuJ+YCowFxuWjp5aGmpXk6sJqnt9DXVTVpSqLDOrEOUMigeBgyQdIGkKmc7qOwbs8xzQACDpEDJB4balEejoqqF51vfIGRZb30p73ZdLVZaZVYCyBUVEbAMuBu4EniAzuqlL0hclnZrs9n+AT0l6FPgecF74UssRS687m/k8T66waOq+3Gtwm9kOnmZ8Aqqb/ke6N72Z7FdwB80NK0h3vK1UZZlZGXmacdtF18YDqeVxcp1ZLOk8qDgrzJtZRXFQTFBdbY9Ry2NkC4ugirlN7/MkgmYTnINiokql6Gp7nDm8lGUnsZZ5fKL1GIeF2QTmoJjIUimubl5BrmssgmouaD2sNDWZ2ZjjoJjgUunj85pE8DVm0lLzw9IUZWZjioPCSPecTlvDjUxiW5a9ROvq06iZvrZkdZnZ2OCgMABSHYtY2vwAuUZCrd60j5dTNZtgHBS2Qyp9PFMn5ZocMLOcqplNHA4K28V1S6dQTe6ZZFvqlhW/GDMbExwUtotUCm5quJk9eYWhm6FEa/eJDguzCcJBYbtJdSxiQ8OHmcT2LHtlwqK98YaS1WVm5eGgsMF1dLC0+QGqcoTFeZ2f8GyzZuOcg8KGlEofz3eb7yfbSKhtTOaC7ovxwttm45eDwrJKpY+nufYesoXFa8ykpbW2dEWZWUk5KCyndNdJKMf1Fa1c5M5ts3HKQWF5ubDhD+S6GK+1+0QveGQ2DjkoLC/pjrflbIIC0dp5sJewMBtnHBSWt3TXSXlMICjObdrmsDAbRxwUNizpntNp1hKyhcV2qlnUtLl0RZlZUTkobNjSN+9FM9eQLSy2sAd10/9YuqLMrGgcFDZ8qRTp5m5m8GqWnUT3pjd7JJTZOOCgsJFJp1nScDvQm2UnsaT73bjDwqyyOShsxFIdi2iefwfZmqCCKhoX7Vu6osys4MoaFJJOkbRC0tOSPjfEPh+T1C2pS9Itpa7Rskv3nE7DtF+TbabZzi3vpq6ulFWZWSGVLSgkTQKuAT4A1AJnSaodsM9BwOXAcRFRB3y65IVaTh0bj6N22jNkC4vubk8HZVapynlGcTTwdEQ8ExFbgFuB0wbs8yngmohYBxARL5a4RstT18YDmVH9etZ9Wluz9WeY2VhVzqCoAZ7vd39V8lh/bwXeKuk+SQ9IOmWwJ5K0WNJyScvXrFlTpHItlyU3TSXXxXgt028sVTlmViBjvTO7GjgIOAk4C/iOpFkDd4qIayOiPiLq582bV+ISrU8qBQ0NyrKHWLLpHNxhYVZZyhkUPUD/4TALksf6WwXcERFbI+J/gD+QCQ4bozo6oLp66O1BFe3df+khs2YVpJxB8SBwkKQDJE0BzgTuGLDPD8mcTSBpLpmmqGdKWaQN3003QbaO7SbaWXjOCc4KswpRtqCIiG3AxcCdwBPAbRHRJemLkk5NdrsTWCupG/gF8JmIWFueii1fqRQ0zH+CbGHxbO++LF7kyQPNKoEisnU+Vp76+vpYvnx5ucswYGbVBjbEjKz7zNnjVV7aPLNEFZnZUCQ9FBH1g20b653ZVsGW3DyD6dqUdZ+1r8+gvfGGElVkZiPhoLCiSaXg2punsX/V82Rrhrqi8z2lLMvMhslBYUWVSsHK7/6StsmLGCosnmM/j4IyG8McFFZ8qRSpGxuZw0uDbt6P56CpyXN8mI1RDgorjVSKq/f4W6bz2i4PT2YzG9iTKrazsPWz7q8wG4McFFYyqesbuJZPsT8rEb3MYQ1bmcJa5hFU8SwLaeo8161QZmNM1qCQdIKkg5Ofj5P0N5I+VJrSbNxJpUg1z2IlB9DLJNazF7v/E5xEU5MnDzQbS4YMCknfBP4VuFnSPwFXAdOA/y3pqhLVZ+NNOr3jx+1MGWInubvCbAwZ8oI7SV3AoWTCoQeoiYiNkiYDD0fEoaUrM3++4K4CNDZCZyeiFxh8EsFJbGNbZJk0yswKaqQX3EVkUqSvHaAvUXpz/J5Zdh0dUFubdZftTKJ99kUlKsjMssn2gf8TSb8CfgVcB9wm6Qrgv4FflqI4G8e6unKuivfJ9V+nveXeUlZlZoPIOteTpGPJnFk8IOlA4H8BzwG3R8SY7HF001NlmT3lVdZvncFQTVBzWMNLbXdlrtwzs6IZ8VxPEXF/RDyQ/PzHiPhaRNw2VkPCKs+6LTNp5hqGOrNYy1xaml72ldtmZeS+Biu7dG1rlq2ilWbqzj2yZPWY2a4cFFZ+XV1DTu+RIbq3v43GuoELIJpZKeQVFJKm9V14Z1YMVzevYAqvZ9lDdHbPd+e2WRnkDApJfw08AvwsuX+4pIFLlpqNSip9PDfUfp2hR0EBiEWtboIyK7V8ziiuBI4G1gNExCPAAUWsySaoVNfnaa69h2xhsYVp1M1eVbqizCyvoNgaES8PeGx8rZ9qY0a666Qce4ju9TWe4sOshPIJii5JZwOTJB0k6d+BXxe5LpvAmhtWkKsJqrXVI7TNSiWfoLgEqANeB24BXgY+XcyibGJLd7yNhmm/JldYTJ+ypVQlmU1oWWddkzQJ+ElEnAxcUZqSzKBj43E0TllG59YTGfyqbbFp62RmT9/Muo1TS1yd2cSS68rs7UCvpL1LVI/ZDh1bTqJa27PsIdZv2oOWxidLVpPZRJRP09MG4PeSrpf0rb5bsQszA7jp5mpy9ld0vrVU5ZhNSPkExX8BXyAzY+xD/W6jJukUSSskPS3pc1n2O0NSSBp0wiobv1IpqJ2/jlxh4QvxzIon58owEbG0GAdO+j+uAd4LrAIelHRHRHQP2G8mcCnwm2LUYWNfV88+TNerbGKoWWbFpa0Hk6JllxX0zKww8rky+38kPTPwVoBjHw08HRHPRMQW4FbgtEH2+yfgK8DmAhzTKtTGtjuSFfEGt5a5tLTW4gsszAovn6aneuCo5PZu4FtAWwGOXQM83+/+quSxHSQdCewbET/J9kSSFktaLmn5mjVrClCajTmpFDc3LCXbQketXERd6wWlrMpsQsgZFBGxtt+tJyK+CXyo2IVJqgK+AfyfPGq8NiLqI6J+3rx5xS7NyiTVsSjHFB+im8Oom/7HUpZlNu7l0/R0ZL9bvaQLyaNvIw89wL797i9IHuszEzgUWCZpJXAMcIc7tCe2dNdJzJmRfZbZ7k1vdue2WQHl0/T09X63fwGOBD5WgGM/CBwk6QBJU4AzgR2z0kbEyxExNyIWRsRC4AHg1IjwOqcT3NVLpqIco6AubX1byeoxG+/yCYrzI+Lk5PbeiFgMjHruhIjYBlwM3Ak8AdwWEV2Svijp1NE+v41fqRRc2CyyDZldyxzaaz5TuqLMxjFFZJ8IVtLvIuLIAY89FBHvKGplI1RfXx/Ll/ukYyKo0+/p5lAGHzILopcL599Buuf00hZmVoGSz/VBm/aHPKOQ9DZJZwB7S/pwv9t5gCfXsbLranuMarYOuT2oonX1qe6vMBulbE1PBwN/BcwC/rrf7UjgU8UvzSyHVIqbGtpyLKFalVkVr729ZGWZjTf5ND0dGxH3l6ieUXPT08TT3ngD53aew/YhB+MFtTxOV7y9pHWZVZIRNT3187CkiySlJd3QdytwjWYjlupYxNL5l5P9+opDaazpHmK7mWWTT1DcDPwF8H7gHjLXO7xazKLMhivVcxUzJm3KsofoXH2IW6DMRiCfoHhLRHwBeC2ZIPBDwDuLW5bZ8C1ZOp1cs8xeeoGnDDMbrnyCom9YyXpJhwJ7A28oXklmI5NKQUNDjusrXtvDHdtmw5RPUFwraTaZNSnuALqBrxa1KrMR6uggx6p4wKWXlqYYs3Ein0kBr4uIdRFxT0S8OSLeEBFLSlGc2UjkWhVv4drltDd6PIZZvvKZFPCNyTKo/53cr5V0fvFLMxuZVAqah5ziQzzLQj7ReR4tdctKXJlZZcqn6ekmMvMxzU/u/wH4dLEKMiuEdBramu9jf1YyWGAEVSzpPsFXbZvlIZ+gmBsRt0FmebFkMr8cjcBm5ZdKH8/KOfVDzjQbVHmWWbM85BMUr0maQ/K1TNIxwMtFrcqsUK6+mv14bsjNa5lDS+OTJSzIrPLkExSXkRntdKCk+4DvApcUtSqzQkml+FLD3VnW2xatnQfTMv3GkpZlVkmyzR67H0BE/A44EXgXcAFQFxGPlaY8s9FLdSziwknfIet625vOo332RaUsy6xiZDuj+GG/n/8jIroi4vGIGHpeZ7MxKr10BnN4Kcse4or1XujIbDDZgqL/ajBvLnYhZkWVSnF1w4/Jdn3Fc+wHjY2lq8msQmQLihjiZ7OKlOpYxIyp24bcHgh1/tyzzJoNkC0o/lLSK5JeBQ5Lfn5F0quSXilVgWaFtOS6yUwaclU80TfLrE8szHYaMigiYlJE7BURMyOiOvm57/5epSzSrFBSKVjacDNzWEO2zu3OTp9Em/XJZ3is2biS6ljESw1nDXkhXh9fX2GW4aCwiamjg/2mrsmyQ+b6isa6npKVZDZWOShswvrSdW8Ehu7cBtHZPZ+WllJVZDY2lTUoJJ0iaYWkpyV9bpDtl0nqlvSYpE5J+5ejThufUiloa34AhrxqG0C0tmbbbjb+lS0oJE0CrgE+ANQCZ0mqHbDbw0B9RBwG3I4XTLICS6WPp63511mm+AAQs6d4mXibuMp5RnE08HREPBMRW4BbgdP67xARv4iIjcndB4AFJa7RJoBU+ngunHID2UZBrd86w9dX2IRVzqCoAZ7vd39V8thQzgf+e7ANkhZLWi5p+Zo12ToozQaXvmEaDXSQdcjs6kNonH5fKcsyGxMqojNbUhNQD1w12PaIuDYi6iOift68eaUtzsaHVIqOthdz7CQ6N73LZxY24ZQzKHqAffvdX5A8tgtJjcAVwKkR8XqJarOJKJVi/rQ/k33GmsyZhUdC2URSzqB4EDhI0gGSpgBnkln3YgdJRwDfJhMSub7umY1az8Y5zJq8gVxh4ZFQNpGULSiSJVUvJrMe9xPAbRHRJemLkk5NdrsKmAF8X9Ijku4Y4unMCmbdlpk0zH+CXGHhK7dtolDE+JrTpr6+PpYvX17uMmwcmKZNbGZalj16ibbvZS7IMKtwkh6KiPrBtlVEZ7ZZOVxX+2/kOquY2XQq7S33lqoks7JwUJgNIdX1eZrn/4hsQ2Y3MJNzWo+mvb2UlZmVloPCLIt0z+k01K4m25lFL1NY1LSpdEWZlZiDwiyHjq6aXdYFHswWptIy+5aS1GNWag4Kszxc2CxyDpldf5ZHQtm45KAwy0M6Dc35hEXnwb4Yz8YdB4VZntJpaK69h9wX44U7t21ccVCYDUO66yQaJucOiws+sTHLdrPK4qAwG6aOLSfRPOt7ZAuL12KaO7dt3HBQmI1Aet3ZOfYQS9afSXvdl0tSj1kxOSjMRijXTLNBFU3dl9NYt9ukyGYVxUFhNkI9G+cwq+oVck5L3j2fupo/l6oss4JzUJiNwrrte+eY5gNAdK+e7TmhrGI5KMxGKd1zOs20kissFrUeicfNWiVyUJgVQLpt75z7bGEa05v+2mFhFcdBYVYIqVTOyQNBbGImNU3vLlVVZgXhoDArkI6uGhbn5roAABBaSURBVObPzz3Nx2r2paVuWYmqMhs9B4VZAfX05Dcn1JLuE3yNhVUMB4VZgaXT5Fxze8c1FjXdpSvMbIQcFGZF0NFTyzReI+c1FqsPoW7KilKVZTYiDgqzItkYM3JevQ2ie+tbaZ9yXomqMhs+B4VZEfVsnENz7T2I3ix7iSu2Xgk1NaUqy2xYHBRmRZbuOombaSLbmcWz7E/16mc9GsrGpLIGhaRTJK2Q9LSkzw2yfQ9J/5Fs/42khaWv0mz0Us2zaOAuhg4LsZ1qWrtPdFjYmFO2oJA0CbgG+ABQC5wlqXbAbucD6yLiLcC/AV8pbZVmBZJO09FwFbU8Rs4V8rpPwOup2lhSzjOKo4GnI+KZiNgC3AqcNmCf04Clyc+3Aw2SVMIazQqno4OutsdpI8X+rCTb2cXM1n+lvfGGEhZnNrRyBkUN8Hy/+6uSxwbdJyK2AS8Dc0pSnVkxpFKk4hZWTnoLk9g+xE5iA3vR1PlJWmp+WNLyzAYzLjqzJS2WtFzS8jVr1pS7HLPcli5lMd8mZzPU6tPcZ2FlV86g6AH27Xd/QfLYoPtIqgb2BtYOfKKIuDYi6iOift68eUUq16yAUinSbbNQ1qCATJ/Fie6ysLIqZ1A8CBwk6QBJU4AzgTsG7HMHcG7y80eAuyMi1/8ss8qQSnFhcxXZzyoARGtreElVK5uyBUXS53AxcCfwBHBbRHRJ+qKkU5PdrgfmSHoauAzYbQitWSVLp/OZRBD6llR1M5SVQ1n7KCLipxHx1og4MCK+lDz29xFxR/Lz5oj4aES8JSKOjohnylmvWTGk09Bcew/5hMWS7nd74SPbTUsLVFeDlPmz0E2V46Iz26zSpbtOymPt7cysswvPOcFZYRktLdTpUVpbg+3JILrt26G1tbBh4aAwGyPSPafT1nwfk9iaZS/xbO++fKKp181QE1ljI+06m+rWf6Obw4DdLy+79trCHc5BYTaGpNLHs7T2Ksg6iWDmzKK1+0RfZzHRtLTQrrPZo/MOmmhnO3swWEgAO84wCsFBYTbGpLo+T9v8v6U665kF9F1nUTf9jyWpy8qnpQWqtB21XkMT7WxhOkMFRJ9Jkwp3fAeF2RiU6rmKmxpuzjHVB4Do3vRmpmkT7S33lqg6K6W6OmhtDYJJZMIhn1mMgsWLC1eDg8JsjEp1LGJl2320zbk053oWm5lGU+tx1M1eVbL6rLhaGp9E6qW7O8gvHPoEtdOeIZ0uXC0OCrOxLJUi9dK3uLD2l+QzfLZ7fQ0103ebvMAqSGNdD1LQ2nkwmY/o4YdE18YDC1qTg8KsAuQ7fBbE6k37UFfV5estKkx73ZeRttHZPZ/8m5j6BBA0N6vgIQEOCrOKke45ndppz5DXmUXU0nLOBodFBWhsBClo6r4cqGa4ASF6aW5YQYQK2tzUn4PCrIJ0bTyQhlnL6fsGOTRxbe/5cM45DosxrK7mz3R29vVB5BsQmb/7OXqJtjbRG1WkO95WvCJxUJhVnI51RxHNF+VcLW87k1DvNtR0NjOrNzovxoiWxiczQ10VdK+ezXDPIBq4i2h4Ly/1ziWVKlaVu3JQmFWidJqu5m9nWfwIdn5LFRu2T+e8pq0eQltG7e0wY48ttHYePMyhrgDBVDbR1nwfHfF+6OgoYqW7c1CYVap0mqXND0DWsNhpG5M5t/UY2ms+U9y6bBftLfeyhzbS1BS8tmUKw++D2E7brIvZFNNIpY8vVplZOSjMKlgqfTxtbZPYc0/Y2W+RrTmqmqbVX/UFeqXQ0sJsvURT63F5XUm9q8zfY8Os5fTGJFLrrilSkflxUJhVuFQKNmyAaLuFmLxHjuYo6LtA77zWd9LeeENJapxI2lvuZa7WoNZrWM8cRjTMNRnF1LHuqCJVOTwOCrPxIpWCG29ksb5D7iG0maaops5PMknbaWl8svj1TQAtdctoan0Xa5nHSK6FaKhdnRnmWuRRTMPloDAbT1Ip0r3NNE+7CbGdfK656GUSrZ0HuzlqBNrbYeHMtVSpl7laQ2v3ieT/sbqzqbBKvTQ3i46umuIVOwoOCrNxKL3xk/TWHkYbKfI5u+g/X5QUNDYWu8IK19hIi/5fmpqCZzfMIajqdxaRj6Bh/hNEiAixvbeqaBfLFYKDwmy86uoi1fYhGuggv7CAvuaSzs6gZsqfilhchWpvp10p5nZ+j1YuYnhNS7CjD6L2Hjp6aotQYHE4KMzGs1SKjngvzc1KZqDNPzBWb30DUiDFhO7DaG+HGVUbM+9F09k00TbMswfoC4j58zNnEOmuk4pTbJE4KMwmgHQaeqOKtoYbqWJLnr+184K91s6DJ1STVGNN946QbGoKXou+4a3D76Cewau0Nd9HhOjpKU69xeagMJtAUh2L+G5DG1PYSP5nF9C/SapK22mv+3KRKiyv9vbMSnKdqw9hZMEAfRP17a/naGu+j1djZtkulCsUB4XZBJPqWMTrbT+gbc8LRhQYwSSaui/f2SxVt6xIlZZGSwtIvTvOHnZOrzESQTPX0Nt8MSt796v4gOijiOH8Ixn76uvrY/ny5eUuw6xiNE6/j85N72I0H44AM9jAkuZHx/yHY2NNd3LG0N9IXzv0vf4qermg4akxdw1EviQ9FBH1g20ryxmFpH0k/VzSU8mfswfZ53BJ90vqkvSYpI+Xo1az8a5j43G0Ndw4grOLPsnEg8zcMby2ZY/rxtT05i11y3bM2Lp7s9JIQiLTOb0/K2lruDEzxDUmVWxI5FKupqfPAZ0RcRDQmdwfaCNwTkTUAacA35Q0q4Q1mk0YqY5FvB7TaWu+D9jGyAIDdnR+bzkfNZ29o3mqpBfzNTbSrrOZoVd2HL+1+8QRzNg6UHJxHNtp5hqi4b2sjIWkOhYVrvYxqixNT5JWACdFxAuS3gQsi4iDc/zOo8BHIuKpbPu56cls9Foan2RJ50HEsNdszqb/hIU7n7OWx+iqPRu6uvJ/qsZGWjpPZwnNSY07VbOV7VQRVI++5B16aaOJVMOLJZ/iu1SyNT2VKyjWR8Ss5GcB6/ruD7H/0cBSoC4iegfZvhhYDLDffvu949lnny1O4WYTTHvLvSxqfQdbmJo8UqjQ6G80n0HFqKdPX9/LqyxpuH3cnzmUJSgkdQB/McimK4Cl/YNB0rqI2K2fItn2JmAZcG5EPJDruD6jMCuC9nbaL7iHc1/7d7YzJXmwmB/S5bDrZ2HD/Ccq6urp0SpLZ3ZENEbEoYPcfgT8KQmAviB4cYjC9wJ+AlyRT0iYWZGkUqQ2XMu22IMI0dwsGNaV3mNZX79Dmmi+aMf8SxMpJHIpV2f2HcC5yc/nAj8auIOkKcAPgO9GxO0lrM3MckinIaKK5oYV7AyM3AsnjQ0765xT/TJtbcmIpbiIMT0zXxmVKyj+FXivpKeAxuQ+kuolXZfs8zHgBOA8SY8kt8PLU66ZDSbd8TYiqnZ8C4+2W2je87vsGh7l0NtvuO+ut75FgSLES1v3JpUqU4kVxBfcmVnRtMy+hdb1Z7KzP2P3UU+j7+vY9TNsKpu5jvNJVX8fbroJJ0F+xtwFd2Y2MaTXnb3rGUdUZW5ttxB7zqSNFHvyCoN988/nNoc1tM25NPN8yTE2xTRScQts3eqQKBCfUZiZmc8ozMxs5BwUZmaWlYPCzMyyclCYmVlWDgozM8tq3I16krQGGM2sgHOBlwpUTjmNl9cBfi1j1Xh5LePldcDoXsv+ETFvsA3jLihGS9LyoYaIVZLx8jrAr2WsGi+vZby8Dijea3HTk5mZZeWgMDOzrBwUu7u23AUUyHh5HeDXMlaNl9cyXl4HFOm1uI/CzMyy8hmFmZll5aAwM7OsHBQJSadIWiHpaUmfK3c9IyXpBkkvSnq83LWMlqR9Jf1CUrekLkmXlrumkZA0VdJvJT2avI5/LHdNoyVpkqSHJf3fctcyGpJWSvp9sjBaRU87LWmWpNslPSnpCUnHFuy53UeR+UcP/AF4L7AKeBA4KyK6y1rYCEg6AdhAZgnZQ8tdz2gk66m/KSJ+J2km8BBweqX9vUgSsGdEbJA0GbgXuLSS14GXdBlQD+wVEX9V7npGStJKoD4iKv6CO0lLgV9FxHXJUtLTI2J9IZ7bZxQZRwNPR8QzEbEFuBU4rcw1jUhE/BL4c7nrKISIeCEifpf8/CrwBFBT3qqGLzI2JHcnJ7eK/YYmaQHwIeC6XPtaaUjam8zS0dcDRMSWQoUEOCj61ADP97u/igr8QBrPJC0EjgB+U95KRiZpqnkEeBH4eURU5OtIfBP4LJmFsStdAHdJekjS4nIXMwoHAGuAG5Mmwesk7VmoJ3dQ2JgnaQbwn8CnI+KVctczEhGxPSIOBxYAR0uqyGZBSX8FvBgRD5W7lgI5PiKOBD4AXJQ03VaiauBIoDUijgBeAwrW1+qgyOgB9u13f0HymJVZ0qb/n0B7RPxXuesZraQ54BfAKeWuZYSOA05N2vZvBd4jqa28JY1cRPQkf74I/IBMM3QlWgWs6nemejuZ4CgIB0XGg8BBkg5IOoHOBO4oc00TXtIJfD3wRER8o9z1jJSkeZJmJT9PIzNo4snyVjUyEXF5RCyIiIVk/p/cHRFNZS5rRCTtmQySIGmmeR9QkaMFI+L/A56XdHDyUANQsEEf1YV6okoWEdskXQzcCUwCboiIrjKXNSKSvgecBMyVtAr4h4i4vrxVjdhxwCeA3yft+wCfj4iflrGmkXgTsDQZXVcF3BYRFT2sdJx4I/CDzPcRqoFbIuJn5S1pVC4B2pMvu88AnyzUE3t4rJmZZeWmJzMzy8pBYWZmWTkozMwsKweFmZll5aAwM7OsHBRmA0janswm2ndbOILnOF1SbeGrA0n7S/pdUluXpAuLcRyzPh4eazaApA0RMWOUz3ET8H8j4vZh/E51RGzLY78pZP7vvp5Mb/I48K6IWD3igs2y8BmFWR4kvUPSPcnkcXcmU6Aj6VOSHkzWmvhPSdMlvQs4Fbgq+dZ/oKRlkuqT35mbTIGBpPMk3SHpbqAzuVr4hmT9iocl7TaLcTIz6OvJ3T3w/2MrMv8DM9vdtH7NTj9I5pv6d+AjEfEO4AbgS8m+/xURR0XEX5KZBv38iPg1mSlgPhMRh0fEH3Mc78jkuU8EriAzLcbRwMlkwma3WUCTRZ0eIzPr8Vd8NmHF5Ck8zHa3KZnpFYBkptdDgZ8n0z1MAl5INh8q6Z+BWcAMMtPADNfPI6JvDZH3kZl072+S+1OB/ciE0A4R8TxwmKT5wA8l3R4RfxrBsc1yclCY5SagKyIGW1ryJjKr7j0q6Twy82wNZhs7z+CnDtj22oBjnRERK/IpLCJWJ8vevpvMjKFmBeemJ7PcVgDz+tYgljRZUl2ybSbwQtI8ler3O68m2/qsBN6R/PyRLMe6E7gkmTkXSUcM3EHSgmQWWiTNBo5PajQrCgeFWQ7J8rgfAb4i6VHgEeBdyeYvkFl17z52nTr8VuAzSYf0gcDXgGZJDwNzsxzun8gslfqYpK7k/kCHAL9JarkH+FpE/H7EL9AsBw+PNTOzrHxGYWZmWTkozMwsKweFmZll5aAwM7OsHBRmZpaVg8LMzLJyUJiZWVb/P2TAiYX37TsPAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "data = np.loadtxt('data.txt', delimiter='\\t', skiprows=1)\n", + "#count = 1\n", + "\n", + "# for a in range(1, 11):\n", + "# for b in range(a+1, 11):\n", + "\n", + "dict1 = {}\n", + "dict2 = {}\n", + "\n", + "for k in range(0, data.shape[0]):\n", + " if data[k][0] == np.float64(1):\n", + " dict1[data[k][3]] = data[k][8]\n", + "\n", + " else:\n", + " dict2[data[k][3]] = data[k][8]\n", + "\n", + "x1 = []\n", + "y1 = []\n", + "\n", + "for i in sorted(dict1):\n", + " x1.append(i)\n", + " y1.append(dict1[i])\n", + "\n", + "x2 = []\n", + "y2 = []\n", + "\n", + "for i in sorted(dict2):\n", + " x2.append(i)\n", + " y2.append(dict2[i])\n", + "\n", + "plt.scatter(x1, y1, color = 'r', label = \"Label 1\")\n", + "plt.scatter(x2, y2, color = 'b', label = \"Label 2\")\n", + "\n", + "plt.title('Feature {} v/s Feature {}'.format(8, 3))\n", + "plt.ylabel('Feature {}'.format(8))\n", + "plt.xlabel('Feature {}'.format(3))\n", + "plt.legend()\n", + "plt.show()\n", + "#print(count)\n", + "#count+=1" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/ParthBakare_180101056.ipynb b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/ParthBakare_180101056.ipynb new file mode 100644 index 000000000..a2b042fb3 --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/ParthBakare_180101056.ipynb @@ -0,0 +1,1634 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Programming Exercise 1: Linear Regression\n", + "\n", + "## Introduction\n", + "\n", + "In this exercise, you will implement linear regression and get to see it work on data. Before starting on this programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n", + "\n", + "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", + "\n", + "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, and [`matplotlib`](https://matplotlib.org/) for plotting.\n", + "\n", + "You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "from mpl_toolkits.mplot3d import Axes3D # needed to plot 3-D surfaces\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils \n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submission and Grading\n", + "\n", + "After completing each part of the assignment, be sure to submit your solutions to the grader.\n", + "\n", + "For this programming exercise, you are only required to complete the first part of the exercise to implement linear regression with one variable. The second part of the exercise, which is optional, covers linear regression with multiple variables. The following is a breakdown of how each part of this exercise is scored.\n", + "\n", + "**Required Exercises**\n", + "\n", + "| Section | Part |Submitted Function | Points \n", + "|---------|:- |:- | :-: \n", + "| 1 | [Warm up exercise](#section1) | [`warmUpExercise`](#warmUpExercise) | 10 \n", + "| 2 | [Compute cost for one variable](#section2) | [`computeCost`](#computeCost) | 20 \n", + "| 3 | [Gradient descent for one variable](#section3) | [`gradientDescent`](#gradientDescent) | 20 \n", + "| 4 | [Feature normalization](#section4) | [`featureNormalize`](#featureNormalize) | 10 |\n", + "| 5 | [Compute cost for multiple variables](#section5) | [`computeCostMulti`](#computeCostMulti) | 20 |\n", + "| 6 | [Gradient descent for multiple variables](#section5) | [`gradientDescentMulti`](#gradientDescentMulti) |10 |\n", + "| 7 | [Normal Equations](#section7) | [`normalEqn`](#normalEqn) | 10 |\n", + "| | Total Points | | 100 \n", + "\n", + "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "
\n", + "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once. They must also be re-executed everytime the submitted function is updated.\n", + "
\n", + "\n", + "\n", + "## Debugging\n", + "\n", + "Here are some things to keep in mind throughout this exercise:\n", + "\n", + "- Python array indices start from zero, not one (contrary to OCTAVE/MATLAB). \n", + "\n", + "- There is an important distinction between python arrays (called `list` or `tuple`) and `numpy` arrays. You should use `numpy` arrays in all your computations. Vector/matrix operations work only with `numpy` arrays. Python lists do not support vector operations (you need to use for loops).\n", + "\n", + "- If you are seeing many errors at runtime, inspect your matrix operations to make sure that you are adding and multiplying matrices of compatible dimensions. Printing the dimensions of `numpy` arrays using the `shape` property will help you debug.\n", + "\n", + "- By default, `numpy` interprets math operators to be element-wise operators. If you want to do matrix multiplication, you need to use the `dot` function in `numpy`. For, example if `A` and `B` are two `numpy` matrices, then the matrix operation AB is `np.dot(A, B)`. Note that for 2-dimensional matrices or vectors (1-dimensional), this is also equivalent to `A@B` (requires python >= 3.5)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 1 Simple python and `numpy` function\n", + "\n", + "The first part of this assignment gives you practice with python and `numpy` syntax and the homework submission process. In the next cell, you will find the outline of a `python` function. Modify it to return a 5 x 5 identity matrix by filling in the following code:\n", + "\n", + "```python\n", + "A = np.eye(5)\n", + "```\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def warmUpExercise():\n", + " \"\"\"\n", + " Example function in Python which computes the identity matrix.\n", + " \n", + " Returns\n", + " -------\n", + " A : array_like\n", + " The 5x5 identity matrix.\n", + " \n", + " Instructions\n", + " ------------\n", + " Return the 5x5 identity matrix.\n", + " \"\"\" \n", + " # ======== YOUR CODE HERE ======\n", + " A = np.eye(5) # modify this line\n", + " \n", + " # ==============================\n", + " return A" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The previous cell only defines the function `warmUpExercise`. We can now run it by executing the following cell to see its output. You should see output similar to the following:\n", + "\n", + "```python\n", + "array([[ 1., 0., 0., 0., 0.],\n", + " [ 0., 1., 0., 0., 0.],\n", + " [ 0., 0., 1., 0., 0.],\n", + " [ 0., 0., 0., 1., 0.],\n", + " [ 0., 0., 0., 0., 1.]])\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0., 0., 0.],\n", + " [0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0.],\n", + " [0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 1.]])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "warmUpExercise()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Submitting solutions\n", + "\n", + "After completing a part of the exercise, you can submit your solutions for grading by first adding the function you modified to the grader object, and then sending your function to Coursera for grading. \n", + "\n", + "The grader will prompt you for your login e-mail and submission token. You can obtain a submission token from the web page for the assignment. You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "Execute the next cell to grade your solution to the first part of this exercise.\n", + "\n", + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# appends the implemented function in part 1 to the grader object\n", + "grader[1] = warmUpExercise\n", + "\n", + "# send the added functions to coursera grader for getting a grade on this part\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 Linear regression with one variable\n", + "\n", + "Now you will implement linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and you have data for profits and populations from the cities. You would like to use this data to help you select which city to expand to next. \n", + "\n", + "The file `Data/ex1data1.txt` contains the dataset for our linear regression problem. The first column is the population of a city (in 10,000s) and the second column is the profit of a food truck in that city (in $10,000s). A negative value for profit indicates a loss. \n", + "\n", + "We provide you with the code needed to load this data. The dataset is loaded from the data file into the variables `x` and `y`:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Read comma separated data\n", + "data = np.loadtxt(os.path.join('Data', 'ex1data1.txt'), delimiter=',')\n", + "X, y = data[:, 0], data[:, 1]\n", + "#print(x)\n", + "\n", + "m = y.size # number of training examples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Plotting the Data\n", + "\n", + "Before starting on any task, it is often useful to understand the data by visualizing it. For this dataset, you can use a scatter plot to visualize the data, since it has only two properties to plot (profit and population). Many other problems that you will encounter in real life are multi-dimensional and cannot be plotted on a 2-d plot. There are many plotting libraries in python (see this [blog post](https://blog.modeanalytics.com/python-data-visualization-libraries/) for a good summary of the most popular ones). \n", + "\n", + "In this course, we will be exclusively using `matplotlib` to do all our plotting. `matplotlib` is one of the most popular scientific plotting libraries in python and has extensive tools and functions to make beautiful plots. `pyplot` is a module within `matplotlib` which provides a simplified interface to `matplotlib`'s most common plotting tasks, mimicking MATLAB's plotting interface.\n", + "\n", + "
\n", + "You might have noticed that we have imported the `pyplot` module at the beginning of this exercise using the command `from matplotlib import pyplot`. This is rather uncommon, and if you look at python code elsewhere or in the `matplotlib` tutorials, you will see that the module is named `plt`. This is used by module renaming by using the import command `import matplotlib.pyplot as plt`. We will not using the short name of `pyplot` module in this class exercises, but you should be aware of this deviation from norm.\n", + "
\n", + "\n", + "\n", + "In the following part, your first job is to complete the `plotData` function below. Modify the function and fill in the following code:\n", + "\n", + "```python\n", + " pyplot.plot(x, y, 'ro', ms=10, mec='k')\n", + " pyplot.ylabel('Profit in $10,000')\n", + " pyplot.xlabel('Population of City in 10,000s')\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "def plotData(x, y):\n", + " \"\"\"\n", + " Plots the data points x and y into a new figure. Plots the data \n", + " points and gives the figure axes labels of population and profit.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : array_like\n", + " Data point values for x-axis.\n", + "\n", + " y : array_like\n", + " Data point values for y-axis. Note x and y should have the same size.\n", + " \n", + " Instructions\n", + " ------------\n", + " Plot the training data into a figure using the \"figure\" and \"plot\"\n", + " functions. Set the axes labels using the \"xlabel\" and \"ylabel\" functions.\n", + " Assume the population and revenue data have been passed in as the x\n", + " and y arguments of this function. \n", + " \n", + " Hint\n", + " ----\n", + " You can use the 'ro' option with plot to have the markers\n", + " appear as red circles. Furthermore, you can make the markers larger by\n", + " using plot(..., 'ro', ms=10), where `ms` refers to marker size. You \n", + " can also set the marker edge color using the `mec` property.\n", + " \"\"\"\n", + " fig = pyplot.figure() # open a new figure\n", + " \n", + " # ====================== YOUR CODE HERE ======================= \n", + " \n", + " pyplot.plot(x, y, 'ro', ms=10, mec='k')\n", + " pyplot.ylabel('Profit in $10,000')\n", + " pyplot.xlabel('Population of City in 10,000s')\n", + " pyplot.show()\n", + " # =============================================================\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now run the defined function with the loaded data to visualize the data. The end result should look like the following figure:\n", + "\n", + "![](Figures/dataset1.png)\n", + "\n", + "Execute the next cell to visualize the data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotData(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To quickly learn more about the `matplotlib` plot function and what arguments you can provide to it, you can type `?pyplot.plot` in a cell within the jupyter notebook. This opens a separate page showing the documentation for the requested function. You can also search online for plotting documentation. \n", + "\n", + "To set the markers to red circles, we used the option `'or'` within the `plot` function." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "?pyplot.plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 2.2 Gradient Descent\n", + "\n", + "In this part, you will fit the linear regression parameters $\\theta$ to our dataset using gradient descent.\n", + "\n", + "#### 2.2.1 Update Equations\n", + "\n", + "The objective of linear regression is to minimize the cost function\n", + "\n", + "$$ J(\\theta) = \\frac{1}{2m} \\sum_{i=1}^m \\left( h_{\\theta}(x^{(i)}) - y^{(i)}\\right)^2$$\n", + "\n", + "where the hypothesis $h_\\theta(x)$ is given by the linear model\n", + "$$ h_\\theta(x) = \\theta^Tx = \\theta_0 + \\theta_1 x_1$$\n", + "\n", + "Recall that the parameters of your model are the $\\theta_j$ values. These are\n", + "the values you will adjust to minimize cost $J(\\theta)$. One way to do this is to\n", + "use the batch gradient descent algorithm. In batch gradient descent, each\n", + "iteration performs the update\n", + "\n", + "$$ \\theta_j = \\theta_j - \\alpha \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta(x^{(i)}) - y^{(i)}\\right)x_j^{(i)} \\qquad \\text{simultaneously update } \\theta_j \\text{ for all } j$$\n", + "\n", + "With each step of gradient descent, your parameters $\\theta_j$ come closer to the optimal values that will achieve the lowest cost J($\\theta$).\n", + "\n", + "
\n", + "**Implementation Note:** We store each example as a row in the the $X$ matrix in Python `numpy`. To take into account the intercept term ($\\theta_0$), we add an additional first column to $X$ and set it to all ones. This allows us to treat $\\theta_0$ as simply another 'feature'.\n", + "
\n", + "\n", + "\n", + "#### 2.2.2 Implementation\n", + "\n", + "We have already set up the data for linear regression. In the following cell, we add another dimension to our data to accommodate the $\\theta_0$ intercept term. Do NOT execute this cell more than once." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Add a column of ones to X. The numpy function stack joins arrays along a given axis. \n", + "# The first axis (axis=0) refers to rows (training examples) \n", + "# and second axis (axis=1) refers to columns (features).\n", + "X = np.stack([np.ones(m), X], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 2.2.3 Computing the cost $J(\\theta)$\n", + "\n", + "As you perform gradient descent to learn minimize the cost function $J(\\theta)$, it is helpful to monitor the convergence by computing the cost. In this section, you will implement a function to calculate $J(\\theta)$ so you can check the convergence of your gradient descent implementation. \n", + "\n", + "Your next task is to complete the code for the function `computeCost` which computes $J(\\theta)$. As you are doing this, remember that the variables $X$ and $y$ are not scalar values. $X$ is a matrix whose rows represent the examples from the training set and $y$ is a vector whose each elemennt represent the value at a given row of $X$.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "def computeCost(X, y, theta):\n", + " \"\"\"\n", + " Compute cost for linear regression. Computes the cost of using theta as the\n", + " parameter for linear regression to fit the data points in X and y.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The input dataset of shape (m x n+1), where m is the number of examples,\n", + " and n is the number of features. We assume a vector of one's already \n", + " appended to the features so we have n+1 columns.\n", + " \n", + " y : array_like\n", + " The values of the function at each data point. This is a vector of\n", + " shape (m, ).\n", + " \n", + " theta : array_like\n", + " The parameters for the regression function. This is a vector of \n", + " shape (n+1, ).\n", + " \n", + " Returns\n", + " -------\n", + " J : float\n", + " The value of the regression cost function.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the cost of a particular choice of theta. \n", + " You should set J to the cost.\n", + " \"\"\"\n", + " \n", + " # initialize some useful values\n", + " m = y.size # number of training examples\n", + " \n", + " # You need to return the following variables correctly\n", + " J = 0\n", + " \n", + " # ====================== YOUR CODE HERE =====================\n", + "# print(X)\n", + "# print(y)\n", + "\n", + "# for i in range(m):\n", + "# x0 = X[i][0]\n", + "# x1 = X[i][1]\n", + "# t0 = theta[0]\n", + "# t1 = theta[1]\n", + "# h = t0*x0 + t1*x1\n", + "# diff = (h - y[i])**2\n", + "# J = J + diff\n", + " \n", + "# J = J/(2*m)\n", + " tr = theta.transpose()\n", + " h = np.dot(X, tr)\n", + " #print(h)\n", + " diff = np.subtract(h, y)\n", + " ans = np.multiply(diff, diff)\n", + " tot = np.sum(ans)\n", + " J = tot/(2*m)\n", + " #print(h.shape)\n", + " # ===========================================================\n", + " return J" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have completed the function, the next step will run `computeCost` two times using two different initializations of $\\theta$. You will see the cost printed to the screen." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "With theta = [0, 0] \n", + "Cost computed = 32.07\n", + "Expected cost value (approximately) 32.07\n", + "\n", + "With theta = [-1, 2]\n", + "Cost computed = 54.24\n", + "Expected cost value (approximately) 54.24\n" + ] + } + ], + "source": [ + "J = computeCost(X, y, theta=np.array([0.0, 0.0]))\n", + "print('With theta = [0, 0] \\nCost computed = %.2f' % J)\n", + "print('Expected cost value (approximately) 32.07\\n')\n", + "\n", + "# further testing of the cost function\n", + "J = computeCost(X, y, theta=np.array([-1, 2]))\n", + "print('With theta = [-1, 2]\\nCost computed = %.2f' % J)\n", + "print('Expected cost value (approximately) 54.24')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions by executing the following cell.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[2] = computeCost\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 2.2.4 Gradient descent\n", + "\n", + "Next, you will complete a function which implements gradient descent.\n", + "The loop structure has been written for you, and you only need to supply the updates to $\\theta$ within each iteration. \n", + "\n", + "As you program, make sure you understand what you are trying to optimize and what is being updated. Keep in mind that the cost $J(\\theta)$ is parameterized by the vector $\\theta$, not $X$ and $y$. That is, we minimize the value of $J(\\theta)$ by changing the values of the vector $\\theta$, not by changing $X$ or $y$. [Refer to the equations in this notebook](#section2) and to the video lectures if you are uncertain. A good way to verify that gradient descent is working correctly is to look at the value of $J(\\theta)$ and check that it is decreasing with each step. \n", + "\n", + "The starter code for the function `gradientDescent` calls `computeCost` on every iteration and saves the cost to a `python` list. Assuming you have implemented gradient descent and `computeCost` correctly, your value of $J(\\theta)$ should never increase, and should converge to a steady value by the end of the algorithm.\n", + "\n", + "
\n", + "**Vectors and matrices in `numpy`** - Important implementation notes\n", + "\n", + "A vector in `numpy` is a one dimensional array, for example `np.array([1, 2, 3])` is a vector. A matrix in `numpy` is a two dimensional array, for example `np.array([[1, 2, 3], [4, 5, 6]])`. However, the following is still considered a matrix `np.array([[1, 2, 3]])` since it has two dimensions, even if it has a shape of 1x3 (which looks like a vector).\n", + "\n", + "Given the above, the function `np.dot` which we will use for all matrix/vector multiplication has the following properties:\n", + "- It always performs inner products on vectors. If `x=np.array([1, 2, 3])`, then `np.dot(x, x)` is a scalar.\n", + "- For matrix-vector multiplication, so if $X$ is a $m\\times n$ matrix and $y$ is a vector of length $m$, then the operation `np.dot(y, X)` considers $y$ as a $1 \\times m$ vector. On the other hand, if $y$ is a vector of length $n$, then the operation `np.dot(X, y)` considers $y$ as a $n \\times 1$ vector.\n", + "- A vector can be promoted to a matrix using `y[None]` or `[y[np.newaxis]`. That is, if `y = np.array([1, 2, 3])` is a vector of size 3, then `y[None, :]` is a matrix of shape $1 \\times 3$. We can use `y[:, None]` to obtain a shape of $3 \\times 1$.\n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "def gradientDescent(X, y, theta, alpha, num_iters):\n", + " \"\"\"\n", + " Performs gradient descent to learn `theta`. Updates theta by taking `num_iters`\n", + " gradient steps with learning rate `alpha`.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The input dataset of shape (m x n+1).\n", + " \n", + " y : arra_like\n", + " Value at given features. A vector of shape (m, ).\n", + " \n", + " theta : array_like\n", + " Initial values for the linear regression parameters. \n", + " A vector of shape (n+1, ).\n", + " \n", + " alpha : float\n", + " The learning rate.\n", + " \n", + " num_iters : int\n", + " The number of iterations for gradient descent. \n", + " \n", + " Returns\n", + " -------\n", + " theta : array_like\n", + " The learned linear regression parameters. A vector of shape (n+1, ).\n", + " \n", + " J_history : list\n", + " A python list for the values of the cost function after each iteration.\n", + " \n", + " Instructions\n", + " ------------\n", + " Peform a single gradient step on the parameter vector theta.\n", + "\n", + " While debugging, it can be useful to print out the values of \n", + " the cost function (computeCost) and gradient here.\n", + " \"\"\"\n", + " # Initialize some useful values\n", + " m = y.shape[0] # number of training examples\n", + " \n", + " # make a copy of theta, to avoid changing the original array, since numpy arrays\n", + " # are passed by reference to functions\n", + " theta = theta.copy()\n", + " \n", + " J_history = [] # Use a python list to save cost in every iteration\n", + "# print(X)\n", + "# print(y)\n", + " \n", + " for i in range(num_iters):\n", + " # ==================== YOUR CODE HERE =================================\n", + " tr = theta.transpose()\n", + " h = np.dot(X, tr)\n", + " diff = np.subtract(h, y)\n", + " \n", + " X0 = X[:, 0]\n", + " X1 = X[:, 1]\n", + " \n", + " temp0 = np.multiply(diff, X0)\n", + " temp1 = np.multiply(diff, X1)\n", + " \n", + " sum0 = np.sum(temp0)\n", + " sum1 = np.sum(temp1)\n", + " \n", + " theta[0] = theta[0] - ((alpha*sum0)/m)\n", + " theta[1] = theta[1] - ((alpha*sum1)/m)\n", + " \n", + "\n", + " # =====================================================================\n", + " \n", + " # save the cost J in every iteration\n", + " J_history.append(computeCost(X, y, theta))\n", + " \n", + " return theta, J_history" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After you are finished call the implemented `gradientDescent` function and print the computed $\\theta$. We initialize the $\\theta$ parameters to 0 and the learning rate $\\alpha$ to 0.01. Execute the following cell to check your code." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Theta found by gradient descent: -3.6303, 1.1664\n", + "Expected theta values (approximately): [-3.6303, 1.1664]\n" + ] + } + ], + "source": [ + "# initialize fitting parameters\n", + "theta = np.zeros(2)\n", + "\n", + "# some gradient descent settings\n", + "iterations = 1500\n", + "alpha = 0.01\n", + "\n", + "theta, J_history = gradientDescent(X ,y, theta, alpha, iterations)\n", + "print('Theta found by gradient descent: {:.4f}, {:.4f}'.format(*theta))\n", + "print('Expected theta values (approximately): [-3.6303, 1.1664]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use your final parameters to plot the linear fit. The results should look like the following figure.\n", + "\n", + "![](Figures/regression_result.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the linear fit\n", + "#pyplot.plot(X[:, 1], y)\n", + "pyplot.plot(X[:, 1], y, 'ro', ms=10, mec='k')\n", + "pyplot.plot(X[:, 1], np.dot(X, theta), '-')\n", + "pyplot.legend(['Training data', 'Linear regression']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Your final values for $\\theta$ will also be used to make predictions on profits in areas of 35,000 and 70,000 people.\n", + "\n", + "
\n", + "Note the way that the following lines use matrix multiplication, rather than explicit summation or looping, to calculate the predictions. This is an example of code vectorization in `numpy`.\n", + "
\n", + "\n", + "
\n", + "Note that the first argument to the `numpy` function `dot` is a python list. `numpy` can internally converts **valid** python lists to numpy arrays when explicitly provided as arguments to `numpy` functions.\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For population = 35,000, we predict a profit of 4519.77\n", + "\n", + "For population = 70,000, we predict a profit of 45342.45\n", + "\n" + ] + } + ], + "source": [ + "# Predict values for population sizes of 35,000 and 70,000\n", + "# print(theta)\n", + "predict1 = np.dot([1, 3.5], theta)\n", + "print('For population = 35,000, we predict a profit of {:.2f}\\n'.format(predict1*10000))\n", + "\n", + "predict2 = np.dot([1, 7], theta)\n", + "print('For population = 70,000, we predict a profit of {:.2f}\\n'.format(predict2*10000))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions by executing the next cell.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[3] = gradientDescent\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Visualizing $J(\\theta)$\n", + "\n", + "To understand the cost function $J(\\theta)$ better, you will now plot the cost over a 2-dimensional grid of $\\theta_0$ and $\\theta_1$ values. You will not need to code anything new for this part, but you should understand how the code you have written already is creating these images.\n", + "\n", + "In the next cell, the code is set up to calculate $J(\\theta)$ over a grid of values using the `computeCost` function that you wrote. After executing the following cell, you will have a 2-D array of $J(\\theta)$ values. Then, those values are used to produce surface and contour plots of $J(\\theta)$ using the matplotlib `plot_surface` and `contourf` functions. The plots should look something like the following:\n", + "\n", + "![](Figures/cost_function.png)\n", + "\n", + "The purpose of these graphs is to show you how $J(\\theta)$ varies with changes in $\\theta_0$ and $\\theta_1$. The cost function $J(\\theta)$ is bowl-shaped and has a global minimum. (This is easier to see in the contour plot than in the 3D surface plot). This minimum is the optimal point for $\\theta_0$ and $\\theta_1$, and each step of gradient descent moves closer to this point." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# grid over which we will calculate J\n", + "theta0_vals = np.linspace(-10, 10, 100)\n", + "theta1_vals = np.linspace(-1, 4, 100)\n", + "\n", + "# initialize J_vals to a matrix of 0's\n", + "J_vals = np.zeros((theta0_vals.shape[0], theta1_vals.shape[0]))\n", + "\n", + "# Fill out J_vals\n", + "for i, theta0 in enumerate(theta0_vals):\n", + " for j, theta1 in enumerate(theta1_vals):\n", + " J_vals[i, j] = computeCost(X, y, [theta0, theta1])\n", + " \n", + "# Because of the way meshgrids work in the surf command, we need to\n", + "# transpose J_vals before calling surf, or else the axes will be flipped\n", + "J_vals = J_vals.T\n", + "\n", + "# surface plot\n", + "fig = pyplot.figure(figsize=(12, 5))\n", + "ax = fig.add_subplot(121, projection='3d')\n", + "ax.plot_surface(theta0_vals, theta1_vals, J_vals, cmap='viridis')\n", + "pyplot.xlabel('theta0')\n", + "pyplot.ylabel('theta1')\n", + "pyplot.title('Surface')\n", + "\n", + "# contour plot\n", + "# Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100\n", + "ax = pyplot.subplot(122)\n", + "pyplot.contour(theta0_vals, theta1_vals, J_vals, linewidths=2, cmap='viridis', levels=np.logspace(-2, 3, 20))\n", + "pyplot.xlabel('theta0')\n", + "pyplot.ylabel('theta1')\n", + "pyplot.plot(theta[0], theta[1], 'ro', ms=10, lw=2)\n", + "pyplot.title('Contour, showing minimum')\n", + "pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optional Exercises\n", + "\n", + "If you have successfully completed the material above, congratulations! You now understand linear regression and should able to start using it on your own datasets.\n", + "\n", + "For the rest of this programming exercise, we have included the following optional exercises. These exercises will help you gain a deeper understanding of the material, and if you are able to do so, we encourage you to complete them as well. You can still submit your solutions to these exercises to check if your answers are correct.\n", + "\n", + "## 3 Linear regression with multiple variables\n", + "\n", + "In this part, you will implement linear regression with multiple variables to predict the prices of houses. Suppose you are selling your house and you want to know what a good market price would be. One way to do this is to first collect information on recent houses sold and make a model of housing prices.\n", + "\n", + "The file `Data/ex1data2.txt` contains a training set of housing prices in Portland, Oregon. The first column is the size of the house (in square feet), the second column is the number of bedrooms, and the third column is the price\n", + "of the house. \n", + "\n", + "\n", + "### 3.1 Feature Normalization\n", + "\n", + "We start by loading and displaying some values from this dataset. By looking at the values, note that house sizes are about 1000 times the number of bedrooms. When features differ by orders of magnitude, first performing feature scaling can make gradient descent converge much more quickly." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " X[:,0] X[:, 1] y\n", + "--------------------------\n", + " 2104 3 399900\n", + " 1600 3 329900\n", + " 2400 3 369000\n", + " 1416 2 232000\n", + " 3000 4 539900\n", + " 1985 4 299900\n", + " 1534 3 314900\n", + " 1427 3 198999\n", + " 1380 3 212000\n", + " 1494 3 242500\n" + ] + } + ], + "source": [ + "# Load data\n", + "data = np.loadtxt(os.path.join('Data', 'ex1data2.txt'), delimiter=',')\n", + "X = data[:, :2]\n", + "y = data[:, 2]\n", + "m = y.size\n", + "\n", + "# print out some data points\n", + "print('{:>8s}{:>8s}{:>10s}'.format('X[:,0]', 'X[:, 1]', 'y'))\n", + "print('-'*26)\n", + "for i in range(10):\n", + " print('{:8.0f}{:8.0f}{:10.0f}'.format(X[i, 0], X[i, 1], y[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Your task here is to complete the code in `featureNormalize` function:\n", + "- Subtract the mean value of each feature from the dataset.\n", + "- After subtracting the mean, additionally scale (divide) the feature values by their respective “standard deviations.”\n", + "\n", + "The standard deviation is a way of measuring how much variation there is in the range of values of a particular feature (most data points will lie within ±2 standard deviations of the mean); this is an alternative to taking the range of values (max-min). In `numpy`, you can use the `std` function to compute the standard deviation. \n", + "\n", + "For example, the quantity `X[:, 0]` contains all the values of $x_1$ (house sizes) in the training set, so `np.std(X[:, 0])` computes the standard deviation of the house sizes.\n", + "At the time that the function `featureNormalize` is called, the extra column of 1’s corresponding to $x_0 = 1$ has not yet been added to $X$. \n", + "\n", + "You will do this for all the features and your code should work with datasets of all sizes (any number of features / examples). Note that each column of the matrix $X$ corresponds to one feature.\n", + "\n", + "
\n", + "**Implementation Note:** When normalizing the features, it is important\n", + "to store the values used for normalization - the mean value and the standard deviation used for the computations. After learning the parameters\n", + "from the model, we often want to predict the prices of houses we have not\n", + "seen before. Given a new x value (living room area and number of bedrooms), we must first normalize x using the mean and standard deviation that we had previously computed from the training set.\n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def featureNormalize(X):\n", + " \"\"\"\n", + " Normalizes the features in X. returns a normalized version of X where\n", + " the mean value of each feature is 0 and the standard deviation\n", + " is 1. This is often a good preprocessing step to do when working with\n", + " learning algorithms.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset of shape (m x n).\n", + " \n", + " Returns\n", + " -------\n", + " X_norm : array_like\n", + " The normalized dataset of shape (m x n).\n", + " \n", + " Instructions\n", + " ------------\n", + " First, for each feature dimension, compute the mean of the feature\n", + " and subtract it from the dataset, storing the mean value in mu. \n", + " Next, compute the standard deviation of each feature and divide\n", + " each feature by it's standard deviation, storing the standard deviation \n", + " in sigma. \n", + " \n", + " Note that X is a matrix where each column is a feature and each row is\n", + " an example. You needto perform the normalization separately for each feature. \n", + " \n", + " Hint\n", + " ----\n", + " You might find the 'np.mean' and 'np.std' functions useful.\n", + " \"\"\"\n", + " # You need to set these values correctly\n", + " X_norm = X.copy()\n", + " mu = np.zeros(X.shape[1])\n", + " sigma = np.zeros(X.shape[1])\n", + "# print(mu)\n", + "# print(sigma)\n", + "\n", + " # =========================== YOUR CODE HERE =====================\n", + " \n", + " mu[0] = np.mean(X[:, 0])\n", + " mu[1] = np.mean(X[:, 1])\n", + " sigma[0] = np.std(X[:, 0])\n", + " sigma[1] = np.std(X[:, 1])\n", + "# print(mu[0])\n", + "# print(mu[1])\n", + " \n", + " for i in range(X.shape[0]):\n", + " X_norm[i][0] = (X[i][0] - mu[0])/sigma[0]\n", + " X_norm[i][1] = (X[i][1] - mu[1])/sigma[1]\n", + "# print('{}, {}, {}'.format(i, X[i][0], X[i][1]))\n", + "# print('{}, {}, {}'.format(i, X_norm[i][0], X_norm[i][1]))\n", + " \n", + " #print(X_norm)\n", + " # ================================================================\n", + " return X_norm, mu, sigma" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Execute the next cell to run the implemented `featureNormalize` function." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computed mean: [2000.68085106 3.17021277]\n", + "Computed standard deviation: [7.86202619e+02 7.52842809e-01]\n" + ] + } + ], + "source": [ + "# call featureNormalize on the loaded data\n", + "X_norm, mu, sigma = featureNormalize(X)\n", + "\n", + "print('Computed mean:', mu)\n", + "print('Computed standard deviation:', sigma)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should not submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[4] = featureNormalize\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the `featureNormalize` function is tested, we now add the intercept term to `X_norm`:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# Add intercept term to X\n", + "X = np.concatenate([np.ones((m, 1)), X_norm], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 3.2 Gradient Descent\n", + "\n", + "Previously, you implemented gradient descent on a univariate regression problem. The only difference now is that there is one more feature in the matrix $X$. The hypothesis function and the batch gradient descent update\n", + "rule remain unchanged. \n", + "\n", + "You should complete the code for the functions `computeCostMulti` and `gradientDescentMulti` to implement the cost function and gradient descent for linear regression with multiple variables. If your code in the previous part (single variable) already supports multiple variables, you can use it here too.\n", + "Make sure your code supports any number of features and is well-vectorized.\n", + "You can use the `shape` property of `numpy` arrays to find out how many features are present in the dataset.\n", + "\n", + "
\n", + "**Implementation Note:** In the multivariate case, the cost function can\n", + "also be written in the following vectorized form:\n", + "\n", + "$$ J(\\theta) = \\frac{1}{2m}(X\\theta - \\vec{y})^T(X\\theta - \\vec{y}) $$\n", + "\n", + "where \n", + "\n", + "$$ X = \\begin{pmatrix}\n", + " - (x^{(1)})^T - \\\\\n", + " - (x^{(2)})^T - \\\\\n", + " \\vdots \\\\\n", + " - (x^{(m)})^T - \\\\ \\\\\n", + " \\end{pmatrix} \\qquad \\mathbf{y} = \\begin{bmatrix} y^{(1)} \\\\ y^{(2)} \\\\ \\vdots \\\\ y^{(m)} \\\\\\end{bmatrix}$$\n", + "\n", + "the vectorized version is efficient when you are working with numerical computing tools like `numpy`. If you are an expert with matrix operations, you can prove to yourself that the two forms are equivalent.\n", + "
\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "def computeCostMulti(X, y, theta):\n", + " \"\"\"\n", + " Compute cost for linear regression with multiple variables.\n", + " Computes the cost of using theta as the parameter for linear regression to fit the data points in X and y.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset of shape (m x n+1).\n", + " \n", + " y : array_like\n", + " A vector of shape (m, ) for the values at a given data point.\n", + " \n", + " theta : array_like\n", + " The linear regression parameters. A vector of shape (n+1, )\n", + " \n", + " Returns\n", + " -------\n", + " J : float\n", + " The value of the cost function. \n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the cost of a particular choice of theta. You should set J to the cost.\n", + " \"\"\"\n", + " # Initialize some useful values\n", + " m = y.shape[0] # number of training examples\n", + " \n", + " # You need to return the following variable correctly\n", + " J = 0\n", + "# print(X)\n", + "# print(y)\n", + "# print(m)\n", + " \n", + " # ======================= YOUR CODE HERE ===========================\n", + "# tr = theta.transpose()\n", + "# h = np.dot(X, tr)\n", + "# #print(h)\n", + "# diff = np.subtract(h, y)\n", + "# ans = np.multiply(diff, diff)\n", + "# tot = np.sum(ans)\n", + "# J = tot/(2*m)\n", + " \n", + " t1 = np.dot(X, theta)\n", + " t2 = np.subtract(t1, y)\n", + " t3 = t2.transpose()\n", + " t4 = np.dot(t2, t3)\n", + " J = t4/(2*m)\n", + " \n", + "# tot = 0\n", + "# for i in range(47):\n", + "# tot = tot + y[i]*y[i]\n", + " \n", + " #tot = tot/(2*m)\n", + " #print(tot)\n", + "\n", + " \n", + " # ==================================================================\n", + " return J\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost function for theta = [0, 0, 0] : 65591548106.45744\n" + ] + } + ], + "source": [ + "print(\"Cost function for theta = [0, 0, 0] : {}\".format(computeCostMulti(X, y, [0, 0, 0])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[5] = computeCostMulti\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "def gradientDescentMulti(X, y, theta, alpha, num_iters):\n", + " \"\"\"\n", + " Performs gradient descent to learn theta.\n", + " Updates theta by taking num_iters gradient steps with learning rate alpha.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset of shape (m x n+1).\n", + " \n", + " y : array_like\n", + " A vector of shape (m, ) for the values at a given data point.\n", + " \n", + " theta : array_like\n", + " The linear regression parameters. A vector of shape (n+1, )\n", + " \n", + " alpha : float\n", + " The learning rate for gradient descent. \n", + " \n", + " num_iters : int\n", + " The number of iterations to run gradient descent. \n", + " \n", + " Returns\n", + " -------\n", + " theta : array_like\n", + " The learned linear regression parameters. A vector of shape (n+1, ).\n", + " \n", + " J_history : list\n", + " A python list for the values of the cost function after each iteration.\n", + " \n", + " Instructions\n", + " ------------\n", + " Peform a single gradient step on the parameter vector theta.\n", + "\n", + " While debugging, it can be useful to print out the values of \n", + " the cost function (computeCost) and gradient here.\n", + " \"\"\"\n", + " # Initialize some useful values\n", + " m = y.shape[0] # number of training examples\n", + " \n", + " # make a copy of theta, which will be updated by gradient descent\n", + " theta = theta.copy()\n", + " n = X.shape[1] - 1\n", + " #print(n)\n", + " \n", + " J_history = []\n", + " \n", + " for i in range(num_iters):\n", + " # ======================= YOUR CODE HERE ==========================\n", + " #tr = theta.transpose()\n", + " h = np.dot(X, theta)\n", + " diff = np.subtract(h, y)\n", + " \n", + "# X0 = X[:, 0]\n", + "# X1 = X[:, 1]\n", + " \n", + "# temp0 = np.multiply(diff, X0)\n", + "# temp1 = np.multiply(diff, X1)\n", + " \n", + "# sum0 = np.sum(temp0)\n", + "# sum1 = np.sum(temp1)\n", + " \n", + "# theta[0] = theta[0] - ((alpha*sum0)/m)\n", + "# theta[1] = theta[1] - ((alpha*sum1)/m)\n", + " \n", + " for j in range(n+1):\n", + " Xj = X[:, j]\n", + " tempj = np.multiply(diff, Xj)\n", + " sumj = np.sum(tempj)\n", + " theta[j] = theta[j] - ((alpha*sumj)/m)\n", + " \n", + " \n", + " \n", + " \n", + " # =================================================================\n", + " \n", + " # save the cost J in every iteration\n", + " J_history.append(computeCostMulti(X, y, theta))\n", + "# print(i)\n", + "# print(theta)\n", + "# print(computeCostMulti(X, y, theta))\n", + " \n", + " \n", + "# print(\"**********\")\n", + "# print(theta)\n", + "# print(\"**********\")\n", + " \n", + " \n", + " return theta, J_history" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal theta = [340412.65957446786, 109447.7964598305, -6578.354844349947]\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#theta = gradientDescentMulti(X, y, [0, 0, 0], 0.01, 50)[0]\n", + "theta, J_history = gradientDescentMulti(X, y, [0, 0, 0], 0.1, 500)\n", + "print(\"Optimal theta = {}\".format(theta))\n", + "\n", + "pyplot.plot(np.arange(500), J_history)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[6] = gradientDescentMulti\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.2.1 Optional (ungraded) exercise: Selecting learning rates\n", + "\n", + "In this part of the exercise, you will get to try out different learning rates for the dataset and find a learning rate that converges quickly. You can change the learning rate by modifying the following code and changing the part of the code that sets the learning rate.\n", + "\n", + "Use your implementation of `gradientDescentMulti` function and run gradient descent for about 50 iterations at the chosen learning rate. The function should also return the history of $J(\\theta)$ values in a vector $J$.\n", + "\n", + "After the last iteration, plot the J values against the number of the iterations.\n", + "\n", + "If you picked a learning rate within a good range, your plot look similar as the following Figure. \n", + "\n", + "![](Figures/learning_rate.png)\n", + "\n", + "If your graph looks very different, especially if your value of $J(\\theta)$ increases or even blows up, adjust your learning rate and try again. We recommend trying values of the learning rate $\\alpha$ on a log-scale, at multiplicative steps of about 3 times the previous value (i.e., 0.3, 0.1, 0.03, 0.01 and so on). You may also want to adjust the number of iterations you are running if that will help you see the overall trend in the curve.\n", + "\n", + "
\n", + "**Implementation Note:** If your learning rate is too large, $J(\\theta)$ can diverge and ‘blow up’, resulting in values which are too large for computer calculations. In these situations, `numpy` will tend to return\n", + "NaNs. NaN stands for ‘not a number’ and is often caused by undefined operations that involve −∞ and +∞.\n", + "
\n", + "\n", + "
\n", + "**MATPLOTLIB tip:** To compare how different learning learning rates affect convergence, it is helpful to plot $J$ for several learning rates on the same figure. This can be done by making `alpha` a python list, and looping across the values within this list, and calling the plot function in every iteration of the loop. It is also useful to have a legend to distinguish the different lines within the plot. Search online for `pyplot.legend` for help on showing legends in `matplotlib`.\n", + "
\n", + "\n", + "Notice the changes in the convergence curves as the learning rate changes. With a small learning rate, you should find that gradient descent takes a very long time to converge to the optimal value. Conversely, with a large learning rate, gradient descent might not converge or might even diverge!\n", + "Using the best learning rate that you found, run the script\n", + "to run gradient descent until convergence to find the final values of $\\theta$. Next,\n", + "use this value of $\\theta$ to predict the price of a house with 1650 square feet and\n", + "3 bedrooms. You will use value later to check your implementation of the normal equations. Don’t forget to normalize your features when you make this prediction!" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "theta computed from gradient descent: [340412.65957447 109447.79645983 -6578.35484435]\n", + "Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): $293081\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"\n", + "Instructions\n", + "------------\n", + "We have provided you with the following starter code that runs\n", + "gradient descent with a particular learning rate (alpha). \n", + "\n", + "Your task is to first make sure that your functions - `computeCost`\n", + "and `gradientDescent` already work with this starter code and\n", + "support multiple variables.\n", + "\n", + "After that, try running gradient descent with different values of\n", + "alpha and see which one gives you the best result.\n", + "\n", + "Finally, you should complete the code at the end to predict the price\n", + "of a 1650 sq-ft, 3 br house.\n", + "\n", + "Hint\n", + "----\n", + "At prediction, make sure you do the same feature normalization.\n", + "\"\"\"\n", + "# Choose some alpha value - change this\n", + "alpha = 0.1\n", + "num_iters = 500\n", + "\n", + "# init theta and run gradient descent\n", + "theta = np.zeros(3)\n", + "theta, J_history = gradientDescentMulti(X, y, theta, alpha, num_iters)\n", + "#print(theta)\n", + "# print(X)\n", + "# print(y)\n", + "# print(np.mean(X[:,0]))\n", + "# print(np.mean(X[:,1]))\n", + "\n", + "# Plot the convergence graph\n", + "pyplot.plot(np.arange(len(J_history)), J_history, lw=2)\n", + "pyplot.xlabel('Number of iterations')\n", + "pyplot.ylabel('Cost J')\n", + "\n", + "# Display the gradient descent's result\n", + "print('theta computed from gradient descent: {:s}'.format(str(theta)))\n", + "\n", + "# Estimate the price of a 1650 sq-ft, 3 br house\n", + "# ======================= YOUR CODE HERE ===========================\n", + "# Recall that the first column of X is all-ones. \n", + "# Thus, it does not need to be normalized.\n", + "\n", + "#price = 0 # You should change this\n", + "area = (1650 - mu[0])/sigma[0]\n", + "rooms = (3 - mu[1])/sigma[1]\n", + "# print(mu)\n", + "# print(sigma)\n", + "# print(area)\n", + "# print(rooms)\n", + "price = np.dot([1, area, rooms], theta)\n", + "\n", + "# ===================================================================\n", + "\n", + "print('Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): ${:.0f}'.format(price))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You do not need to submit any solutions for this optional (ungraded) part.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 3.3 Normal Equations\n", + "\n", + "In the lecture videos, you learned that the closed-form solution to linear regression is\n", + "\n", + "$$ \\theta = \\left( X^T X\\right)^{-1} X^T\\vec{y}$$\n", + "\n", + "Using this formula does not require any feature scaling, and you will get an exact solution in one calculation: there is no “loop until convergence” like in gradient descent. \n", + "\n", + "First, we will reload the data to ensure that the variables have not been modified. Remember that while you do not need to scale your features, we still need to add a column of 1’s to the $X$ matrix to have an intercept term ($\\theta_0$). The code in the next cell will add the column of 1’s to X for you." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# Load data\n", + "data = np.loadtxt(os.path.join('Data', 'ex1data2.txt'), delimiter=',')\n", + "X = data[:, :2]\n", + "y = data[:, 2]\n", + "m = y.size\n", + "X = np.concatenate([np.ones((m, 1)), X], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Complete the code for the function `normalEqn` below to use the formula above to calculate $\\theta$. \n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "def normalEqn(X, y):\n", + " \"\"\"\n", + " Computes the closed-form solution to linear regression using the normal equations.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset of shape (m x n+1).\n", + " \n", + " y : array_like\n", + " The value at each data point. A vector of shape (m, ).\n", + " \n", + " Returns\n", + " -------\n", + " theta : array_like\n", + " Estimated linear regression parameters. A vector of shape (n+1, ).\n", + " \n", + " Instructions\n", + " ------------\n", + " Complete the code to compute the closed form solution to linear\n", + " regression and put the result in theta.\n", + " \n", + " Hint\n", + " ----\n", + " Look up the function `np.linalg.pinv` for computing matrix inverse.\n", + " \"\"\"\n", + " theta = np.zeros(X.shape[1])\n", + " \n", + " # ===================== YOUR CODE HERE ============================\n", + " t1 = X.transpose()\n", + " t2 = np.dot(t1, X)\n", + " t3 = np.linalg.inv(t2)\n", + " t4 = np.dot(t3, t1)\n", + " t5 = np.dot(t4, y)\n", + " theta = t5\n", + " \n", + " # =================================================================\n", + " return theta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[7] = normalEqn\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optional (ungraded) exercise: Now, once you have found $\\theta$ using this\n", + "method, use it to make a price prediction for a 1650-square-foot house with\n", + "3 bedrooms. You should find that gives the same predicted price as the value\n", + "you obtained using the model fit with gradient descent (in Section 3.2.1)." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Theta computed from the normal equations: [89597.9095428 139.21067402 -8738.01911233]\n", + "Predicted price of a 1650 sq-ft, 3 br house (using normal equations): $293081\n" + ] + } + ], + "source": [ + "# Calculate the parameters from the normal equation\n", + "theta = normalEqn(X, y);\n", + "\n", + "# Display normal equation's result\n", + "print('Theta computed from the normal equations: {:s}'.format(str(theta)));\n", + "\n", + "# Estimate the price of a 1650 sq-ft, 3 br house\n", + "# ====================== YOUR CODE HERE ======================\n", + "\n", + "price = 0 # You should change this\n", + "price = np.dot([1, 1650, 3], theta)\n", + "\n", + "# ============================================================\n", + "\n", + "print('Predicted price of a 1650 sq-ft, 3 br house (using normal equations): ${:.0f}'.format(price))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/ParthBakare_180101056.ipynb b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/ParthBakare_180101056.ipynb new file mode 100644 index 000000000..23834a64c --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/ParthBakare_180101056.ipynb @@ -0,0 +1,1140 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Programming Exercise 2: Logistic Regression\n", + "\n", + "## Introduction\n", + "\n", + "In this exercise, you will implement logistic regression and apply it to two different datasets. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n", + "\n", + "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", + "\n", + "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, and [`matplotlib`](https://matplotlib.org/) for plotting. In this assignment, we will also use [`scipy`](https://docs.scipy.org/doc/scipy/reference/), which contains scientific and numerical computation functions and tools. \n", + "\n", + "You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "\n", + "# Optimization module in scipy\n", + "from scipy import optimize\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils\n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submission and Grading\n", + "\n", + "\n", + "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n", + "\n", + "\n", + "| Section | Part | Submission function | Points \n", + "| :- |:- | :- | :-:\n", + "| 1 | [Sigmoid Function](#section1) | [`sigmoid`](#sigmoid) | 5 \n", + "| 2 | [Compute cost for logistic regression](#section2) | [`costFunction`](#costFunction) | 30 \n", + "| 3 | [Gradient for logistic regression](#section2) | [`costFunction`](#costFunction) | 30 \n", + "| 4 | [Predict Function](#section4) | [`predict`](#predict) | 5 \n", + "| 5 | [Compute cost for regularized LR](#section5) | [`costFunctionReg`](#costFunctionReg) | 15 \n", + "| 6 | [Gradient for regularized LR](#section5) | [`costFunctionReg`](#costFunctionReg) | 15 \n", + "| | Total Points | | 100 \n", + "\n", + "\n", + "\n", + "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "
\n", + "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once. They must also be re-executed everytime the submitted function is updated.\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 Logistic Regression\n", + "\n", + "In this part of the exercise, you will build a logistic regression model to predict whether a student gets admitted into a university. Suppose that you are the administrator of a university department and\n", + "you want to determine each applicant’s chance of admission based on their results on two exams. You have historical data from previous applicants that you can use as a training set for logistic regression. For each training example, you have the applicant’s scores on two exams and the admissions\n", + "decision. Your task is to build a classification model that estimates an applicant’s probability of admission based the scores from those two exams. \n", + "\n", + "The following cell will load the data and corresponding labels:" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [], + "source": [ + "# Load data\n", + "# The first two columns contains the exam scores and the third column\n", + "# contains the label.\n", + "data = np.loadtxt(os.path.join('Data', 'ex2data1.txt'), delimiter=',')\n", + "X, y = data[:, 0:2], data[:, 2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Visualizing the data\n", + "\n", + "Before starting to implement any learning algorithm, it is always good to visualize the data if possible. We display the data on a 2-dimensional plot by calling the function `plotData`. You will now complete the code in `plotData` so that it displays a figure where the axes are the two exam scores, and the positive and negative examples are shown with different markers.\n", + "\n", + "To help you get more familiar with plotting, we have left `plotData` empty so you can try to implement it yourself. However, this is an optional (ungraded) exercise. We also provide our implementation below so you can\n", + "copy it or refer to it. If you choose to copy our example, make sure you learn\n", + "what each of its commands is doing by consulting the `matplotlib` and `numpy` documentation.\n", + "\n", + "```python\n", + "# Find Indices of Positive and Negative Examples\n", + "pos = y == 1\n", + "neg = y == 0\n", + "\n", + "# Plot Examples\n", + "pyplot.plot(X[pos, 0], X[pos, 1], 'k*', lw=2, ms=10)\n", + "pyplot.plot(X[neg, 0], X[neg, 1], 'ko', mfc='y', ms=8, mec='k', mew=1)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [], + "source": [ + "def plotData(X, y):\n", + " \"\"\"\n", + " Plots the data points X and y into a new figure. Plots the data \n", + " points with * for the positive examples and o for the negative examples.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " An Mx2 matrix representing the dataset. \n", + " \n", + " y : array_like\n", + " Label values for the dataset. A vector of size (M, ).\n", + " \n", + " Instructions\n", + " ------------\n", + " Plot the positive and negative examples on a 2D plot, using the\n", + " option 'k*' for the positive examples and 'ko' for the negative examples. \n", + " \"\"\"\n", + " # Create New Figure\n", + " fig = pyplot.figure()\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " # Find Indices of Positive and Negative Examples\n", + " pos = y == 1\n", + " neg = y == 0\n", + "\n", + " # Plot Examples\n", + " pyplot.plot(X[pos, 0], X[pos, 1], 'k*', lw=2, ms=10)\n", + " pyplot.plot(X[neg, 0], X[neg, 1], 'ko', mfc='y', ms=8, mec='k', mew=1)\n", + " \n", + " # ============================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we call the implemented function to display the loaded data:" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotData(X, y)\n", + "# add axes labels\n", + "pyplot.xlabel('Exam 1 score')\n", + "pyplot.ylabel('Exam 2 score')\n", + "pyplot.legend(['Admitted', 'Not admitted'])\n", + "pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 1.2 Implementation\n", + "\n", + "#### 1.2.1 Warmup exercise: sigmoid function\n", + "\n", + "Before you start with the actual cost function, recall that the logistic regression hypothesis is defined as:\n", + "\n", + "$$ h_\\theta(x) = g(\\theta^T x)$$\n", + "\n", + "where function $g$ is the sigmoid function. The sigmoid function is defined as: \n", + "\n", + "$$g(z) = \\frac{1}{1+e^{-z}}$$.\n", + "\n", + "Your first step is to implement this function `sigmoid` so it can be\n", + "called by the rest of your program. When you are finished, try testing a few\n", + "values by calling `sigmoid(x)` in a new cell. For large positive values of `x`, the sigmoid should be close to 1, while for large negative values, the sigmoid should be close to 0. Evaluating `sigmoid(0)` should give you exactly 0.5. Your code should also work with vectors and matrices. **For a matrix, your function should perform the sigmoid function on every element.**\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [], + "source": [ + "def sigmoid(z):\n", + " \"\"\"\n", + " Compute sigmoid function given the input z.\n", + " \n", + " Parameters\n", + " ----------\n", + " z : array_like\n", + " The input to the sigmoid function. This can be a 1-D vector \n", + " or a 2-D matrix. \n", + " \n", + " Returns\n", + " -------\n", + " g : array_like\n", + " The computed sigmoid function. g has the same shape as z, since\n", + " the sigmoid is computed element-wise on z.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the sigmoid of each value of z (z can be a matrix, vector or scalar).\n", + " \"\"\"\n", + " # convert input to a numpy array\n", + " z = np.array(z)\n", + " \n", + " # You need to return the following variables correctly \n", + " g = np.zeros(z.shape)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " t1 = (-1) * z\n", + " t2 = np.exp(t1)\n", + " t3 = np.ones(t2.shape) + t2\n", + " t4 = np.divide(np.ones(t3.shape), t3)\n", + " g = t4\n", + "\n", + " # =============================================================\n", + " return g " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell evaluates the sigmoid function at `z=0`. You should get a value of 0.5. You can also try different values for `z` to experiment with the sigmoid function." + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "g( [[-1, -2, -3], [1, 2, 3]] ) = [[0.26894142 0.11920292 0.04742587]\n", + " [0.73105858 0.88079708 0.95257413]]\n" + ] + } + ], + "source": [ + "# Test the implementation of sigmoid function here\n", + "z = [[-1, -2, -3], [1, 2, 3]]\n", + "g = sigmoid(z)\n", + "\n", + "print('g(', z, ') = ', g)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After completing a part of the exercise, you can submit your solutions for grading by first adding the function you modified to the submission object, and then sending your function to Coursera for grading. \n", + "\n", + "The submission script will prompt you for your login e-mail and submission token. You can obtain a submission token from the web page for the assignment. You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "Execute the following cell to grade your solution to the first part of this exercise.\n", + "\n", + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# appends the implemented function in part 1 to the grader object\n", + "grader[1] = sigmoid\n", + "\n", + "# send the added functions to coursera grader for getting a grade on this part\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 1.2.2 Cost function and gradient\n", + "\n", + "Now you will implement the cost function and gradient for logistic regression. Before proceeding we add the intercept term to X. " + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup the data matrix appropriately, and add ones for the intercept term\n", + "m, n = X.shape\n", + "\n", + "# Add intercept term to X\n", + "X = np.concatenate([np.ones((m, 1)), X], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, complete the code for the function `costFunction` to return the cost and gradient. Recall that the cost function in logistic regression is\n", + "\n", + "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^{m} \\left[ -y^{(i)} \\log\\left(h_\\theta\\left( x^{(i)} \\right) \\right) - \\left( 1 - y^{(i)}\\right) \\log \\left( 1 - h_\\theta\\left( x^{(i)} \\right) \\right) \\right]$$\n", + "\n", + "and the gradient of the cost is a vector of the same length as $\\theta$ where the $j^{th}$\n", + "element (for $j = 0, 1, \\cdots , n$) is defined as follows:\n", + "\n", + "$$ \\frac{\\partial J(\\theta)}{\\partial \\theta_j} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} $$\n", + "\n", + "Note that while this gradient looks identical to the linear regression gradient, the formula is actually different because linear and logistic regression have different definitions of $h_\\theta(x)$.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [], + "source": [ + "def costFunction(theta, X, y):\n", + " \"\"\"\n", + " Compute cost and gradient for logistic regression. \n", + " \n", + " Parameters\n", + " ----------\n", + " theta : array_like\n", + " The parameters for logistic regression. This a vector\n", + " of shape (n+1, ).\n", + " \n", + " X : array_like\n", + " The input dataset of shape (m x n+1) where m is the total number\n", + " of data points and n is the number of features. We assume the \n", + " intercept has already been added to the input.\n", + " \n", + " y : arra_like\n", + " Labels for the input. This is a vector of shape (m, ).\n", + " \n", + " Returns\n", + " -------\n", + " J : float\n", + " The computed value for the cost function. \n", + " \n", + " grad : array_like\n", + " A vector of shape (n+1, ) which is the gradient of the cost\n", + " function with respect to theta, at the current values of theta.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the cost of a particular choice of theta. You should set J to \n", + " the cost. Compute the partial derivatives and set grad to the partial\n", + " derivatives of the cost w.r.t. each parameter in theta.\n", + " \"\"\"\n", + " # Initialize some useful values\n", + " m = y.size # number of training examples\n", + "\n", + " # You need to return the following variables correctly \n", + " J = 0\n", + " grad = np.zeros(theta.shape)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " t1 = np.dot(X, theta)\n", + " h = sigmoid(t1)\n", + " hp = np.ones(h.shape) - h\n", + " l1 = np.log(h)\n", + " l2 = np.log(hp)\n", + " t2 = np.multiply(y, l1)\n", + " t3 = np.multiply(np.ones(y.shape) - y, l2)\n", + " t4 = np.add(t2, t3)\n", + " t5 = (-1) * t4\n", + " J = ((np.sum(t5))/m)\n", + " \n", + " t6 = h - y\n", + " t7 = t6.transpose()\n", + " t8 = np.dot(t7, X)\n", + " grad = t8/m\n", + " \n", + " # =============================================================\n", + " return J, grad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you are done call your `costFunction` using two test cases for $\\theta$ by executing the next cell." + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at initial theta (zeros): 0.693\n", + "Expected cost (approx): 0.693\n", + "\n", + "Gradient at initial theta (zeros):\n", + "\t[-0.1000, -12.0092, -11.2628]\n", + "Expected gradients (approx):\n", + "\t[-0.1000, -12.0092, -11.2628]\n", + "\n", + "Cost at test theta: 0.218\n", + "Expected cost (approx): 0.218\n", + "\n", + "Gradient at test theta:\n", + "\t[0.043, 2.566, 2.647]\n", + "Expected gradients (approx):\n", + "\t[0.043, 2.566, 2.647]\n" + ] + } + ], + "source": [ + "# Initialize fitting parameters\n", + "initial_theta = np.zeros(n+1)\n", + "\n", + "cost, grad = costFunction(initial_theta, X, y)\n", + "\n", + "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n", + "print('Expected cost (approx): 0.693\\n')\n", + "\n", + "print('Gradient at initial theta (zeros):')\n", + "print('\\t[{:.4f}, {:.4f}, {:.4f}]'.format(*grad))\n", + "print('Expected gradients (approx):\\n\\t[-0.1000, -12.0092, -11.2628]\\n')\n", + "\n", + "# Compute and display cost and gradient with non-zero theta\n", + "test_theta = np.array([-24, 0.2, 0.2])\n", + "cost, grad = costFunction(test_theta, X, y)\n", + "\n", + "print('Cost at test theta: {:.3f}'.format(cost))\n", + "print('Expected cost (approx): 0.218\\n')\n", + "\n", + "print('Gradient at test theta:')\n", + "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*grad))\n", + "print('Expected gradients (approx):\\n\\t[0.043, 2.566, 2.647]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[2] = costFunction\n", + "grader[3] = costFunction\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.2.3 Learning parameters using `scipy.optimize`\n", + "\n", + "In the previous assignment, you found the optimal parameters of a linear regression model by implementing gradient descent. You wrote a cost function and calculated its gradient, then took a gradient descent step accordingly. This time, instead of taking gradient descent steps, you will use the [`scipy.optimize` module](https://docs.scipy.org/doc/scipy/reference/optimize.html). SciPy is a numerical computing library for `python`. It provides an optimization module for root finding and minimization. As of `scipy 1.0`, the function `scipy.optimize.minimize` is the method to use for optimization problems(both constrained and unconstrained).\n", + "\n", + "For logistic regression, you want to optimize the cost function $J(\\theta)$ with parameters $\\theta$.\n", + "Concretely, you are going to use `optimize.minimize` to find the best parameters $\\theta$ for the logistic regression cost function, given a fixed dataset (of X and y values). You will pass to `optimize.minimize` the following inputs:\n", + "- `costFunction`: A cost function that, when given the training set and a particular $\\theta$, computes the logistic regression cost and gradient with respect to $\\theta$ for the dataset (X, y). It is important to note that we only pass the name of the function without the parenthesis. This indicates that we are only providing a reference to this function, and not evaluating the result from this function.\n", + "- `initial_theta`: The initial values of the parameters we are trying to optimize.\n", + "- `(X, y)`: These are additional arguments to the cost function.\n", + "- `jac`: Indication if the cost function returns the Jacobian (gradient) along with cost value. (True)\n", + "- `method`: Optimization method/algorithm to use\n", + "- `options`: Additional options which might be specific to the specific optimization method. In the following, we only tell the algorithm the maximum number of iterations before it terminates.\n", + "\n", + "If you have completed the `costFunction` correctly, `optimize.minimize` will converge on the right optimization parameters and return the final values of the cost and $\\theta$ in a class object. Notice that by using `optimize.minimize`, you did not have to write any loops yourself, or set a learning rate like you did for gradient descent. This is all done by `optimize.minimize`: you only needed to provide a function calculating the cost and the gradient.\n", + "\n", + "In the following, we already have code written to call `optimize.minimize` with the correct arguments." + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at theta found by optimize.minimize: 0.203\n", + "Expected cost (approx): 0.203\n", + "\n", + "theta:\n", + "\t[-25.161, 0.206, 0.201]\n", + "Expected theta (approx):\n", + "\t[-25.161, 0.206, 0.201]\n" + ] + } + ], + "source": [ + "# set options for optimize.minimize\n", + "options= {'maxiter': 400}\n", + "\n", + "# see documention for scipy's optimize.minimize for description about\n", + "# the different parameters\n", + "# The function returns an object `OptimizeResult`\n", + "# We use truncated Newton algorithm for optimization which is \n", + "# equivalent to MATLAB's fminunc\n", + "# See https://stackoverflow.com/questions/18801002/fminunc-alternate-in-numpy\n", + "res = optimize.minimize(costFunction,\n", + " initial_theta,\n", + " (X, y),\n", + " jac=True,\n", + " method='TNC',\n", + " options=options)\n", + "\n", + "# the fun property of `OptimizeResult` object returns\n", + "# the value of costFunction at optimized theta\n", + "cost = res.fun\n", + "\n", + "# the optimized theta is in the x property\n", + "theta = res.x\n", + "\n", + "# Print theta to screen\n", + "print('Cost at theta found by optimize.minimize: {:.3f}'.format(cost))\n", + "print('Expected cost (approx): 0.203\\n');\n", + "\n", + "print('theta:')\n", + "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*theta))\n", + "print('Expected theta (approx):\\n\\t[-25.161, 0.206, 0.201]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once `optimize.minimize` completes, we want to use the final value for $\\theta$ to visualize the decision boundary on the training data as shown in the figure below. \n", + "\n", + "![](Figures/decision_boundary1.png)\n", + "\n", + "To do so, we have written a function `plotDecisionBoundary` for plotting the decision boundary on top of training data. You do not need to write any code for plotting the decision boundary, but we also encourage you to look at the code in `plotDecisionBoundary` to see how to plot such a boundary using the $\\theta$ values. You can find this function in the `utils.py` file which comes with this assignment." + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot Boundary\n", + "utils.plotDecisionBoundary(plotData, theta, X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 1.2.4 Evaluating logistic regression\n", + "\n", + "After learning the parameters, you can use the model to predict whether a particular student will be admitted. For a student with an Exam 1 score of 45 and an Exam 2 score of 85, you should expect to see an admission\n", + "probability of 0.776. Another way to evaluate the quality of the parameters we have found is to see how well the learned model predicts on our training set. In this part, your task is to complete the code in function `predict`. The predict function will produce “1” or “0” predictions given a dataset and a learned parameter vector $\\theta$. \n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(theta, X):\n", + " \"\"\"\n", + " Predict whether the label is 0 or 1 using learned logistic regression.\n", + " Computes the predictions for X using a threshold at 0.5 \n", + " (i.e., if sigmoid(theta.T*x) >= 0.5, predict 1)\n", + " \n", + " Parameters\n", + " ----------\n", + " theta : array_like\n", + " Parameters for logistic regression. A vecotor of shape (n+1, ).\n", + " \n", + " X : array_like\n", + " The data to use for computing predictions. The rows is the number \n", + " of points to compute predictions, and columns is the number of\n", + " features.\n", + "\n", + " Returns\n", + " -------\n", + " p : array_like\n", + " Predictions and 0 or 1 for each row in X. \n", + " \n", + " Instructions\n", + " ------------\n", + " Complete the following code to make predictions using your learned \n", + " logistic regression parameters.You should set p to a vector of 0's and 1's \n", + " \"\"\"\n", + " m = X.shape[0] # Number of training examples\n", + "\n", + " # You need to return the following variables correctly\n", + " p = np.zeros(m)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " t1 = np.dot(X, theta).transpose()\n", + " t2 = sigmoid(t1)\n", + " t3 = 2*t2\n", + " p = np.floor(t3)\n", + " \n", + " # ============================================================\n", + " return p" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After you have completed the code in `predict`, we proceed to report the training accuracy of your classifier by computing the percentage of examples it got correct." + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For a student with scores 45 and 85,we predict an admission probability of 0.776\n", + "Expected value: 0.775 +/- 0.002\n", + "\n", + "Train Accuracy: 89.00 %\n", + "Expected accuracy (approx): 89.00 %\n" + ] + } + ], + "source": [ + "# Predict probability for a student with score 45 on exam 1 \n", + "# and score 85 on exam 2 \n", + "prob = sigmoid(np.dot([1, 45, 85], theta))\n", + "print('For a student with scores 45 and 85,'\n", + " 'we predict an admission probability of {:.3f}'.format(prob))\n", + "print('Expected value: 0.775 +/- 0.002\\n')\n", + "\n", + "# Compute accuracy on our training set\n", + "p = predict(theta, X)\n", + "print('Train Accuracy: {:.2f} %'.format(np.mean(p == y) * 100))\n", + "print('Expected accuracy (approx): 89.00 %')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[4] = predict\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 Regularized logistic regression\n", + "\n", + "In this part of the exercise, you will implement regularized logistic regression to predict whether microchips from a fabrication plant passes quality assurance (QA). During QA, each microchip goes through various tests to ensure it is functioning correctly.\n", + "Suppose you are the product manager of the factory and you have the test results for some microchips on two different tests. From these two tests, you would like to determine whether the microchips should be accepted or rejected. To help you make the decision, you have a dataset of test results on past microchips, from which you can build a logistic regression model.\n", + "\n", + "First, we load the data from a CSV file:" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Data\n", + "# The first two columns contains the X values and the third column\n", + "# contains the label (y).\n", + "data = np.loadtxt(os.path.join('Data', 'ex2data2.txt'), delimiter=',')\n", + "X = data[:, :2]\n", + "y = data[:, 2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Visualize the data\n", + "\n", + "Similar to the previous parts of this exercise, `plotData` is used to generate a figure, where the axes are the two test scores, and the positive (y = 1, accepted) and negative (y = 0, rejected) examples are shown with\n", + "different markers." + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotData(X, y)\n", + "# Labels and Legend\n", + "pyplot.xlabel('Microchip Test 1')\n", + "pyplot.ylabel('Microchip Test 2')\n", + "\n", + "# Specified in plot order\n", + "pyplot.legend(['y = 1', 'y = 0'], loc='upper right')\n", + "pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above figure shows that our dataset cannot be separated into positive and negative examples by a straight-line through the plot. Therefore, a straight-forward application of logistic regression will not perform well on this dataset since logistic regression will only be able to find a linear decision boundary.\n", + "\n", + "### 2.2 Feature mapping\n", + "\n", + "One way to fit the data better is to create more features from each data point. In the function `mapFeature` defined in the file `utils.py`, we will map the features into all polynomial terms of $x_1$ and $x_2$ up to the sixth power.\n", + "\n", + "$$ \\text{mapFeature}(x) = \\begin{bmatrix} 1 & x_1 & x_2 & x_1^2 & x_1 x_2 & x_2^2 & x_1^3 & \\dots & x_1 x_2^5 & x_2^6 \\end{bmatrix}^T $$\n", + "\n", + "As a result of this mapping, our vector of two features (the scores on two QA tests) has been transformed into a 28-dimensional vector. A logistic regression classifier trained on this higher-dimension feature vector will have a more complex decision boundary and will appear nonlinear when drawn in our 2-dimensional plot.\n", + "While the feature mapping allows us to build a more expressive classifier, it also more susceptible to overfitting. In the next parts of the exercise, you will implement regularized logistic regression to fit the data and also see for yourself how regularization can help combat the overfitting problem.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [], + "source": [ + "# Note that mapFeature also adds a column of ones for us, so the intercept\n", + "# term is handled\n", + "X = utils.mapFeature(X[:, 0], X[:, 1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 2.3 Cost function and gradient\n", + "\n", + "Now you will implement code to compute the cost function and gradient for regularized logistic regression. Complete the code for the function `costFunctionReg` below to return the cost and gradient.\n", + "\n", + "Recall that the regularized cost function in logistic regression is\n", + "\n", + "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^m \\left[ -y^{(i)}\\log \\left( h_\\theta \\left(x^{(i)} \\right) \\right) - \\left( 1 - y^{(i)} \\right) \\log \\left( 1 - h_\\theta \\left( x^{(i)} \\right) \\right) \\right] + \\frac{\\lambda}{2m} \\sum_{j=1}^n \\theta_j^2 $$\n", + "\n", + "Note that you should not regularize the parameters $\\theta_0$. The gradient of the cost function is a vector where the $j^{th}$ element is defined as follows:\n", + "\n", + "$$ \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left(x^{(i)}\\right) - y^{(i)} \\right) x_j^{(i)} \\qquad \\text{for } j =0 $$\n", + "\n", + "$$ \\frac{\\partial J(\\theta)}{\\partial \\theta_j} = \\left( \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left(x^{(i)}\\right) - y^{(i)} \\right) x_j^{(i)} \\right) + \\frac{\\lambda}{m}\\theta_j \\qquad \\text{for } j \\ge 1 $$\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [], + "source": [ + "def costFunctionReg(theta, X, y, lambda_):\n", + " \"\"\"\n", + " Compute cost and gradient for logistic regression with regularization.\n", + " \n", + " Parameters\n", + " ----------\n", + " theta : array_like\n", + " Logistic regression parameters. A vector with shape (n, ). n is \n", + " the number of features including any intercept. If we have mapped\n", + " our initial features into polynomial features, then n is the total \n", + " number of polynomial features. \n", + " \n", + " X : array_like\n", + " The data set with shape (m x n). m is the number of examples, and\n", + " n is the number of features (after feature mapping).\n", + " \n", + " y : array_like\n", + " The data labels. A vector with shape (m, ).\n", + " \n", + " lambda_ : float\n", + " The regularization parameter. \n", + " \n", + " Returns\n", + " -------\n", + " J : float\n", + " The computed value for the regularized cost function. \n", + " \n", + " grad : array_like\n", + " A vector of shape (n, ) which is the gradient of the cost\n", + " function with respect to theta, at the current values of theta.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the cost `J` of a particular choice of theta.\n", + " Compute the partial derivatives and set `grad` to the partial\n", + " derivatives of the cost w.r.t. each parameter in theta.\n", + " \"\"\"\n", + " # Initialize some useful values\n", + " m = y.size # number of training examples\n", + " n = theta.size\n", + "\n", + " # You need to return the following variables correctly \n", + " J = 0\n", + " grad = np.zeros(theta.shape)\n", + "\n", + " # ===================== YOUR CODE HERE ======================\n", + " J, grad = costFunction(theta, X, y)\n", + " \n", + " t5 = (np.multiply(theta[1:], theta[1:])*lambda_)/(2*m)\n", + " J = J + np.sum(t5)\n", + "\n", + " grad[1:] = grad[1:] + (lambda_/m)*(theta[1:])\n", + " \n", + " \n", + " # =============================================================\n", + " return J, grad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you are done with the `costFunctionReg`, we call it below using the initial value of $\\theta$ (initialized to all zeros), and also another test case where $\\theta$ is all ones." + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at initial theta (zeros): 0.693\n", + "Expected cost (approx) : 0.693\n", + "\n", + "Gradient at initial theta (zeros) - first five values only:\n", + "\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\n", + "Expected gradients (approx) - first five values only:\n", + "\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\n", + "\n", + "------------------------------\n", + "\n", + "Cost at test theta : 3.16\n", + "Expected cost (approx): 3.16\n", + "\n", + "Gradient at initial theta (zeros) - first five values only:\n", + "\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]\n", + "Expected gradients (approx) - first five values only:\n", + "\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]\n" + ] + } + ], + "source": [ + "# Initialize fitting parameters\n", + "initial_theta = np.zeros(X.shape[1])\n", + "\n", + "# Set regularization parameter lambda to 1\n", + "# DO NOT use `lambda` as a variable name in python\n", + "# because it is a python keyword\n", + "lambda_ = 1\n", + "\n", + "# Compute and display initial cost and gradient for regularized logistic\n", + "# regression\n", + "cost, grad = costFunctionReg(initial_theta, X, y, lambda_)\n", + "\n", + "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n", + "print('Expected cost (approx) : 0.693\\n')\n", + "\n", + "print('Gradient at initial theta (zeros) - first five values only:')\n", + "print('\\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5]))\n", + "print('Expected gradients (approx) - first five values only:')\n", + "print('\\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\\n')\n", + "\n", + "\n", + "# Compute and display cost and gradient\n", + "# with all-ones theta and lambda = 10\n", + "test_theta = np.ones(X.shape[1])\n", + "cost, grad = costFunctionReg(test_theta, X, y, 10)\n", + "\n", + "print('------------------------------\\n')\n", + "print('Cost at test theta : {:.2f}'.format(cost))\n", + "print('Expected cost (approx): 3.16\\n')\n", + "\n", + "print('Gradient at initial theta (zeros) - first five values only:')\n", + "print('\\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5]))\n", + "print('Expected gradients (approx) - first five values only:')\n", + "print('\\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[5] = costFunctionReg\n", + "grader[6] = costFunctionReg\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.3.1 Learning parameters using `scipy.optimize.minimize`\n", + "\n", + "Similar to the previous parts, you will use `optimize.minimize` to learn the optimal parameters $\\theta$. If you have completed the cost and gradient for regularized logistic regression (`costFunctionReg`) correctly, you should be able to step through the next part of to learn the parameters $\\theta$ using `optimize.minimize`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Plotting the decision boundary\n", + "\n", + "To help you visualize the model learned by this classifier, we have provided the function `plotDecisionBoundary` which plots the (non-linear) decision boundary that separates the positive and negative examples. In `plotDecisionBoundary`, we plot the non-linear decision boundary by computing the classifier’s predictions on an evenly spaced grid and then and draw a contour plot where the predictions change from y = 0 to y = 1. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.5 Optional (ungraded) exercises\n", + "\n", + "In this part of the exercise, you will get to try out different regularization parameters for the dataset to understand how regularization prevents overfitting.\n", + "\n", + "Notice the changes in the decision boundary as you vary $\\lambda$. With a small\n", + "$\\lambda$, you should find that the classifier gets almost every training example correct, but draws a very complicated boundary, thus overfitting the data. See the following figures for the decision boundaries you should get for different values of $\\lambda$. \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " No regularization (overfitting)\n", + " \n", + " Decision boundary with regularization\n", + " \n", + " \n", + " Decision boundary with too much regularization\n", + " \n", + "
\n", + "\n", + "This is not a good decision boundary: for example, it predicts that a point at $x = (−0.25, 1.5)$ is accepted $(y = 1)$, which seems to be an incorrect decision given the training set.\n", + "With a larger $\\lambda$, you should see a plot that shows an simpler decision boundary which still separates the positives and negatives fairly well. However, if $\\lambda$ is set to too high a value, you will not get a good fit and the decision boundary will not follow the data so well, thus underfitting the data." + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Accuracy: 83.1 %\n", + "Expected accuracy (with lambda = 1): 83.1 % (approx)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Initialize fitting parameters\n", + "initial_theta = np.zeros(X.shape[1])\n", + "\n", + "# Set regularization parameter lambda to 1 (you should vary this)\n", + "lambda_ = 1\n", + "\n", + "# set options for optimize.minimize\n", + "options= {'maxiter': 100}\n", + "\n", + "res = optimize.minimize(costFunctionReg,\n", + " initial_theta,\n", + " (X, y, lambda_),\n", + " jac=True,\n", + " method='TNC',\n", + " options=options)\n", + "\n", + "# the fun property of OptimizeResult object returns\n", + "# the value of costFunction at optimized theta\n", + "cost = res.fun\n", + "\n", + "# the optimized theta is in the x property of the result\n", + "theta = res.x\n", + "\n", + "utils.plotDecisionBoundary(plotData, theta, X, y)\n", + "pyplot.xlabel('Microchip Test 1')\n", + "pyplot.ylabel('Microchip Test 2')\n", + "pyplot.legend(['y = 1', 'y = 0'])\n", + "pyplot.grid(False)\n", + "pyplot.title('lambda = %0.2f' % lambda_)\n", + "\n", + "# Compute accuracy on our training set\n", + "p = predict(theta, X)\n", + "\n", + "print('Train Accuracy: %.1f %%' % (np.mean(p == y) * 100))\n", + "print('Expected accuracy (with lambda = 1): 83.1 % (approx)\\n')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You do not need to submit any solutions for these optional (ungraded) exercises.*" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/ParthBakare_180101056.ipynb b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/ParthBakare_180101056.ipynb new file mode 100644 index 000000000..00034d6a1 --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/ParthBakare_180101056.ipynb @@ -0,0 +1,1033 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Programming Exercise 3\n", + "# Multi-class Classification and Neural Networks\n", + "\n", + "## Introduction\n", + "\n", + "\n", + "In this exercise, you will implement one-vs-all logistic regression and neural networks to recognize handwritten digits. Before starting the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics. \n", + "\n", + "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", + "\n", + "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "\n", + "# Optimization module in scipy\n", + "from scipy import optimize\n", + "\n", + "# will be used to load MATLAB mat datafile format\n", + "from scipy.io import loadmat\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils\n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submission and Grading\n", + "\n", + "\n", + "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n", + "\n", + "\n", + "| Section | Part | Submission function | Points \n", + "| :- |:- | :- | :-: \n", + "| 1 | [Regularized Logistic Regression](#section1) | [`lrCostFunction`](#lrCostFunction) | 30 \n", + "| 2 | [One-vs-all classifier training](#section2) | [`oneVsAll`](#oneVsAll) | 20 \n", + "| 3 | [One-vs-all classifier prediction](#section3) | [`predictOneVsAll`](#predictOneVsAll) | 20 \n", + "| 4 | [Neural Network Prediction Function](#section4) | [`predict`](#predict) | 30\n", + "| | Total Points | | 100 \n", + "\n", + "\n", + "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "
\n", + "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once. They must also be re-executed everytime the submitted function is updated.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 Multi-class Classification\n", + "\n", + "For this exercise, you will use logistic regression and neural networks to recognize handwritten digits (from 0 to 9). Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes)\n", + "on mail envelopes to recognizing amounts written on bank checks. This exercise will show you how the methods you have learned can be used for this classification task.\n", + "\n", + "In the first part of the exercise, you will extend your previous implementation of logistic regression and apply it to one-vs-all classification.\n", + "\n", + "### 1.1 Dataset\n", + "\n", + "You are given a data set in `ex3data1.mat` that contains 5000 training examples of handwritten digits (This is a subset of the [MNIST](http://yann.lecun.com/exdb/mnist) handwritten digit dataset). The `.mat` format means that that the data has been saved in a native Octave/MATLAB matrix format, instead of a text (ASCII) format like a csv-file. We use the `.mat` format here because this is the dataset provided in the MATLAB version of this assignment. Fortunately, python provides mechanisms to load MATLAB native format using the `loadmat` function within the `scipy.io` module. This function returns a python dictionary with keys containing the variable names within the `.mat` file. \n", + "\n", + "There are 5000 training examples in `ex3data1.mat`, where each training example is a 20 pixel by 20 pixel grayscale image of the digit. Each pixel is represented by a floating point number indicating the grayscale intensity at that location. The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. Each of these training examples becomes a single row in our data matrix `X`. This gives us a 5000 by 400 matrix `X` where every row is a training example for a handwritten digit image.\n", + "\n", + "$$ X = \\begin{bmatrix} - \\: (x^{(1)})^T \\: - \\\\ -\\: (x^{(2)})^T \\:- \\\\ \\vdots \\\\ - \\: (x^{(m)})^T \\:- \\end{bmatrix} $$\n", + "\n", + "The second part of the training set is a 5000-dimensional vector `y` that contains labels for the training set. \n", + "We start the exercise by first loading the dataset. Execute the cell below, you do not need to write any code here." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "# 20x20 Input Images of Digits\n", + "input_layer_size = 400\n", + "\n", + "# 10 labels, from 1 to 10 (note that we have mapped \"0\" to label 10)\n", + "num_labels = 10\n", + "\n", + "# training data stored in arrays X, y\n", + "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n", + "X, y = data['X'], data['y'].ravel()\n", + "\n", + "# set the zero digit to 0, rather than its mapped 10 in this dataset\n", + "# This is an artifact due to the fact that this dataset was used in \n", + "# MATLAB where there is no index 0\n", + "y[y == 10] = 0\n", + "\n", + "m = y.size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Visualizing the data\n", + "\n", + "You will begin by visualizing a subset of the training set. In the following cell, the code randomly selects selects 100 rows from `X` and passes those rows to the `displayData` function. This function maps each row to a 20 pixel by 20 pixel grayscale image and displays the images together. We have provided the `displayData` function in the file `utils.py`. You are encouraged to examine the code to see how it works. Run the following cell to visualize the data." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Randomly select 100 data points to display\n", + "rand_indices = np.random.choice(m, 100, replace=False)\n", + "sel = X[rand_indices, :]\n", + "\n", + "utils.displayData(sel)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 1.3 Vectorizing Logistic Regression\n", + "\n", + "You will be using multiple one-vs-all logistic regression models to build a multi-class classifier. Since there are 10 classes, you will need to train 10 separate logistic regression classifiers. To make this training efficient, it is important to ensure that your code is well vectorized. In this section, you will implement a vectorized version of logistic regression that does not employ any `for` loops. You can use your code in the previous exercise as a starting point for this exercise. \n", + "\n", + "To test your vectorized logistic regression, we will use custom data as defined in the following cell." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "# test values for the parameters theta\n", + "theta_t = np.array([-2, -1, 1, 2], dtype=float)\n", + "\n", + "# test values for the inputs\n", + "X_t = np.concatenate([np.ones((5, 1)), np.arange(1, 16).reshape(5, 3, order='F')/10.0], axis=1)\n", + "\n", + "# test values for the labels\n", + "y_t = np.array([1, 0, 1, 0, 1])\n", + "\n", + "# test value for the regularization parameter\n", + "lambda_t = 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 1.3.1 Vectorizing the cost function \n", + "\n", + "We will begin by writing a vectorized version of the cost function. Recall that in (unregularized) logistic regression, the cost function is\n", + "\n", + "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^m \\left[ -y^{(i)} \\log \\left( h_\\theta\\left( x^{(i)} \\right) \\right) - \\left(1 - y^{(i)} \\right) \\log \\left(1 - h_\\theta \\left( x^{(i)} \\right) \\right) \\right] $$\n", + "\n", + "To compute each element in the summation, we have to compute $h_\\theta(x^{(i)})$ for every example $i$, where $h_\\theta(x^{(i)}) = g(\\theta^T x^{(i)})$ and $g(z) = \\frac{1}{1+e^{-z}}$ is the sigmoid function. It turns out that we can compute this quickly for all our examples by using matrix multiplication. Let us define $X$ and $\\theta$ as\n", + "\n", + "$$ X = \\begin{bmatrix} - \\left( x^{(1)} \\right)^T - \\\\ - \\left( x^{(2)} \\right)^T - \\\\ \\vdots \\\\ - \\left( x^{(m)} \\right)^T - \\end{bmatrix} \\qquad \\text{and} \\qquad \\theta = \\begin{bmatrix} \\theta_0 \\\\ \\theta_1 \\\\ \\vdots \\\\ \\theta_n \\end{bmatrix} $$\n", + "\n", + "Then, by computing the matrix product $X\\theta$, we have: \n", + "\n", + "$$ X\\theta = \\begin{bmatrix} - \\left( x^{(1)} \\right)^T\\theta - \\\\ - \\left( x^{(2)} \\right)^T\\theta - \\\\ \\vdots \\\\ - \\left( x^{(m)} \\right)^T\\theta - \\end{bmatrix} = \\begin{bmatrix} - \\theta^T x^{(1)} - \\\\ - \\theta^T x^{(2)} - \\\\ \\vdots \\\\ - \\theta^T x^{(m)} - \\end{bmatrix} $$\n", + "\n", + "In the last equality, we used the fact that $a^Tb = b^Ta$ if $a$ and $b$ are vectors. This allows us to compute the products $\\theta^T x^{(i)}$ for all our examples $i$ in one line of code.\n", + "\n", + "#### 1.3.2 Vectorizing the gradient\n", + "\n", + "Recall that the gradient of the (unregularized) logistic regression cost is a vector where the $j^{th}$ element is defined as\n", + "\n", + "$$ \\frac{\\partial J }{\\partial \\theta_j} = \\frac{1}{m} \\sum_{i=1}^m \\left( \\left( h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_j^{(i)} \\right) $$\n", + "\n", + "To vectorize this operation over the dataset, we start by writing out all the partial derivatives explicitly for all $\\theta_j$,\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "\\begin{bmatrix} \n", + "\\frac{\\partial J}{\\partial \\theta_0} \\\\\n", + "\\frac{\\partial J}{\\partial \\theta_1} \\\\\n", + "\\frac{\\partial J}{\\partial \\theta_2} \\\\\n", + "\\vdots \\\\\n", + "\\frac{\\partial J}{\\partial \\theta_n}\n", + "\\end{bmatrix} = &\n", + "\\frac{1}{m} \\begin{bmatrix}\n", + "\\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_0^{(i)}\\right) \\\\\n", + "\\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_1^{(i)}\\right) \\\\\n", + "\\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_2^{(i)}\\right) \\\\\n", + "\\vdots \\\\\n", + "\\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_n^{(i)}\\right) \\\\\n", + "\\end{bmatrix} \\\\\n", + "= & \\frac{1}{m} \\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x^{(i)}\\right) \\\\\n", + "= & \\frac{1}{m} X^T \\left( h_\\theta(x) - y\\right)\n", + "\\end{align*}\n", + "$$\n", + "\n", + "where\n", + "\n", + "$$ h_\\theta(x) - y = \n", + "\\begin{bmatrix}\n", + "h_\\theta\\left(x^{(1)}\\right) - y^{(1)} \\\\\n", + "h_\\theta\\left(x^{(2)}\\right) - y^{(2)} \\\\\n", + "\\vdots \\\\\n", + "h_\\theta\\left(x^{(m)}\\right) - y^{(m)} \n", + "\\end{bmatrix} $$\n", + "\n", + "Note that $x^{(i)}$ is a vector, while $h_\\theta\\left(x^{(i)}\\right) - y^{(i)}$ is a scalar (single number).\n", + "To understand the last step of the derivation, let $\\beta_i = (h_\\theta\\left(x^{(m)}\\right) - y^{(m)})$ and\n", + "observe that:\n", + "\n", + "$$ \\sum_i \\beta_ix^{(i)} = \\begin{bmatrix} \n", + "| & | & & | \\\\\n", + "x^{(1)} & x^{(2)} & \\cdots & x^{(m)} \\\\\n", + "| & | & & | \n", + "\\end{bmatrix}\n", + "\\begin{bmatrix}\n", + "\\beta_1 \\\\\n", + "\\beta_2 \\\\\n", + "\\vdots \\\\\n", + "\\beta_m\n", + "\\end{bmatrix} = x^T \\beta\n", + "$$\n", + "\n", + "where the values $\\beta_i = \\left( h_\\theta(x^{(i)} - y^{(i)} \\right)$.\n", + "\n", + "The expression above allows us to compute all the partial derivatives\n", + "without any loops. If you are comfortable with linear algebra, we encourage you to work through the matrix multiplications above to convince yourself that the vectorized version does the same computations. \n", + "\n", + "Your job is to write the unregularized cost function `lrCostFunction` which returns both the cost function $J(\\theta)$ and its gradient $\\frac{\\partial J}{\\partial \\theta}$. Your implementation should use the strategy we presented above to calculate $\\theta^T x^{(i)}$. You should also use a vectorized approach for the rest of the cost function. A fully vectorized version of `lrCostFunction` should not contain any loops.\n", + "\n", + "
\n", + "**Debugging Tip:** Vectorizing code can sometimes be tricky. One common strategy for debugging is to print out the sizes of the matrices you are working with using the `shape` property of `numpy` arrays. For example, given a data matrix $X$ of size $100 \\times 20$ (100 examples, 20 features) and $\\theta$, a vector with size $20$, you can observe that `np.dot(X, theta)` is a valid multiplication operation, while `np.dot(theta, X)` is not. Furthermore, if you have a non-vectorized version of your code, you can compare the output of your vectorized code and non-vectorized code to make sure that they produce the same outputs.\n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "def lrCostFunction(theta, X, y, lambda_):\n", + " \"\"\"\n", + " Computes the cost of using theta as the parameter for regularized\n", + " logistic regression and the gradient of the cost w.r.t. to the parameters.\n", + " \n", + " Parameters\n", + " ----------\n", + " theta : array_like\n", + " Logistic regression parameters. A vector with shape (n, ). n is \n", + " the number of features including any intercept. \n", + " \n", + " X : array_like\n", + " The data set with shape (m x n). m is the number of examples, and\n", + " n is the number of features (including intercept).\n", + " \n", + " y : array_like\n", + " The data labels. A vector with shape (m, ).\n", + " \n", + " lambda_ : float\n", + " The regularization parameter. \n", + " \n", + " Returns\n", + " -------\n", + " J : float\n", + " The computed value for the regularized cost function. \n", + " \n", + " grad : array_like\n", + " A vector of shape (n, ) which is the gradient of the cost\n", + " function with respect to theta, at the current values of theta.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the cost of a particular choice of theta. You should set J to the cost.\n", + " Compute the partial derivatives and set grad to the partial\n", + " derivatives of the cost w.r.t. each parameter in theta\n", + " \n", + " Hint 1\n", + " ------\n", + " The computation of the cost function and gradients can be efficiently\n", + " vectorized. For example, consider the computation\n", + " \n", + " sigmoid(X * theta)\n", + " \n", + " Each row of the resulting matrix will contain the value of the prediction\n", + " for that example. You can make use of this to vectorize the cost function\n", + " and gradient computations. \n", + " \n", + " Hint 2\n", + " ------\n", + " When computing the gradient of the regularized cost function, there are\n", + " many possible vectorized solutions, but one solution looks like:\n", + " \n", + " grad = (unregularized gradient for logistic regression)\n", + " temp = theta \n", + " temp[0] = 0 # because we don't add anything for j = 0\n", + " grad = grad + YOUR_CODE_HERE (using the temp variable)\n", + " \n", + " Hint 3\n", + " ------\n", + " We have provided the implementatation of the sigmoid function within \n", + " the file `utils.py`. At the start of the notebook, we imported this file\n", + " as a module. Thus to access the sigmoid function within that file, you can\n", + " do the following: `utils.sigmoid(z)`.\n", + " \n", + " \"\"\"\n", + " #Initialize some useful values\n", + " m = y.size\n", + " \n", + " # convert labels to ints if their type is bool\n", + " if y.dtype == bool:\n", + " y = y.astype(int)\n", + " \n", + " # You need to return the following variables correctly\n", + " J = 0\n", + " grad = np.zeros(theta.shape)\n", + " \n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " t1 = np.dot(X, theta)\n", + " h1 = utils.sigmoid(t1)\n", + " h2 = np.ones(h1.shape) - h1\n", + "# y1 = y.transpose()\n", + "# y2 = np.ones(y1.shape) - y1\n", + " y1 = y.transpose()\n", + " y2 = np.ones(y1.shape) - y1\n", + " \n", + " J = np.dot(y1, np.log(h1)) + np.dot(y2, np.log(h2))\n", + " J = (J/m)*(-1)\n", + " \n", + " grad = (np.dot(X.transpose(), np.subtract(h1, y)))/m\n", + " \n", + " J = J + (((lambda_)/(2*m))*(np.dot(theta[1:].transpose(), theta[1:])))\n", + " grad[1:] = grad[1:] + (lambda_/m)*(theta[1:]) \n", + " \n", + " # =============================================================\n", + " return J, grad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.3.3 Vectorizing regularized logistic regression\n", + "\n", + "After you have implemented vectorization for logistic regression, you will now\n", + "add regularization to the cost function. Recall that for regularized logistic\n", + "regression, the cost function is defined as\n", + "\n", + "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^m \\left[ -y^{(i)} \\log \\left(h_\\theta\\left(x^{(i)} \\right)\\right) - \\left( 1 - y^{(i)} \\right) \\log\\left(1 - h_\\theta \\left(x^{(i)} \\right) \\right) \\right] + \\frac{\\lambda}{2m} \\sum_{j=1}^n \\theta_j^2 $$\n", + "\n", + "Note that you should not be regularizing $\\theta_0$ which is used for the bias term.\n", + "Correspondingly, the partial derivative of regularized logistic regression cost for $\\theta_j$ is defined as\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "& \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta\\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} & \\text{for } j = 0 \\\\\n", + "& \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\left( \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta\\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} \\right) + \\frac{\\lambda}{m} \\theta_j & \\text{for } j \\ge 1\n", + "\\end{align*}\n", + "$$\n", + "\n", + "Now modify your code in lrCostFunction in the [**previous cell**](#lrCostFunction) to account for regularization. Once again, you should not put any loops into your code.\n", + "\n", + "
\n", + "**python/numpy Tip:** When implementing the vectorization for regularized logistic regression, you might often want to only sum and update certain elements of $\\theta$. In `numpy`, you can index into the matrices to access and update only certain elements. For example, A[:, 3:5]\n", + "= B[:, 1:3] will replaces the columns with index 3 to 5 of A with the columns with index 1 to 3 from B. To select columns (or rows) until the end of the matrix, you can leave the right hand side of the colon blank. For example, A[:, 2:] will only return elements from the $3^{rd}$ to last columns of $A$. If you leave the left hand size of the colon blank, you will select elements from the beginning of the matrix. For example, A[:, :2] selects the first two columns, and is equivalent to A[:, 0:2]. In addition, you can use negative indices to index arrays from the end. Thus, A[:, :-1] selects all columns of A except the last column, and A[:, -5:] selects the $5^{th}$ column from the end to the last column. Thus, you could use this together with the sum and power ($^{**}$) operations to compute the sum of only the elements you are interested in (e.g., `np.sum(z[1:]**2)`). In the starter code, `lrCostFunction`, we have also provided hints on yet another possible method computing the regularized gradient.\n", + "
\n", + "\n", + "Once you finished your implementation, you can call the function `lrCostFunction` to test your solution using the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost : 2.534819\n", + "Expected cost: 2.534819\n", + "-----------------------\n", + "Gradients:\n", + " [0.146561, -0.548558, 0.724722, 1.398003]\n", + "Expected gradients:\n", + " [0.146561, -0.548558, 0.724722, 1.398003]\n" + ] + } + ], + "source": [ + "J, grad = lrCostFunction(theta_t, X_t, y_t, lambda_t)\n", + "\n", + "print('Cost : {:.6f}'.format(J))\n", + "print('Expected cost: 2.534819')\n", + "print('-----------------------')\n", + "print('Gradients:')\n", + "print(' [{:.6f}, {:.6f}, {:.6f}, {:.6f}]'.format(*grad))\n", + "print('Expected gradients:')\n", + "print(' [0.146561, -0.548558, 0.724722, 1.398003]');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After completing a part of the exercise, you can submit your solutions for grading by first adding the function you modified to the submission object, and then sending your function to Coursera for grading. \n", + "\n", + "The submission script will prompt you for your login e-mail and submission token. You can obtain a submission token from the web page for the assignment. You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "*Execute the following cell to grade your solution to the first part of this exercise.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# appends the implemented function in part 1 to the grader object\n", + "grader[1] = lrCostFunction\n", + "\n", + "# send the added functions to coursera grader for getting a grade on this part\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 1.4 One-vs-all Classification\n", + "\n", + "In this part of the exercise, you will implement one-vs-all classification by training multiple regularized logistic regression classifiers, one for each of the $K$ classes in our dataset. In the handwritten digits dataset, $K = 10$, but your code should work for any value of $K$. \n", + "\n", + "You should now complete the code for the function `oneVsAll` below, to train one classifier for each class. In particular, your code should return all the classifier parameters in a matrix $\\theta \\in \\mathbb{R}^{K \\times (N +1)}$, where each row of $\\theta$ corresponds to the learned logistic regression parameters for one class. You can do this with a “for”-loop from $0$ to $K-1$, training each classifier independently.\n", + "\n", + "Note that the `y` argument to this function is a vector of labels from 0 to 9. When training the classifier for class $k \\in \\{0, ..., K-1\\}$, you will want a K-dimensional vector of labels $y$, where $y_j \\in 0, 1$ indicates whether the $j^{th}$ training instance belongs to class $k$ $(y_j = 1)$, or if it belongs to a different\n", + "class $(y_j = 0)$. You may find logical arrays helpful for this task. \n", + "\n", + "Furthermore, you will be using scipy's `optimize.minimize` for this exercise. \n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "def oneVsAll(X, y, num_labels, lambda_):\n", + " \"\"\"\n", + " Trains num_labels logistic regression classifiers and returns\n", + " each of these classifiers in a matrix all_theta, where the i-th\n", + " row of all_theta corresponds to the classifier for label i.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The input dataset of shape (m x n). m is the number of \n", + " data points, and n is the number of features. Note that we \n", + " do not assume that the intercept term (or bias) is in X, however\n", + " we provide the code below to add the bias term to X. \n", + " \n", + " y : array_like\n", + " The data labels. A vector of shape (m, ).\n", + " \n", + " num_labels : int\n", + " Number of possible labels.\n", + " \n", + " lambda_ : float\n", + " The logistic regularization parameter.\n", + " \n", + " Returns\n", + " -------\n", + " all_theta : array_like\n", + " The trained parameters for logistic regression for each class.\n", + " This is a matrix of shape (K x n+1) where K is number of classes\n", + " (ie. `numlabels`) and n is number of features without the bias.\n", + " \n", + " Instructions\n", + " ------------\n", + " You should complete the following code to train `num_labels`\n", + " logistic regression classifiers with regularization parameter `lambda_`. \n", + " \n", + " Hint\n", + " ----\n", + " You can use y == c to obtain a vector of 1's and 0's that tell you\n", + " whether the ground truth is true/false for this class.\n", + " \n", + " Note\n", + " ----\n", + " For this assignment, we recommend using `scipy.optimize.minimize(method='CG')`\n", + " to optimize the cost function. It is okay to use a for-loop \n", + " (`for c in range(num_labels):`) to loop over the different classes.\n", + " \n", + " Example Code\n", + " ------------\n", + " \n", + " # Set Initial theta\n", + " initial_theta = np.zeros(n + 1)\n", + " \n", + " # Set options for minimize\n", + " options = {'maxiter': 50}\n", + " \n", + " # Run minimize to obtain the optimal theta. This function will \n", + " # return a class object where theta is in `res.x` and cost in `res.fun`\n", + " res = optimize.minimize(lrCostFunction, \n", + " initial_theta, \n", + " (X, (y == c), lambda_), \n", + " jac=True, \n", + " method='TNC',\n", + " options=options) \n", + " \"\"\"\n", + " # Some useful variables\n", + " m, n = X.shape\n", + " \n", + " # You need to return the following variables correctly \n", + " all_theta = np.zeros((num_labels, n + 1))\n", + "\n", + " # Add ones to the X data matrix\n", + " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " \n", + " for k in range(num_labels):\n", + " initial_theta = np.zeros(n+1)\n", + " options = {'maxiter': 50}\n", + " res = optimize.minimize(lrCostFunction, \n", + " initial_theta, \n", + " (X, (y == k), lambda_), \n", + " jac=True, \n", + " method='TNC',\n", + " options=options) \n", + " all_theta[k] = res.x\n", + "# print(res.fun)\n", + "# print(res.x)\n", + "# print(all_theta)\n", + " # ============================================================\n", + " return all_theta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After you have completed the code for `oneVsAll`, the following cell will use your implementation to train a multi-class classifier. " + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "lambda_ = 0.1\n", + "all_theta = oneVsAll(X, y, num_labels, lambda_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[2] = oneVsAll\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 1.4.1 One-vs-all Prediction\n", + "\n", + "After training your one-vs-all classifier, you can now use it to predict the digit contained in a given image. For each input, you should compute the “probability” that it belongs to each class using the trained logistic regression classifiers. Your one-vs-all prediction function will pick the class for which the corresponding logistic regression classifier outputs the highest probability and return the class label (0, 1, ..., K-1) as the prediction for the input example. You should now complete the code in the function `predictOneVsAll` to use the one-vs-all classifier for making predictions. \n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "def predictOneVsAll(all_theta, X):\n", + " \"\"\"\n", + " Return a vector of predictions for each example in the matrix X. \n", + " Note that X contains the examples in rows. all_theta is a matrix where\n", + " the i-th row is a trained logistic regression theta vector for the \n", + " i-th class. You should set p to a vector of values from 0..K-1 \n", + " (e.g., p = [0, 2, 0, 1] predicts classes 0, 2, 0, 1 for 4 examples) .\n", + " \n", + " Parameters\n", + " ----------\n", + " all_theta : array_like\n", + " The trained parameters for logistic regression for each class.\n", + " This is a matrix of shape (K x n+1) where K is number of classes\n", + " and n is number of features without the bias.\n", + " \n", + " X : array_like\n", + " Data points to predict their labels. This is a matrix of shape \n", + " (m x n) where m is number of data points to predict, and n is number \n", + " of features without the bias term. Note we add the bias term for X in \n", + " this function. \n", + " \n", + " Returns\n", + " -------\n", + " p : array_like\n", + " The predictions for each data point in X. This is a vector of shape (m, ).\n", + " \n", + " Instructions\n", + " ------------\n", + " Complete the following code to make predictions using your learned logistic\n", + " regression parameters (one-vs-all). You should set p to a vector of predictions\n", + " (from 0 to num_labels-1).\n", + " \n", + " Hint\n", + " ----\n", + " This code can be done all vectorized using the numpy argmax function.\n", + " In particular, the argmax function returns the index of the max element,\n", + " for more information see '?np.argmax' or search online. If your examples\n", + " are in rows, then, you can use np.argmax(A, axis=1) to obtain the index \n", + " of the max for each row.\n", + " \"\"\"\n", + " m = X.shape[0];\n", + " num_labels = all_theta.shape[0]\n", + "\n", + " # You need to return the following variables correctly \n", + " p = np.zeros(m)\n", + "\n", + " # Add ones to the X data matrix\n", + " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " h = np.dot(X, all_theta.transpose())\n", + " p = np.argmax(h, axis = 1)\n", + "\n", + " \n", + " # ============================================================\n", + " return p" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you are done, call your `predictOneVsAll` function using the learned value of $\\theta$. You should see that the training set accuracy is about 95.1% (i.e., it classifies 95.1% of the examples in the training set correctly)." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Set Accuracy: 95.30%\n" + ] + } + ], + "source": [ + "pred = predictOneVsAll(all_theta, X)\n", + "print('Training Set Accuracy: {:.2f}%'.format(np.mean(pred == y) * 100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[3] = predictOneVsAll\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 Neural Networks\n", + "\n", + "In the previous part of this exercise, you implemented multi-class logistic regression to recognize handwritten digits. However, logistic regression cannot form more complex hypotheses as it is only a linear classifier (You could add more features - such as polynomial features - to logistic regression, but that can be very expensive to train).\n", + "\n", + "In this part of the exercise, you will implement a neural network to recognize handwritten digits using the same training set as before. The neural network will be able to represent complex models that form non-linear hypotheses. For this week, you will be using parameters from a neural network that we have already trained. Your goal is to implement the feedforward propagation algorithm to use our weights for prediction. In next week’s exercise, you will write the backpropagation algorithm for learning the neural network parameters. \n", + "\n", + "We start by first reloading and visualizing the dataset which contains the MNIST handwritten digits (this is the same as we did in the first part of this exercise, we reload it here to ensure the variables have not been modified). " + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# training data stored in arrays X, y\n", + "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n", + "X, y = data['X'], data['y'].ravel()\n", + "\n", + "# set the zero digit to 0, rather than its mapped 10 in this dataset\n", + "# This is an artifact due to the fact that this dataset was used in \n", + "# MATLAB where there is no index 0\n", + "y[y == 10] = 0\n", + "\n", + "# get number of examples in dataset\n", + "m = y.size\n", + "\n", + "# randomly permute examples, to be used for visualizing one \n", + "# picture at a time\n", + "indices = np.random.permutation(m)\n", + "\n", + "# Randomly select 100 data points to display\n", + "rand_indices = np.random.choice(m, 100, replace=False)\n", + "sel = X[rand_indices, :]\n", + "\n", + "utils.displayData(sel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 2.1 Model representation \n", + "\n", + "Our neural network is shown in the following figure.\n", + "\n", + "![Neural network](Figures/neuralnetwork.png)\n", + "\n", + "It has 3 layers: an input layer, a hidden layer and an output layer. Recall that our inputs are pixel values of digit images. Since the images are of size 20×20, this gives us 400 input layer units (excluding the extra bias unit which always outputs +1). As before, the training data will be loaded into the variables X and y. \n", + "\n", + "You have been provided with a set of network parameters ($\\Theta^{(1)}$, $\\Theta^{(2)}$) already trained by us. These are stored in `ex3weights.mat`. The following cell loads those parameters into `Theta1` and `Theta2`. The parameters have dimensions that are sized for a neural network with 25 units in the second layer and 10 output units (corresponding to the 10 digit classes)." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup the parameters you will use for this exercise\n", + "input_layer_size = 400 # 20x20 Input Images of Digits\n", + "hidden_layer_size = 25 # 25 hidden units\n", + "num_labels = 10 # 10 labels, from 0 to 9\n", + "\n", + "# Load the .mat file, which returns a dictionary \n", + "weights = loadmat(os.path.join('Data', 'ex3weights.mat'))\n", + "\n", + "# get the model weights from the dictionary\n", + "# Theta1 has size 25 x 401\n", + "# Theta2 has size 10 x 26\n", + "Theta1, Theta2 = weights['Theta1'], weights['Theta2']\n", + "\n", + "# swap first and last columns of Theta2, due to legacy from MATLAB indexing, \n", + "# since the weight file ex3weights.mat was saved based on MATLAB indexing\n", + "Theta2 = np.roll(Theta2, 1, axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 2.2 Feedforward Propagation and Prediction\n", + "\n", + "Now you will implement feedforward propagation for the neural network. You will need to complete the code in the function `predict` to return the neural network’s prediction. You should implement the feedforward computation that computes $h_\\theta(x^{(i)})$ for every example $i$ and returns the associated predictions. Similar to the one-vs-all classification strategy, the prediction from the neural network will be the label that has the largest output $\\left( h_\\theta(x) \\right)_k$.\n", + "\n", + "
\n", + "**Implementation Note:** The matrix $X$ contains the examples in rows. When you complete the code in the function `predict`, you will need to add the column of 1’s to the matrix. The matrices `Theta1` and `Theta2` contain the parameters for each unit in rows. Specifically, the first row of `Theta1` corresponds to the first hidden unit in the second layer. In `numpy`, when you compute $z^{(2)} = \\theta^{(1)}a^{(1)}$, be sure that you index (and if necessary, transpose) $X$ correctly so that you get $a^{(l)}$ as a 1-D vector.\n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(Theta1, Theta2, X):\n", + " \"\"\"\n", + " Predict the label of an input given a trained neural network.\n", + " \n", + " Parameters\n", + " ----------\n", + " Theta1 : array_like\n", + " Weights for the first layer in the neural network.\n", + " It has shape (2nd hidden layer size x input size)\n", + " \n", + " Theta2: array_like\n", + " Weights for the second layer in the neural network. \n", + " It has shape (output layer size x 2nd hidden layer size)\n", + " \n", + " X : array_like\n", + " The image inputs having shape (number of examples x image dimensions).\n", + " \n", + " Return \n", + " ------\n", + " p : array_like\n", + " Predictions vector containing the predicted label for each example.\n", + " It has a length equal to the number of examples.\n", + " \n", + " Instructions\n", + " ------------\n", + " Complete the following code to make predictions using your learned neural\n", + " network. You should set p to a vector containing labels \n", + " between 0 to (num_labels-1).\n", + " \n", + " Hint\n", + " ----\n", + " This code can be done all vectorized using the numpy argmax function.\n", + " In particular, the argmax function returns the index of the max element,\n", + " for more information see '?np.argmax' or search online. If your examples\n", + " are in rows, then, you can use np.argmax(A, axis=1) to obtain the index\n", + " of the max for each row.\n", + " \n", + " Note\n", + " ----\n", + " Remember, we have supplied the `sigmoid` function in the `utils.py` file. \n", + " You can use this function by calling `utils.sigmoid(z)`, where you can \n", + " replace `z` by the required input variable to sigmoid.\n", + " \"\"\"\n", + " # Make sure the input has two dimensions\n", + " if X.ndim == 1:\n", + " X = X[None] # promote to 2-dimensions\n", + " \n", + " # useful variables\n", + " m = X.shape[0]\n", + " num_labels = Theta2.shape[0]\n", + "\n", + " # You need to return the following variables correctly \n", + " p = np.zeros(X.shape[0])\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n", + " z2 = np.dot(X, Theta1.transpose())\n", + " a2 = utils.sigmoid(z2)\n", + " a2 = np.concatenate([np.ones((a2.shape[0], 1)), a2], axis=1)\n", + "\n", + " z3 = np.dot(a2, Theta2.transpose())\n", + " a3 = utils.sigmoid(z3)\n", + "# a3 = np.concatenate([np.ones((a3.shape[0], 1)), a3], axis=1)\n", + " p = np.argmax(a3, axis = 1)\n", + "\n", + " # =============================================================\n", + " return p" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you are done, call your predict function using the loaded set of parameters for `Theta1` and `Theta2`. You should see that the accuracy is about 97.5%." + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Set Accuracy: 97.5%\n" + ] + } + ], + "source": [ + "pred = predict(Theta1, Theta2, X)\n", + "print('Training Set Accuracy: {:.1f}%'.format(np.mean(pred == y) * 100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After that, we will display images from the training set one at a time, while at the same time printing out the predicted label for the displayed image. \n", + "\n", + "Run the following cell to display a single image the the neural network's prediction. You can run the cell multiple time to see predictions for different images." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Neural Network Prediction: 3\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "if indices.size > 0:\n", + " i, indices = indices[0], indices[1:]\n", + " utils.displayData(X[i, :], figsize=(4, 4))\n", + " pred = predict(Theta1, Theta2, X[i, :])\n", + " print('Neural Network Prediction: {}'.format(*pred))\n", + "else:\n", + " print('No more images to display!')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[4] = predict\n", + "grader.grade()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Exercise4/ParthBakare_180101056.ipynb b/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Exercise4/ParthBakare_180101056.ipynb new file mode 100644 index 000000000..ab72cb7e1 --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Exercise4/ParthBakare_180101056.ipynb @@ -0,0 +1,1257 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Programming Exercise 4: Neural Networks Learning\n", + "\n", + "## Introduction\n", + "\n", + "In this exercise, you will implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n", + "\n", + "\n", + "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", + "\n", + "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "\n", + "# Optimization module in scipy\n", + "from scipy import optimize\n", + "\n", + "# will be used to load MATLAB mat datafile format\n", + "from scipy.io import loadmat\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils\n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submission and Grading\n", + "\n", + "\n", + "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n", + "\n", + "\n", + "| Section | Part | Submission function | Points \n", + "| :- |:- | :- | :-: \n", + "| 1 | [Feedforward and Cost Function](#section1) | [`nnCostFunction`](#nnCostFunction) | 30 \n", + "| 2 | [Regularized Cost Function](#section2) | [`nnCostFunction`](#nnCostFunction) | 15 \n", + "| 3 | [Sigmoid Gradient](#section3) | [`sigmoidGradient`](#sigmoidGradient) | 5 \n", + "| 4 | [Neural Net Gradient Function (Backpropagation)](#section4) | [`nnCostFunction`](#nnCostFunction) | 40 \n", + "| 5 | [Regularized Gradient](#section5) | [`nnCostFunction`](#nnCostFunction) |10 \n", + "| | Total Points | | 100 \n", + "\n", + "\n", + "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "
\n", + "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Networks\n", + "\n", + "In the previous exercise, you implemented feedforward propagation for neural networks and used it to predict handwritten digits with the weights we provided. In this exercise, you will implement the backpropagation algorithm to learn the parameters for the neural network.\n", + "\n", + "We start the exercise by first loading the dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# training data stored in arrays X, y\n", + "data = loadmat(os.path.join('Data', 'ex4data1.mat'))\n", + "X, y = data['X'], data['y'].ravel()\n", + "\n", + "# set the zero digit to 0, rather than its mapped 10 in this dataset\n", + "# This is an artifact due to the fact that this dataset was used in \n", + "# MATLAB where there is no index 0\n", + "y[y == 10] = 0\n", + "\n", + "# Number of training examples\n", + "m = y.size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Visualizing the data\n", + "\n", + "You will begin by visualizing a subset of the training set, using the function `displayData`, which is the same function we used in Exercise 3. It is provided in the `utils.py` file for this assignment as well. The dataset is also the same one you used in the previous exercise.\n", + "\n", + "There are 5000 training examples in `ex4data1.mat`, where each training example is a 20 pixel by 20 pixel grayscale image of the digit. Each pixel is represented by a floating point number indicating the grayscale intensity at that location. The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. Each\n", + "of these training examples becomes a single row in our data matrix $X$. This gives us a 5000 by 400 matrix $X$ where every row is a training example for a handwritten digit image.\n", + "\n", + "$$ X = \\begin{bmatrix} - \\left(x^{(1)} \\right)^T - \\\\\n", + "- \\left(x^{(2)} \\right)^T - \\\\\n", + "\\vdots \\\\\n", + "- \\left(x^{(m)} \\right)^T - \\\\\n", + "\\end{bmatrix}\n", + "$$\n", + "\n", + "The second part of the training set is a 5000-dimensional vector `y` that contains labels for the training set. \n", + "The following cell randomly selects 100 images from the dataset and plots them." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Randomly select 100 data points to display\n", + "rand_indices = np.random.choice(m, 100, replace=False)\n", + "sel = X[rand_indices, :]\n", + "\n", + "utils.displayData(sel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Model representation\n", + "\n", + "Our neural network is shown in the following figure.\n", + "\n", + "![](Figures/neural_network.png)\n", + "\n", + "It has 3 layers - an input layer, a hidden layer and an output layer. Recall that our inputs are pixel values\n", + "of digit images. Since the images are of size $20 \\times 20$, this gives us 400 input layer units (not counting the extra bias unit which always outputs +1). The training data was loaded into the variables `X` and `y` above.\n", + "\n", + "You have been provided with a set of network parameters ($\\Theta^{(1)}, \\Theta^{(2)}$) already trained by us. These are stored in `ex4weights.mat` and will be loaded in the next cell of this notebook into `Theta1` and `Theta2`. The parameters have dimensions that are sized for a neural network with 25 units in the second layer and 10 output units (corresponding to the 10 digit classes)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup the parameters you will use for this exercise\n", + "input_layer_size = 400 # 20x20 Input Images of Digits\n", + "hidden_layer_size = 25 # 25 hidden units\n", + "num_labels = 10 # 10 labels, from 0 to 9\n", + "\n", + "# Load the weights into variables Theta1 and Theta2\n", + "weights = loadmat(os.path.join('Data', 'ex4weights.mat'))\n", + "\n", + "# Theta1 has size 25 x 401\n", + "# Theta2 has size 10 x 26\n", + "Theta1, Theta2 = weights['Theta1'], weights['Theta2']\n", + "\n", + "# swap first and last columns of Theta2, due to legacy from MATLAB indexing, \n", + "# since the weight file ex3weights.mat was saved based on MATLAB indexing\n", + "Theta2 = np.roll(Theta2, 1, axis=0)\n", + "\n", + "# Unroll parameters \n", + "nn_params = np.concatenate([Theta1.ravel(), Theta2.ravel()])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 1.3 Feedforward and cost function\n", + "\n", + "Now you will implement the cost function and gradient for the neural network. First, complete the code for the function `nnCostFunction` in the next cell to return the cost.\n", + "\n", + "Recall that the cost function for the neural network (without regularization) is:\n", + "\n", + "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^{m}\\sum_{k=1}^{K} \\left[ - y_k^{(i)} \\log \\left( \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) - \\left( 1 - y_k^{(i)} \\right) \\log \\left( 1 - \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) \\right]$$\n", + "\n", + "where $h_\\theta \\left( x^{(i)} \\right)$ is computed as shown in the neural network figure above, and K = 10 is the total number of possible labels. Note that $h_\\theta(x^{(i)})_k = a_k^{(3)}$ is the activation (output\n", + "value) of the $k^{th}$ output unit. Also, recall that whereas the original labels (in the variable y) were 0, 1, ..., 9, for the purpose of training a neural network, we need to encode the labels as vectors containing only values 0 or 1, so that\n", + "\n", + "$$ y = \n", + "\\begin{bmatrix} 1 \\\\ 0 \\\\ 0 \\\\\\vdots \\\\ 0 \\end{bmatrix}, \\quad\n", + "\\begin{bmatrix} 0 \\\\ 1 \\\\ 0 \\\\ \\vdots \\\\ 0 \\end{bmatrix}, \\quad \\cdots \\quad \\text{or} \\qquad\n", + "\\begin{bmatrix} 0 \\\\ 0 \\\\ 0 \\\\ \\vdots \\\\ 1 \\end{bmatrix}.\n", + "$$\n", + "\n", + "For example, if $x^{(i)}$ is an image of the digit 5, then the corresponding $y^{(i)}$ (that you should use with the cost function) should be a 10-dimensional vector with $y_5 = 1$, and the other elements equal to 0.\n", + "\n", + "You should implement the feedforward computation that computes $h_\\theta(x^{(i)})$ for every example $i$ and sum the cost over all examples. **Your code should also work for a dataset of any size, with any number of labels** (you can assume that there are always at least $K \\ge 3$ labels).\n", + "\n", + "
\n", + "**Implementation Note:** The matrix $X$ contains the examples in rows (i.e., X[i,:] is the i-th training example $x^{(i)}$, expressed as a $n \\times 1$ vector.) When you complete the code in `nnCostFunction`, you will need to add the column of 1’s to the X matrix. The parameters for each unit in the neural network is represented in Theta1 and Theta2 as one row. Specifically, the first row of Theta1 corresponds to the first hidden unit in the second layer. You can use a for-loop over the examples to compute the cost.\n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def nnCostFunction(nn_params,\n", + " input_layer_size,\n", + " hidden_layer_size,\n", + " num_labels,\n", + " X, y, lambda_=0.0):\n", + " \"\"\"\n", + " Implements the neural network cost function and gradient for a two layer neural \n", + " network which performs classification. \n", + " \n", + " Parameters\n", + " ----------\n", + " nn_params : array_like\n", + " The parameters for the neural network which are \"unrolled\" into \n", + " a vector. This needs to be converted back into the weight matrices Theta1\n", + " and Theta2.\n", + " \n", + " input_layer_size : int\n", + " Number of features for the input layer. \n", + " \n", + " hidden_layer_size : int\n", + " Number of hidden units in the second layer.\n", + " \n", + " num_labels : int\n", + " Total number of labels, or equivalently number of units in output layer. \n", + " \n", + " X : array_like\n", + " Input dataset. A matrix of shape (m x input_layer_size).\n", + " \n", + " y : array_like\n", + " Dataset labels. A vector of shape (m,).\n", + " \n", + " lambda_ : float, optional\n", + " Regularization parameter.\n", + " \n", + " Returns\n", + " -------\n", + " J : float\n", + " The computed value for the cost function at the current weight values.\n", + " \n", + " grad : array_like\n", + " An \"unrolled\" vector of the partial derivatives of the concatenatation of\n", + " neural network weights Theta1 and Theta2.\n", + " \n", + " Instructions\n", + " ------------\n", + " You should complete the code by working through the following parts.\n", + " \n", + " - Part 1: Feedforward the neural network and return the cost in the \n", + " variable J. After implementing Part 1, you can verify that your\n", + " cost function computation is correct by verifying the cost\n", + " computed in the following cell.\n", + " \n", + " - Part 2: Implement the backpropagation algorithm to compute the gradients\n", + " Theta1_grad and Theta2_grad. You should return the partial derivatives of\n", + " the cost function with respect to Theta1 and Theta2 in Theta1_grad and\n", + " Theta2_grad, respectively. After implementing Part 2, you can check\n", + " that your implementation is correct by running checkNNGradients provided\n", + " in the utils.py module.\n", + " \n", + " Note: The vector y passed into the function is a vector of labels\n", + " containing values from 0..K-1. You need to map this vector into a \n", + " binary vector of 1's and 0's to be used with the neural network\n", + " cost function.\n", + " \n", + " Hint: We recommend implementing backpropagation using a for-loop\n", + " over the training examples if you are implementing it for the \n", + " first time.\n", + " \n", + " - Part 3: Implement regularization with the cost function and gradients.\n", + " \n", + " Hint: You can implement this around the code for\n", + " backpropagation. That is, you can compute the gradients for\n", + " the regularization separately and then add them to Theta1_grad\n", + " and Theta2_grad from Part 2.\n", + " \n", + " Note \n", + " ----\n", + " We have provided an implementation for the sigmoid function in the file \n", + " `utils.py` accompanying this assignment.\n", + " \"\"\"\n", + " # Reshape nn_params back into the parameters Theta1 and Theta2, the weight matrices\n", + " # for our 2 layer neural network\n", + " Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)],\n", + " (hidden_layer_size, (input_layer_size + 1)))\n", + "\n", + " Theta2 = np.reshape(nn_params[(hidden_layer_size * (input_layer_size + 1)):],\n", + " (num_labels, (hidden_layer_size + 1)))\n", + "\n", + " # Setup some useful variables\n", + " m = y.size\n", + " \n", + " # You need to return the following variables correctly \n", + " J = 0\n", + " Theta1_grad = np.zeros(Theta1.shape)\n", + " Theta2_grad = np.zeros(Theta2.shape)\n", + "# print(Theta1.shape)\n", + "# print(Theta2.shape)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " for i in range(m):\n", + " a1 = X[i]\n", + " one = np.array([1])\n", + " a1 = np.append(one, a1)\n", + " \n", + " z2 = np.dot(a1, Theta1.transpose())\n", + " a2 = utils.sigmoid(z2)\n", + " a2 = np.append(one, a2)\n", + " \n", + " z3 = np.dot(a2, Theta2.transpose())\n", + " a3 = utils.sigmoid(z3)\n", + " \n", + " y_ = np.zeros((num_labels, ))\n", + " y_[y[i]] = 1\n", + " \n", + " delta3 = np.subtract(a3, y_)\n", + "# print(a3.shape)\n", + "# print(y_.shape)\n", + "# print(Theta2[:,1:].transpose().shape)\n", + "# print(delta3.shape)\n", + "# print(np.dot(Theta2[:,1:].transpose(), delta3).shape)\n", + " delta2 = np.multiply(np.dot(Theta2[:,1:].transpose(), delta3), sigmoidGradient(z2))\n", + " \n", + "# print(delta3.shape)\n", + "# a2 = a2.reshape(26, 1)\n", + "# print(a2.shape)\n", + "# print(a2.transpose().shape)\n", + " \n", + "# print(delta3.reshape(delta3.size, 1).shape)\n", + "# print(a2.reshape(a2.size, 1).shape)\n", + " \n", + " Theta2_grad = Theta2_grad + np.dot(delta3.reshape(delta3.size, 1), a2.reshape(a2.size, 1).transpose())\n", + " Theta1_grad = Theta1_grad + np.dot(delta2.reshape(delta2.size, 1), a1.reshape(a1.size, 1).transpose())\n", + " \n", + " cost = np.dot(np.log(a3), y_) + np.dot(np.log(np.ones(a3.shape) - a3), np.ones(y_.shape) - y_)\n", + " \n", + " J = J + cost\n", + " \n", + " J = (J * (-1))/m\n", + " J = J + ((lambda_)/(2*m))*(np.sum(np.multiply(Theta1[:,1:], Theta1[:,1:])) + np.sum(np.multiply(Theta2[:,1:], Theta2[:,1:])))\n", + " Theta2_grad = Theta2_grad/m\n", + " Theta1_grad = Theta1_grad/m\n", + " \n", + " Theta2_grad[:,1:] = Theta2_grad[:,1:] + (lambda_/m)*Theta2[:,1:]\n", + " Theta1_grad[:,1:] = Theta1_grad[:,1:] + (lambda_/m)*Theta1[:,1:]\n", + " # ================================================================\n", + " # Unroll gradients\n", + " # grad = np.concatenate([Theta1_grad.ravel(order=order), Theta2_grad.ravel(order=order)])\n", + " grad = np.concatenate([Theta1_grad.ravel(), Theta2_grad.ravel()])\n", + "\n", + " return J, grad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Use the following links to go back to the different parts of this exercise that require to modify the function `nnCostFunction`.
\n", + "\n", + "Back to:\n", + "- [Feedforward and cost function](#section1)\n", + "- [Regularized cost](#section2)\n", + "- [Neural Network Gradient (Backpropagation)](#section4)\n", + "- [Regularized Gradient](#section5)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you are done, call your `nnCostFunction` using the loaded set of parameters for `Theta1` and `Theta2`. You should see that the cost is about 0.287629." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at parameters (loaded from ex4weights): 0.287629 \n", + "The cost should be about : 0.287629.\n" + ] + } + ], + "source": [ + "lambda_ = 0\n", + "J, _ = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,\n", + " num_labels, X, y, lambda_)\n", + "print('Cost at parameters (loaded from ex4weights): %.6f ' % J)\n", + "print('The cost should be about : 0.287629.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise neural-network-learning\n", + "\n", + "Use token from last successful submission (parth.titan@gmail.com)? (Y/n): y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Feedforward and Cost Function | 30 / 30 | Nice work!\n", + " Regularized Cost Function | 0 / 15 | \n", + " Sigmoid Gradient | 0 / 5 | \n", + " Neural Network Gradient (Backpropagation) | 0 / 40 | \n", + " Regularized Gradient | 0 / 10 | \n", + " --------------------------------\n", + " | 30 / 100 | \n", + "\n" + ] + } + ], + "source": [ + "grader = utils.Grader()\n", + "grader[1] = nnCostFunction\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 1.4 Regularized cost function\n", + "\n", + "The cost function for neural networks with regularization is given by:\n", + "\n", + "\n", + "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^{m}\\sum_{k=1}^{K} \\left[ - y_k^{(i)} \\log \\left( \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) - \\left( 1 - y_k^{(i)} \\right) \\log \\left( 1 - \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) \\right] + \\frac{\\lambda}{2 m} \\left[ \\sum_{j=1}^{25} \\sum_{k=1}^{400} \\left( \\Theta_{j,k}^{(1)} \\right)^2 + \\sum_{j=1}^{10} \\sum_{k=1}^{25} \\left( \\Theta_{j,k}^{(2)} \\right)^2 \\right] $$\n", + "\n", + "You can assume that the neural network will only have 3 layers - an input layer, a hidden layer and an output layer. However, your code should work for any number of input units, hidden units and outputs units. While we\n", + "have explicitly listed the indices above for $\\Theta^{(1)}$ and $\\Theta^{(2)}$ for clarity, do note that your code should in general work with $\\Theta^{(1)}$ and $\\Theta^{(2)}$ of any size. Note that you should not be regularizing the terms that correspond to the bias. For the matrices `Theta1` and `Theta2`, this corresponds to the first column of each matrix. You should now add regularization to your cost function. Notice that you can first compute the unregularized cost function $J$ using your existing `nnCostFunction` and then later add the cost for the regularization terms.\n", + "\n", + "[Click here to go back to `nnCostFunction` for editing.](#nnCostFunction)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you are done, the next cell will call your `nnCostFunction` using the loaded set of parameters for `Theta1` and `Theta2`, and $\\lambda = 1$. You should see that the cost is about 0.383770." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at parameters (loaded from ex4weights): 0.383770\n", + "This value should be about : 0.383770.\n" + ] + } + ], + "source": [ + "# Weight regularization parameter (we set this to 1 here).\n", + "lambda_ = 1\n", + "J, _ = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,\n", + " num_labels, X, y, lambda_)\n", + "\n", + "print('Cost at parameters (loaded from ex4weights): %.6f' % J)\n", + "print('This value should be about : 0.383770.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise neural-network-learning\n", + "\n", + "Use token from last successful submission (parth.titan@gmail.com)? (Y/n): y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Feedforward and Cost Function | 30 / 30 | Nice work!\n", + " Regularized Cost Function | 15 / 15 | Nice work!\n", + " Sigmoid Gradient | 0 / 5 | \n", + " Neural Network Gradient (Backpropagation) | 0 / 40 | \n", + " Regularized Gradient | 0 / 10 | \n", + " --------------------------------\n", + " | 45 / 100 | \n", + "\n" + ] + } + ], + "source": [ + "grader[2] = nnCostFunction\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 Backpropagation\n", + "\n", + "In this part of the exercise, you will implement the backpropagation algorithm to compute the gradient for the neural network cost function. You will need to update the function `nnCostFunction` so that it returns an appropriate value for `grad`. Once you have computed the gradient, you will be able to train the neural network by minimizing the cost function $J(\\theta)$ using an advanced optimizer such as `scipy`'s `optimize.minimize`.\n", + "You will first implement the backpropagation algorithm to compute the gradients for the parameters for the (unregularized) neural network. After you have verified that your gradient computation for the unregularized case is correct, you will implement the gradient for the regularized neural network." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 2.1 Sigmoid Gradient\n", + "\n", + "To help you get started with this part of the exercise, you will first implement\n", + "the sigmoid gradient function. The gradient for the sigmoid function can be\n", + "computed as\n", + "\n", + "$$ g'(z) = \\frac{d}{dz} g(z) = g(z)\\left(1-g(z)\\right) $$\n", + "\n", + "where\n", + "\n", + "$$ \\text{sigmoid}(z) = g(z) = \\frac{1}{1 + e^{-z}} $$\n", + "\n", + "Now complete the implementation of `sigmoidGradient` in the next cell.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def sigmoidGradient(z):\n", + " \"\"\"\n", + " Computes the gradient of the sigmoid function evaluated at z. \n", + " This should work regardless if z is a matrix or a vector. \n", + " In particular, if z is a vector or matrix, you should return\n", + " the gradient for each element.\n", + " \n", + " Parameters\n", + " ----------\n", + " z : array_like\n", + " A vector or matrix as input to the sigmoid function. \n", + " \n", + " Returns\n", + " --------\n", + " g : array_like\n", + " Gradient of the sigmoid function. Has the same shape as z. \n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the gradient of the sigmoid function evaluated at\n", + " each value of z (z can be a matrix, vector or scalar).\n", + " \n", + " Note\n", + " ----\n", + " We have provided an implementation of the sigmoid function \n", + " in `utils.py` file accompanying this assignment.\n", + " \"\"\"\n", + "\n", + " g = np.zeros(z.shape)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " t = utils.sigmoid(z)\n", + " g = np.multiply(t, np.ones(t.shape) - t)\n", + "\n", + "\n", + " # =============================================================\n", + " return g" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you are done, the following cell call `sigmoidGradient` on a given vector `z`. Try testing a few values by calling `sigmoidGradient(z)`. For large values (both positive and negative) of z, the gradient should be close to 0. When $z = 0$, the gradient should be exactly 0.25. Your code should also work with vectors and matrices. For a matrix, your function should perform the sigmoid gradient function on every element." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\n", + " \n", + "[0.19661193 0.23500371 0.25 0.23500371 0.19661193]\n" + ] + } + ], + "source": [ + "z = np.array([-1, -0.5, 0, 0.5, 1])\n", + "g = sigmoidGradient(z)\n", + "print('Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\\n ')\n", + "print(g)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise neural-network-learning\n", + "\n", + "Use token from last successful submission (parth.titan@gmail.com)? (Y/n): y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Feedforward and Cost Function | 30 / 30 | Nice work!\n", + " Regularized Cost Function | 15 / 15 | Nice work!\n", + " Sigmoid Gradient | 5 / 5 | Nice work!\n", + " Neural Network Gradient (Backpropagation) | 0 / 40 | \n", + " Regularized Gradient | 0 / 10 | \n", + " --------------------------------\n", + " | 50 / 100 | \n", + "\n" + ] + } + ], + "source": [ + "grader[3] = sigmoidGradient\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Random Initialization\n", + "\n", + "When training neural networks, it is important to randomly initialize the parameters for symmetry breaking. One effective strategy for random initialization is to randomly select values for $\\Theta^{(l)}$ uniformly in the range $[-\\epsilon_{init}, \\epsilon_{init}]$. You should use $\\epsilon_{init} = 0.12$. This range of values ensures that the parameters are kept small and makes the learning more efficient.\n", + "\n", + "
\n", + "One effective strategy for choosing $\\epsilon_{init}$ is to base it on the number of units in the network. A good choice of $\\epsilon_{init}$ is $\\epsilon_{init} = \\frac{\\sqrt{6}}{\\sqrt{L_{in} + L_{out}}}$ where $L_{in} = s_l$ and $L_{out} = s_{l+1}$ are the number of units in the layers adjacent to $\\Theta^{l}$.\n", + "
\n", + "\n", + "Your job is to complete the function `randInitializeWeights` to initialize the weights for $\\Theta$. Modify the function by filling in the following code:\n", + "\n", + "```python\n", + "# Randomly initialize the weights to small values\n", + "W = np.random.rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init\n", + "```\n", + "Note that we give the function an argument for $\\epsilon$ with default value `epsilon_init = 0.12`." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def randInitializeWeights(L_in, L_out, epsilon_init=0.12):\n", + " \"\"\"\n", + " Randomly initialize the weights of a layer in a neural network.\n", + " \n", + " Parameters\n", + " ----------\n", + " L_in : int\n", + " Number of incomming connections.\n", + " \n", + " L_out : int\n", + " Number of outgoing connections. \n", + " \n", + " epsilon_init : float, optional\n", + " Range of values which the weight can take from a uniform \n", + " distribution.\n", + " \n", + " Returns\n", + " -------\n", + " W : array_like\n", + " The weight initialiatized to random values. Note that W should\n", + " be set to a matrix of size(L_out, 1 + L_in) as\n", + " the first column of W handles the \"bias\" terms.\n", + " \n", + " Instructions\n", + " ------------\n", + " Initialize W randomly so that we break the symmetry while training\n", + " the neural network. Note that the first column of W corresponds \n", + " to the parameters for the bias unit.\n", + " \"\"\"\n", + "\n", + " # You need to return the following variables correctly \n", + " W = np.zeros((L_out, 1 + L_in))\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " W = np.random.rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init\n", + "\n", + "\n", + " # ============================================================\n", + " return W" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You do not need to submit any code for this part of the exercise.*\n", + "\n", + "Execute the following cell to initialize the weights for the 2 layers in the neural network using the `randInitializeWeights` function." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing Neural Network Parameters ...\n" + ] + } + ], + "source": [ + "print('Initializing Neural Network Parameters ...')\n", + "\n", + "initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size)\n", + "initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels)\n", + "\n", + "# Unroll parameters\n", + "initial_nn_params = np.concatenate([initial_Theta1.ravel(), initial_Theta2.ravel()], axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 2.4 Backpropagation\n", + "\n", + "![](Figures/ex4-backpropagation.png)\n", + "\n", + "Now, you will implement the backpropagation algorithm. Recall that the intuition behind the backpropagation algorithm is as follows. Given a training example $(x^{(t)}, y^{(t)})$, we will first run a “forward pass” to compute all the activations throughout the network, including the output value of the hypothesis $h_\\theta(x)$. Then, for each node $j$ in layer $l$, we would like to compute an “error term” $\\delta_j^{(l)}$ that measures how much that node was “responsible” for any errors in our output.\n", + "\n", + "For an output node, we can directly measure the difference between the network’s activation and the true target value, and use that to define $\\delta_j^{(3)}$ (since layer 3 is the output layer). For the hidden units, you will compute $\\delta_j^{(l)}$ based on a weighted average of the error terms of the nodes in layer $(l+1)$. In detail, here is the backpropagation algorithm (also depicted in the figure above). You should implement steps 1 to 4 in a loop that processes one example at a time. Concretely, you should implement a for-loop `for t in range(m)` and place steps 1-4 below inside the for-loop, with the $t^{th}$ iteration performing the calculation on the $t^{th}$ training example $(x^{(t)}, y^{(t)})$. Step 5 will divide the accumulated gradients by $m$ to obtain the gradients for the neural network cost function.\n", + "\n", + "1. Set the input layer’s values $(a^{(1)})$ to the $t^{th }$training example $x^{(t)}$. Perform a feedforward pass, computing the activations $(z^{(2)}, a^{(2)}, z^{(3)}, a^{(3)})$ for layers 2 and 3. Note that you need to add a `+1` term to ensure that the vectors of activations for layers $a^{(1)}$ and $a^{(2)}$ also include the bias unit. In `numpy`, if a 1 is a column matrix, adding one corresponds to `a_1 = np.concatenate([np.ones((m, 1)), a_1], axis=1)`.\n", + "\n", + "1. For each output unit $k$ in layer 3 (the output layer), set \n", + "$$\\delta_k^{(3)} = \\left(a_k^{(3)} - y_k \\right)$$\n", + "where $y_k \\in \\{0, 1\\}$ indicates whether the current training example belongs to class $k$ $(y_k = 1)$, or if it belongs to a different class $(y_k = 0)$. You may find logical arrays helpful for this task (explained in the previous programming exercise).\n", + "\n", + "1. For the hidden layer $l = 2$, set \n", + "$$ \\delta^{(2)} = \\left( \\Theta^{(2)} \\right)^T \\delta^{(3)} * g'\\left(z^{(2)} \\right)$$\n", + "Note that the symbol $*$ performs element wise multiplication in `numpy`.\n", + "\n", + "1. Accumulate the gradient from this example using the following formula. Note that you should skip or remove $\\delta_0^{(2)}$. In `numpy`, removing $\\delta_0^{(2)}$ corresponds to `delta_2 = delta_2[1:]`.\n", + "\n", + "1. Obtain the (unregularized) gradient for the neural network cost function by dividing the accumulated gradients by $\\frac{1}{m}$:\n", + "$$ \\frac{\\partial}{\\partial \\Theta_{ij}^{(l)}} J(\\Theta) = D_{ij}^{(l)} = \\frac{1}{m} \\Delta_{ij}^{(l)}$$\n", + "\n", + "
\n", + "**Python/Numpy tip**: You should implement the backpropagation algorithm only after you have successfully completed the feedforward and cost functions. While implementing the backpropagation alogrithm, it is often useful to use the `shape` function to print out the shapes of the variables you are working with if you run into dimension mismatch errors.\n", + "
\n", + "\n", + "[Click here to go back and update the function `nnCostFunction` with the backpropagation algorithm](#nnCostFunction)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After you have implemented the backpropagation algorithm, we will proceed to run gradient checking on your implementation. The gradient check will allow you to increase your confidence that your code is\n", + "computing the gradients correctly.\n", + "\n", + "### 2.4 Gradient checking \n", + "\n", + "In your neural network, you are minimizing the cost function $J(\\Theta)$. To perform gradient checking on your parameters, you can imagine “unrolling” the parameters $\\Theta^{(1)}$, $\\Theta^{(2)}$ into a long vector $\\theta$. By doing so, you can think of the cost function being $J(\\Theta)$ instead and use the following gradient checking procedure.\n", + "\n", + "Suppose you have a function $f_i(\\theta)$ that purportedly computes $\\frac{\\partial}{\\partial \\theta_i} J(\\theta)$; you’d like to check if $f_i$ is outputting correct derivative values.\n", + "\n", + "$$\n", + "\\text{Let } \\theta^{(i+)} = \\theta + \\begin{bmatrix} 0 \\\\ 0 \\\\ \\vdots \\\\ \\epsilon \\\\ \\vdots \\\\ 0 \\end{bmatrix}\n", + "\\quad \\text{and} \\quad \\theta^{(i-)} = \\theta - \\begin{bmatrix} 0 \\\\ 0 \\\\ \\vdots \\\\ \\epsilon \\\\ \\vdots \\\\ 0 \\end{bmatrix}\n", + "$$\n", + "\n", + "So, $\\theta^{(i+)}$ is the same as $\\theta$, except its $i^{th}$ element has been incremented by $\\epsilon$. Similarly, $\\theta^{(i−)}$ is the corresponding vector with the $i^{th}$ element decreased by $\\epsilon$. You can now numerically verify $f_i(\\theta)$’s correctness by checking, for each $i$, that:\n", + "\n", + "$$ f_i\\left( \\theta \\right) \\approx \\frac{J\\left( \\theta^{(i+)}\\right) - J\\left( \\theta^{(i-)} \\right)}{2\\epsilon} $$\n", + "\n", + "The degree to which these two values should approximate each other will depend on the details of $J$. But assuming $\\epsilon = 10^{-4}$, you’ll usually find that the left- and right-hand sides of the above will agree to at least 4 significant digits (and often many more).\n", + "\n", + "We have implemented the function to compute the numerical gradient for you in `computeNumericalGradient` (within the file `utils.py`). While you are not required to modify the file, we highly encourage you to take a look at the code to understand how it works.\n", + "\n", + "In the next cell we will run the provided function `checkNNGradients` which will create a small neural network and dataset that will be used for checking your gradients. If your backpropagation implementation is correct,\n", + "you should see a relative difference that is less than 1e-9.\n", + "\n", + "
\n", + "**Practical Tip**: When performing gradient checking, it is much more efficient to use a small neural network with a relatively small number of input units and hidden units, thus having a relatively small number\n", + "of parameters. Each dimension of $\\theta$ requires two evaluations of the cost function and this can be expensive. In the function `checkNNGradients`, our code creates a small random model and dataset which is used with `computeNumericalGradient` for gradient checking. Furthermore, after you are confident that your gradient computations are correct, you should turn off gradient checking before running your learning algorithm.\n", + "
\n", + "\n", + "
\n", + "**Practical Tip:** Gradient checking works for any function where you are computing the cost and the gradient. Concretely, you can use the same `computeNumericalGradient` function to check if your gradient implementations for the other exercises are correct too (e.g., logistic regression’s cost function).\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-9.27825235e-03 -9.27825236e-03]\n", + " [-3.04978709e-06 -3.04978914e-06]\n", + " [-1.75060082e-04 -1.75060082e-04]\n", + " [-9.62660618e-05 -9.62660620e-05]\n", + " [ 8.89911959e-03 8.89911960e-03]\n", + " [ 1.42869427e-05 1.42869443e-05]\n", + " [ 2.33146358e-04 2.33146357e-04]\n", + " [ 1.17982666e-04 1.17982666e-04]\n", + " [-8.36010761e-03 -8.36010762e-03]\n", + " [-2.59383071e-05 -2.59383100e-05]\n", + " [-2.87468729e-04 -2.87468729e-04]\n", + " [-1.37149709e-04 -1.37149706e-04]\n", + " [ 7.62813551e-03 7.62813551e-03]\n", + " [ 3.69883213e-05 3.69883234e-05]\n", + " [ 3.35320347e-04 3.35320347e-04]\n", + " [ 1.53247077e-04 1.53247082e-04]\n", + " [-6.74798370e-03 -6.74798370e-03]\n", + " [-4.68759764e-05 -4.68759769e-05]\n", + " [-3.76215588e-04 -3.76215587e-04]\n", + " [-1.66560294e-04 -1.66560294e-04]\n", + " [ 3.14544970e-01 3.14544970e-01]\n", + " [ 1.64090819e-01 1.64090819e-01]\n", + " [ 1.64567932e-01 1.64567932e-01]\n", + " [ 1.58339334e-01 1.58339334e-01]\n", + " [ 1.51127527e-01 1.51127527e-01]\n", + " [ 1.49568335e-01 1.49568335e-01]\n", + " [ 1.11056588e-01 1.11056588e-01]\n", + " [ 5.75736493e-02 5.75736493e-02]\n", + " [ 5.77867378e-02 5.77867378e-02]\n", + " [ 5.59235296e-02 5.59235296e-02]\n", + " [ 5.36967009e-02 5.36967009e-02]\n", + " [ 5.31542052e-02 5.31542052e-02]\n", + " [ 9.74006970e-02 9.74006970e-02]\n", + " [ 5.04575855e-02 5.04575855e-02]\n", + " [ 5.07530173e-02 5.07530173e-02]\n", + " [ 4.91620841e-02 4.91620841e-02]\n", + " [ 4.71456249e-02 4.71456249e-02]\n", + " [ 4.65597186e-02 4.65597186e-02]]\n", + "The above two columns you get should be very similar.\n", + "(Left-Your Numerical Gradient, Right-Analytical Gradient)\n", + "\n", + "If your backpropagation implementation is correct, then \n", + "the relative difference will be small (less than 1e-9). \n", + "Relative Difference: 2.38295e-11\n" + ] + } + ], + "source": [ + "utils.checkNNGradients(nnCostFunction)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Once your cost function passes the gradient check for the (unregularized) neural network cost function, you should submit the neural network gradient function (backpropagation).*" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise neural-network-learning\n", + "\n", + "Use token from last successful submission (parth.titan@gmail.com)? (Y/n): y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Feedforward and Cost Function | 30 / 30 | Nice work!\n", + " Regularized Cost Function | 15 / 15 | Nice work!\n", + " Sigmoid Gradient | 5 / 5 | Nice work!\n", + " Neural Network Gradient (Backpropagation) | 40 / 40 | Nice work!\n", + " Regularized Gradient | 0 / 10 | \n", + " --------------------------------\n", + " | 90 / 100 | \n", + "\n" + ] + } + ], + "source": [ + "grader[4] = nnCostFunction\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 2.5 Regularized Neural Network\n", + "\n", + "After you have successfully implemented the backpropagation algorithm, you will add regularization to the gradient. To account for regularization, it turns out that you can add this as an additional term *after* computing the gradients using backpropagation.\n", + "\n", + "Specifically, after you have computed $\\Delta_{ij}^{(l)}$ using backpropagation, you should add regularization using\n", + "\n", + "$$ \\begin{align} \n", + "& \\frac{\\partial}{\\partial \\Theta_{ij}^{(l)}} J(\\Theta) = D_{ij}^{(l)} = \\frac{1}{m} \\Delta_{ij}^{(l)} & \\qquad \\text{for } j = 0 \\\\\n", + "& \\frac{\\partial}{\\partial \\Theta_{ij}^{(l)}} J(\\Theta) = D_{ij}^{(l)} = \\frac{1}{m} \\Delta_{ij}^{(l)} + \\frac{\\lambda}{m} \\Theta_{ij}^{(l)} & \\qquad \\text{for } j \\ge 1\n", + "\\end{align}\n", + "$$\n", + "\n", + "Note that you should *not* be regularizing the first column of $\\Theta^{(l)}$ which is used for the bias term. Furthermore, in the parameters $\\Theta_{ij}^{(l)}$, $i$ is indexed starting from 1, and $j$ is indexed starting from 0. Thus, \n", + "\n", + "$$\n", + "\\Theta^{(l)} = \\begin{bmatrix}\n", + "\\Theta_{1,0}^{(i)} & \\Theta_{1,1}^{(l)} & \\cdots \\\\\n", + "\\Theta_{2,0}^{(i)} & \\Theta_{2,1}^{(l)} & \\cdots \\\\\n", + "\\vdots & ~ & \\ddots\n", + "\\end{bmatrix}\n", + "$$\n", + "\n", + "[Now modify your code that computes grad in `nnCostFunction` to account for regularization.](#nnCostFunction)\n", + "\n", + "After you are done, the following cell runs gradient checking on your implementation. If your code is correct, you should expect to see a relative difference that is less than 1e-9." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-9.27825235e-03 -9.27825236e-03]\n", + " [-1.67679797e-02 -1.67679797e-02]\n", + " [-6.01744725e-02 -6.01744725e-02]\n", + " [-1.73704651e-02 -1.73704651e-02]\n", + " [ 8.89911959e-03 8.89911960e-03]\n", + " [ 3.94334829e-02 3.94334829e-02]\n", + " [-3.19612287e-02 -3.19612287e-02]\n", + " [-5.75658668e-02 -5.75658668e-02]\n", + " [-8.36010761e-03 -8.36010762e-03]\n", + " [ 5.93355565e-02 5.93355565e-02]\n", + " [ 2.49225535e-02 2.49225535e-02]\n", + " [-4.51963845e-02 -4.51963845e-02]\n", + " [ 7.62813551e-03 7.62813551e-03]\n", + " [ 2.47640974e-02 2.47640974e-02]\n", + " [ 5.97717617e-02 5.97717617e-02]\n", + " [ 9.14587966e-03 9.14587966e-03]\n", + " [-6.74798370e-03 -6.74798370e-03]\n", + " [-3.26881426e-02 -3.26881426e-02]\n", + " [ 3.86410548e-02 3.86410548e-02]\n", + " [ 5.46101547e-02 5.46101547e-02]\n", + " [ 3.14544970e-01 3.14544970e-01]\n", + " [ 1.18682669e-01 1.18682669e-01]\n", + " [ 2.03987128e-01 2.03987128e-01]\n", + " [ 1.25698067e-01 1.25698067e-01]\n", + " [ 1.76337550e-01 1.76337550e-01]\n", + " [ 1.32294136e-01 1.32294136e-01]\n", + " [ 1.11056588e-01 1.11056588e-01]\n", + " [ 3.81928666e-05 3.81928696e-05]\n", + " [ 1.17148233e-01 1.17148233e-01]\n", + " [-4.07588279e-03 -4.07588279e-03]\n", + " [ 1.13133142e-01 1.13133142e-01]\n", + " [-4.52964427e-03 -4.52964427e-03]\n", + " [ 9.74006970e-02 9.74006970e-02]\n", + " [ 3.36926556e-02 3.36926556e-02]\n", + " [ 7.54801264e-02 7.54801264e-02]\n", + " [ 1.69677090e-02 1.69677090e-02]\n", + " [ 8.61628953e-02 8.61628953e-02]\n", + " [ 1.50048382e-03 1.50048382e-03]]\n", + "The above two columns you get should be very similar.\n", + "(Left-Your Numerical Gradient, Right-Analytical Gradient)\n", + "\n", + "If your backpropagation implementation is correct, then \n", + "the relative difference will be small (less than 1e-9). \n", + "Relative Difference: 2.33438e-11\n", + "\n", + "\n", + "Cost at (fixed) debugging parameters (w/ lambda = 3.000000): 0.576051 \n", + "(for lambda = 3, this value should be about 0.576051)\n" + ] + } + ], + "source": [ + "# Check gradients by running checkNNGradients\n", + "lambda_ = 3\n", + "utils.checkNNGradients(nnCostFunction, lambda_)\n", + "\n", + "# Also output the costFunction debugging values\n", + "debug_J, _ = nnCostFunction(nn_params, input_layer_size,\n", + " hidden_layer_size, num_labels, X, y, lambda_)\n", + "\n", + "print('\\n\\nCost at (fixed) debugging parameters (w/ lambda = %f): %f ' % (lambda_, debug_J))\n", + "print('(for lambda = 3, this value should be about 0.576051)')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise neural-network-learning\n", + "\n", + "Use token from last successful submission (parth.titan@gmail.com)? (Y/n): y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Feedforward and Cost Function | 30 / 30 | Nice work!\n", + " Regularized Cost Function | 15 / 15 | Nice work!\n", + " Sigmoid Gradient | 5 / 5 | Nice work!\n", + " Neural Network Gradient (Backpropagation) | 40 / 40 | Nice work!\n", + " Regularized Gradient | 10 / 10 | Nice work!\n", + " --------------------------------\n", + " | 100 / 100 | \n", + "\n" + ] + } + ], + "source": [ + "grader[5] = nnCostFunction\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6 Learning parameters using `scipy.optimize.minimize`\n", + "\n", + "After you have successfully implemented the neural network cost function\n", + "and gradient computation, the next step we will use `scipy`'s minimization to learn a good set parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# After you have completed the assignment, change the maxiter to a larger\n", + "# value to see how more training helps.\n", + "options= {'maxiter': 100}\n", + "\n", + "# You should also try different values of lambda\n", + "lambda_ = 1\n", + "\n", + "# Create \"short hand\" for the cost function to be minimized\n", + "costFunction = lambda p: nnCostFunction(p, input_layer_size,\n", + " hidden_layer_size,\n", + " num_labels, X, y, lambda_)\n", + "\n", + "# Now, costFunction is a function that takes in only one argument\n", + "# (the neural network parameters)\n", + "res = optimize.minimize(costFunction,\n", + " initial_nn_params,\n", + " jac=True,\n", + " method='TNC',\n", + " options=options)\n", + "\n", + "# get the solution of the optimization\n", + "nn_params = res.x\n", + " \n", + "# Obtain Theta1 and Theta2 back from nn_params\n", + "Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)],\n", + " (hidden_layer_size, (input_layer_size + 1)))\n", + "\n", + "Theta2 = np.reshape(nn_params[(hidden_layer_size * (input_layer_size + 1)):],\n", + " (num_labels, (hidden_layer_size + 1)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the training completes, we will proceed to report the training accuracy of your classifier by computing the percentage of examples it got correct. If your implementation is correct, you should see a reported\n", + "training accuracy of about 95.3% (this may vary by about 1% due to the random initialization). It is possible to get higher training accuracies by training the neural network for more iterations. We encourage you to try\n", + "training the neural network for more iterations (e.g., set `maxiter` to 400) and also vary the regularization parameter $\\lambda$. With the right learning settings, it is possible to get the neural network to perfectly fit the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Set Accuracy: 95.080000\n" + ] + } + ], + "source": [ + "pred = utils.predict(Theta1, Theta2, X)\n", + "print('Training Set Accuracy: %f' % (np.mean(pred == y) * 100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 Visualizing the Hidden Layer\n", + "\n", + "One way to understand what your neural network is learning is to visualize what the representations captured by the hidden units. Informally, given a particular hidden unit, one way to visualize what it computes is to find an input $x$ that will cause it to activate (that is, to have an activation value \n", + "($a_i^{(l)}$) close to 1). For the neural network you trained, notice that the $i^{th}$ row of $\\Theta^{(1)}$ is a 401-dimensional vector that represents the parameter for the $i^{th}$ hidden unit. If we discard the bias term, we get a 400 dimensional vector that represents the weights from each input pixel to the hidden unit.\n", + "\n", + "Thus, one way to visualize the “representation” captured by the hidden unit is to reshape this 400 dimensional vector into a 20 × 20 image and display it (It turns out that this is equivalent to finding the input that gives the highest activation for the hidden unit, given a “norm” constraint on the input (i.e., $||x||_2 \\le 1$)). \n", + "\n", + "The next cell does this by using the `displayData` function and it will show you an image with 25 units,\n", + "each corresponding to one hidden unit in the network. In your trained network, you should find that the hidden units corresponds roughly to detectors that look for strokes and other patterns in the input." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "utils.displayData(Theta1[:, 1:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Optional (ungraded) exercise\n", + "\n", + "In this part of the exercise, you will get to try out different learning settings for the neural network to see how the performance of the neural network varies with the regularization parameter $\\lambda$ and number of training steps (the `maxiter` option when using `scipy.optimize.minimize`). Neural networks are very powerful models that can form highly complex decision boundaries. Without regularization, it is possible for a neural network to “overfit” a training set so that it obtains close to 100% accuracy on the training set but does not as well on new examples that it has not seen before. You can set the regularization $\\lambda$ to a smaller value and the `maxiter` parameter to a higher number of iterations to see this for youself." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Exercise5/ParthBakare_180101056.ipynb b/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Exercise5/ParthBakare_180101056.ipynb new file mode 100644 index 000000000..5ab1c1d70 --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Exercise5/ParthBakare_180101056.ipynb @@ -0,0 +1,1110 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Programming Exercise 5:\n", + "# Regularized Linear Regression and Bias vs Variance\n", + "\n", + "## Introduction\n", + "\n", + "In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n", + "\n", + "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", + "\n", + "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "\n", + "# Optimization module in scipy\n", + "from scipy import optimize\n", + "\n", + "# will be used to load MATLAB mat datafile format\n", + "from scipy.io import loadmat\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils\n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submission and Grading\n", + "\n", + "\n", + "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n", + "\n", + "\n", + "| Section | Part | Submitted Function | Points |\n", + "| :- |:- |:- | :-: |\n", + "| 1 | [Regularized Linear Regression Cost Function](#section1) | [`linearRegCostFunction`](#linearRegCostFunction) | 25 |\n", + "| 2 | [Regularized Linear Regression Gradient](#section2) | [`linearRegCostFunction`](#linearRegCostFunction) |25 |\n", + "| 3 | [Learning Curve](#section3) | [`learningCurve`](#func2) | 20 |\n", + "| 4 | [Polynomial Feature Mapping](#section4) | [`polyFeatures`](#polyFeatures) | 10 |\n", + "| 5 | [Cross Validation Curve](#section5) | [`validationCurve`](#validationCurve) | 20 |\n", + "| | Total Points | |100 |\n", + "\n", + "\n", + "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "
\n", + "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 1 Regularized Linear Regression\n", + "\n", + "In the first half of the exercise, you will implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, you will go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s.\n", + "variance. \n", + "\n", + "### 1.1 Visualizing the dataset\n", + "\n", + "We will begin by visualizing the dataset containing historical records on the change in the water level, $x$, and the amount of water flowing out of the dam, $y$. This dataset is divided into three parts:\n", + "\n", + "- A **training** set that your model will learn on: `X`, `y`\n", + "- A **cross validation** set for determining the regularization parameter: `Xval`, `yval`\n", + "- A **test** set for evaluating performance. These are “unseen” examples which your model did not see during training: `Xtest`, `ytest`\n", + "\n", + "Run the next cell to plot the training data. In the following parts, you will implement linear regression and use that to fit a straight line to the data and plot learning curves. Following that, you will implement polynomial regression to find a better fit to the data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load from ex5data1.mat, where all variables will be store in a dictionary\n", + "data = loadmat(os.path.join('Data', 'ex5data1.mat'))\n", + "\n", + "# Extract train, test, validation data from dictionary\n", + "# and also convert y's form 2-D matrix (MATLAB format) to a numpy vector\n", + "X, y = data['X'], data['y'][:, 0]\n", + "Xtest, ytest = data['Xtest'], data['ytest'][:, 0]\n", + "Xval, yval = data['Xval'], data['yval'][:, 0]\n", + "\n", + "# m = Number of examples\n", + "m = y.size\n", + "\n", + "# Plot training data\n", + "pyplot.plot(X, y, 'ro', ms=10, mec='k', mew=1)\n", + "pyplot.xlabel('Change in water level (x)')\n", + "pyplot.ylabel('Water flowing out of the dam (y)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Regularized linear regression cost function\n", + "\n", + "Recall that regularized linear regression has the following cost function:\n", + "\n", + "$$ J(\\theta) = \\frac{1}{2m} \\left( \\sum_{i=1}^m \\left( h_\\theta\\left( x^{(i)} \\right) - y^{(i)} \\right)^2 \\right) + \\frac{\\lambda}{2m} \\left( \\sum_{j=1}^n \\theta_j^2 \\right)$$\n", + "\n", + "where $\\lambda$ is a regularization parameter which controls the degree of regularization (thus, help preventing overfitting). The regularization term puts a penalty on the overall cost J. As the magnitudes of the model parameters $\\theta_j$ increase, the penalty increases as well. Note that you should not regularize\n", + "the $\\theta_0$ term.\n", + "\n", + "You should now complete the code in the function `linearRegCostFunction` in the next cell. Your task is to calculate the regularized linear regression cost function. If possible, try to vectorize your code and avoid writing loops.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def linearRegCostFunction(X, y, theta, lambda_=0.0):\n", + " \"\"\"\n", + " Compute cost and gradient for regularized linear regression \n", + " with multiple variables. Computes the cost of using theta as\n", + " the parameter for linear regression to fit the data points in X and y. \n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset. Matrix with shape (m x n + 1) where m is the \n", + " total number of examples, and n is the number of features \n", + " before adding the bias term.\n", + " \n", + " y : array_like\n", + " The functions values at each datapoint. A vector of\n", + " shape (m, ).\n", + " \n", + " theta : array_like\n", + " The parameters for linear regression. A vector of shape (n+1,).\n", + " \n", + " lambda_ : float, optional\n", + " The regularization parameter.\n", + " \n", + " Returns\n", + " -------\n", + " J : float\n", + " The computed cost function. \n", + " \n", + " grad : array_like\n", + " The value of the cost function gradient w.r.t theta. \n", + " A vector of shape (n+1, ).\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the cost and gradient of regularized linear regression for\n", + " a particular choice of theta.\n", + " You should set J to the cost and grad to the gradient.\n", + " \"\"\"\n", + " # Initialize some useful values\n", + " m = y.size # number of training examples\n", + "\n", + " # You need to return the following variables correctly \n", + " J = 0\n", + " grad = np.zeros(theta.shape)\n", + "# print(X.shape)\n", + "# print(y.shape)\n", + "# print(theta.shape)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " h = np.dot(X, theta)\n", + " t1 = np.subtract(h, y)\n", + " t2 = (1/(2*m))*(np.sum(np.multiply(t1, t1))) + (lambda_/(2*m))*(np.sum(np.multiply(theta[1:], theta[1:])))\n", + " J = t2\n", + " \n", + " grad = (1/m)*(np.dot(X.transpose(), t1))\n", + " grad[1:] = grad[1:] + (lambda_/m)*theta[1:]\n", + " # ============================================================\n", + " return J, grad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you are finished, the next cell will run your cost function using `theta` initialized at `[1, 1]`. You should expect to see an output of 303.993." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at theta = [1, 1]:\t 303.993192 \n", + "This value should be about 303.993192)\n", + "\n" + ] + } + ], + "source": [ + "theta = np.array([1, 1])\n", + "J, _ = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n", + "\n", + "print('Cost at theta = [1, 1]:\\t %f ' % J)\n", + "print('This value should be about 303.993192)\\n' % J)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After completing a part of the exercise, you can submit your solutions for grading by first adding the function you modified to the submission object, and then sending your function to Coursera for grading. \n", + "\n", + "The submission script will prompt you for your login e-mail and submission token. You can obtain a submission token from the web page for the assignment. You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "*Execute the following cell to grade your solution to the first part of this exercise.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[1] = linearRegCostFunction\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 1.3 Regularized linear regression gradient\n", + "\n", + "Correspondingly, the partial derivative of the cost function for regularized linear regression is defined as:\n", + "\n", + "$$\n", + "\\begin{align}\n", + "& \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left(x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} & \\qquad \\text{for } j = 0 \\\\\n", + "& \\frac{\\partial J(\\theta)}{\\partial \\theta_j} = \\left( \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} \\right) + \\frac{\\lambda}{m} \\theta_j & \\qquad \\text{for } j \\ge 1\n", + "\\end{align}\n", + "$$\n", + "\n", + "In the function [`linearRegCostFunction`](#linearRegCostFunction) above, add code to calculate the gradient, returning it in the variable `grad`. Do not forget to re-execute the cell containing this function to update the function's definition.\n", + "\n", + "\n", + "When you are finished, use the next cell to run your gradient function using theta initialized at `[1, 1]`. You should expect to see a gradient of `[-15.30, 598.250]`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gradient at theta = [1, 1]: [-15.303016, 598.250744] \n", + " (this value should be about [-15.303016, 598.250744])\n", + "\n" + ] + } + ], + "source": [ + "theta = np.array([1, 1])\n", + "J, grad = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n", + "\n", + "print('Gradient at theta = [1, 1]: [{:.6f}, {:.6f}] '.format(*grad))\n", + "print(' (this value should be about [-15.303016, 598.250744])\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[2] = linearRegCostFunction\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fitting linear regression\n", + "\n", + "Once your cost function and gradient are working correctly, the next cell will run the code in `trainLinearReg` (found in the module `utils.py`) to compute the optimal values of $\\theta$. This training function uses `scipy`'s optimization module to minimize the cost function.\n", + "\n", + "In this part, we set regularization parameter $\\lambda$ to zero. Because our current implementation of linear regression is trying to fit a 2-dimensional $\\theta$, regularization will not be incredibly helpful for a $\\theta$ of such low dimension. In the later parts of the exercise, you will be using polynomial regression with regularization.\n", + "\n", + "Finally, the code in the next cell should also plot the best fit line, which should look like the figure below. \n", + "\n", + "![](Figures/linear_fit.png)\n", + "\n", + "The best fit line tells us that the model is not a good fit to the data because the data has a non-linear pattern. While visualizing the best fit as shown is one possible way to debug your learning algorithm, it is not always easy to visualize the data and model. In the next section, you will implement a function to generate learning curves that can help you debug your learning algorithm even if it is not easy to visualize the\n", + "data." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# add a columns of ones for the y-intercept\n", + "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n", + "theta = utils.trainLinearReg(linearRegCostFunction, X_aug, y, lambda_=0)\n", + "\n", + "# Plot fit over the data\n", + "pyplot.plot(X, y, 'ro', ms=10, mec='k', mew=1.5)\n", + "pyplot.xlabel('Change in water level (x)')\n", + "pyplot.ylabel('Water flowing out of the dam (y)')\n", + "pyplot.plot(X, np.dot(X_aug, theta), '--', lw=2);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 2 Bias-variance\n", + "\n", + "An important concept in machine learning is the bias-variance tradeoff. Models with high bias are not complex enough for the data and tend to underfit, while models with high variance overfit to the training data.\n", + "\n", + "In this part of the exercise, you will plot training and test errors on a learning curve to diagnose bias-variance problems.\n", + "\n", + "### 2.1 Learning Curves\n", + "\n", + "You will now implement code to generate the learning curves that will be useful in debugging learning algorithms. Recall that a learning curve plots training and cross validation error as a function of training set size. Your job is to fill in the function `learningCurve` in the next cell, so that it returns a vector of errors for the training set and cross validation set.\n", + "\n", + "To plot the learning curve, we need a training and cross validation set error for different training set sizes. To obtain different training set sizes, you should use different subsets of the original training set `X`. Specifically, for a training set size of $i$, you should use the first $i$ examples (i.e., `X[:i, :]`\n", + "and `y[:i]`).\n", + "\n", + "You can use the `trainLinearReg` function (by calling `utils.trainLinearReg(...)`) to find the $\\theta$ parameters. Note that the `lambda_` is passed as a parameter to the `learningCurve` function.\n", + "After learning the $\\theta$ parameters, you should compute the error on the training and cross validation sets. Recall that the training error for a dataset is defined as\n", + "\n", + "$$ J_{\\text{train}} = \\frac{1}{2m} \\left[ \\sum_{i=1}^m \\left(h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right)^2 \\right] $$\n", + "\n", + "In particular, note that the training error does not include the regularization term. One way to compute the training error is to use your existing cost function and set $\\lambda$ to 0 only when using it to compute the training error and cross validation error. When you are computing the training set error, make sure you compute it on the training subset (i.e., `X[:n,:]` and `y[:n]`) instead of the entire training set. However, for the cross validation error, you should compute it over the entire cross validation set. You should store\n", + "the computed errors in the vectors error train and error val.\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def learningCurve(X, y, Xval, yval, lambda_=0):\n", + " \"\"\"\n", + " Generates the train and cross validation set errors needed to plot a learning curve\n", + " returns the train and cross validation set errors for a learning curve. \n", + " \n", + " In this function, you will compute the train and test errors for\n", + " dataset sizes from 1 up to m. In practice, when working with larger\n", + " datasets, you might want to do this in larger intervals.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The training dataset. Matrix with shape (m x n + 1) where m is the \n", + " total number of examples, and n is the number of features \n", + " before adding the bias term.\n", + " \n", + " y : array_like\n", + " The functions values at each training datapoint. A vector of\n", + " shape (m, ).\n", + " \n", + " Xval : array_like\n", + " The validation dataset. Matrix with shape (m_val x n + 1) where m is the \n", + " total number of examples, and n is the number of features \n", + " before adding the bias term.\n", + " \n", + " yval : array_like\n", + " The functions values at each validation datapoint. A vector of\n", + " shape (m_val, ).\n", + " \n", + " lambda_ : float, optional\n", + " The regularization parameter.\n", + " \n", + " Returns\n", + " -------\n", + " error_train : array_like\n", + " A vector of shape m. error_train[i] contains the training error for\n", + " i examples.\n", + " error_val : array_like\n", + " A vecotr of shape m. error_val[i] contains the validation error for\n", + " i training examples.\n", + " \n", + " Instructions\n", + " ------------\n", + " Fill in this function to return training errors in error_train and the\n", + " cross validation errors in error_val. i.e., error_train[i] and \n", + " error_val[i] should give you the errors obtained after training on i examples.\n", + " \n", + " Notes\n", + " -----\n", + " - You should evaluate the training error on the first i training\n", + " examples (i.e., X[:i, :] and y[:i]).\n", + " \n", + " For the cross-validation error, you should instead evaluate on\n", + " the _entire_ cross validation set (Xval and yval).\n", + " \n", + " - If you are using your cost function (linearRegCostFunction) to compute\n", + " the training and cross validation error, you should call the function with\n", + " the lambda argument set to 0. Do note that you will still need to use\n", + " lambda when running the training to obtain the theta parameters.\n", + " \n", + " Hint\n", + " ----\n", + " You can loop over the examples with the following:\n", + " \n", + " for i in range(1, m+1):\n", + " # Compute train/cross validation errors using training examples \n", + " # X[:i, :] and y[:i], storing the result in \n", + " # error_train[i-1] and error_val[i-1]\n", + " .... \n", + " \"\"\"\n", + " # Number of training examples\n", + " m = y.size\n", + "\n", + " # You need to return these values correctly\n", + " error_train = np.zeros(m)\n", + " error_val = np.zeros(m)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " for i in range(1, m+1):\n", + " theta = utils.trainLinearReg(linearRegCostFunction, X[:i, :], y[:i], lambda_, 200)\n", + " J1, grad1 = linearRegCostFunction(X[:i, :], y[:i], theta, 0)\n", + " J2, grad2 = linearRegCostFunction(Xval, yval, theta, 0)\n", + " error_train[i-1] = J1 \n", + " error_val[i-1] = J2\n", + " \n", + " # =============================================================\n", + " return error_train, error_val" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you are finished implementing the function `learningCurve`, executing the next cell prints the learning curves and produce a plot similar to the figure below. \n", + "\n", + "![](Figures/learning_curve.png)\n", + "\n", + "In the learning curve figure, you can observe that both the train error and cross validation error are high when the number of training examples is increased. This reflects a high bias problem in the model - the linear regression model is too simple and is unable to fit our dataset well. In the next section, you will implement polynomial regression to fit a better model for this dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Training Examples\tTrain Error\tCross Validation Error\n", + " \t1\t\t0.000000\t205.121096\n", + " \t2\t\t0.000000\t110.302641\n", + " \t3\t\t3.286595\t45.010231\n", + " \t4\t\t2.842678\t48.368911\n", + " \t5\t\t13.154049\t35.865165\n", + " \t6\t\t19.443963\t33.829962\n", + " \t7\t\t20.098522\t31.970986\n", + " \t8\t\t18.172859\t30.862446\n", + " \t9\t\t22.609405\t31.135998\n", + " \t10\t\t23.261462\t28.936207\n", + " \t11\t\t24.317250\t29.551432\n", + " \t12\t\t22.373906\t29.433818\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n", + "Xval_aug = np.concatenate([np.ones((yval.size, 1)), Xval], axis=1)\n", + "error_train, error_val = learningCurve(X_aug, y, Xval_aug, yval, lambda_=0)\n", + "\n", + "pyplot.plot(np.arange(1, m+1), error_train, np.arange(1, m+1), error_val, lw=2)\n", + "pyplot.title('Learning curve for linear regression')\n", + "pyplot.legend(['Train', 'Cross Validation'])\n", + "pyplot.xlabel('Number of training examples')\n", + "pyplot.ylabel('Error')\n", + "pyplot.axis([0, 13, 0, 150])\n", + "\n", + "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n", + "for i in range(m):\n", + " print(' \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[3] = learningCurve\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## 3 Polynomial regression\n", + "\n", + "The problem with our linear model was that it was too simple for the data\n", + "and resulted in underfitting (high bias). In this part of the exercise, you will address this problem by adding more features. For polynomial regression, our hypothesis has the form:\n", + "\n", + "$$\n", + "\\begin{align}\n", + "h_\\theta(x) &= \\theta_0 + \\theta_1 \\times (\\text{waterLevel}) + \\theta_2 \\times (\\text{waterLevel})^2 + \\cdots + \\theta_p \\times (\\text{waterLevel})^p \\\\\n", + "& = \\theta_0 + \\theta_1 x_1 + \\theta_2 x_2 + \\cdots + \\theta_p x_p\n", + "\\end{align}\n", + "$$\n", + "\n", + "Notice that by defining $x_1 = (\\text{waterLevel})$, $x_2 = (\\text{waterLevel})^2$ , $\\cdots$, $x_p =\n", + "(\\text{waterLevel})^p$, we obtain a linear regression model where the features are the various powers of the original value (waterLevel).\n", + "\n", + "Now, you will add more features using the higher powers of the existing feature $x$ in the dataset. Your task in this part is to complete the code in the function `polyFeatures` in the next cell. The function should map the original training set $X$ of size $m \\times 1$ into its higher powers. Specifically, when a training set $X$ of size $m \\times 1$ is passed into the function, the function should return a $m \\times p$ matrix `X_poly`, where column 1 holds the original values of X, column 2 holds the values of $X^2$, column 3 holds the values of $X^3$, and so on. Note that you don’t have to account for the zero-eth power in this function.\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def polyFeatures(X, p):\n", + " \"\"\"\n", + " Maps X (1D vector) into the p-th power.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " A data vector of size m, where m is the number of examples.\n", + " \n", + " p : int\n", + " The polynomial power to map the features. \n", + " \n", + " Returns \n", + " -------\n", + " X_poly : array_like\n", + " A matrix of shape (m x p) where p is the polynomial \n", + " power and m is the number of examples. That is:\n", + " \n", + " X_poly[i, :] = [X[i], X[i]**2, X[i]**3 ... X[i]**p]\n", + " \n", + " Instructions\n", + " ------------\n", + " Given a vector X, return a matrix X_poly where the p-th column of\n", + " X contains the values of X to the p-th power.\n", + " \"\"\"\n", + " # You need to return the following variables correctly.\n", + " X_poly = np.zeros((X.shape[0], p))\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " X_poly[:, 0] = X.reshape(X.shape[0], )\n", + " for i in range(1, p):\n", + " t = np.multiply(X_poly[:, 0], X_poly[:, i-1])\n", + " X_poly[:, i] = t\n", + "\n", + "\n", + " # ============================================================\n", + " return X_poly" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you have a function that will map features to a higher dimension. The next cell will apply it to the training set, the test set, and the cross validation set." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized Training Example 1:\n" + ] + }, + { + "data": { + "text/plain": [ + "array([ 1. , -0.36214078, -0.75508669, 0.18222588, -0.70618991,\n", + " 0.30661792, -0.59087767, 0.3445158 , -0.50848117])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = 8\n", + "\n", + "# Map X onto Polynomial Features and Normalize\n", + "X_poly = polyFeatures(X, p)\n", + "X_poly, mu, sigma = utils.featureNormalize(X_poly)\n", + "X_poly = np.concatenate([np.ones((m, 1)), X_poly], axis=1)\n", + "\n", + "# Map X_poly_test and normalize (using mu and sigma)\n", + "X_poly_test = polyFeatures(Xtest, p)\n", + "X_poly_test -= mu\n", + "X_poly_test /= sigma\n", + "X_poly_test = np.concatenate([np.ones((ytest.size, 1)), X_poly_test], axis=1)\n", + "\n", + "# Map X_poly_val and normalize (using mu and sigma)\n", + "X_poly_val = polyFeatures(Xval, p)\n", + "X_poly_val -= mu\n", + "X_poly_val /= sigma\n", + "X_poly_val = np.concatenate([np.ones((yval.size, 1)), X_poly_val], axis=1)\n", + "\n", + "print('Normalized Training Example 1:')\n", + "X_poly[0, :]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[4] = polyFeatures\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.1 Learning Polynomial Regression\n", + "\n", + "After you have completed the function `polyFeatures`, we will proceed to train polynomial regression using your linear regression cost function.\n", + "\n", + "Keep in mind that even though we have polynomial terms in our feature vector, we are still solving a linear regression optimization problem. The polynomial terms have simply turned into features that we can use for linear regression. We are using the same cost function and gradient that you wrote for the earlier part of this exercise.\n", + "\n", + "For this part of the exercise, you will be using a polynomial of degree 8. It turns out that if we run the training directly on the projected data, will not work well as the features would be badly scaled (e.g., an example with $x = 40$ will now have a feature $x_8 = 40^8 = 6.5 \\times 10^{12}$). Therefore, you will\n", + "need to use feature normalization.\n", + "\n", + "Before learning the parameters $\\theta$ for the polynomial regression, we first call `featureNormalize` and normalize the features of the training set, storing the mu, sigma parameters separately. We have already implemented this function for you (in `utils.py` module) and it is the same function from the first exercise.\n", + "\n", + "After learning the parameters $\\theta$, you should see two plots generated for polynomial regression with $\\lambda = 0$, which should be similar to the ones here:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "You should see that the polynomial fit is able to follow the datapoints very well, thus, obtaining a low training error. The figure on the right shows that the training error essentially stays zero for all numbers of training samples. However, the polynomial fit is very complex and even drops off at the extremes. This is an indicator that the polynomial regression model is overfitting the training data and will not generalize well.\n", + "\n", + "To better understand the problems with the unregularized ($\\lambda = 0$) model, you can see that the learning curve shows the same effect where the training error is low, but the cross validation error is high. There is a gap between the training and cross validation errors, indicating a high variance problem." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Polynomial Regression (lambda = 100.000000)\n", + "\n", + "# Training Examples\tTrain Error\tCross Validation Error\n", + " \t1\t\t0.000000\t138.846777\n", + " \t2\t\t0.114107\t144.125230\n", + " \t3\t\t106.956580\t70.863286\n", + " \t4\t\t121.740879\t78.372963\n", + " \t5\t\t102.949459\t63.845046\n", + " \t6\t\t97.169857\t59.532632\n", + " \t7\t\t83.326539\t59.585493\n", + " \t8\t\t76.491825\t58.699842\n", + " \t9\t\t71.297176\t59.564455\n", + " \t10\t\t64.350636\t59.731344\n", + " \t11\t\t58.997943\t60.409869\n", + " \t12\t\t57.977080\t57.842195\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lambda_ = 100\n", + "theta = utils.trainLinearReg(linearRegCostFunction, X_poly, y,\n", + " lambda_=lambda_, maxiter=55)\n", + "\n", + "# Plot training data and fit\n", + "pyplot.plot(X, y, 'ro', ms=10, mew=1.5, mec='k')\n", + "\n", + "utils.plotFit(polyFeatures, np.min(X), np.max(X), mu, sigma, theta, p)\n", + "\n", + "pyplot.xlabel('Change in water level (x)')\n", + "pyplot.ylabel('Water flowing out of the dam (y)')\n", + "pyplot.title('Polynomial Regression Fit (lambda = %f)' % lambda_)\n", + "pyplot.ylim([-20, 50])\n", + "\n", + "pyplot.figure()\n", + "error_train, error_val = learningCurve(X_poly, y, X_poly_val, yval, lambda_)\n", + "pyplot.plot(np.arange(1, 1+m), error_train, np.arange(1, 1+m), error_val)\n", + "\n", + "pyplot.title('Polynomial Regression Learning Curve (lambda = %f)' % lambda_)\n", + "pyplot.xlabel('Number of training examples')\n", + "pyplot.ylabel('Error')\n", + "pyplot.axis([0, 13, 0, 100])\n", + "pyplot.legend(['Train', 'Cross Validation'])\n", + "\n", + "print('Polynomial Regression (lambda = %f)\\n' % lambda_)\n", + "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n", + "for i in range(m):\n", + " print(' \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One way to combat the overfitting (high-variance) problem is to add regularization to the model. In the next section, you will get to try different $\\lambda$ parameters to see how regularization can lead to a better model.\n", + "\n", + "### 3.2 Optional (ungraded) exercise: Adjusting the regularization parameter\n", + "\n", + "In this section, you will get to observe how the regularization parameter affects the bias-variance of regularized polynomial regression. You should now modify the the lambda parameter and try $\\lambda = 1, 100$. For each of these values, the script should generate a polynomial fit to the data and also a learning curve.\n", + "\n", + "For $\\lambda = 1$, the generated plots should look like the the figure below. You should see a polynomial fit that follows the data trend well (left) and a learning curve (right) showing that both the cross validation and training error converge to a relatively low value. This shows the $\\lambda = 1$ regularized polynomial regression model does not have the high-bias or high-variance problems. In effect, it achieves a good trade-off between bias and variance.\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "For $\\lambda = 100$, you should see a polynomial fit (figure below) that does not follow the data well. In this case, there is too much regularization and the model is unable to fit the training data.\n", + "\n", + "![](Figures/polynomial_regression_reg_100.png)\n", + "\n", + "*You do not need to submit any solutions for this optional (ungraded) exercise.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 3.3 Selecting $\\lambda$ using a cross validation set\n", + "\n", + "From the previous parts of the exercise, you observed that the value of $\\lambda$ can significantly affect the results of regularized polynomial regression on the training and cross validation set. In particular, a model without regularization ($\\lambda = 0$) fits the training set well, but does not generalize. Conversely, a model with too much regularization ($\\lambda = 100$) does not fit the training set and testing set well. A good choice of $\\lambda$ (e.g., $\\lambda = 1$) can provide a good fit to the data.\n", + "\n", + "In this section, you will implement an automated method to select the $\\lambda$ parameter. Concretely, you will use a cross validation set to evaluate how good each $\\lambda$ value is. After selecting the best $\\lambda$ value using the cross validation set, we can then evaluate the model on the test set to estimate\n", + "how well the model will perform on actual unseen data. \n", + "\n", + "Your task is to complete the code in the function `validationCurve`. Specifically, you should should use the `utils.trainLinearReg` function to train the model using different values of $\\lambda$ and compute the training error and cross validation error. You should try $\\lambda$ in the following range: {0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10}.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def validationCurve(X, y, Xval, yval):\n", + " \"\"\"\n", + " Generate the train and validation errors needed to plot a validation\n", + " curve that we can use to select lambda_.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The training dataset. Matrix with shape (m x n) where m is the \n", + " total number of training examples, and n is the number of features \n", + " including any polynomial features.\n", + " \n", + " y : array_like\n", + " The functions values at each training datapoint. A vector of\n", + " shape (m, ).\n", + " \n", + " Xval : array_like\n", + " The validation dataset. Matrix with shape (m_val x n) where m is the \n", + " total number of validation examples, and n is the number of features \n", + " including any polynomial features.\n", + " \n", + " yval : array_like\n", + " The functions values at each validation datapoint. A vector of\n", + " shape (m_val, ).\n", + " \n", + " Returns\n", + " -------\n", + " lambda_vec : list\n", + " The values of the regularization parameters which were used in \n", + " cross validation.\n", + " \n", + " error_train : list\n", + " The training error computed at each value for the regularization\n", + " parameter.\n", + " \n", + " error_val : list\n", + " The validation error computed at each value for the regularization\n", + " parameter.\n", + " \n", + " Instructions\n", + " ------------\n", + " Fill in this function to return training errors in `error_train` and\n", + " the validation errors in `error_val`. The vector `lambda_vec` contains\n", + " the different lambda parameters to use for each calculation of the\n", + " errors, i.e, `error_train[i]`, and `error_val[i]` should give you the\n", + " errors obtained after training with `lambda_ = lambda_vec[i]`.\n", + "\n", + " Note\n", + " ----\n", + " You can loop over lambda_vec with the following:\n", + " \n", + " for i in range(len(lambda_vec))\n", + " lambda = lambda_vec[i]\n", + " # Compute train / val errors when training linear \n", + " # regression with regularization parameter lambda_\n", + " # You should store the result in error_train[i]\n", + " # and error_val[i]\n", + " ....\n", + " \"\"\"\n", + " # Selected values of lambda (you should not change this)\n", + " lambda_vec = [0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10]\n", + "\n", + " # You need to return these variables correctly.\n", + " error_train = np.zeros(len(lambda_vec))\n", + " error_val = np.zeros(len(lambda_vec))\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " for i in range(len(lambda_vec)):\n", + " lambda_ = lambda_vec[i]\n", + " theta = utils.trainLinearReg(linearRegCostFunction, X, y, lambda_, 2000) \n", + " J1, grad1 = linearRegCostFunction(X, y, theta, 0)\n", + " J2, grad2 = linearRegCostFunction(Xval, yval, theta, 0)\n", + "\n", + " error_train[i] = J1\n", + " error_val[i] = J2\n", + "\n", + " # ============================================================\n", + " return lambda_vec, error_train, error_val" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After you have completed the code, the next cell will run your function and plot a cross validation curve of error v.s. $\\lambda$ that allows you select which $\\lambda$ parameter to use. You should see a plot similar to the figure below. \n", + "\n", + "![](Figures/cross_validation.png)\n", + "\n", + "In this figure, we can see that the best value of $\\lambda$ is around 3. Due to randomness\n", + "in the training and validation splits of the dataset, the cross validation error can sometimes be lower than the training error." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "lambda\t\tTrain Error\tValidation Error\n", + " 0.000000\t0.028896\t53.311438\n", + " 0.001000\t0.112624\t9.822076\n", + " 0.003000\t0.170984\t16.308886\n", + " 0.010000\t0.221490\t16.910132\n", + " 0.030000\t0.281853\t12.829364\n", + " 0.100000\t0.459322\t7.586806\n", + " 0.300000\t0.921755\t4.636851\n", + " 1.000000\t2.076201\t4.260598\n", + " 3.000000\t4.901371\t3.822930\n", + " 10.000000\t16.092273\t9.945554\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lambda_vec, error_train, error_val = validationCurve(X_poly, y, X_poly_val, yval)\n", + "\n", + "pyplot.plot(lambda_vec, error_train, '-o', lambda_vec, error_val, '-o', lw=2)\n", + "pyplot.legend(['Train', 'Cross Validation'])\n", + "pyplot.xlabel('lambda')\n", + "pyplot.ylabel('Error')\n", + "\n", + "print('lambda\\t\\tTrain Error\\tValidation Error')\n", + "for i in range(len(lambda_vec)):\n", + " print(' %f\\t%f\\t%f' % (lambda_vec[i], error_train[i], error_val[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[5] = validationCurve\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4 Optional (ungraded) exercise: Computing test set error\n", + "\n", + "In the previous part of the exercise, you implemented code to compute the cross validation error for various values of the regularization parameter $\\lambda$. However, to get a better indication of the model’s performance in the real world, it is important to evaluate the “final” model on a test set that was not used in any part of training (that is, it was neither used to select the $\\lambda$ parameters, nor to learn the model parameters $\\theta$). For this optional (ungraded) exercise, you should compute the test error using the best value of $\\lambda$ you found. In our cross validation, we obtained a test error of 3.8599 for $\\lambda = 3$.\n", + "\n", + "*You do not need to submit any solutions for this optional (ungraded) exercise.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.5 Optional (ungraded) exercise: Plotting learning curves with randomly selected examples\n", + "\n", + "In practice, especially for small training sets, when you plot learning curves to debug your algorithms, it is often helpful to average across multiple sets of randomly selected examples to determine the training error and cross validation error.\n", + "\n", + "Concretely, to determine the training error and cross validation error for $i$ examples, you should first randomly select $i$ examples from the training set and $i$ examples from the cross validation set. You will then learn the parameters $\\theta$ using the randomly chosen training set and evaluate the parameters $\\theta$ on the randomly chosen training set and cross validation set. The above steps should then be repeated multiple times (say 50) and the averaged error should be used to determine the training error and cross validation error for $i$ examples.\n", + "\n", + "For this optional (ungraded) exercise, you should implement the above strategy for computing the learning curves. For reference, the figure below shows the learning curve we obtained for polynomial regression with $\\lambda = 0.01$. Your figure may differ slightly due to the random selection of examples.\n", + "\n", + "![](Figures/learning_curve_random.png)\n", + "\n", + "*You do not need to submit any solutions for this optional (ungraded) exercise.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Phase 3 - 2020 (Summer)/Week 6(May 3 -May 9)/Exercise6/ParthBakare_180101056.ipynb b/Phase 3 - 2020 (Summer)/Week 6(May 3 -May 9)/Exercise6/ParthBakare_180101056.ipynb new file mode 100644 index 000000000..921c99cdb --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 6(May 3 -May 9)/Exercise6/ParthBakare_180101056.ipynb @@ -0,0 +1,1183 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Programming Exercise 6:\n", + "# Support Vector Machines\n", + "\n", + "## Introduction\n", + "\n", + "In this exercise, you will be using support vector machines (SVMs) to build a spam classifier. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n", + "\n", + "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", + "\n", + "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Import regular expressions to process emails\n", + "import re\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "\n", + "# Optimization module in scipy\n", + "from scipy import optimize\n", + "\n", + "# will be used to load MATLAB mat datafile format\n", + "from scipy.io import loadmat\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils\n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submission and Grading\n", + "\n", + "\n", + "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n", + "\n", + "\n", + "| Section | Part | Submitted Function | Points |\n", + "| :- |:- |:- | :-: |\n", + "| 1 | [Gaussian Kernel](#section1) | [`gaussianKernel`](#gaussianKernel) | 25 |\n", + "| 2 | [Parameters (C, $\\sigma$) for Dataset 3](#section2)| [`dataset3Params`](#dataset3Params) | 25 |\n", + "| 3 | [Email Preprocessing](#section3) | [`processEmail`](#processEmail) | 25 |\n", + "| 4 | [Email Feature Extraction](#section4) | [`emailFeatures`](#emailFeatures) | 25 |\n", + "| | Total Points | |100 |\n", + "\n", + "\n", + "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "
\n", + "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 Support Vector Machines\n", + "\n", + "In the first half of this exercise, you will be using support vector machines (SVMs) with various example 2D datasets. Experimenting with these datasets will help you gain an intuition of how SVMs work and how to use a Gaussian kernel with SVMs. In the next half of the exercise, you will be using support\n", + "vector machines to build a spam classifier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Example Dataset 1\n", + "\n", + "We will begin by with a 2D example dataset which can be separated by a linear boundary. The following cell plots the training data, which should look like this:\n", + "\n", + "![Dataset 1 training data](Figures/dataset1.png)\n", + "\n", + "In this dataset, the positions of the positive examples (indicated with `x`) and the negative examples (indicated with `o`) suggest a natural separation indicated by the gap. However, notice that there is an outlier positive example `x` on the far left at about (0.1, 4.1). As part of this exercise, you will also see how this outlier affects the SVM decision boundary." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load from ex6data1\n", + "# You will have X, y as keys in the dict data\n", + "data = loadmat(os.path.join('Data', 'ex6data1.mat'))\n", + "X, y = data['X'], data['y'][:, 0]\n", + "\n", + "# Plot training data\n", + "utils.plotData(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this part of the exercise, you will try using different values of the $C$ parameter with SVMs. Informally, the $C$ parameter is a positive value that controls the penalty for misclassified training examples. A large $C$ parameter tells the SVM to try to classify all the examples correctly. $C$ plays a role similar to $1/\\lambda$, where $\\lambda$ is the regularization parameter that we were using previously for logistic regression.\n", + "\n", + "\n", + "The following cell will run the SVM training (with $C=1$) using SVM software that we have included with the starter code (function `svmTrain` within the `utils` module of this exercise). When $C=1$, you should find that the SVM puts the decision boundary in the gap between the two datasets and *misclassifies* the data point on the far left, as shown in the figure (left) below.\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SVM Decision boundary for example dataset 1
C=1C=100
\n", + "\n", + "
\n", + "In order to minimize the dependency of this assignment on external libraries, we have included this implementation of an SVM learning algorithm in utils.svmTrain. However, this particular implementation is not very efficient (it was originally chosen to maximize compatibility between Octave/MATLAB for the first version of this assignment set). If you are training an SVM on a real problem, especially if you need to scale to a larger dataset, we strongly recommend instead using a highly optimized SVM toolbox such as [LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/). The python machine learning library [scikit-learn](http://scikit-learn.org/stable/index.html) provides wrappers for the LIBSVM library.\n", + "
\n", + "
\n", + "
\n", + "**Implementation Note:** Most SVM software packages (including the function `utils.svmTrain`) automatically add the extra feature $x_0$ = 1 for you and automatically take care of learning the intercept term $\\theta_0$. So when passing your training data to the SVM software, there is no need to add this extra feature $x_0 = 1$ yourself. In particular, in python your code should be working with training examples $x \\in \\mathcal{R}^n$ (rather than $x \\in \\mathcal{R}^{n+1}$); for example, in the first example dataset $x \\in \\mathcal{R}^2$.\n", + "
\n", + "\n", + "Your task is to try different values of $C$ on this dataset. Specifically, you should change the value of $C$ in the next cell to $C = 100$ and run the SVM training again. When $C = 100$, you should find that the SVM now classifies every single example correctly, but has a decision boundary that does not\n", + "appear to be a natural fit for the data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# You should try to change the C value below and see how the decision\n", + "# boundary varies (e.g., try C = 1000)\n", + "C = 1\n", + "\n", + "model = utils.svmTrain(X, y, C, utils.linearKernel, 1e-3, 20)\n", + "utils.visualizeBoundaryLinear(X, y, model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 1.2 SVM with Gaussian Kernels\n", + "\n", + "In this part of the exercise, you will be using SVMs to do non-linear classification. In particular, you will be using SVMs with Gaussian kernels on datasets that are not linearly separable.\n", + "\n", + "#### 1.2.1 Gaussian Kernel\n", + "\n", + "To find non-linear decision boundaries with the SVM, we need to first implement a Gaussian kernel. You can think of the Gaussian kernel as a similarity function that measures the “distance” between a pair of examples,\n", + "($x^{(i)}$, $x^{(j)}$). The Gaussian kernel is also parameterized by a bandwidth parameter, $\\sigma$, which determines how fast the similarity metric decreases (to 0) as the examples are further apart.\n", + "You should now complete the code in `gaussianKernel` to compute the Gaussian kernel between two examples, ($x^{(i)}$, $x^{(j)}$). The Gaussian kernel function is defined as:\n", + "\n", + "$$ K_{\\text{gaussian}} \\left( x^{(i)}, x^{(j)} \\right) = \\exp \\left( - \\frac{\\left\\lvert\\left\\lvert x^{(i)} - x^{(j)}\\right\\lvert\\right\\lvert^2}{2\\sigma^2} \\right) = \\exp \\left( -\\frac{\\sum_{k=1}^n \\left( x_k^{(i)} - x_k^{(j)}\\right)^2}{2\\sigma^2} \\right)$$\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def gaussianKernel(x1, x2, sigma):\n", + " \"\"\"\n", + " Computes the radial basis function\n", + " Returns a radial basis function kernel between x1 and x2.\n", + " \n", + " Parameters\n", + " ----------\n", + " x1 : numpy ndarray\n", + " A vector of size (n, ), representing the first datapoint.\n", + " \n", + " x2 : numpy ndarray\n", + " A vector of size (n, ), representing the second datapoint.\n", + " \n", + " sigma : float\n", + " The bandwidth parameter for the Gaussian kernel.\n", + "\n", + " Returns\n", + " -------\n", + " sim : float\n", + " The computed RBF between the two provided data points.\n", + " \n", + " Instructions\n", + " ------------\n", + " Fill in this function to return the similarity between `x1` and `x2`\n", + " computed using a Gaussian kernel with bandwidth `sigma`.\n", + " \"\"\"\n", + " sim = 0\n", + " # ====================== YOUR CODE HERE ======================\n", + " t1 = np.subtract(x1, x2)\n", + " t2 = np.sum(np.multiply(t1, t1))\n", + " sim = np.exp(-1 * (t2/(2 * sigma * sigma)))\n", + "\n", + " # =============================================================\n", + " return sim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have completed the function `gaussianKernel` the following cell will test your kernel function on two provided examples and you should expect to see a value of 0.324652." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gaussian Kernel between x1 = [1, 2, 1], x2 = [0, 4, -1], sigma = 2.00:\n", + "\t0.324652\n", + "(for sigma = 2, this value should be about 0.324652)\n", + "\n" + ] + } + ], + "source": [ + "x1 = np.array([1, 2, 1])\n", + "x2 = np.array([0, 4, -1])\n", + "sigma = 2\n", + "\n", + "sim = gaussianKernel(x1, x2, sigma)\n", + "\n", + "print('Gaussian Kernel between x1 = [1, 2, 1], x2 = [0, 4, -1], sigma = %0.2f:'\n", + " '\\n\\t%f\\n(for sigma = 2, this value should be about 0.324652)\\n' % (sigma, sim))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[1] = gaussianKernel\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2.2 Example Dataset 2\n", + "\n", + "The next part in this notebook will load and plot dataset 2, as shown in the figure below. \n", + "\n", + "![Dataset 2](Figures/dataset2.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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WpRZV7Rbl3ngnp3BD1uev4xXZBUVADbehodwvFqCu0QjfjnEYr5+jJD/HrTEvTTlNwZlETFZQ12vntOrI2jIDr1pNKf7t2E0veP8yNqxd7TakFTx0kXT+3K+WUHLpW8IGv27z1n1DSF/5IvpjG6VtZq8AAoJC2Lx1xy31cCIjI/num2M81P8BvFQq9N98RuiAqWjqt8OvUzwRL6zEq57t2vKQuojgZMzBeVVj/+65FQqOFC1tKoyOKzx3q5rCfYsRrRbUje9CbsjC4BFo+9vgMeh3COzLXoCSS98RVB5uANuPMX3NWLQte7B5+y76D0hwKgZ6evjzENIQXeJKqehG9/EL5O1fxo3VYyj65TAleVkExk9zYkIolErCBr1CcPQYRIsF3fbZyEwlFHy7hdxd8/Fu+wDW3Ovo9i9DXbs5n27ZzjdfH+blkU+jOr2VsPAaCGGN0Z/4HFVYQ4xmke0HjqGKSpC2lZSUcOz4CYxlZfjHTiGwz2hSdCXkGwUKDEX4x04hOHosMo0vNz58Dm2zbhT/dpzcjeVx960z8arXlsJjmyi+cMJtvNu/cwKygnQKPn8Z3YHlaCK7VIj3P0vOV0vI3vE2qrCGyDS+lKb8hEy02EI/+5cRFhJEE28jGpUcuV8YefuXMW/OW/To0ePWNMJzSWib3usUEkKQEzb4dYfCqziMJhO+/SdXOuk6xqDt+ixGKy6GOe3D4VI83ZEVJH3fD55EXb8dZl2aw4T1Jm2bNpAKjgxnE9HtX058/xjpep9t2Y53d9fxVbWNQfQNR9P8PvIKCtH8DoE2D/4ceEIudwiSk5OJiY3n0pWrTjS/m+yOzpjzbmAtK0HuH0ZZ2q9oGnbEK+c8Bfl5mK0i2ib3YMxIRijOY+LYUcx7bxFekXdTnPwt2si7nJgQOdtmEfjAi04sGN3+pYQNfh1VrWZkrxmFrKwYq9WMf9x0t9opthDRC/h1HYLhbBIIAqrQBjYxqLsfsm2zmPCRlWGu2cYpZJG9fQ5BvZ93un5e0ipEsxFVrRaEiPlYrVbiY/vx2eYtFJeU4B871ZlWmLSKkLjJKAJqULD5FcJUZVxMSUXuXwOZUo1Pmz7kJa3CajEjiBbCBr+OunZL0teOR7RasOSnI9P4ERwzjqLDq9AKJoY9/SRrP9nIhrWrAdtEu3PrZlJTUysNaeV8uZjiX4+iDIxwyxayhYTeRBFcl5pDF0ghnpw9C/HtGIdfVKxTiGfD2tXSteQ+wbZwEyI+bfvaqj7veYSSSycBm7HXH/8MZXBtfNv3Q7d/Kb5RsRh+3ofcLwxlcG1Kkr/BNyoWIfUn1F2fksI22qb34n3jlNP1qmLVmHKuVUpBrYzp5MHtwxNy+R/D7+HqRkZGolAo8S7nNQMUnNxG1pYZNwtqAJlaizHlZ6diILNVtBWQxIxFkCsRRAvvL/uQkIRXbSEAnyC0ueedQgY1hy2rUIK/FHVES9R1WlGWdh6LoECm9cc/bnqlSa/Zc+ejimgheeOCIKMs6zL+dz8kbRMBuUxGY3UhmetulvHXevaDCprdHyJazIQNfp3wh9+gSO7DxPFjWbZ0CWHh4SgbdnEZF2Vo/fLYuoA1rCnJl6+iaXwXMoUaTeQ96I9vInTAVORePk4edGj8FASrhbCH3qD2iDW2HEFeFsVBTdiXeIjUq5cBJFZLzwej6dUnplKDF/zgSHxrNsTHku82VJa9az5eDTshkyvJ3DClPNE5m4Buj9tYQBsmYzibSOnXa9iwdrVTklIZFGEr29enk3d4NeqIZhSfP0r4Y3PQNumK4acvCR0wHVN+BnkHlhPQ7UkC73+a8CHvYDHkUnLxJEEPjiDw/qcIePw9KWwT3HcMlvOJbFy3xq0eTOryYRSc2uHEqkEUkXkHSftkLX+atOXPUpZ7vVKmkwd/LjwG/S/GH5F8nT5lEiUXTkg0v/zyhJrdEAXHjMVSnO/E7vCNikOQK52MlSqolhP7wK/LYORyOZ0ia7qlsOV8sQBB7Y25MJvrix4ja+tbqGo0waT0djKieYnLmTv7psEa0D+G0uvnCOw13CbaJVeiCnOoPi3fdleXThQXF9GtdQNytrnJDex5D0QLQQ/+R7rnMnUgL02bzv79+6UYdv6n08g7tIb8rzdIFZCixUjmxmkU/rCrPF49BgBBEGyx5rpt8L93CMUXjpOxbqJLEVFpymmyt88mNOFlgmPGuqXjZRaLKEPquoQ19A60TbMmmDx9oVOozA6/zgMxXv2BgAdfRNv0XttEM3A6Pq164X/PEMhLhe/+y65tW+jRo4eLPkvRVwtYsWQRjerUwnjjPIE9h9mea3k8XdOgHQF3P4I8oAaGswelQjAvucgDPbph/nmXyz3ZY96iKDpRPQ1nEsndNgssJoovHHOgly5FHdGcrI1TKDx9AP2u2VjNRtR1WpG9aRqFZw5IE5IH/3/whFz+QlSUiy3Y/Aqdm9TiQNIRQge9Km17tG83Dh39mp1bNxMZGel0rHffidKS2P/eIbcsDsnePge5TxAyL2+3+xSc3Ib++EamTBjHOwsWSoJajtCf2k7Bic+wmk14N7mbsuwr1Bj6LlmbXkbbtCuqsAZkbZmBd9OuNNEWS0yW3n1j0DS5B3N+BuFD3sacl07OzrkE9hxWIZSzDK86LShL+5WQBDfXP7mVwu92IfcLocbj76Dbt4yicwcldog+J4NDhw7RL24AJosVbZN7nMJHWVtmEtxnhHP45sBy/Lo+hiqsATk75xESN5micwcxpicTMXyZdO30NWPdFuZULMrSJa5AGVgT3w6xUljDeP0XREsZioBaUlK00pDFuglYC7OIGL3J6fnlbHuL2TNeY/LkydJ7MGLMOLZ9/ilf7NnLgoWL2LhujVNhUmXXSF873rZCk8tRluYzavhQ3l/2YaXMp6C0E6SmpSKv35HGagNDHhnMzNlvU6DXE/7wm6hrt0R3YAVFZxMJ6PY4vlFxZK6bgMyQiSjI8Y+dgrp2S3I3TUFuyGL71s306NHD5d48uD1UFXLxGPS/EO4qNrO3zCTIxdgsc6pulMlkLsfaY6w+Uf0xZ1/FlJ3iWs247BkCuj2Bd4vu5O59n7LMZGo9u0T63E7nU0c0x5R+3q0xhZvGQB3RnKDez0v62TIvH/IOrwGrhdABUyutcMxYNwGvBh0IvP8pp/PaY8ferXpRfP7rKil0mRunYjbkoQyshTHtnBTvzlg3gdZ1gjiffBGfmJeQeQeSvWkaXkE18Os7vtJ4tVwAIaAWloIcNI07492yBzk757rcg0mXRtbmN5GpNITETXY7IeYf20hg7xcw69Iw/PgF2hbdKbvxG8Gxk9Af+5SSi9+gbdpVmhRKU06Ts3s+vp0G4tfxZkWsbt8y6k7YLN2nvfJVvP4T+pwMjh49WmnVpbv3I/fLxfi0j8GvY7x0Df3Xm5D7BCEIYMq+UuUzz1g3HlUt2zPXfz6dLk0iXLj5FemrJl0a+Tvfxrfnc1VWId8K9omrolNTcdu/Ef+IGPo/QSPCHb2tMrnYipV1FZfZWVtn2UIFZxIp/q0Scasom7hV6bUzlFw6SVDvF6TP7AYjbPBrWIr1eEXe48KUcIyR+nWKp/TK9zZNkrZ9KDi1jbykVcjUWqeiHncFJb5RcRT+8IXL/eXsno8yrBHGtPNuKk2fRX9yq4Nmd18QLZRlXEDbzJkxcvpKunQ9VXAd/O57Cosu1S3jJGf3fHw0XlhlSkSrBU3Teyn67RhZm98EhQrd/uVOHZeUQRHUfPYDREMu+TudhcdKU06T//UGtJF3Y/jhCwLue5LQga9QfP5rlKENyNk5F+Olb1DIZRQnf0PG2gkSowSjAf3x/5KxbqIUsgi493HpvFlbZyHT+OJ312DMXgHcdfc9xMTGYZRr8ek8yK32jj0PkXdoDVlbZ+LTri/Fvx4lY93EmzK59w7Bp20fjNlXXZ657uMXKPreIS7eMb48FDUBdedHSDxyXBIssx9jLszBmPqLk+ZN6NPv/6HuR//2TlR/BHeEQa/sAcfExpF85ToDBj/sVIJ9O0b+r5woHOltbmPFu+fjG9Ufv04DXJKM9mNfHvk08h8/p1GDepT9uBOLQVdpNaNfx3hEk5HsrTNdPE9HHnJo/BRMOSkOBmcmCDKKfjnkUnloZ4+IFjMqhRzfZl0l+lulTYj3LyXg3iEu9+fbOQFz7nVEiwlT9lWHysfZWEsKMJzeLxm8vMSVhMSMp8aTCzDr0p1Fs4bfnBQLTm4jP/FDLCISn9tO6zTp0gjoMoii0jKsFgvq8MYYU8+C1QKCDNFYjCq8Mdk73nZK+pWlncdiNuHb86bwmOOEGBwzFlG0krtnsbPqIwIWi5Wgga8QMWINVkMOefuXoZALhAx+g4gRazDpM9DtW4Z/18fx6zzQdt4tMwAb/1/35fsEx07ipwtXKLOAulZzdF++j6xZL6cEo/39SOjZ2cZQibyb4vPHCH9iLqpaTSn8YTfaJvdQcGobuoMrCXrgPxjTL5CxboKkk/7B/NnULThL4ZZXMZxNJPfLDxDLSlCG1kd//L+EPTGfsozLpK8dL01MorEYq7kMc26qrWK1AooqSAhUR9zr98hbeGDD/7xBr+wBx8TGUWYBrwYduHDpKgvefe+2Z/G/wxOw84cDH3CXHBuEMfUXRNHq1rORy+VMnDCezBvXOX/uDPKyQvccZAfP1rdjHIJc5bRP9kfD8W4UJRligBpPLsC7ZQ9bQi7hZRQBNbAYi9E26Yr+2CZC46fiVa8NuV8uJrDnMPy6JFAjPJT6pmsoLKWYcq6T9dlr0r3ajWjWjrfxjYrFr9MA1+8bFYvCP5SGId54ybFd6/gmZBo/tM3uJbDnc5iL8tEdWAFKNV712mApzMVqKkWm9bdNPA6we8yayHsQfUJR12klGV5laANy9y5C2z4GeUBNfFr1JDhmLDK5EpkAMkU55z5mLCCgjmhO8YVjpC4fTtaWGQQ+8IIzVXT7bNR1WiFarWSsm0Dg/U9hyr7ipPro3fw+ZEo16jqtkKs0hA15B9/wOgQNtE3AcpWGoJ7PoVAoEK59h+FMIvpds1EoFTf5/yYj+UfWIZrKCB04neDo0VhNRgoPfuji9R45coSdX+y1JX9jxoJoRfflEorPH5WSxAgCCoUCRBGVqZBnBz6I6sxWdm/fytChQzl5/Cgvj3ya/H3LEOSKm8eJUPDNZsyFOahrNUN/bBMy7yC82zxIcN/RWM1lBPV+3uUZq1pHs2jJchITE6v1W/urOlH9U/E/b9DdPeAtXxygzEL5Cz4GmV84r7/xxm3N4n+HJ1BV+T3clIst/G7XLSvrjhw5QpnRSFnm5QoaKo9h+Okrm+d1NpGSI6tpWDcC3SYbM0a/+x2WvfcODczX0agUgED2lplOrAhBkGFM/YWQviNdKg/tGuW+UbHkW9R06tCOMn0WmkadENRaaTKyG1G5ly+l189J2+3L+pKrP5KxbgLa5vdx8cpVNPc9iyqsATK1N0EP/Iey9GRyts9GU78NiFZC+o2TzivXBlB65SeCHTRIKnrMMqWXzWPePttBJ13E8MMep/BRSPwU5IG1CR14k37p1zGeshvn8e/6GGJpIdqm92D46UtE0SpRIjWNO1Oa8jPZW2dJHmyNp96TpAmUofXRn/gMUbSg2zS5Sn2Wvbu224qxztiKsdSN73YKKZlyU6UVliCT4xcVhygI3HfffU7vRMXfik0K4EqFY+OxGIvRJ65g+QeLWbZ0CTeuXZWqiy9fvszECeOp16A+WocuVMExYzFlXyFs4DSCH3yRiP+sJOCeRym+cJzc3QsqXSX6RMVKVcjV+a39XiE5D2z4n0+KJicnu+hIuEvEFHwxF+/7h1U7EfN7JWX/CNwmNnfNw7eCXKz+2CYCOsZSt+CsW7nRimyZwu932/Q7osfgVbcNVnMZ2WtGoRLL2L75M+677z4WLlosMSK6d++OxWJh1OjRrFi1mtBBrzl7n7fQ+MjcOBVt03uxFOVR+MNuqaFz5sZpKAJrUXLppJQkzdw4FXNhNuqIVgg3fmbp4oXMmvMOFy9fQdvkHoovHMe/62Ooy0Md9ibT5oIcwgZOsyU+N0xGGVRHKkiy0x4d78/dO+G2OOnwGlsdXtEAACAASURBVMSyYhT+4YQOfLnSpKmmfjtKU3+VvkfGugkICi/Ksi4RNvg1RKuF7K1vVfndMz6ZjCIgHPHaD6gCa1apz2JHcnIy/eIHkqIrdZuElSY1Lx9mvzyBSRMn3taxWVtngWh1YSRVTLZu2LDBphsUXJeQfpUll2eAINgSvg4SyLlfLsanQz/pnU77cDjq2i1vyRSy/9YsFgvPDht2S02bfyvu6KSou7LqkLjJLs0Hgp9cdFuz+N/hCbgkNrfMwLt1Lye52LzEj0CQoazfgYsZemrUrsP69eudmjJHxw5AVu+m6qIqrAEapRxz1hVEqwWZQoX3XY/g7e0NQKt2HYiL7c+Na1clj//IkSOs3/hfJ2MO7jU+Upc945Kg1H+7GcNPX7jw4MsyLzl5hD5t+yJaLBgvn2T39q3UqVOHG+nphA16tVzqthb6E5+TvX3OTU9aEFAG1rzpafabgCn7is2YH/8UdZ1WTvz3a+89hLJGI5dYfsXipLz9y9DIRQSrBbMhz63uS/aueSjVWkyp52wMk/KQSsD9T2EttSk4IkLOjndcvntFb9i3fQwlF79FFOS31GexIzIyktLSUqxlJWTveMflGCnkdfcjzJztnKSNjIxEJpNVemz2rnkIgkDY4NcIih5Tpac8e+58vCLvRhkU4V4f58vFeLe4H9FqoST5W5dVYsGx/5K7ySYhoDQXU8uSUe3fmj0sWd0x8+Am/ucNOrg+YGVQBP73PoYpL93GTiiHPW5r+GpRleGKpKQk4hIGs3HdammicEycwf+PmJBjYrPg4Aq86rQk4L6hhD82B2VoA/L2L0O0mFFH2BJfilYPkpObxzPPvUCaLIz+8Qn0H5CAonYrLCk/ULj5ZdfGv+snSVWFk8aNqbLFnKxeR0Sr1TZmZw+i+/gFvBtFUZZ+gdTFj6P/ZjNZW2bgo7ASlnlSSpYVJq0kQKtizUcfElKcQuriIZRc+aHciL4PItI58xJXoMLE/r273ep+h8RNRuETSOjAaU5hAVPONdLXjpcMQGCPYehPfI6mcReM189yY83Y8iKiT6QEoGguwx5CqoicXfNQ1WhImcmEVZAjILplBvl1HohV7YOfnw+a7F+cQio1hy2VQirqOq1tDJD1k25W1z69qEIyeBmixVJpI4qK+ix2mMvKsBh0BPUa7nKMb4dYCk5tR3fgQxRyucvnL095CbE4z+2xglyJJvKuasWnp0+eRMlvX1P82zG34+TTvh9Fvx4Bq5WQ+ClS/iN0wFR8WvXC9+6HKctNRfXzVr7YuZ1fz/xcpXzz6Bdf4MXRY1m/fr00wdjfI0fWUWVj5oEN1TLogiD0FQThN0EQLgqCMNXN5/UEQUgUBOG0IAiHBEGo/WfdoLu4c2nKaXJ2vIOmfnsEpdolblsmKFj4wTInA2bPpttfmDRZGA9E92f9xk+RNbnPKXEmitb/N0/Antg8d/pHmgUrKdz8CkXnDmG+cBilUulQ4WhB99USm9FLeJXAPqO5qitG1qgrwXFTUAbWomurhqjObGXm66/y/rIPCY6fhrb5fej22QpjXnn9Tcnz+u1aBjXr1JfYBDu3bibcmEr2trdQhtYnP/FDFs+dRYjuLOaCbDSNO6E/8Snz5swiPzeHX07/JIlufbl7B7lZGdSuXZuc3Fy0jTuTW97AwfE55CWuwEerZs+unVJBiWNVp+RJD1vq7Ekf/IiQgdOxFGSh2zHbpYGF3C8Uc+71m9WfMWNRhdTFrM+kLPOSW31v3y4JWIwlmIxGZAqlFDOvCL+O8cjkSrre1YWS0lKnpKAjiyV0wFQUATUBsUoPVqbSVEkNdCyHT0pKom6DRuTp9ZXGpH2j+oMgQ4aFzZ9ucvrMrshZGbc87OE3Kbl0ipwNL93SU375tTcQBXkVDKo4FAE18Wnbx1nlsXxf/04DEPzCierQjh49elTpdQt12jHvvUWkycJ4YeQYhLpRiFarlIvI/GQShu+2ux0zD5xxS4MuCIIcWAJEAy2AxwRBaFFht/nAOlEU2wAzAFf+0u9ERR0JSb9k4PTy5JfGhS4m0/jx6+Vr0ixunxRSihU889wLTiXb6totnErR7UnJ6ngCv5fy6LhCsCfD/PwDUTRwaNLQf4KN0+uQrPONupnMU7R4gO9//IkNa1cz7dU3oEZzvOq1QR3eCBAoMYNJ5Ye6TiubZGxeFkWBkZKn7tjPMzh6DN5h9Ug6dMghHDIW3xoNQLC9IvaJ6Ma1q/To0cNpog2KHos8sKbLc1AE1sLX15/7779f+u72VQoF6WRve8tlbHL2LCSw13PIZAqwWvHv9YJLA4vQuMkoAmo6qRXaQjvmyg1QVByCIENQqiUvFRxCSo7dfjrFs3ffAcmwZKybgH/XR11YLKoakZhyrlXqwRafP0qDuhFSiM0dNdBeDm8fzxvZecjrO8sDpy592inkZS9Gso+r/Z0b/sKLLporjhOIKrgOAT2eQcxPq7LRiSiKZGZlo21yt7NM8YphFDjeR3lyWbrPJUPRn9rmxLDa+9X+KskApSmnMfx6VHJaVMERqNJ/JnvbW9IErgmpjVdyosuYeeCKWyZFBUG4G3hDFMU+5X9PAxBFcY7DPueAvqIoXhcEQQD0oij6VXXe35MUlTXrRV7icjSRzqXd2dvfJqj3cy7Jr/AgPyelOF3iSlRhDZyP3fEOQb2GOx2rP7aJiP+slNTtbly76lKlZtcYl9ePor48j9M/fFet/omV9WbsFzeAMkGF0ieQoP7um1fYjSUiGPbOY+brr/LqmzOlRhGCxp/i3AwEuRxN4y6UXP4OrwYdKb30LaEDnRURV65e84eSwhX7gZp0aW7L+vMSVzB39kwnhb2kpCT6xye4lYrVn9pGwfFPEa0iYYNsmuXVbWChjmhB2MNvSMm54sQlqNrE4N3hZkMK3f4PERRKFAE18G0XQ17iCmRaf6zGItu29jHk7V/OS+PHMO/dhYjYkn7m/HTCh7yN8dpZm8xueCOMab9UWc6f/+k0Xhn9LDKZzCUhbU9S2w2T/R2VeQeStXEqMt9g/KLiyTv4EQE9hpF/eA0Kv3B8o/rZvoNM4O2Zb9IxqoP0PtUVclEqVaToSpA160XpsTUsXbyQRUuWcyWnCFnzXhQd+gjKy/IrK/dPSbmCSZSjVsgQ/MLRtI2hMOkjfHy05BcYUARG4BvVH92+pch9gvG/+xHyklYR2HMYhp+/AsCnbR90+5YSEhSEr78/2V51nMgAxYlLULeNQX86EVV4Q6dktmHvfDRu3sOJ48ZKY/ZvlhD4Q6X/giAMxmash5f//STQRRTFUQ77bAS+FUVxkSAICcAWIEQUxdwK53oeeB6gbt26USkpKdX6Ao4/gLmzZzFn3rtczjZUbvi2vYVSBnu/2MWIMeNIKVZiNuQR2P1p8o+sA5FKZUxzts1EE1QTdYd4Sr9ew65tWwCcjPCQhwcxefqrBD74It7N7ydz/UTua9OQxMNHCXzAts2dVKg7LRfHMnlVrWZkfjgMhXcAoU+/73RvaR8Ox7/rY/i06oXu4xcYOexJSYfDfq6iGxcxiwJhg16xMS82TMFcmENI/wkuP47Diftv2YWmqg4zycnJtInqjNU7tNLuSdk73san7YN4p52SJoVbUTdvao4oqPHkfMkAFB34AJPSB6vFTK1nFruMjaysmJo1a1Agerkas2wDsha9ywt4hpB/bCOCXIFM6UVI/wm28ftuJwXfbkaQK/Dy9kNpMqAvLLo5lhunIai8MKb+Qtjg11ycA0cjpW3v3NHoViypiuynspzrZP53urRS86rbBl3Sagq/34Vc60dwzFjyj3yCvCANmULp9A5MH/FUpRPI7LffwVBUTGD8tMrL/deOx5R3A23TrhQnn0AmWhFlSrCY8GpyNyWXvweLCUGmQFB7Yy3SgUwuyeaKVguF3+9G//UnyLCiadaNOuSgVqu5mus60fyWmo3RaESmUFXKzPF0OnLGX8FymQTcLwjCj8D9QBpgqbiTKIorRFHsKIpix9DQ0Gqf3HG5/8QTT7BowVxUJTnots922Td71zxEq4WatWpy//33M33yJMrSfkEVVp/8I+sJf2IeytD6buOexYlLWLl8KW9MGoXq9FYnY+4Yi35p2itomtyN4acvQRDwiYrjQNIRNJH3SNvkzXszd/67TucfMWYc1mBbowlzfoaUlFJFJZB3cBXF57/GbCpzqki0w87/tsf3Fy5Z5sLPxztY6kxjY6NE2wyUmxjp7+kz6ojIyEh+PHkCH7N7Sdic3QvQNrkby6+JTstjx76o9ntKXTLUSZnQr5MtKZrpUMU4avhQTLnXK00Uil6+BAYGMn3EUJdCmUf6diNv/zICuj1O8a+H8W52L3XGbCSk3wRyv1pC2tKnUMhl1B65jpB+EzGWllBoKJJCDnYWi1mfJYV0QuOnYEy/QPracbaOTdtmsnjuLLfhlFuhIuNKFVKHOqPWU3vEGknxseinPXg370bEix+jqd+ekP4TkPuFuyQ031v8vvRbcWQ0rVy9Bi8vDcqGVTOYfDvFI/Pywbtld1vCc9Br1B79CYrQegiCAOYywga9Su3RnyBXa5HJZE4a6Pbn59f1MYSAWgQ8OJIbhRYee/ghKbxofzbvvDUDU146lqI81KW5bjsdFXk6Hd0W/pSQS4X9fYDzoihWmRj9veJc9pJ/o8nq0lYLIOerJZSc/xq5dwCP9uvB1h27UEUlSM0U1HXbUHz+qFuetb23ZmWiR8ZrZ230Ojs/et0E5P5hGK+fc+BdT0MZ1oCic0k0blif8+fOcPjwYUaMGcezQ59g2quv4xV5N8b0ZAS5Er9OA8g7uBJNo04UXzhBaMJ0NPXauXxvG//bJorlG9WfvE0v4Sc3Uyz3qdTDrsgnz1zxLB8unCdxeKvTZ9Rd2MjxWVR2fOGp7Ri++Yw9O7fSq1cvabujZ+/boT95Savw65xAwcmttph7+2jyDq7Cu3VvDD9+ga+vL2+8Mp3pr71ZKVvEzo0XSvXMmj6p0lWRY/jGaizCUpiDpnEXSi+fokZYKPoyKNZlomncBR/db+To8qrkYWdufgNNgw6Ysq6i1mh486XRjBs7ximcUt3QQFXc6+yVw3l98nj++/lWkjP1lYqOufNk7d9fqBtF8W/HkPuHIyi98G0XXa4xb0IZXBuZSlMeJllGaMIr5B9a7cLrdxdSyz9qm3jcP5ObIm4VQ3eOz8WR0+/6Hm2jvuGXKt/Dfxv+qId+CogUBKGBIAgq4FFgZ4ULhAiCYD/XNODjP3LDlcFuzMssrj0SwfZSl/x2zMYfNpvY/OURp3ZnotmE4cyBSotm7InQkaNGSYlNu/ekWz+OrG1voa7busoqPp+2fSg6e5DAXs+RbZQzctQoKSE77dXXpaYSglyJaCq1/YDKkz/KoFqUZV2RvktFgSyfdn0o/H6XjXveth8Wi7lyXZg9C51+fABe7fpLzJ1bhT5ulRS+1fE+UbFow+vx08+nnbZHRkay/P2FmHTXyd2/jMCew/G/azAhcS9hMejIP7Ie/7seoujMAXyjYvH29mbFx2uQ16/QYMGNeFdZaYkL+6FiUt1SmItQmIm5IEvSOJcH1EKXnUWZPlvalpNfKPGws9yuQObj1zGOsIRXiPjPSrw6DGDBwkUuyePqojIWSGnKacrMVtZu2MS3x4+S0LML+m0zXOh87lZUTonrvqNRBNVGGdYQU04K+UfXEzpwOqrwhni37Im2SVfyDq1BBPTHNhJw/1MutR4utMwDHxLcb/zNZ1Kh6YVPO5uIm7t6DkfabM7OeVVUmsZ5aIq3gWpVigqCEAMsBOTAx6IoviUIwgzgO1EUd5bH2ecAInAEGCmKorGqc/4eD71567YkX7mOV8Mop8q0nD0LbR5uueetrt2S9HUTpHZn0jY3sdmKEqN5h9Zg+HG3k3ztwYMHiYkdgKpRF0qufI8isBYhMeMq9Yq1zbphyr6CqlZzin76gtBBrjHXyhKyusQPCer1AvmJy1F4+WAylqDwD8e3Yxz5B5aj9A7AbBVRmouZ9ebrvDZjllujWnBqB8UXjhE+5G3sc61jG7CKSdHbjQG7q3qtzvFSUrhuFCUXT4AmkOAHR9xso5d/A1NeBtrGnRCvnmTXti3Url2bAYMf4XK2Ae/2/clLXIFCJlIrojYZRSLeHfqTd2AFcsFCzVq1OfDlHkledf369Tw/YjSqwBooW8dQemwN/n5+6P0bOz2Lgt1z8e5+cwLUf7MZwzefYhFx21bNcXyN187+oThvZZOjo4Su8cr3DHvyUdau/4QyC2ga34U5/4b0fG+1upTeua2zCHrQubWg/vgmgvuOIXvH2+W68Icoy0ym5tOLyd37PqbsK9R8epFTWz/9tjfxVojk6PQIXj4IpYUovf2wegcDtqRo3sFVqFUqli9e4FLZaa9qvXQlBU3kXVVWmlY3F/FvwT9GDz05OZn4QQ+RfDnFJrDUtq+UXc9LXImmcWfnvpjumikc+NDWiCAqTjpW/81mZEo1XvXaOZWy25OWn23ZLon1Z26ciqXUAKJIxHPLne7Pnrj0btHd1mMx+wreze4jKHqMrbnDrnkgilWXdCuVaBQCr02fyvTX3kRRvyNa3XmUShUTxozi5ddnoGzQicCiFHS6XHz7uW8qbO/zaM1Px6fLYCdNbNVp56RoZYwIe1LYnadZkX1UneMrJoUz1o1Hpg2gLOOiU8hKrvXDnPaLU7jGYrHw7nsLmTXnbRRyOVs++y/dunWTtokWCyaLFWXDTk7sIXsy27/gCmaziU3r1xIREcEjQ56sMnyh3zUbBJlTn9KK42sPKZjP7HEpR7906RKLFy9g48ZP0OkM+PtradCgPlevXiU/v5igIB+GDHmCMWMm0n9AgmR4zfmZGI6uxHj5JGVlIiqtGm2Lnhiu/oJJl4ZM6SUxluzX9+sU77ZnpzvZDHfvXOiAqeTuXYx/18dQ+IY6sakq/ts+6Xo3uYf8o58gCjK0kXdRcvEEIQNfxqtOa1vTi1+SCOr9HwRjIXULzjJ39kxGjZvgpGXerFUbLl7PRDSVogyuLfV4tf2eP0IRFFHOOlrGgS/3/KuZLY64o0v/HREZGcmZH7/n0YRYxMJsp8q08CfmUnzhBOlrx1VZuaeUCyT07EJB0kdoA0IAJNW5ktN78XbQ2tb2HsXWfV9LsVs739lSkOUkDGWHT/t+GH76CgQB3/Yx+Pr4EelloHDzK4BN0dCuk10RUkl3t6Eo5HJen/kWgfHTCOk3DrMmhAd6dueNWbMJjJ9GcMxYskvB4hPuFIbIWfU8BQ7JRd/2MfhovWhY9KtLos6xatWd2p49KVzZj+j3HO9aJToFa7HeJWRVlnXVJVwjl8t5adJE9LnZ5GZl0L17d2nb9s2fYQX8Y6dUKv5kELRMGj+O7t272+79m2Mk9OxSaUI4KCjYqU+p1FbOgWft064PhT/scilC27t3L507tyE3dyULFxYya5aIxVJEkybnWLy4iH37RBYuLCQ3dyWdO7dh4tjRtrDeujHoPnmRvq2/ZdUqkf37YdWHRh5stA95cTpy70CnugSftn0o+PbzSgtubpX4tr9zXnXb4NOhH/mJK8jaOqtSY+5Yq2HMuowIkkKlIrA2pqwUjNfPUXz+KNrIezD8/CWadjGSOFfFiuWXp7wExkJC4ia7VJr63f0I5oIc8vYtY/7bsz3GvJq4ozx0QOJ/u0uQ5X+7mYJvtqDwC3VLbRMtFjq3bsrxY0edqFw+vr4c+HIPAI88PpRLWYX4PDimUuGmgG5PupWDtbcSUwTXofTSKWqFh6L18aFF0yYknTqD173PVCp8pT+5FcPp/chL9VitVrwi7yKw782ElDuOeEHSR/jUbOjkIS/8YBnJGfmo20RTdPhj9uzc5iTO9XdyeO0e4y/XcwiOdU8btY+PuSC7ymW2Y11AXMLg2+bU3yohHJZ1yolqp9u/DN+o/hhTfwFuhhRC46eirnOT3z8gPo7OndswY0YxLVtCWhqMHAlvvQUtW7p+j3Pn4LXXtHz66Q4SEmKYM8dU6X6TpqoJGvIBysCaNqngXXOoW6cOOWWKSldUVX1Px7ARokjmugmIMjk1npxPxtrxKMPqExw91unft0qQiqZShxXEVBSBEZQknyA04WVEq4XcnfOYNHYkfR58gNiBg1BFJVB07hAhcZOxFOZKIR1FQA0y1k2ga8v6pGdl/+u7FDniH+Oh21/OytgO6vBGYDFJ1DZHfRbfDrEIKi++OXlSkgnt0L4dxrIy8nzq8+gTT9GoUSPmzp5JcfpF91WMu+Yj97/ZaLc05TTXPxhKzr4l3Fg9BnN+Br4d4yhJ/oaAXs+RZZRxJVPPzt27KdTnSQJUlZacK5SUGUtR1IjEkvID+Z9Ow3D2IDk75+IdPclptVFydLWT7KrdQz514mveGPc8qtNb2bNzGz169ODq1atcS0nGVKKjd+9ehIb6MXbsCC5dunTbz+DSpUuMHTuC0FA/5HLZbZ3L7jFG393GbSI394sFePn4Yy7IrpLyV1FHe/vmz6hhumHjUFdS0l505GMUcjnr16+nXqNI+sUNkIxcRR0fbfv+TlQ797o7y/Fp3g11nZZO3vHixQuIjr5plLdvh3793BtzsG2PjjYxZcp44uKq3i+uv5mSn7YDULh/MSuWfsCvZ09XuiKqjlyzaDFTcGqHzePvGIc1L5XU9x9HXbctJRdOkLtxMtpm3Sj+rRq6NVYLmsZdHKie4yjLvEhowss2QbOd8/Bq1In3Fi/hxdFjsQbbGoYrQxuQtXkGmZvfRBlav7yITMCrfnsOHf3a06XoNnBHGfSKjAWpocOpbZRc/dEpW16xsYEipDbmgmxkcoUkdBXdP9appdczzz5L7ICBWGVKgh74j8v1vSK7YNFnoAqtT9bWWWRumYl3qx4Unz0kbcv9agnqiOYUnd6Hul57TPkZIFeB1VKNZhTxCAo11jIjZhQUXP8V3b6lThozADlfvEtoaAg9evRw4hxfunSJCRNG8/ac18lMu8bDD8cTH9+Pjh1bS8v/isv9vXv3Vnv8K4YSfs+5jhw5wv6DSW4bfAR0ScBbIaI/8CHL3l/kFHt3VJusqA44e84c0tLSkGn93aoMFu5bjNViIkcWwDPPvUCuti5lMjWq2i2kZhXKkHpkb5mB1WKSDLSd011Rd0e8fJzVHy2nsULnEsrauPEToqNN0rUTEyEmpuoxiY42ce7cWafj3CGuv4WSX5MA0LaPZdGS5QiCUCmrxt3vJXXJU06cf98O/dAf34ThbCJFhz9GIZehbdQZ47kDfLlrO2+Ofx7NpSQa16sjJfIrouCrRTwyeCDBvhoXVcVaz35QIWQzBq+Q2jRvEokx9RcpjCMo1fi07m3TMTIbydw4TdLq8XQpqj7uKINupxDqP5suyc961W2L/utN5OyYi6ZRp5tdahwaG9h5roIgIyThFQL7jCZFV4KxzCy1+dL0GuHUOKMySqRdDEqQK1ErBAw/fEHY4FelbZoGHQh76A1Ei4XC73cgyG1CUOGPzabk8ndklLfvkppRnN5P+rrxknRuSPxkRKuZsoJcBLldrOumxgyAb6cBpGXmsODdm3xld8Z22rRCEhP3MGtWCcOHm4iIALkcIiJg+HATM2YUM3BgDEFBPrf0si9dusQTTwxmxoziSs/1xBODqzxHdaiSeqsKeXAdZs5+m+atnMXU+sUNIDp2ANRo7qIYqO44iLKMi24Lj1RtYzDLtZSm/krAAy8SHD0Whdaf9A+etHUj6v28TRdIpSFn9UiXoqDbyRfodAZq1Lh5bb0ep7/dITwcyspuvZ/FApbSYjKXPkRu4krOnf6WHj26cfDgQberpg8Wvusk12zYO4+HYh/E8M2nUgOUvIOrUKg1lBxZDaIF/7jpBEWPQRtej9NnzjJxwng2rFvN9Rs3EATB7fh6tevPhYtX2PTJOmQyGfe3bewUs6+oxePTZyxJp84QWs4esjcVkZqOxE3GUqx30urxdCmqHu64GLo99j3nnXno8/MQ1T4Ex9i62Rh+3IMisBbW0gLUtVsQHDOuSr2X/KPrifjPKjI3TEHbrBtF5w46FVPYKZG+HeMoOnvQRXMie+tMgh4c4UIBi3hhpdsGC4Wn91N8ZDUWqwU0Afjd/TC5Xy1BUHghIBISPwVN/Xa2TvNbZhLcx/25RauFjE8m41WSjT4vl0uXLjnFbe1YsgRUKnjOtfBUwooVkJ8PQUFK9u5V8sknm4mOjnbZb+zYEeTmrmT48Mq9yI8+UnD+fFOuXbuGTmdwYnI0atSo2lTH/CPrsBqL8W5yDyUXvyF4wDQQRbK3zca7yT1Yr/+IKigCbe9RKIMibtmUo+Tqj2RvfQttk3sw6zPw7zqE3L2LsZYUSDot/l2HoPvqA9SiET8/v9+dawgN9WPhwkIiytMDCQnw/vtIf7tDWhoMGwarVlW+37ffwuzZ0LcvxMXZjH9Ghu35nTwJCQlyYmIs0va9e23Pc+3aT/ntQrKTFECzVm24mleGpVhPcN8xyH2DK805bFi7Wqr9qEyhUrRayNn4EmZdGgTUwpRznaA+I/Bp2dN2rrMHyTu4EkVADUL6T6xUi0fuE0TY4Nc95f+3wD8mhg42tkOH9u0oNZYSPOg1ao9Yg6Z+OwLvH0rtMRtsovtmMyUXT5Gx/iWHpZ9zJ/K8gx8R0n+CVCJf+MMup8YZ9hWAb/sYSi4cB0HAeOM3J41ux+bE9mbIgkJdaYOF/MQP+Wj5EkoK9bw1bTy6r5bY9ERiJ6IMroP+6w3SsRHPVTz3MrwaRDkslWNQqtUALnFbO6qz3O/XD7755tZedsVQgjvExJi5cOFcpeGYig0+HNUHM9dPlFYpVmMJYYNeJSh6DPKg2hSdPWQLp5VvUwVF0CjES/ICc/ctkVZn9vGyl7Q7huKCY8ZiNRnJ2jwDa6nhplSx1UL2U83LqgAAIABJREFU1lmoI1oQERFB6tXLvztxPGTIE+zdq5T+7tUL9uyp+pi9e5W0bNnK6ThHpKXBnDk2g/7ii0irI4Cff4b58+G55yxuV01PPfUIA+LjnKQAdm3bQouIQHyDwpD7Blcpo/vUs8MxmiyuCpUfDHVSqNS064cZOaaca2ibdpVa9tl+ayvxatgJsz7LrUyE/quF+Plo6dm5tduiqYK97+Lv50dEhdnur2zwfqfgjjPoUEksfelTFHy3E9+o/tQeuYaAns9SlnWJ7G2uei+5Xy4msNdzUqw97+BH5ZWaEYQPmYO2SVfyD69Go1ZR+O1mgvqMxqdlT0RzGaLJaGu9VQHZu+ahbX4/qhqRbmmJObvmoQpvyHvvL0UQBOQyAQQBTcOO5H7xLoF9RqIIdN8dJnvXPLxb3I8p+4rU2chwaCWfb9oAVG5sq7vc1+tt/7Yn6N5//z2X/SqGEio7V3ExlYZjZDJZpaGL8c88RMHBj8BqRdv0Zi9Le7ciR2qjvHlvfvz5DF7dnqE05TSWonxKU34mY+0EaSK2FOVT+N0um25+ubG3S+8qgyKcNG9828Ug8/IhOGYsWSXCH4rVjhkzkb17lZw7Z/t7wAD44gukvyvi3DmbQZ83b5HTcY7Yvt02MVecsKubcK34PKur4yOKIrm5OrzqtaXkwnEyPnmpfHzfRB4QTsGxjWRsmGxTsdy3DGtZiS0kWa4fn/X5Gzap6wFT8WnVHdFkJOhB19yJT1Q84TVqkXToMMX6XFtitDxnVJpyGmNpCXr/Rjz6+FApMfruu+/Su28/0oSbCVN32/5tuCMNujtP761XJlP2wzYyyj093VdLUam8CHrgBZfjHYWu7PrbFcWFgvtPpMRYhlfjzui+eh/fjrFoGnTAUphNcJ+RLuf065xAyYUTFCV/41Yf27dLAqIVLmUVEh0Tw6SpL0vxeIVfGFn/fZmSSyfdd9HpkoAp9zphj75lm2wOraZunTqSHnZlxtbf37b8rgqZmbb97IiONrFx43qX/YKCfG77XHY4GhbH0ni7xyiXy5kzezYlhfnE9euD8dLJqkvO9y/D/94npGRbWMLL1Hp+BZZSA3kHbSGp8IffpNbzH4LCi+LkE2R8Mrlaq7XfE6t1ZP40aRKJ2SwydaqCjz5SADB5MkyfDsuX27xts9n2/2XLYOJEMJtFduzYzLx57/Paa1pWrlQ67bd3r81wV0R1E67unuet2ry99fY8ovvHUqb0JuD+p/AKqYOYm4Ju/zICuj1JjSFv4xVSByE/Fcu3G1DI5Xg3v8+tmJmd4RI22H3jDW37/lxOz8VsFR0aiog39fUTXiY4eiy/pefz3sJFvPvuu0ya+jKhg14lsK8tYWr/TTlu+zcmUe9Ig+4uSTV58mTystJ5sHNL8vYtRa32InCA+5ifb1R/Kcno2zEOw89fUXL1J9KWPoXhux3Oy/TosWC1kr5uIkXnDlaqf+3XMQ6Ffxiq0HqVNlgAEGs059D3v0hsHFv4pL+Nv1sZpTEqzqaBXt6xPmLEWrKNcumFrczYVme5v2cP3HWXLd4+YAA8+STo9QWoVIJTsvT/2Dvv8Kiq9I9/pqX3QgtNkSadgKiAq/SEFJq6ssCuFJEOSpGq4iKwWJAAIaEo3UILCURpQQSRqiIoIog0AwnpPZPJ/f1xcidzZ+5MJoj7W919n8cHmblz53LP3Pec877fYl1KsHcuCx0uRdhLLJYhEDCHCYicjt6vjjoBa8/buNZqhHeHqIpmm1h9a/Uu1HzmdfS+NfD/yzCyDq3FlJuO/xND0bp5Q7lJdWdlvVvLO7ymWj6yas3olSuL6NYNEhIkJkzwYN48LXq9F5cvt2TcODd69YIXX4S8PFi9GlauLCIjYw3Tpk1gyZIYgoJeYMoUH/r00TJlig+Fheo7LWd3YJmZ+YrXnGlOW4IG7myegefjz+ERXI8ag+bhUuNBYfDx0GMEBAaRlX6Hc1+fxi39e26vf8mmbGg5TmChxWNRsgEJj6ZdrCCPPyl2Zm6tw1j4ryVMfWUmGhd3tJ4B5sb4wS/PoPMOROcV+F/dRP1DJnRAdaXn4uJCYsIumjRtistD1q40w22Frs4m4hMaiWQykbbtdfS+NSk6vcNmmx7YdwplWb/iUcEilc95w4o56N0hCmPaz+b3rWGJXm17U3jxC2qPiLVZcbrWfVgFjrlLKXRkhxVoL9k6s91PTIQvvgCDQST1/fth3Tp4+mkwmQo4dy6eRx5pTZs2HeyWBORz7dkjvlMt1BKLZVgmGY1Ga3+30rEf2txUcj+ZjUezrhRfOk7W1hk23qOG4AdIT1hExr6V6L38MWZcV91ZKXdr7+Lu5uJ0480R8mfKlDIWLzZRVFRIeXk5Wq2GVq3aAKJJmpgoVu716ilLU9OmTWDChCmkpeXw44+X6Ns3EhcX6NVLNFhXrBCrdnB+BxYQ4KV4TRXOaJVgvTpEoXP3JjBsInrfGmTuX4Xv4LfNuyJDcEPyTnzCpg+EDl9qaipFRUVoPXxsJuKgqOkUXfqKzK0zRMlmxz/x7TKYoktfin7V+YOYCnMpvfOzlcn3ChsugRZAo8O9YTvubJlBacYNTHkZSGWluNZpTkbyexT98g1ZB2IdTsx/1vr7Hw7l4kxY64xkHojDv8cLFJzbh1Rejne7MDIPxBPc7xXcG7YTzbjD71N37HpyPplNDRcjl3+5jiGwnrkrb+ma49W6N5n7V+IdGqlgDmbuW4nG1Qv/J4ZV6sQc3YrGzROfDlFk7oulxsB5uDWoXBXdXPl3vNpHUnz5JJJULmRND8bj320kBd/tRyo34d0unPzDa6hVsyZ3szMpK86ntKgUPz8Phg79O9HRg3j66UgblAsIdMSCBQIdER0tEuudOyL57tkjtvX/+pd9FuPs2TB5MsTEeLBkSQzTpk2gS5dSSkvL+OoryM0Fd3eQJMGItFcCuHULpkzxIS0tR/V9GQHj3vwv3N39L7u7FVmzpEurBznz9TdsfH8tGzZuZNfBL3G3YOLKYmw1XEr59U66Q2chWRNFkkzUuH2SH85/65RUqzPIn/h4AUns3x+SkjQkJkrMnQudOqkfv2aNgaCgF+jduy/PPdef3r1LFKiWvXvFuM2cCadPV41iWrkSjMYIdu5MNL/m6PlAkvBq24esg6sJ7jcLtwatK0Tj4vFo/JhC7C7zw1d4bfIo2rdra2Z9ynaOaizuvC8/xM/PD3c3N9JLdARGTKX46lnyziYS2GcirvVakLbtdcpy0mx0ktJXj2TiC/9g2YpVeEdMF1pAG1+mLOcOoFFoAZXevcaMKRNYuFDdCdOea1hVbmP/KeEI5aJ77bXX/s2XIyI+Pv61F1544Xc5d2BgICOHP49LeQnHd6yldq0a5KVexa1lTwrOH6T4xgU0kgnT3atIWj2Z++MIjhbGy7o6zcn8/ii+vSdiys8Smtxtw9C5++DZshtSaTFZKWvxDo3E/y//sHhtHVK5CUNACAXfpxDcbyam7NsUXj6J3sufgu8O4ttlMF4tnlRerAT5p3bi1bE/xouH0adfpF5IHQrvXMOlZQ8Kzu1Hn/4jc2fOIOXQHvr2zmfqy2WMHQtPPGHk3LlvefPNj5g0aQaLFp0kLw9q1izHwwNSUyElBS5eFA/+5s2wZg18+ql4rVkz6NxZJHu1qFFDlAVu3xbH5uXVYejQEcTGJtGqVTlTpgjUhQwI+egjeOABqKuihP/xxwY6dRpOnz7qGb93zx4cSviIX77YhXujjnh3iEKj0VB87Ry3N09HAlxrN0Gj1VEmaUj7NoXN69/n+VGjOXX6DF49J5CVsha9fx0Kf/wS94Zt8WzWhdvHE/Bo+jjeoRbn2zoTAJfajcV2X6sl9+R2giKnknkuBZfyEh5/7LEqf2fDhj3HhAlF+DgwW6xVSyTVUaOgQwdo0wZefx26dkX1czVrlrN48Q9s2/YhCxYUEx4ujtNqxZ+hodCqlTjHkCFiPFu1EmNlHRcuQFwcXLt2naeffo6AgABA/fkoTLuOd49xaHQGcS8ipuLWoHWF+mEMAd1fEBDSsImiBKLRUo6Ok7s/4GDKYbINQRT8oO4zAOBapymFP32FG0Zy8/LQ+Nam4PwhgvpOqTCcrknJ9fPknthGcNRU9L41rc4g8cXODegadsA7NFKUYOq3oujnswSFTzJfExotxTfOUVBQwKgRzwtTDouw3Al6tY/g9ulPufjNSea+/obiNWd/A/8f8frrr6e+9tpr8Wrv/SlX6NZhMpmoXa8+Gdl5BEW/gnuDNpTevUHW9nmUGk3o3L3RuLgrjAwcYZtzTu2k6NJxhTRtzskdFFxIodawd0jbOrsisRw1Kzfe3jwdz2ZP4NMxWnEuqdxExtYZ6PLTSNi5XdVzsn79+qo4czlkPZBPPklk9+4dbNq0nqysfAwGQUbx8BBb9n79KnHOFy6Ihpwj7DOIlfWECaJMMHGiF1Cu0CnZtUs053JywMtLfN/8+dC+ve31nTx5jkaNGjkcp9lz5vLusuW4BdfH0KIXxcc+IDK8DzsS9uAWXA9Dy14UH/2A+fPmMPvV+egahFJaIe9acOEwmfti8WjyGMaMG9Qa9jZlWanKnZXKbi3rQDxBFrs1Z6VadTot+/ZJZgihWpSVQe/e4h7JsXq1WLWPs60AUVYmGqADBmgYPdr+synzBwoKBA69Xz+IiKjcgVmu5L/7Tqz6ly5dbve+2zPXuLliGN6hUfg+OkjxevG1c6TteIPRI57npSmTad2+I7qGHRSWfHeT3sK7Yz+F0mfm/jj03oGYLETZ5PM54hJI5SZyPpmFZ3kBuZKbXZVMWQK49ORHNhaQoC4pXB0v3f+E+FPh0O2Fo5rYzz//zLHPD9OmZQuMJz8y23zVHP0+9cZvwL/bKErvXCF91yKg6h+X3KSUmZtQocVicCP/7F4CwydhTL+qbHy2DSfvbKKNE7tGq8OtTRie3t4K1IclndsezlwOGUWye/cOevfuiySV88wzGtauFavxlSvFCn3cOFGCkT/jDDtRhjXWrAn5+fnm6zhxQpzPxUUk+337xPdERsLcufDllyLhr1ljYN48DzZt2uYwmcv/7kUL36QwN4t5E0eYG95bt2yhMDdTvHZuB/PnzWHe/H/iGzlDMDwNbmTsXUbWwXgzaxepnLzTiQooataBVdQICkC6eAjPVj0h6wamE5tp9EADjKc+qbaj/L0if8LDlQne+niAiAjHC62+feHQIThzRpTUysrExNu7tyAplZaKenunTlU3pB0hXrw79if35A6z7IQcGZ8uw7P5EyQk7RF2hKe+ovTnk9ze8LKZCe0dGkXeV9u4vXmGmZXq/mAHYS5i9WxZskmhUqYgz6Kur2/Ri5LiEvJ/taO1tPdd/J58HveGbW2UJ+WwtvuTMfhIkPrBJPLPHzJj8P+INfU/RUK3FmtSw6TKwlvlOank7lZi0zP3r0SjMxDQQxQj1X5cN5YrfS+92vYm5+gmG9ec3NO77Li7rMKjaRcFmcZZ70lnSD1hYUY2bVpvbtKNHi0pmnSjRokHf+HCyqaaj4/zsMY7d0CjEd9z9iy88YY436hRStz56NGiJj9/vljR6/XPEhXVn2HDnnVazMsetFF+bfX76xUyvGYRKAtpWe/QKPLOiglXhqIGPDGEOiF1mTV2GK7nd/HZ3iSy0u84FLhyFPeK/LHE/lvHnj1gNDo30RqNUFQkdkPjxsGOHfDXv4pJddy4yp2XdUPaWmAtLKwbujpN0Hnb+vz6dIhC719bsXgBIRVd9ONRNr6/FoBmzZpx7sxJglyMZFX0gHwfHUTI+I14NutKzrGt+D76NMVXT+HZrKsNAMD1gfaUZd5SkPrcH+xA/lcfK54TrQbxrKpoLXmHRpL9+QfkfXeArP2xqs+UGgbfUvcp+9Bqli99B0mSbHKKHNbaQs1bCYkK69f+PyaCP3xCf+edd+gR1he3rs+bRXw6derE1Fdm499rjBmTOm78BCL7D6KsVitKJK15xVF87RzlRXkKSzs1xqimJI/i09srce77YvHz8SLvTKKZ7JO5fxVSWYkN0y1910K8vTxxvfZltXXHwXlST1ZWfpUr+b59RZkERKLZvVv9WBCJf8ECsdobOlQ0PletgjlzRNJw9D2DBhno2vVJEhN3YDR+/JuFwaDyQVr2zls2qyxrVm5WylqBZ7YIj3YRXL1bgEajtTtZVIchak0isg57yB97eP0LF8QE4OxE6+OjPI+j79PpygkO9lEVa1u7FsLanCZzy3hyjn9C5rrR5J+u3EF6tws3T45y+HSIwrNGA7Zt32FOXs2aNeP6z5cZ8tdBlH2rnExDRq+h7PynLJz/GrXLUs2aRmnb5yOVlVLyyzdIgM6nBpn7Y0Hvgunn4+zdvcP8nMyfN4eCwiJVBylxTdHofYLJ2reSJQsX2ChPytcp70i0TZ7g1urRpO1cYNZ90vnV5uVpM2wE4GSIsOXisW9UPyL7D+SXAj1/H/kC10s9za9dK9TTOrQjP/74o+OBvM/xh07oKSkpTJs5B/fGj5H/zaeg0aB7uCdnvj1vph+j0aBr3oO4Nesstuju5hVHVspa3BsrjQzSPnkV9yaP4d74MXK+3Ir7Q4+AzsDp40fp9UgLsg/E8eLI5/ELCKRJ/Tpoi7LJOrQOqawUyssV6ojF186BVI6pTmvq1avPE088AVQviTi7tTcYICnJSPfuthA3OSy3+507i4SulpBOnBANz1atRGNNhjPWqQPl5aJe6yjatzfy2WdJv0nMyzIsH6RX5rzKiWNH6Ni4Nunb37A5NuPTZXi17kXGZyu4Gz/CXN4quXGB/Kx0Fi95S3Hee4WvNWrUiE2btjFvngerV+sVZKDVqwVCaOZM2x5FQgLUrq2xe3zPnpCU5Ph+7N0LtWtDt25Vf9+ePWICdiTW9uJoE28tKqHo5HrmTptE4clPuL1xqrlU4t2urzD4sNilGlr1Jn7tOsUq9siRI3y8bTsFhYWKRU3xtXMUlRqJjV/DzZs3canTjOwvNiBJEm4N2oJOj0fTxym5cR40Wjwe7EDNWrUUiqKr31+P7gElnj19zUhF+dI7NJKAwEBeeukl1d9ORPQAIvoNMCNyXGs3RevigUvdhwUzOeJl8iRXM0bfEtNu2VB1b/4XLv98FZfQAZTevoxH48cpSf2Jn678jEvoAEpSL6OrH0qPPn3/rYzVP2xCl29u8EBhuiyVlZC+ayFZh9ZYsM0gc39cBbttiMLpJfvoJu7Gj8CzUShlmbfEj/e7g6TvfBPfzs9RcOEQhd9/jkfTLhRdPoFb40fp//Rf+enKz7y/ehUbtnxIultd3NzdGfZ0FJKxEK2LO8H9Z5nVEe0x3eyFPa3x8PDIKrf2CQkCYbJihahnx8TY1s1Bud0/e9ZA794RzJvnQXy8zpxg5JLKwoVK7ZCQEPF3Z2rvx45Rpb63PZkB61CTzI2IiGD/oc8JUHWOCifv6z241GpCcKAv9XK+I3PTFNJ3LsA15GH8/QM4ePAg9R9oRET0AHNCUnutqocxLCyMJUtiSEiQePHFyhr27duVNWzLkLH/P/4oMWKEOH7CBHFP580TUMR9+2Dnzqr5A1euiOOq+r69ewVs8vhxAV11NCYDB+i4desaZ08eF+WTA/F4te5F9tHNeLbqTtGl44pdqm+Xv5nHZNz48WYhLxkTLi9q0hMW4VqvFdd/vYPJqyYeTR5DMpZQ8+kK9VI0GNOuIZWVml2QUvPLeefdyt+HGkM89p3FNuVLWRJD7bdzLauIUp2HGV4ZGD4Jrbs3v8aNttBoUvJE5Jq6JX4/+/AHuIY0V5xH5+GLW92HFa+lFfFvZaz+YVEuzhrgZh5YRUDPF/FqKQqZsuuQa8jD1NIX4O8fwPeXf6EkNwOtwQ2dVwD+3UeRnrAYnYcfprw0agx6VSBV1k+h3FiMVJhF0IC5Zt9RclLJyS/ArWE7gvu9gkajJf/8ITIPxBHQc3Sl6pyD7nlycjJDhgwiLMxIWJhRoZq3Z4+esrJyFi0qsYtymTlTUMmtV2cylnzePPFA798vsOPe3lBeriMhYR8NGjQgJuZdBTomOlokb7VwRkGwXz+RYKpC0DjCpsthPdb55w+RuW+lXad42U/Vo2kX+OUkkV3b8dG2nfhHz8S1bgsyNk6mJOs2JklDjYFzcK3bguyPZlJ4+womdObXrD061UJN6fLECTEZ9u0rdkQy8iQhQSTXsWNFIu/VSyRvnc72M19/DcuXi0nRmj+we7fYJel0rnzzzQUuXbqk+O2oIV06dXJe+VEeExltNXP2XPR1mhE86FWQJLK/2ET+13vw7fwcPh37AxXqpQdiKUdrvn+3N0+nPPMG5ZKG4AGzxWubplOWcQ20etwf6lSlB7Dxq43kZKSbr88SASarR1qjwix3vGp54u7Of+LfU5knsg6sQucdRJ2RsYr7kblutNkv1hK/b+gwiLu7lzjlY3y/ETN/GpNoy3DWANejWVeM6VfNEMOby4fi2ao7fl2HkrttDpFd27F568cEW8ALjenXqDFoHpkH1+BS4wHFgKUnLCag+0jlj2F/LO5NH6fop6/QBdTFp30EWYfWmA11ZWf4nKTFJCfusmEi2pO/lUMkZVdAQ0SESfHQOkNWiYsTSaBfP5EsKkkqOj791JVNm7bRpEkTs6lxbm4e69bZf/BXrBDlHUc0gm7dxORRFaSvTx8tZWUm+wdhO9Z3d//LVuY4cQl+jw7Cs32lDG/Osa3UePp1BSyt+No50ncuQOvph2tIc6UcsorEclUPoz1ykTWk02AQJa7hwyvvq5xgwdambsUKKCkR9+jIESF6ZjCI/7p2FRj05ORKOOKVK1eIiXmXLVs2cvduLr6+0KOHEqravXvlBFKdMXHmWctPXoKvjy85vo0Uz0tO4mK8nlI+L5z+EAmJnKIy9L41CAybpP787lyAn48XGWlV1BsdhDPXnpO02KwFr2ZHWD/3vJloZAnx9OjzEmkfzUXn4UdQlLql4u8h+/unhC3K3WpHdVT/biMI6PGC0hzikQGC3anR4NFjPLsPHSfYCl6o0RlwrdeS4OgZGO9eU+hT1Hl+ma1QVNchBIZNwj24PuXpV8nct1JhqJt3OpG8fcuIW7FMdWCdgSVGRJTTo0dvfvihCSNGiNXdiBFi1Tdxov1kDqLebTBUIlJu3xbJJjnZRFZWIVFR4bRp05SMjNUsXZpHWZnjkkq/fmL156gk4OJyb7R0tbBGJshN69ubppsba8MHD6JB3nmyPnqlokEdi2/nwTbSsBn7VuDe+FGCB8wVje/NM+yKdslbbUexceMGMjKMDBiAoncBlciTDRvE/Z87VzlJylo7aqqJBw/Cs88KeYC4OBg4EDw9BarlxAnxmfbtBbJp0qSxPPpoO2JiViIv0LZtUyJd4N6lApxVZzx8cB9N3PLJ3FophBb0d+XzUnBkHds+2sJXR4/QqmkjpJzbpO+yZXTe3fsueq3Eto+2Kl6vbs+jqmuX3azUkjlUCIdZNEUtIZ7l+VmUG0tU5Q5A6A4tX/rOv1XD/Q+b0EHc3AMpR+zUUfuSdWgtxqxfzbotxdfOUXAhhfLiPLKPbFTVgc7aH0vtGoHkbZsDQK2hb2MIblilUJRGq8M3bApan5oK/LlXG/HdHu0jFc7wluEsLDEpKYHmzS+xdq1Y/a5dC5GREitXKuvk1lGzpmB83rolECojRwrmYUwMrF8viEeLF5sYObKMkJCqH/yQELE6nzpVNOMsm3urVsErr+gJC+tbZd0/OdnA4MFDHR4jh+WDZAgIwbfzYIx3r5F1aA1aNy9Of/MdC994jZL062QdWoO3pyfGb2whPK5SGUFFNyg+tJKAPhPsShbn7V/GymVLHT6MycnJFBYWEBBQicVX613IssLWIWvt7NtnK5kgC2+p4f3l75g/HzIy8m0sAT081MfPWW12tTGpSp3xvRWraNSoEae+OsaAbp3sJn55UdO4cWOWLPyn8ADuYbvV8w6NpGHDB82KoqAOT1Z7rbrXbjJ44lL3YaBSitsS5aN7uAczZ881u2d5hU8za9r4Pf4Mpbcvq+oOeXWIZubc1/6tTdE/LPVfHkj/6JnqVOPaTSj88Rh5Z5Io/PFL3EKakXtqF271WlKWl0HxtW/wfewZxWdyd8wjfvlS1q1dw8VvTvLdvo/Q+IWQc/yjCpEiJR1Zq9FiunqSsqun0NVujiEgBO/QCPNxMm06KPJl3Jt2sUspnjlzNmPGiCRrLzw8RPJ9771yBRW8Y8dKKrg9OnlqqpgAEhPFg75kCWZK+aZN0Ly5kv5/9y78/LOgmVvGrVuwcSMsWiRILS4ugqn40UdicvnsM/j+eygpkbhz5xbff2+kTh2Jhg1tz/Pee/DZZ+UcP36a5cvf5tatGzRp0sxMT7cMa3XA4mvnuLt7MTUGzCag54sUXTpO2s1rbP3wQ/z7zSKgx4tknz+MtlYzPBp1VJxLAmpp8+gS2oJvktZTdOeq6tgiSVw8uleVPg6iTBYW9hSLFhnp08c+Pb9rV3GPDh0SK27L8PERjWy5rm45/gkJ0LSpKMPIWjxq33HoELz8cjkhIZXvZWXB5ctCasAyQkJg6VLHUgGrV7uxZs0GxThUpc5oqNXY/NsuLS1h7utv4NFjHHrfmmY7SLf6rdG4enLx6F7yc7N5skdvPvzoY/wcPL+WMgzVoeyXFBcTHtWP3j17cO7cOYfX7lK7CUU/fE7RmV1IBg/uJr2NS1A9TFdPY/rlFEZJQ+a+WFzqNifxk83oG3bEKzSSu7sWovevTeHFo/blDmo3If3sPjx0Jh5//HHbG36P4Yj6/4ddoasb4A4jz1L9MDQSyVhCy2ZNKLl+rtKQVmfArUEbm3PKK43Dhw+bcaqOGKOe7SPM7jlqLvZ3k97C44F2uNZtYaOQaBmWsMRbt8SW3XoLf+6cOnYZbPEH3yi6AAAgAElEQVTl1pGUJFbQjzxii3JQ09RWU2m0Xinu3y/o56GhokHn4iKuYe1a2LdPYtmyAqKiYPFieP31SgTN3r2CfBQUVHlsVdh067G2dCiSiUWSu695chdjH03R5RM2nABtYH2+/e48W7Z+TFF+jt2xtd5qW4dcJvPzUx8vP7/KMUlIEBLFatGpkzruvHt3odNSlYFFdLTtuD/2mHjNuiQWEiIapDNnClav5c7KEaNX7VmzZjvrHu7Bm4sW20y8lkbt7m3D+f6nq0ybORuXBx/BWGHULZ/z5sp/kGsBi7R8XiyvwdJL1hpeuPBfSxQr9rETJqOpH6q49gyra/cKjURrLCLrgEDD1fjrQgz+tXkw0I2s/bH4df0bwQNfxeBbE5+cK+Rtm4NHs66U3PzBxinrxvKhSuP3jtG88eYi9QH8HcKppqhGo+kDvAfogDWSJC2yer8+sB7wqzjmFUmSHG7u7mdTVNusO8XHPmDlsqW8OH4SJs9gvEMjyDq0Fv9uIyn7NtFGq0H257QMqdxE9kczKU6/hn+/2WQdWmvTfMv4dBle7cIV+hRZ+2MJHmiLuMg/tYuiMztxCaiD7uEeFB/9QJVEJDfWWrUyKpAOlgp7CQnQti3803beACo1V3bsUL4ua7Z07iyQE9YoB3uNMkvURWio2N5bNu2sv8MRyuaVV/S4uLiRnV2Ai4vEW2/ZP4+a5ov1WBceWUuNmrW4UygREDFVvaG2602k8nI8mjxG8ZWT+D3+LLqgBhXSyI9QeOUkHo0ftR3b9hH4VIg/OdJ1CQ724fnn84iPVx+vPXtEWUpWW3zqKZg2TW3kxASg14uJTg5nfEbl46zHfcUKscv6+mtbpM3evaJBHhgImZkuFBaWVXi/DmXChCmq8gz2nrX3Vqzi6t0CtM27U3z0AwIDA8nyegD/PhMouX5eGLX3n1WhgvgKGhd3Sm5ewK/rEPLPpyCVGdGYSvDp/Dcy98ei9fCD0gLcg0JwbR2meF6q29yUUUpPtn2IxL2foQusL5RMD6zi2YH9uHTlF8W1B/j7k+3TqEqNF8O57UydMpm3l75HuclEenYeOu8gfEKjyNwfi8HVDZPWBa27Nz4dosg6EP+bG7vW8ZtQLhqNRgdcAnoCN4FTwHOSJH1vcUw88LUkSbEajeZhYK8kSQ0dnfd+iHOpQZguXrxI995h3L6Tjn+P0Xi17Kb4jKVOCxIKk+KSGxfI+HQZpsJc6k3+kLKsVG5vnYVUVoJvp0HkntwhJHG/2obW4Ip3aKRgjHb9mxm+ZRnWUq/2jIevXLlCaGhLysuLWbjQfrKbNUusrNQecFkEasMGW4GmVq0ETPGzz2yTtyMom4zUkB1z7MEYwbHglCwJK0nlVcrNysdaC0lZj3XXrl15fvhwtn/2BcH/iFFe94phlBXlUfOZ180yry5FGWTl5lFjoICbpm54Cam0CK27D95tw8g6GI+flWSxvQkYQKvV4OPjeJKbPVvUwqdNE70Fe2N7+LDYyVhPdM4ihazFvyzRM5ZIG19fMYHLTFJnIKNymEwmxk+YwNoPNrAufhVDhgxRvLY2LpZHH33UnHTzMtNwrdtCgXhJ2/4G3m17k3P8E9wbPUJJ6o+U5dxBo3dFg4T7Q49Skvoj7mX5eKkYdTsSEctcNxovFy35gc1skrJr579jTL8mellNu+D56yluXL2igDrWrVvXKSSPJWLlp59+4pnBQ/nx6g1KjUaC+k7BrX5rpHITeWeSyD21E0N5MclJif9RKJdHgMuSJP0sSVIp8CEQbXWMBMjVW1/g13u92OqEmuaHGv3YMjL2vI2bly9lOekKXZW7GyaZiScaDeR8PIvsLzYjFefj0egRco5/hJuPqCu6GPMY3r8XuQfjcavXAu8O4nbY24qe+fob8ypPrRvfqFEjnnqqO2FhjrfX4eH2yyp37ghdclmgSSarrFgh3HG++kq92emoURYSUpmgo6LUj5HDkeCULA6l1vy1LjElJRlZty7ehkFqPdZHjhxhR0Ii3t1sxcC9OvZDo3dB4+EnqOd9JlOMAZ1XgNnRxic0ClNJoRDoOikEuqQfU/Bs3ROybsLpDx1KMnh5GVR9PuWQx8vTU/w5ezbMmAFxcUqGaGys6EloNC5m2zr5/epo7ViG3FCVx2/HDjE2O3ZUIl+qMhyxjiNHjrBxy4e4NX6Md2NWmlmh8mtLl8eKpmiFk5i/m5Y6ptsKeYbAXmMrSTcVpU+dmzeYysyEPK3ewJC/PafKoK6Kherp7UUjl1wb4S33hm2FrWSfiZguHmTLhg9smNrOInksE3Pjxo05/dUxno7oiZePn42NpYebK3ErV/zHoVxCgBsWf79Z8ZplvAYM0Wg0N4G9wAS1E2k0mhc0Gs1pjUZzOj09Xe2Q+xKOutp+nQYQ4OmC4dx2s67Kc08PoCgzVTA6wyeh96uNLv82RVdOUKOCyWbwr0PDIC9czgkFwGeeHkS9enV5wKvczFzLSVqMl4sWr1+OcHvDS+SfP0j2gVVsfH9tld34I0cOV5k0IyLgwAH19/buhbAw+w9vTk6lJoulLIAzrkb2LNAsw5HglJw8rDVp1BAcK1aICcCR1ktVTTqfDtEY/GuTtmmq2dGmuCBP4WiTd3gN/p6uNgJdrt/t4tM9idy9/atDSQZJUvf5tAzL9zt1EmWrPXv0Znu5SZO8uHy5BR4enpSUGDEYXLl4sSmTJnnRp4+W8nKDQ60dEGNXo4ayhu/ufv8go6DO1H1++HBVvRM5Uaal3uKH775VwIotRe80Wh3B0TPQeviavUbl3seuxD2q1xAeGUVJaRnlJYWk7/6XmYWatuOflJcZuXrtJn99emC1krJlOIPksX1uq/+Z3zPuV1P0OeADSZLqAuHARo0sFG4RkiTFS5LUQZKkDsHBtspu9yOc8UvM13gwdcpk88DOee0NhZ9hUNR08iR3s8uNrN734+WfzSvtyP4DSXevh6urK7PGDoMzH4JkIts1mOvXr+FSpxk5R7fi5l+LDRZwJ2uxHzmysgqcSpq5uZV/l1e30dGwdavYnqvpt8ird1mTxRJad/OmqH/Pni3eV2uU+fq637NBtPxeQICXTfN34UJ1xcYxY3Co9WKvSacQlAqNBODulhnmEpvMC8jZ8xarY5eTkXb7ngW6CgvLnBqvoqLKv7duDYWFJtLSckhMTEKSymne/BIxMQXs2ycRE1NA8+aXkKRy4uNX07//0yQkOJ5sd+6EJk2UkMb69R2LrkH1IKPONiStG/7WsGJL0Tt7vI7sg6vMtnaW8ffhIykuMaLR6SvM2u9y671nhXCeVov7A+2R0PL6G/+8pwTrTN6wfm7v5TO/dziT0G8B9Sz+XrfiNcsYAXwMIEnSccANCLofF1jdcLYjb/njS9y5DenG19ze8JJdPYfM/bG8tWiBzWrl6t0CvvvuO4qLi/GNmkVZfjbuTToT0HM0IS+uwTfyFad+/M4ScQwGkQy//FKUUi5cELK2MrLuwgXxuiUuPSlJrN7tyekaDPD445CYqGPCBA/69NEycaIXP/zQFNCQk1PE6NHqk4Ucjgyi5eRhKTerRqaxDEdaL2qaHsvfetNGUMq/x4vgGagwGvZq05syid+8croXLXR5YnPkRTpypJFnny1k3LgRmEwfM3GimGzj45WTbXy84AE8/zy89JJybGfPFn0PRxNBcrKBCRPsSxpYhj0NcUckLEutJfk4WZte71eH9ITFNt+T8ekyXL0D+fqbb83nkEuUZWVGsarvP4vAsIkY/Gqj0erR6PTm1/R+tSgqKr6nBHsveeNePvN7hzMJ/RTQWKPRPKDRaFyAvwLW8/91oDuARqNpjkjov19NxUHYe9gd6Y/37NmTrLRU6npC+g51+OHIfwxl8uTJikEsuXGBgrwcdnx62Pwj8unYj6LLJ7i9fordH3/hkbV07tRGIcIlSQLP7Sj27BEQwfHjPXj1VZHE27WrFORasUL8XaMR2/tbt+SHV928uUULwTidNQtcXSP49tsfycwsIDExCSinefMfWbasgP37xerdYLAV+4JKsSi177BMHpZys2pwSeuwZ8wg1ztnj/uH2QRj2LBhCkEp/24j8WrZzYb9mZWylsCIqb955XQvWujJyQbCw6N45ploSkoKmTjRVhXz1i3YskU0SEeOLCM8XLxvNCoNLE6cEKqMzzxj+70hIWJSnzbNFp64erXeacMROe6lvmwPVpx5IJ6iKycJ6D7S5jze7SMplTS89e5SmxKlu7snHk0eUxi3a31qKDXw24Wj1RsU33l37QtOJdh7yRvWn5FLrjXTT5s/U/D5OvQ63b9NG91Z2GI4sBQBSVwnSdICjUYzHzgtSdLuCmTLasAL0SCdLknSPkfn/D0t6ET3fSJr31/PutWVHfn+AwaSlPwZixfMZ1oFhiwlJYWxEyfTLyKcxW8vVRV8yjm5k5IzO8jNSOPq1auER0bz89VroNXi/tCjCr2W9F1v4vZgR3S/foPGKwh/q258WuzfMZBHZKSkEOHavVv89/zz6g+pjHLR672IiIhi+/YtLF5sH2ExY4bY8t+9Kz6nJg1w65YgFqWkgNGoISBAnHvnzu0sWFDsEG0TEyNWhHv26Ni504QkieTUt69SjGrfPle2bt1JWFgYUClClpVVeN+0XizDZDLh6eVNqaSl9rB3FJaCaTvewLNNHwK7jaiW1ZwcV65cMevdZGbmYzBINvoslvdp9uxKgTIB33RFr9fSu3cRkZH3bvy8erW4t3Fx6ugkGXLatauwBPzqK9Hf8PAASdKxbFk8w4cPd/rfDY7LC9Z6J6BiRL0/FveHHqHo8ilzzdw6pHITtzdMoXenVhz6/Ahe4dNs4Yf+dQiKVNdNSd8xnwcfeIC0Yi2GFr3IPBBH/bp1CAwM4mpGoRmiOH/eHFa/v57dO7bRuHFj879vzIRJ9I+KYP2mLXaFv0BMVvJn5Vyzet0H6LUaXBo/xkOu+Qx+dhCLlrxNQX4ehkaP3lfj6T+lOJejsHb1/tebbzB42POkpd3Bs1kXpBvfkrDtQ0aMHkN6WhpS8EOU3Lzg0Bn+9oaX6NWpBS9NniyaM8Zys6rcnS0z0fvXMTuiS+Um0ne+aSPEb8xKJXPTGN76V5ndZDl1qlh5PfusLfywXTsdbdq8yLp18UREGB2KY8XHi8bo6tVQr57t+9bqfnJycUbsKzZW1G59fLwoLi5i6lQTjRvbQuTatYMzZ9w4c+a8YjV45coV2rZtzqpVxvuixmgZY8eOZdWadXg07UJZdmrlRJuwCPdGHSm89CW+3UZhPL6xWu5E9tQwd+8WYzN2rNjt3LkjdivJyWJSbdBArMyTknSAxIIFJfj5qd+rkycF29MeNNXyvgwdqg5pvHXLVujLMpz1d7WMqmrFMjzXWplSTnbxa9fh22UIBRdSnOJ15B6Kx63x4zbwQ03jLmR/sRmDv9DdsYybsc8jFRfw3DMD+GT7Tkx6d+FkdfIjZo39O1qtlreXvsfUyROZN/+f5txw6vhRPv/8c0W+sJd4rfOK/FnrfJC7bQ5RT7Rn+67dikmpKuVOZ+O/KqFb/vgqpVLv4PrQo5SmX6XWsHdIXTsOqSALt8aPUZp+FUkC15oPqhjc9senQqcl//xBsvbF4u7hTonOw0apT5bNlDUe1BiIuSkr6fXgZ7w42v6KMy5OEELS0pTY4RYtICZGPIgPP/yQQzVEEA/28OGoHufMQ2+5ulQ795QpPjz33N+qxJXHxWlwcxvM++9vUrxuT6XQMuxh0u2FnMxluWPrida1bgtub3gJU9ZN9n+a7HQyd0YNc+pUoY7o6ysm4tRUyM8HT08Xhg8fRU5ONkbjx1WSx4qLBV+gqp1L377qY7tihTMrfD3BwaOdvq/WErTF184p+BuOSFiWny3LSiV91yI0Wi3eoVFkpay14XUUH/2A2Jj3eDdmpQITLqNZNFqtucxiGTmndpH71ScYDAb8wl9SVcy0zg3VSbyOPquWD35P4+k/pdqidcgNlJGjx5hrd3mnd1OYkUrggDlmM+HMT1dQXpRH0MC5Zvcij0YdKEm9RKqFLZaXAXK+/FCY3p4/SOa+lbi4e+EVPo0aA+dVqP1NM9fJZQ9RAc3qqNooKfo+hcgIx+WDiAiRMGNixIMdEwOurgZiYirrns54TppMAlo3ejQ2DkabNlEl5t2RlIAMQ3RGVCwiQmLr1s02EERn7Nuq07hLSUlh1Zr3cWvY3lxn9Wrbh8KfjuNfIXcso5XKJU21rOacUcMcMEAoIu7cKUhECQmC5OXi4sLSpctJStpN+/ZGu8ieUaMEuUivh2+/dXw9d+6I34RaDd+Z3kR4eBkbNjhnhA3268v+N49xa/lQck5sM9eXrT02Le0C88/tpywnlZ4dm5ObshoPP4GbkHkdsh3j0KFDbWr2GZ+JyUctmQP4hEai962F0Wi026y9V7ROVZ9VywdVNY1/r/hDJnRruUwZo/rT1RtodVoaueSSuXEy2ce24NG0s9JMOO0Kwf1nKlAPBRdScDHm0aFRbbIPxvHOvxZy6sRXtG7ZAlNOKpn7YvFuH0nNMR/gVr81prwMTIU5lN75mfSdSsPpoKjpFF76yuyZaNlcKSkocgrqVlhIBVZZw5gxBhISICurkGHDnmXSpLG4u+sdIixOnBANtP79xYrfUqFvzBiBZa/KQq4qZ3pfXw9ycvIYP952wrD+9xiN2EAQLe3b1qwxOK0rYhmWySOy/0A8m3am+No3pH4whfzzh8g6tAaPCntCGbOcuX8lY0ZVr37szMSldr/kiS85OZnMzHyOHasa2RMVJTRcHIXQhumiOiHKpCJHUbMmZGcXOm3/p9aErlevHr+mpooy1skdJGz/BMDsp/n8qNEKu8Bne3cm/+skPJt2ITUtg4KcTF6bOt7M64hduUIBGbXGd2t0enNTFCp6IvEjFNov3u2F9LUc1s3ae0HryOHos7L6Z/ndX8jdY6vcWRX+/X7GHy6hW3e/Dx48SO/wSEpN4PZAe365kUrL5k0ozrxNQK8xlGX9aqVnbot6cNFCbMx7fHnsC8qKC5g8eTKNGzfmrUUL0AIeTR6n5Nb3SjutOk3R6PT491DubQ0BIfg89jQ+FCoQGCe//AJvb1enoG6BgT6sX/8hvr7uREXBqlVG9u/HLGKl0UjExal/XsZ3v/mmrX3cqFHiPZ1OoGUchSOi0Jo1OkpKiujfX7jqOLK8k6F7ahDEsLAwTp48R1DQC2ayzZQpPgQFvcDJk+fMjVS1kH8HcvLwCp9GQNhEDP61MWbeUNWkT09cQnivHqyQBcudDGdNuq3vl2zkPGTIIPz8PJxaPUdHC6VLRzuXTz+FkyePs2RJjM2E6O3tHPzV0xOn7P/ksMTpS5JkLj8EhE3Eo2YDM9dC9tMMGjDXzLkYOWoUG7d8SPDAeQRUmLYvi1luF/evVrMP7j9bPMsbp5l30dF9uhF0+ysziS/r4GqC+lZ6iVrjzu8FrSNHlbrqny1FZ3DB8y+2Mrr/ToLRH6qGrlbHMt69RmFJWWWDcvMMyvPSCYh42ayrcGfLLExFOYSMWqU43624kfh2fg5NSb5Nlz4lJYXwqP74Rs6wX4/dPB3PZk/g01GphCA3iV78aySFBVlmVISHh5769cuYNUuyW/9es8aAwfAMu3fvtFuzPXxY0MVl+VpLjY5du6p2E1q5UtR3p0+3f4w9sS97uiNyWNffV60S5Z9+/arf4LQXlr8DNVeptO1vENh7rI0Ym1e7vpSe3UXO3dvo9Xqnvy842IelS/OqLZIl69u4uOi5eLEZX3993m4j07JJqteLY3r0UG+Oz5wJ330n+gsTJkwxOxWJ35iOsDBjlbo7WVlw+vS9jYdlXbzk+nkyD6zCVafBs9tosg6tRevhi6kgm+DoGQDVrifbq9m7tO6DqRzyv96DR7Mu6K98QXFJCdpGnSm+eobAPhNxa1BZjlFr1lYXrWMZ9j4ruysFD5hdrabxvcafpoauVscqNfhQY+CcyhJK2zAkncF8Y/NO76Y07TKBKiYY3u0jyf/2M9zbhnPhehqz58wxv/ePkS+ga9jBolwzCWP6VQVJxbttONlHNwuPS7lO/ksFQcmrJkvfXagwH1i1ykjLlhJjxqgbUsh14/JyyW7N9sQJoWndv79Am1iujseOFQ99VZT06Ghhawb25Xo3b7Z1pl+zxsDbb+sYMEDnVP39wgVRHujXr/raIWDfnSYssh/Uam7XVSpk1Cpb7HnYRHw6RFHm5kf/gYOqdR33gjm/cEEkX2H7V8bly5dVyWNq8gfr1onPHTqE2XjaUpunU6dKjH6jRo1YunQ5x4+fZdy40ej1LlWyS/fsgb/+tfrjIYdcfsjcOJn0XQtxqdUEo8ET13ot8e4QTcnN73EJbsjdxLfQ+9eudj3ZXs2+Qd73aK6dwrfzc5h+OIiXtze6hh0Fia9COdURsee3MDsdfTYrZS3uVuWg/y+C0R9qhe6sj6icdIUh9HyHZsJ3tryCR9MuaN29yD0UT3F+Lp9//jm9wiIwSWAIbkBQ+GQFnjkrZS3eHaLJ3LcC15AWlN76npq1auJi0HP95q+4NWhL+fXjvP22Y5z4/PmCDn7njkjkyckGNm3axrBhzypWhPIKztLguVcvpV+kfN4JE5xT6OvVSySLLVtsERd79ojmXufOT3Hu3BkyM/PNEqsbN37AsmUFVa5WR48WK83cXJGYqgtBtAcRi+w/EG39UEzXz+ISEIJHj/Ho/WqRkRxDSeqPhFiZ/N5YPhS3+q0IinjZjMbIPhBHWXGBU9cBzqFcLLH51ubM8v1u3741jRqdM6+efwva6Pp1UULz8fE2Y+IbNdIwYoTEvHniOiIibKVz5euqW/e37ZgOHDhAeGQ/AvrPdogocqnTjNJfLxIUNR1DQAiZ60YzbsRQdiXtVeDArcMZM2hLhURHsr4yPPV+oXWsP2to2J7M5BgkyYR32/Aqr+O3xp8KtuhIQvNm7HD8uv4Nr5ZiqZT6wSQMwQ2VcMS97+IdGqnUMz/8AZSb0Ll5MfzZaNZv2oKuQShF175FKs5D71+HOsOXmycM90aPUPjTcbza9sG9YVvSd76JZ5PHKbr8FYH9ZmK8coyeDyYz5kX7/464OA179ugpLDTZ6FHrdFr27ZNU3eDVyCiWePGICPuEEzlu3RL1daPRcelEDa9seW32QpZ0XbJE6Lfv2FE9CKKz8LIaLkZ+ySjAv8cY+1DRUzvJ/fJDtD618OkYRea+lby16E1eeuklO9+uHtY4dEvy1J49Aj9eWKgsf1lOyKNHC1KPVmti0SJxz52DGNrKEp84IXokYWGoEpRatxbX4eamLp0bEgLx8Tpq1HjRaeiidTRo1JgMjwYOHe+zDn9A3tmkCk6AIN9lH4ij4MIhPJp2vi9kG2cSv5xEndV1V0u8VX1W0+wp8lPW4O3jw7YPtzi8jt8af6qE7mjrk3sqgcJLx6g5eJH5R5axdymSyYh3+0gy98cilZvQ+9RA5+GDV9s+ZO6PQ2NwJThyGmW56WTuj6XGoHlCM/v9CZTlpFFj0DwFvty1bgtub5pGWU4aSOU2NfWiU5tYE1t0z6QZuWYL1V/BLV4sGnGOaqirVsGpU2IicFRrV0vC1aknh4WJZPTkk9Ujs1ivhuzhejP3r8Sv6xCzJKu9XVjWRzPJ//UyGr0rdWoEcu3qlXtKIleuXFHUqwMCvKhXrx61a19g6lT7n1u9WrA1O3YUBCJ5gt6zxz7WXw7r2vzZszBvHg5ZwtOni6a3o8l66lTYs+cg3bp1sz2gikhJSaFvVD/K3APRuroTGDbJZrece3In2Uc3OeQE3M+6srNRnQngfn72fsafJqE7w1i7vXk6Zdm38X30GbxDBTbv7p53Kb58ggcfaEiG0YCmWTdyvtgEGg1B4VNwa1BZnvHrOgSfjv1sGh2pH0xSsNyMmbdI2/FPAnuNsWm+mXJu/yZau0y6KSkxVnsFd/26SNJVlXuMRpg0yTHqQnbNkWUBBg8eQm5uLkbjxwpCkHVTz90dgoOF7EC3bnqOHnVh06ZtDlErluFMaS1t++v4dR3qNPsw+8hGQl5ce9+TyJUrV2jR4iGH93v2bJFgZUcn+X5t3149A4u9ewWqKDpa6W5kHaNGQZ06As+u5lhkyTq+lxW6POH69RpL5qcrKL1zxYa5eX3pM3g0fkyxgk9PWExABScA7i/Z5r8p/pRNUagQ/LH28GsXDhothZeOcXvzDAq+P0zR5ZMYDAbOf/s1jQJcyNq/CrcGbdH7BONav8Kn8tNlBPQag09HoTCVsW8F7o0fNX+Xd4doCi8d5/bGSvJAiJUio9x8c3HR/CY9apl0o+YGbx3W+GedTqBcZHcjpTCTSC5z58I771Qq+NkLGUNu6fu5c+d2kpK05qabWlMvLk74l5aVgUbTp0oIonVUBRG7m7gEQ41GeHeIIihqOqW3r5hJYekJi/Dt/Bz55/aTagVnc0Qcuddo1KgRrq7uzJol7u+tW2JSXbxYlL8mTBAEr+LiSqiobDzh5+ccxNDHR8gJxMSIsXXEIThxAm7cEJP6ihVislczPRk50sTGjeuZNGmsQiRu0qSxCnz6lStXbI7pFNqSelI6WZtfpuin4wT0sF1xeLUNo+jySYVInbVU7r+LbPPfFH+ohG7d/U7f8QaGgDoi0W6aXsHojMWr6eP4PPYsxrvXyD6ykeD+M5G8gnnwwUac+fY7agyap8AnQyXiRZLEUycZSyj66ThZW2dUbO9X4ddtBGU5d0hTUWTM+HSZuX7oUqcZibZmSYpwpEctk27y8qpvLLF3r3CIf/zxyiap9cNct66AH5aV2RpeWIaMIbeUdl2woBiTCebMcefdd/W8+aZ9TfO33oIvvjjk+B9gJxyalDw6CFPGNVLXT6Hk1kXK8u5iCKxH5sF4dJ7+AJTl3EHvE0zOsbp/+FsAACAASURBVK0E95tl3oX9HknkH//4B08+qae0VDSaX3hBJGFZf37lSoFKGj9eiW5y5BYlR0ICGI06VqwQ58jPt/+bkDkIpaVVOxZduwYFBfkKFJa1WXdycjKPPNLa5piysk+4df0HiitMYdR2y/5PDEPnE0xDf8M9mU38L+4t/lAJ3Zqxtn5tPM1reePhokfvV5us/bHMeHkyD+kzyUhcgkeTxwkZsw73hu0IippOWjEK0wqvNr3JO1uR0EMjFAnep2N/tB5+eGuK0H39MY0eaIDxmyQoNxHYy7ZAbTkheHWfREKC5jfR2sPCwvD396yW5rYlVO7ECbFKt36Yb96sXFGrGV5YJhw1jXPhNF9OdPRAbt5sVqWEgD1Nc0dRVWnNs30E3rUfoKarkayDcdToP5PgyKnUm7gF13otydwXi8HFjRoD5pjhbKkfTCL/s/d+lyQyceLLHD3qQosWYkJ7+21bUteYMaKRuXBh5cTpjFvU3r3QsOGD6PWifKJmIyiHrDFf1cr/1i2xg3j7bVT12OfPL2Tw4IE899wAu5rtCxaUoNeWoPUKBNSheu4PtOfyz7/8x7j5/DfEHyqhg5KxNmTIEE4dP8qc8c/jm3+Vg58ls3DhQk5++QUj//43ii4dt8Inx9mYVrg2bG/+AXq1VSZ4nYcvmQWlTJ/6MrHLl1GSdccsG2Ad3qERSKYyck8l4BJYF0OraKZO1diltS9ZEsOyZW873O4OGTLMLv5Zxo+PHg3Z2WIbPmOGWB2GhKhTwB25BFkaXsg66vLkYB1hYUb27t3N9evXiIx0PF72NM0dhXPGAT359XYawQPmmMej5MYFCi9+gUfTzpgkKC8vMyOTDMEPUKrRs3R57D0lEbXSgzxe8o7q7bd1TkxwlRo5ISH23aLk8tjYsXD58k8UFYnx7N7dvhuRzEStauW/axdV+qH26VNMSEiJY5mCyHJyt89S1Q+XES7BA9Uhw/8fbj7/DfGHS+jWoWYUrdPpiFu1iqK8LOp6qZtWpCcuAYMbZXd/4c7WmeZyjWvDdhYJvg+lxUW8vfQ9Roweg8tDj2It2J+j0JLoS+6XH5J//iDllz5n9eoNqrT2JUtimDZtgt3t7rp165g0aSwbN25g2zajzQpOrlsbDJWr7Lg4oQMSHy/eV1vJOeMSFB4Ob7whUBADBqgjMOx5hKrFvRCKnDEbyNwXi2+Xvykm6EqruYnofYK5s2m6wn5O6+7DDz9fr3YSsVd6sCxPhIWF4eLiVqVGTkSEkNaVk3fdutClixibsWNty2O9eon/lw2j+/XDLnFInsSrWvnv21e1lk9EhMSvvzoGTERFga6swEbiYva4f1B0LhnPZl3+I8g2/03xh0K5VDfeeecdpr4y265pRc6xLUhocKvXkpLr5/BqF07h+YPofYLxCo0k60A8LnoNC+a/zpxXXzfDtLxa9yYrZS1uDdtRfPUM+oC6eLcLIy9lDcP/PoSEpL12IUzOSrFqNEKO1dNTUOcfeUTgio8dEw94VaiKxx6DgAAlQmbAAFFeqQomN2IEvPKKYKSqwepkyKUkSU5BGO+FwOIIIjZz9lz0dZoRPOjVSn7BzgW4NX60SgmArM8/oGaAj9PICmfGS4ZkNmnS2GmMvq9vJT780Ufh6NFKNIslbvyxx0Tp7KmnKsezTx9wdbUlDo0eXclBOHFC7Lj69BGoGPmYPXvgww+rh65xdIw9pNZvwXz/LxzHnwa2WJ1wlMxBQBxT109Bo9VjzLpFcP+ZuDdoS965/ZQd3whaLXqdjm0fbWXsxMlmmFb+2b3kntqJixY8O/bHvU0Y+V8nk3NiG34eLmSkOS56O6MDvmqVYFi+/LJYlck45uho8bp1oraO+Hj49VfhfmOJV+7eXazOnH2Q1UgtUIlPl6Ty+65p7kyoJYt5s17htQWLMLkHEhRlx9Fm5wJc9Br2Ju52OonYGy9LqGZ2Nnh6itKYM6Yd1povMpP0uefUNdLd3b3o1Cmfo0dFkk5KEkQmDw/lBFCjBrRvL8put26JBu0jjwh9fctJQjYTr+o6x48XjGFHxziarP9TcNt/tvjTwBarE7PmvW6WzgXxQN+KG6mAOPp0jKa8KBeXoAYY065RfO0cJV9uIGHndnIy0slIu81TTz1lLgHk73gVrZsXbloTq5a9Tf3c8+TvfA2tuxfuetj20dYqr8sZKdbISJHA5cT7zTeVjbYTJ6qGMvbtC8ePgyTpmTFDx7vv6rl1q3Lb7ihkmByoS8La8whVi+pqmjsbanKu06dPJzv9Nq3q+ZG+/Q2bz+R8thRvT7dqJXNQHy9rqOb+/SKR169fZre+LYdao1m+5/Y00nNz8/niCxg8WOy+DAahxfPkk8qm99y54vwXLojJJipKvGbdGO/Zs2p0ze7dUKeOxuExjpBaYL8cak9l8X/x2+NPm9ATd25DuvG1DT658IcvKuU2DwncuIx2UYNSpaSkEDVgEFs2vG9OIG+8OpeFS95m8/p14rUKYX5nfqDVlWK1rns7q3ddVgbx8WVERpZz8KDE2LHuFBbab6jJkZQkVP4sr8NeQ/fRR9uRnV3Eyy/DuHEazp6tvqb5vYZasjh27BgXf7pCgIoQm1doNI0easJf/vKXan2P9Xg5aizPmiWRnFy1MJZ1ozkxUSRZy5Cb3nPnCi7AK68I3Z0nnhD3GMQOzpJrANCmjSjZJSfbn/idQdckJcHly9K/fbL+X/y2+NMm9J49e5KVlkqXFg+QuT8WN58AXOs0o9bQt/Bs0U3gk6NfATATgqyhVJba63/7+3CmTJ7E5vXvM/f1NxSvVWe1ERDgVS0oorWGtiPYmvXnQ0Jg9GiJxYtNaDTlJCcfZO9efRUPaWXCuXNHrEKrauiuXSv0Q2bPht69NU5rmt/P+C1Keo7CerwcNZZDQsQ9mDHDltQVFyfemznTVlAtMVGZ5K13AL6+UL++SPCurmL3Vlgoyi7nzonSiNxMrVFDTDaFhfYnfhld44h8Nm+e6KVMnSos6+7FgOR/8e+PP20N3TJkQa8d+44SOEz5QMua6F4tuyt0i9u3a3vP/oOOwpkaumXt2rrufa+CTitXgqfn3+jatRvjxo0gOlqUdtSU+GSxL+v6d3UahP/uB/23KOk5CuvxcqaxfPasSOre3mKH4+UlkmH37kp98z17RBnEYMBMRFNTYFQb86qUGqOjqzabfvVV4V175466gBdQUa5rxvXr1xWqm7KQ3P/i3x//FTV0e9rZzVu1YcuWLWzftRvPJ23dRLzahZPz5UeUm4wKKJWa9vqOfUdt/AcX/muJzfc6Ckd151u3BGRw+3bxoA8YIHRRLD0mndkuq23ro6Nh+/ZPGD58OCtWrCUxUceIEepa2/J5kpK0ii21M96a90Ikuh/hDNRR9r2sTliPlzMlr9atxeo5JkbssBISRKPa1VXJ3M3OFq8VFlZ+Vm0HoDbmjjDs8fHi/6sqr505A3PmqDNJ5XjmmTKuX79OWloOZWUm0tJyWLp0+f+S+X9o/CkSurUtXXl5uapFmaq5bIdoNAY3UuNfUDz0ah6CgcPeU+CeC46soyA/T/G9VYU9L829ewXsLCgI1q6tNK6IjBTbX5nBafkgy9oh1ttl6209iFVhfn4pAMOHDycxcR8ajRZPT0F2kU0o5PPMnCngkpbhTEM3LMzIqlUrVMlSjsg5vzXUGqWWuOjq9DksQx6v2bNdWbnSeYs3g0GZUNVo+P7+0LmzmLTlULOpszfmdeuKmvru3ZUY9tGjBbTVZBJ1cEcTv6OyjBwmE2Rn5xIY6HXfx+x/cf/jD5/QLWunsofh88OHm18ry8/GrfHjKoQgJdrFVJxP0ZG1BAYGUrduXYVAVG6SrfFrVvK7lJuM+EbNMn+vs/VZay/N3r01xMQIRIMlZRzEg6vTiQe6X79KLRZZeGncOAF5GzvWdpVtGXI9XE6q/fr1ISqqnNhYdQGn2FhB8bdcbTvb0LUW9BJlmvl06NCKtLRVCnJOWtoqOnRoRXJyslP3zlH8XqiKJk2aABoyMpxb+e7ZA127OreT0mrFOavaAXTqpBTb6tVL/OnqKlbkCQliMoiLE2P9imgPMXOm+sQ/cyaqDkqWYWk2vmxZgSqh6n/xnxVO1dA1Gk0f4D1AB6yRJGmR1fvvAvLT4gHUkCTJz9E5f4se+tiJk81uJ81bteFaoYGy/CxVD0NZ5lajd8EnNIrM/avQeQVgKsjEENQA73ZhZO6LRR8QginrFp7Nutg45Kit7nNO7qDgQgq1//EeGo32N0mBWtZpZXzzp5+KxBgdLeBnlm5Ce/cKrPG1a+K4khJRnnGkbR4fD7t363B1dSUsrJSkpDKnsMiWOON79dZ0zofUlTNnLvxHbuWtx8dZjfpvvxW7rH79BAnIul8xeDBs2AAtW8LFi/eukW4ZMo+gcWOBeImKUkobW9bJFywQx6hJ8Trz7/z/6pf8t8dvqqFrNBodsAIIAx4GntNoNA9bHiNJ0hRJktpKktQWiAFUfmq/PdRKK7OmT6X01ve41FD3MDTlZVCWexfXOk3J+XIrAb3GgEZDcP/ZeDbrSs6xrXi3j6As8ybBA+eZV9vjxo93iJrw6RCN1uBO3ulEVRU/RzV963q7XMqQ0Q0lJWLlpiby9MIL4iGLiREP9PLlgkW4e7fj1eDu3VBeLlWILZU5reRoSdt35K1prS1jqeB44oRILI5q7716lfDPf77q+IL+n8Ky1OSo5BUbK1Ahsp5OeLhIvLt3i/si74CysgTdf8sWwQieMKFy9e3MDkANyy6HXO65eVPcc0eKiyNHVnq/Woczei+9ehXd05g5Kr39nmW5/4ZwpuTyCHBZkqSfJUkqBT4Eoh0c/xxQNcOmmmGvtDJmwiSCBswlMGwSSOVmtUQ58g8sp37dOhiyb1Dj6dfxatmNkFGrcG/YFp+O0QT2mUje2ST8ug5VNDvXfrDBRiAqddXzilKNLOZljV93VNNXq7dnZuZTXl6Jb5Zp3Y4epoEDDbi5GdBqYcgQobntaHttMmnp2VNjPqez8EdLzfaJE18mKUnHG28oTaXnzROsREttGUsFx5QUkVwchdy0/S3xeyUD61KTdflDTtQ7d8Izzwzm118rJ73wcLE7CgurpPsfOSKS6+TJlXVsOfHGxcFnn1W/6S1HQoKoy8tiXo6idWuxeJg92xa+mJxctdl4RITE1q2bq1V6caSL07ZtC0JDWzjUzIHftxfzRw9nEnoIcMPi7zcrXrMJjUbTAHgAuDcRbAehhjpJOPilAnXiHRplVkuUw71tXwKDgunQuLYqg9Da2EJeba+LX6VATeQkLYbSAgoupJjFvLIOrSWwjxK/XlVNX63eHhDgZTZrbtFCvTFmHWFhRkpLjYweLVZT48aJpP7NN5W45PHjBYTOZNKh1xsoLTWZE3FpqZg8HBlcWDMBL126BEgEBVWaWcTEQO3aYqXZpo26giNUmjvYC8um7b2EMwJa9xp+fh42k5/lynfDBpHktVrYtGkr27YZeeMNce9XrFCWR558snJVvnSpaIhantvRDmDlSkEmGjxYvSQjy+0OH+78hO3nJybknTuVk5MzDVO5XzJkyCCnVtdXrlxhyJBBqpK8YWFGNJoSFiwosSvpO2TIINatW/e7jfOfIe53U/SvwDZJkmzVegCNRvOCRqM5rdFoTqenp1frxGqoE8vSSvG1c2QdWg0VwkxyeLSL4FJqNvsPHlZlEFobW+Qmv4Ovjw+dOnUyoyaMX22kpKQI7+4vUvvvS/Fo0tlMTHJr0FpBWnFm4rF2zRk8eAhffFGZxKvDBo2LEyvj+HhRnnn4YZEwQCT4Gzd0zJnzBkZjCQEBSlehVq0q5QSsw5oJKD+MCxaUqGp9L1yo1PqWo0ULsTrfWsWeTW7a3ks4ShSWyaAqJx57q7yGDRuSlKT+3XKZzM9PRicJopXRKIg7er1y8gsKEuMTGChW6OXltiUWtR3AqFHC3GL8eNi8Wej9WEMVX35ZNMdD/o+86w6Potz6v9mW7GbTGxBCMVRRKUFQQT8vKJBOEwQUpVyDQkB6FVRCkSZFIHQEBEEglQQJoUoRKQrEKyWUkEB671vm++NlZmd2Z3ZnI+iVe57HR91smZ2dOe95z/kVP2nmGUzr5tIlE4mJactIdVNydSXJeMqUT20Ova3BXuPiCKLL2q60e/c6TJgQadfv/L8WUhJ6NgB/zv83fvyYULwLK+0WmqY30jTdmabpzt7e3tKPErCJOilIXgnaoIeDX1vk7pqCiotxbGvEsX0Q5BoXUR1zxtii5v5V1FRVIKeoDH0HvAOKotCpYweUl5WBUjmh7Jd4gKKg8mkOtVIOQ8FdCylQKQuPeb99/PjJvIpIanXl4mIaoJaVAatXA+XlBFs8ZIgScrkGq1dvxLJl0Vi+XNhVaNEisNUktxI012xv27YF3n5bmFAEkBsuJMSk9c2NiAjSZrAWhw4BKpVwf95W2IuPT0lJsQtxc/fuPVYjhRtcGYDISD466epVMgPhPm5+zleudMQ332zB0aMai/dmdgDz5xPFzU2biPlzaCj5fQwGE8Jp3DiygNTVkeQPSOcrtGtH/t2wIX9hsWdBCArSISUlCdHR1YiMpHnfNzKSRnR0NYYM6YedO3eIwl6l7Err6vQICdH/V/Ig/ltCSkL/BUBLiqKaUxSlAknaFmMbiqLaAHAHcO7JHqIpTp06hX0H4gQJQs6BoVC4N4RHn3FQezWG4600k3Z26gZ4Bn8KgCTUB9+8j9JfYnm98NKf9xM0jFwBdfNOuJFxDx+OGIHgsHBQChXUzTvBUJaP/ITlVkkrtvwwhfRiAgIC4OqqZpO4lJspKYncwFyBqC1byGIwezZQUdEbFy5cxW+/XbSZ7EJDybabwTEfPqy0oPg7OcGmmYWQmBdAdhNVVbaHtv37D7b+ASIhFR+/e/dOZGRkYMiQfhbJBwD0ehpGYzVCQoLh6allK/bS0irMmGFqg1y+TM45M+g0P7dSdOfDwij06zcAI0eOFOQlZGeTKnz6dPK53BYLk+zj4ohC41tvWXqU2iIezZoFdOxI2j5Dh5ICgauzbg+BjWm92Bp6l5dXiu48pexKz583XYPMEJ47y1m7FujUyX5DlWepJ28zodM0rQcwDsCPAP4DYB9N0+kURX1JURR31PUugO/pp6QlwPSmXcOmW0WdVFxOhsOLfWCkjZg99kPIL/9A7OMu7EXFtTTkHfgCSveGqL55jmdsAQNRN/LuN4sYJLg1xPf741Bn4D9Wk3HeJmnFmh+mmPXW8OEfIiVFAUDazRQXB0ydatvLU6q6o0JBSDM9eyrQv/9gTJ0axdvalpXZ72/KBNNOsTa0rasDaNpYr7aIPUYb0dHz0Ls334mHq52ybh1ZHFevrkRu7nq0a9cCMhmNU6fIonf/PkmSCgX5R2jYK6XaDA2lER9PtGm5vISPP1ayfeykJODLL4V5BUxwF1HzQoBp3ej15Lro1Yvos+zfT1o9mZkkEcfEkP4/ANbsGiA7glmzLNs75gQ2rkKnWEREkGtUbOcpZVfKXINCxuTMEP7LL4HCwnLrb8QJsdkL89u7umr+UcldUg+dpulkmqZb0TQdQNP0gsePzaVpOoHznM9pmp7xtA5UyJYsK2YUyn6J51XaZb/EovhoDJYsjMbkSROR+/AB1n+zGsraEsgu78WOrZvxfEMXqJVyyJ19WB9SpZyCptWrbO/bM2QiKGdvePebZRq6dgyGXCaH3+NySS6Xo1PHDnB1dUXjxo0B1F8kilDMVUhPtz4Y27iRVG2vvEIGbELB3XpKTXZlZaRS++knFSiKtqjq7REFM49Dh+Ro2rQp6upIL33UKJKIPv6YaHXTNOknGww/2DQoNh9+paSkQKmkJSN29u/fx0vC1tQTx4whbRONhjw2bx5ppSxbRo5dDPopdQZSUlKFL7/8EgDZpa1c+Q0cHByxYwfpY1dXk0GzrfdhFlExiYCxY8kOaO5cQCajQNMkkXfuTK4nxsj6uecInJLp3TNKj8eP8wXAzAls8fHiMErucdbVAfv2Kdjzzq2wrQ3pmecqlUSVct48IDCQXENCQ3iVipaUgK3NXpjfHqjGvXub/jED138MU9Rcq6MobgFUMqDq5hke6oTW1UDl2xxfr1nHok76DhyEaq/W8G/SFA0bNkR1VRUiB4eyPqS9evWCX8OG0BbfQs63E9ned8MPV5l5kK6D0q+NVSiiND9MS+stc0mAxo1JT7y4mFxcvXqRJKLTkYtu9Gjr54tpMWg0CknJzsEB2LuXKOglJMRZVPX29FS5kZ4OxMYa0KZNNrZsMbWG+vUj38XDg1SAwcFksBUVVYX+/UPQv3+wzeHXsWPH8N57A9Gtm+1jYxA7lZU6XrKV0h4JDSVolC5dwPMMFVvk7JmBLFo0D8eOmUBh3AXY3kXUmorihg2E3DV37kJRI+vZs4HTp/k665s2EWhjdLSw3gvTLnvtNdvHqVQCR47osXSpZYUtNqTn2i1yr58GDSxNzQHy2/TtS0nqo0uZvYSGAiqV/h8zcP3HJHSuVgcufQ+5nIJLnwnwHbKIhzpxeW0waCOQkVduARe8nVOK4LAIZMt9kHrsBLLu3QFN0wjrNwD5Gn+4ODvDUJyF/LjFFp+fn7gUzoFh8B4wzyoU8c+IRJlLAowcKcPFiy4YNWos3n9/KMLDlRg7llSGjHgXt3/IrW6YFgNFkarNWhw6BFCUDBcuXAUAFBdb9jpffVXcyxIgj8fHk+eZtuYKTJkCjBgBTJyoF2wNXboEvP8++Q5z5gBLlgAURaNvX1tmyzpMnToeQUE6jBxpu0W1f78OO3Zsh0rFFzuT0h5h2hpXrvBbLGKLnNTF7+23SSti2rQJ7ONcud76LKJdu5Iq+8IFE0JmzBggMVGOtWu34NGjTNFzKyT/6+tLiFJCksDr1gFz5qghk8lhi/R96BBJytOnk3MptCMyH9JnZgILF5LnMmQtMVNzboSG0pL66FLakcxv/08ZuP7j5HNttTRoowG5u2fCwb8dqKxfWQkAgHhKlpzeCb8xWwTlcB9uHgNDZSl8BswR9CCtvnUOvkMXQ1/8iCcvwLw3Q/1/GtZbjHTt4MFV2LaNVLghIXy7Mq78LUPbLygog4uLbap6eTmFmzdvoUuXl6DXV/GkV3/+mdw4HTuSGy0khPzDUNmTkshnu7kROVadjoKnpzP8/f3RuPENTJyoF/1eGzeS7fZrr5GErlAQWJ8t6VfG+3TLFpOH5qJF5Li4PpuJiYQkM2MG0RRPSCDH+tln5DzZY8sH8J8rRo+3Rx4AILut6mpyH9ZXZoBbMc+YoYBK5YjS0ip4eGgRHBwOiqKRlJSAsrJybN1qW/537lxSQZeWkt2JwUAq6Nu3TRICDRtSePnlYaBpIw4c2M2zOzQ/zunTib6NVkt+YyG5ASY2bKBw6JACtbU69OtHFiSxEJKKtuZ1yg25XCbZAzYtrf7+uE86nilPUTHda9VLwXDqZNK9Lj2zB36Rm9nX1dy/iryD0fDuPxvqpu2hK8rmJWXGc9K7/2zRhSJnxyQ4Nu8E9//7gPe3mvtXUZGyFElxB3nolfpERkYGVq9ejt27d3H0p9/D+PGTcfLkSYwdO8qGJgq5wRMSgNu32+Hu3XsYPboSGzdaJjtmEfjoI2D7dhcMGTIMhYWbUVurY/W3zZMK10uztJQMUmtqAJlMjkGDhuCzzz5ntT3s0X7p2ZMcf8eOxMRYSpLt1Ytvdswc29Gj5NhcXEgVzNX3Zs7TzJmEqh8VJc04OyqK/Lf5c3/+mbQj/P2Jj2t5OTknLi5kcevXT1jHhVl4me9hNNLs78/VnBdbqOLjyXt98gl5fW4uaSulpCixa9d+1lgkJSUF7703EEFBOgQF6TB8uH2+stxzZr54MAnu3LnL6NChHSiqFmFhltdYYiJZEDZulH6+7TEhHzWKtO+4xtqLF9tOvPZqE0ldKJ52PFN66GItjabl18ljj1Ernn3G815XcfQb+Ps1hO7CXkF8eOGRtVC3fMWqB6lz53CUX7bsXwhBEesTtgaBiYkHMWCAwiYWfOtWgk1v3PgGdLpa/PKLXJCqzgy3Hj4k/WVmC8odrpn3mM21QeLiSLukeXMjEhMPPmaTkrDHbi8tjSTE4GDpvWNztUDm2N56i8D5GAat+Q3brh3Qpw/ZznftKr2t0bOncPtKJiPDy7VrSbJcu5bQ72kayMgQPufMQJGZXzBhbZYSGWmapfz6KwWjUYbNmzU8RymuS5TQ0K++w20hngHT1gsICMC+fbGQyRxx6RKfqfzrr+Q8zJ1LfgdmYCwGO2TaPEVFFXaqe5qQLnPnAq+99ob1F8K6NhEThw6ZWlrmUhj/jfGPS+jWdK8H93kdxanr4fb6MDg25VfZqhf7QOvsgtDuHQTx4Q60Hl7VmcgR9CCd/HihWAe37kMsXqt4oTeWrFjJs64TM70Qg+IxAz5rg8CUlEMIChJvXwAkIZ45QwZjEyfqsXixHufPG3DrlrBIU0mJiRHK3EBclI01b0omQkKAR49oi8GRmN0e92bu3Zv0VisqTKgRKb3jlBQl2rVrJ3hDSumLR0QAd++Sc3XggDS8dd++fBE0BiGzcKElgSgyElixAvj9dz4D03yBSUwE2rV7gfeZzCxFqRyEUaPIDOL8eTKQ3bmTHMPGjTSWLjWCooAbN24KGk8IDf3qO9wGLHkGubmAWi1HRkYGgoKCcOnSdWRnm34PiiLM5ZgY0wLm6kq0asRgh2PHkv93c9PYZddobqx98mSazQGmFJPz2FigeXPy/7ZMsf8b4h/XchELSb31nZNAl+bAva9lW6XyUjz8S6+hV883sWLVN3Bw84FL6HQo3Bqg6OhGVF5Pg9vrw+Dycj+R956M6CljENipE8L6DYCsaSArwyt7DPI13/4y/e+UFCXi42l07kxj3jzxuc+lOQAAIABJREFU7VyPHvwWg1Aw2+UdO0ytkZIScrMEBFAYNYrGSy8Jb9HNt6DZ2WRgaeszMzNJH1irJZ/l5KTEyJGjUVJSCoPhBwQF6SyO5bnnyGvatyfnICGBtBG2bCHvaat3PGOGAuvXb2Op4OZJS2rLRqUiVbODAxl4hoeLt0fS0wm8UqMhbZSiIoLSsWYHuG4dUFhIevZC32PKFODQoTT06NHD4u9S7ArNbQK5wfyeAP9acHCwJWUsLOFr3opZt44kPLkcCAoKwbJlq7B69XKrx/zVVwRJY6vfbjTK0Lt3EJycjki2a5R6XrixdetWjB07yqrE8e7d5Hdfs+a/Qy74mWq5iIU5XLDsQiwerByEohPb2JaJQ9OO0BloyLWeFq/XdAzF3YJKeHv7oLq8BP17vY7KlGWgZHLUPfwDmjbd4dyZiEzW3L+K/E38dow2MAzzvogWFeGypTeyeLEely4ZrIplubhI2y5rtfwKiIF62TJyNt+C+vnZ1vTgmiAwnxUTo0Nh4WYkJMTiwAEaH39seSwdOxISyMWLJoRDt24ksVvD4cfEEMJL5840JkyIxIQJ0y1YllJdhdzcCHzPyUkBg4HsEJiedGQkaXOsXk0MRTZtIrsegOCg6+oIPjswULx1AJCdwJkzluScmBiSzGfO/EIwmQP2MWCFoqioApmZltdCVJQwamXjRnHHK+acMa2Y9HRSaW/dSn5PleoQOnd+Ee3bd7Za9ZaW2oaJBgcDHToYcfx4Gg4dsm5qLqY8ae28cOO33y6iVy8F9Hrh1tigQaQ9t2yZ3KYp9n8D4/SZqdBv3bqFwcOG415hFQzerVB+OQmaVq+h6vbPUHo1gWPjdii/nAhN6+7QlzyCa7ehKExZDefAUGJD93iYiovfQ6NWo7CwCM6h01iTjMLkVQAFaF/qjZK0GHh7eSK/vBYKF29o2/dGcdpmqLUu0L49VhD5IqXaWreOJKLHXBOLWLOGIAQ+/lj8PDDuNUuWSDMm4A5hCwvLLao3a6bUtlAY0owtTNVgdjZJpEz1JjSA1ekIrK1TJ9N3+eGHRCQkHMTu3TtRVFQBjUaOkBA9IiPFr21uZRcTQ9ohKSlkkTX/XGbYFhZGzsM77ygxerQOPXqQRZYZWAohjgIDyQLasWM7pKdfR20tqZDbtXsBS5asEk3mgHQUhtigzsNDC4OhEgsXWp7/7GySjM+cIedBq1XC31+PWbNo3g6Nex7UalLBtm0L/PQT31Cc+T0ZDaCpU6PYnShT9aakKLF/v45FJokFM4gMDlaioqI3Tp8+ZvFejNGL+TFIOS/ckDoYnTBBi4ICcQaq0O77t9+ALVsoZGTQ0OkoHsDhz1T5zxTKxVoYDAaMi4rCxi3b4D1gLhwat0PuzkmoKyTlks9A8ljOrmnQFdwHQEPm6AyZxhUuncNReXIrDPpa6I0yC+gibTSg/FISyi8lwkGtRtSId7E/Nh53HxLVSK/giby+vTnyReqFw4XimcflyyQBmidI5sY7coRUmWo1STDm6A4mmO1o794hFhfhjz+ShSU4mFSXBgO5uYSSAsPeE3NKWrvWNkTNfMucnEyq4v79+dDI+Hgy6J09m38DC22tjx07hoiI3li8WFjISWghsXbemcjOBsaP1wIwIiqqStJiNXeuNMSFUEi9ZsSgdIGBL6BVq3Sr5z8mBrh9mywu3HNmjq5hFisG9vnJJ8Jzik2bFPjjj9a4e/ceKioqIZORRdjdXYv33vsAa9askwwV3LHDhKKZMuVTpKQkQacj11yPHsCwYeK/l1SI4Z9dNAFLZBIgfv6EkEj2xv9EywUgVPwTp8/Aqc3rJgp/2DTInb3gM3Auj8IPAD4D56HRRxthrK5A8ZH1AG0ArfGEpjXfgzR/02iUXYyHc2Ao/MZshmNgX2zauh0PHz4EJVdCrnK0GMKaI1/smdgLKRYCwOXLSvTuHcprMZw9S24uhcKkQ7Jxo2nAJCSNGxSkw86d3wq2gIKDCWuvsJAkuZEjKQBqzJihQEwMZZcJQloa6UtaC2bQxgxKGWbiwYPk85n2R0UFgRmaV2PmW+uUlBS8804YOnemMWsWORfmjEnztgJz3qUMYt9//wPs2rUfy5fLERFhvXUQEkIqtPoO0qSgMMwHddxt//Xr6TbPf1gYcPPmbd45W77cROgRIv989ZXpvJpHcLAeN2+mY82aSqSkkEVyyBAlaNqI3r1D7Bp0ch2zTp06huXLybUSFkYMtq0tdFIHmLaOJzubnA+FwijaRjEfPluTk2AADkOHDngqrZhnqkIH+K0Xdc+xUHrwf/Wa+1eRd+BLODR+Hj7vfA7qsX562aElcPq/UZBrPVGYvAo0bYBzh2BUntoKo0EH2tkXMpUa2va9UZa2EXI5BXXXwSg99wO8+84QHLI2KbvODkWlVlvjxhF0gLlf5IkTwPLlcqhUjigpqYJGowBAo65OjxUr7Btw6fUk2TCtA7HgVr8ZGRmIjp6HPXu+YzG/JSXWB6b2kHacnYWrwR9/FN9WM69nqifzSslWy4YJ5rzTtHQPTU9PLVavrpS040pPv12vLbZQ5WftmOqDOc/MJDus5ctNba7588mw2lprT2wYKYRh5x5reHg/6HT7JA06+/YlVTbDj2Be8yT9Tq21QpkqOyiIFCZiVbb5vf3VV2Q+Y21ntG4doNOFIjY2UfxJIvE/UaEzUEEArHxtSYIlhb/w8Gq4vvYu6Loa5O6ewWLSPd8nui1KDz+4dhsKFGcBl76Ht5cnHFp2Q4PhK6Bp1Q0lp3dBpXWzmswBSxGuoUPfk+wVWVrKryq/+EKOr74iCAzGfT0mRofQUAML+RMKMY3y3FyyaNgzcAsICMC2bbsQG5sMNzcNgoOVNoePUjHPSqV4NShG7+a+nsEGm1dKQpj5vn2Bc2YCz8nJBLsuNojdvFmJuXM1vKFYSUmVxB0XVe9+qTkm3dox1RdzvmcPeFIAfn6EEGXLMjA4mJE85g+BxQTaGOo8TVM2oYLMoJOpss2Hw9aG5kK/lbUQgy6Kad0LmWlwd98//0yG5bZ2RhERQEpK0hOv0p+JhG4uknXixAnsOxAH5x6Wkzxtx2BUXE2F9+AvoXBrhPy4RRbPKf9xJby8vHDu9EmkHTmMFg7lKP5uMiqu/gjfwdHwGrUBleknoG7RRbII1/jxk20aOR86RCjwarUC//63DH36UBg/XouLFyksWwb8+98GC/OAr76ynvCENMpTUpSoq7PfJBrg683o9ZSokw9AFidrfwdIbzwgwHbrQqwNlZREobq6GnK5DBs2rJeszcEEN4EIOQWNHg0LNBBge6sOkOTm6els/Uk2wlzfR4xEVF/M+enTljr39jhmcfHjP/9s3cA6KEiH5OQE7Nq1H3PmqC1QNlxZXiF+BDeEfqtRo4R/KyaEUCirVy/H0qVrLBbNXbv4QmzmwdV2Ya4FZhGQem/pdHji2jD/+IRu7uF5IzMHQaER1nXTVY54uOEjVN86B4+3+BO9mvtXUVtTjVLXALw7bDgCAgKwZOF81BbnQuXbAoUpq0DTRniFT4O+6CFLRCpNXAhnRwV88n5hRbgqT2yCUqnErVu3EBAQgF69gjF9urAmOHMhX7woh4GWQ9GyB9oHvoJhw95DRARV74RnrlHOWMu5uzvZbRLN3BCvvNIRa9asg15PCzr5cI9NzFWeOZaEBHIjWgsx44z0dCAxkcYXX+hw5AgNnU66ZjsDHTTvp3Or+iFDlIiMHGtB2AHq19+ubzDSunl5pdDrDYIkIiGIoxRdfSHvUHvYpFyhrAULyO8pZmDNFAhBQUG4ePEadLpQjBplYpXW1pIh8rVr/CpbbPE093V1cXER/K0A6yzsqVOjsHTpGt6iKaXKZnawzLXAsKql2ve5uMBuMw5b8Y9P6Az+nDYakbNjEmpr66BqwafwZ60bYUHhp+sqLXRbau5fRX78Ynj3nw3PoAm48ajksariQHj0mw3P4AmsXZ3Sww++QxdB6eqDkqPrAUqOcveWcHBwxMyPh4O+sBug5CjWNkNoRH+0eeEljB8/CQqFI3JyhDGvWi1wMNYApx7jWBz7t99uk1x1CtGp588HHB3JTdajB/GdfOONfyE0NMKuhCR0Q+j14Dn5mOOsly0jFHyhv2/aRLDQOp10zW9zpb8ZMwhhp1MnsmuRmogUCpJAEhMJYUSoP2/uqWoeUliG1l7/pEOoipXSmuA6ZTFRHzYpI6fQsaP4sDI3F5DLjWxlvGzZSqSn30ZU1FgolS6Ii5Nh8WLb/AihsLZ4SvGcnTo1ClFRE9lFU6ejJO9gmWvhyBFyL/bsaekTax7M+TPfAf/Z+Mcn9ISD++Fbm4X82AVQejcDQKPq5jlC17+WhryD0TBUFqH0zB7k7JxCdNOPrIeD3/MWSJbClJVsG4WSyeEaNBEHj/zEVvuUTA5t+94ov0wGGZRMDqeOwTCCgmvYdKjb/h+uXv8dZ8+eQW1dHfvY7bv3cCe/AtNmfYbp0+fi7FkKb71Fqop9+0iyjU8ApkyhoO76AbTt/sWaSZeX10o0S7CkU48eTeRpQ0NJomdIPU5OR5CQEIv4eBmPxs5dDCIigPh4I8LD+4veEK6uRMFQSCemrIz06Rnijfnfc3IAhcIRrq7SdgpqtUkjZNQoIg/LpZQD0hJRQgJ5XmwsqQZXrhQ2EZkyBVb7sFL725mZmRYyEMePH0fTgJYIaNXG4nExyQhbIVbFclsT48YR1BC3ZUOcsvjJ0h77OW5ERADXr4sf46FDpL3D1Se6efOmzd2HlMXz0CEFiouLBUk99nrOAtJbah4eWvZaYKQrzCUihI+XtFeftDbMPx7lwm250EYD8g8uhHf/2ajLu4vSs3vg0LA1ah/+B9795qAu7y7Kzn6PwQMi8MfNDNzKLYXDi0GoPLUVG9auxlfLv8bNjHuQufrCK2SSIEImP24hHBwc4fRyP2g6hiJnxyQovZvBqd2/UJCwBOqAl1F75xd4hE8DBRny4xdDHfAyqm//DEdXL+hK86HwfxGGR9dA6atRV2uEUgXI3BrDSDmiwfAVrIpkWdJiyOhKKBQGlJebCC7m+HIhfLo06VUHABQ6dtTh8mUDQkPNCTJyHD7sgO7d34Szc6oFEsAa6QggPdX584l/ZmgozcOUJyYCvXoFw8PD3SbqYf16koDd3FwwdOj72LFjO9assUSYSPnOkyeTBO7vb3qNOYmoRw8gNpaCwWC0fBOzyMjIwJo1X7OkJkIeeR9RURORmZmJ4LBw6KBE64CmuHblEk6ePInewWEwyFUAgPbPt8bF82dw8uRJUckIKVFfmQAxJI05wkNMDoEbYggXQFzqVyoaxRzBwyUrJSXJYDAAERFGC0mNlBQl9Hoa69ZV24Xnr8/55CKf5swhwmQREcIKpzNnktaSFHkC83imiUVtX2yPLMoHHkHjkfPtRCh9msMzaDwLRyxIWAL3HqMs2JsP7mYIapav+Hol5n3+OeQuvvAczncVylo/AnJdJTbFrMeqtTG4W1AJukknlJ3ZA5qSwWfAZ4TMtHsmFO6NUJ1xAd59ZzwmM02FriCTJTeR5zRA1Y1z8Bloep2mdTe4vByBvPUfQIlyBAfrEBpKi+qeAwQN0r49XzfaFsMzLo7gyKuqyPO6dQNGjrTcLjN6IwsW8KF+zPvYXjQc0bXrqzh58jh0OtI37NmTVCeXLyuRlCQHQGPBglqrELQffkhEfPx+ltHq6kqQKeaL288/Ewx1UBCpBk2a7RQOH3aE0Qi7b+76xPHjxxEcFo46A6Bu0RW1dy9hyIBwfL/vAAygoG7RFdV3LkLpqMHgsF48Xf6y/XPwbp/uOHH6DBIO7kfLli3Z9/xk/Ke8x5iwB+IIgCfRrNU6wmjUIShIj4gI0znbu5ckZ4UCbEEhdM65523UKGDgQCUv6VpbBKRqrjDf0XzxDAkJQ2zsASxYUCN5ERcKc/KQvZBRwFLLfswY4nJ15Qqfcdy3L9lRS13MzOOZTug7d+7EiH+PgdyzCdy6D0Xpme8BCvAMmiBYYdvSLbcm8lV64SB8ci/gP9d+I1rNjxcEhUKJYm1TeARNsLqQFB/bjMbjv2Ofkx+7EB5vR/KeU3p2D3wGzUfRro+xbIleEE+t1RIG55dfEhr55MmWLMf+/YV1p8UYbGI3XXY20ft+8IB4XJrvEpj3Cw7mMzuZ6oihgVu7MWbMcIBCIUNwsA7BwXr2PRISgORkBUaPHocdOzZaiJoJHTNXn/zRI9L6cXEBGjWikJ3tgH/9q6dNwSd7koxYNGkegKyHOfAZMIcs1t9Nh7HkIQxGI7z7zTIxlosfwsWrAc8spfjEdlRcSYJTm+5stS6lgrdWxTK4aQCCAnH79imQmqqHTCb8O9vajTHnTakcBFdXN+zevRMFBWWSFoE/s3hKqaTXrycL0rRp4u8jdBxSzicXTSNVyz45WYHDh1X1Zos+0wmdqdBpmoYu/x4aDF+BwpQ10OXfRcMP+RV20dZIfLNsIYYPHy74XlIUG8v2z8GccSMweZJp2MVdVLxCJoqSmRSuvpA5OokuNvnxi+HddwbqMn5Cr+d+xJhIg2gCTkoirQu53AFVVXUW9GUhUo+9DjjmydqaO9J33xH8rU4nY9sO4eH9MX78GNy5cwsGAxmAajTEuea990w3+ObNSuTmdse5c6dAUQZUVZGE8sorQF2dHGfOGCTpwQDSdgwURSE6uvpPk1KsRUDrtshV+cEz2PoiX3J6Jxp/sp19XdmFWJT89B27kxNy1hK6Bpmw1gICYLXqtKa9k51tsoiTet6eBK3eVkgl7BEbPvHniC3i1s6nGJqGuwgYDMSw5fRpoLKSyAIPHz5C9PVS4plO6Ldu3UJwWAQy7t6Dz4DPABpsYrTF3jQPMTckh/bB0HQ0uSGprhLBLcC0CDj1mYzK9BOCC0nW2uFQePjDd/CXootN1vqRUNI6aLsORMX5XdgcUw3AdoIino4yC9aiUIUupcrisvSk9KMdHMigra5OgebNI9kbIiUlBYMG9QNN1yI8nL8gJCSQdg+jyyKmUcMcszW9GO4xA9KqSDHBJyk6G0JtD6HHbt26hcFD38cfj4rhHjxZdJFnJCmYyFo5GOpWr8KD0zYsT14KzRsjeQuB/MoPyH34QPyLCoSUavaLL+S4eJFCRASFTp10OHOG7AzLykhxoFLJ0bcvxdtJiZ23P6tFIyWkLhq9epH74Wku4kzYuwjYG880UzQrKwuPcnJsJnOAz94UQhTMmjYFuru/oHTfLMkGzwxsEgCqMy7AvYclqNr55X7Q5WWgJvOa+HMCQ9HA1xv+pddQW1mNBg2kOdKHhOjRrFlzC6SCEOLDHkNkKZ/dvz/pvTNu7i+9RJrsGRkZGDKkPyiqFl99ZWnwy1R6CxeSG/rMGcJMFPqstDTrejHZ2USXPDGRGFUcOmRpmM2NoCAdzp49JYmsYx7mBDaj0Sj4GECMWJYsioYu/76g6XhB8teQOWpZpBUTLoEhMGZeRsnemSyL2eO9lez1XHP/KoqOboCrqwv7WVJDihzv6NEGqFSOqKjojdmzSf+cQUht3w689RaF+HgaUVHiTklM/BVYfaloFDc3jSTG7ZMIKbyBpxX/+ITO1UEvPr4F6hYvC9jIxfLYm4uWLBW8MT+OmgBFs0BojZUWbkizx34I1dWDSIw9wDN45sImxRYSl87hULg3Qv7BaCvPiUCJwQFDBg2Es7MDcnKkJeCgIB3u3btnAesSgp5ZYwAysMVx48jAJjGRJEpr+uwhIcRJ56OPSHU9dWoUCxPz8yP+ktahYqbZgBjV3Nox//wz2UV4eJAZQmqqaRciJkzGtU2z56YzJ7Ddya94zFEQ1r8/fvw4wvr2g1GmhMdbllsG584RoA06lP3CByw7dxsGOLqgPPumyEKwEu49/438Wjn7WVJDqkBcSUklTp8+hmXLyOLLXYwnTtTjq68MVp2SmPgrsPpSF43hw0fUaxH/p8U/PqFzPUY1bV5Hzc1zKN4z/TEGfT4cm7ZH+bnvUbJvFmFvntyKyopy0RvTo894VMq1mPzpBHZwKpfLMXnSRDzMvMdL5gCpxBQKJZxad+MtJIVbI1H+i2khce4UAkqu5BOe1vMJT/Ln38LXq9fgww9HIiVFKZmCXVpaZYGJ9vUliZZrZCBmkMEkRpUK+OYbE17dw0M8MZo+m/w3F8u7e/cuZGfTNhejsDDTdl7se4qRhawp2jHMRSFJBKZasze4hQMlk0Pz1jjEp51l5y0Mb4CRevhg5GjU6sgAVHABDwyDwrUBSs99j6xv3ucVHTUVpYDIQuDSOQKV11Iha9OT/SypIbWadXJS2I3bNg9mYdfraUyeTAaTT6MytmfR+Dsr578qJCV0iqL6UBR1g6Ko2xRFzRB5ziCKon6nKCqdoqjdT/YwxYPrMaq+cxxfLfgSakMFZJf3InLUCDgX/YHF8z+Ho64Mskt74e3lCWXAq5JvTMCS8GFODEmI3Y+G+kfI3TGJIFWSvoKzSoaGhZd5xtUyGNh2TnHCImgVRvjkXLBo5zAXqVYrjfno4aEV1PzYvt0FAwe+ByenYZg40QVVVZaDofomRuazuUJMDBW6qKiCJVlYC2ZBMDd75oYYWUhaO8pSEiExEaipqUZKSor1gzMLbuHAtEIcu49A8bEt0BVlo+b+VdSc2Y6+ocFQaZzx6FEONK1e5S3guRtGouzCQVTfu4KcHZOgaf0qoNfB6cWeqLx2FFlrhqHg8BrQtVUWevxMKL2bQFfwAOXHNmD3ju0ApBOSpFazNM0XbhNiIBcWEglm4fcwsYrXravGpk0EZTJmDOllR0VpnlhlbI+A2f9C2EzoFEXJAawFEATgeQBDKIp63uw5LQHMBNCNpul2AD59CscqGkwF/d232zBv/gJUe7WGf5OmWPvNGnz37TZ8Hr2QfSz1cLLFjek27Gtej7LmzHbezcJtz6SlpSE4vB8K1U1wP7cIg4cNR2ZmJrKzswGZHKXHNoI26lHh2RoqlQNmjf0AuPg9HFQKOLZ8lW3n/HgoASWFBfj92q8W7RzmIjUa5TbFrbg9SKEKZPv2ndi2bRfy8krx+++3kZqq4VUz9U2MgCX9m2lneHhobSoxmnSmyUAzMlK49y3GWrRnHsBEejoxyZgyxcAzs5YSTOEQ9npHVKYsY1FJSu/mKExZhYKkZQjt0wsxm7dCFdAFBlAw3r+Mgu+msoxlXw8XeD48h/yDhNVcenYvFF5N4Ni0AwyVxVC36IKq/5yC2my3l71hNMp+iUf1vSsoSFgKdYsuoGVyvPHGG6I9fKGQWs1WVel56oFChs4eHkBlZYXFwijEKvb3J5DBxETyWorCExsQAtIFzKSE0OL4Zxi8f3VIqdC7ALhN0/QdmqbrAHwPIMLsOf8GsJam6WIAoGk678kepu2Q2uNMOpTMuzHNg2tMsWLFCrzVJ4Qn/NUnJByuYdPhGTwBCmcvpP/nBoLD+8E5ZBp8hy2BzNUXytZvwr13FG4+yEX0oq9QVVkJ1/BZ8OgzHiV1gMFghN/j0b9YOycoKAjx8UeQlGTdU9GeHqRQNXP0qP2Jkflsc/o3s1sYOvQ9+PlRojR8Jkm4uREbtNRUYjyhVFq2ePz8iFHvlCl8swrp7ShLAbQ337TdMhCKU6dO4UBcAmSt3mCH755BUQANyLWe2HcwDj4D5xFYqlsD1NbVojb/PkpO74R3v1koqDIg80EWfAbOhWfQeDj5NoOzvoSdv3gGTYDKqylqs35Hzq5pqLiWhvy4hXDtNgQVV48QFnTfGfAMGg+5iy/cPb0QGtFfsIcvFFKrWXP1QLHd2/LlsFgY60OzfxLxJNop9gy9/1tDSkL3A8DFR2U9fowbrQC0oijqDEVR5ymK6iP0RhRFfURR1EWKoi7m5+fX74hFQmqPc8myFbwb89H2CdAVmcpCyr8DIseNx7fffoups+ZA3eo1jvBXDRxamto1nsGfgla7wj1ipskNKTACNXcvofZBOqpLCqBv8CL0Gk84+L9AHivOQ6V7S0kXR48ePbBvX8IT3U6aVzO2EmN2NlGzq6w0EU0++YQIY5mbCTO7hfHjJyM72wGJiZaVtTWd6Y8+Io8vXEiMFxiHod27if6LTmfSg1EqpQtxcQXQGAKSVBNhJrgFA1c6mVwHE0AbDfAZOI9zHYSDpgGvAXPR+JPtUDfrgNqaaiifM73Opc+n0Du6wWfAZyatoA5BgEEHXYFpIdC+0BOQyVknLUomh2f4NFTJtazvrVCrUChsVbOtWrVCkyZNkJgobfdmnpz/jLG1vSbLT9KU2d6h939r2MShUxQ1EEAfmqZHP/7/9wF0pWl6HOc5SQB0AAYBaAzgFIAXaZouEXvfp2kSLepUdDAafg18UFRSAofOA1B67geoA7pAX/IQvkMXozbzOtFeea4zqm+fh9tbkSj/+QD0pXnQtO4GXUEmIFOAklGi5KCC2PlQOrmhrqoC3v1m8qUAbp9nWYKFOz+Fk0yPn06k2aR2P01cqzWssBixKCmJtFsiI4G7d0n1XlJCtuVDhgzDnDlf4ObNmxY4dF9fUtXZcnNhtFscHIiCnxDLsD5+pdywl9DC5Sjoix8RBIpMBu/waVZJYtxWXlnSYjT290dBrVz0Gs2PX0zgjI2fZyUsau5fRcGhFaAUKsid3OrNgrYVDCmme/c6nDihB0AG6lJx5BkZGWjbtgWcnMigW0x7SOjcC5ksW/PgtPf5tsKcg6IrykZlyjIeg5dr+v53xp/FoWcD4KogNH78GDeyACTQNK2jafougJsAWuIvDPMep3mwcK86OerkGtZtyDMoCrSuFnm7Z5i20cEToHBvhNr7V2GoLGG3yJRcCU3r16Bwa4SChCUWn1GVthabY9ZBbqi1qOB0+XdZxINQpW5ta/c0p/NigzJuJW2OI4+MJFX0mjVATQ35N4OM0en2oXPnFxENnzQOAAAgAElEQVQT8w2USgXrDzpyJBmISdGZDg8HZDKipjh2rHBC6dsXVrXYxRQBmTDXercV3KFobfYfUNYW4+UWDZF/cL7FcwuSlvEYoQC5NmLWrsF/rv0meo0WHl4N9x6j4DNw3mOtfTJkz49fDLc33gel0kBXmCVoymLuYWtvcHvfEyfqMWsWJA+2i4oq2EFo//4EKcX02oUgpEI6+7bkbbmtHXufLyWEht7WZmuA9X77zp07/5ZevJSE/guAlhRFNacoSgXgXQDmar9xAN4EAIqivEBaMHee4HFKCqaV4vj6CIu/OXcOQ8mJbXB5/X3I5HK2emK2x7V5d6AOeJlNwl5hU1H36Cbvedr2vVF+MV6UHOTQPhiffRGNyopy1OXe4VncNfyQWNwxio3e/Ym++t+9tRMblEnZbvftS7TWzW+o6OhqpKUlY86cShw+TBL9O+8o4e6ukawzbcv1xc+PQDInT7Y0go6JIX8zbwlxw15CS8uWLbFk4Xwoa4ohu7wX0Z/PxeXfrkHbvrdF2865Sz+U/RKHh9vGs487tA9G9KKv0LxFK+w/GC94jWo7haA4bSMqb52Hz7vRcGzeEUVHY0A5auHQqA3c3/wAtNFgYcrCvP+qtTH17vGa9767dhWHuXKDIJ00bIIdM8Y2Usr83Evpu/fqVY3o6HmSn29vn95WQWi+YFrrt9+vUmDEvyP/ll68zYRO07QewDgAPwL4D4B9NE2nUxT1JUVRDB3kRwCFFEX9DuA4gKk0TRc+lSMWCVs6LC6B4VC4+qAoNQYNRq61WHm3bYyBW/ld5OyYxCbhRqPX855XfGwTjLVVVpmouVU0HJt3AigKMrUbW8nX3L+KR9snoCh1PdQtutoFm3yaITYoS021PSwNDRWWSm3XjsiGnjtnWTkplbSkJGENyshE06bE2LmkRFiLXStSgNeH0HL8+HH0HTgI1V6t4e7ugZmffQ51l4EoPfcDi3ShaXKTugSGA6BAyZXs4zLPJrh1+zayHuawfW/zcAkMh8LFF+UXE5C3dw6Uno0BmQJyByfkfD8bBQlL4WNmysKEuYetvSHU+377bdv68ikpSjRr1sxmgmWQUkLn3lbfPTsbKCmhsXv3d5KtBu2dkQDWC0Lugmmt364K7I/aR7fh1f+zv6UXLwmHTtN0Mk3TrWiaDqBpesHjx+bSNJ3w+L9pmqYn0TT9PE3TL9I0/f1TOVor8cn4T0E1CeQTd9Z+gDIuuScwDMbqcpRfNIGxmZW3SZMm0GqdEPzqS6LbYciVFthic3KQunkn1N7/DSqf5qi5/yvc/jWCB3GjlI6oyfodeTsnS97aPe0QGpRZI/swYW5vxw0hZEy7dkBAAIWkJMrq+yYkEDVJMSgjEykpSrzwwgvw8FCyRtAHDxKI3Jw5T8ZEmOEc9AkNZ2/IuzlFqNMbUPLTHrZtZ9TVIC92IXsduLwcAWNVKWiDHoXJK1GQsBQKF292sAlwCWhxpmv05QjI5EpoWnVD6Zk9cGrTHbrCTMiUDhYetkLEtPoWAkIsUilGFykpSty7d1eSq9bhwxA899YYrFw28NatsMtq0B43ICZJqwL7s9wCJmruX0Xp1TTceliIr1eusgrAMB+Y/9UF2z+eKcpEwsH90N+7yDoV5ccvhnNgGMrP/4Cc76YTp6JjW+DcIYh1HAJMW+GwfgOQWa1EYnIKHF8fwVbUzA+r7RgCY20lqm6dx6Mt48hnxC6AobIEFVdTkbtnJopPbEf55US256708kfl9RM8iBslV8K5Qx8YywtQkmBJ7a5KW4uojyPxcdSEv7T/Zt6n9/R0luwrKRRiyX7UKBpxcbTVJHH4MLl5xaCMzPOIPO8qwZYR49STk0M0uvv0oezGJjM3eaGmCQxyR6gaP4/aB+kwVJdD06IrFG6+JsRKYARq7/2KRzsmsdeaZ9B4eIVORt2jW/DuOwM+A+aS3viuqaxW0NplC9GsIh25OyfzXqfyaQ6ZrgqV6cfgM3Ae+9rc3TPY69u121CUnd+P/O+mCuoM2RNCLFIpFna7du1HSUmVpARbVQXBcy/GYBWDTUq1GrRnRvLJ+E9h9HyO3XHlxy9G4ZaPUJy6HvnxiyHXuKEiPxuLlyxl++1FOz/Fwy2fAABblHmFT0NdTgZyvp34txRsz0xCb9myJa5cOAcvlQ7FaRvh3mM0XF8ZiEZjd8KpzesoPbMHrq+8g4qrR+AZNJ59ncyzCW7fucvbKjEiX9yttMqnGShKBk3LV0AbDShK2wQY9ejV81/QODpA06obyn9NhqZNd1MfPmQSGYaa9+EvJUKvq4NzD0tqN+XfAUu/XvW3Y2GlsAqtObyLJfuXXgLq6ihBKOaGDSR5zJpFyChiUEZuMunRo4cotjolRYlff9UgNjYZer3RrmEyd1vtGTQBco0rstd+iPy4RfDuNxOewRMgU6pRfjGRIFiOb8SPhxLQrV0zwgp21ELu7Mlr3Sk9/ODabShQnAVc+h7z532GRUuX47tvt2LiiHdQdmwTVGonFB5ejbKkxaCNBmhak+uJeW1d/n0Un9wGr/BpoGsqYDToUJ17lyWmXblyBY5aV6Smpkr5mdkQ+725FnZjxpC2lvnCKFVSwNPTRfDci3222BxHitWgvTOSWdOmoC77d1PhRVFo3+Y5VKYfg+ur76Au9zY0LbqCksktjOPzYxey8xKlhx8c6Fq08tagIDba4nP+7PDaVvzj5XPNw2AwYOSoUYhPOwu3YfyhSPaG0dC07g76zjlWEpexkKvLuwuV73Nwev5NFCQs4TkNKT0bozrjIuexaUBJFhZFf4m5X0az2yldUTYKk1cBoOEZ/KmoZCoAC9lU5u9MNV8fLewnGVIcW8wtxbhGHCUlRPs8KAh49VXST2cMOpRKAm+kaQrJyQkoLCyDRiMOUQQsbejMIZtPGtopBGPLj10Aj7fHWBiS0EYDtAojSgoLAABHjx7FwHeHwqDQwPNDItRec/8qio9vgbKuAutWLoG/v7+FYcWKFSswffZcODRpD39VBVavWIa+AwfD6OQN58AwFB/fAvceo1Dx24/QlxXCWF1C4LR3L6KypBCrV6/GlBmzoWn9GugHv6G0IAcKhULS962PQw8T9bW/s/XZYiYtUnT97ZXDFYMtylq9wSLiHBq3Q86OSXi3T3fsOxAH17DprO2lpvVr0Jc8gu/QxSiIXYDqe7+yvARu2JLwlhLPtB66eVh3HIpF+bnvsXn9N1izfiNrIVd+bh8cPRtBZwT0JblQt+jCMybIOxgNz14f825k3fmdMBgMFp9DGw0oTFmDurw7aDRiNe/zC7dGgtJVw9i4I/v+XM310qtpUPk+x7PQ+zuxsAzWt0+fOp7+9aFDJLmOGAEMGkSeK2bEsXkzUWTs14+PZefihYcPH/zUdbPtDYbXcCe/Ak5vR1nFmtfm3GadrE6ePIng8H6QNw1E3WPde5bfENAFdQ//g6beLniUk8NbpLu0bIjU46egad0N+uJHcFTKMCTodWzd/i3qamtBKVQsAan05wMoPbMbPgPnsUmmsZbC3cws1hgjZ8ck9Or6AhLjBTQbRMJehx4m/sxiwP3s/v2D2evE15fsBsxNWpiw5ZRlr6b9zp078dEnUVC5N4C2j8lP+NH2CRa2lmVJS+D05iieXDfDN6GUjqjNThdM5oC4SY498T+T0KU4DpXsnYm5E0bj0wnjWQu5HVs3Y+euXYg7egbw74SKX5Oh8Ggs6j5UkbIUHu7uKHEJsDDDoPw7oOI/p0UNNnzzL0KpVOF+UTVkbXqi5sx2rFu9EqvWxuD2oyJUVVVBplDBS4Sw8mfJI/ZGRkYGBg2KwM2b6ayTUM+eQPPmBC4YEgIEBhI7PPOKSWolVVxchdRU4RuXiT/rbFOfYHZ7B4/8ZOEvm71hNFy7DYH2hZ7sTfpun9exfed3cA2bLuotm7t7Bury78GpdTfWxKLi+jEUHVkHt9eHoeL6cYKQUjiiLvcmaEoOmVzBU218sOpdqFt05RUd+Qej4WFWdJSkbYC+ptKu72y+03F11aBZs2a4d+8uSkqqWGmH8eMn85Kz2GKQkECctRwc1Pjwww8tXseNDz4YhosX9+DRI5rdyZlbK/J+g2xhpyxruzImRzA7oyHv9Mesz+ZBrlBA+VwXqHJ/R0VVLXS6WniFT4PC1ReFyatA62vZe7Lm/lUU/vgNDFWl0LR8hfc7PNoxCZpWr8AzyFSwFSQtg/srA0RNcuyNZ9rgghvc6TPw2AxgayQqL8WzSADli72xfOUqnoaKXC7HgbgEqN8YCff/G47GUd9B6dFYlDy0bvVKpB05zBIRmAHX2FHvozL9mFVYY3aZHkMHv0MEucw01+d+GgkvNYU3O7QQxcL+1QPTgIAAZGZmIibGhCIZO5ZURkxvde5cUk2ZJ22p1HEnJ+UTH3I9iWBgbE5vWnIOtB2DUXp2H4wGHYsw2bLtW8ibdeYRyupyM8xmKH1AyZWoy/j5sRLnMRSnbYTSlwzkVD7PgaJkMFSVgFI5gaJkFhK8XuHTUH3nInK+FYfYFqWux7LFC+z+ztzheGJiEgAj2ra9gdWrK7F9O43OncuxZct6tGzZAu7uGpZqz0VKRUVp0KsX6bmXl5OB6rp11Sgs3IwuXV4SVbqcO/dL5OSoMX8+sGMH0KwZWRDEws8P8PBQIjJybL007W88LMbU6TOhNwKuYTPg3jsK5bQjaqvKoG7RBQVJyyF384Xv0EWgZHIUxEazjHNDZQkc/V9E1c2zePTtpya+yfAV0Bc9YofXeQe+xNRxH1k1yXmS8UwldC7bS6rjkFBVX/sg3Sp5aNXaGAQEBLCyvUxijktKZodYgPCCwmieM4uJueb67h3bceb8BUEs7N81MBWDlfn5mZT4hAwqpBp00DRt9cYF/ryzjb1hk9fQOQIylSMebv4YFdfTUH58M7ZsXI+msiIeJLXRyDUWbkPQ1yExdj9ebtEARUfWQd2iK3R5d+EVMY1FQjn4tQWtq7GAyWZvGA1dwQM0itwMY10V8kSYqqM/fB+fflp/0VNzNmZWFsH4e3iQ4XVqKvDNN9UoLNzEJumAgABERU0ERZFFvk8f0m778EPy2tpaHaKixFmcDCdixgwHREYCLVsSxNOTEqczhxu6Bk2E0tOflSqmZHI4d46AXOMKzyAivlcQ9xVqH6RDV5IDp5d6Ie9gNCiZDD4D5sC730zIXRtAl3cPeQfI76D08IPv0EXQtOqG4qMxmD75UyxatMiqSc6TjGeq5QKQgdSwD0aCNhqwb893ePPNN3mP7d29i3cyzYchXJNesbZN6Q+z8FnUKIseGFdPxrydcregErK2PVHz03bRH9RaEvk7B6bW9F6ys0kvXUi/Y/hw8R4oE4zfo1Zr3YB4zhw1Ll689pfpWgv5yxYkfw3nwDC4dI5gt87FJ7YBoKBVUWjk1xixP+zFwkWLcODH0/D+cA3vPbPWj4RrtyHQp6filbb+SD120tTz3jkFmuffgOvL/VhDaeeXI1BxJQW0QQ/nTqGCQ1GhXm3phVjUXY6zayhqHtxBpz1DyNWrl+Pq1Y24csVgMU9hzMU7dpSjffsxogPSzp1fZE28zWczTCsnOVmBw4dVdmm2SNF7yo9byO6IKq6loSh1PSiFCt59Z6D42BboK4qgfi6Q7akz7TKhfPEkBqBC8T/XQ+ciB06ePGnxGPfkMibTdzOz4dylP8rO74Wm1WvQPP8vlJzYBufOESg5sQ2uXQdAGxgmahTNDFmee+45rFy1GouWLIVGrUbakcPsY8tXrsJ3324TXZ2tmVT/nQNTMRQDc7P17k0qdPMb12AgNHwpjuxz5ojduKQH+9ZboYiLs2Lb/oSDOxRVPP82io7GwKnNG6i6cRoKt4Zw7hyOoiPr4dwpFHXpqQBtgDLgFTSoe4iHjx4JD+V/iUP1zbPw6BOFgoML4NH7E95vV3R0Axp+8DUv0VTf/RX5sdGQOWrhFTrJYigqVnTUZyjKDe4iLsVcnEGx7NixHQZDJRYuFE/+s2YBCoUWFy78itWrl2P37l0sMqlJkyZo2/YGRo/Ws6/hoqdKSwl6qnXrF7B3b5zdC7zUuQhBpH0BGUXBsVU3eAZPgL74EfL2z4expgxKDz9oOwSh+Nhm0RbrkxiACsX/RA+9vvKXjMm0OuBlVP1yENMmTkDVHz8h/2A0lN7NUJS6Hm2aNULp2T0sQakoNQYRocG8z2VaHhRFoVPHDqipqUGJy3N4970PcOLECWzeth0n01LZZC7VpHrsqPdR+fMPcFDKUfvwxt9CWBDSe7l8GZg/n1Rt5r6TjH4HTQN79/Lfy9z9JjIS8PEBGjc29eS5NP66OjJwPXv21BP/XtaC0fb4LGokVNcOInLUCDjkXoVSTkHVqA1KftoNhdoJ+t+PArQBruGzoG77f7idcceK/EQYYDSi5s5lNPp3DL8Vc2QdFB5+vLlNzf2rKEhcAp+BRIKXeX7Z+R8sWntZa4fzbOycO4cj5Uf7sOjc4LbZxFpn3N9yzx4dYmLWorKyUpJhSnl5BetqtHJlOY4cobFyZTlu3EhHUJCe9xqmtcewgWNigMzMzHrt1qzNRZw7haHitx9B08bHbavhuP7bFVYSpDb7D6gMFVBRBtTl30PR0Q0W7N0HZnaCf4a9W594ZhK6vZ6PAH8R8AgaD41vU6zbsBE0JTexPT2bICM7D54RM1mCknPHEMQnJUtaRG5k5iA4vJ/NvreQSfX8eZ9hzfoNkDcLhKtcb3Ng+rQIC+Z6L8nJpEdqywQ6PJzcgMxCIOR+s2EDIa+MHQtkZfFvXGYA+9JL4jTuJ6mJbR7cwfn6dWvh7e0NVYvX4PF2JBqP2QLvwQuhdPWGazjZohelxkBj5jaUv2k0j56v7chnKgNAfuJSKNwawFCay5vbENPzLhYzGW3b19mhKBm8fYGRQwag7nIcch4zTouOrMeShZb9danBJQsJaeYL/ZZbthBkSkiI+PtmZwPFxURrp7i4CsnJOsTFkZ2dnx9QXf3kaf1MmLc0zdngzoGhMNZUIjtmNDTteuC7fQfRokUL3M+4iYVTP4Hq2kFEfz4XBr0BoI3weDvSgr3r2ORFlJ7Zg5zHjOGnOQAVimcmoddH/lJoEaA1HvwhScdg6Aw0Sk5sgzqgMzz7jIfhjzRMnTgBQWF9gQZtRRcRRibXPWKm1V3D2HFRFibVb/d4E599MZ/3WN+wENGBKTOsTUtLeyqIFwbFoFAMxpo1pBq3JYPL3NhTpgDLl8uwcKGw+w0DfZw3z+RZydVw4SJcuAlcJqPwwgstkJcXw6vybKEpbIW5ZywArFixAhm3M1Bz6yxK9kyFrigbhvJCVOuMkGs9UXP/KgwVxazKZsW1NBTFLYC+ugyV6ceRu2fm493dBnj2Gc/7PLeu/aEvy4NXxDQLNIu+6CEebY1C9obRKIydj2+WLUQLZQnUzu7Qleag8Mg3cNE6YerUqSjOe4heXdqhODUGXh7uCAsLq9f3B/jsTXOqvTUnI2sJ2VyXJTXVUmL3adD6meDe70y/XOnVFHn7v4TRoCOyDuUFcPR/AXVZv0Pn4Ip+AwbyLC6nz5oFPU3DZ+A8aNv1YAegpWeJro9X6GQo3BrAS1n31AegQvFM9dCtsUSLtkbim2ULMXz4cPYxqUMS2miEptVr0Jc8hLKmBONGD8ea9RsgaxIIQ+ZlqDz8oHlrnMXrszdFwqFRGx5OVajvXXZsIxxbvQb3PlGozbyOoqMxcJBTcOoRaZLcTVwK1FXD28qwtmTvTNTk34dDy1cF5wVPIph++p49OkkDz969gdTUNEydOgEtW17HmDH854gRkpg+/MyZwLVrpD/bu3cIi3Xu1EkniH1noj5sQYAkc4YYVPPgGl5qHYBhgwdi6szZ0LTqhprMq3BVK1FjoFBdXgJK6QC51gOgaah8msOjzzhUXE5G2cU4OKpU0L49Fg6N26H8UhJKf94PAPAfx1cBpI0G3lCUOzuh3Al8VtOiK2rvXEBtRSmOHTuGkIj+UDZ/GdW3z0PT6lW0UldJmhlJDS5Z6MQJfg/dWk/9z7A7Z88mrGJ3dyL5IBbWWKfWgrnfb2XloaooF979Z7MkLH15AWDQs4/l7p4JuasPajMusFj+ti+2x42bt6Bp9RqPGFiQtAzOXfrBJTCcnbEVHVkPfU3lE7//gP+RHjogXf6SCdumGF+DNujhM2AO6x2pU2qx9OtVpMLuEwWVhx8CvBxZ42DuFs6B1sO5+BZyOZK8QruGrZtiWLGf/LhFUDVoBZ3SiVdJALAwD85aPxKlHKU+5Yu9YVCon6pEJyN1KlRJmffHBw4kW/CBA8Nx8+YNmBeMtjwrFywAPv8ciI01oHPnV3gwunPn7LdHsxVMMud6xl7//Q9MnTGbeIUGT4DCxRslVTpUlRSAksmgbt4JhvICKDz9UX3rPPJ2TYU6oDMaf7wNXqM2sDs9lU9zwGiAd/hUQTirc2Aoys58T+QEEhfC2VEBpzvHUBC/GD6PtfPlrr4YMPAd9B04CO4RRE9G4ekPhVcz3MmvQFBwMM8D989cA9w2W12dAklJptaZNTiqmM6KVDPyvDzCQn5SUEVuMPe7htJB3fIVk+ZS+DTIHLXwfixNzGguVd88B09PT3aXlnBwP55r1vQx9nyiyXzk/z5A5fVjRBjwehqKjqyDg9btb7Gre2YSui3csJhe9KlTp7Bv/wFUVlVZSGYCFCiVGqrGz7NEEaNBD6/+n/H68ld+u8YaB3MFveTNOyMvPw+G2ioLoR5iV7cAc2fNwHvvvYclC+ejpiiHCD89XjwKk1cTqGK/WfAdshC1dy+haM801kXetftQVN88S7b419NQnLYJXiGTnqhEp3mPurCwHA0aWN64Qj3VtWsJfNFgqERFhc5iKy7lJu/XD2jWzIiPPx6Bl16qZZ8rFeNujyb2J+M/NSMGfQo4ucPnnXmcG70PQBtAyQjhxzNoPGSOLqi5fR6OLbqiruCBoIpmYdIy0HU1KDi0EmVJi/HNsoVwzzqDrFVDkLV+JKpObsFHI4eD/mU3QMlR7t4SuXkFcHq8iFMyOTzDpiH59C+8uZBzh2BUXDkEzVvjcPyXdHgP+Ex0ZsSEVGd7ps3WvHkkADUmTyaaOtZ8aMUkd6X8XoGBwO+/ExiskMLjhg2kfbd06Rq7B6LM97tz5w5+OpGGVupKlOw1GdD4/XuDGTFrHWQUhWqv1uycq2XLlohZuwYwGkFRchSlrodXxHQonL2hLysAZHIUHVkPbadQOL025C8dhjLxzCR0KSxR84nz8ePHERwWjjoD4NCoLZuIGcy3Y5MXAaMB2etHmYgiI1ZbMPI07XrwLO2YZFzxn9Nw7T4MxpoKuL/9Mfu57Pu3fAVfLFwCvV6PYR+MhAOnamBt6x5DolSe/nB54wPIyvMgv/IDApo3Bf3HMXj0joKmdXeUntkD776z4Nj0pSeGeGFsxbhIBMbFhnvjWqu0x4whSomOjsBvv/HfX8pNHhICPHoELF6sx+XLBravbsvcGrB/eJZwcL8lMWgEnxhUfGwTjDUVcA4MY+ckhvJ8yJ294Nj0JYCiBFU0tS/3BeRyODRuh8b+TeDn54cHDzJBA3D0fwGN/Ztg4IABqK2rg2vYdLj3joLSzRcOeek8ZFPDUZaMUNduQ2EoLwRNG1GUGgNdUTZq7l9F5clNMOh1CGjdlk3U9jrbM8zR0tIqpKffRsOGY60adDOSu7NmET/Sy5fJAl9ZSXgJ5vMRbpw5QwbpgwYJI54IZ0GBq1cvS/5Nhb4zo5ZY+fA28mMt2bQFiUuh9HkOMveGcOs1li0EGZMTn3fmocH7S6H0borK68eRH78YPv1mosF7S6D0aoLK68dQfXrbXzoMZeKZ6aHXh9TTpHkAsh7mwGfAHNJL+24ajIUPYAAFH6a/tmsaDIX3IXfxRsNR63mfmb3uA4JOqK22wIjnx38F7Qv/Qum5H+D66juoTD8Br/BpMJQXEi3rV98huh0GHZp7OeFmxh0o3f0gc1CLmgAXJyzCj4cS8Oabb9o9L7A3xASXuP1Tpv/dqBHQoYP1vmdMDHDtGnk9Ez17SiMe9e5Nkv/GjYBOR3YCYr1abtRH0MtgMGDEyJH44fBJ+I5Yy/tb9obRMFSVQendDBQF+A5djKz1I0HXVkHT6lVU3TwH7/6zoG7aweJ9aaMBOd9Ng6Z1d9TeOA1dwX0YZUqTafie6XAyVqLaqw1P8a8ieSnqVC4wVpWi4YeWuGnKUQtjVSmMj/u8mpavQF+ag7r8TCgoA/RGCppWr6C1ugpLFkUjYsA7PCJafchpUtQVly8HfvyR/Lbh4cI8hZkzCcKJib59+eqdgCUG3dkZMBqVuHLlP3bLIHO/8569+2EAZSGrAAClv8Si+sY50DQNpzbdIXPUQnXtIFxdXZFF+bAaPAwBjOsfy/AJIkd9iPXr1goc0Z+P/4keOtMfE9JIEaPcKlUqllrN6Jc7uDdgbb4YlIuRBq/CZsK1S3/Iyh7BUaWwwIg3GrEaleknoGrQgiean38wGirfAJNuh9IRt7MLoHDxJmJAbo0ETYALkr+G2lHFwhLtnRfYG2K+jdzKnNHKzsy0DlUDCMQxI4O/FXd1JVU7t+8uhHBhdNVDQkwuSE9KE9u81XDq1Cns3R8L156RFs917hQGuZM7QNOgjTQKk1eDrql4PGMZD6WHH+ry7gIw0fRLLxxE9b0ryNkxCZpWr6LiyiG4h0wGrXaHTOMKudYTlEwOx5eCQBsNFkgt9esjUZdzW1CGQtsxBLqCTBirywGAHEfwBBgqSwGjDnoj9fjYJuDGo/9v77zDo6jeL/6Z3eymbioB6WAMRRCRUETEAtJCCk1/iigKCCLSLBQLKiCgX0QECRBARRBRlBqKlMQOAqKCWAkCBggJ6XWzZX5/TGYzW7OBJCDseZ48sLOzM3On3Ln3fdIgsxcAACAASURBVM97Ti4PPfIoqqYdEc1m0j98Bm2n+y2sLERI//AZaBJVaajAmQ+tjOPHpeuk0cD8+c7rFJQ+o4CdS5azMF6/fga3WUyOmGyfb9/rtDMH2UIQvILqkX9ok2W2qzTRka9PyL0jLQ5HskSyb0RHVn2w2mpWVFuG0ddMhw7WvGFbjZRzZ07Z0Yd2JW2lhU8h2R9PsVygOsNtQyoJBHcf5vDC+3eIQRvakLs7tHbIEdd1jEef9ruVaH77W1qjP1uxTDSWIRpK8G7Qmotb5lFy4oBDE2BdVCz16tW38zSsCX9JcO7zqHSxSUyUlrnLHS4rk8yb33xTepBbt5b47MoH1pbGpjTRULoguWuP5ip5ZjsV37dvH31j4gmJn+7wvOqiYlD7BWIqysGnSVsMmf9YSr4FlRpdhxjyyhObkqPQQ+R/u47Mja+jCW9G3vefEHjHQ1J4pKzYEuYrOfUzRV+/x6cff2SVpFfKPTg6HovpSos7UHn7WxyVzCX5+LXoZuWo5NOuHwIq6unTyNwkH896goa+pTB0aUbBDxtY+8F7Lq+lMx/as2ele+LFF6X14uMrT4Ju3lxxvZShHFdhvLFjYeZM55owSjiiMxvUPpakKFQQDPIPVYRnA9r3oeTEAdTmMoICA2nYsKHFRCfAmEvmxtlW1pJZO98hc9t8tA1aUHLiIN43dbXcU/I9FhM/iFZt27FmzRq7ZdXVsV9THXpVERkZyaED3zGoRxfHPqLb38L7hpvQdZTe2M7i8j/+9LNDUa28/Z9YPCSlbPpU/jqXa+kEZN5r3UEv4t/mbox5Gc5HDR3jySgV7DwNXR3X5SRlXPk8Kl1sRowALy933eGlqXdKCjz5pDc//ghvvOGc4fL665La3oABFdvQ6aT/u2uP5q7y3snMQgb/30NobrT17RxhXRjUvi+iUU/xH99IIRBR0swu/DWZnOQV+N98j4WT7KULB1VFkZpXcH1yU1ZaWRIiQs62N1m+ZBH33HOP1czLUWHR2YThFB7eQsmpnyTT6CEzJPckX51LR6Wir99j2vPPkJb2L+qAUAJvH2LJ9WRsnI3KV0fg7UPwDW/CTz//4vCcKeHIh3bkSMnYZMkSSfahsllbVBTs3CnVM4wfDyoVzJ5dEWapDhaTIyabzO9XKiIGd3+Y4r++q6gX2L0U34jOGI0m8oIiePDhRzGbzZw/fx69wUjArb3trqNXYB30Z45Rd8gMQvtWFBXK99ip7GJSz2Xx+BNjrJadyimrNmG9ayaGfqlwNdotPLyFoh8+pcxoxrfFHZT8+TVzZ77KyvdXc/J0GrougzD8tIWZM16yci6CcneiTa+j0vqi9gumTtzzLnnq6asnWwnpl54+StauRQTc1p/AjhX8Vu3RjXy1b89liYC5A1eCXJbjPys9iD17Sp2xLcdciRUrpBfAuHHSaGzaNDXx8YKVZoctEhKkF8VMyeSJxER1eQevsmhuHz0Kq1YJpKaKGAwCYWE6t5yKHDnU5G+dQ6nRjMrLW3II2peI2ahH5e2Ply4cXcdYcvYmYjaWUXfIKwiCSlLf03hjLtMT2ms0ulvus+zDkTlC5uZ5hN73hFXMNefL92nfppVVjNvKAUuAgHZ9yElZhZdWi7G0GAS1vRHL57MIs9GHyfv+YzSCyLKFb/LSK6+Rdi4dvxZdMeaeJ7TveDI/n4W5JB/fm27HmHsO/1t64/3rpkvSA1LG1nv0wKXGvTMdoK1bpU5eFN3TAVLmSBwZV6SkpDB8xCiysrLRxVQUbolmk1QbcGADgtaHhqMTQRQp+DGJgiPb8G52G8W/fUX4oBfwaXwLWR9P5ZHYe1mzbj0B0c+Tk7zK/tpueYPQnqMs+zi7fBTejdpY69bbrFN4bB95331MQGi425ov1404V1XhjiFG+oeTMRvKMBVk4t+qOw2M6aSdTUPVJIqyk4fYsXUjT096xqlio7ZBK84uHYHKV0fDUdZJ1bSExxC0vqh9dQTd8SB53623engD2vWm6Jed+NdrhqZtb6tO2mQyWQS/1n34gSVR6o4ImDtwJ/Eld9LNm0uhkvnzXReNKBNeMTESDc2dF8bGjRWFQhs2bGPr1o2XbTXnrKhMNJvIP7yV/AMbEA0lTJ/yPJ9s+JzU1BMIai9ABSoVvjfdTsnf+yUu+k1dKEk9DFof6vQdT0nyUrxvjUbTrAMXt7wJKoHwuKlOXY/qxE2h6Lu1dknR0tNHKdr7LmJoU/QX/iEsegJZOxehbXgzal0oRT/twiukPnVin3PpqFSWnkqd9B84ffoUIfFSEvbCR1PxCm1kZb6R/tEUyjL+4cknRl1SQk+ZSJ861fn1dafI6NlnpfurcWPn+1OanjgT5ouOjUNvMFuqv20hmk1cWDcdv5bdCOwUb7U8/aMp+Le6i8BO8VIBYEoiPpFSAaAx5zyZm+chqFROzWjyk+bRqHFjzuToCYu1H9Apr5ExL9NtYb3rIil6KXAUukh79xHyDilFjuKlsMiQVwjtK02RVBHdLNovP/9y1E6HPe/7dfiXm0WXnf0DzCbCetsnVQM7DQSVGlPmKXJSVtmVEYsn97Ny6bvMmDDSLqlb1XxBVeFO4mv7dqmyLzHROXd4xQpp+fTp1g+3u3H3vDx7U2jZgMEdUwNniIyM5M05szDnnSd/6xzLcv2/xyn+7Ut8fXz5YNVKXn/9dcoMBgQvb+oOeYXGk9ajDWtM6cmDVlx0r+AbMBfnkrt1rkWDvzR5KeayYgxZZx3T43a8TfA9j+PbrD0+t1onRWVxtiVvzaVlMOjC6mLMz0RjLKah+QJeGX8R3GMkposVWtxKZO1aZGFf6DrGcTq7BIIaVNBi+0+2MzDXtY9GpfFmS9L2Kp1LGcrYev36gp3GvVx4NmaMlAB9+WXHFEZZB+jjj13vT5YAcKWpZPQOsoQ9oSI8mW8VRutD/qFNksCZolBPd1s0BUcqQlbvJS6zXB/92T/Q6HPoFFnfqRn0siWL+f3YL/Tr2s7hOvI1QqTahPWu6w7dtiPO3DgLTWgDSv7abxVLC+7+cMVNHxVP6T8/WhVu2DJsVq9MJNKnkOwPJ1pims6SbBj1IAiE3jcGQaUmsFM8DcesxKdJO7xvjWbx0kQmT5pYLZ10VaB8OJcudd5JyxWbSu7wiBHWaolLlljT086eBV9fGDzYMbNFxoULUoJU6TBfXZA6gSHodY0oysuiLCvNMmJS+QZRXFzIvPkLMJvNdmyogNuiQRQJuW+05b7waXKLlKD087NiVwWojKhUAqG97ONRuqhYcr/6gIJjeyn66j0+Wbe2UqbW9q2b+f3YL7w47jE4vB5RpSasz1N227Z1VNJ1jEPMPWtVtVz/sXesCQD7EsFoYM37qy75vMqx9W7dHmbrVsfCbLJBhm0CXIn4ePjmG9f7kllMroT56g6egTH7HOlrn7cyvWmQ/RMZa5+TCvL2JiKUFaEqK6LgwKeWZz9n3wrMRgOZm2bz2ssvWAoAzblnyUtOZOK4sfx87LhDBpzMNPvyyy/Zk5zicB1Z3bFo77vVJqx3XYdcAKswxZtzZrNg0RL+uViEuV4riv/4hrC+E6SCEaynSIi49Pc0mUyE1auPXtcIU3Ee4fFTMRVkkbVrEd7NbqPs3J8EdhpAzt7llpJjW9SUnnJV4MxTdMAAacTtiA/uiiMux0779pWojK64yZeq2VEZ5BJ/385DyNu/Ad8bO1J65hhmQynBdzxgWVaSepB5s15lQHwcAwY/wImMAnQd48lJXiklzHLPUW/oPAoObSH327X4tehGQ3MGv//6CyqVyu2QnldxFklbNlXphe3Ots+vnoxZX0TwnUMp+uo9Zr3yEtNffAkhqAENRlibb5xdPorArv+H8fgeZkwcVS33286dOxk6dDDdupXwzTe41Ei3DcnJxieLF7v6jTc//ngcs9nsUpOp5J+fyd02F11gIJ+tX2cJTy54eyGz587DS63mpelTefm1Wfj3fRZD5mnyD23CVFIgvSAib0dM+5nNG9YTN3AQeoMZ7wYtMZz/w1I1bgvRbCJrzSQM+RedsqbkcI+XfxAtg3Fbd+eyQy6CIPQVBOFPQRBOCIIwzcH3jwmCkCkIws/lf6Pc2e7VAGWYYtiwYRza/y1xd3VASPtZGik3rbgQymlsZXK1X3/9NSVFRejP/o42vBkZG2eTsXkufi3vpPh4CtrwZpKesg19qrrZKpeLiIgIPv10C1qtH4sWVUjayg+eo4pNZxxxJRXtySddc5MvR7OjMjw1YRLUubGiurecJaKt08RqmTqwLq/Nmk1kZCTvLPgfxqwzZO9OsKsIlvIlktaLzESS91MZG0nXMZ6g4OAqz76chgsVoYTATvGIJXlweD07tm5i0ZKlmFATep+DStYOMRQd24NX297Vdr/169ePw4ePcf58W/r1c5/CCBWzs+nTHYfxpk+XmDRQuSZTScpSVixL4GL6Oavw5PPPPUteViafffIxL7w8A73aD6/AcLR1m2PWFyMIAuqAEAK73o/RJ5i+0TGUmSS+v6k4D59I+1CO8trqS0vsWFNnl4+yo0fqL/xTbdpLlXbogiCogSVAP+Bm4CFBEG52sOonoii2L/9bedlHdoXgqmBHKYCvbRfNk09P5M8//7R8LxcLrFmzhujYOIyoLLQywUuLd/1ICo/utizzCgjFL+uPKnmgXgm44h37+tpTFrt2lVyGbOPv7lDRoqNhzhyhUtrh5WDrxs8QM1PxjehkmaaHx0/FVJJvFVMO7BiPvswgcYkHDERUqfFr1c1SmBPU7UELF91RYc6leNxWpQ22234vYSE3Fv1ute29u3ZwMf0cAFlZ2U6Tg4FRsYgmE4UpK6r1fpNMxk/bCbPZIjq6omgMIClJICJCYOlSx6YnS5dCfLzZQlu8nEK74SNGlY+6W5G18x0ufvGuxKEUVHg3aEX2rsWExT6POqShhVYcHj8V/fm/JN3z8nDtS89NsLq2pqIcwkv+5YK8TnltQuGvey166TnJqwjrN6HaBm7ujNA7AydEUTwpimIZsB6Ir+Q3/zmkpKTQpHkEMfGDnE5jdVExIELB4W34d4jB5B9Oz9797PQwRo8bjwGNlaJbeNwUTIVZ1h1GF0lruSrVrVcKjnjHkycH0rJlW3burPCt/OEHiWbYqZOk55GYWDG62rPHPe2Wf//VVHvMXInIyEh+PvwD6nNHrRzbG46y1knJSV6Jd3BdhjzwIHqDmdDeT1F2/oSiUGg9Nwx/22lhzqVUL1elDVXZ9lMTJqFu3snafGPlKOvZQododIGB1X6/uappkKEsGjt+HDZvFhk5UrRzK1LOEGXxtcsptEtJSbG86ML6TUA0maDwIoLZaFlmcZlSGH6bCrJQl+bRt0sbcvYuxbtRaz75fDMHvv2K/+tzJzl7luLTuA0hoaFMemwI+Skr8AuuAyJoSnMY1KML+cnSMmNBZrUN3CqNoQuCMAToK4riqPLPjwBdRFF8WrHOY8BcIBP4C5gsiuK/DrY1GhgN0KRJk6jTp09fdgOqAwsWLOD5F14ClQbfiE6ERU9Ef+ZXsnYvwawvIrDzICtj4LxvP6bhkystCZVhDw6x0sPI+fh5/MwlZOUVoAqqR53+zzikLOUlvcHObZtrxGWotqCkqgUHW9PRbHU4RNE1NxmsqWg1hZSUFB4bNZozp06BAJqQBjQYmWC1TlrCcFB5EXTHQ+SnJOJ1Q0uM+RmYinItOj8X1k3HK6SBFfUve/00Xp30xBXLeTjD5RqYXw7crWl4+mno1Qt27/YmP1/v9r0S2bqtUy9ev9tiHPoAy2h9y612+iz529/E/+6Rdnz+hmMqAg8ZiSOZOOZxFi9d7pY2zqSJE6qNZlwbtMVtQDNRFNsBe4DVjlYSRTFRFMWOoih2DA8Pr6ZdXx4WLFjAc9NexDfyDryC6lF68jDp7z1F5qbX8a7fEpWPjrz9n1mmSNm7l4Ls+J68ipD7xthZ3Wnb9UdQCcx59SXUedINYouC3YtYvmQRoijWms5DTUAZjpkzRyA6uiKkYju6Cg6uOTcadyEnRDPMOhAEVBofxwyUTgMRy0rIT17O85MnYjj3h3Q/2Mgp21L/vG/pe0VzHs5Qk7OFyqB0P3KGLVsk4bWsLACBkBB/t++VSw1tpaSkkJeXS8lf31vps4Q98o7NTG2FNFJXwKd9DO8sWYbQJMot28uaphnLcKdDPwso6f2NypdZIIpiliiK+vKPK4Goajm6GkZKSgrPT3/JUpotqDVoG96MIe8i4bKpgI8O32bt8Sq+iPboRt5fsYy6Og05exMJ6TGKgLY9HJpWfLxmNR2jOqDy0qBpdY+V8QUAYU15bOQTRMcNsNISkcM+jqRMr1bI4ZgzZ7xclnv37CkxWVzBHUGtS4U8NQ+KnYohNx1BrXEh0BSLV3B9RFFkUcIy6gx+uTx5Gsi5xDHOqX97lxEfU0nNuws48kgdPvxhHn982GX7ptZWp2ILd2oaduyQQnUvvwwxMSaaNWte6UtAvlcu5WUlv9jzA29CCKyLV1hjMjfac8UvJs3Hp3mU3T2i6xiHEFiPwLwTVbK9rGm406EfAiIFQWguCIIWeBCwKhkQBKG+4mMc8Hv1HWLN4akJk/BvpTAQiJ6IKT/DSnAp4NY+lJ48yPatmzl35hTDhw/nzMkTDHtwCMZfttltU2a/iKJI7MDBaKMGWdQWZb31/IObKD55RJrSx04jpM94TqTn0Te6P2nn0tHFTKlR16GaQEREBMXFRpex0gEDpAe3Jtxo3IGSGaLy0li45eCYgaDrEI1ZFKxGXIEd4zGXFpJerpkuQ5J6mI3vjVGXXJjjSH/+8ccL+PzzdRQXf1Stvqm1CeUs7s03Jf2egQOlF3xMDEyZAm3bSh36Dz9IsfFTp05V+hJQ3itVeVkpX+xh0RMRRDMlfx8g1EHxn67TQEr/+ZGSUz+TtvRxq8Ij3/bR0nPuhF1Tk8btzuAWD10QhGhgIaAG3hNF8XVBEGYCh0VR3CoIwlykjtwIZANjRVH8w9U2rwYeuiWueLEIXweeoKWnj5Lx+UymPjuJuXMrJG1dJWGKftxCk/xfKS4q4nSJBv35vytKqz98BtGgx1huiODdsHWlOh/ulgNfDXAnVrpjh8QtjouT+ObffSeFY/LzJZpav379mT//nRphtyjjyJqOQ8j77mNEkxFdhxhyUlYR0mMkhb98AUDArX3I2ZtI+MAXresQNs7EbDSg8g1EHRBK/eFvoz/zK5lb5uEb0Yniv77ni6Qt9OrVq0rHZqs/f/YsrF0rCZmVlUnhKiX/H+DLL+Gtt9RotT7k5haXyyAMIz5+CFu2fMa6dWsV8gjDmDDh2Ro5r+5i7ty5vPbaC8THW+u3bN8u3RdDh8K6dbBoEYwYoWLbtiSLh6ys3XPhgtSR79ypYe3azy4pca7U8dGf+dVl8Z9oNpG+9jlM2f/SqGEjzl64iCqoHrrboin66j1697ibbTt3E9TtIYK6DAYg/+Am8vZ/QkDr7tykya12b1+PlosLmEwmovv3J+XQcRo8sczqu7PLR+HToCUtAsosF8Wdgo78z15izP/F8M67CaibdbQS50n/8Bl8W9xOYJf7yd65CBAJi57k8GXiqnDpSiI1NZVFi96y6zDy8/MpLv6IsfYDHQtWrJDipHv3SnKptuYHl/uwVgalMYj3HcPJ3Pw6Km9/6sQ8g0+TdlaiTWq/IOqPWIwgSA/jheUjKM3LQuXtK+m3nDyMT/OOlJ48ZPXS7t2lLdu2bK7kSKzx+OPDKC1dx5gxoqX4ql8/aQTrqPgKHK+zcqWaAwdMDBqkJjraVGvn1Ra290hwsB8lJcVMmSLi6HaWi4vuuktKdh4+LIlupaamsnjx25et3aOE8sVemJ+Ltn4LK1G8izsWoouKsSJCGPavoUH9BsT278fyxBVovL1pGdGc/Yd+xK9lN0pO/kjDcaspPLxNKjIrXxYQ3ogZE0ZWa5Lc06G7gJwUlcMsSuQf2kLxn9/ho1Hx8vgRPPvMZLu3e9buJXiLRnyj4vC7LQb9v8elAiQfFV+n7OO+vv3J1KuoUy7Oo1TQC+0znvwfPseQ+Y+dG03mylEsXfDGZbkO1QR27txpNWpSdhhJSWr0+tJKRbqeegreftu1mNeMGX4cPHi02keU8gtZDoU50xp3JNqUd3CjZPU36EV8Gt/ChY+mYshLJzxuitWsKnffcotTvDvYuXMnAwdGs6q84r4y4aoXXgCzGebNs17HHdGrmjqvSji7R5KSJCVFW6ciGStWQE6OVPI/cuS4aq8QVkJ+sX+2IxmTVmcliocgoPINRKX1Q9chmqKvVoFoRhNxu0X46+mnn2bZyveoO+QVy4vcVJSLWV9otUwUVNTRlFXrLNsjzuUEyqSoU945YPCrY1cskrFyDBmfz8S7fkt0/j4UHPiEC4mjpGUNb8bbx5cBQx5g767tPNinmyXGpgltSL2hc/EKbkDGhpcpST3o0I3G77bYy3Ydqm6kpqYybNgQZs4sZtQog1Wl56hRBl5/vRRQMWWKa5GuLVsEBgwQLlvr2l0oC77k2VXBz7sshUVg7TCkrOIrOFKRJwnsGI+mThMMGafLv++HSuNj5/H5v7n2yTVnkM9pWZk0ynZPBxyaNLFfx53f9uxZTJs2NxEYqGX48IernFh1tz2O7pExYxw7FcmIjoavv5ZCTIsXJ1xy8tcdfP3113z8yWeUmcyE9h1vEcULuv0BBC9vUGvwa9WNnD3LMRoMBMW9YCX8tWzl+/i1vFPhdTAFQetL3SEVhuK6qDiMWWdqtTDwuu7QlUlRqCifLlQkPgLa96H0xAHLRYmMjGToA4PRF2RbKj6zDWpMmgDKivMtyzJKRE7llBE/6H4+27TFqoJN/+9xiv/6HrGs1OkIsTpch6obzmzpZLRpA4MGqRBFFenp9tV9S5ZAQACkporExLieGcpFI5cLZcHXmHETEJpEIZrNmIpyKT39C+dXT7aYHATc2pfCo3s4r6ComkoKnCrw5aSssqKzXUyaz9RnJzF5svvTa/mcypROd4yzY2Ph3Dn75e78Nj5equ6NiTHw+efraN++TbUmVpXtcWQtGBxsX+Yvo149SYXzvfeo0eSvbA5vQsC7QWuyv1iMrmMsYX0nkHfgU3ya3IK5MAtQofILxDvyDjtqYlC3hyg5eZj01RViZw2fWGbndjb2iZG1Whh4XYdclElRVev7yNm7lCnPTGJ38lf8k1mI6ub7yNmzlP/NfZ1nnnkGkG6G+/r1x7fFHYT1q1y4XuXjh7ZBa4t8boWin84qKWoxtOgQQ2BUrMtiiCsF900v/BAEnCazcnNL2L1brPECI7lgLKTXWPxb303ehhcwZ6dRWKIntM9T+LW8k/PLR2LSF+Pd8GaMBRcx5p5HUEkH5t3wZrxyz1BcXAwqLwJvH0z+DxsJj5/GxR0LCe7+MAFte1r2JyfEq5IEk8/p5s1SUnj9+qoZZytRVdPt48elGZNK5cOPP/5aLWGY8PBAHn+8gMREqeOOjrbPAYweDStXSvUJSij175Wo7lCRI3N4w8UzIKiszOEN2Wn4aNQ0atyYi3q1nfCX2VjGueVPIGh9aWiTf0tb8igjHhpMouzTWI3whFycwMJfffpxtMc2su+LncydO1firz79ONqjG/nf3NdZ8X6F4etTEyahrXsjJX8f5Pyqp61Moa2KEVJWERY9wSK3Ky+/uOl1fCM6ET7o5XJZzymSzsOm1wns+n9SkcNHU64aHRcl3C3hzs8vZcOGbfz+e0tGjpRU80aOhN9/b8mGDdsIDQ245AIjdw135XCab2RXCn/eBYKAV5s+FJbo8WvZjcKfd6FP+w2zQXIfqnv/q2A24eWllfw4QxoQPmQGpYIWEQHfmzqTf+Az6sRNwadpOwI7xlt0fWRcyqxKPqeyR2pAgHvFV4GB9suDgty3AgRpRhUbCw0b6qstvJWVJXXmjrxAZQG2xETJqs4WSv9YJao7BOfIHN4rIMzOHF4QzRa5YkfUxMIj2zHrCx3KF+s6DeSjTzdiNDp35KoJXNcdOrjmr360+n1mzJxtVeTzwpTnMGScxDeiE6bSAi64MBdAhNx9y/Dx9bNUsM2ZOQPx35/ISpqPf7telF04if771UQ0b4r4Zwr+7XpBThocXn/V6LjIcNURy+YFQ4aAyWSmf/+etGhxnFWrpHL/Vaugdes/uf/+WO688y63i0aUsDV1ttXQsV0WPvhlwvpNRDTqydw8l5zklZYiMkS4uG2+RW9HMlcuIGzQSxY/zuxdSzAX51s0PTRhTTBkSnIVuqgYKXGa8KhLdUxHhULKuLB8TmWPVJNJSh66wrZtUL++/XJnKpdKbNli3WlGR8O5c2Kl4a3K2iEjIEBjVS1sC1mAzc/PerlsmCL7x9qiukJwUGEOn/H+OM6tkjrjBuXhktLTRzm7YgwFX65g3xc7uPfeex0Kf0muZGstMXNbBHaMw+gTzF1331OrleDXfYfuDM5cUMaOn0idQVLVoNo/BN+mt9r9Vilcv3JZAq8+9zTaYxuZ9crLvL/mIw5+9zW9O7chL3kFY0ePpFGjRmzd9DkvjnsM72Ob2bV9GxfTz11VnTk4L+FWmhfMmCGNAOfPhzFjRLvE6cyZxezdu4fNm83Exzs2uHBUYOTKlUa5zNZEW1CpqRM7BWNWmlWJfsCtfRANpQTlpVLw2Utk712Gtn4k2ftWYsxNJyx6ImUXUi1a9fJv8g5ssIqn+/ponZaYOyoUso0LK89ply5Scc22ba6Lr7ZsgVOn7NeRR/mufqs03QZ5RiXNFJzBWTsyMpbRtu1NqFSCpYM3mcRKzaHl7+WEeUKCY1crJerVc32MVUFkZCRvzp2NqSAbbb2bLAV/cjjUu35LGjRowF133eWUpiwZwN9pnX9b8ih5B5VuZ3EcOHS40gFIdeK6jqG7giMT4aKdrumq0wAAIABJREFU8/Hp/rhL0R5wLFz/1VdfOfQ8tF1WnQUI1Q3b4hewp8otWSJ17E/YS24DUuc/axbExAjExopW8dWkJOjQQc3Ro952fGl3r4f2WIWJdmpGAQG9JzgpGHsN74ZtUOWcYlBcfz5L2o2+MB+/Fl0xZP3LDY++ZeGfy7/J3TaXxo0akV4s4t2uH0VfvceOrZu466677ESWHJ0rJSSjbC9UKg16fQlvvVUxqpV56HIMWs5BOOKhy0Yh8jorV8KBA9Ky+Hjr3yYlSXTHbYoCZ1kUS6OpMFtWIjk5mfj43qhUJgoL7Q1OZCrqjBlw5IiGzz4z8MorkoSyM8jmFSqVitDQAEpKSnjtNQMdOjj/ja0h9OVALvsPip3qVGTtwppnmf3ck6z64EOHwl9Co3YU/vm9ZB4eFUv2ngRGPfaoFGbxDZGW7U4guPswdFFxLoW7qspR98TQFXA3BqsU/HGm0yCzHCoTrh/39Hi3R5dXMxzpom/ciNUU2xXTQja4eOMNePJJ0S6+OmcOHD4ssGHDNrviF3euh6ybIfuFFp3726GXZ+a2/6GLiqXu/a9g9Alh3cefUFZSRN0h0swL0UzBYWtZh4I9i0hMeJc/jh/j1Umj0R7dyI6tksuQoxJzdxhB/fsb6dathFdekbjly5ZJ5ygqSu4gpdyDIzu/Ll2k/+fnW69Tp440yt+0yZ5ldPfd0gtAiR07pE6/W7e77I5x586dxMf3ISbGREKClHC1tY2TzSn275dmYG+9JfHjHdESZVy4AHXqBFo8YUeMGMWRI1UPwbkDR8/2oPv/jzJRhco/1CL5UXYh1XoG1yGWWXPmORX+ukmTi65uY0RBsLBZEhMTyck4R+/ObcjevRTvRm3QlRcnuRLuqk5cVx26uzFYgLS0NIqLi7j71psc6jRcTJqPoPHBmJdpJVx/3oFw/ar3Vzv1PKzJi1sTsNVFT0rCaortyMFIhjs86fh4ga1bN9p9V5krjVI3IyUlRTKkUGsdKikGdh6EPu03EATqxE2xMi6Q+cMy/7z09FHOfzAR75Z3Sep6gkCH29oTFBREo0aNnJ6ndevW0q+fwen3II2iDxyQRrMJCVL8fNw4afQ6Z04AqakSf99WB1xGw4bwzDOSSqFynQYNpBelUkP8nnskjrcy3HL8uDRaT0uDlJS9VvHw5ORkHnggFpXKyKefSi+FZeVEDlt3KaU5hcyTd0RLlGHbObsj3vX55wYWLVpSJW66o2d7wYIF5OXn49vsNi58NIWyrH8lUoNC67z09FFy9iXipVa7FP56efwI6mjK2PfFTpYsWUJKSgq3RnViwfz/8efxo7SuoyV7zSRLnL42hLuum5CLMhZWmXZxh9vaEztwMOaw5pSd/d2hb2DewY3kH9yMWV+Eb0g9/DoNJmvPUrThzTBknsI39AZ8Ow6k9NsPWLr4Hd5enODU8/BqLvOvDGq1yoqC6MpP1NV3MlxNrd3R0Dm0/1uaRURaaGnOvTyfwad5B0LuHm71na1vrKTR0hljzjl8NCoe6tedNevWVxomsz0vjuCMfih7qX700YdWNFFbffmgIGmk/v33UlxdxrJlcPiw9O+FC9J3O3ZIFbq9e0vLtm+X4umiKM0Gjh2r8G/duXMnDzwQR0yM0an0QJcu0sumrEwqGFK2Qw7DOerUlV6gSgqibXWpHCpSHnufPu7LGDh63ju3aMCe5K+oO2QG3o3acH71ZExFOTR+2jrZmpYwHLWxhF3bk9zOY8n7U94XycnJRMcOQBvRBWNeOvWGzrOE8bLfG8O78+dcUiW4J+QCLp3BlaPkuW/+z1Iarj9/wqkJbGDHeLwCQhgzagSzpk1Ce2wjT44aQZi6hN07tjFzygSLbOcjjzzi9ujyvwKZ9aDRiPTuXZHY7NLFuZ9oYaEUr3WUCJXhLPnlrivNuKef5kJGpp2SYtqSR60LhDrGUXDEnk4iM5SKfv+GjM9nEtJzlOQfCpQaRRJXve9WmMxdaqZMIVRCZnQoE6bKxPPixRXhj+BgaWT/ww/Sb+Wk6cWLfhZXqR07vGjTRpoF9O4NI0ZInXn37hKFsEuXin3KlZ7z5hkZM8a176s8Mrdth5xorcwL1LrNto5YAiNHSro/y5dL+7JNrg8bNsTpSN3R8/7lj79ZKanKWi220HWMp269+owdP9ESqpEdzSJatrZa1jQikgaNmliczmTl1JA64fSPG0jowBfLw3iS05k84/OK7FYjleDXzQhdKcjjapQcGhJCbmAEpempaOvdaFf4o+sQiy7KsQtKSkoKT02YxNaNnxEZGWm17IUpzzF2/MRKR5dXc1JUhjOtjh07pI5CEKSH3jbJ16ePtRCX7YgPnI/QbZOijlxpcr78gMKfkvBt3gEx/Q/EgDr43dqf7D0JaOpGYMyRYu8Bt/axJKwCOw202k/ewY0UHtmBsSgb/1Z3YsyVRlbGnPNkfj6L0D5PuaWGOXz4wxw6tI709IrRtK1aojzCHTfO+rdyUdWff/5F587tGD++mIULK9d36d5d6mCnT3+NGTNmWL53d7bQt6+KcePGkJW1klGjnIeLbEfmDz5o3Q450XrffdazCbn9O3dWzAacYeLEpyo9Dnkm42g77jzvmZvnONTDl2ZwkxHUGto0CuXNObOIiR+A3mDGr8XttPQt5s25s4kZMBh10yiK/z6AX4uuViJ8GRtnE9Z7rLXGzzdrEI1lVjM+WSOqKvCIc5VDqbQX/LB1kYI8BeratavEkEjPodRgQvDSWkR7QnqMJP/QZgRBha5jnJVll6Mpl8xiEZpEUfL3fsIGTMe3aXu745IVGi8l413bcIe9MWWKNJqKjobOnaUknavO6MUXpdF6w4bOH9LKLNRM4S0oOJJkmU7nrJ9G0fkTiIK6nGkQS/pH00ClwnAhleDuD9t15iBdi/OrJ+PdsDWh9412GZpxFiaT6IiD6d27xOkLLCDAut1KKF9qUvgjlpgYE2PGOL8uCQnw7behrF27gR49elh9526F7+TJgYii6GY1sDRDkDtxZTsSEqRwS0CA/UvMtn3OUJVjdrYdV897WsJwgro/gu6W+yqqtG+LtrOa9AsKpTg9FbNKMkPxbtSGzI+ex5CVRtggqdL0/IfPYMq7gHdYA0Kin3XMqto4C9FkpN4Dr1mYNeqgugQV/FPlSnBPyKUc7jiDR0REcGj/twzocTs+aiyiPeEDphHQtifeZj2DenSBw+sJDQmhUaNGVgp+pekn+ftclhWLJbTveNTB9bmYtBBD9llJ5+G9MeTsWcq59ydgzE2vNtfvmoY77I1OnaRE3dGjUny2Tx/XiVBZ28OVwUVlrjQlR3fi36pCLEnXdxLqkEbUHTKDwE4DpGXt+2LI+Ae/Vnei6ygpKDpiKAV2kqp75QRp4U/2hhXOwmRyyGL27BLGjnUcspg1C6ZNc867ViYN+/Xrh1brQ0yM82sCEkXRaDTadebgngWcvM+qGDpv2yZdZ2U7jh+HL76QCskWLwaVSmLhxMZWhNjc4ZS7exyutuPqedd1jCf3y/cpOLrXQmoo+uULi9WkTGoI6DMRIbCeVdLcr31/RK1fRegmKg40Phhz0sncPM9qP7LxiaDWUO+B1/Bp0g79v8cxFWVT8vf31V4Jft2M0N3VMVcmRZ2FR0LSvuPc+fOW0XhRUSFnSrToz/+Nb0RnDBdP46MW8b1rhNWUKyd5JSr/YLSGAiY89ST/e/sdfCK7oj//N1pDAUmbN151xUQyZH3r5cuXYjA4DiGA9MA+9ZREQZTEuioSoY6Sej17SiyPV14BrdbvkvW63ZliZ2x6nZCeoyn65QtE0YSufTTZexLw1oVg1uoQvDSSsUXyKsLjpwGQ8fnM8tCMdQmjszCZO6GChASJWTJnjv13x4/Dc8/BAw8MZcaMmURERFQpZOJI+8adWZWslXL77be5NTIeN04Ks0yaBD16SHF0Z/K4ckjo7rslps3o0fDBB85H1qmpqbRv35plywxVHqG7E+IE2bjieYx5FwiPn4pPk3aYjWVkrHoSY0khvqE3EBgz1XGopjxpLleWZm6eS9AdD9jJMcvr+kZ0ouTkYRqOW03Zv7+RuXkOvhGdCSk6zenUv6ocZvWM0LFOkgCWUbJt2bacFHV2I6jCmnDi5D9WibHWLSIpO/sb4QOmEdZvPIJag7luS3KSV1lG5DnJKwi8/X7MRTnc1q4ti5culypO+01E5aXl0YeHXrWdubJScNUqx3xkGZs3S4YLcsch0xidJfW0WikkU1QEBw8evWTzBZl7bs47T97WCu65nITK3DIPBBXFPyXhf2svzFn/kr1nKT6N2+CvUeGtMkuzse8+Jjx+Gj5N23Fx2/8ko4NO9vXoznRb3KErxsdLHHPbpGFiohSGGT8eTKYNlmrSy9G+Acf1A/I+V67UMGOG9CKNiIhwazS/bRsYjWp6945h5cpA+vSBJ5+UtiknxpVo00a6J7y9pRlKQgJER8c53LZ8rzVpYqyy/6ySpjh63Hi75z3t3UesKzk79AdjGabMfxDNJlReWgK6PUx4eB0G9e7ukMAgJ83lvuFi0lsgqJx25lKfMAEvXR0urJ0iLRv4AmHREykU/Kq99uS66dDddQb38/V12fHnfvkBfi2s5TRTDh2zsGEElRrvRjdT9Os+i49oZtJb+N/ah/wfPsP3pi78cOQo/v2eteI9X6oPZU3Dlb61LesB7AuLgoLgl1+kdZSCTenpUue/c6fEiNBoYPbsVy5Z+zolJYX+8YMw3nALZaLaqpRbE94clY8Or9CGiPpCir56D5PZSGifpwgf/AoF+CDWa0Vgp3gajllpsZzTlfPV5W3J90FZVhoFXy2n5MKfPP/cM1bcaHdDBQaDdA6URUEGg9QhRkdbMzmio2MvSftGCXsWicSAqVNntNWL1B1OeFKSmq1bd7N58zYyMvIIDdWxbJk9T14JmREja7kIgn1kQHmvvfCCWCX/WVtpCE1QPbQXfiW9XB45c8s8tL5+FP+wgfQ1z1rkkac8M9GuD3h+8kTnoZpyWQ9ZlE3XKR5RNOF7U2erPiNj42yL3r6sl27WF1sVL3kKiy4D7jqD79u9y2XH79e6O6V/7yf3k+kOKxbzD26i4Mi2chGo8SCCV0AohUe2W97W6pBGGDIkkacr4QxeFbhX8VjBObYtLOrZUypHVxYU2Y7W9+yRNLBLS9ddkvZ1SkoKfWPiCYmfbhHWytqxSDFCGo/aLxBzUS7qG1pSVmbAr2V3iwqjUhFTicCOcYhGPRe3LbDcB0FnviFn7Vh6Nd/JqsQy9uyBhQsLuHBhKe3bt0Kn83ZrNO3nBwcPetG1q9pp4ZCsMqhSCVUyTHaGiIgIFi58l4yMPEuV5sKF71rxweXR/IsvepOQYD+DmDoVBMELvV5v+U1V4u4gzVC2b7c3WFfea7JY2Ysv2s9kli8XrGYVYE9T1PV7FoOXP9oGrSw5ML/ODxAaGkzvzm3I3buct96YU6GuWt4HzJzxEjNmznY6Q9dFxVgoiFBOXw6qhz7tNy6smya9PDbPQTQbKUk9ZNFLNxVkodJ4ow4IA6TnPm/fMjQaTbWKdF03MfSqwGQyWbQ51n34Affcc4/Vsg/fW8matWsdZs/PLHwAv0hrCpNDrfRyDZjLKTCoDSjZBq5i4LNnS51SfLw0pVYWw4wcKSXJ5G1Uh02akiJ6X99osvya4t/mXnK/fJ/gu4eTk/K+neF2zlcfIJaVWJgwF9ZNR1O3OcV/fGMpJLKlQhb+uo/cvcvZu2s7jRs3dhqLPntWeil9+6000nZk6ixj6VJITW3LyZP/sHhxkVtx4tWr19eIYbIjpKamEhXVlqioUn76yZ52mJtrfY3c18mX7hFn8X5H27G95wIDwWTS8NNPv1vdH+7Skisr3nNEj724fQG6TgOsfApyUt6n0bjVFZ+/+hDMRhAEBI0vPo3bENLrSTI+fgFjYTbIdMXcc9QbOo+0dx9BNOgJaH1nlXWcPLTFaoarBGvOVx9S+MsuvALDqRM3pdKkytXOQZcTcocPWwtG2VLx8vNh6FANW7aYiYuDJ56oeFh79JBG4Wp15eJd4JpfnJKSwmOjRpOZkYHXjZ2J9CniXNq/XLiYhWg24d+qO8bc81ZVeaWnj5K56XXMxjJC7n7UQlcsPLaP7D1L8Q0Ox7fTEDsqpKp1TytqqpTwXMGoUdYa17ZiWq549soXVosWkVVKdtaEYbIjVJUD7s76Ss69M7rh5SZ/9+7dy5AHh2Ly8iPssSVAheaSpqyQhIVvVjpwWrNmDaPHTcA7rBFeN/ei5Jv38dZqyCvW4xVSX3KsSl6BwWjAK7QRgR3jyNmbiCiKhPYaQ+EvX2A26BGNZYhmI+bCHAS1l4XyKAuBFf+932KmUVXKsicpWo2ojC0T3P1hVAFhkHuOi5vsvSVtkypXo9WcEqGhAQ5j4LZxdK0WvLz+jy1bdrNrl7dVeEC2VwP3bNKcaV/LKnlZvk0w+oUR3Hscf55J52J2Nn6RXdGENiS079NWU2KAi9sX2HXm8oOuu6Unof5aNEc/dxqGk5PVUsJT6sxl/fcBA6QOWxSlDgsc5xiOHLFPQFY12elOyKQ64E5iV3mN3Im7K7XOncX7L9f4JHbgEIw33IJeVNnlUMoELxa+u9RlZWZKSgpjx0/Eq1kUAeYitMc2MnzYQ+Tk5ePb8k6MOenk7FmORqNG26A1huyz5CSvAi8tdQe/REDbntR7aC4BbXpgLinEXJiDWhdm0dyXhcAMmf9YmWlUZyzd06FXASkpKfSLHYCqaZS1uXDCcCsfUr8bozCYzYT0Gmu3jYDb+pObvJLCw5udmiJcTRg6dBirVgmVimoNHCgQHBxEjx497BgV996LhbHgSrxLhiN+sfwiDYqdWh4n9yF71xJKci8SNrDClKLwyA4CbrU2d9Z1jMMr+AYL9xwqXqzBPUdTKPjx3ORJdgYnSvVEqIgVK3MAS5ZIs4+EBMesnzZtpHL7V1/V2CUgq8IPr01UlQOuZNGsWOHl1Bxcltt1Fu+/1POhlMN1lkNR+Qby+8kzTgdOyoFaaN8JFKkD6NXjHpa/9wH+re8krPeTNJ7wETcMfxvBN5iy9BP4t+xGo/FrJS0YEc4uH0XBj0noomLw0oXi17KblTOZnHOr/9g7NSbS5Qm5uAn5gnNDa8T0P/AOa4iq1X1k71mK9w03Ysg4hTqkIT5Nby1Pijp2MhHNJrLXTyNIKKXYS2c3rb/akJqaSps2N1li4M5gO41WhgeysgrQakXmz4eXX740gS5HeuiZW+YR2vMJh+XVSgqZLMbl1+YegspH6HkHN1Hy137qPTyPouMpbnm3hocHMn16QZUqX521Rz5H7vLDq3sU7gqXWqUpX/MPP3yf3Nxi/P0lOYIHH5RmLZXF+y/1fLS+5Vb+Jdw6b7V5nsMcSr3QQIfX2Znevlf7WAp/2gUChPWbaAmhlmX9S8YnL6P2C0YXFWupJC/85QtEgx6/1t0p2P8p/jc0x+vm+8jftwzv0IbUGb7Iar+ZK0ax9O03qpRD84RcLhPKt3dY3FQ0IQ24McyHnL1LCe4+jLoPzsX/hmb46LMpOJJk52Rydvko8g5V8F+92/XFLJodTuuvNkRERGAwCFUeVSvDAyaTmY0bdzBjhh/16wtV5heDYz30Bo/bSJ6mrEI0mwho15usnYusqj91HePI/+Fzy/YCO8YhmvRcTFrgtnfr0KHDWLGicglgW1d7ZxWNVeGH1yYudaQsX/Ps7CL+/vsEI0eO4/DhQEaMcEyRtMWlno+tGz+jqSqbjDXPVtwbDuRwtRicXmdnevsBbXpQb+hcBI0PmQq7SXNhDmaDHm2DllaV5PUemosmqC5FP3zGjq2bmDFhJBxej5fWh4B7R9nt169DbLWKdLnVoQuC0FcQhD8FQTghCMI0F+sNFgRBFATB4dvjvwpbSpR/r/GcOJ9L+OCKsnJNmz74BwQQH9MP/clDFRSm8rLigv2fkrN+mpVNmaNp/dWIyy1sgQoedKdOD7Npk/v8YhlKPfT8pDftfieHUAI7D6Lgpx2YSgoo+GkHFz6eLrEQ9iZiLi2i8LCik4+KQ5960O0X6oQJz/LPP5XnAJT64OD63LjLD69NuBMTr4wmeanx/ks5H5GRkRw9coj7e3Uld+s8u+/zvliIzt+HHdu2Or3OrvT29f8eR5/2G6HlZtBybL7uwOmE9R4r1S6UvzwElZqw+Gn439CcY78ep8Nt7dGXlREUO9WlSmh15dAqDbkIgqAG/gJ6AWnAIeAhURR/s1lPB2wHtMDToii6jKf8l0Iu7lKiZr3yMi+/Ngv/vs9iyDxNwZFthPWbgE+TdhQe3oTP38mYRbPFpuy/gstVvrOFM+3ryqbkylip7cORf2gLxX99R90HX+fCh8/Sq/PNJH/9HWV4IZqMmEsLmfb8s+xO/sohg8VdqNUCu3dTJZ3zqpybqwWXeo2uFNzVynfFJHO0DVtWGsD5DyaiqdvcSok174uFBETFW9FdtUc3EhQUVKlKqK1qa2W43JBLZ+CEKIonRVEsA9YD8Q7WmwW8AZS6dVT/IbjjljN+7Bhefm0WAdHP49usfUXVYflN4N8hjiJ1AM9Omvif6syhekZsSlzKKEyZFHVe8CFSeGQHuk7xHPnlGLmZ6fS75w4Eo54F/3vDrojkUkJdoaG6KumcV/XcXC24GmcOzuCuVr6rUbCzbeSkrLKrAtWUFVKmLCTa9DregsmhWbi7FerVJdLlzgh9CNBXFMVR5Z8fAbqIovi0Yp0OwIuiKA4WBOFL4DlHI3RBEEYDowGaNGkSdfr06WppRG2gshFA0Q8bUDfrSKjirX25b+KrCVd6xOaw4GPH2+iiYq0kT3O+/ABfL2osL+HObCUxUSq+CQ29Okez1xrc0cqv7Nlztg2vyG4Unz4GgiDJaO9dyvsrlrPw3aX8cfIM+tIStBjYsW2rQ7NwqLxQsaozdlcjdERRdPkHDAFWKj4/Aryr+KwCvgSalX/+EuhY2XajoqLE/wqSk5NF/6AQsd5Dc8SmU5Ps/po8v0UMatZGbNCkmRhyYzsxLHqy6B8UIq5evVrs0LmrtKy/tCw5OflKN+eSceLECXHixHFieHigqFarxPDwQHHixHHiiRMnanzff/31l3hbp9st51frpxODQsPEFje3tZxfrZ9ODA2vV6Pn+MSJE2JoqJ/47ruIKSn2f+++i+jtjRgaGlBr5+Z6h+29cSnPnqttBDe/RfS7+R5RpfURFyxYIIqiKBqNRnH+WwvE+o2b1vozDRwWnfXXzr4QKzrsrsAXis/TgemKz0HAReBU+V8pcK6yTv2/1KG3attO1LXrJTaZslVsOjVJrPfgHFEX3lCsc98TYpPnt4hNpyaJYf0ni/UbN7Vc5JSUFFEUr+yFv9agPJdX8vzu2LFDDA31Ex9+WCOuXYu4Zw/i2rWIDz+sEUND/cQdO3bUynF4UIHquDeulvurMlxuh+4FnASaIyU8fwHauFj/mhuhV8cIwINrC1dytuLB9Y3L6tCl3xONxHRJRYqVA8wE4hyse8116KL433l7e+CBB9c2XHXonkpRDzzwwIP/EDyVoh544IEH1wE8HboHHnjgwTUCT4fugQceeHCN4IrF0AVByASKkCiP1yvq4Gn/9dr+67nt4Gn/5bS/qSiK4Y6+uGIdOoAgCIedBfevB3jaf/22/3puO3jaX1Pt94RcPPDAAw+uEXg6dA888MCDawRXukNPvML7v9LwtP/6xfXcdvC0v0baf0Vj6B544IEHHlQfrvQI3QMPPPDAg2qCp0P3wAMPPLhGUCsdemWepIIgeAuC8En59z8IgtCsNo6rNuBG258RBOE3QRCOCoKwTxCEplfiOGsK17sfrTvtFwThgfJ74LggCOtq+xhrEm7c/00EQUgRBOGn8megEsfW/w4EQXhPEIQMQRB+dfK9IAjCovJzc7TcKOjy4Ey1q7r+ADWSSuONVMjv3myzzlPAsvL/Pwh8UtPHVRt/brb9XsCv/P9jr5W2u9v+8vV0wNfAAdxQ6vyv/Ll5/SOBn4CQ8s91r/Rx13L7E4Gx5f+/GTh1pY+7Gtt/F9AB+NXJ99HATkAAbgd+uNx91sYI3R1P0nhgdfn/PwN6CoIg1MKx1TQqbbsoiimiKBaXfzwANKrlY6xJXO9+tO60/wlgiSiKOQCiKGbU8jHWJNxpvwgElv8/CMkc55qAKIpfA9kuVokHPhQlHACCBUGofzn7rI0OvSHwr+JzWvkyh+uIomgE8oCwWji2moY7bVdiJNIb+1pBpe0vn2Y2FkVxe20eWC3BnevfAmghCMJ3giAcEAShb60dXc3Dnfa/CgwTBCEN2AGMr51DuypQ1f6hUnhd1uF4UG0QBGEY0BG4+0ofS21BEAQVsAB47AofypWEF1LY5R6k2dnXgiDcIopi7hU9qtrDQ8AHoii+JQhCV2CNIAhtRVE0X+kD+y+iNkboZ4HGis+Nypc5XEcQBC+kqVdWLRxbTcOdtiMIwn3Ai0gOUPpaOrbaQGXt1wFtgS8FQTiFFEfceg0lRt25/mnAVlEUDaIo/oPkDBZZS8dX03Cn/SOBTwFEUdwP+CAJV10PcKt/qApqo0M/BEQKgtBcEAQtUtJzq806W4Hh5f8fAiSL5VmD/zgqbbsgCLcBy5E682spfgqVtF8UxTxRFOuIothMFMVmSDmEOFEUrxUrK3fu/c1Io3MEQaiDFII5WZsHWYNwp/1ngJ4AgiC0RurQM2v1KK8ctgKPlrNdbgfyRFE8f1lbrKVsr0tPUqSLuAE4ARwEbrzSGepabPte4ALwc/nf1it9zLXZfpt1v+QaYrm4ef0FpLDTb8Ax4MErfcy13P6bge+QGDA/A72v9DFXY9s/Bs4DBqSZ2EjgSeBJxbVfUn5ujlXHve8p/ffAAw88uEbgqRT1wAMPPLhG4OniqaGTAAAAP0lEQVTQPfDAAw+uEXg6dA888MCDawSeDt0DDzzw4BqBp0P3wAMPPLhG4OnQPfDAAw+uEXg6dA888MCDawT/D98xSK4gSXXSAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load from ex6data2\n", + "# You will have X, y as keys in the dict data\n", + "data = loadmat(os.path.join('Data', 'ex6data2.mat'))\n", + "X, y = data['X'], data['y'][:, 0]\n", + "\n", + "# Plot training data\n", + "utils.plotData(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the figure, you can obserse that there is no linear decision boundary that separates the positive and negative examples for this dataset. However, by using the Gaussian kernel with the SVM, you will be able to learn a non-linear decision boundary that can perform reasonably well for the dataset. If you have correctly implemented the Gaussian kernel function, the following cell will proceed to train the SVM with the Gaussian kernel on this dataset.\n", + "\n", + "You should get a decision boundary as shown in the figure below, as computed by the SVM with a Gaussian kernel. The decision boundary is able to separate most of the positive and negative examples correctly and follows the contours of the dataset well.\n", + "\n", + "![Dataset 2 decision boundary](Figures/svm_dataset2.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# SVM Parameters\n", + "C = 1\n", + "sigma = 0.1\n", + "\n", + "model= utils.svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n", + "utils.visualizeBoundary(X, y, model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 1.2.3 Example Dataset 3\n", + "\n", + "In this part of the exercise, you will gain more practical skills on how to use a SVM with a Gaussian kernel. The next cell will load and display a third dataset, which should look like the figure below.\n", + "\n", + "![Dataset 3](Figures/dataset3.png)\n", + "\n", + "You will be using the SVM with the Gaussian kernel with this dataset. In the provided dataset, `ex6data3.mat`, you are given the variables `X`, `y`, `Xval`, `yval`. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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G14ZERFJY9/7Uq8/tVFtb6/AYzxi0Xqfh/A4PzW0x5uj+46hbj550c+KtDuc38pru1G6Q7ZienwEejhw5QlOnTqKYmHCyWi0UExNOU6dOoiNHjjSes3XrVoqKCqaQEFB2Nqiw0PlXdjYoJibcxHfk2QDYR07sqnTpSFTRsBO3dOmNhKCL2PvFbnz66actjhGRQ233lZ2vxsWormiXPFWom8XZuJJTh8DapTeqTv2Ijo8sga3Ek40Gvfyrc19A2v0PaNa1i9DH80o+n31sNCwWizDtvBnYu3EyMoBt22wJWs6oqQEGDrSgpqbWuEF6EVKHLxGKCIOmRw6Bs3F98FEOIlKmIzCuG35dNxPBv/sDwm/5rYJ3g/EMqDyLy9HXafb169G+0MjYkNHYJ2gNHQosXQrN2v3WjPThS4QiogSDHpJNZ+OKSJneOK7QHgNwfu9Gh75uqqsVkjcgImbw7DNPofrHvTj3wbOGx4aMxr4Mcr9+wNatrs+X9XPUIw2+RDGiEqpESzZ5xlWWvwr+dRUOjec/1mULWYSULGbOkqvGPzYFls49EVR1tjGA3qlTJ1y8WI4JfxpkaoVS0dgnaKWnA1u2QNbP0Qlp8H0UPStUitid65G/4G5c5/65GGEhQcjb/LFT9dGuXbvwwUc5DhchvxsH4NVFi7kWIZ7FLD8/32FyVXLqEESkTEd08uM4WxeEJ6ZOAREhZcj9OG7tgG35O1H801GfMPZA0wSt2Fhg5kxg1izgzTfRRNmzciVaKHskypAG3wcRUUHR3YKhdXeuV1kCV+MK7Z2G+Gu74q677gLQshRxYWFho7F1tAiFJabhyK8XsOg115JA3sVs+Kg/t3BBbdi2u6kLqlcK/vq3F10mialZ3D0pk7V5gtattwLLlwNVVcCUKcCAAcBf/gIUFd2IPXsOyGJpGpAG38cQUTLZ3YIxOG0oBqcP1bQ71yN/QetTwyNjx8N6VdNF6MTrf8a5PRsaF6HQ3il4Ye48l+PgXcwcxQzajVrS1AVVsBosMMRpfETN4q41k1X0YuGoDHJsLDBpErBhA5CZCQQEBOMf/8iRO3uNSJWOj6FVIcKjwPn1nScAix86jJyvSUEiuhyvVmVLfNfrcOyXUvhFtEdo9wEoK1iNwDbBqPEPhiWgDUJ7DEDZjlVoGx6K0pPOi8QoyVi+6667nFYnLX59DNreMRyhN/ZrPGavXmpw8yhRS2nNZG1erviKK4BfftFerliWQRaHlGW2IrTKHXkXjPOFbyK04zVCSjCIQmtpiMOHD+OPwx/GDz/+D1XV1YgeNA1BnbuD6mpxYf9mnN+7Ef51Fcjb/LHbwC3vYubqqeTcng24fPhLdBg+rzFvwL5EtZrFXUuNer3LHsgyyGKQBr+VoaWmPe+CsemjD/Htvw8YkvDjqiZ883o7Wp8aRPYD4Hlf7pKrmucN2DdVKSoqUry4a6lRL6KhiUR/pMFvZWjtwmSk0XMHb0avqI5SRnawcuSCurAtEwE9khGemNbogjr32XuInbAaQMuCaEr/VlarBdu2kapMVlENTST6IhOvdERP+aPa8WiVO5pZ398eo/sFq507tZ8BR4HriDb+uLD/Y/z63kyUf5ePsvw34U81TtVLSv9WWmrUi2poIjEPafA1oHeDbjVolTuaWd+/OXo0Vee9H8A3d2o+Aw2LAYDG6qS0dx3atWuHrLfW4Kau18By+SzKdq5FgBV4PXOhQ/WSmr+V2hr1gLbFQuIZSIOvEqN3n7xolTvq3bZP7XsxooWf0rlT8xlovhgwxtCr582orKpCWehVmPHcX7Hn83/hhWefQoeocJdJYmr+Vo4kkPa4ymS1LRZ+LudQlj3wcJyV0TT7y9PLI193Y3cK634vdX4ml7pM30xXjnuDIuISmpSybZc8jTp26qLrOJyVAo7peCW17xinuGSyq5LGZpTcrampoVGjR1NEXILLssZE7ss1896Pt9y00s+AfbljESWh1f6tGkoRjxjhT9nZoO3bbSWHR4zwp6ioYNq6davD+61Zs4aCgkDLljkuW7xsGSgqKrhJ2WOJ8cBFeWTTDbuzL083+Pb/2a4c90YLY9ThwbkUEhHZaDT0QFTt9uaIrrGvxRC7qwnv6n07mwsRCwOR8s+AHpsEtX8rnhr1zc+PigqmiRNBERGg4cPRZLF48EFQUBBozZo1Dl+bkTGRoqPDyGJhFB0dRhkZE10uDGpeo+d1vAlXBl+ISocxNhDAEgBWAKuJaF6z3z8BYCyAGgCnADxKRMdcXdMbVDpmqllElCg2Ai0qG96a8A8OvBPvrHuPay569bxZqOpHyWdAj5LQRmEvySwpAXJygPx84Nw5ICLCVuWyqsoPV189oYkkU02ilqjkLr2SxDwdXWWZjDErgEMA7gVQDGAvgIeI6L9259wN4CsiusQYmwigLxH9ydV1vcHgGynha4439GnVuijxZs6ez1+FoK6/dzsXtHcdKquqVI2nqKgImZkLsW5dtl1S0Ejccsvv8ZfJGdyfASM2Cc7GmpHxpOoEJjWSTDN72rbm3rh6yzL7ADhCREeJqArA+wDS7E8gokIiulT/45cA4gTc11TMVrMYHdBUwyNjxwOderUoDsarsuENoq5ZtZJrLkLDwlSpfpzVnjl9ehXGjnsY/jencn8G9Ja8aq2T4ww1kszMzIVISqpW1J9WzWscIeo6voYIgx8L4H92PxfXH3PGGAAOP3WMsfGMsX2MsX2nTp0SMDT9MFvNokcDEZEUFhbi1MmTqDj2b5x850mnxcFcLUq8TdUffvhhrrnY8clWxYtkUVERRo4chjlzLmHs2GrExtra78XGAuPG1WLhAqBi//uoLjvh9jOg9ybB1VjHjq3GnDmXMHLkMFVFztRIMu0bmzgjKaka69a9o+o1roq4qbl3a8BQWSZjbCSARADzHf2eiFYRUSIRJcbExBg5NMXoUe3RFY4SeZYsWYLs9z+A380pLc4XmSClNLGowbCFp8zAleNXoe7CaZzZNLfFeef+udjtomRfwrjhvOZljQG+nXN8fLziRZJnp5iSXInz+cvcfgb03iTouatVo99X81TA+5rS0gsun2RkkphjRBj8EgCd7H6Oqz/WBMZYfwCzAKQSUaWA+5oK7+5TRD0ZR4k8ixYtwlMzZqFNwu9R/u0nIGpq2EW5lNQkFtkbtqqS/0NNTQ0i+k1oce2gmwcLWZSU7JyVulR4doppqUBNyQG3nwG9NwladrXuSh6r0e+reSrgec2BA0BAALl8kvH3J5kk5gARQVs/2IK2/WAz9HsBDCeig3bn9ASwHsBAIuKqN+ANQVsjcBb4zH7vA8Tc/3xjY27/9leDFX8rtMm12qBrgxrlcPFJXCo7hZghM12qbLQqiXiDu7Tnt6At73i01J5xhOiS0PaoHSuvmkVpCWM1xdZ4XjNpEkP37sCECc5tF885vlroTdegLRHVAJgM4J8AvgfwAREdZIzNYYyl1p82H0AogA8ZY98yxnK13re14Ky8QMz9zzdpzH3pYL5wl5La0gYNTz/Wqgtoc22fJi6MkjfGNmkowuvCcOVWyly0gGvnbB+0bRiPO5eK6HICvC4qJe+/4ZiasSrx+yclJWHPngOIjh6PadPCMXCgBdOmhSM6erzDTlRqngp4XlNURBg82PVGdcwYQk4Oyd64zZDVMj0cHu32qQ1zsPCVl/H4448LLVGsRTdeWFiIQanpqGnTDpbANraGIoVrEHnPGJz/4gNYA4MR0mswV918Hi3/l7s/RebSZS53znFxcYrr5RtREthd+efi4mKu3IFp0ybrsqPW8v7UNDZx95qzZy9zPckMGMDQtm2bVtdUxdUO3/SMWmdfnp5payRKygu4Q2mWqZp7Ny8hEHnPOPJre0VjRmmnJzdQULsrKTwq2m3WruhyBEozUxsyTPUqJ+AuQ7jr9TdScHhbrvevZqzR0WGUne34/Iav7GxQTEy4qvfXMIdKsnrdvUbJmNXc29uB3pm2eiB3+L8hKsFLTdarmntrbTVojyckmDXsOAcMqMSgQbVNdoqbN1sR1vYKFGzf1qQRCw9q2km6e/9Kd9SiYxRGIBuxuEbWw/diRGm3tVR2VHpvkWoUT0gwS0pKwpIlb2DjJj+MGWfFfffZMkorK5NRVRuAcxHXqiqFzRMjCe2VirqyEu73r9TPbkTJYyOantvTWv3zPPiUwfe0ZiQiEKXdVhOAVXtvkZJVT0gwKywsxF8mZyAi7a/oMHkDIq66CUnJQ5C3fRfCU2aoLoXNu5jlfZyj6P3Hx8dj8eJlOHnyHGpqanHy5DksXrzMYQkBLfXxedAj8zc+Ph7Z2esxe3YwVq/2R0mJ7SmkpMS2s589OxjZ2et9rmSCCHzGpWN0Kzyj0NqY29F1eAOwou6tFTNrFgH6upV4aut06tRJt/evZ80Z2fTcHHzepeOpzUhEIGq3rGanbGRymSMKCwvR+ep4DE4bamoHLj3dSu4SwV6aNx+D0/V7/3rulvWuZ6PkSUZiwyd2+J4Q2PMGzN4pK6FhrBU1QNDVvdAueWpjAPj0lkUIT0xDWGKqkAQzHvSocslT/rl50FZLANwVeuyWZdNzc/D5Hb4nBPY8HbOreyrBfqztH5qLyh/349S7T6H8P/k4lTMXEXeMwIU9H+HM+9OFJJjxxH5c7cRZp5sxYXIGfvjhB5fXbA5PjCS0929BWz1rNumxW1Zaz0Z0cFfSEp8w+J4Q2PN0zK7uqQT7sQa064SYB18GXSzD2X+9g5ghzyKs+70Iv2s0LBdOanYr8dQKys/Pd7pYVhw7gPLv/wVr597oP3CQokb2vGqmrbkbTXOraUGJAkivss6SpviEwQf0rzPuDDXKIDPUREZX9xQ11uozJQiI7oQOE95C3GNrEdS5OyqOHUDl529j08aPXJYjcAdv7Oehh0c5XCzLtq/EqU3zEJM+A+2Sp+LkZSiKHfHGSPr3799YeqFTp06YOvUxXHFFJJ55+klUXz6DnJwPPXIXzKsASk5O1a2ss6QpPuHD522FJ7rdn5ZEJjPURHoW7hKNEZ2heGM/1m8+RGxcHA4Xn0TF5Uvwr7mE15cvxYTJGUC7a1B76Rxi0qYDgK6xI29r2cer0klNHYLq6g9kIpUgfN6Hb4a7QmsikxlqIrWFu8zA1RNbQPffnti0PBnxxn7+se4dvDr3BVSdO4XA2BvQqXNnjBw5Eq8vXYKq498jIOYqnP54AfwiO+oWO+Itcpadna3L06Ma/zqvAmjz5lzFZZ2lv18dPmHwzXBXaE1kUlJ9srXh7oktpJctwJw+ZCj6DxyEEubaV+4M3tgPESF92B8RmTaz3nXD8OdHH8WkqdMQc/9stEueClAdLuz7uMnrz+ctQkR4OGLtZCpqjS+PxHHAgEqMmzBWUe8CHrT413kyf5UGd6W/Xz0+4dIBjHdXaE1kUlp9sjXhqBbPqY/nI/zWoQjv/ZsU88y2FWg/7K9um6G7gkeqevFiOY5br3Dr+jn3+XuInbAaQH0V040voU3Crfhdm0vY++Vnmlx3vBLHsRMC0f6xDxQ3jHeGEc3Alcg3v/ji61bbnJwXn3fpAMa7K7QkMnmrmkjPYLP9dRqe2M7+YybK/5OPkx/9DX6RHXH50Bf49b2Z9cZ+JdreOVLTkxGvVHVI6mC3rp+ywjVol5TR+POpTfMQM3QW2iVNxQ8nzmp23fHugisvVwl9ejSiGbiS8g6yObk2fMbgm4EaZZBZaiKtqGl1qPba8fHxeHXuC6g4+RPKClaDWQOBujoQEQLjuuHcZ++h/f2zEX7LEAD1BnfHSsW+ct7YT1b2OpcLdeknmYi8ZwxAtoByad7ixsYvzGJFRNI0zcaXV+IYEMCE5qIY0QxcSTE02ZxcG9Lgq0RNIpPZyU9qd+h6BpudXTt92B8RmT4LcVOy4d+2PSyV52ENaYuKH79G7ITVCOry2/yd/ng+nnniccVPRkpiP64W6tCeyTj35Xpc2Poqli2Yi65drkTl0X34JftpYcaXdxd87bUJQp8ejWgGrqS8g2xOrg1p8FWiRhlkZvKTlh26nsFmvhLBKaCKC6j48RvbTroZbW+9H9sKPm0xfncLHK8OHoDLhTo8MQ0WvwCMHjkco0aNwvtQBUMAACAASURBVIGv92Hu//ccrOdKcH7zqy3OV2N8eXbBmzdbcfSnE0KfHo0onwzwl3U2ajy+ijT4KlGjDDIr+UnrDl3P0hU8176wczUqKyrQfthsx6qd3ilOn6bcLXA8sR+ehTosMRU5H29pfH1i716w+PkjpG/LBUqN8XW3C541KwgV1X4IT5kh9OmR58kiNxfo1KmTZkkkT3kHvcs5+zrS4DuAx/WhppKkWdUnte7Q9Qw2u7v2hW2ZqK6qQMgNd7Vohn5+r+MnI9EuKKULtRbXnSt9uatdcLv2XRBw7e3Cnx55niw++QSIi/vBEEmkbH6iDZ+RZYrC0+rqu2twzdNWT4QcVM9Km66ufX7PBpzfuwnW8BgwiwWh3QfgzPaVCEm4FdXnTgEMCO0xAGXbV2LHJ1tx991361I9VYnsV22LRy2ZtHr2LmgY18CBVUhOrmlsm7h1K7BlCzBzJnDrrcZJItU0Rm9NyCbmnIhumC1qPM4aXDcc40FLI3T7eWn+2i7TN2ual4KCAgoKCSW/qFi6ctwbjdfs8OBcCugQTx3HrqTAK66loHZXUvD1fckSEEQzZsygXn1up7ZX30TBN9iOLVy4sPGahw4dop633EaR13Rvck37a4dERDY2MReN/f3bJU+jkIhIysrKol59brcdG2Q7Zt8wXUSzdN4G7UeOHKGMjIkUHR1GFguj6OgwysiY6LapeK9e3Sg0FGSxgCIjQcOGoUUz8REj/Gnq1EliJtIFrbE5OS+QTcz58KS6+jwNrpUk0mjZoYtsSt6czlfHo/j4Lwjuejtqzp5AxB+G48Ini1FZcRltEm5DzdkTCOl+H2q+zEZISAh3Up0RtXhcoTQR0KjG3FqeIoyuby/i6bY14mqHL8TgM8YGAlgCwApgNRHNa/b7QABvA+gNoBTAn4joJ1fXNMPge1ImrMjFR2txOb3cBYWFhRicNhRhg59BYFw3/JL9DOpKf4LVPwBtU2c2HsPZYnyy5WPF1xblgjLC8BhhTLVmzVqtFmzbRrBand+jpgYYONCCmppaVWNswNNcq96Erpm2jDErgOUAkgDcAOAhxtgNzU4bA6CMiK4F8BqAV7TeVw88KRNWpDJGqxxUr2DzYxmPw3r1LY3B5OjBTyAoOg5tU2c2BpPDeiYjLDxcmLEHlClW9Ew4s8eVvrykBFi+HJg8GTh9+rzqQmFas1SNkkSaXWTQlxGxPPYBcISIjhJRFYD3AaQ1OycNQFb99+sB9GOMMQH3Fo6nZMKKXHxEyEH1KF3haFFr9/CSFova+vfXKbquqHwHIw2PM2P61VfApElAQACwbBmwfTtUFwrTmqVqlCRSRN6HrKbpGBEGPxbA/+x+Lq4/5vAcIqoBcA5Au+YXYoyNZ4ztY4ztO3XqlIChKcPsTNjmiFp89Nqha62to9cTlah8ByOrmzoypiUlwMsvAy+9BIwbB82NQbRmqRolidT6dCuraTrHoxxgRLSKiBKJKDEmJsbw+4vYGYoqMCZ68RG9Qxfl6uBd1JTMK88CN2f2c3gs43GX1zOyV7IjY5qTAwwaBGGFwrS6ZJSUQNCClo0Ab9+A1rrTF2HwSwB0svs5rv6Yw3MYY34AImAL3noUWneGIv29ntyDVpSrg3dRmzR5suJ5dbXAvZv1FmbPedHt9RISEvDq3BdQW1aMC1vntxhf+Y5lwmI6jozpjh1AcrLr1ykpFCbCJcNbAkErap9uZTVN14gw+HsBJDDGrmaMBQB4EEBus3NyAYyu/34YgALyQD2oFteH2dmdRiLK1cGzqNW1/x1WrXlL2Lwq+TsVFhYiJX0ILlysQPBdjza5TsWxA6ioqsErCxY1Gh6tpaKbG9Nz5yC0UJgolwxPCQQtaHm61auappKYgCfHD0TJMpMBLIZNlvl3InqJMTYHtgSAXMZYEIB3APQEcAbAg0R01NU1zcq0VYvZ2Z1GIkq+yiP3LNvxOoK7/gFRSRlC5pX374T97+Ni+QVU1QIxQ55tYnga6t23ib8FFUf34eW/PY/E3r2ESwZFSTWLioqQmbkQ69Zlo7T0AgIDgfR0hsGDySOzVLXkfeghHVWSu+AJfYd11+HrgbcZfE/S8BuBqMQmd4vaKy+9gNeWrhA2r7x/p6jISBw/VYaga3qjXf1iU3HsAE7lvgqqrUb7obMa8wSs50tg8fPX1FnKESKSsRwZoH//G1izhqGoiFBdzdCuXRiGD38YU6ZM84guUVryPkTnMyjJXQDgEd24pME3CLOyO83ISNSztk5zRM8rz/Vuv/12pN3/AA4fPQZr244I7TEQZYVrYAkMRmBcN7RLntr4hHB+86sI6TtGeDa21kQpI9oTisT+SeTMmXKEhgYAFn+sWPY6RowYwfV0KzpjWcn1iOoMyZZ2R6tocegJmKHhNyoxyNE9jZKvip5XnuvFx8fjP9/sx4NDU8Aunsa5z99DTPoMtH/gb6gpO45fsqb9ljswqmXugAjljlZVjNEBTC2+a0dSyhUrKpEyqBIZGeORl5fHpSoTLR1VEhPwhm5c0uALwgwNv1kZiUYqiPiVPFO4ZJtK/k67du3CxtzNiEiaZuuy1bk7/KNi0WH4PDCLFac3vtji9aKzsbWoYow0QFq07yKllKKlo0pyF7yhG5c0+IIwQ0ZpZGKQPXooiJwZ56SUdFi69HY7r6vW/J3rKYf37/Tyq/OdLgyV/zuI6rO/IPLeiS3eh6MnDi073wY3x7vvvo3S0guIjAzBQw+N4PK3G2WAeA12QUGBw3l48cW/Cn0ScbVIfvjhx/jkk4+5/xZKche8oRuXNPiCMENGqTQxSFRSmOjMXVduKb+4G1F77GtcWD/L6bye2bYSEXeM4HrK4f07Bbdp43BhKNu+Eqc2zUNMOl9nKS07X60Zo0YZIB7XUffulUhLG+Dwvbz33rvCn0QcSUcHDBiEBx5IUTSfSnIXvKEblwzaCsQMGSVvQNOs6oPuAsrFxcVuy0Cf+3AW7uwej/3ffNtiXmfOeh5+V16HmGF/5ZZt8vyd4uLiHCpFJkzOgLVLYmPQ1pVk8F+F+aqDpiICrkaVXHanjCkpAR57DJg713HW8D332GoE6VmFU+18SpWOQXijwTcDHrXMq3NfQNr9Dwirra90bK4WmYsXy3HceoXq/AU95bCOFob/+7//Q/+Bg3Cq0oKwXikuJYM5OR+qNrgijLVRKh132vfly23F38aNc/z7oUOBpUuha2loLfOppMOWJ3TjkgbfR+Gtc+9fUYqKmG6GNnbhbeDylwcH4587CjUZbKPlsLxPclo04aL05EYYIHdjdWfQly8H/P2B8eOd30Prk4jW+SwqKsLSpa9h3bp3cOZMOaKiQp3mLig5Vw+kwfdReDMSrV9/iNi4OEOTwpRkHv/vxyJNBtvInAAlaMn6FJkxqrcBcrd77tcP2LbNucumpMRWAvqllxy7fEQ8iRjZvMVspA7fAEQFRJXAG4B8/923DW/soiSgrEVnn52djUEpSaipuYST78/CrysewPnCFaguOwHA+JLW9mgJmooMuDqrffPzzz8L+cy6076HhsLle4mNte3un3oKulXh9AYFjRFIgy8AM5KfAGVqGaOTwnhL3BKR6vyFvLw8jB8/CimDq7DmjUps305YvfIy7r16K85kT8SlI3s0yWHVyCntDaYW1Ybeig+Rn1l32ve6Oiu2bnWxtQZw/Lg//vSnkbpV4fQGBY0RSJeORkQ3G9d7jK58/Q8OvAM7//WZsBIN9ve1hsXg8rcbcfn7QlSWVyCgjR+iIqMQFByB0226KC6UxROQfOophqDbHkH1NzmKG7yoKYLVPEj9fnYWbrmlO156qcIUlY4z9PrMOnMdpaYOxQMPpJiqXvG2MhNakD58HXHlqwYBZYVrEHzdnWhztBDHf/5J9xo3PGN0ZFTLdq5F+TebEXLdHUJkm/ZGhaqrcP6Tl5E2uAYpg2sbjWfux8DWLf5o3/EalNUGKiqUxaO6eOMNC/I+CcbGDbmKjL0a4+DMiP7jw4/AWCVSU+qQOrhWUdBUr4CrHpVd3eEJ6hVPGIMRSIOvI85kgU1K6B75Etu25IIxprvu3d0YHRnV2piuuPD1ZrQfNlvYjq/BqITeOgxl72VgwbxKl8Zz/ITHkZX9Lnf+guiqiPaokfC5MqK2p5scXDq4HVWXqhAdHc4dNNUj4GpWZVez1SueMga9kQZfZ5rLAhuMfUz6DATGdcOZ92dg6D19THXzuJISznzueQRee7uwmvPAb0blUNEhDLr3LCb+xbk/WI3kTk/VhZrFxNvKY5tV2VVvmlfctBn0kcjIeNJnDLo7pEpHZ5oHRMsK16DNtX0aa9yED3wcOTrVuOENLLpq+Xfw398gIahcaO/WhqCtteYiUlNcB//UFPDSU3WhpgaNXg3Z9cKMyq56I5uXu0cafI04CohGpz6DmjPH8eu6GY0GNFKH5teiPuB6GSur1Yry8ipdCnjpqbpQu5iINKKi2uQ5us6wYWkYlJpuaGVXvVFScdOTWxDqjTT4KrCX3jmqvnghZw5Cr7sdfm2vwOncV1u8XsRuT2RJWUC/HZ9eO3HRdc/tUbOYiCyPLWohd3YdP79cUG056qorARhT2VVveGv/P/XU46Y9BXjCQiN9+AppLr1b9/ZbSBlyP46dqUBY71SczX8dq19fgbmvzMeRoz+i/f3P65L9KbIwFq9sU03MQc8CXnqpLgoKCpCWNgDz5tVwq3S09GG1R5R8kFu2eusjqP42h7uFoKfCG3cZMwZYuFC/jF5nGNnrVvrwBeGo4cjcl19GSUkJAq68Duc+ew9BkVfgqz17cPzECYfGHhDzyCyyuYWIWv7OMo035xVgyxY/1TtxVxnM1157rermIM7Iy8vDAw+kIDGR8OyzwKpVaJJItGqV1WHmp6jy2KK6VPFcJz2dgf79oeby1p4Ab9ylutqxsQfEdwBrQPTTuBbkDl8BvPrl84WrEJTwe827PVeIVKloaRoNuK+K2b6iGGWnf1a8Eze6pHPzXXFJCZCTA+TnA+fOAWFhQE2NFbm523DPPfe0eL2I8tii5KZ6ylY9Ed73O3kysHGj63NEz4lRZaobkDt8QfDWh/n7qtd1b4Yi0jeupaEJT5vF0mp/jJ/wuKKduBntG5vvimNjbUW9NmywGf2cHCA93YLc3A0OX+9KCeWqD6s9orpUabmOJ/ialcITd9m0yVbIzRU8c6t0fjyp163c4SuEV7+sdzMUo3cNztAra9OMbFBP2BWbvcM30tcsEr6Yha0iZ69ezq/jbm7VzI/RlTp12+EzxqIYY9sZY4fr/410cM7NjLEvGGMHGWMHGGN/0nJPs+FVs4jY7blCT5WKksqfvE89T0+bqqgyo9L2jSLwhCbUouSmaq7jSb5mpfA0L7/vvmR8/bX6uVUr/SQi3H+/re5/SYnj+xpVqVOrS2cGgHwiSgCQX/9zcy4BGEVE3QAMBLCYMdZW431NQaT0Tis8H3A1JWWVVlHk0fBPmTgBz//tBUWVGc1IZPKEErqiFnI11xEVMAbMcQu5al6+Z88BLFqUqWlueefnySebSj+3b/+t69ekScBXX7V8rVGVOrUa/DQAWfXfZwFIb34CER0iosP13x8HcBJAjMb7moIINYtI3H3AlT56q/Wbu3rqCeiejPmLlyIk6UnFvnijs0E9oYSumoXckXHNzFyI+fOXKrqOKF+z6IxXJYuHs9r/8fHxLuf2tdf8MGOGFXV1hK5dExzeg2d+evWqxj//udnhU8C4cTaX0ssvN93pa3kaV4omHz5j7CwRta3/ngEoa/jZyfl9YFsYuhFRi/+pjLHxAMYDQOfOnXsfO3ZM9dj0QKuaxdNR4zdvWCQCeg/FxYM7EZ36TJMCcmcKVgN1tQjp3h8Rtwzh9sWLzg3gqbHiSSV0eYt8ufMpz5+/FAcOfM1VLEyEr1n0HOoRU2g+tyEhgairq0ZaGpCUVOP0Hjzzs3Qp4OcHTJzo/JxVq4CqKmDIEON1+G4NPmNsBwBHns1ZALLsDTxjrIyIWvjx63/XEcBOAKOJ6Et3g/bkoK2ewVgzUVMA7PqbeuDYJX9UnjiMNvF9UHWyCIF1lbB07ony7/+FNvF9UHniB9ReKEXH0a9xFxUTlcgEKDMaapK5HJW8NqIMtmjjKiJgLFJMYMQCrOQet93W0+38pKfb3Dc8CWDh4fxVU5WgW7VMxtgPAPoS0YkGg05Ev3NwXjhsxn4uEa3nubanGnxfR2kVxXfeeQd/HjcB0UOfR2BcN/zy9jTcknAlvty7v8mxq9oG4NeLtdyVGUU9TakxGkpK6BqdK2CPaKWWiOuJVDoZoURTcg+iOrfn3nMPsH278/69gP69c/XU4ecCGF3//WgAmxzcPADARgBv8xp7iXko9ZvPfXUBgn93R2Nl0OjU6fjup18RPfT5xsqgYb3TcOTHnxT54rXkBtijJhDpyg9sjxm5AvaI1neLCBiLVDoZoV9Xcg+e+QkIcN2/FzC3d65Wgz8PwL2MscMA+tf/DMZYImNsdf05fwRwF4BHGGPf1n/drPG+Eh1Qo0LikU+e2b4SEXeMVKxsEiFt1dNo2AfxmcWK4P6TsUmnMtiOEC0jFaH8Eql0MkImq+QePPOTlDTI9MC/KzQZfCIqJaJ+RJRARP2J6Ez98X1ENLb++2wi8ieim+2+vhUxeIlY1KiQ3MknS7csROAV8QhLTOW+ZnO0SPz0NBpm5ArYo4eMVKvyS6TSyQiZrNJ7uJufBQuW6JYfIwJZWkHSiNoCYK7cQBF9hsJy/gTOfzhLVZkJrRI/PY2G2U1PRMtIGxbW227riaVLV4CIMGnSBHzxxdcOXVqOEJkQaIRMVs091Eo/teTHiEIafEkjavzm7txAIb0GIyAqFnfcdI1iX7yIzE+9jYaZnaNEGldR2nmRBk/U+3P1hKhHxrro/BiRyFo6Ek2IlE82R4RKQ09pn559BHgR0RNAjzkS1Sxc6/vjkeQC0KWvglnIJuYS3dAzGU2UxE+vRil6LnZK0GpcPaUQnzPUvj8lCxkAIQuUJyANvsQpIpKG9EpGE1llUNSO0x5fybz2hCqheuDpC5leyHr4EocoLZTmDL0qg4oMuLoKtKlVAYnKFXCFEUXIPKFKqB54Uh16Hoz4W0uD30oxO2mIByNUGlqDlTyLndr/yKKLkDnDE6qE6oG3LGRFRUUYMmQwunW7FkuXrkR19QWkpxNmzhT/t5YunVaKGQ1GlKJ3LRUjarWoLf5lZCE3X3V9eIOrKi8vD8OH34/77ruM1FQ0fj62bgW2bAFmzgRCQ5X9raVLR9ICnqShS7vW4A+39uDamerxOMoj8Zs/fykyMxequq/I+u+O0CIr1Xts9ujZTMdM1DaBMaqOf8Pn48UXL2PiRDgtpdy2rbi/tTT4rRR3SUPnty6Ev6UCgYF5bt0JeroeXGma589fiqefnqL6vnr7eLUYbSP9z2YkCxlhWJUuZEa50Brg+XwMGmTrpSzqby1dOq0YZzry6rITOJM9EQtereGSs5lRQ16ry6OoqAgJCdfqWtlQi0vB6D6ogD5KJkcY2TeXV5JrRi8E3s/HlCnABx/w/62lS0fSAldJQ5e/3Yi0VOLamRrperBHy30bdnLBwfpWNtQSNDQjkMpbJVQLRvfN5c16NeNzzPv5OHdO3N9aGvxWiqtCaZf/W4iUwa53Eg2PmGZJ39Te197gJCXZgmOu0KIC0mK0PaHdIiDe9WKGYeVZyMz4HPN+PiIixP2tpcFvpbgqlFZ58TL3ztQs6Zva+9obnPR0mxJCr2ClFqPtCYFUPXzanqqNN+NzzPP52LoV6NlT3N9aGnwfprCwENff1AOHDx9ucQyA06ShsLBA7p2pWRputfe1NzixsTbZ26xZwJtvokmwcuVKaA5WajHaZldd1Mv14qnaeDM+xzyfj02bgP37g4T9raXB91F4smgZYw6Thh555FHunalZrge1921ucG691daDtKrKFhwbMMD278aN0FzZUKvRNrPqol6uFy2GVU9ljxmfY1efjxUrgKeeAvr3H4z9+78T1+BcqnR8D/uAbGBcN5xf/xxS7+qFj3JymxxzVsVRadEpb1LpmJGMY5T6RSR6zZPaJC+9lT1mqHTs7y3y8yGLp7UyRGTRKqkwqVc1Sneoua+vZpWKRi9ZqNqm8kYYY1efp82brbj77n7YvXuXnVEeiYyMJz1u0ZayTC/HlS/e/lgDIlrvKXEnmOV6UHNfs4OhRmZyakEvn7YaN5dRyh5nn6fy8gFgjCEkZJshCVm6QkQe+dW7d2+SEBUUFFBIRCSFde9PvfrcTrW1tQ6PNaempoZGjR5NEXEJ1GX65iZfYTGxlJWVZcK7sXHkyBHKyJhI0dFhZLEwio4Oo4yMiXTkyBFD7r9161aKigqmESP8KTsbtH07KDsbNGKEP0VFBdPWrVsNue+OHfrcV8T8ZmRMpBEj/KmwEE6/Rozwp6lTJ6ke49SpkygmJpysVgvFxITT1KmTHI4xOjqMsrOdj6Ow0DaPMTHhqsbibpxRUcG0bJnj+y5bBoqKCjbss8sDgH3kxK5Kl44Ho8UX7yqx6uL+Teh8/jvs/WI3LBZjH/KMzLJ0hdF+dbPcEmrn10yfdnPMyDpuwBtdgNKH76Wo9cVrbb0noimKIzzJiBiNKMNRVFSEzMyFWLcuu4UvGRAbQDcrNtMcM6teekPFzeZIH76XotYX7yqLlupqwSxWWG/oj4UOat2LaoriCLPKMHgCIhKO3CVCPfFEhtD59ZRm3GZmHXtq3oBaNBl8xlgUY2w7Y+xw/b+RLs4NZ4wVM8Y847nHC3BX0fJS/nKsyFzcqKFvwFUW7YWPnkf5d/mo2L0W72a91eR1ejdF8dQsSyWoDbpqNRw8iVDbtm1Fr15i59eI+jruMDPQLip47SnBeq07/BkA8okoAUB+/c/OeAHALo33a3Xs2rULH+XkIujOP7f4XWCPZCxZ/nqLHbfa1nv2TwbMYkVw/8nYlP95o2uIWaywXu/4yYAHb98taSk1oNVw8DwdpaQAn3/u+h6i59cIQ2Zm1rGIpwujyy67QpMPnzH2A4C+RHSCMdYRwE4i+p2D83oDeBrAJwASiWiyu2tLH752X7xS7Jtyt+k3Cf5RTR2XFccOoDxvPjbnbGjxVMGD3v5QV/5trcZAa/xBqw+fd+4mT7ZlCdsfy8kB8vNtVRfDwoC6On988833mufE6AC8GQlsIspwGx230tOH34GITtR//wuADg5ubgGwEMBTGu/V6tDii1eDWhcSL3r4Yht2mJGRwUhIuBZr1qxEYuIFrF0rdhelNf6g1S3B+3R0/vxvP3/1FTBpEhAQACxdCmzbZisjMWhQjeY5MbrMcQNEdc2khuriSbxofbrwtLiVW4PPGNvBGPvOwVea/Xn1+k9HjwuPAdhKRMUc9xrPGNvHGNt36tQp7jfhq6j1xWtBjQuJNzFMtC/2t0flN7Fs2WVs3w688QYQFWWrh1NcLM74aI0/aDUcvC4hf3/bPJaU2NrjvfSSrV2evUGeMIE0z4nRhsxMt4iW4LWnxa10d+kwxt4FcCeAOgChAAIArCAiV/5+6dKpp7a2FouXZGLh4iVY9/Za9O3bt8mxd7PeauGL58GRzHLRokV4+tnnEHnvRIR2u6fFaxy5kBrcTpYuvZEQdBF7v9iNTz/9tMWxBr2/KKkfz6PyrFm2HW1srHattCgtuFq3BK9L6OLFAdi1qwBXXHEZPXoQxo93Pl4tc2KkXNGb5bxm5BDopsNnjM0HUEpE8xhjMwBEEdEzLs5/BNKHbzrOjHT/gYPQpuvtqDn7CzoMn4fKn7/DpfzlCOyRjOCeg8EsVpR/l4+AAzbdv9rEMBG+WB4D+OabtiqYkyZpNz5m67GVFrS7+ebr8frr1bqN10hD5o3JTw2Y8bnR04c/D8C9jLHDAPrX/wzGWCJjbLXGa0t0wJX0Mub+59EuaSqophKnP17k1oWkRNVjr+bo2jUB7777Nh56aAR++OGQKqkfz6NycrItWAloV6eY3YFKiUsoPj4ely7V6KqIMrJ+vKe5RZRg9uemOZoMPhGVElE/Ikogov5EdKb++D4iGuvg/LU8u3uJfvAY6bBeqag8usetnJM3MWzqpInC/a9K+oEC2o2P2UXXAGW+ZL0NspGGzEw5r1bZqSd8buyRmbY+Am/glNdI7/hkS5OmKA2NUuzjBTyqnr/OmolXX50jXM2hpB8ooN34mN2Byn4czhKh7I1TaekFTJhgi2GUlDi+Vm4u0KlTJ1WBWyMNmVld1UQEij3lc9OANPg+gJJyCKKll+5UPa8t0UfNwdsPtF8/ccbHU0oNOKK5cWpQLPn722IYX33V9PyDB4FPPgHi4n5Q9ZRlpCEzwy0iUnbqSZ8bWTzNy1ETOBVVSZMnMezXpUOwZnWd8KAVTxDz2WeBvn39sHt3QAv1j55JWkbDOxdLl9qM1tattubtM2faWjxqUbkYkQxlhkrHmwPFsniaD6O0HII7Ix3cczB3vRyexLCqyjpd/K+udpgrVwJPPgkAbXD11RNa7KI8KdXdFbz+Yx5N/IABwNixwJgxwIcfAkTAvn22+dKimTei1o4ZbhFvDhS7Qu7wvRyl5RCal1yuOHbApfSS996W6/qh4rO1WJG5GEuWv44fT1+E5fp+KN/xGtasga7lFJTsML1F062kbAGv9G/CBJubp+Fa9jv9uDjPKvHrCCNLK5hZg18rsh6+j1NbW4tHx4zBpvzP0XZE013amb9PwLIFczFq1CgAfEa6Yvdah8XVnN3bVWLY7/v0QFBQnsc8GnvDo7rSRYnXOA0Y8JtM1f5as2YBmZnAo496nvEyC7PzLrQgXToegNK+tEpQUg5BbSVNZ9greBypel55ZZFHydLMflTncdMoLVugVLHU/FqDBgHvvy9e5eLNeJp+XhTS4BuAnk1F1PjkYd/UqQAADBhJREFU3RlpNaUanOFpsjQ9NN28vnbe2IHSRUmJYgmwzf3y5cDQobZjW7YAO3cCycmpit+T0Rg1Lk/Tz4tCGnyd0bupiJKKmno+ZbjCk2RpojXdvEZcicxP6aLEY5y2bAHS051X0ExJATZt+gh5eXmag9p6GWUjg+2etlERhfTh64zavrS88Prk58x+DrPnvMhd5MwRviBlFOnDV+Jrz8xcyH3fd999W7H/2FlRutxcm97+2WdtgdlJk2wVNJ0XnAsCYwwvvnhZVVBbrxr5ZgXbzajBrxUZtDURvZuKAO4Dp089noHZc15UXOTMHqObXeiFSMOhZPFQYsQfemhEi+s2b2TSpg3wu9/diA8+yGkcpyPj1KlTJ8TF/YBp02qwfLltZz9unPMxTJrE0L27rYSyu/fUfEHU0yh7Q7DdU5AG32SUqGj0QOtThrdIGZvj7ImkR49EPP30FM0lmpUoOUpLL3DL/H744VCT+f7qK1tt+0GDbAXhflts/ZCX1zKprPkcNFzr+edtbhxX401P/62ktLv31FydoqdR9mbVjNFIlY7JqGkqIhLe+jnr3l7r8PWe1rWHB1f+3qefnoL585dqjiko8bUriR3Y+49fe80Pc+c6bmQydmyN2xR/+2udPQu3471wwf05zoLaeiqgvL0fsqcgDb7OiMxsVYvW+jl6/UfWK7jHEyB9+ukpmDJlmqYMUSVGXKnMryHQXVx8HZKSHPvcAb7FtuFaISH+bscbFgbVQW09jbJZBdR8DWnwdcbovrTO0PKUocd/ZD0VF0Y9kSgx4jxKmo8+qkZm5vLGhQ8Afv75GFJSXI+DZ7GNj4/Ho4+OdTve2FiGzZuZy3Oc6c/1NMq+qos3GmnwdcaMvrTN0fqUIfo/st4NsI1KrlKi1XYl81uxApg+3daHd/t2NFn4RC62POMtKQnEJ58EYefOplr9oUNtP+/c6Vx/rqdR9lVdvNFIg68zojNb1aD1KUP0f2S9d+BG+XuVarVb5iMwjBkDlJbaatwkJ7dc+Pz9SdhiyzPe997bgGnTZuCVVwA/v9+0+kuX2n5+5RVg6tTpDl1fehplX9XFG41U6bQCtNbPEa3S0VtxYbSiQ8/G5BMnAj16MPzlL8plkmrGC0DT31pUk3o1Y5fG3oaUZUrcavXfzXrL5VOGyP/Ielci9BbNNs/C9PXXtqSphQudJ0uJlMSKmDtplM1FGnyJEET9R9Z7B15UVITExJtUZ4saBe/Cd999QEiIH9LSmC67Znuk3t37cWXw/YwejMR7aWh2oXVXbIsJuN5FalFcHDp0CDU1dZg+HUhNtSUsNRjJTZuAbdsC8d575vt7bcFw18a1ocqlxRKE6OjRmDat6WK7Z4/YXbPUu/s2MmgrMRw9g3sNCqB58yrxxhtAdbVN/TJggO3f0lIAYOjataum9yCC4cNHIjfX9TkNVS7Pnbuke2cpQOrdfR1p8CWGo6fiwl4BFBtrKxa2YYOtBs2GDcDzzwODB9cKzQpWm0CWkfEkcnPhtsrl739vnIGVenffRpPBZ4xFMca2M8YO1/8b6eS8zoyxbYyx7xlj/2WMXaXlvhJzEZEhq1fJZKMbnGhJIIuPj8d99yVj+nTgzTfRZOF7801bJ6qZM4GvvzbOwEq9u2+jKWjLGHsVwBkimscYmwEgkoimOzhvJ4CXiGg7YywUQB0RXXJ1bRm09Uw8vWqmkb1IRchVi4qK0Lv3jejduwLffGOrhBkRYXPjpKcDZ88aH2DWW1op0Rc9i6elAciq/z4LQLqDm98AwI+ItgMAEZW7M/YS7ejR7ETvDFkRGOmDFpFAFh8fj/fe24Bvvw1GcrI/3n4b+OADm7HPyzMnociTGtZIxKJ1h3+WiNrWf88AlDX8bHdOOoCxAKoAXA1gB4AZRORyeyV3+OppKKWgpdmJI7xB327kGEVKGKV2XSIKTTp8xtgOAI6EWrMAZNkbeMZYGRE18eMzxoYBWAOgJ4CfAfwDwFYiWuPgXuMBjAeAzp079z527JjLsUlaYl83R22zE2d4g0bbyNr9RrqPJBJeNLl0iKg/Ed3o4GsTgF8ZYx3rb9IRwEkHlygG8C0RHSWiGgA5AHo5udcqIkokosSYmBje9yexw75uDrNYEdx/Mjblf95YOI1ZrLBer646pzdotI2suSIljBJvQ6sPPxfA6PrvRwPY5OCcvQDaMsYaLPg9AP6r8b4SJ2htduIKbzFwRvmgzZIw6tVHQCTeMMbWiFaDPw/AvYyxwwD61/8MxlgiY2w1ANT76p8CkM8Y+w8ABuBNjfeVOEFrsxNXeJNGuyErWM9EJTMkjHr2EWhNY2ytyFo6Poir+vcX929C5/PfqQraemtvWz0xUsKo5/w76/+bkfGkomvJz4j5yJ62rQg9WyrKmuQtMVLCqFcfAZE7cm/sf9yakDt8H+P6m3qgxNIBkQOngDELKo4dwKX85QjskYzgnoPBLFaUf5ePgAMbcPznn1TdQ0oIzUEPlZS39TqQuEeWR25FaG12IvFc9JCBis5bkFJV85EunVaEJ7RUlOiDHiop0bWHvEXJ1VqRBt8HsVqtePKJaTj+80+Nahz7Y9LYeyd6qKRE51Z4k5KrNSINvkTiJeghAxW9I5fVNj0bafAlEi9BD5WU6B25VHJ5NtLgSyRehGgZqB47cllt03ORKh2JpJUj69/7FlKlI5FInCJ35K0HucOXSCQSH0Lu8CUSiUQiDb5EIpG0FqTBl0gkklaCx/rwGWOnAKjtcRgN4LTA4Xg7cj6aIuejKXI+muLt89GFiBy2DPRYg68Fxtg+Z0GL1oicj6bI+WiKnI+m+PJ8SJeORCKRtBKkwZdIJJJWgq8a/FVmD8DDkPPRFDkfTZHz0RSfnQ+f9OFLJBKJpCW+usOXSCQSSTOkwZdIJJJWgk8YfMZYFGNsO2PscP2/kU7O68wY28YY+54x9l/G2FXGjtQYeOej/txwxlgxY8x9w1IvhWc+GGM3M8a+YIwdZIwdYIz9yYyx6gljbCBj7AfG2BHG2AwHvw9kjP2j/vdf+er/jwY45uOJejtxgDGWzxjrYsY4ReITBh/ADAD5RJQAIL/+Z0e8DWA+EV0PoA+AkwaNz2h45wMAXgCwy5BRmQfPfFwCMIqIugEYCGAxY6ytgWPUFcaYFcByAEkAbgDwEGPshmanjQFQRkTXAngNwCvGjtI4OOfjGwCJRNQdwHoArxo7SvH4isFPA5BV/30WgPTmJ9T/Mf2IaDsAEFE5EV0yboiG4nY+AIAx1htABwDbDBqXWbidDyI6RESH678/DttmwGG2opfSB8ARIjpKRFUA3odtXuyxn6f1APoxxpiBYzQSt/NBRIV2NuJLAHEGj1E4vmLwOxDRifrvf4HNiDWnK4CzjLENjLFvGGPz61d5X8TtfDDGLAAWAnjKyIGZBM/noxHGWB8AAQCK9B6YgcQC+J/dz8X1xxyeQ0Q1AM4BaGfI6IyHZz7sGQMgT9cRGYCf2QPghTG2A8AVDn41y/4HIiLGmCOtqR+AOwH0BPAzgH8AeATAGrEjNQYB8/EYgK1EVOwLmzgB89FwnY4A3gEwmojqxI5S4o0wxkYCSATw/8wei1a8xuATUX9nv2OM/coY60hEJ+r/wzryzRcD+JaIjta/JgfAbfBSgy9gPm4HcCdj7DEAoQACGGPlROTK3++xCJgPMMbCAWwBMIuIvtRpqGZRAqCT3c9x9cccnVPMGPMDEAGg1JjhGQ7PfIAx1h+2TcP/I6JKg8amG77i0skFMLr++9EANjk4Zy+AtoyxBr/sPQD+a8DYzMDtfBDRCCLqTERXwebWedtbjT0HbueDMRYAYCNs87DewLEZxV4ACYyxq+vf64OwzYs99vM0DEAB+W5mptv5YIz1BPAGgFQi8g2BBxF5/RdsfsZ8AIcB7AAQVX88EcBqu/PuBXAAwH8ArAUQYPbYzZwPu/MfAbDM7HGbOR8ARgKoBvCt3dfNZo9d8DwkAzgEW2xiVv2xObAZNAAIAvAhgCMA9gC4xuwxmzwfOwD8avd5yDV7zFq/ZGkFiUQiaSX4iktHIpFIJG6QBl8ikUhaCdLgSyQSSStBGnyJRCJpJUiDL5FIJK0EafAlEomklSANvkQikbQS/n/hm2CO0fFw0QAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load from ex6data3\n", + "# You will have X, y, Xval, yval as keys in the dict data\n", + "data = loadmat(os.path.join('Data', 'ex6data3.mat'))\n", + "X, y, Xval, yval = data['X'], data['y'][:, 0], data['Xval'], data['yval'][:, 0]\n", + "\n", + "# Plot training data\n", + "utils.plotData(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Your task is to use the cross validation set `Xval`, `yval` to determine the best $C$ and $\\sigma$ parameter to use. You should write any additional code necessary to help you search over the parameters $C$ and $\\sigma$. For both $C$ and $\\sigma$, we suggest trying values in multiplicative steps (e.g., 0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30).\n", + "Note that you should try all possible pairs of values for $C$ and $\\sigma$ (e.g., $C = 0.3$ and $\\sigma = 0.1$). For example, if you try each of the 8 values listed above for $C$ and for $\\sigma^2$, you would end up training and evaluating (on the cross validation set) a total of $8^2 = 64$ different models. After you have determined the best $C$ and $\\sigma$ parameters to use, you should modify the code in `dataset3Params`, filling in the best parameters you found. For our best parameters, the SVM returned a decision boundary shown in the figure below. \n", + "\n", + "![](Figures/svm_dataset3_best.png)\n", + "\n", + "
\n", + "**Implementation Tip:** When implementing cross validation to select the best $C$ and $\\sigma$ parameter to use, you need to evaluate the error on the cross validation set. Recall that for classification, the error is defined as the fraction of the cross validation examples that were classified incorrectly. In `numpy`, you can compute this error using `np.mean(predictions != yval)`, where `predictions` is a vector containing all the predictions from the SVM, and `yval` are the true labels from the cross validation set. You can use the `utils.svmPredict` function to generate the predictions for the cross validation set.\n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def dataset3Params(X, y, Xval, yval):\n", + " \"\"\"\n", + " Returns your choice of C and sigma for Part 3 of the exercise \n", + " where you select the optimal (C, sigma) learning parameters to use for SVM\n", + " with RBF kernel.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " (m x n) matrix of training data where m is number of training examples, and \n", + " n is the number of features.\n", + " \n", + " y : array_like\n", + " (m, ) vector of labels for ther training data.\n", + " \n", + " Xval : array_like\n", + " (mv x n) matrix of validation data where mv is the number of validation examples\n", + " and n is the number of features\n", + " \n", + " yval : array_like\n", + " (mv, ) vector of labels for the validation data.\n", + " \n", + " Returns\n", + " -------\n", + " C, sigma : float, float\n", + " The best performing values for the regularization parameter C and \n", + " RBF parameter sigma.\n", + " \n", + " Instructions\n", + " ------------\n", + " Fill in this function to return the optimal C and sigma learning \n", + " parameters found using the cross validation set.\n", + " You can use `svmPredict` to predict the labels on the cross\n", + " validation set. For example, \n", + " \n", + " predictions = svmPredict(model, Xval)\n", + "\n", + " will return the predictions on the cross validation set.\n", + " \n", + " Note\n", + " ----\n", + " You can compute the prediction error using \n", + " \n", + " np.mean(predictions != yval)\n", + " \"\"\"\n", + " # You need to return the following variables correctly.\n", + " C = 1\n", + " sigma = 0.3\n", + " \n", + " model = utils.svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n", + " predictions = utils.svmPredict(model, Xval)\n", + " ans = np.mean(predictions != yval)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " for i in [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]:\n", + " for j in [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]:\n", + " \n", + " model = utils.svmTrain(X, y, i, gaussianKernel, args=(j,))\n", + " predictions = utils.svmPredict(model, Xval)\n", + " err = np.mean(predictions != yval)\n", + " \n", + " if ans > err:\n", + " ans = err\n", + " C = i\n", + " sigma = j\n", + " \n", + " \n", + " # ============================================================\n", + " return C, sigma" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The provided code in the next cell trains the SVM classifier using the training set $(X, y)$ using parameters loaded from `dataset3Params`. Note that this might take a few minutes to execute." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 0.1\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Try different SVM Parameters here\n", + "C, sigma = dataset3Params(X, y, Xval, yval)\n", + "\n", + "# Train the SVM\n", + "# model = utils.svmTrain(X, y, C, lambda x1, x2: gaussianKernel(x1, x2, sigma))\n", + "model = utils.svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n", + "utils.visualizeBoundary(X, y, model)\n", + "print(C, sigma)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One you have computed the values `C` and `sigma` in the cell above, we will submit those values for grading.\n", + "\n", + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[2] = lambda : (C, sigma)\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 2 Spam Classification\n", + "\n", + "Many email services today provide spam filters that are able to classify emails into spam and non-spam email with high accuracy. In this part of the exercise, you will use SVMs to build your own spam filter.\n", + "\n", + "You will be training a classifier to classify whether a given email, $x$, is spam ($y = 1$) or non-spam ($y = 0$). In particular, you need to convert each email into a feature vector $x \\in \\mathbb{R}^n$ . The following parts of the exercise will walk you through how such a feature vector can be constructed from an email.\n", + "\n", + "The dataset included for this exercise is based on a a subset of the [SpamAssassin Public Corpus](http://spamassassin.apache.org/old/publiccorpus/). For the purpose of this exercise, you will only be using the body of the email (excluding the email headers)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Preprocessing Emails\n", + "\n", + "Before starting on a machine learning task, it is usually insightful to take a look at examples from the dataset. The figure below shows a sample email that contains a URL, an email address (at the end), numbers, and dollar\n", + "amounts.\n", + "\n", + "\n", + "\n", + "While many emails would contain similar types of entities (e.g., numbers, other URLs, or other email addresses), the specific entities (e.g., the specific URL or specific dollar amount) will be different in almost every\n", + "email. Therefore, one method often employed in processing emails is to “normalize” these values, so that all URLs are treated the same, all numbers are treated the same, etc. For example, we could replace each URL in the\n", + "email with the unique string “httpaddr” to indicate that a URL was present.\n", + "\n", + "This has the effect of letting the spam classifier make a classification decision based on whether any URL was present, rather than whether a specific URL was present. This typically improves the performance of a spam classifier, since spammers often randomize the URLs, and thus the odds of seeing any particular URL again in a new piece of spam is very small. \n", + "\n", + "In the function `processEmail` below, we have implemented the following email preprocessing and normalization steps:\n", + "\n", + "- **Lower-casing**: The entire email is converted into lower case, so that captialization is ignored (e.g., IndIcaTE is treated the same as Indicate).\n", + "\n", + "- **Stripping HTML**: All HTML tags are removed from the emails. Many emails often come with HTML formatting; we remove all the HTML tags, so that only the content remains.\n", + "\n", + "- **Normalizing URLs**: All URLs are replaced with the text “httpaddr”.\n", + "\n", + "- **Normalizing Email Addresses**: All email addresses are replaced with the text “emailaddr”.\n", + "\n", + "- **Normalizing Numbers**: All numbers are replaced with the text “number”.\n", + "\n", + "- **Normalizing Dollars**: All dollar signs ($) are replaced with the text “dollar”.\n", + "\n", + "- **Word Stemming**: Words are reduced to their stemmed form. For example, “discount”, “discounts”, “discounted” and “discounting” are all replaced with “discount”. Sometimes, the Stemmer actually strips off additional characters from the end, so “include”, “includes”, “included”, and “including” are all replaced with “includ”.\n", + "\n", + "- **Removal of non-words**: Non-words and punctuation have been removed. All white spaces (tabs, newlines, spaces) have all been trimmed to a single space character.\n", + "\n", + "The result of these preprocessing steps is shown in the figure below. \n", + "\n", + "\"email\n", + "\n", + "While preprocessing has left word fragments and non-words, this form turns out to be much easier to work with for performing feature extraction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.1.1 Vocabulary List\n", + "\n", + "After preprocessing the emails, we have a list of words for each email. The next step is to choose which words we would like to use in our classifier and which we would want to leave out.\n", + "\n", + "For this exercise, we have chosen only the most frequently occuring words as our set of words considered (the vocabulary list). Since words that occur rarely in the training set are only in a few emails, they might cause the\n", + "model to overfit our training set. The complete vocabulary list is in the file `vocab.txt` (inside the `Data` directory for this exercise) and also shown in the figure below.\n", + "\n", + "\"Vocab\"\n", + "\n", + "Our vocabulary list was selected by choosing all words which occur at least a 100 times in the spam corpus,\n", + "resulting in a list of 1899 words. In practice, a vocabulary list with about 10,000 to 50,000 words is often used.\n", + "Given the vocabulary list, we can now map each word in the preprocessed emails into a list of word indices that contains the index of the word in the vocabulary dictionary. The figure below shows the mapping for the sample email. Specifically, in the sample email, the word “anyone” was first normalized to “anyon” and then mapped onto the index 86 in the vocabulary list.\n", + "\n", + "\"word\n", + "\n", + "Your task now is to complete the code in the function `processEmail` to perform this mapping. In the code, you are given a string `word` which is a single word from the processed email. You should look up the word in the vocabulary list `vocabList`. If the word exists in the list, you should add the index of the word into the `word_indices` variable. If the word does not exist, and is therefore not in the vocabulary, you can skip the word.\n", + "\n", + "
\n", + "**python tip**: In python, you can find the index of the first occurence of an item in `list` using the `index` attribute. In the provided code for `processEmail`, `vocabList` is a python list containing the words in the vocabulary. To find the index of a word, we can use `vocabList.index(word)` which would return a number indicating the index of the word within the list. If the word does not exist in the list, a `ValueError` exception is raised. In python, we can use the `try/except` statement to catch exceptions which we do not want to stop the program from running. You can think of the `try/except` statement to be the same as an `if/else` statement, but it asks for forgiveness rather than permission.\n", + "\n", + "An example would be:\n", + "
\n", + "\n", + "```\n", + "try:\n", + " do stuff here\n", + "except ValueError:\n", + " pass\n", + " # do nothing (forgive me) if a ValueError exception occured within the try statement\n", + "```\n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def processEmail(email_contents, verbose=True):\n", + " \"\"\"\n", + " Preprocesses the body of an email and returns a list of indices \n", + " of the words contained in the email. \n", + " \n", + " Parameters\n", + " ----------\n", + " email_contents : str\n", + " A string containing one email. \n", + " \n", + " verbose : bool\n", + " If True, print the resulting email after processing.\n", + " \n", + " Returns\n", + " -------\n", + " word_indices : list\n", + " A list of integers containing the index of each word in the \n", + " email which is also present in the vocabulary.\n", + " \n", + " Instructions\n", + " ------------\n", + " Fill in this function to add the index of word to word_indices \n", + " if it is in the vocabulary. At this point of the code, you have \n", + " a stemmed word from the email in the variable word.\n", + " You should look up word in the vocabulary list (vocabList). \n", + " If a match exists, you should add the index of the word to the word_indices\n", + " list. Concretely, if word = 'action', then you should\n", + " look up the vocabulary list to find where in vocabList\n", + " 'action' appears. For example, if vocabList[18] =\n", + " 'action', then, you should add 18 to the word_indices \n", + " vector (e.g., word_indices.append(18)).\n", + " \n", + " Notes\n", + " -----\n", + " - vocabList[idx] returns a the word with index idx in the vocabulary list.\n", + " \n", + " - vocabList.index(word) return index of word `word` in the vocabulary list.\n", + " (A ValueError exception is raised if the word does not exist.)\n", + " \"\"\"\n", + " # Load Vocabulary\n", + " vocabList = utils.getVocabList()\n", + "\n", + " # Init return value\n", + " word_indices = []\n", + "\n", + " # ========================== Preprocess Email ===========================\n", + " # Find the Headers ( \\n\\n and remove )\n", + " # Uncomment the following lines if you are working with raw emails with the\n", + " # full headers\n", + " # hdrstart = email_contents.find(chr(10) + chr(10))\n", + " # email_contents = email_contents[hdrstart:]\n", + "\n", + " # Lower case\n", + " email_contents = email_contents.lower()\n", + " \n", + " # Strip all HTML\n", + " # Looks for any expression that starts with < and ends with > and replace\n", + " # and does not have any < or > in the tag it with a space\n", + " email_contents =re.compile('<[^<>]+>').sub(' ', email_contents)\n", + "\n", + " # Handle Numbers\n", + " # Look for one or more characters between 0-9\n", + " email_contents = re.compile('[0-9]+').sub(' number ', email_contents)\n", + "\n", + " # Handle URLS\n", + " # Look for strings starting with http:// or https://\n", + " email_contents = re.compile('(http|https)://[^\\s]*').sub(' httpaddr ', email_contents)\n", + "\n", + " # Handle Email Addresses\n", + " # Look for strings with @ in the middle\n", + " email_contents = re.compile('[^\\s]+@[^\\s]+').sub(' emailaddr ', email_contents)\n", + " \n", + " # Handle $ sign\n", + " email_contents = re.compile('[$]+').sub(' dollar ', email_contents)\n", + " \n", + " # get rid of any punctuation\n", + " email_contents = re.split('[ @$/#.-:&*+=\\[\\]?!(){},''\">_<;%\\n\\r]', email_contents)\n", + "\n", + " # remove any empty word string\n", + " email_contents = [word for word in email_contents if len(word) > 0]\n", + " \n", + " # Stem the email contents word by word\n", + " stemmer = utils.PorterStemmer()\n", + " processed_email = []\n", + " for word in email_contents:\n", + " # Remove any remaining non alphanumeric characters in word\n", + " word = re.compile('[^a-zA-Z0-9]').sub('', word).strip()\n", + " word = stemmer.stem(word)\n", + " processed_email.append(word)\n", + "\n", + " if len(word) < 1:\n", + " continue\n", + "\n", + " # Look up the word in the dictionary and add to word_indices if found\n", + " # ====================== YOUR CODE HERE ======================\n", + " if word in vocabList :\n", + " word_indices.append(vocabList.index(word))\n", + " \n", + "\n", + " # =============================================================\n", + "\n", + " if verbose:\n", + " print('----------------')\n", + " print('Processed email:')\n", + " print('----------------')\n", + " print(' '.join(processed_email))\n", + " return word_indices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have implemented `processEmail`, the following cell will run your code on the email sample and you should see an output of the processed email and the indices list mapping." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------\n", + "Processed email:\n", + "----------------\n", + "anyon know how much it cost to host a web portal well it depend on how mani visitor your expect thi can be anywher from less than number buck a month to a coupl of dollar number you should checkout httpaddr or perhap amazon ec number if your run someth big to unsubscrib yourself from thi mail list send an email to emailaddr\n", + "-------------\n", + "Word Indices:\n", + "-------------\n", + "[85, 915, 793, 1076, 882, 369, 1698, 789, 1821, 1830, 882, 430, 1170, 793, 1001, 1894, 591, 1675, 237, 161, 88, 687, 944, 1662, 1119, 1061, 1698, 374, 1161, 476, 1119, 1892, 1509, 798, 1181, 1236, 511, 1119, 809, 1894, 1439, 1546, 180, 1698, 1757, 1895, 687, 1675, 991, 960, 1476, 70, 529, 1698, 530]\n" + ] + } + ], + "source": [ + "# To use an SVM to classify emails into Spam v.s. Non-Spam, you first need\n", + "# to convert each email into a vector of features. In this part, you will\n", + "# implement the preprocessing steps for each email. You should\n", + "# complete the code in processEmail.m to produce a word indices vector\n", + "# for a given email.\n", + "\n", + "# Extract Features\n", + "with open(os.path.join('Data', 'emailSample1.txt')) as fid:\n", + " file_contents = fid.read()\n", + "\n", + "word_indices = processEmail(file_contents)\n", + "\n", + "#Print Stats\n", + "print('-------------')\n", + "print('Word Indices:')\n", + "print('-------------')\n", + "print(word_indices)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[3] = processEmail\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 2.2 Extracting Features from Emails\n", + "\n", + "You will now implement the feature extraction that converts each email into a vector in $\\mathbb{R}^n$. For this exercise, you will be using n = # words in vocabulary list. Specifically, the feature $x_i \\in \\{0, 1\\}$ for an email corresponds to whether the $i^{th}$ word in the dictionary occurs in the email. That is, $x_i = 1$ if the $i^{th}$ word is in the email and $x_i = 0$ if the $i^{th}$ word is not present in the email.\n", + "\n", + "Thus, for a typical email, this feature would look like:\n", + "\n", + "$$ x = \\begin{bmatrix} \n", + "0 & \\dots & 1 & 0 & \\dots & 1 & 0 & \\dots & 0 \n", + "\\end{bmatrix}^T \\in \\mathbb{R}^n\n", + "$$\n", + "\n", + "You should now complete the code in the function `emailFeatures` to generate a feature vector for an email, given the `word_indices`.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def emailFeatures(word_indices):\n", + " \"\"\"\n", + " Takes in a word_indices vector and produces a feature vector from the word indices. \n", + " \n", + " Parameters\n", + " ----------\n", + " word_indices : list\n", + " A list of word indices from the vocabulary list.\n", + " \n", + " Returns\n", + " -------\n", + " x : list \n", + " The computed feature vector.\n", + " \n", + " Instructions\n", + " ------------\n", + " Fill in this function to return a feature vector for the\n", + " given email (word_indices). To help make it easier to process \n", + " the emails, we have have already pre-processed each email and converted\n", + " each word in the email into an index in a fixed dictionary (of 1899 words).\n", + " The variable `word_indices` contains the list of indices of the words \n", + " which occur in one email.\n", + " \n", + " Concretely, if an email has the text:\n", + "\n", + " The quick brown fox jumped over the lazy dog.\n", + "\n", + " Then, the word_indices vector for this text might look like:\n", + " \n", + " 60 100 33 44 10 53 60 58 5\n", + "\n", + " where, we have mapped each word onto a number, for example:\n", + "\n", + " the -- 60\n", + " quick -- 100\n", + " ...\n", + "\n", + " Note\n", + " ----\n", + " The above numbers are just an example and are not the actual mappings.\n", + "\n", + " Your task is take one such `word_indices` vector and construct\n", + " a binary feature vector that indicates whether a particular\n", + " word occurs in the email. That is, x[i] = 1 when word i\n", + " is present in the email. Concretely, if the word 'the' (say,\n", + " index 60) appears in the email, then x[60] = 1. The feature\n", + " vector should look like:\n", + " x = [ 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 ... 0 0 0 1 0 ..]\n", + " \"\"\"\n", + " # Total number of words in the dictionary\n", + " n = 1899\n", + "\n", + " # You need to return the following variables correctly.\n", + " x = np.zeros(n)\n", + "\n", + " # ===================== YOUR CODE HERE ======================\n", + "\n", + " for i in word_indices:\n", + " x[i] = 1\n", + " \n", + " # ===========================================================\n", + " \n", + " return x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have implemented `emailFeatures`, the next cell will run your code on the email sample. You should see that the feature vector had length 1899 and 45 non-zero entries." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------\n", + "Processed email:\n", + "----------------\n", + "anyon know how much it cost to host a web portal well it depend on how mani visitor your expect thi can be anywher from less than number buck a month to a coupl of dollar number you should checkout httpaddr or perhap amazon ec number if your run someth big to unsubscrib yourself from thi mail list send an email to emailaddr\n", + "\n", + "Length of feature vector: 1899\n", + "Number of non-zero entries: 45\n" + ] + } + ], + "source": [ + "# Extract Features\n", + "with open(os.path.join('Data', 'emailSample1.txt')) as fid:\n", + " file_contents = fid.read()\n", + "\n", + "word_indices = processEmail(file_contents)\n", + "features = emailFeatures(word_indices)\n", + "\n", + "# Print Stats\n", + "print('\\nLength of feature vector: %d' % len(features))\n", + "print('Number of non-zero entries: %d' % sum(features > 0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[4] = emailFeatures\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Training SVM for Spam Classification\n", + "\n", + "In the following section we will load a preprocessed training dataset that will be used to train a SVM classifier. The file `spamTrain.mat` (within the `Data` folder for this exercise) contains 4000 training examples of spam and non-spam email, while `spamTest.mat` contains 1000 test examples. Each\n", + "original email was processed using the `processEmail` and `emailFeatures` functions and converted into a vector $x^{(i)} \\in \\mathbb{R}^{1899}$.\n", + "\n", + "After loading the dataset, the next cell proceed to train a linear SVM to classify between spam ($y = 1$) and non-spam ($y = 0$) emails. Once the training completes, you should see that the classifier gets a training accuracy of about 99.8% and a test accuracy of about 98.5%." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Linear SVM (Spam Classification)\n", + "This may take 1 to 2 minutes ...\n", + "\n" + ] + } + ], + "source": [ + "# Load the Spam Email dataset\n", + "# You will have X, y in your environment\n", + "data = loadmat(os.path.join('Data', 'spamTrain.mat'))\n", + "X, y= data['X'].astype(float), data['y'][:, 0]\n", + "\n", + "print('Training Linear SVM (Spam Classification)')\n", + "print('This may take 1 to 2 minutes ...\\n')\n", + "\n", + "C = 0.1\n", + "model = utils.svmTrain(X, y, C, utils.linearKernel)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the training accuracy\n", + "p = utils.svmPredict(model, X)\n", + "\n", + "print('Training Accuracy: %.2f' % (np.mean(p == y) * 100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Execute the following cell to load the test set and compute the test accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the test dataset\n", + "# You will have Xtest, ytest in your environment\n", + "data = loadmat(os.path.join('Data', 'spamTest.mat'))\n", + "Xtest, ytest = data['Xtest'].astype(float), data['ytest'][:, 0]\n", + "\n", + "print('Evaluating the trained Linear SVM on a test set ...')\n", + "p = utils.svmPredict(model, Xtest)\n", + "\n", + "print('Test Accuracy: %.2f' % (np.mean(p == ytest) * 100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Top Predictors for Spam\n", + "\n", + "To better understand how the spam classifier works, we can inspect the parameters to see which words the classifier thinks are the most predictive of spam. The next cell finds the parameters with the largest positive values in the classifier and displays the corresponding words similar to the ones shown in the figure below.\n", + "\n", + "
\n", + "our click remov guarante visit basenumb dollar pleas price will nbsp most lo ga hour\n", + "
\n", + "\n", + "Thus, if an email contains words such as “guarantee”, “remove”, “dollar”, and “price” (the top predictors shown in the figure), it is likely to be classified as spam.\n", + "\n", + "Since the model we are training is a linear SVM, we can inspect the weights learned by the model to understand better how it is determining whether an email is spam or not. The following code finds the words with the highest weights in the classifier. Informally, the classifier 'thinks' that these words are the most likely indicators of spam." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sort the weights and obtin the vocabulary list\n", + "# NOTE some words have the same weights, \n", + "# so their order might be different than in the text above\n", + "idx = np.argsort(model['w'])\n", + "top_idx = idx[-15:][::-1]\n", + "vocabList = utils.getVocabList()\n", + "\n", + "print('Top predictors of spam:')\n", + "print('%-15s %-15s' % ('word', 'weight'))\n", + "print('----' + ' '*12 + '------')\n", + "for word, w in zip(np.array(vocabList)[top_idx], model['w'][top_idx]):\n", + " print('%-15s %0.2f' % (word, w))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.5 Optional (ungraded) exercise: Try your own emails\n", + "\n", + "Now that you have trained a spam classifier, you can start trying it out on your own emails. In the starter code, we have included two email examples (`emailSample1.txt` and `emailSample2.txt`) and two spam examples (`spamSample1.txt` and `spamSample2.txt`). The next cell runs the spam classifier over the first spam example and classifies it using the learned SVM. You should now try the other examples we have provided and see if the classifier gets them right. You can also try your own emails by replacing the examples (plain text files) with your own emails.\n", + "\n", + "*You do not need to submit any solutions for this optional (ungraded) exercise.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filename = os.path.join('Data', 'emailSample1.txt')\n", + "\n", + "with open(filename) as fid:\n", + " file_contents = fid.read()\n", + "\n", + "word_indices = processEmail(file_contents, verbose=False)\n", + "x = emailFeatures(word_indices)\n", + "p = utils.svmPredict(model, x)\n", + "\n", + "print('\\nProcessed %s\\nSpam Classification: %s' % (filename, 'spam' if p else 'not spam'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6 Optional (ungraded) exercise: Build your own dataset\n", + "\n", + "In this exercise, we provided a preprocessed training set and test set. These datasets were created using the same functions (`processEmail` and `emailFeatures`) that you now have completed. For this optional (ungraded) exercise, you will build your own dataset using the original emails from the SpamAssassin Public Corpus.\n", + "\n", + "Your task in this optional (ungraded) exercise is to download the original\n", + "files from the public corpus and extract them. After extracting them, you should run the `processEmail` and `emailFeatures` functions on each email to extract a feature vector from each email. This will allow you to build a dataset `X`, `y` of examples. You should then randomly divide up the dataset into a training set, a cross validation set and a test set.\n", + "\n", + "While you are building your own dataset, we also encourage you to try building your own vocabulary list (by selecting the high frequency words that occur in the dataset) and adding any additional features that you think\n", + "might be useful. Finally, we also suggest trying to use highly optimized SVM toolboxes such as [`LIBSVM`](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) or [`scikit-learn`](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.svm).\n", + "\n", + "*You do not need to submit any solutions for this optional (ungraded) exercise.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Phase 3 - 2020 (Summer)/Week 7(May 10-May 16)/Exercise7/ParthBakare_180101056.ipynb b/Phase 3 - 2020 (Summer)/Week 7(May 10-May 16)/Exercise7/ParthBakare_180101056.ipynb new file mode 100644 index 000000000..1dca70f28 --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 7(May 10-May 16)/Exercise7/ParthBakare_180101056.ipynb @@ -0,0 +1,6753 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Programming Exercise 7:\n", + "# K-means Clustering and Principal Component Analysis\n", + "\n", + "## Introduction\n", + "\n", + "In this exercise, you will implement the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n", + "\n", + "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", + "\n", + "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'autoreload '", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'autoreload '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 32\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'autoreload'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2315\u001b[0m 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\u001b[0m_bootstrap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'autoreload '" + ] + } + ], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Import regular expressions to process emails\n", + "import re\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import matplotlib as mpl\n", + "\n", + "from IPython.display import HTML, display, clear_output\n", + "\n", + "try:\n", + " pyplot.rcParams[\"animation.html\"] = \"jshtml\"\n", + "except ValueError:\n", + " pyplot.rcParams[\"animation.html\"] = \"html5\"\n", + "\n", + "# Optimization module in scipy\n", + "from scipy import optimize\n", + "\n", + "# will be used to load MATLAB mat datafile format\n", + "from scipy.io import loadmat\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils\n", + "\n", + "%load_ext autoreload \n", + "%autoreload 2\n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submission and Grading\n", + "\n", + "\n", + "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n", + "\n", + "\n", + "| Section | Part | Submitted Function | Points |\n", + "| :- |:- |:- | :-: |\n", + "| 1 | [Find Closest Centroids](#section1) | [`findClosestCentroids`](#findClosestCentroids) | 30 |\n", + "| 2 | [Computed Centroid Means](#section2) | [`computeCentroids`](#computeCentroids) | 30 |\n", + "| 3 | [PCA](#section3) | [`pca`](#pca) | 20 |\n", + "| 4 | [Project Data](#section4) | [`projectData`](#projectData) | 10 |\n", + "| 5 | [Recover Data](#section5) | [`recoverData`](#recoverData) | 10 |\n", + "| | Total Points | |100 |\n", + "\n", + "\n", + "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "
\n", + "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 K-means Clustering\n", + "\n", + "In this exercise, you will implement K-means algorithm and use it for image compression. You will first start on an example 2D dataset that will help you gain an intuition of how the K-means algorithm works. After\n", + "that, you wil use the K-means algorithm for image compression by reducing the number of colors that occur in an image to only those that are most common in that image.\n", + "\n", + "### 1.1 Implementing K-means\n", + "\n", + "The K-means algorithm is a method to automatically cluster similar data examples together. Concretely, you are given a training set $\\{x^{(1)} , \\cdots, x^{(m)}\\}$ (where $x^{(i)} \\in \\mathbb{R}^n$), and want to group the data into a few cohesive “clusters”. The intuition behind K-means is an iterative procedure that starts by guessing the initial centroids, and then refines this guess by repeatedly assigning examples to their closest centroids and then recomputing the centroids based on the assignments.\n", + "\n", + "The K-means algorithm is as follows:\n", + "\n", + "```python\n", + "centroids = kMeansInitCentroids(X, K)\n", + "for i in range(iterations):\n", + " # Cluster assignment step: Assign each data point to the\n", + " # closest centroid. idx[i] corresponds to cˆ(i), the index\n", + " # of the centroid assigned to example i\n", + " idx = findClosestCentroids(X, centroids)\n", + " \n", + " # Move centroid step: Compute means based on centroid\n", + " # assignments\n", + " centroids = computeMeans(X, idx, K)\n", + "```\n", + "\n", + "The inner-loop of the algorithm repeatedly carries out two steps: (1) Assigning each training example $x^{(i)}$ to its closest centroid, and (2) Recomputing the mean of each centroid using the points assigned to it. The K-means algorithm will always converge to some final set of means for the centroids. Note that the converged solution may not always be ideal and depends on the initial setting of the centroids. Therefore, in practice the K-means algorithm is usually run a few times with different random initializations. One way to choose between these different solutions from different random initializations is to choose the one with the lowest cost function value (distortion). You will implement the two phases of the K-means algorithm separately\n", + "in the next sections." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 1.1.1 Finding closest centroids\n", + "\n", + "In the “cluster assignment” phase of the K-means algorithm, the algorithm assigns every training example $x^{(i)}$ to its closest centroid, given the current positions of centroids. Specifically, for every example $i$ we set\n", + "\n", + "$$c^{(i)} := j \\quad \\text{that minimizes} \\quad \\lvert\\rvert x^{(i)} - \\mu_j \\lvert\\rvert^2, $$\n", + "\n", + "where $c^{(i)}$ is the index of the centroid that is closest to $x^{(i)}$, and $\\mu_j$ is the position (value) of the $j^{th}$ centroid. Note that $c^{(i)}$ corresponds to `idx[i]` in the starter code.\n", + "\n", + "Your task is to complete the code in the function `findClosestCentroids`. This function takes the data matrix `X` and the locations of all centroids inside `centroids` and should output a one-dimensional array `idx` that holds the index (a value in $\\{1, ..., K\\}$, where $K$ is total number of centroids) of the closest centroid to every training example.\n", + "\n", + "You can implement this using a loop over every training example and every centroid.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def findClosestCentroids(X, centroids):\n", + " \"\"\"\n", + " Computes the centroid memberships for every example.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset of size (m, n) where each row is a single example. \n", + " That is, we have m examples each of n dimensions.\n", + " \n", + " centroids : array_like\n", + " The k-means centroids of size (K, n). K is the number\n", + " of clusters, and n is the the data dimension.\n", + " \n", + " Returns\n", + " -------\n", + " idx : array_like\n", + " A vector of size (m, ) which holds the centroids assignment for each\n", + " example (row) in the dataset X.\n", + " \n", + " Instructions\n", + " ------------\n", + " Go over every example, find its closest centroid, and store\n", + " the index inside `idx` at the appropriate location.\n", + " Concretely, idx[i] should contain the index of the centroid\n", + " closest to example i. Hence, it should be a value in the \n", + " range 0..K-1\n", + "\n", + " Note\n", + " ----\n", + " You can use a for-loop over the examples to compute this.\n", + " \"\"\"\n", + " # Set K\n", + " K = centroids.shape[0]\n", + "\n", + " # You need to return the following variables correctly.\n", + " idx = np.zeros(X.shape[0], dtype=int)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " for i in range(X.shape[0]):\n", + " idx[i] = 0\n", + " mindist = np.sum(np.multiply(np.subtract(X[i], centroids[0]), np.subtract(X[i], centroids[0])))\n", + " \n", + " for j in range(1, centroids.shape[0]):\n", + " dist = np.sum(np.multiply(np.subtract(X[i], centroids[j]), np.subtract(X[i], centroids[j])))\n", + " \n", + " if dist < mindist :\n", + " mindist = dist\n", + " idx[i] = j\n", + " \n", + " # =============================================================\n", + " return idx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have completed the code in `findClosestCentroids`, the following cell will run your code and you should see the output `[0 2 1]` corresponding to the centroid assignments for the first 3 examples." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Closest centroids for the first 3 examples:\n", + "[0 2 1]\n", + "(the closest centroids should be 0, 2, 1 respectively)\n" + ] + } + ], + "source": [ + "# Load an example dataset that we will be using\n", + "data = loadmat(os.path.join('Data', 'ex7data2.mat'))\n", + "X = data['X']\n", + "\n", + "# Select an initial set of centroids\n", + "K = 3 # 3 Centroids\n", + "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n", + "\n", + "# Find the closest centroids for the examples using the initial_centroids\n", + "idx = findClosestCentroids(X, initial_centroids)\n", + "\n", + "print('Closest centroids for the first 3 examples:')\n", + "print(idx[:3])\n", + "print('(the closest centroids should be 0, 2, 1 respectively)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'grader' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfindClosestCentroids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mgrader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrade\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'grader' is not defined" + ] + } + ], + "source": [ + "grader[1] = findClosestCentroids\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 1.1.2 Computing centroid means\n", + "\n", + "Given assignments of every point to a centroid, the second phase of the algorithm recomputes, for each centroid, the mean of the points that were assigned to it. Specifically, for every centroid $k$ we set\n", + "\n", + "$$ \\mu_k := \\frac{1}{\\left| C_k\\right|} \\sum_{i \\in C_k} x^{(i)}$$\n", + "\n", + "where $C_k$ is the set of examples that are assigned to centroid $k$. Concretely, if two examples say $x^{(3)}$ and $x^{(5)}$ are assigned to centroid $k = 2$, then you should update $\\mu_2 = \\frac{1}{2} \\left( x^{(3)} + x^{(5)} \\right)$.\n", + "\n", + "You should now complete the code in the function `computeCentroids`. You can implement this function using a loop over the centroids. You can also use a loop over the examples; but if you can use a vectorized implementation that does not use such a loop, your code may run faster.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def computeCentroids(X, idx, K):\n", + " \"\"\"\n", + " Returns the new centroids by computing the means of the data points\n", + " assigned to each centroid.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The datset where each row is a single data point. That is, it \n", + " is a matrix of size (m, n) where there are m datapoints each\n", + " having n dimensions. \n", + " \n", + " idx : array_like \n", + " A vector (size m) of centroid assignments (i.e. each entry in range [0 ... K-1])\n", + " for each example.\n", + " \n", + " K : int\n", + " Number of clusters\n", + " \n", + " Returns\n", + " -------\n", + " centroids : array_like\n", + " A matrix of size (K, n) where each row is the mean of the data \n", + " points assigned to it.\n", + " \n", + " Instructions\n", + " ------------\n", + " Go over every centroid and compute mean of all points that\n", + " belong to it. Concretely, the row vector centroids[i, :]\n", + " should contain the mean of the data points assigned to\n", + " cluster i.\n", + "\n", + " Note:\n", + " -----\n", + " You can use a for-loop over the centroids to compute this.\n", + " \"\"\"\n", + " # Useful variables\n", + " m, n = X.shape\n", + " # You need to return the following variables correctly.\n", + " centroids = np.zeros((K, n))\n", + "\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " sumc = np.zeros(K)\n", + " for i in range(m):\n", + "# centroids[idx[i], :] = np.sum(centroids[idx[i], :], X[i, :])\n", + " centroids[idx[i], :] = centroids[idx[i], :] + X[i, :]\n", + " sumc[idx[i]] += 1\n", + " \n", + " for i in range(K):\n", + " \n", + " if sumc[i] != 0:\n", + " centroids[i] /= sumc[i]\n", + " \n", + " # =============================================================\n", + " return centroids" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have completed the code in `computeCentroids`, the following cell will run your code and output the centroids after the first step of K-means." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Centroids computed after initial finding of closest centroids:\n", + "[[2.42830111 3.15792418]\n", + " [5.81350331 2.63365645]\n", + " [7.11938687 3.6166844 ]]\n", + "\n", + "The centroids should be\n", + " [ 2.428301 3.157924 ]\n", + " [ 5.813503 2.633656 ]\n", + " [ 7.119387 3.616684 ]\n" + ] + } + ], + "source": [ + "# Compute means based on the closest centroids found in the previous part.\n", + "centroids = computeCentroids(X, idx, K)\n", + "\n", + "print('Centroids computed after initial finding of closest centroids:')\n", + "print(centroids)\n", + "print('\\nThe centroids should be')\n", + "print(' [ 2.428301 3.157924 ]')\n", + "print(' [ 5.813503 2.633656 ]')\n", + "print(' [ 7.119387 3.616684 ]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'grader' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomputeCentroids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mgrader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrade\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'grader' is not defined" + ] + } + ], + "source": [ + "grader[2] = computeCentroids\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 K-means on example dataset \n", + "\n", + "After you have completed the two functions (`findClosestCentroids` and `computeCentroids`), you have all the necessary pieces to run the K-means algorithm. The next cell will run the K-means algorithm on a toy 2D dataset to help you understand how K-means works. Your functions are called from inside the `runKmeans` function (in this assignment's `utils.py` module). We encourage you to take a look at the function to understand how it works. Notice that the code calls the two functions you implemented in a loop.\n", + "\n", + "When you run the next step, the K-means code will produce an animation that steps you through the progress of the algorithm at each iteration. At the end, your figure should look as the one displayed below.\n", + "\n", + "![](Figures/kmeans_result.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load an example dataset\n", + "data = loadmat(os.path.join('Data', 'ex7data2.mat'))\n", + "\n", + "# Settings for running K-Means\n", + "K = 3\n", + "max_iters = 10\n", + "\n", + "# For consistency, here we set centroids to specific values\n", + "# but in practice you want to generate them automatically, such as by\n", + "# settings them to be random examples (as can be seen in\n", + "# kMeansInitCentroids).\n", + "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n", + "\n", + "\n", + "# Run K-Means algorithm. The 'true' at the end tells our function to plot\n", + "# the progress of K-Means\n", + "centroids, idx, anim = utils.runkMeans(X, initial_centroids,\n", + " findClosestCentroids, computeCentroids, max_iters, True)\n", + "anim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 Random initialization \n", + "\n", + "The initial assignments of centroids for the example dataset in the previous cell were designed so that you will see the same figure as that shown in the cell above. In practice, a\n", + "good strategy for initializing the centroids is to select random examples from the training set.\n", + "\n", + "In this part of the exercise, you should complete the function `kMeansInitCentroids` with the following code:\n", + "\n", + "```python\n", + "# Initialize the centroids to be random examples\n", + "\n", + "# Randomly reorder the indices of examples\n", + "randidx = np.random.permutation(X.shape[0])\n", + "# Take the first K examples as centroids\n", + "centroids = X[randidx[:K], :]\n", + "```\n", + "\n", + "The code above first randomly permutes the indices of the examples (using `permute` within the `numpy.random` module). Then, it selects the first $K$ examples based on the random permutation of the indices. This allows the examples to be selected at random without the risk of selecting the same example twice.\n", + "\n", + "*You do not need to make any submission for this part of the exercise*\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def kMeansInitCentroids(X, K):\n", + " \"\"\"\n", + " This function initializes K centroids that are to be used in K-means on the dataset x.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like \n", + " The dataset of size (m x n).\n", + " \n", + " K : int\n", + " The number of clusters.\n", + " \n", + " Returns\n", + " -------\n", + " centroids : array_like\n", + " Centroids of the clusters. This is a matrix of size (K x n).\n", + " \n", + " Instructions\n", + " ------------\n", + " You should set centroids to randomly chosen examples from the dataset X.\n", + " \"\"\"\n", + " m, n = X.shape\n", + " \n", + " # You should return this values correctly\n", + " centroids = np.zeros((K, n))\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " randidx = np.random.permutation(X.shape[0])\n", + " centroids = X[randidx[:K], :]\n", + " \n", + " # =============================================================\n", + " return centroids" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 Image compression with K-means\n", + "\n", + "In this exercise, you will apply K-means to image compression. We will use the image below as an example (property of Frank Wouters with permission to this class).\n", + "\n", + "![](Data/bird_small.png)\n", + "\n", + "In a straightforward 24-bit color representation of an image, each pixel is represented as three 8-bit unsigned integers (ranging from 0 to 255) that specify the red, green and blue intensity values. This encoding is often referred to as the RGB encoding. Our image contains thousands of colors, and in this part of the exercise, you will reduce the number of colors to 16 colors.\n", + "\n", + "By making this reduction, it is possible to represent (compress) the photo in an efficient way. Specifically, you only need to store the RGB values of the 16 selected colors, and for each pixel in the image you now need to only store the index of the color at that location (where only 4 bits are necessary to represent 16 possibilities).\n", + "\n", + "In this exercise, you will use the K-means algorithm to select the 16 colors that will be used to represent the compressed image. Concretely, you will treat every pixel in the original image as a data example and use the K-means algorithm to find the 16 colors that best group (cluster) the pixels in the 3-dimensional RGB space. Once you have computed the cluster centroids on the image, you will then use the 16 colors to replace the pixels in the original image.\n", + "\n", + "#### 1.4.1 K-means on pixels\n", + "\n", + "In python, images can be read in as follows:\n", + "\n", + "```python\n", + "# Load 128x128 color image (bird_small.png)\n", + "img = mpl.image.imread(os.path.join('Data', 'bird_small.png'))\n", + "\n", + "# We have already imported matplotlib as mpl at the beginning of this notebook.\n", + "```\n", + "This creates a three-dimensional matrix `A` whose first two indices identify a pixel position and whose last index represents red, green, or blue. For example, A[50, 33, 2] gives the blue intensity of the pixel at row 51 and column 34.\n", + "\n", + "The code in the following cell first loads the image, and then reshapes it to create an m x 3 matrix of pixel colors (where m = 16384 = 128 x 128), and calls your K-means function on it.\n", + "\n", + "After finding the top K = 16 colors to represent the image, you can now assign each pixel position to its closest centroid using the `findClosestCentroids` function. This allows you to represent the original image using the centroid assignments of each pixel. Notice that you have significantly reduced the number of bits that are required to describe the image. The original image required 24 bits for each one of the 128 x 128 pixel locations, resulting in total size of 128 x 128 x 24 = 393,216 bits. The new representation requires some overhead storage in form of a dictionary of 16 colors, each of which require 24 bits, but the image itself then only requires 4 bits per pixel location. The final number of bits used is therefore 16 x 24 + 128 x 128 x 4 = 65,920 bits, which corresponds to compressing the original image by about a factor of 6.\n", + "\n", + "Finally, you can view the effects of the compression by reconstructing the image based only on the centroid assignments. Specifically, you can replace each pixel location with the mean of the centroid assigned to it. The figure below shows the reconstruction we obtained. \n", + "\n", + "![](Figures/bird_compression.png)\n", + "\n", + "Even though the resulting image retains most of the characteristics of the original, we also see some compression artifacts.\n", + "\n", + "Run the following cell to compute the centroids and the centroid allocation of each pixel in the image." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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jRQvPKm7fvPNE4/mdEtgf42wJvZe5r0ycmf05fC1UPvBZL1hoW4pJncgN04KkSBAQW0IpWMmYGkbCxAWEopAHAolAQ2inqBXUBoooRKFLDbmYCwaZYBKREHAZqyTJBMA005/MKQqqgbSeUAuUETNXGnyPIboELT7R04AkQpog7QSVhJSAakG1YKGlmtoUNbJCVkHEkGiYnGDqAsNiJITEJE0ptkCLkRcjWQdGHVmERJCAZej7kZyVrAUJEAJ0s4YQ3YIuOVCKkUclWEYozJoI0hKk4WQcGYsxaIJQIEDUgESXRkOKBIs0dJieoDaieYnaBLEIAQKKsARaDCimSPbBU7QqUaZ++2oMxYhWCOLWuqqiWpCqlCmFUBQRo1AqCwHjMEIbCanFYsHUGMcBzQVLiumIWiaY0jUd53dnpOfPAcb+buRkOXIwL9w5HLAwoAZqAReXWt8vQMCqCS4ia83dWRTxtvW3IPj14Za9miuBp2xK5VtWZNGqT9ikderO2qgK5zVLZMapzf+u45FjGc6O51/yGVefXZPmKcMMihbu3LuHqkF6KyW4nw7UAneW5znXndCFAYBhyNy+c0C6kojRlXAwVI279w4ppQrLB1m5m6SfwDhkbt+5x3NXLtGkxPzOa8zOX2XWzZhduEpsOtCeTx5vsjMR7s2vku5kvA6KuIJfr0BN6fvlO/1I3nW8UwL7HwGfUZet+xhedebL3vQIC0SMMedKSxZygSKRGHcQm4Io4IKt5CMEIxLQ0JLVKONAlOBUb2icjiWRZQdNR5gNSKOoBAqQDIIJSMICEALd3gUs91ge0CGvGdkUon8SjZFLoShkdiA0WDDQAWEkUkhaEEsstWMoMBqEoLRRCG2gWEbVsFEolkEybRJME5ojZRjACoXMWGAssMwFSkE0I0FQCktgyJlSFCtKkIKJ0ltDiB0xBVQixQI9M7RkxBRhwnKp6HyJ6eBUdmzIGlAVpDhdF+JAFwULDRp20ZQwRqIdoCVjJTN0U6JFkkWsCIqRgTIGQomUCCkITRCaJqIImURRJQApJIpBwQgGYi1ikTIqASOGCVkXFB0pi7t0NkPCjG7nEppHlkNPqyOiiZAHrI+oCV3T0kaY7c74pBdhb7fjzvGSV28dkG4qkxQYEAYTdroIYpQSWS4y46ggCTVD1DBzpcEEogRAKG73bljUK1rG1mJ9JeAD4uwFxqbTZ+VkCFSXj3G6deXioTI/a3fDu44nH8tbPBT3Dg44OVm6sH6P4njcYVEmgFPe7L/InaNXkKMD2PlM6K9BfxPdsGxv3T67ZoeWwvXXb7K7O2N/f48rly+xXC65fefeI89/6eIFZrMRuMvHbhU+fF04t7NDP44s+iV7s1l9vqcuuOVi+TSV3rcN74jANrMsIl+D14SN+NJnP/nQ9sDq3apatcaMEFaUYsQICAVFnD7kVDVzjaoelzMhRgIJ1eCn1+B+1xggur85EEBrL2qouZUlEkESEhQiyMqP7rYjJlonXEU1s4rbWzWzACufr5UBU6dnQog+fZu4z1qVYIpYBstgoVLihphgquQykhVKUXJRKEpQ71sNshm52PqZGd4nuWA6YpqwqPWZGYqARfpR/N5LxrRU4RAoEjCEYkpUI0WIjWFB0ahuEcqK4B7X70kNigUUty4Lgml9T8XvSc0guZZeQofoUN9ZQ8Eops5o1N6LKoYgkrx/VSCTitPyElvEwBgpqu7TKgXJo8dCBEEEmq5lR1ywXjg3YXenYTqJLHqI6hbyZNoSAuQilFEpWdH1Wz+NYNiMo1gJY5NqJtxfeV1OPfz+3p00X/nTbO283+h1UyYL953NqtB+d/GkY/lpIKSWmFrG/mQd7/CsoagyDCN9PzCMb6x0qqUwDgNN2yIiDP3ZqsAxRlLbPvI8Q9+v2aGmbQkxPuKIJ0exQCkbFH7sGJmBjqAN6ARs5l+LDVAWjPnsCqkGDOPoYxZom0TObxRHJQ+Mi2MAVDPtZJd2AqnruXwy58JuZL8LHA3CWDIgTLqp959HctFTK//jAO+YD9vMfgD4gcdqq9D3HnxFqFaFAsGFtgbBijpdbAYESA11JndpXwVezgMSW2LTEQ00KFIWTq0GIHREiQiRjLrAKspYlk5dE0kkkjTQKhKEKEIpI6VkSo4YA5Bheej+dCLWTJDUuC87BMSMpIeghaiFaXuRGCLBDNGC2ghygisJIGGKqjFSmMQEFBb9Ass95My4LGAj2IghZBWWGefARSiVYo4SSKMCPSajB2qFQAxgNCiRw/mSQCaSybkK9AIxebDZIhdaaWklkWKBUNA0J4RAwMglVIE6EiioRbKCruzO4G4NMYXikieYMh0VCS3EfUwPnClh6rS0CVMxXBEopFKdIzEymFHKSCxCiUbJuD9c3EovWRlCIebosQbjQBYIbUcz2yF1HaSWl19Y8MJzB9y4c8LhyZxsRsI4f3GflALLYU7uC3kojLmgZhR3uqyx+svt5kAECnkVe7h2PBtViWQjOM8E3RDIVDq9erD9Zx23tyGuRVbc+2OPv7cTTzKWnwa62TlmF65y77UPUcZnkwYdh5Hrr9986P6h77l9/TqXnn+e1CRuXb9+5huY7e1x4cojYpPMuHfzJuPgdPXFq1eZ7uy8Ldf/SEw3Fg7rrvoPwHAD5j/3lrvtj+/SH7t13u2c59zzvg5J7heIZj796gEHNw74p9dOhfLe/kWiCGjh6OSExVZgv70wlKIDMUSPLCZAkCr8ImYRNUUFQpqBqfstUQgKbYCSsVKwYu4LHk9QaTxyGCGESIgRzQkLgSDUcDMBOjSCBg96KjX6S1UREUIImAbM3HJ3VjQh7SpQKaNlQFAkJiD41BqT+3dN0fk91IRiQsniwtlGQvT+SzAKEaQhmwdKLPNIDBlpCrNOydkYs2EMrkiEwHyMDCpki3SNMEmKyujyQAt5VGcnQqokbmAYTv22oRRMjMF6muwR103r6kwQ6EfXWhkysfH3okSyRlQLMSgWQUN0FkAC0u2hRTATzJakJpGalmGZkRigg6KGmTCyRwwexNczYiWgVuhEaURpcZ1sVMGCkjWTh4HUJleKYkcUIZqiWRnJaFBoIk0MdGGfQGLSBV64fI7P/JQLlDJw+6Anzo2lCDsTITVCDJF7SdBgmAQwRRBioIYfnNJssiK3z2yrlresXdbrYDFYB98D3McSnYahJQk1cG9DTbgvyn6LsxgWh5Q8oPmBa3S8ozDg4OCIYfBz7+7OmE0nT9xP07ZcfO45+sWC+eHI/cpZv1xw58aNM9t29vZouo6D27fXGTN5w5I9PjhgWC7Zv3jxNG7iITg5OqLvl5y/eAkJp9bzoB13l3U5e4kwLNmZtezuPG60+CPOu1hSyh0unD/3mP2dxfte2iXrZX7ixk3alIizGc+1x4TYoOcusRwzi/6trGH0bOLZENhWhaeEKrDrzBYCSERVUKt2SkiYKmbi1o4YUqO413S1FawolEAwMBNC4xaRqlPTiNOSVj3hFhoXMOpWvGKoN0BX9CyBaE4xiSjVkMS0EK2Aivs8V2lJq2hvM7RfUAxGE0pxmldDIUr0a1HqPUaKjWRVBi100anplPw5ZaX6ofGgLIWxBEaLxChkxC1fM4IZpbiQDiJYKM4IlFhpasUJaaVYQRRCNJrGFScDci6V8a1kdYxoaJ0C14AVc9EvgawBQiLSkVUp6pHfhEhIiZyXzpEnp/EVYTSPAUhBGE0wNZRECjUQ0FgrOgI1XWusbo+IyCpWwdDiHnTTQswjoRQ8aj+SGtjbnfH8lX3miwV7O7coRSk5MO0iTSNY8fQwW31/1ahd+6DfwHz7uz4NIuP0ncvKJ31Kg1O/Ilv9v04bXOH0m7F6v5vzrJydwz9hoGoULaQYHyh4ythTxndnUi7qGQWbWCwWLHu3aruuhalvX103QH6ElRdioJ10zI+O6BeL9TaRQCmFMmYW4/GZY5q2RUJgMZ+vafBNDMslJWd29jwz5o3nTOvnmXNm7HvyOCKpxcTp96F0HI/7rL/8IRNCYNKt5kEhRqnzjGeu3HcWCKvFxjzWZxPjmMk5s7Mzo5QncGeIEFLLpYs7jGVk2t5izJESI3uNkhqYW8ftd8Al8DTxTAjsINCkhKWIaXDrIxjBWiiJXDISFAmBMQtWoOQCazG6i9Jg0pL1GLOM2QgheqQ4I5amRBqiCMHc+ssUF8Y2VtdXAtuBUBAppKCUcWQclmho3Q9OSzAPLmskQhQsQGODpyXlIzRFLASGHMjLTOkz0bJ7wUUYco/h9xxSQ0gNOghFe0Y9oLFCKYWFGkk6UhQ0HiMITQiMJTjDYAPTptA2gTFeQGnpCaSxEMzTxYq6YBjtiJqiTDDQ4j4jmwwEhKYk8ljIKKpCH0aQmqZkkUiHZXdbjNF94ZDoF5mclKELSJx6LuU4sCiQDbpQc6gtsBAhGDRDRtUFl8oJJTQQp2iJSEhIDBQCwTKjZYol1BKaYdBCKXN2zIgCSaBYBCKlDEQKoHUSX1LGBWGyg6SOxi7w8kswmU750Z+9Qby+oJTMi1d3SSHwsdeMVloi3s867cpWSqWtBbZP2qsJZiNT31b5oKdWyuYUtpHh7XEUrPgYVxRUTyPCT6PLlZrJ9wmJ+cmCO3fv8vxzV1wgPkUcHc05ODg8s+1MDYMNzE9OuFODqB6la/WLJXdu3DiTM7x77hyz3T1uvvYa5T4fMMDh3bvIvbvYmwSwlZK58dprD9x36epVuqlrF3vnz7Ozt8fNa68xppcYdz5to+XZL+/weOBo7oJ3Z9pw9cqMm3cW5Ky89Pzu2XoT7UVoLtSLOYKjNxbIM4MbN28/9B4ehNR0XHjxM2gmM5rU8oEXX+XDN40P31N2L77MTjLSnVe4FZfceaKen208EwLbg3GEoB7huypEYuoWl5l/rCYuqFfUN7jWqjoAhlnCJFUaUj1QSQCC+45XflWcJrd1oFfBqjVNcfrcQmDMvadEZSWEgpnnQwdGhEIJLSIFie5LFXDrv86u0QJGhmAeCFUVhZCcvoomSDGEjI4uIENIjItCzgqaGXPALKAleahcarzgAqtoZCNQaGxBoSNbQsupdq8ma8YAXUUxC2oZApRVURm0toVhNAieVmU0eECaPxsxKGWsecMJI7uvtmQsNAjCWJTYzYippQkCoWEMzdpi1bF4jjjGOMwp1hG0pUhHQmiDMgzLdeBZ0YBaJKEUzw4jj8UDCJMQa/GZsCEktVjNqR4xNU95i4nJZMb+XualyzvkXlnOlb1Zh0ggthAaZ2Maazw9UJVxdOUQao68rYLGVkT1ylp+wMS5MtQ3fdecbqt8zDrAzT/000hzpMa0PYWAs2cFbZvY39uraUNvH3IuHM/nZ7Z1bcv0TShtqwWb9vZ2KaUwP1m8oY2acXQ0R1XZ39/l6NjrRTwK91vJ/XLpY/cB1nO9mIcX3Fm3YaPo0VmcHB8z1NSnRdmjzw0hGxrB7Gxe+M6soW1923JZWPSZ/b2WGIS7Bz3DUDyV62BZCybB/m5LCD6fHx0PkAt7D7vMJy1uUgszdbNz7F0wXrwUyAWyBc6f2ydJ4eiOrZrSrAP2zGtaPMPFVN4Mz4TAdmErHjQUgBrOk4tUmrP4DBmCV8pRrcaNOpdsBiEBEaPBpArs4pS5C89alESdelcACzXK2dbduDETEIRl7tHR/eVtwPN+FwtgRETJYsRoxMYInpLsfm8tLkiDkMSQ5IFYpu4Lj1VxiBaR4tU7yjBAaohpynIYyKOBFcaxkItAbmiSECOscs1CcMofU5ItGc0//KGoKw4lV5qdWjHIPacizmAQnPb3+D6tgXsBd1srIsUt6xAoQaAEz6zTnhwbiiQixf2tZURT9WuZ0nZT0mSPIImsxqBGGwTLhTyMWIqoGGM+9uelkJtdOgm0odCX6MF2IgQNBAusipaYQZYCBCR5ipWYhwsixQO+ir+3kkdUC0ESEhq6yZRdU16+skO/HDk87NnfmVAMYmuEVghtpMEp/2KFsSioEKoiuaq+tE4bWWki9+dLryLBZdNGWVHerGl2F/W22XyjJTU6XPhEldld29K2bRVQ9paUF2c/zk7SOWfu3jusz9WVrv29XbpJx2blwhVWgiiEwP7+LgjNesIAACAASURBVMMwcrJ4Y5CbFuXg8JDZdMrFC+dZLPrHilS+n7buF0v6BygEbxfmR06xC8btYcK8zHhxEuoY07XhBLAzS+zuuNC7R8+yz+zvNAyj8vrN+brt3XvumghB2Jk162d2eDxg48DOTiDIW4vkNzOsxhWtBkM722c3NLx0OYJGxISL5/coJSMS6ngNdF237sPdfKtAz/cWnhGBjdPMIh40JuYCeTC0WCU5QcUDrs56/QyxHtWEigeWmbag4pHheO6rHy9kwYPDquByH2Uga6npQUochajCsJxjZUB1cL84SmGknbTE1DGOLaMWbMjQBKKs6HWX/m1qaFJk0iYKkb4EljnRSCZQCHmsPmhDgjCMhcXJgsNlphQh2A7JIikJXXJhNQxGXzw9SkJLiq1bewY2KjYsWA6ZsRi9BUTc7+e06+BCuBhFlIKSLSDi1Lxo9LStFNAiaI7koGCZYsJJ8UCrpAtyhBIDqWkpGl0gJ4ipoZvsomWfcTFlWTJDv2Tsl+STBTpm8pCZ7k9IrafqpamQNKAnR8QE1gknvcctzLpAlI4IdHLkMQK5UEjuZjaIlhExUpx4ygkBAY99GAs69EgjhLZFYqRpW97/wiV2244r+3vsnpswXxTOdTucn42My8Bx7DGNtQKaT+5DHtZCFdyVIyKMK6mrq5K1a/e1/6ObIWO2ITxk7Zheu8xPWXbnjwyP0ajf8CcsDG7eugMiXLl88YndA6bG6zdunRGcKwF+/tw+k8mE12/c4nh+wmK55Lkrl1FVbt1yQjWmyNXnLrO3u8vObEaKiTiJvPTC1TPnOTqec+31m2hR5ide6etR/muAbjLhuZdePrPt+OAe88OjJ7zTx8et4UXUIs91XnlWLXJt+anQRzh6jfOXn6NpXEjfvrvk7oEL46Ju5PzUT/0zryRWjHMXL9F203XfasZr1+fs7jRcuuCMxaAdrx6/n4uTG+w2hzwpxsURd1/7/9i/8kmkbrbenlLil/yLH+Dluwd82p1DmvYGhyfC3SufyvmDjzEvBwAs+4GToWc6nZJzfk8WVnkmBLYINAm31tY6rRJiLTRRqWuf/zzAyATEVkTiqXfRCC7AJLk/cXUCgluQnNJqUmlMwTw1LBfy2HsOcYEyLNwfTiYTMEmMsUWkxaRhIKCiqGW6EKrffaQweOCTCCkkjxxXv44gHnmMBYoJ2aCYT+q5eNR5wCPicxFnE4rQhVqeUqsrQCqRGsLa91lMMVmliYEUr++tiAerERBxzVKrTqTFRUHNVPfsoUq0G64wmTjdtcQFUSPBU9nwoBUNDcjEq71ZYiiJshzJpXB0cszQLxiXC3TwWAEtwgA0k0TbQOup8uRhiaVAlMBy9PxMnXZIyBByFcSuDK2s6lD8XsxC9bdTvx5PodLiVdRC/RYkJGKacP7cBTypK1Ek0ff+nU3alp2pMpRM0UBRI8SM6Oq9nQaVVXv79KSbAWL+MZ76oTf32KmQBtkoeLZpoa+Ef3UP1bfyiYxcytriexyoGoulT8pmToWWTXpZEjQXyWGXkRYaL0drAos8Q8jretcxerBqiIEYnRr2DJJUz6Uslj3jOK4jtU2t1mp4NCQEUo3ONlWWi8WTBWG9BRRrGLXlpOwzmlcpzNYQY7POB19huVgyjiMHi7xWOI/uHa0VoNhNmdVnm5qWlBpyVvq+cHziQt0sMFrLIs8IokzT/IHfdN8PiAjTyeQMq2SmXtTqPopfQmD/4hUkNoQAi2XPfFHIY6FtGqbTHYZ+SUqJTpUh53X55/cangmBHYMwnQag1MnXC4tY9JKUYTSCBopF1Bo3ZlBE6wceMisu0YhYXYjCqBZRSAgJNFWfrJehDKsyZlqQsXj+7smB09pmjOPCfb5RKHGfEib0ac+pYA305lm6Fo2uTWiABiWPC0wHgixJMVJCJOsAkmkpoBEt0GfIBDLiwZM6AgtmTcdQAgfZK5gpME3V365afejKKJ5ipRYYRo+gLgKxDV6wJAijeeBYX2rBGPHa7IqnS5Uxe+X1MtJFIQXAUhXYkMeBaouzYKSExKSbEGSCyAQNAWlnNN0ejEopkcWQOLh9wPzwhJs3PsY4nJDHJalpiLGjbfY4yoVup2V/f8JUoFNjuTzmpAnMywQWI22CvbhPiRnTgVEjnreupKiYGGEsaNNSgnkI/arOenBLIOdCo7kKbUXChNQELj3/EpOdA6bTO1y/PXJwlBkGYTbpQIS+LBmyB/iFMFZFp/r1qnW8intYhYitDOr1VHA/67YhvVd1wjn9b+2PXFnncRUliFXldIsnQS6Fm7fuPNxfGSew+1kcGRz1wMzznA24M8I0Lbl65VQ4POwNGO4Pv3nz9tuiUpWSPYXrXfCzZmu40Z+17LvJhHMXLp/ZNj865ODwkB9/9YgH6RFBhPMzFyf75y+S9jxNa7HMLJZnlZbD4SLzvMf7dn8ekTeyDweHR5ycLHjxxauPpaBJiOxd/iRSO6MJxmvXX6eMJxzfvU6TLrC3f5E7t67RNQ1tStw9fudYi3caz4TA9mrULcWyC9x1SJXnMIsMRDGCjl6C1KRa4nXyq/96mVG3rixEsK76B6NbsVVcez6XUEp2x7bhwUsSmeS0XuFlkmaYFjSP9IsTsowMbUNvDWYuPMz8fAmlbYRpGyhhCqFl1rVIFFSEYTCnmVURHQH1Ra5KQWqVrhQDs3ZGGV2M9w1IdB/QOPSMBYYsTl0bTpLa4D7dUhewMEVLJKjR1UpqorAYfZUy9997nrGK5zb74iQjwTwHmTFSYqK0kZIFUSESiEGwGBjTDAstQsOOtFiZoMuO2zduspgvuXeYmR/NGZc9QUZiECbtlMnOhBAbUmyYxEhTBI5HhuUxJQ3IRChNy9BOKcf3KGYMNiFYJABD2CXogqBzsjqToGJYoTIJqxQ0X7QjS/EAuZpTr2Uk0ECITHYvoQRyHklHtwlJIQbaaXIFbC4wgA3msQJBau7+qT/0tHJxZWmEjQlG1tbx2gqXWkHN7L6JffMYXadvrVx1q+C092qgzNsCgUsXzj/RISlGrl65zNHxMSeLJZcvXWAYRg5WNHNZwPFPcdBfYp5Pa3qLwOWLU6K0vH7iQjxK4fLkrqfrbcCAu3cPWPb9WxPWZty9dcvnotUmfUgA4z8HjvN55mWfy+1rRHmw1R8ksH/xMk3bYBhHd+9weLLko3eWHNy7xXK5QNpzbKoue5PISxcm7LSnzOXJ8RFD33Pu4iVCzX09vOeuhf3zF3m46vP2ITUde5fehx4eMJnf4y73j7n3Jp4JgW2Ip+1gmFaBbTWKGy9JCcVpSCsbU6DTiVKDxFbmzekkmVaxQLXk58YrM0GrbzFI8jKkArFpkFqqNMYGK9kLqfSDC3I5Qa1DrUHCKa3dZwMRT7Gq9HcRryOuQF8i5sXR1xHrQWrpFvOKbjEKKUbIAykobYoQBBMh95kxw1AjtanR37EGT5iWKpgMU5csYqvyp36+VSCzasBCqExEWEe+FpxtsFJcYMfocQECsVLCQkRDg0oLoanpVoEhK0f3Fhwfn3DvYMGw7DEt7O00tE1D27R0sykxJmJomXYdKUYP5FJBsiKaEPMCNSoNRWCwSCKQiJTYORsiJ66Y1Kh2NV+9jbIK4vL89Fh9bf5MFNOMhRoQmDpSO6GddDQpECP+/AkU9cI6qx/ZEMT10dcAs9VX6NgMLFvt8boCrI9dE+j3seirwMD1Ftv83TYUhGcfIsELV5R/vkImBozltECH1KDGsUAMhbgRvGRAVp/OmuACKQRhOu0YRnfPTCYdIQQWi2Vds6DAeICODWUUiFOK1toFahSLLLL7X1MoDNqcUri6WFvAy76n78/mF7/5jdnpMpNm9MsFZXw86vytYrSWRdml1wlt6Ely9t3EmIjJF/Awc2bt4HjO3aMFN+4uOD5aUPLA8zthHfSVUmJ/2nB5f/qG823WEfe/dc1GrQysQVsa8eJQ98MzSMY1Ld80iUcFG4YYfUw3LU0rNF1kku4xhH4dr/RexzMhsCFQbEIpoNmX1iziRS/8Jce69KMhMhDMSARyjBgRodYMr5b52gtYfHnJXEYsF4yRtrO1sFZp66pdDWElrGi9RKkpEfHlNNOSMFwnDnO6xTEx7UPcoWefEwucqNFYQ7BAzjBfKlkVzXXCN2NxAqJCJ0ITGpIoszAATu3Odrywi5m4goCynyKlacgm3FoouQyoZjRMKGaMqsxCIaKgI5Y9Ml5LcCFmUllco2uVsYgvBLLyZxOBGUgBxpoDXCjDQG4nlDSBGAg2Vj9/RwgdKh3W7mBpQr/smR+fcO/uIa9fv80w9BjK7t6USTfh4rmLtG3nP9PWayKHxGTSkFIk4n7iXJTFcoH0gXAkDLvPQwNHBhNtaZkRun0vgqJLRl2AQdN0DCWhRBqrdda1EGPCl9b1wLFcRhiOSQSiOOsRm4adc+fYmd1hdxLYaQvLQbxq2yBeAhXPs7fg4lLFuZ0QqnJgsq4T/gbLYcMiP0NvipyZPO4X6KuIcKvKxlnf9rOP1HRMz11hfufB+b+PC7XA64srFH1jOtfl6R12m42ULBNuLC6TJHN1dutM2/29Xfb3dkFgOolMX+i4/votlrUC1oXuFucnh7D/AQ6OC7fvvjEYKWvk2nwVYFbg8MdBV/7xJ7uvUgq3rl07Tdd6l5gTQ7jRv4+ddMiV9tUz+3b2zzGZzbh9/ZqXgAZ+5rUj5r3XFdjdPUebAr/oxT1icEX00vMvklLz0OyFTbbp3MXLZ7YVjXxs/imcb29zaXLjDcfmXLj2um8XCbz0wtUzy3c+CN3sHO3M12EvHAFPHtj2rOOZENhmnjPsLuWMVcJbgpu9pl6wxGINjzJf2SmwsgA7ICI1XWvt7pPsOckF/6fAMBSvjFVNmgDEUK1vAauVrkoV6kZEQ6KkWvNaBtpGaWJPDAvEEqYJES/XuRyUYczkMnCY3dLLBfrlSBcK51pjECUFX1YzV+vfavCXsnoWRhIPixKEWK2vYpDEyHj6liIECbBaQ6RAKVYX1PDgNglCkzxdDfWVoQIgFlF6QD2WT53ZKPhqYloCMbUg0FumHyNFAzIKNC3GhJu37rKc9yyOe8Douo7d6Q5t29E0DdPZLtPpjOlkRupaYkq0TUfXJlKMxFjjDczoF3PGPLLse4alelpV15LjhBCEIi0qred7Z/e9Dwqi2VfSim1d3lIZSyGpoRpRDUgRF7Za6o/fdwgtO9Mp5/cGrl6c8OqtgX6uXnoVakqVVuGJl8z1/yi2Ug5Pqe9TweqT8UpYV3G/nrBCDZ5k1Wptcct9Etw58U3j+1lHKSPDyVubLLNGDoe9qugIauGBqsrxOKMvm+UxhazRa9/fB3+Ewr1+nyZkdpr5G9uQYflRpmpcnhRimEANUD06HuiHzX4Nhkt0csxee/CW7tNW+YnvMgyhL1NuD88z6tkiNGrwyp3ThTr6UelS4Oq+t2tS5PzFix6wJc4GTrrI/q7vt9Bis5fxiVSRk1folz3H8/EBvuiVMfbwj/r08Sj3Dg6ZdB27uzMWh7fJ/YLp/mXOaAsidR15YdoZ739h5OeXyjsYaP+u49kQ2Jgvt2g+Oa7LOgl1tMn6ZbjrWBE1hEiQWJfSjPhKW3XKXL1IMSR7v2qFnEv1DUYvLbrKraw8ZQxCQTxqu1rsSkDTtBYTEFKCLmWows5rYruA74uR80guI6MW+lHoszD0mVmjTOoKZFGU6A71Os27oCmrvOZqboVK5ccV4y9CkLoyWfEVnEyqkVxcaBfTStWvhIvQBNbCUdQIa8uwRpZzag2q1WjZEpDk0eJjGOktoSXRlFAD5yJ37pyQlwt0GAgh0DYd589dJAan2CbTXWY7O+zMdohN62lf3Yy2cWHdNF7ZDIG8nHAyP0LHJfNx8IIx2qKh9eA6AoXoa42bxxpkE2JdC9yr3blfO2vxlcyq4iVaUwZ1JbR9qa4QE7PJhL2dnkvnWl4/yB7WIHFD8VtlIuDBX6eJ2Bsct6wFA2uxbVVYn27xNMRTy+PB3uy6XWRjtbj3DrRkxuXxoxs+6FgLHI87G0/FXUf3oy8tfemcRdqA4atJBeyMv9kQjscdutgzTQusrsoH1Ih/Q/vX6YCuFeD5OtnAYtEzPxnX/ZgJanvsNsZee+BBWAZPUtclhLD+vlZ08duBUpNHolQPUe13UzcYrWXMl84cZ2aUorx+sKTPGcEXDNptIs+f88JCKSV29/YJIaxzxts2ce589f/HKXr+/X4BVgj3Djg+PORk6QbAg+7Rn2UkPCD47LSNp8upGbu7M4aTA0ru6XYvIMHLt25CQqRrhRcvZa6/5oWwRPx7eK/HgTwTAtsHpRJCJMaJC5FgtUrVSFklpFYrxaofNhCoMpQQBalUzcqXHVIDmpg0LaXUilxVOzQBjb6kZC6Nx2qLU8hBlCRKZnShAARf0w3KLirKIOr1jTWzb0tOhoFskSIJsYEmKJNJZD4EdAjMF8KQ3Rt/MB/cv3wuMsFoyeR+7oJGcKuYgBYYhpGiRmqUlHwhlGEsYIXEQF4KWQ3MV5fyMjOKWkDXcl/AEhLd0o5FUC2UnFnUmt6NpBq05r5AyxEbhb6dYTGjjVCm+1jskDTj+HjJ/OiQG6/foIuR/ekOOzs7dJMZu/sX6JL7kvYuXmAyndBNWl/3OSRi0zLtWpqUSF1DjKv8+XPsLE/Y2d0nv/pzLMcejkbYDZRGmN+5RWYOwZjGVX304NqKZdQyISRSainjkrF43fFxTBhKCLjYD/5UVgGFe+f2yKrs7hgvX060seXuSWAxh+UCIpEkkU6aqtkp2cpajkpYOWJWCmfdjst2tVNBvPqS5ZQs59QvvkkjujfDg+lOx8nHO5qQeWnn+mPdabHI9ZPnzgjtZe742PELXJneZpreSG2f5CnL4wk6uQqdn+XC5B7TcOT501ql7/HPrJX+y8m4VEt03esvcXd5gbs3rmOzJS/swIc+1jJm4bM/pX+s4jYxRZ576aU1+3Lr2jXyA5bcfFIY8HPXhBTh/c8br94UDuZ+Qd2u0L7Jwl3zg3uM5Q63b11DUsfe3nk+4+rOOphs//wFJrMdRIT9vZYL52o1uO4ieu6z/PczqQ8RPf8vMO1u8L7uQ1y7Mafv3yiUj8bznOQ9Xtz5CE14/DiAMvTcfe1n2bnwApPdC6c7RNi78j5odrl2/XVm557j/ACXhmOOjo+YH781RuRZwTMhsD0Aq1q+2NqoRgwVI6xWgcAFkwkEKaiA1XzkYLlSvCtr/JRylOAVx3U1VUotzlJ9ibn4JIwplm0dhBVWM2VclUoFxCuWDWYMGUSVaErUhWul1oCpl2zJQsTootK10MRITImUXOA2MXmFMS2+SihCCcELcBhAJudCLh4tvy5GagVBSSK1mMxpOVSg0vuGRyUHLyeqhgS/5yzUUD4X1maep+3rbCtFWxf4lkixI0hLDJEx7FAs0S+MxeEJi8M5jQiTpmM63WW6s0/bTWialq6b+M90l6ZrPJjPIIRIig0ptaQm0baesxmjVylrmoa2SRydHHB8csRxv8Cj/QPLyo40JCZNru9wpV1X7VnEmRai31fOtJq9xG2tq27r9k6Px6al7Sac2285Olb6ZeSFSy23TcknihArg5NZLd1RDeBaouWNNvBmqNlpJa3/n703+bXsys78fms3p7n966Ilg2Q2SkmpRMrpUqFsS1UGCjBs16BmBXhg2ICBGhvwwAX/BTUy4KkAD2zAAxuwDXtYrrIFo1yWVJaQqkwp1WQmk2SSjO7Fa253mr338mCfe997ZAQZzAzKkbIWQUbwvXvPbc45e+31rW99H1xP0vv3vTuCDBC7fuI4+0rxF6vS/llCRLGfUW3deKwqE7/OBFVyMk6DvsEm1HsS2i5GbjsgIDdDpWCbpsC13reG/XkYxBcBqOyGaWHY1AWuUKBhUqfnjjo9L9pmSxgIZr4osmTmKzytIQp9hKeXsGkzQQ/ApXwtVaPRjYo0pUiz3XC57Vk2Pc6XjKqSk2nBYjahHubQXVFifZET5KjAjIZ2hJug5gX67uKRYoqM7zI6bLHbLZuLRzeub9U81vq88wJQFh7vPZtPKb4NcsjPkW011uGLgulkzMlBIKTEo1UNYct6AH7EGLz3xBBvOuO95vFaJGwAYwI5ZbGHfvewuCMvkCnfpLuEpYS8YGuX4Ss1qDqQoUJVS56dchh3tftLKFEG56qkhBiyLWNKaJ/ndQ0JaxQngnMmbw4G9ve2t7QR1m3CEilRPBs8IDg05LGvPgjGKmOrhBEU1lJUBXXMbPe6tBAUDWm/0ITkSDEhGino6NuWPkaMcajJPfuUetBEYSWT2zRX+zK4cpkB6hbL3n9ZYszfqQoRQ9BhbFktmqDvFaQnSSTEghAtKXkKP8Iag2NCCAVtqyzPlqyeXrK+vGBalozHU2azIyazxWClKVTjmrIaUY0mA/vUIClijaVwHudLrPcUZUnhK7z3Wa+dMUkXBOk5O3/K6id/BlqQMGxiTtCFKfLsvRnwP7F7S0qLIOKyuI1CCD1RLVYtKWYpWpLkTY5GNAWMnVBWkeOjMauLSGwDX7s/wnYdF086zPCPmGEnuRM+4cpZbs/i3hPKrkPj7B+/r6aB673rXYrPzPLnJOi/+sX1Fw4riaMqeyWrCh+tC7pBA/uym9x4rJHE/fFD7FAYXI+nzQHLdnH9xLwwxn7JyK1oF1+ltA1wwf3jl2d4by6XbFY5a0wWi2sa1z977JkQw1+aDt57/JzPIsJkfoB1V8t+33U02y1PVx2PLjtGkwW3pgXvHI84OjzEF+X+sdYVHL31rbwWvcwbE8DPUD9jMYNmdcbm4vHVBvQlDjIa1cymU5q2+0L7Gu8dx0eHJD1lUnasNjVd43k47MmMNZSmoklb0pcsUPMq47VJ2BE7FNFDFSlmEEZJYOLeuCKRx3Kwit2VIxqz6EjUvSKaAaLNftrWVGh2pqbHkettQVLIVbX2mJjHOdq+oxtGO2IEZ5TSK95kZyjr0t6dpy0skjxJlSggRJxGfLdF+8g6VogvMN4zloQz2XPZ+RLRSLSSYX9snmvWiNOedcgJ2bpEjErqFSk6ui73w1OIDMO5pJRNTpoQMKZAjEVsJpVlc5H81cU+j6qoGDo1aMquXCqGoMoyBkprsVhSn6AUxDm6TaJpGi7OzlmvO0I3+FEbZTT23D15gC8qXFFRjmqKoqSuKlw5wvqCEAaLSzFYWxIRtq0SrVII1Kagi0IXE7PjGdZbxBsWs4qqWTG5d5uzzZLVdkXfbzDNmq5dEbNyOKAkI0QxOAxJTT633oHJnACiDmWSQ3vA5f/PWsMekZKyNNy7fYezJ1u67Zq7945oVqf8+KfnJM3sfCWrvoFirOw5DUmBgT2eVPeV9/VqeadBvvtR1Pz6SN6I7lL7rmLPt8LVwvbJyvuv4xMhykl9utvy34jlcs1qvebRqmdUlxwsZjd+Py+WjMwlTy7TfoP7mS8lyu3RTzF+CuNvwfZdCC/u2Z89eYKIsDi+KUayWa1otz/fSFdI8O5D2Vf47Wcg66rK+dPHXDaRD86uFOBC39GGvD597daIqjDPP0DqMOf/ChndRau7n/m+jIVfecfg82Qtf/GekuKUu7/ym4CSQsfjH/0hGj//s4sIt06OvpA6mStqFne/yvmqxfsNv/ZOy9kq8IP38u9jiHRd+2Jjldc0XpOEnXvPer3iuF55qOGK9mMGSJw9Ny07d6Qhce9uWRmIEbu6JktdmpSGqjwTejRlVyybcuWUrTOzPGHQQcJzgKuz9ytYq3iEupDcb1aHqMeo4ADXZeMJSS0acjIRo3tXLzQn/RAtoYfQKU7tIPYZhlI+7f29kwrESAi5EtbdxkQ1j8Bp1kFXSdm5yuwShEHSDvbP36maYTZZ2VeFScnkrZ1s6yDlmjBs28Bm27FcN2zOV8S+xxooxzV1NWI0meFcgVg3VM0l1meBlOxQliUi444gN4zgpeiJwVO2LhNHjKWIBcZmE5foAVNmEpC19AbK9RjVQOg3A2SvAyFsxzTPEFsajGIkU+GvZrFl+MwD4Wx33QGDx29FXXnGteNoUjObOKp6x5u4Lml5rVq+xgkbujifurafe8VfL6KFfRP7Cki//udncWn/OiB/hYV9frbqbEewuT+aUtp7VxsRfOHxJmBcoiyL50KsfR9uSppCrq5NwU2UBELfk1LKZiXDtfWiPJNCID3HNvPzIips23zQEGHdyMtB8qqcr7acbwJnlw0h9IDiXEHlDXVtOZiNcDvpVfOJxK0J+hXEl/Me/yQR3hhHNc7iNzG01JWjaxMhRtqUe+KFvXnskBxtKin9deTp80OMxZUjjHVYIxwtHIcTy7S86uv/IsZrkbBFwJvBrGMg3+yJJEMBrQOZSsWS54cL1AwLqWkxMSIa86NUUBxoBVoCBUkbSD0SOywGIzYrYyXQmHF3I0JlI8bkKmnjM+syJWiG2eVSLdYLdQGzmlwdq9AGk6ti7VCTML6lO9+yjdD0SrIWxTCrO9pmSR8VrwuWF4HtpuV44agEnIa8K9WcYKIRggj0hi70NH1PiD6/qRBI0g59fLJQhSa88wgWVYcx+TN0YeiFmoxSZA30mKFxNYi1dINMau2FII62Mzy9vKRrW0Lo2G4vIAQW8znzyYL5/JjJ5AhXeKyzWDfGWA9UaMwQtSkyKa7rAiEI4mpcdUgMNdo7ztcwntXUk5KL0xHJGWIB4kuwY0LhsDNhWnpSWLIxgXW3pE0e0byDTwaMGeTgkyUlobAZwha7M+dIOE1ZWzxGxNoMQhtLCn0uc23BYjHBSeKgHnHnoObenYofvfcs25HKbvM4iNWQ9oSwHapznU62Y4yz+6/m1Ltnqt5c69mNhQk5maSrX+R5/ld94/3/JKbTMdNpZlyt1xs+fpjne8uy4O7tW/n7NYbbt46f+/wnND0fagAAIABJREFUT06fa6NJuITl92/8aH15yWa14vYbb2AG2vji+OQVfhpoWvjzn35xZfmk8MPHG5qQr6zV8oykicPD29yZl9xd1BzfvoPdJ+qf/YpLEf7kxy/eRVgj3L015vwCTp81PN7cY+TW3B2/f+Nxy37MenPCvfFD/AsU2j4vrDXcuXXCV+60/NrtU55cQO8stR3RbLeEl6jyX5d4LRK2aiR1l3n1sg5EMGbwwx7agoNqdp6FxaCDEhaiWPwAV2Y1qzyU3COyAXqUSVZN0wKG/rdJPdHmlV5w2N38t0l7SNMMMKZejW3jbUKswziLk1x9iSrWZAZ3rrYdxvY0HWibkL5lmyB1Hc26Z2IjyUITVkRtEDpcKjEmw6rWZtcsaxRbZGGUftujxIGcZwlAmyzOFtn5y4RB7y27bCF26OUCRnOPXlP2ETdpGC0yhBgJKkQKMI5kDK0IXYS+3SIqmCDIRqnKGjsSFoe3mR6cMJ4cZIa3WKx4NCZi6kkCvqxxRYWtTgjR00fP+Wqb57glMR5bqrKgrEc0q8DqomUt5+AKjJuQxtmMwdSJw6KgNmMKV9G7AuccoTf0mufYZehb2tQjJmIUUshz6L0xlOrzRIEmUgrEGPFaDgTHSNcuyWYzgXpUYmWCjSNuHUz51Qcz/uiPHtFtA03MCAU6zPbrzU71LtHu+JEw1Ma7dU9ulhzXi+sdv2Cfpq9xzXaz2b9I4imvU1xPO2VZcHx0COSF/Do68qKYTieUZcHZ+eWekHp59gznPOPZ7DOe+eXFy14JYiyj2S2cr6+eNzx5NJ5SWMNXTkacHC6YzcaDPsCnv43prbeop4cw8qgbkxS+96NnNAPz+xtvzVlMrvrxISnf++Ez5uOCr9x/jgu2caTp19HmI+D9574mAN0zNDWctVuq0jObfgbV/TkxnYyxukBEuHMy4tvfPOJfvLdk1cVfyB3wa5Gw0TzCRZJc+UjuE6VrgKAM89myN2AA3Zsj7Hy0QenJItARdvCyFqA+w7EDaUg0kswApcqu4xhzFcVVxZT5Q/m3WapSh/Exg7HDlPbQj1TNmwxXOFR6fLlGU5uNK4JCDHRtZFwaEMO2b0A7rNlNGefPaI3L8I9kn22xu35mnknNc6lCwGA022fKzr1LTd7oiBlEPoaUIgO0ryCS8hw6hpj6weVLsiCJdfQYohpSzEYlRJAuUVYlRVUwnh1Qj2YU1TjPZIoBXJZEtVlKFVuCGxNlTicFrXqaYIkhbxq8F5wVNArNOrBZbznnDLEV3guhSUgllAmm1lGXJb4o8b7AFQUa/eDlnUhiswWr9sP5hhgzGcyk3TnPOu7535gVynKGJfYNqhFXGIrCYamIm5L5uOatWxMWY8d60+8rk93192nP5Jxlc5vmqrq+gXPv8/d18tpVj/uqEzRcpwCDWNBfp+vPj6zwt8M6wJh4Y132zuEnX2zZq6sS5yxn51diMF3TosWnz4gYkzfcX2KIQOEyHP5ZPXcxFusKynoOMsj3XotxPWJcOm4vambTEfUoE/XicP9D3oCWhaWe36Je3GHTZk5I6gNPzxrWg7HH3eMRhb+C0PugvP9wxclBxZ3jvFkwRii93V/TlCeYYol1kltmZNKttcN6rD3ENRrXrFuA0Usl7BTD/t4qS4/E/PqLWcnbb8yZVI7lJtD9ApHNdvFaJGwVS/AHiCacK4EEaYOEFmKfE4EMVaMGoAPWVDL0OylQ41BjSDhQwSSHCdtcCqUVzjjAEFOHJkNIjk2b+9QxAbZAxOEkUZBwEnHEIY2aoTcKQSKZoKwkLM4q1sX8lmMipIhxHm8847rGS8JLh2uGXnMIdJqNNKZi6QslOUtRRSwRqyBaowli2EAQJEFlLM54XMwiKM4BMeMOYAiaEQGjMpDLQpYxTVkydWJKOmVQQAsZYjWC2EFTWQ2xnEExwVqP0xoTKx4/eUJqNzi7YjF/wHhywNHRXRw+J8NiRG8MGzGU9ZSynHF48A7btmS7tfz0/SV9WhE18Na9KaX3WCm4vFxxennG+0/fx5cOV1i2XUJsQefPSRdT3LRmejAnlJ52XDFxR/hSKEtP9+wU06/ReEYsJmAMQbcYySz/JhWU1lHZghQb+pRI1mJDj7ORGHrEGYyzqPak2BO2BqcOaxxrVY5nYxb1Xf72v/6I7//onP/zu2fs0q1xeSRbkpJZD1mxbp+ETW6hXJHHro9v5Ut81wPfTRjmQ2eOhqJXiny7TP+LWBL8JUcTS55sM7TtTc/d0TVW8isKEeH4zp3nno7pYsF0Pv90//cVRl3Crz5IfPBEOF2++JoYz27jq+nzm+gC75zUHM1GnNy+e+P9PutmvL+9A8DxouK3fu0uxli2beCf/v6HhKAYI/yd79xlXOcU8vt//IT/50+e3HiJPiY+fLzh4dPcTjicl/yd79wkq00nBZN7Uz58uGLbj/hg9VVunYwYlxEuv891XYOXClWWT94jdAOp7touZTodU1UVv/X1j/jBB5f8wQe/eBJor0XCzn0/lxngcdcLtIAfVr5hx6QBm+m9qBlUoFSxJmWW7sAW1pR54DFt8wmTlOFiY1GxWfhUA26ozqPNRI48/xzpJFPUonEZGBe3d7nqIpgQMBKwasFl5aqQ8g5W1WBNhrMrZ0nW0TuPcQyVfd599qTsEEW2vETzQmAR1usVKSnGRUxMuKR0OnROjSAp7b2rs5qXYousQR5TImqer87iZy4T5xSC5t9hs+mHiJCohmp8hC1mUMwgWcI20q83uBgw1lJNF4zrGaNqQlGOsbbCGE/PmIAjqKcwEyJjlpuCs8uGTZNtQAsRkIJ2uSW6Fu8toWshtDhfYJwiJmKT0HU9y+WSxcmUIhp0BTKpMXVFVQRsIeBb1D7LUwEpV6FWwHuPy5PlQ0MDSFnuVYzgvBvOf6bUJQVJidAFUh8QHN7bQUCny77AbswvP7jFtoE//PNz2hZCgKzlc22mmys0JueHAWkYru6dFeeuhr5h9AFDUrnibVwZHVxV73+drj8/nESmfpXnsCNcLJcDepYrup3c6I0IF0jaMJmMX8hENmKYzyZsm2z0cT3BbddrNCVG0yk7Y4zrsVmtctUHV6YfLxlJ4XT5fFJZ83mHErN/n6smctkEYlLqwrAYeWpvsy64MQOGKTzpDrjsx0Qsb92dcDyvMNbx8dMNT88b2i6rIIqB9x+uKAdG+XLd0YdPv8lMcFUe3MnH+lT4KWb8JrPZ+zTbhvW6B2zeEVd3M08grKC8Bf7l0tVOzRCgqGcY62hWZ4hk0Zpv/+pdKCt+8LjLfJZevvB5+f8qfuaELSJvAv8tcJu8qvy2qv5XInII/PfA28BPgH+gqmefc7Ts+qQgYYDCrcWYKqcd3Qw2kRFrsl9xsjZXnwyEX5Pyqm18JlmroU2ZYAQJIwYjPve5pSfRUZi8qAeUvhdSVEyKmU8uBrUuC32IQyUnxG1QTOgwqcclhxYG1GaHLs0uT1ZyEhZr6J2jcSXW5+RgE6yanpgSHsWKyf3vlKFlK8J6eUlIkXrqsNHiEmyG0bEkA+w6EJFSzKIoxjMwxhNdZ4hksloyA78+5hEnlYSRMt/IYgZmgEP9GFsskHKBbpRufUbzdElBT1F6JrMDxuM5VTXDF1NsOQZbs1qXRC1JjEhS08eC9bnh0ekFm2bFneMJ3tR4KtZnTxAT8HWCkGHqspqA3YJp8eLYtpGziw2Hh1AkSzqPMKsxo5JqbLE+kuwlvShJ8uy+lYQTQ+WLbF2qcZjHJovSDAnQ2AKMHdTDcluElAhNR+zyHLu1BdYbMC3WeQpb8c2377DZRg7n7/HsmaAhs/P35LLr0PeO7MA1UtBudvtKziyPfYlce37mQ9yE2rnqsX7JePirvZ+f+wI3G6hfUhS259CeEzaOTYDzy0vyN2xh9pWB2X09FDan2HDJaFQP/gXcpDcjWGs4WMyRi0u6rr8hcblZLokxMppMnlPNKuuLC7r25ZjVnwxVeHQmdP0X/OZ2OhbDMc43PR+e5/cwrx0PDivAoIMHg5IJmx82J4SUuS9fe2POwawgqfLexyvef3g1uqYJ/vQn5y/9dr56f8bRotyPxDK8Om6Kugnz7hmFTWwGCVjEIfUD2L6PxjVU96CIwLMbn/EzJ71EKCcLXDmiWZ8PxZ3wN779BsmX/NPvnhIHLde/8gmbrLL5n6nqH4rIFPgDEfnfgP8Y+Geq+o9F5B8B/wj4zz/zSJoQbTHiUDdA39bml9CI9NlyktgT1KKmRP0Mikz4InaQOjS1GOmyeIgAZXbMCp3SdpFAwBmDqgcsXnry35RxZVFxtFoQ+0QIWZs6qqHDkJpIih2hW2FCh9VISj2hN9Abmpg77GqEceHwVrC1o5QKxbIVSwiRvgvgc3+4p6MLDlVLGyIVkRHKetOiROqJkJ2zs4xmH6AL2e1HAfGKqEWjpQvZgSoLpmTHEw2JnoQOJD00oxOBNlfgKnSpJlifE25QUtxw+eEZfbMi6ZqRnVAUE+rxCTJakKpDKL/Js0thtVbOludUY8v0YMTjx0K3XXP+5F2KUcKXynqtlK4hmRXttmHbbrhcP8OWuam+eXZJWVmqUcHtN77GfOKZ3RdOH33A40ctyW6o3yuo5563bnkKWeH1gr5ZU1jL0cEbTEYjSqPYiw0hdPRtwIrivcVbh3MVznuKcoJ1+doyxpFiJDSRiEUqz2h+SOkzGS22DW0TWTeBui54896U3/wbd/k/fveU5bYh5qUOK5bcaBj05gbmt0EHkZ8B7x74TbKvtOHqL2k3Vr9Hvq8D4HmN+9L7ba/ufv5EuHLE9OgNVmcf02//cmDIw+qMRQmMb3HZTVh2EzD+5oPiBtZ/ASlb5z589ITJeMx8NuXx02f7RfzoYEFd5+pwOplQ1xWPH58SsmYyi+Pjq/P8isMY+Nq9xPlK+PD05Y7viprx4i7GeLqQ+PNHG7rnVL/Pqm9RzW/z7W/e5wfvnvPewxVx0E9Xhd/93iNsdh1i27yc+txzQ+H3//jx/liQkZBfGr9/pWoXG8pCeOPeFGcFZyK36qcs45plk2D5A4gV1LnPbn3F9OQB1vrnvSKuGDE9fgPj/KesPgHePBT+g79p+J//AH78KP8sqy46ttvta6s5/jMnbFX9GPh4+PtSRH4A3Af+PvBvDw/7b4Df4SVu8Pz17ByONMOdhiwzabM0JWS9a8TkPq01QwPQ5ecmBU1XRhmDiIi1glPyxkByz1zVoRIxGLwYorV5XlocloQYJQafRfGD0nWJFBRS1vhOmmhDQkTw1mTjDQa3q8gwOpar4MJZ+uDRJPSShqV9IFrEwSfWZHnQbQj0KWWh+mSuZpjJIjKqkGJW9pKh2sbkx+SQQZdrICylQWNcJO9ad0lbU/b8NXaAzS1h0xJjT79eAgHrHL6occUE3AFNmLDdFGxDy6YR2i738fsusbnc0DYtoW3p0orK1HhXQPQ0bc+2X7FarmnahuX2EuNjJsp1PaoVMVpOn57iC4tzwvr8nK5viXZL6wxNssx8ych3jO0WX4wpCkNZjygKiyeA85gQcDZhbUbQjAXjbLZQHdj32UZU9zPZrqqznGFZZxJdykSlFCN9iIxHjumk4q3bU2ajC0690HWZTZuB7KtxrOsMs6sa+xM3/3Da9gn6RuWZE/q+UB9eZTdI9mXFq76fP3FsYuiG9pTBV2NS3xHDz1Z1vkw4k8c7G51iXUEtkln+w1fYtJHUR2gthRGcGeathyQcQqAfBE3SNWaXtQYRfyM5xxgyf2VI8CJCUVX5+um6LyzOERVWW6HySumh8i+NBuf3noRlZ4FEHxPbLg5kU5hWjqoas3W36OwcYcTTleWy8zSxvHGc9fazxp2UdvV4mMrJIdZRjm8BgmqiWz/eQ9PNJRhbUIzziJs3gc4scZLFNKrJEa4u8eMKCUtUt5mA5gpGw2ap9FfnQURwvnzuJslV44xWFgME/5yEXRWWeycjJnVL4SOdy4Ja6bNYfK9BvJIetoi8DfxrwO8Bt4ebH+AhGWJ73nP+IfAPAe7dmg8yoh0mZ0Nil3fm4gpwI5JRkknQrVFNaFijyaLGouIxWMRUxH4DorlnbEvEepwFrwHoaZIhqCNQEULAi6GwnjWWHgdujHeZANZtPLHtic2azbonxkTpRyQNaOqIXceBEaYYoloiAxMzZulPNIu4FFbobEFKhmQSqvkGT6GH1GMwuGJOCJH15ZYuJbwzkEpi3xJSyKdKBzuukGG9kFJmxUugNz02FTgtBgGUDItLjKhGOjI5QIzDxoIu9WxjR6xLkqlJ0bE9vSBsO2hWlOMp5XjBqD7ElsdE94An58JqnTg9/1Mm84LRpOTk6E3aVcvD9z4gyiOMV0YHc+rJEZWfQ+M5ffoRTz7+Kaerj4kkrHf0YYW38OadO4S+YHXp+OCD72JSSykRPx5hCgcjoa0CjU3MVp7kwXk4vvN1xnXFyCllWGHDhujHeLJ9Z+W6PE9vQcoC8QXi7YAiJMwg/yoSGR+cYF2R2fmpIaZAiImQeoK22HrK4mDKt9484vcOnnDxbMtlb2hV6YZUusu+u9Z0Qgcv9OHXOwk+hgcMBLOcmdO15C77vp+91g4dAPO/lPi57+eTm2M8sdty+fgnABhXMDt5i+3lEzbnj76Ed38VQR2PN0csyktu1ad8uL5DGCrI07MtTZOANzmpP2ZWfHGUfxeXz85ot1dz2sZabr/xBs1mw/nTp5/xzOdHH7KJx71DuHPwxRPIpot8+PDTFqJG4CvHNbG+y9P6O/m1NoF//t2HX/g1VBMXH/4eoblCTHw15/iX/j1ELJoC5+//C2J/9b0U42OOv/bv8Cm2nghHb32Los62qlz+CbHteLw94qB03J5+AflWESaH9z73YUVVcXTnDvNZYnpmiElpm4a2ec68/WsUP3fCFpEJ8D8C/6mqXsq1HY+qqrxAnkZVfxv4bYBf+8Z9TdYCdeZk24irW1Jc07draGtwBeoKTFnkXV3sSalDk+ZEKA4xDoNDyGIhQVzuXplc1YoohfQ4tcSk2LIc6lGHCQajQrdbfBUmtqVxgd4kgkAPhC7hrcPbirIQrO1Rbfd9c/B0/VDdj4eKVoUYu2y7GXqct1nQRAvWbUvbtvTtKSEobW+prEcsdCHQhqxilunjWXBgq02uEpMlEImqSJ+ds3uJdAmCQpcsyXgE8JJFXZMkoq4GQRhDsCV9cmwvO9Jmi/YBX9T48YxqcYQrv8527Tj9i2f01iDWce/4AKxHo+PZo2cYDZQ+0KURKSrbc2Vz/hFJPyR0l4h2yLTjzsmcovSMxyOsN1jr8OWMdr2h3WzwC48RR+E8h4d3KKsxbjxD5wUyFib1GfNR5HCqzG/dp7KGMm7gYkOKKWuee4dz4LzPxapRiqLCOZ81kH0+47acYUVwAqlrIUVsXZHEo0SatiOkgFilDwW+8Nx7o+AbX3uGAj/9YM3jZeBsOxiooFeU73yFX+m4DzQ49hKkWfVN2LUaLVeCKjvhlOttSIOV59KlXnm8ivv5W1+//cIsk2Lg8sl7pPDyzkw/b6z6MV0sOCjP6WLBRfecueAhNtuGEJ4SrimQXVwu2TYNR4cL5DkV3ezggDSfA1k4pdluefbkcZYQ/oLx8TNhuc2X0ulSWA2GY91LaHso8DTMadKnYeLjiedwWrGcfJtt2/HsJ7/D9M638dXBpw/0gkih4eLDf0k5u8/o4B3m9//mJypsz85YRIxj8eDf2FfYAPPZhN/49Tsg2Ypp6ha5jy3gyvql38docQdfjZ/L0n/ZKMuC2yfHfO3WKf16y5/riLUxmNbSts1fPUgcQEQ8+eb+71T1fxp+/EhE7qrqxyJyF3j8ecfJBG3HlTlCwNhIjIPWa2zJTB1DsibD5JoySzcpmgI6GDPIoBCVYWA3QJbZrlNEcQaMZsKVs5kFzjAOZdJgu5mUmJRyMMNwFqwTghpCdHhTYCx4GzESBj/bOEDY2Y8aGcwmADQ7bCUNxBiwxmWBgjxgTYiJpm8JamiTpSgtoPRDsk6qiKbhKzCoBKJk5nFKOkiXypWjtmZP76BmmD8SrMQ9DJx0GFfT3WeCbtMifczc/HKMK6fYYk5IU9o2sr5cYyce7xyjuiTiScmwbVYYm6gqg0n1IOUqNH1LF1va7pKqMoxqz/xgQVVXTKZjfJVlSIOUVBtHvxXKzUFmyjvPdLLA+8xc18kIGTmKMlHOlPrAUk4WeFFMp6i1+TMagzMWL2CcG8avE3ZwA8vM+GzxaVyFNXnwKjXbQfYwy6aqGvouu/gYI8Sg2Qt4MuX2yYyL5YbNeceyS1y0cQ+zpt3FfOPKHtjiQz4fXLU/AeXtOtZXDHLZ3Q2DoMqXNyR07V28svtZ6FLeKBbeIKSsswCg6VN9bBGDcZ44aOi/6uiTI6rlsDrDiLIJNYUT4jA3bFwBpoLUEEK4kawB2q4jxkjXZwte/YTmZlFVA+wfMDYbzLTPU0Z7iVi3sNzma6PpoOm+WFbapoL2WsIWgdIZRvWI8XjGIx2x6XtCu7yRbF8U1gqjytG0kSYooVvhYwsiHJ68gTGf8f4mD/Z/3TSBeuSZjIrh+lYKk+91625W0IU3WDXE0GEGFUfjiv1mqagnuHL0Rb6WT4WzFjuy3D0oubwoeLyGlAJ9DPyM/MC/lPh5WOIC/NfAD1T1v7z2q/8V+I+Afzz8+b98/tEMiRorlmiy4GNMFXnGKyDhnNQuie0FMjrOi205w6SEpIDGzTBf3QxHG4wZ1KAYAo6d/FRLmRnj1mBSkyt6SVTeYBX6EAkh0LUBL2sKI5xMC7yHdXCc9nM8ayo21JqwGuiCEEMkxo4mbLNhurW0TRhEVsAasJJheWssJKGLBu8LRgKrZ6s8E24Mfci/j31DUSSchUTCSoHzFVKskJj7r6G1ZJ0ApY8dQXuS8ZkN6xzqPBjBWINXsKpsekNIQieGZtPTt4F4cUk1WlDVU27degf1J8RwyE/+7BQ0MZmBn9/ClBWN6ZiNYVR6gs7xVqi84OoK4z2uLIc5cuXicYPxgi8NhwdjTGGJpRB9Dy4yLnrm9QnjakTfC00XWG8a3v/ph1yeX3L2o/cZH00YzQvevNMxKQ9RPcHIHEyiJ+KKETY1FJSUYilMIhXzoertsS73HPsY8b7A+wLrHKnfENszJMX8fSl50xWEZtVR1pZ6XLJePaXwBfP5Ae+8dRvnC7rTnmWvXPSBpk1EldzWibvFPGIki0Skwf4R0f3PICfvfV96nwD2jeuBtzAwxvciKl9OvMr7uU+ej9a3EYRf/uoET8Pl43df+HhfT5iePODy0U/omxebaLyKqN2We+MGc/uEJuSerTAB7eDyX+WW03MixLiXNAU+VYGFvufJRx+9dpVZ6QzfvD9hXX2dR/5tHv/FP8HXB5z80r/PdZvNF8XhrORvf+cu//KPn/D+w5ghbRGsEX7z1+8wHT2f9HU9FPidP/iI04uWf/K7P82Inwl8c/ojDu884PCNX73x+Dfv1sxsxflHP80bODHMb7+Dcfm1XuWA47d+5Yhbxx6NH/O9Dw2Xm1d26C8lfp4K+98C/kPgeyLy3eFn/wX5xv4fROQ/Ad4D/sHnHUghV4uSR2YEcGIwxiGmQFyuMA2aoeaUoM9M56RA8kOv0GLoBwBS86yskUw22t9HhqCWFB0pNFiTDQBSVhKhEtj2hmgMbRJMyt7bZWggWFopMbEnpkhMHUYCRgZDjgQ25UpeUsq65cZinKXZzS+qJ7UBjRGjKyRl8pc6BzEiMdCZAWb1gqEAlWwdaS3GQuE8kGgGf+ekStBItJlh7/wYxJHEEWO+5vvkiEmJqrRRiZolWel7JBnMyDOe36aqjlmvF6zbhk3zHpt+S1WPKaf3mJ/MKEcjqtpTFp7CeUw1xjtLWQh+7DHeYiqLmPzeDh80GAfWC7cWFYW3GGdoSARNBHpcyrPmU+lIXulGHlfd4mw5IpYNo1HJaFywuHfE4nDB4vCQ0WSM04iYnrR1GJO5CNZYsIq4jMKk3hJCVmyy1g/ohoXQkvqWvuspXIE6jxpolpdsl5f0MWCC4oOQ+oYkCaVlMfVsFhW9hdHUcSIlH3zYk+JuNGsHi++6zjsSmQ58/U9e91f/vyetfWI9ugLZv9Rk8Mru54wm5A/x+LSnNB0eqMaLKyIQgCrby6fEvmVz/oj4M8LkTSjZxoqZX2HNFQRrJXJQXrAJNW0suehmVLZl5DdI+wiJBZR3WG8CXdux4LM3RapKXZVUVcnF5WpPJlsvl3RNk30NXqN8XR+8TTU6YFUVLJuOy9Pvk/otVIuMUgJ1afnam/MXEtxHVTbPeHB3wuJaL9kY4esPPOP6qlHTB+XDx/qp70AZRsSmDT/8aZagdtYyv/s17tw74N79mxuHSRJcdEwObg1kTsFY91IbjE9Guz6n3Sw5P7+gLDxVVXG5XGKtZTIeMZ9O6Dow8pA7swJn4Hs/2dDF1+hEXoufhyX+z3lxF+HvftHj7cw+YlAMgncmOz4ZA87ux2Sk79CYq1nF5SVQLZIEIZOIjO5IPAPE6Ex2iNC8N4vR0wVP6sFbyX1xk3sppRF6L3S9pUsWqylbZsYWE4WJLWlTIIaAao+aODC6gZgr2L0GYOwxheCsJcZISoIRl6UwQ4ukdX5fSTKTShXpEyEpyRiwDodDUjYqsWKxVvC2IKaYBWBSRFOiV83wt3O4YjJA3kDoSFHpcPSDcEqvhqQO8HmTgGBHNdX0hNLf5vRxwdnFYy4vH+PnFlOPqGdHzA/njKcjDmbzYTPlKOdzfOEoSksxFkwhUCnWAUZp+zbbfVrl7rSgcobSWtY9dEHZ9JFueUlYrxixQrxBygJ3sGC0rThLp1TOUpcFizt3WcynzOdTRq7ApR4jPY1zqBG8dYhV1JJn8mNGWGIU1IArch/biEFDSwxZ7MGVboDQhWa7Yr06o4u+cMsNAAAgAElEQVQBG5UQFO1bsIpqy2zkWE8KooXx1HNSCB98uB4EXHY64DlpC5CFwnPy1hspd0czG5jm11bMnZzs/jGfmgt+9fGq72cYeqpnLbXruTu2FKM55WhOHJr7qonN8oK+a4n9k88+2AtCxNCmiot2xshtMehe/MQJLKoN2jn6tuKymxC9oXYN0j9GokeLE7bbls26Yf68MepPRFEUTCcTlqv1/jZvNptMOvuip0gGl0Idpj/Sz36aFUhq9teSGEu9eItqdp+LFFif/4DNsx8BYKzDuZz8xiPPL7+94IXI9oAOvnEyQo+vYGgx8MZtQ11ePXHTwEdPdD+iqDvBfeDB7YpJbXjv4QpVKArL4b373L5refPOzRcPzyA1lnr+85umdJtL1henXFxcMptNKKuS1XpD4T2T8YjxuGbbKmIMt6Yli9rzJx884+Xsz/7y4zVROoM+5T5sJmRZxFf0MfeHU2dy1SqKSIGYHqMdRjcYhYAH8ShFTnQkjEToGlQb2lDunaQqn1AVugBSHSBOMIUZ1MMU6xNuDOMSLtcFIUV6AvXMYtoN1ZMfg1ZEHNVIcGSyWpQ8JqZEnGg28hBD6gNdSjgSOEMyhg4hdELoQWLE9lCqR52BAlKjxB62WhKtobDCYgRl6SgKj2kKVDtCe0nf93m+GwgxEUNk0yzzYmCFmHLijqEnJCFiCEVNGxJN01HPb+HcCa3/JR6eK6Fp2fIhR/cPeOvb3+b4+Feo6wXj0TE9WQzGeE85LvF1QbnwWZDOQXWk+AKcVZpVR9dGpEu5/xcD7/70MV6USWGxGnFGmdYFm27Dulnx4+//X/QxklxJPz0hljV3DiYcnxwxn8955+iQynpKHF5Nzot4vClJtqSNG5x1OGvR0OWFw5hMJBOLWk+IAQ1hEFIZMZqNMmITHQRPDJEQWlQDXZ/QJlIikJRu0+CSMnbK/Tsn9K2ybQLfs2f0Eq96zSqIuBurb6Y2stfHH5gVn1jjr3rVmp+UCWiJrHv/5d6CX1r4csTB/UPEOLo+8WfvrvbjMykeUbstx9WzzznKp0OsZ3H7HdqzyLOm49HmhPnU8s79m3rTcxX6KPzZu1kB7cN1ydGBYRbXPHr6RxyaxGKsec34nFiuVqzXm/34F8DB8TF91/H04cMvlHHH0ymT+ZynDx9ydhl475HwM/DU8vuKI07DlIjBj084ePBvYlxJ7Dec/uifUR+8w8k3/h4A92/N+I1v3Qcy8vRZm5TpWPiVd4QffaCcnl99NlX4/g/TDWW4pLr/+JoCH//p/03sm/3vfT3n3/1b3wGBwgu/8U1HWXyZjZ5Ph4hw+9bxDVjduoL5yVdICdq+x5iPyLIEr1+8Hglbs8NR1JT9m1FMSINns6DqYBDaRAySDFYtJna5qpH8HMEwyFqhJvttKSkLq5isciWS5fgKT56p1mwCb9QM5CBFjOJdwntBUobdRQuIgdILhJagLSTNUKkaQuyvBvT3O+esL65xUCazUFhD6vKMeXa56nGSlbkSeWZaJCEKoY2YSrDOZAMC60Cz9rXGK5WzkHae0EpKiSjdQMyzJOywe08kzapmiRHJWLSwBO6Q0oy+iThvKXzBZPSAw4NDDg+PGI+PKMoxvq6whUO84CaWcmqpasFNwFjF2ETUQNj2xO2WZrkhdAEZnNSSJvr1OSa0NGGN7zY4Ik1lWcfEpg9s25ZeEwHwEqhKy+1bx4zHY2rnCKsNjXEE46jqPFEgg6Y74hHrB8JhhqJzdWowkhXrMis794XF+LwBRIGQ5+dTQrEgBc7n35PIvTMxhDaBKF4Mtw7nrFYB0ZbS50QUo14tBPuFW/dlmw7X6X4KjCu6Gdf+ZCe4onLFRZNfXGnSPsLZEiAQQsqaBvvvx9BFz6ofU9kGZ14+Ywn53Ixqy9ECzpc9TW84WyrTidubURjyNXo493RNom9bnMtV6KS2tG2kf0lf6pQysfN6GGv3Vpqf9R1cboRJrVSFUI3GlHWFdVlVLKWXY4J/MhRhHUs2qSBgqab3seWEbvWYYnILEUsxucXJyS1u388TeUfzilH54qW/roTJCM4ulRDgYgXL82esz1tG89sZDVLoewj9lubyKW3bDKqS4H2BsYbQrvPoKlDPT5gsFrx1z7NcQx+VyzXYbb4ODueZQHu+hLgaY4KyKC9fiRaNEWE8HlH4IrdbP3GunIXbh8JyY+hX4JzH2nBjU/a6xGuRsJWBma2B1DuMQKTFOIeIzWpeSYkpZJa4Wmx02JDrFOt7hDYvdMGgUpBsCVJi6NG4QpJHNC/shRecVdabIdkFsOIxYjHB4G3AuUBZgk+QUiRpCSkxnozw2zP6fkMIdYafxNB2PaqRwgoy9MOTKjEkUhJ8aXHWYIwnWgWbMjnM9ogLhBTpUiKGNDC6E6HZYvyYwnhmkykxCG0HOsDcMQl9VHpNQyLP/uDRgUpWUEPcwLeLxGH+POkc/AwpF2zDA0Ib6Tcfc/eNExYHC45PvpOTtK8gZgiN0jC6XePGDjNRJtPEqE6IiXiT8BJ4+HTN+mLD2QcPCesLNPaMZnNMkfvW29Uz4uqU8PjHuMunmH6LM4mNGdGYMdXBIbH0dKWhntcsjuZ86ytv028aum3D+cOHqLUYXzI/uZ2dWFPEq8NJiXV1NvqIPX5gfItxeFtkoiFxGP0zWD+C1KOhBc0jcW3oSJSIm1COGjT0EAO2LCBBt464ArxY3rp7wqOnG/p+xbgs6Hplq4MzlA7M/UEgB9jLkGbW+FBZD3C5HZ6zy2ERiKoYvQaLf8mksy8ztk3kxx98ei54F20seLI95M7oyRdK2LtYzDyzieNPfrhks82v9fW3Jzfco4wRHtwd0axbVk9Phx86To4PeXp6Rr/6ciuqphN+8kh4+zaMKsvB8TFizM9NUosqPA4LInkCYnr310n9lmfv/u8cvPVbVIsHLN74W3ztqwd88ysvN8K1mMJX3zR8908jy43yp+8qT378Ls3qGfU3j7MF8hDt+pzHP/5Dzp482s8wzxaHjKczrhMoF/e+wcmdQ77xtuEv3k88ego/fC/DzmLgO79saHv4s58k4IjKTpmXOx34nzGGjbGxhqOjg92PPhWlV37pjZ4ff+w4X0FZlbThrxP2C0M10fcdiuA0glE6b5A+IBqw0u/kmQc6WVY9M1IgCrHNv88i9hF0i/bboY+bSLGj1HNsXIEsUFOCKah8HEwy7EDsMkT1pJQBkbbrEQKWjtJHjFFMHCFxjdE2q6FhslKYWNBsfWmNwVpD7BNEiDEhLqJ9JPaJy8uWrusoJSIm93t9bDJk3RtUIilFPCtqqalwbDdC6FuaZkvfbQkxZktJY0hq0WRzZakJ6UzWzPYmG4yIIVKQmBLtArP4DiZOMN2Y41mkLhMH07cYje/g/JxA9uS2dUU9m6CVI04M7gSKOuIksFlu2D5tmVWGZntJt3zKs9NztpsNy/MzYt+isaP5YIV2S1K7pGu3xBjoY4eG3LcTb7Bji5/UjOcTCm8ZGeVkdEgpNT/+4buE7ZrUt4htKaZzqnlNKlKubEyN4y6umhCevTf4dytIyNeDqxFnM3QnoOqI5I2gDkiMRqHtep6eXbB+ek63uaCw/y9779UkSXbl+f2uchUeKjMys7Kqq7rRDcwAGIxYLDk08oF8IZ/5HfbD8FvwG1AYX0mjkWZjOzM7ioslVDdal66UIV1cxYfrKaqrqgUGmC2QPGbZXRkRGeHh4XHPPef8xY7ge6LvkXqCjJHgOkptkMIwr3LcWGB7yd5+hcXTnPcDHesqScub+fMw2BPXtlzxWgglXKfyxAa4Qo+HmNrsIiTevpR/qE3x30+E4Fg+//z69z3t2eUZF930NY+NfPZwh/Qt498xR255dkbXtt95AL3bbNiuVvhvWd1/NS5czU5MmH3vv0IMGuk6q6lKwY/+/PscfHAfUR7yd7/4bviAs8vItvE8/PBntNuE2u/bDa7v+NW//V+uN5Cbyye4bkffrHE2dfVGs2O2mzVd2zLbXyDlTTW7ayL/4TeBpn39eRpXgj/7o/ThWJvx4eP3WZTnHJTfXdRm13i+fLLDr88J7dn17ULAYn8P5xwXlyv25lOMMdw5XLD2OUsvmNVjrHW0bUueJ5Bk17Vveql/0XhLEnZMrROhhmpQEDVJtjMksw6pZPrwY1rwokgKZ1e8YoGAMEhQRIcMdrDFDOAdCAc4cA0pP8troEWIATHMLJKWbiDEgPduuN2R6YCSEaElSiuCUwlsRtrlxqENLuWNb7a8ggGnfj/Be7q2o+sszgVMliookAh5Q+IJgQRiEkkgJtqevrc42+Nsi3d2uD+BJUSMxCASt3ugsyFVuk+KdB7IQM0Qeh/0PlrVKFUxHu2oisCkBmWmoGpsUEST9LWzeU7MBV0OQbQ46xChx262xF2DWbfYzRnN+RO2Fxd0bUO3WxF9n9TgdpeEbkVol2nzJBXRZASZJeORKMijh+gRMaBQiU9NRDhH7xtE8EgFZVWgqwJT5GidNkVKKIQuic7hvEA6ORipKKSQqCgSvU8koZgwaHkHAjGkNrZ3kqYVXGwiu63DNz3jMoD3RO+xLqCu0mhM570wgirXjKuMyciw2mqkTFao185bV4DvGx7XS1WyEDcAna9Wz+J6zHMFQnt14v3/trDBIF8D9pEiYORrklqMuO6mci/zAqFg53qia7Cdo/eJChRCpGt3GHp4WYETrRM2xPb2tWc4ix5JpBUvL5cheJx19F2L/Zbk3d7Crov0XUfftvTtVSL49v2TgKAPGmvmeHOIGR0hb+mkK2UoygOMKYjq2z9vJNLv1jTecuYcZ08+p29uPMCD91ycnRBDAvT67SN8CLiQNgtyALKl9cm/0j3wHlbrSJ4L6lswAyGSZrrWMK3T8TYtbJpAGT0jHGWemCdfF3YYt6BHbDvHervCbiF2kKlueC1BN8jFXnnMSykoipzRqGQyVsxHhmanWSl1/T18W+KtSNhET7BbAjlRZqmZ6SPeWYL3xGjRyqTZQpZsIQURoUZEWeBElj6AEFExoPwO5XcQugG5m+bTUQToLxHGgghEZgDI2KPoUwvFl9jg8NFBaPHB4oMlK1VSTNM9spAYmePaDmIg4pFCoxTkOoHORAwoFRED3Ud5Td86lssdrUsiJp0HGQwEBdIidEBrT9/HYVYW2K7PabdrKtNBdHhn8TZ5XmvpyY1GKoOTMQ3JlSZKDSJZaHYOgs+x3ENmH5Dld3BuxHhSM51NmZQLggsslw29qgm6pBjXjEYjikXJ6P1AEAF2ls2TZ/jtFtVtMaJDhYblw5/TrZ/RXj7Cby/wrsXaBmILMQlhCCNQmWAyrVF5hRpVkFUEMpplsqMU/ZZw9gJXj2A2ZXv5jLIs2Nuv2Tt6h3q6YJLt0QRYe0+mB46zB+cabCtoNjtUtGghyU2OFgLlfZKJRRKDTnrRrocQsdbR9ZbG1TRW8mJTYTcC0QeKIonpiCBo2h6jJaVRCWMQPJnumVYKouHBXkHfdrw4b7HWvUaPeAD2DMVGvCEwDPfKa1aDRAxjFq5R51cEsbeM4vs7j7N29tq0VZsti/IbQGlCMD54wMj16Pg5fiU4vdQ82d65xtvfG52gXpP4p5MxdV3x+Mnzl/yTr+LIbymi4xMzJ9w6wq5pOX/x/Dut50/OBaeriJbPUL9lx6QPmkd2wfToX7O/932+muw3ruKXm+8hft0Bz7/1dROc5flv/pb1xSmr8zOWp59d+0rfRHoyKeBHDwJejnnRPXj1yb4mHtwR3Nn/yif91V99h7z8GSc+cK7gT34w+UaA2sXS8uWThjD/AQSH4JTz7oB2V3G//hQpUpJ+cXLGqCo5vnP40utOJzn3Zc5P7hiykHGxq2ia3VvVGn8rEraUSXjDOouPAe810arrHVAg8Yyj78CFVHHEhEokxEQLcT3BWaT1SDokLVo5lAKlNV7IAYBkgIiMLTFu8YOXs48WGQOaTZoXhoAXQ7Xte6x3EALap5VXyIRmlwFUdOSGYTYqUqUFqBAw0oPwKJla9i6J8RGip/c9OqaL3/skbylVQCgBUeFthtIGqRVBeGIQuKDZ9Q4b0h8GnwA8V37eMQailLgAfQisXYZjhMzvUeb75MUEU4zJ8zLZWe4EEYOoJhRljcwLsllJfZRRH4GzAdu1NMtL/Pkj/PKM9uQhvnmGa0/pV58hfIMMHXWRYUaacjQhyyZoDZlRqEyjc01VzzHFiLyekdcHKD0CMaZpOpqm52LV0iOxEuajKaPRhNlkQZHNkZREFFpGKq7oeRHbe3abhn61xu22KJmU6YTS2ABt74kqA2FA5DjnCd7R7ZZ0nafdRTrnsF7Qe0fXAz00XcREiYkaayNKRHSukANlS0mB1pHMBEwmyDJBWQgKo3E+stumL3liKw6zOnF1w6ARcFWFD5W3EDF1a9Jf3lpo47X62R9CaOkYmR1b+93VqK7fcn8GbglAKy2nu1QhGaOZTMavnokIu8vn9E5x1u4BKUnP85t26rIfDz2LyDRbX1ftbzq3eXTs+4byDWIqLx/wt4v9SWRSpg7c1Yz16blg+y07rheuxpo508Ofkg1GG68/LsH770yYT1L1O5+83FaIwOr5p7SbS7arJV2zpG/XbM+fYLsW27UcjjuKQjGevjxiKIuC0ahkVkdUNoHRu8jRAqFvOPYxRE6enuCdBwHGSIpc8M6RYFq/+bABnp5Elsur6z8SguDxs+ZaVS1W74C6JWUaPWL7BReXO16cNsT1h0BEdA1d9/pkO+A4X4qRaYhFkzo6WjOuSsrM0FvLcv0v4zL3TfFWJGwhBEYnhLb3NsmKOn99QgMRQrLaVKmJQYgqKVQFi21bfN8QXI+wASk9WgUSz16h5I0wihcJjiSjRcSWQIYngyCQMWJEh4wREQRS54n2gMAHjxQBGcQgb5lazjJ6JEmNTAlJUskc0KnSp6pcMsxQRdo4DO/KB3e9UFwJZAkZkTI5agUEalBNiyREuHUiCZ/4iJJxAMTFAR2dQCwB6ALsXGDtM4IaUY8OUGZKlo8YlRVSG0SUSYZPGfRkTjap0GWGnufkMyjG4BuP3bV0y0vi5TP8xVO6p7+mXX1Gv3tKiC/IjKTKc+rqkFGdM1uMqSpDliuqwqCzDJ1lFKM9sryiGk2ppnfJignZ6IDVestqveXzL5+xaz3bLjItSspiRGkmSApC0GlTIgRGMmihh2tltH7XQu9QKuKQ6JCc05wTBJURRY5U5XB+HNt+TdtKdruItUl8JuKxLoITdDYhyhUKXEhNEKFhQJYLkc6/lgFjJHkmKTKJkjK5l21ft8i/Wnmn/yad+6/eff2riC/Nxt/2kCKSScubYWZviJi864GUrLtkDmKRWKGSImFumEzG15xjKa5q3ki/W9K4gp3dJ0SJlo5plYBLAcG6qfEhtTlqs0sOb7dCDbKiVwh2EwN7g3piLyQ6husK++Z7/N2iymFSDZS2CM7D+Tpdb28KOzBfhJBsQkGQMxb7P+BNWU9KQWYkdw8q7i5e3TQlXI9lc/aY9dkTzp4/odu8oG+Sv7UQyf97MZPMpjn7Ry8n7Ml4zGya9NhNOWa8OMTsfw+VT4YDUHgf+L//7v/Cdgmb1GuJ1rA/S0dtXUxr/q2IEZyLnC0jFxek4kqmte9sBVffiBDHoCe3/tAhl1+yXsO2FdCeEWO4RqhrKa5tQ6+kTlOuSdLD15KnoqVSFqUkWhmKDHSR0/Xd/5+wb0eMgMzISkXoe6yHXd8iI0ksRbZczfM6r5OxhwfaDcE2tJunicOsFeV0QZmPKasRRZ4Q58SADYE+JjtLmeBoZKJDCk+BoCfDx4yNHawAvWOat4hMIMoS54e5jPKAToussSRUWTL0UFKgMjXYfKYZtg4CESR9eiRIkDomBbUg8d5jgwcXUYNVp9dpZmp8oo31UeFizbbdcbnaJG51iPSdo2MwJSFxjJ2PbDtLR0YrRujRu5TVHRb7xxRFRZblCD240zQ9Kh8jyxqzv095AGYEukhIdXfpUf0OLl/gH/+azcd/g1s9RtovqcueYgqTvQeMRwXz6Yj5bEaR5xRFhtIKKSVGRcSwYZFqh4wdulnjwhnBlPjVHYp8j2o84+5P/xwXPZ1vefT4nM12xbNPX6Bn99D1HtX+HjrTKK3Y2ci29zzd7WjajtB7pnqBzhXaKDrrsWQ0jIniCClLKhT1SJKZSAwzvFjj7AqUJPYdzXpNiImms3Op+pVSgLNIIWidIMs1Ughiv6RtFX0L02LMXh04nPbszUZsG8vzszUCxZVW/bX62TDUFkJeLxTRh2s3LzFUFYKAv67I/7CiD5plP/nmB341dp+ATVX1S5ri2T5U73FYnZCrpIZ20U1pXc7x6AVC3Dy2UB336qecNPu0ruDx9ui6e3FUnlwroX2V1S6l5PjOIev1lovL5SuHZmLgA3vBFRHwUzP77u8PeHwmOF0K/vidwNlK8ORcfK1Ghw/w8+ceaSrGkz0Cgm8SA13MCv6LPztC69dfO83yBaef/XvOnz9hu75gdfI5d/cc+4fpQKp6zGx/n7vHC4o8e0Vh7LbIj202XDz+kIntMVVK4np8jBrt85P/9C+AtL7/4lPJZhf5x1+m854ZwZ//sUDfYlgtN5FffRrS+ZAFYf7T179B8RUKndCE2Z9TTSPFvfT87eacF7/5ewBc0DzavM8sP2OeJ/e0pm15/OQZBwf7lEXqPixXa84vN4zm96maSzh9wh8tFF2vePjs7ZhkvxUJGwQOjUQlsS4RMMETHdeIWeeSN/Fu12JtoLceHVoEPSiNNAUqy1F5jcoLdFampCEiIXTEmIw5bPBIHDIGUJLUKXc3FTCpUg0DeEtGkDLgBvhPEAqVanSiSFKg1xU13HggDmDgBEQTydZxAGuImCwvIX1ZrQNDAo1pBfSJZC1FxA5c67ZP3rYxhmE5j9gQcSHgYhKeSVxrj5AjtKzJzSFFfURe7mOUQWIGlLQCaRDCEEwFWUksFE4GIGC0RfoO2bU0z79gd/6Q5tHP0P0jcnPBaAyjsqQsNPW4oMw1dakppMPEiHI9IiSEdJAhqdUJidA6obKVIcYNwWapK+I3kK2Q8gglNbmUzMeGXEu08iybMza7hqUV5OOaYjJm5yKtBRt1cmVTBV22wBmNNAbrHL3XNL7EbQDfor1nWmdUhcQ3eZpNyYA0gohC6golxygCSlqIyfSBkPiotvfJ9UsJQnAE7/EOjNRUJmNaZeyNK7TqMEYQ/GC3ek20Ftd+5DcCKQOU7AqXMST0GIeKWvBSa/wPIqK4Vi58U3Sdp+0czW57MzfuejSRg5lPFZQaqkM9BqFTR0umM5eMaxTrvibXHcU1qCiiiIx0gxKBra2uUCxIEVDi9dlRiNQhe50bF6SPUA04g/Bb9DoyHTmcwWp7k6BD/GZBLSFgv5JYKfFIyul9TPX1CmBSXJmuvBwxRjbnj1idfMnJ49+QxRUTvSObWOazguk0bQXyosAYSd9bpHdMQsdGZjgk09AR85JQXqHGktpit73A9UmIO0eRDVweoQtEPuZgL1IXltBestpY+kbw7OTo+vME2LXpe3bz5l9NTxF4/GJLM7S6D+YFszpL14dIqmwAUt3e1iSd/3jrmrzS/YhDpb3d7Wi7ZDYlhKTKJMdjyXQyYdP0mOwM59wr3uZG33ije//q/b/reCsSdoxpNiuExhiJVA7o6GICePs4AIR2HeenO7rOJZWuwmNywWg+IivGyLxObdZMp0pMJrlSaIHES+5CStZKRjwahSCTHik9SWjFEAcyVwwKIT0SS5qIJREXiSLh0TVCBKTQSes8QcZBiaEwSu0WJQTeJxEVEQIEn9r5RJwXdE5gTLLONDLJZgYfUpXlwIXIrgk4l+anMQ6a4CHS24jzyfgjDuh5le8j8z2y8j3Gs3cw+QQlDUSD9waXaaQqUGqEy8eQ54RS0kWPd55SdAi7RW6WrD/+GbvTj9k9/XsW85a6hsODino0oixLcpOhhEeLPgH9fCD2YXCJjngZruf9JtMopZMQiU2zem8vifYEacb4sEGaCTKfsT8tmI6hLBsuP3nO+VlgvcqoDgUTPab1ER8kNmYEVRBMpBVz0IaoDFscnRM0O0F3scQ3DX63ZT6tGI9yZrkmxmLQmhdEaVBZg9EOIxSZbxBdwNk+8fRlpO88eZ6K3hg8ziXefCY1dZaxXxcsJhVaJ1GZrrlRqb3ZwcmBF8CN69N1khBINYhzRHlz81XG/sMrtl+Jq7nkZmc5u9hx9uxJojUNc+SqMOxPA9LsQXF8/XdXZLjbEaLkvJsyjWtyOeiQD5Kw42xDETp2rnxpof7Ox/tb/+XLkRl4ZxH53Cc3rq/gDt8YUsCDuWblFS+cZLT44TC7fn0I8XIFfEVUSYnVcfHo15w//ZTnX/ySH9yNjCuggtn+iNHkdps5slyusNGycEt2asxOGvbtBX5+iC9uErYA2tUt3+8YkD5toGS1h85q7h1AtD329AmfrXacriKfPNxHSP2Nl/XNe0jx0cMlJ+dpVPGvf7RgWmfcnE0xEHNuf6+u7pKk4dJX0eue84vlS6j2cS55f18xme4TjKMontA0zUsJWSAwWRKJAWib8P+RhM0AfEIiVIL4x1Bxcb7mctnw+NkFq8sd6+UWHRuKXDKfGkw9JRtVyNECZXK0zsiNwmiBUiFp2UYPkaENHmn7PgmTSHDeJUONWCClQ4lAUfhk1uENXiaUcwipOo4+bYlDFEQh6aOCqEFmSO9SIrUBnBhmTundIZNxh5ZgcAifbDYb19BahQ0KSgMyJeMYVHLiChbvJd7BZtWl1p8wWOexPvlbRyFBklTQzIRoZuSTP0XmB6jyLmW5j1QFMRqEzkFl5NUCLw1OGPSiRBaaaDyFXGO6Fet/+pDzk09xJ5+j2s8ps4ajdxVHdw6pSkMmIfaOsNqxbdtBkjkZD5EAACAASURBVDAgBvqLjgwo/JhwB1KgJRQlGKMoqhxtHMqAzrfobIXOKkxvidmYkM8R+QSkplaCxXhN2zl+/uGXZCvNOBwwnkikEAQfiW5K8BXrDrpW0gfB5szRXF6yfvaUuH2Gii2TMiLytIB0LuCDwgVNcIk/b0xNNZlT5JEq7uO3T3DrJ9jLHuEDrYPCWiQSaTSIHh8CmTZMRwXK1JSVBhF49+4ej5+2XK7s0GkZui0Rooi3tLIE4orbNfyeevFX87YbvvYfSoH9pji7aNlsWi5OX/DkouXpRcOBeE6RF1STQybzPfIi5/E2Mjdj6gHDVOmGWb7CyNcPejd2ROPTgwvVsV8koJkWjuPqOct+zM59dwDcTho+MS+Ljez5hln47v6LTQdfvBB0NiknfvRIfisZ0igN3d3/FqnGLFDo7M1e3kLAf/6nRy8BzJrO8dc/e07Zfc6o+5yLJ7+mUFt+eD9w53hBUabHqltiKM12y/oyncM8ywiLPc4uluyahrPoqfpLKpva0qNgOfYvO6zZ3YZ2nsYK+WjOyA2IulvJrLOBv/rHJ9w9nPLDd1/lzd8OHyJ//bPnNF0qvzfNy9dB8JYXH/895eSA6fEP+Hc/f4EUgb/48X/50uOas095fPoRd8pH6FvXkjaau3cOWa7WXA52XVlRo01yCCtyzfsPvs+Xj7/g4vKcsiy52j7e7hD8S8RbkbCTlkQyamhbh+s9m2XDk6dLzi82PH1+Qds0iYxfBbTOqOqSosrIijyJfMjEqxYDrtx7QfAJqBZDSC5tiESViYOPtogIIXFeoIVEyGS1qZQi7ftS0rmqjuNQHTNIYvoAyVAktciJgaHTDSJpag/v8Pp9KhUJ4qprYPE+Jt41mhAFfuD8xBgZkGTEEOg6i5AhibX4hJyEpKomhSSqCvIFMT9EV3eRZobMxgiVDzKcOVEakIYgM7zWWK3JS4HMAsJ1+PYFcfeC9stf4S4/x68fMqu2jDLBfFwyyioypYg24K3AdRK7G477mpaU2vtiSDYieJSIKBGI3uGMIHqBMQKlI1kFobcEvQV5gcwt0gVk8KCTZ3auGkrjaDcNcWMpmwhlOuXegu8kvhXsNp4+RGwQbC82dJcX9JfPGekdhfHMKs00j0yygJaRgMRj6FuL80m8R6oppigQ0SYwofB0mzU+driQFo8QA2rQ9o4xImUyq6mEJh9Q4oezCZeXkfU2DP7o3Gzgrq75KG5dHSlu2uM3V04CpIlXK4Y/gGi71LWxtuPZyY6LVcPp8xPWTY9re4pxQ50LJnVgPMkxearcxK2WZiSJGnX+JhFJEShUR+dzfJR4L8lVTwQaV5CpHiUCmbIUqnu5Y/GaiDHSdv1LXtgB8Qr3eicNhhvw2Svv14J1glERXzLU8EGw625uaL6FMVnI5oRsn1AcIVT1jbNrIWA8MtRlOubLTc/lcsvq+W+w3Ze4/hHKX1AVkfm0pChyTHZzTiOC1pU0XtANMss+5pz7KdsQ6YKhBXpbY3cZhdqhsey+svOQ2xVCCEI5wipNt0q2pFIqdF6RSUsme5abjvn0DXamIXJ22SawrY9crDvaoQ0+0umayes5ZaERiOSXPWw6MiMRMRBchyknaJPeo20PkM2O1q/I/IZMdantLyV5liXQ4RBGRYxxZHkqAA/nY87OclYqyRy/7uOXUqFU/L3SwN6KhI0QCC0JXnDyzHJ+tuazjx7y+ZMnXKyWBLulHgkmE8nB4YS9/ZK79xaYfIKQBheSxaXE4kOktwLvRFLbCql6yUxqg+c6Dgk8IgczmV6AlxotIwUdSiiMiigGJywLzrph7mwR0UHw9C5pfquY+LkxMHxY6eusRIBBC9379IVQRuBsshFt2w4b3WCDmSFkEoGJw6YCJxGDG9eua1KCkMlU/kojW2tFkDk+u0es3kMUD9Cj90BkgMZ6jRKGohxjvcKhcFFjtcLWkr1xQMced37K6pN/pH/xG/j8r8jzLUXVcXx4zLSu2avHxE7gtrDrHJ3L6b0mdBOCVASlCCKBy3Ip0TLRjkUIyGBRWPrdGi0dXWvRwmEUjCYlUjQIuaNsHbpcoqsVpl4jswKKggxHpST0FtN56gYql+b43cazu+hpNh1nJ9vkIoZk9flnxOYFuvuS7/3RfRazmoNxwbgsqLKMsioRJoOs4HK14vxiya8+fMJ8cRdlDvHUmGKB2TtmuzzHd5f0oUlGLlGQoSFKgk8Ls9HJFKXMMozK+MG9e1wsYb0LbJqOYfdFHERVJEnMJZKYAVdOTSlhJ7OxAbt4C4z2hxenZw2r9YbzF8/4+MWOF6uG89OnvDMRvDeXfP8oMJt59o46qLOEevxKNK6gccVLty3Kc6bZmsfb42HeD4viHBc1z3YHHFWnVDpJZdbZljrbfm3rNYTIyckZ/htampey4FIWb7z/ZCk4Xwt+/CAi1Rsf9q3CTf8Mu/eXv/Xf/+LTCx4/ekz55f+Ij46thB8/CMxmI/YOj155fIiS5807+Khhki64HthtBZgHXO0YemC1i9yvP2UnI5+blyvkO5s1++szmnd/hG3W2CYhrHVeMbvzAZNsTV9ukLzzxmNvWsdf/ftn+NfYXN4vnrOYau7++EdcEfIOP/hPgJRHf/rDBe36jGcf/jUH7/+Uei8ZnYz27lHN7vDkFy2Zf8qd6hEXl0syY7h7/PKYoVQbFvljDo7v0oYK70ecnFRcbN782Wd5Rgya3XZ3hUz5ncdbkbC9jXzy4QVPXyx58vAFu+2GdndGNhK8s1exV40Zl1CXsDiYU5QjtBkPeMm0q0lMqEQLizE1wImSEAJd7+h9RMqk2yxFRKlIppJspyVZd+IjVqQmpIwuARNswHY2ocRxSNGnx4aI61PL3EWPGqpv7xxhaGJmkgTACdBaj/UxJZShXS6FQYtIFIHoBkMQKUEoImFQN0tzfOcCQoESAkEy9ohC04k5Xk6gfB9THKPzfRA5HjU4dUVEsHRuQywnxMJAFVEjyGpoHn5IXL/APvuIcPFrZPOCeuypR1PqcUmdH2BEjrMlLuR4oQlFhhSGTBiIBVKpBPDLFFoqCqXRKs3fCKlVLqKHsIXQI12L8B0QscYg4xbFjq7d4nzEuB6/u0Aag5rV0AeUNdRZILMNXJzTugxH4q/2XU/oexQe11l85/CbxxRxxX4ZKZs1IbScXfR0haHKNZPFHJVniCyjXbfI1nNvPqHKOmI45/JsRV4YquoOB+//JX79kN2T/0BEpmqpCTgfQELT9bgYEAYWRUaFYduXTEdLxqOW7a5NX+BEAb7OvlfmIy7eoMdvZ5XBriRt1F5D+3pbw7rAlw/P2G3WfPhkw3K95eLsOeMs8E4pee/77/BgEXhwEJhUkJUzGH0P1KvJ+k2x7mt2oiJGQaUbarNDS4/zry5pr4CvgMvL5HR3fdstOtc/J/bHkXF5A376rnHhaqyeMjn+V8TizbPqr8bxouK9uzW/ebiEbsm0/w3doyfky3NE9GTlhLIaMz/oUdmY57uED9Basj9PnGaJ4KCo08xfCOLoPaJ6fYISMaC6DMKrBHKnl3R6R/zKjsXbntXJl7i++a3gGLXecSc/o1QtrpOcfPKPQAL17u8V185hAshdz9GiIrtlciIApGTvwU9QTQWrR+l4vePk9Jz+1vXQ+BEn3X1mUaO1YbY4YDp5zvh8xaZpf28J+ZvirUjY1noeP7rg80cnnJ6cEnxLkXfszSZM5wVHY83IRKosMt6bIXUBsiCl3zTvY6hWXIjIGJACVLxqp0WsT21sIxKAKCU/QEY8CTAmY8QFMQBXfDLY8AFnHW7giArlk3/1dZvcDT8xtcSDxw0VtvdyQCGKNHMOV0AIBahBZzct+iJGRBxUv4YWe/CDz3YQqfWKSG3/mNDqPmY4OcHLfaQ5RJg5yoxSUo2SQcocQsQLj6oVMs8gS8h45Tu655/hLx7inv8SY5+g2FCNM0b1jHE9J8/maJ0BGagSITO0rjAqA6UR0qCVwhiFyQ1aSQqdoRSJB38l6xUj0TVE3xO6hth3aaYlVfIFjzt8fAGuQ7Qd4JAmEkyT0PXOo7Eo1xF3G3ws8ELjbQY+InxAhYDtW+yuIfYXaLllJD2iWxO8wIoeGw2WDO8yhOyRwiC8xwjJvC6QoifYNX27g1gjZMXs4D2Cgu78M4LoU/vcOdyAAO+tJ4iI1oLMaKQwlAbKPKfIkkPQSxKkUdzGmV3HS7fFq9qBYZBzVZ2//WGd5/GzC7brFSfnDZumZdtZ7ow1B2PNZD7h7sJzd98lFz2zl6hb3yE6n13/W8lApvorWChaOgQxgVmjegkdHkJqWTZNS28tSqmkZfCKOt3Xh3fu2p3qdlQ5VPl3/5wCAh8lu5DTM6Ya/1Fy3PuayDN1Td2a1IbZOOcXH35Jv3xC1/wSvzkh6xtkXlJUE8rxnGpi8aKmbeeJSqsNsagwyqOE5yY9C+LeYULov+GI5eXFaxO2yiZgNsgrKmxIbe8YHP1umdZUp79Dyouo2FLENRNxBh5clHSr5wmvZCRFGKG0Bqnp+vQVqicToorE0IHM6V2gd4Fqcog0W2hm4DaE4Njumpde0UXD1hlC7NBSYLKcIi8o8pJt2/5HUx18KxL2xeWaf/s3/4DMA+99cMBif8F77xwym43IjaQ7fUxwPTE4inqB0gaiwHmJi4omlgSXEqeRlkxGchNRKgmyGKMTotcHNts+8YI1mJEkk1DpHiUSWn3Xa4x0aOmwNv100cJAuRFCk2bMnuBcShB9gx6KIxkBnQBRXRuw3mGDw1p/rZMefKrBTaYGjXRBYZIoC16gMeiYqn41mHiUQg8tVEEkow8lqzAn5veR+QFleYjOpwgzwsec3sZBEU0hs4zRwZz6+IhiMsHaHd3pZ6yf/hL7yf+Mak8oxI7J3oRRvc/+3vuMihlVPqWYzMnzjDLPyEuTlNwUaCUHta88/V+RkPlKorUZKHMJl3BFf0Ooa41279yg4w3CW/A97eVDQnNOv3mGUJIQPe3pikbsaF3Erk7BV/i8ZpzfAWMwZcauy+lCoG2WxIsT+ssXlP452u9oL3sumo56kvHDn7zL/t6cyXhONt5DqgKpCnonaNue89MzlptTOifYn865WF7w8PlzsuqnjEdz7v5FzvKjv2W3TDM54SUBTdc0SCMpS0OeKSQCHRyV0dRljkQMQEUxzKTFdeJOgMg4oOpv5+9bFC9S9fOHIk16sVzzv//DL6jrOT88HjF7sMds8RfcWVSMywibnyMHq1zqH72sWvVbxKYfsbUlx6MXFLrl3ugZQkRc0DzZHjHLV0yz1JbdNQ2nZxfEGMmzjDtHB5xfXLLefHuZlxgDp8+e4ezXqJ18x2hCxjO7R+RawfYb408+mPO945RQP3m84n/7my8xn/8PqPaEJYHR7JjJ/n32j46vKXFyWpNryQMkYfonRDMjSsGd8ROOqtvyrwK9MMjsTa0CAfFP33Bfoq7ak49oVy/YnD1+6d6L5ZLTZXitDOzrX8lzsPs7TNxxukxfgrou+eDPvs+DuxUHexlSCmS1j5rc42cfBbZD/hXr3yCbR4TZv+LTx2t+9fkl//Vf3mNcvUd4798gHv9PiN3nX/v6ru85ffoErQzz+SHnq2UCM/9HiLciYRsjuP+9mslsxP0Hx0wmIxbzMTJGou3xfQcyonKDNFmy3YyknmuQCJeUz3wAFTqCgigFUWpi0lNBS0nwgd4mGU8fI3YQN1HRDZQtgYgCooNgUwVNAhUpodLS6ZNQSvQWgif6iLNJxluQ5FKlTK/fxuS9FEjVtxg8jpUMGBUocn3dWolCYGPEuWS1GaNHRI0LYCODUUYSfNtFhRUFzuyj9QxlxgiR44LCOui6DhfSAl9NR2SjmtmdowSUWi/ZPv457uJj/NkvKP2GPNdM6mPG8z3K0Zh6fMy4qKmLEZNZTZ5rqlKRFxKlBUKGZLwhb6RYpRBILdLCoJN0KyJpnt+ko+TXHXD4ODiN5WW6L3h0obHbGX2WYzdrvO+wAXpvsbYF0aNVT2Z6QmgQXqAwyOCI1tKtz/DNJdKuyWRAx47oL5iNJfO5ZG9sGOc5pcrRMUNGmZgBmaLSGVl+jD5bs9l2WNuhfIOODc8efo5dVOx//4cwekzoA7TPaftAs3WE6Ci0ZjLSEDzOx7SYD50eKQfe9a2Od7z+D0lEJd5qsg2ZOQDXRiLcVNxve+RG8cG9Q6bTPb53t6auCsrRiKrKUMqmXe01HvPK8ua7RaUbjHSs+joRi6JMnGzVUZuE9JUiMM1uUb6AzBimk5uqcbla0/c39/ddR7fbMZpMvtbj+pqS98+MGOHZJtARCUZQzt8jGy2G8/L6KAvN+/fGdL3n118kdbJnTx5x8fRjFv0GLSV5NWM03aMez9jfqweDDcHdo2qwHRXEYkNUqfod520abd16bWUMvPEwrjqFb3pjEjU+IhOKyjtOznt6mxJ05zaE+N3cryQe3+/o2zX3747Y3xPsFSuqzKBNhRotEFmN1IrjQ0lv02dz9mzBbluDkIzklgP1gs8eltw9qvnpj2pO14e0voXuGRcbyXKj8EFQF4G9iSczkc4myWe4UUV741kRgizLcP73Y8/5diTsXPD+D6bcObrD4uBuUssyis35mrZx2LZBVxmyKBBaI5RBMkiPBoH0yXLThYgOSVs7CoXXCkGieGkFKEnrBJ1LPtU2pDZqFt3g4jQsicESsWkzQPJTNUInfXEXiN4mXfOYWmneXfGuIz7E1A4WYMOQpEkznytCoZIedAChUpUZIAhJHx2ts/jgB6tMM/hkA8ik8BZg5TXelAi9hzETpK5BZLggcT4pmEWhEMZQT2uq2Zy9xT67szOa81O2H/89cfMxavcRo5Gkrmr2D+5TThbkZc14dMCkLJhUOfNpTlFIygryXCAH5TIh0wIgzI1ZRRxsd4RSiIHzGF1/DfxL0hOBGHqC0ERpiGVBFMmcJRNz+u0UtGbZPca6Dc4pun5F3zVo7TGZoygsMe6IDiQ5OEt0Pc3m9CZh5xETe4Q/Zz6tOZwXzMo0X8+CRliJihEtekymoSgY7S9AKrRZc/Z0iQo7DFuePfyUGO/z/Z/8hFB/ROwaRP+CTR+43CSdudxoprVGkBycnO2vKW1SiuQaRkgmNDHeZrhcj68HMPkVhPza8ON1HOS3OYos48fv3mEy3+PenZosSwt7jKRrIVx1D745Wd/OibfXyso0lKplY6skigGs+ppSayrdJAEVEZjlq5eeL8sMWZbQU13X8+TZi5fut13HermkGI2Q6lW/6t9m0xTekNsH2AxP15GgYDqFcvYexeTeG59LSkFdav743Rn/9KvnfPZ4hRCR3enn7F78nJj3mKKgnh4xnu4zntTszQqkFGglOD4sya+r5jWIG0qWkApVH6b24z83hETVh8l/Pjh2Z2s2vUsXuT9B0KJkolN+XcQYEtMnRqLr6LYnHEwUx/uGabbEiJoQBarcB6kJ3nM0vzlXXbdPQzrXY73jOH/BLx/VZFnkeFGzHh/RNg76Ey7WkienCiEC4yrw7lF63c5ebZiTJPGrB/nSG8dkGbH//aDF34qEXZYFH3zvPrnOiSGy2/VsfaA5O6XfXuJcTylqcjOja0kJWEKmoFCRot7RaUfXWS4vlzS9oOsUdfRkmULJRPtSQjKtBJ31tF3ER0NjPaH36CwBRVS0OOfpXSQzBi1INA5v8bZju70gG1C8UoRULZvk6R5iwPoW16sETiKQSYGRCbUefAK2SBXRKhC9p++hs4J8rBB4YkhiKr2X9ELQhkDrI1oKtr1m3Wri7D3IZkg9waKTf3cjcTi8cJjCUE7HTI/uUtUHBB95+oufcfnF39GcfEh1+UumI8V8b8HxvQeMxhMme4skhJIZ9uuMcakYl5Fq6tCZROcamYkBSZbocGnb7q4GsyQXcQn0ac4fAedIrCSBwKQdqkozdEQkig1RGBCaKDNEViDGx3QHGWG1ZPX4C9rG4H3Gn/34LuP5Pab7x3z06zN2TY8TOR6L91vazRPi5hTRXpIvesbZhtl4ycIEyj5y9ulDolwTRY0UKond4BmVknI0Zu/uD9iv9xgf7hOB7osdbrkGu+Lsmeev/o8x9955QHU8Q4hzts++4OTsnEWhmE5z7h1N2a13BG8RwlLmhvGoIssMXZ9AasFLAomLmxAGMYEjY+rOpFMZudIPh8GzK1md/0FENSr40Y/uI6RE30JfnV007DYrsuVj6oPvUx/+MM2w3xAhRJ483+JDOi9HBxV5dlPVKRE4Hr1g3dcs+1Q1tz7n8fYOi+KCUn93D+OqrsnLEqU1fdtxfvKyl3Q9mTCavJkL/dWIwGfPBNvu1UT/fBN4vI6U4wOU+ibSFiDgP/vJIVoJ/te/fcTJF//A7uxT7mZnTPOS996ZcHDwPSbjijtHqUMgh8304X7G0aIgMzfHoWf3EdlXfS7/mdD2r4Qsp5hsBJeeoNbI1a8AyHXgv/nBCWpcAos3/v327EPas484OlRMR5F3p4EqT0Jaj58+R65KRJEhfvNPL+3o6krz/v2Kdxf3OFpM+PnHgwEPkT+qv2T34pL/7r+3/MX332F/foRE0H7xc9ann/CDe5G70wX3jt/jxekZ7FLi3ddrpLngE27rKCRm0Ff9sr+60ftdxVuRsKWQGKORRJzriUEQrMfbLcE3SGOQyiAwSRUpBKRRhMGYQ+tIbpLCty0N1kV6J2h8wDuB9snAkEHoXUnITGpViyiIXhHj1W66I2UUdcObjTH5cscbu8MYB7S5BCWuuNFheGh6bIiRoFP1H4LHx8TpVsNzOpuSs48kHjlJwtS7JFkYRUAKiQR2vWTnMxpRkaspUs1QagqyIoocHxRRpnb8eDalHM8oizH9dk23WXL58Bd0Zx8jto8YF4LZuGZvb8F8fkhV10wmY6rCUBjFdCypckFZQJalDbdScEU3h5uq8KbgGP4RA1e2msSI8DaJqhARIkvnNl75zCZ7SiElUSqEkiij0XlGXo5w1iOzktCkccBib8RollHVEaMbRBT0bY4oDEYFlOhxsYPQYoSnUJZaW4Sz+M7S9I6tXdPahqbpMHhy6TmY5tSjCiUs2fwAUU6ZVPtcZGkTpVUg+B3Ly2eMZ/v4UhBtTR9yhFAYDbmRFLnBtYZMB3ItqXJNlQcyk3jC0csbyc54TcMm+WxfncHIte748Pur2kxvd0ghyLKbpcX7SNM57PaM2J5jMoPSOcj8jc+RKYtSjiJLznOCJGcaQqQsNL3P2A1nRYrAyKShpQ+K1mc0riACpW6x3mCDptQtcvCwb9uOrnuVDC2kREtJN+g++K/MqfuuRW7Vt5q/dha2naDpBfYW3ThEuGgiW69BaZQymLwmGx2gzMvzfCHhzn6FHkjdvfWs1g2nTz9htz7B2w154RkVmnpSM59PqeuCosioKz20v2E80rcq6+G51SCm9C0i9ts0Bvy6kBKZj3kZSakQWjGdBSQ9q6HhoSS8s5CIWsBYcHl6zqaNnGxz9qe3XL9cj+83RMYoAbkG27VsUQSzj2t7LGcsG4fCkQvLpIr044ypmaFGO2I2Y3/yHm0JF0AmLBvbc7q19MEQhKJpE4hXmwIt1xgZ0EZTlQVja9mfeqI19H1Fnq2IfRpdAoP8tMT7mxzx+4q3ImHD0CnB4d2G4CKxd8R+hYxbsnqCyQpEVDjbEaRECIOMCpREKkWeKVRuyDLJuvGcrRxrCyp4pIlDxRvRAqQKFCqpcgUvsCSdbREjCItQGUIYhFwTQyC4JPpBDEidDS2acE3JCjKy2aWELJRK1LKQRFR8EDgt8LHHhYgNEu1Sa3DXBrogCUJQKIGPgtDe6IcLaVPXIWqeXmoaXdPl+2RqgVJ75Nkx0RREqfFeI2VE55K7D95FmxrCiM+++HcsX3zMxSf/J7U8Z2xa7t3/gPn+AfODYw4WC6oyYzoxTLJIkcGoBmWGHy0HLfABujxkjniVVKK6QTPHoYUVOogDX901EGz6oUAIQyRHqCs987RooTLi0NGIUTKqRwgEq8ke643G94Hjw5q8Vsi8pShWbLaO3XrLpLiLMZBpR5RpppzLnkp1zFSP/3+oe5Mfy/Isz+vzG+785mdmbmY+hHtEZGRm5VBV6lJJUGpUjRAtNS1WrGi1kED0FgkhUO9YwIIVsAK1xAKJRUm94h+ABqGuVlPVVZWZVTlExuiDuc32xjv8Jha/a+Zu6R5TpkcSfSSTm5tde+/e++6953fO+Q6blk2Ts8kEj8+XnFzWPH5yRKkt89LznTszJpVie/UjJuMx1eQOe3/rP+ayylBZIEPShC3r9YccPzekmcY2Q5yvKNKCPK3JU0meptjEYVPBuPRMrKF2gTIX4DTCiijM0ZMFQohwM98nZXl9XkWvoN/f/JGzLb88GukbFp1xHJ9sSdbvk/lTZnfvIsrPp3ANkg2DZEO3c4ALMdE8OVojBNzdH7DsBiwZADBOV+wVURpzawua7U6vblZwt3rO2lSszIC71RFSxIfs1dWSpv0MxbIQWFycY16T0Ov1hvpLAtSWteDTk1cra+MC7587sqJiNI4mIkk5Y/rW335lWy0lf/DdHYqenvR//Nkznj19wsUn/w+EQKoV1eSQ0WTMaDrnzu6QtE/M+7sZ0/FndzC+SrjNGb6+/NxthM6Ru+/dnl308fBQsioFP34cF6pKCR7dL0nHGXIIf3n0IU+PHf/8gx3+6Hfv9HKjr4/V5SVee7rxD1lcXrJcnPCjJytK1uwlC75739NMCvwmUteyaszv/73/FHMlePqa17PW8/xsg6Wimh4gRfx8BTCdjCmKDhFOeF+MacKQUbUGtqybuEiUSpIXBU1dY+03PGGLyD34M+BpCOHvCyEeAX8CzIE/B/5hCOFzdX3qxvPTX6xQOjApNJkO5LqlHGikHOKzCdYXNLVC6Zw8k+S5jAAxIaLEX4irbQtQkwAAIABJREFUHJmWDKQl0R3LOlJvWhMr10R6yhwSBYkUpMogEihSzXXpKEQCTsb2o4uzsSSJ1K8QPN65a2Ys2ntwDt8ZjHNxDqsUXRvFUaQC3ScxF6JdppASZzucMZiuwQqBU5Kuy9m0jvPGIL0koCGZsN4GlrXgwudIOUQnM2Q2QKQZTnukjdW68x3lcMBoMsAtT2jaJ2wXG1ZPf4JdP2en2rI7njIdlBzcf8h0PGU2mTGfFJS5YlxBllmUDiRZ3E8hIpiHoOCa3yo8iOsK2oPv+iTtIk0r+Ajau+7hOhO/fAckIBJQJULHFUEIFvwWYXJCVkOIi6wsy3A2Qem0z24GaZ4hfYuSHdNxnBUvLldslyd0NuLnpXKEpEMpi1YBlUjQGa1XfHp2yQdPFjw9XXN5fhGrZx0V7/bHKnY09BVOPWf5N/8bz58Grs4c4923KJOMQaK5ODvhrG1Yrj5mNzfs7E05nFfMBjnSGBIZyFJJVSQMOs2o1cyLGWlo2coOG5YY53r/8tAjwGWfpPvJdRBxISOucQ+yn519/XX2G7mf65af/PgDhBCMpjNybTmsniHyFCkOEcPvsmoSVsv4YCwyzXRyu9JbdQO2togmPH3szoovHOZnqmW/PO2PJS5+humaQje3jD9m0zFt13F+cXV73zcbNssl1rxegetNhxCS8b0/JClnr/29dZ5/8eOTGy/ox7/4UzZXx0DgYJwxH2bs7N1hMqnYmVW8+7CiyOLKrihev8IT6QA9vINICoJtcYsnX7ifwdRfvI3rsBcfIssdZPEaNzNV4sc/iF7n3auOaJWq+fbgYz751NKEwedS7Zw1XJwe8/7j5zw+ueDocoUMlg+kJYiKw20HHDGcTJBK89P/+5/yyRPDxVnLeDpnqLd8e/AxH37k+UlnOfn5h9zbGXCwu898sGEwfr2UrZSK8WQHoVcoJVlv66hk+FuKN1Fh/2fAT4Fr5fj/DvjvQwh/IoT4n4H/BPifPu8FjPE8Pd6idMCOsugZW3WoUkVx9STDGh3lRr1Ch0gXEr08ZKRAC2QQaCFAeLJEUJhAZwONk9HdKsTKVYkAkh70ECvuWM3IyO0WMWH73jFJiJ53La4tLV6OAERJ0zinja5gzoVeFD685KwYedTGeaxzeO9AxfawMYG287TGk0YhaaxLqDvHpgNDSipzkqREJBleKyxRsEUCSZaQpTlpktGuV7SbBavzE9zmGG2vGBWSybBgOh4yGlWMhyXTYcG41OSZoMo8Ko3sK9VX1OIawRsgON9TGRzQIoKJkHXX9MnaRgT9dUv8+ux4/1KFraI8qnYQMvDJTdscWRNC1y8QJIoBSliUtEhslAr1K6RPkaSUZWBQSarKcbWp6RrXL1wc1nV47wjBIkRk51ofWG5rrtZbrlYbNnWD8x4vAuebhizV7HXQ2ZbMbGgvf8Z2UdFuKuQsoLRCaU27PmO12dAYixikFOOEqtqQJpLgLAKPEqC1IlGCVAkGeYFAorVibbb4LuC6OL++hRq/7mCIftbwUvsc+G0hz37z+9k6np1cIRBsbMKodJTDGqUySApIJoTWRhaA8TfJ6NZreI3xtx9PgyL0v/vs91bCvzK7TqQlkS8SsACy7PUVXAgh8qu/phlkawO1BaUTVE8tSatddPZ6O9IQ4PSywbsOZzasr45wzSVlIhlXGbNRyXBUMRkXzKc542FCnn1+K0YIAVJH8Kxt8O3q5s2caX8tURClU4QE364RuiC8ptUeXCDIBO8CpuuwXYMyDcI25KlgWCp2R3B22XFex8/Qe4e1ET/k9IvnineO7fqKq+WSs8WK9WbVY0AEZxtFkUkO67rHIygWx5+wOhfUK8loPCVRjrHe8PTihLO1Y1Ur7qmc4WhAnhUk6euvDwEMMoWwGu0Suq6jtWCt/a3QLn+jhC2EuAf8e8B/C/znIg4m/23gP+w3+V+B/5ovuMHrxvCvfnpMUJZ5MWZYavZ2JO+8VbIzz5imOZlWyEyy2CZYo/BbzXSQkWiJdQZnHcE4gjVILFoYUrYk0pOENLo6eYXtDFIHVAioJInJW9hepEDgQ4KXkYokvQMZ8EGgXYhCJsQkr4RAqPglExjpOLtuXYsJnk4EsiRyrKUE7XoerQisG0drPB5FmgxQacFiCfUaZBcgVxgvuFg0nK0Fy06TDMeUxZRhNcUVBUYouq4hR1FkCQ/eeYRwkmAcp0dL6s0xm+WHTPwZg9RxZ7bH3ixhOpbsDxw7c8n+nZwii0lRyWg3ipSIkL4AkgVihWw7sKsolBDWUX7NG/DRkir4gDeRK4/ruB54C6FjUgbQ8XyhVZSgVClCx5ldALzriOaIGT7ZQVhBzgWZv8S6NUlYooNEec/ufEqeZ6i04Ofvn7NZr3CdZ7XcsLq84F4FA2p84XDG0HaS1UZQtzWdiTgJ5yStD5xuPEnmuWsj0wBvEdtn+NUAczWiGy1Jhxo5qVhePGW13bL/3u+yf8eyP22Rq7/BOUdn+9PiQPDCoexgOsCKEicD3eOGy+WW5ux2WzW2wPvBds/5emEyZSOI72vO2G/sfu4cP3ka0cfi6YpRoSMfe77LcDTk/kQyGqYMqoTHR+vPe6lbsVNcRBrU9ssrgH3VKAcDiqri9NnT17bEf9P4dOE5ryWz+d4rPtOfF83yCYvH/4JAYJApfudgwHg2ZzQec+9gyOFezv3D4gtpRwC+XeFPf9H/70WW8d6xOP4oejB8xRjtPSTt/bDd5hS3OXtlG7u1qMs1y+NfYlZPuLz7S4bNmqpb8/Zhwnx+F7/zkI9+dAx1pOZt6zUX50csUk05BfrC19mO5dnHtPWrSOznZkJmJb/DGcvLS7arFXt379JuVyzPluwc3EMnMSEvnv6/rLaKnXf/XQ7urbm7cwWrzz6HSnjuJueEscOPNH9Fxfmq5nj5asfg64jftML+H4D/EriGTc6BqxDC9Sf+BPhsjkIfAXpbskDrApnrW84SEiGwrUPqQKE9uugISHAJpnYEKdEyJmtrPduuA6JueJVqtAhk2pOHSL9xmqhzLQKuvzCllPi+jatCggsxSUstwTmEjZKn3nZgLV64OA9XgkQrRJbhvMXZgHeQSgMy+lkL4qpdKB0VyxBYFyU407LAO4HddKzbGiMC+aiKKHEbuNg2bEOFSwqqckaVTyn0hJVJeulRQTndoyqHdC24dolrlzSbI0J9RmWXzIcpkyrh/uEu+7sls0nOfGfKcDQkrwq02ESJzGu6ggeEi4DvEPCuvamQg2n6Fjd9knaE6IxC8A5vG3AWb9r+5x4wvViIRKRpRK8lEqUbhMqQaQW9i5jUGmc7fLvEssRbTWIVChcXPipFCY/2K8pkjBpqpB6x3liEgMuTjLIY4tspUnYE4bFIgnQoZZlmlmUlMJ3muBWEXqp2PEwYDzVlJciKDJ15mi76iztnMHYLJkOZPHZFAmRKk452SPYSwuYDAgZ/rU7n4q2V6IwiFUyHY5wKWAXV6SXbrQc2L2wfX0Lw3ZjT4AB1QwN7U7zfL4g3dj9f7+r19yHAIFkwzdr+5SPdbTbObpku3HqdAJeL5qY1momSPAnM8isE4IK8QYd/lQjAarn+zBm2EILBeHKjZLZdrz43eZ8uosf1nWnotRjg6FLciHcANDZwtPSs2uv6VZAND8jH96Jy42fta3CsT/6abnNKILA/StkZl7zzziEH+2Om04JhlTKcTNDjGX59SvBfRtDl9rXUbhaYehWv75d+J7qWZHHKF0WnEromdgmyYkRSRHyBbbc0mzh2MMYzywwb1WKFpJzcIS1HRNYJlIXg3QeK5XbMeJDys4+vyNKcwXBKOS5Ii5YIG4NEB+7teK5cwdIrrtYbrsVMJmrDWEVh38NCM8hTtkLQI0N+9QQDUaKafIcwmCLWP7k5Q8vlmra/ToqqYuQzbLuJng8BhpuarV0Ary5Qvo74tRO2EOLvAychhD8XQvzxr/H3/wj4RwBpVsRZ3jWmSUCiIVWCVIKzjkQGEg1F2uK8oDEW13qCkCRJABvAetrORBcuGUi0RmhPLg0JFgnUSveULPDBInykGzkiqEwB0bCDWHF6D97jrSFYg3CWQNQLFzpFK4USAmvBEFCtIBEeZEDie5B5iJWjEL33d+RfSp1hmqhV3pgaIVOSosRuDMZZrhpLm2h8WpBnY7JkiJYl3ki8UEidkQ+nFMWAdmux9RKzPcVuT0jMgpKGeTViNh1wsLfDwd6Q6bRgNKtIs5w0SxAuOtuA6Eem/T47ore12caqOXSErotEcEtvoBJn+sG7OMt3huAswURt7+AMIawRQiKlQmRlPKepJJEWqVJIHSKrECkIMjAGV1/hjMP5BO1mSG97JGaKxCF9Q5Z5klSRliWLZXTb+qjIyfMSl49ArPG02KBQyqGUYZwlTCtBZzWLlcB7iRKB8UAzGmqKKnZsZepxTc+P9xEfIFyLNzXe2dhSTXPS4Q56MgKVxZGKUL0UowChot1rqhlUA5wMWA15WpDoF0/y0LfAb1ri9OC+6+/DtQ/7q8OYNxlv8n7O8x7pLECLqIKXqMAgrRmmFoiyoSEEhp8DLgoh+mY7FxPhclCBdNzJTpEEXJCsTfV6buwXxGZbfzbojFhpEyLzo23qz03Yyxqs44b/6wOcLcQt+8zOwrNViCyVfoGi8zHF5OEtZ7Jbx+8tzjZsz39JsDWJFNwZFxzuDnnwYI8HhyXzaTx/sqxQxRS/vejHT18cwbsb+lFXr2jXF69sI5whufrihN3kFa6LVbEKRLVDwNbrG6/sEKBSnkR0CCkphnN0muCdRUpFnsFgLlhtS9JE88GnF2RpSlkOyYqKJF1xk7AV7I0DsyblwmRcXEqsBYRkljaME4VUinmRMC1TPvDRRlHKa/bPS8eIIE0kMpsQigwh0xv8SN00Nwk7zQvKMKAezaNapfeU5Ypsa+Lo7bfQE/9NKuw/Av59IcTfA3LizOt/BCZCCN2vyu/Ba4F5hBD+CfBPAAbDcUjwpFrxzkHOnZ2Ub78tmOUJuZYYHFsrWXSKSudoLImo0WIbPZFFgdcpQaVUSLy3BG/w1mECmDQj0bHtW4jwQpmLeENaF9Ayop0bs8H3Gd0ZF2lWTuBri29blG1ii1fEC15JFYFRIc4xtiZWBCIIRGd6cQRBs2ow1tCZluBThMypt7ED6lNJLqaYDtoVLDctV3Xgyg8oin2yao9W79AZgWjP8emQajzj4NG7hC5gVpesTz/Frhe4zZLKfMhkJDm4M+bdewfsTIY8OpgxGGnyQpPmWRQ9sQ2YfhZtr+d2UfwEB6GXc/XeR1pK4wnGQNcRfI33XZxtexBe4HwSjVdc6KlyKUFWsTkeBMpIsD5SNfwKSUCKi9jGEwLSJIqpqALnLNZuqFdHuO0SQSAVAu22iOYcXbxFSAxKbXh4f8B4mHNxfMEv3YLllWdRe9IEahKmhSUNhmHb8GA3YXei+fbdMUIkKJkymXSMhp79uwlJammC4+OVY+mmpMU+ViaRmtbVbDdbnM559Id/m2qYYqVBVCOklwgdkF2/CBQ5SZqTSU3WZaybLevVGtyWRBjKVEVudj+PjWn6GiEOBNGbUQQEkd73ObJTbyLe2P08Hk+u1918607JwVTw/YctavgIsjkIyWLZslh23N2v0Pr1xyWl4O6dwa3/d07xdH3APL+kTLYcVsdfG+mtbVsujo+/kML11t51zfzFMRhOyPISIQTb8w9oFo+Zv/PvvNbnen36UzZnPye4jlmV8HBe8IMfPOJgf8zvvDuKrn19+PoS0yxudLu/TKzOnmDaOJr5sjKhnxXZyeMX6PDHv6Dt2/1mMIZ5RGt3Xcfx6fkt3e52fcn26oTx/ts3V/c79yU7Q8uzH3/AT08vWIXA6vwTiqGFX2lGfG93xbfnK9y7A9Z2wmW3x/3qMeMB7B3c4UrAhXccPzvC2gmj3beRv7JAGpQJf/ffuEeWJrG7OPw+qCsQC3bnM9qu4/gkLjqkUuzcOWSzXrK6ip0DpRRlWdE2DfbXGCd8lfi1E3YI4R8D/xigX5H/FyGEfyCE+KfAf0BElv5HwP/+ha/lPUo6xoOUuwcj9uY5wzJaNAYRUMKRBkfA0hpB6wPSewplSTXkEpRwOEykVYmAlbEy8SFgnceJ3ipE+psOiBB9EzIIgvNxXOscN97D3uGtw5ougqxwSOF7ARSwxoGORWM07wAlVOTtililWt+DLa65yAS6rsV4SZKUeCXwUmGsw/R583wjuOoUSlcIVYDIsMb1qmmC0XhENRyTioJtvaTbbrCrE0R9SWoX7A5hZ5Jxb15xMMmZjrLIw8wTdKajraRz/Vw68toj26W/ZYKAG+MRFfffOpyNAHBnHMEEgguRp+p7nnrfpSCIXj/8WlylXwRI1XOME4SIdqXe9YC14PGm7gVZ1jid4JzHtRuCqxFKxg6MsTjfoMcdMjSEsCRLPcOB5ODuhMvLMWfnA0zdsm4TTpcpqfbkSWA0gNQ6jIcQNEo4tLIMCkteBrQOtMawbTyXC43xGXlVkFUDWhe4Oj5F6Jx8NIdqhMFAu2GiUoRMkcqCjLNnYx3OO4KLABlnPcYERoMJPujICLhaY9pYDb3crAs3X/FnIvgei/b1zbDf9P2shWE+rLh7MGN3rEkqG3EL4tqzWFEWmk1tb57zpV6TKk9VXuMaBBtRvBgd0EMqQhSfEcT7/quEMZamaW+pUHVNg7WWoqpunWMlJUVV0tSv8rFfjk1zm2ft/O2RwPNFx6oNQFQAlL2pR/AWb1/auA/vOprFY2T9jCGXpIMRe9OKe3dn3N0fsjMvSLTspygCmU8ItiWY7Vc6F3Gk9WLHQwhstnVUowOGoUV3MbluRIJRyc1n87oQ3qHXV+DsC5zkVvbmQYC1jM2Glbdc+3XFLp2NXUhn8JtzVD4kyzSzvUPmlysWl2es2wzdwGDpGFeBpMfVJdKTSEAL8lIw1pIHSpNlEJSirmuauuX40tFCLzcr2DYtJ5drfLpLOZiSpxole/yI0JE/DiglCSQsuwnGpxACbV3f6MjPqgTvSlbTOWfnp9j6G5qwPyf+K+BPhBD/DfAXwP/yRX/ggyfRlt15yrtv7zAbV6QovF3Hh7PsKEWg8IEntabtHKZ1TFNLlXgmqouzUanZyApzjR63vVOXtRgR05HCI0SkHEkR0eZ4hQttpF85gbB9ez4EvDN07QYdDAjXexc7nAkEGXnIWilC7IKTywQpHNY7Giui6YgNRAtrgdKKpl7SmMCgTPA6wylN3dRYK7Be8nQl2diMdDBByJLgE6xtQUiE1szu7FPkM6TJ6FYt9WKBvXxGEY4p5YK3dvfZ3xnw1p0Rh7OCapBSVhJZJsg0gyQHt4WuBWtiDvXRQewGoexdb+GsCQ6CdVgrcDbQdYHQQOjAdF3fBvYobC8JqSBVBCUREpTSUddd6xcPxGAgGIKrCbaJX2GF8B3CGdxgjgsqttl8jVQa6QPBOUzTUNgaqdcoOjJtEFXBo3d2uFrucnZxweknGy4bgz0vKLVlNgzM5xEB7IJFCE8iLJlsKRMQOtCJwOXacbEQnJwNqKqC8aQin0zYni/45INPyPceMdh9QJdVmO05arvmUOXx2GUDSuBxNJ3B2gbnItCxM47OCnZnhxTVlk4JVq2JmIuXrR2vx0KA621ao6qK+//LE/sr38/OWTI63t7d491HB1SDVx/yZaEpcsXjZ2tM32Y4KJ9SFC3z2R4RACppbIZ9AwT0HsdH23acXdzmE2/Xa+rNhrwooj5AHzpNmezscnF8TP05CfvkCpbb1y+mQoBfnGzZGBhXg9u/vMEu3N5Tb7Ysnv5L5uqKWVozme+yfzDlW+894NGDAYPqJctIodDju7jtBe4rJuz4bi/C+8DF5QLnHAIYm0t0iMn3QuVskoq7u3deActdH4HoatRmcSPHDKCaDaqJVXwGVMBFMNS8OgoJtsEuHqPlQ5QeM3/4A+6tN7jFM370xFFvNoSu41t3QRcvzdn7f8eVZzLveNsmeAQfE9isVlxdrvn0RJJVgmoct75a1/zlLx4zf+fvMNp/+LmLYeNTTptJ7Hp5y/LqHO991ASY5lSZpnGazXpNXX/1z+CrxBtJ2CGEfwb8s/77D4Gv5Lqep4Effqfie99/xHA0QKok5gqhCS7FWosKDYot98cprVGsthldC6vOIbaWsvDkmUHLLQpNKhJqZK/yaHEh0PmADAEte+9pHwEHwcWVbvCOQItEIbxkua6xztDZhkFfhXa1jZQsPNZ0kS9Nim0inStJA97E6vFantMHH1WTnMO5QKYkkhANJpJoTVkMc55fWN5/bmjIEekA0h2cS3DBILVlOLrLePqAYEZsO8uifkx99jP89oih+IgH04R74xnff6tiNq3Y2auoZkN0nqPSBBE8omsI1sVWuLFgAzfWjz3t18uA8BoRUkSoo267kAiREqTEZgorO1xi4uKoV3WTtkUEi/QdwnmEdygrEViEcAjVRF9s1yF6zraQ152HBC9mEDqEqwkXWxwgREaiJ4isAuaYbaA+v6QYXKGrDqoSkTi0yJkPh9w7hM1mxOIMMBlpdsD7J6BONkyeb7kzgelAMMkdUlqcg5Na0jjBZSc5vyjY1Blpss9o5yHTg7f5m59/zMXJBZvFlr3f+x3GD77N8+fnjMSKEQaR5AS/wbkNQWQgJUG4SEVpwdQG2zp8B+PBECUyRkVNnpyj1QZnPVrGh6B3vWcn1w/TKMYTr6XfDq/rN72fi0Tww7dn/PB336UoP0NJqz1Bdif88P6YLpRcNhMWV3dZrzYIcRKLRynZmUkaX3HVvp729KWPyQdOz87pPoNf7X104CoHAwbj8a/9PncmgVEJHz4XnKw9TxYBlRRM8pTJZAetXzxyy+kjqp1vo16SB10++1e4zVPu6hOGgxHj0R5//IcT5rsjZvuDVyhbwTvM+QexS/VrhPUJp3XkgIcAvtqB7pzQPOWJHiL7q7BD4b3n+fEpLy8yqmC443pGgA+3kvVnxXAygWyEeA2dD8Atj1D6kh9+6y0mYYeRepefnfyYJFSMZmOOrp7z7DxW/vF+ju9ZbzaYtmUZHAHoxIZPjy1Xm5TB7C4Hh/sc3L3Dhx8csbqK+/z77+1w/629N9K9SrOMPC9omoZfBfW9qfhGKJ0lWjIbF+RZStMEOhG5MT5EbW2cIgkSTUzuUQaUyK/2AWkkMglo7ZHSRQ4xUeozEBHh4HvThdjODsTXAW5QuCHQJxCBkNHY3LoXftdR59u98CYOxIozuNjaIoqlSBGQRP1vJwA8xjusdbStj6IZQsRqVIHSgk0t2XSCi9oTdIJQaeSBB4FAkeQDknxIkg6xxhFsi1mfotpjMnfGTtmxP065u5OyNy8ZTQoG4xJdZdHdrHeEIgSE6cC5Xo3N9yheD71IRbiZQF6rkckokRoSlHYo7/DWRACWzPHORstMsQVvkE6hRETJE2TPEAuxBe8dwXax9+8jf12qOJ8MvX62CBrf1VFvW2fRTjXJowe2KzF2SLdpI/VOhvhZa0emNbOx4vBgxEfzIe26wVuP13OgpAlLFk2L84Z6293w8LdW0TnFuk3Y1gOsq8jGM1AFTee5urxgtV5hSQj5iJANaTY1w9KTZAmClGAUzsbWd6SjG4KXvad6f935iJXQUqOlvtG3d9e44V5ql/70yxB4IUwqXmJtf7Mj0YrxaIhOUrrO4719YUUpojmC6Bx0noGLXtAhBDqf4oxjsU1IZEuqDN5EtUEsoCqklKSyQ4nemKEzKCVJ9Bc/yrrOYF9qhQfvMV13wxYRQkRd6CZygKWUn8nHhXgJ191tcFmaQJlHcFlrA5dbE4GpMmoyhNCbCRVz0mqXpIg+4N612GYB9XMyc8xO1TGZaWY7Q/bvjBlNKsrydccYvpSoyetCpzmJU6Sh7F9JYHQRR1l2xesgee3NqDuA26BcR9NLloYQMK2j0JJcffa1qpMkXgNCYIOmcRmmbSJ4FVCJReEZ6A07s5z2wQE7008IXYtOJZ2bEGzZ78+aZd2w7iCRjly/PIt3bLoMKwoGgwk6zfHOc7nesq7j0fkQ6Kzn9KphUGjKlz7uQH/NGEuhYNtKTNfLTzuLd4a8GqF7fXYpI6DwGjb6dcQ3ImEXRcZ0NOXk+Zamk1gbcM0WJzVBSlAKTUoiYJREXrNXhrVXBKHohEBKhw6eJAkEYfFYFNGLOFUgg0MEj22jEqZIA6a/0KRKcEIilCRVabTHBJyKD5KkXzx477Chi1xlIVHkuM7RmQ6to/+2VgmKKH4sM433jsZ4bHBsWsPFRUdSSpI00ojyUiG05viJ4fmV56L2DMYVWuZ09Ya0yEnyitn+d1CypLMB212g6ivSi48Yuw8Zp0t++KDiwX7O4V7C3sM5yXCKGu0Q9SxFpJS5WEXjGnoINN65iHi0Bi9zEDoC0q5H0MkQqSQ6UUgn8B5UENgQcN5Tr2uaZku3XdLVS7Ad2nVoERnVAY8QjoDDba96TAGEqDaAMluU9qhrbrxSqCTFdFE32jtLMlAkeU7tCjp2MFqwvvyEpF6Tmy3p1CCLkiwJ3D8YsLMzZbPqePLpOR/97DmPHv2AskywdsHTJx/z18+esb48Q4tArhVpkpDojCobMprsM5zOSPfusbja8Pivf8nJs0/oXEIYHbJUJd5JhusV2XjAaFqhlhWYDLNRNGtLUztst8a6HBMSGuKizoXuBsfgCUghUVLFjkYczvSSpdzMrEWINLEg/NeMOXtzkRYD8tFdnh7HVmjb1FyePAeiYMjO/iFClMADnm4gLg43/VMu5dnmATvFMVpecHJ6DpzHbYbfJ9UJ+1V02HLWc3xyxnBQMZt+9arYGMPp0RGEEBHFd+6wXa85e/YMgKws2Nk/+My/X9WCD59/dmLqjGGxWTMZDBDWcHlxwmg8p8qHzB79cWRJXG+7OeXi4/+Lw+TfpDVvAAAgAElEQVSCedHw3l3Po3cz7r81YXb3YZREfsNRzQ6pwgvrDRMUPzp9D7+9g5Bf4FEePCz/iq30fCSjqpntOk7OrnhvlPJw+OX2d2sKnm92kSdHpCo+j4e7byGVxpx/wM5sn9nh7/FvfnzO2fEpV+en7L/1XdIsos+ef/zXfPz4E/7imWOnFLw9u32TDKa7jMYzZnsHrFdLnj39gJ8+XVH36jt/9YtzfvY8doF+7705377/kmRugPOLS9q247CC969SLi9jh6Orl9SrU3YP/wiXenh8WzHv64pvRMJuWsnPHyscDt/zU4MNOOXw0qN8pIIIQPecPURs3WotYCwoZFzV+QCO2P4WeLQUiMwjsRBcnFHjEbiI0BUSqQNSRNCBUpEDG4In1739tQs3Il6Sa6MQwbY10QYzSq3FB2uw2NDbbSqJ0oFUibjKVg6lAZkSZIpOJmxaxXbleX5p2NZQJAXeGgwtKi1IyzFluYvyKbJ3xxKbI3RzxMD8mHfmHQdj+J1DmO6WjOcz0tE8ypeKtFcIDbEDYEMEk7mIAscKjFFRmc0DQROQKOlRPirBCemjqL/QiKJASY0kmlj4EJBZi7YdiZnhuppgDaGtwUUKHF2D6HncWiisqQkypfNLPDVdt0V3Ad16tDRInaBJ8arESY9xnjybkA72MSagyxmj8SF2KTD2CndxgRcXJGaNHkdePHnKt769T5pJlotLzs4vUBeKyWzMbOcR09khzlxGkIu16LRAypRElgQ5pCXh+MkZV4sLLi7POFuuSAa7zA8foBHormW+m5JKi6s7TNchbJSs7Vygu+5exKY+IkRKoG87RIA0TZnP5lTHR6y3UTUtBHGDEo9/I/sRTewCePjXxq1r23T8+c8/vfl/JmGew/GyY9s1fLp4/FpI9eE4o+jduIazmttjzsDlokHJDl1H+lHsinnquuH0V6wMR6NhRP1+YfRdNu+5Oj9/0Qkg4jMuT0/oPof+dR2phoN5oO3go2PBL88cl61kWJRI+SKJVPNvMdp9B9FbWIbgWT3/K2TznDv6kvvTjv0h/GCWU+a6B+S92c6K6P2jfzUkgndLTbAzMN/lk6NA8xKbTXiD2HwUL0QhoXwLzBLao3gs/ddRbVn1CXGaKe5V8XPYWs8Hq47h4S7z2QQhBJPdOfnOgLTO8M2CenlKszrDtlvK6T4ASgp+/3szno7hb34OHx1dEhB85+E+4517ZNWEwd1ApgTD7Pa50mmBVAmry3NOrrY8v9xwcXVBUu6y8+AP0MWEItN8/53pLdOR18ZnlM1Vqnhnr6ReldiuZbvdfm0l9jciYRsnOL6MblNCmdgWRWFUxMlqG9GyvuduRnCkQClBngpGmaYuPJ1zaOFxPmB65xSvBE4HXERO4Z2IXF4sndUgZeQNJgKJQgqJD1G5S18vDAJ9SxOuLQ9DELSdJdBzvkNcIATrcOJa2jP0jl69KIaIrfYgVG8pmbOpLRcbw9XG0VpFolJMiPuutSTJKtJsGPWmg0d4g2qOybpnjOUz7o6H3J/nHM4UxSQnG41Q+QBUAUHFlrcPeOPBRMS69xK8JNiA6R2kPCBClCIV2F7fWoBUCKkQSiPzHFSKki8WLT4zKO9IvO0ToMFtt1E8xRrCdg2uRdiWoAWqywkywQUIUkZQWdfhTRSkUT7Of0k1TgQ63zJIBxTFjK4z6DIjnx6w6U6wWzCbK9SmBhp0NUCqglRX7O8PabuGnTsVH//8jK6FrBwzGIwpckmSjLDWYLoOmeSAxlvNplHUjef07IKrxRlXy1NqY1EqpZztoZVEB0uVF6TSgTM44+OiDoH18fqL6m4BIRxSxOutv4BIkoRhMqLIc5JU36IlvaylEuhfR0rEjaf4Nz+M9Tw920ShIWBUpkwOZiyblot1C8s+AQpQukDgUaGjVFNcHrN0MxRYr9EvSYpuaosgsOY2sKczhu5XQGF5MYhystJ9qXQXQqBe31Zd89axXb2qxBaI7fCXW+FSwrAIXK7h6UXgeB2wXpL1LfV470vSwR7F+MGtV7PLT8jdKTtZzcEIDseSg1GJy3O8Sr5UvnZe4sLt+baW9oXftJCI3jpTJgXyNdrlEtgtAUoIJae1J7zcbfct0h1FESXvINkBq4FLrA0IJVBpycp2LLv4eXgCO3lMM2vjebY1vJdkDIYjgrPkg4zhZB97saVZSFZXF/h6i7OGfDgjOIMMhvt3xwg8z887/vqTE+rWUO/PEUlJkZY8nMlI1e2PMRABptYHjDFcXi04u6o5WTQ0TYOuUsrp22SpYlQlPDwY9s7Br2IBbuRqRYpSKoLyhEQq3XO4FXfGBR8XGWlyO6Vez8bfFEf7G5GwERKb5HGmKW2fsFNc8LGCdRCEwAtBovo5cQKZytBKs3EZ23ZDk9RMRksUjuA8XYiJCRto2w5jWzyBgfakiae1BUFItAMnHF4EZN82t53Etg5rwZFjnCVY31Me4hzDNEvi3Frj6ZlL/Q3igM524Dwaz7qGVR1XmbmOgmFXVy3vP1/w6cUWwwAbYocgySp0WlFkY7KsQucZIe0Q3RpRXzC++FMOijV/8HbCew9gPhMMDnfRwwNUdQAhI1hJ8IFu3eKMx9rIi/YhwfTJlgBGRPi87DsUqRLkRZyry0RCWUULQK0hK6OFl9axQhCaVMkoCIOIYwNrsc02UsW8x9sWVy+x2wXCdATb4bsN9WZF02w4P3tGc3pOfXFJWB6RYCi3lzAucEqwtQ17oynV3iHtk0/wSuJHu+jRPUIypDOS9epjks2SJD9FVh5ROKblAfqtEVX1A7T4Ec+eXPDpJx8SRHR329vdoywKympOu+mrtGdHXF1csdmsWdWnNJ3BGMf+wXcY33mPyfhtynlJMZCEDqphyc6gRCxzvE/wQd3o2gcLCQGpA6PcAIIgM0SWkJQVu4Oc4WRMvrzEn5/fLFIlKqqThh5dHwI+eERPM/nXIXQ+YbD3PZbP/gKAVk05qv4tGv2nwOOb7YRQzN7+Owzkmnn9l1xUv8+RjpVf6p8TNmfcG3yEEl8dUHXeTEkZcFCe8Kbh9SHA+88kL2upNB389FPJT55t+Oi8ZVgNeu58DK1TJtNdsuxX7DMJHKQXTPUV7+x7fn+nYGdU0Tx4j3J2SDHe/VISpmfNlMer/Vs/e2f8mGke/SxVtYsa9JKuXwZgJeA7b8tfWSTmEP4Wbn2KWz3vfzaEcJcPPt2wWBnuTD2LZ3/F5uTnABzXlrMeJX6N1C+n+4x2H7B4/iGlSEimD9Czh1g/4ulmy25xTilqrp79krJeUU4W6Pm73C/m7E0T6uUp7398wf/55z8HAlpJvnc4YDQaMZrEhYhpWy7PjvnwdMvpsuH87CjaHgOT6R6DUVS5+YPv7nBnXn7uKTFdx9nREbr8AbO9fc6Pn5GVY7JyjNQpOknYObhL+tHRr5xCQVGUcSzafHVv9tfFNyRhx0pUCImxPQDKt4ReUCOEqHEttYwzThkTd5JmsdWsdVwd9YIpSgayTICNCGQfPN4rvE9BWpy3dF2H84IgPAIFTiJlBAhZ7+m8w4W+9pG2n8NG+VTn43ZJovrvLS5Ez20l5I0UY/TQDnjRg9V8QJIQnIwdaVpcCAQh6VxcjSVaEqRAKIVMc4QMBN/QLZak7Rlpc8zh0HB3BAfTwHCUkA4KVDGKXrRJ1dPAJG0n2NgM6wVGJAg0oHAy9glkiJ0tJSBTgSwRaC1QmYo66Vr0XM/+rnWmpxgBAYQM0S5TxqQtpEYmEil79TQfCL7AFwV+OL4xCfGuI2kaStOS333E+uyM7fkZV+//BLYXtNtT5PVb6QKl48pWS4sMNXQrlAh4nSLyHbr2HGNb0sWaxAcSGrSUpKJgPsl591t3KauKbfOE9crQdZ6uc3gaatuxXqyotw2LyyXb7Yqua3Ae0rwkH+UcPHqban5ANVIUmSfXhjKzETBmPd16TdY16HANPFEkWQYiAzReKVQSkEkgzTKSPKMYjhiUFWVexJapj8YwEBdS4Rqy38POEOFr5WG/yfC2ITEXHE4yTlcdrtuwOv0F5tpkgmh4kQ3uoJISLzSb/FtYOexZF+DRt5y6AIbJ1ZdetITuAk8NZaBuWpqmfUGdewPh+0nYy+E8CKnROtB0HVop0l7xC/ECVOi9YXvxS3IaStlwZ7DlTiF4d5wySiRaa9Rkn6QY3FTF13FWT+jcq63+lalwPp6vTHfM8wXFoEDlsc0rs0HUQfgK8apibBxKymKIkrcPfjd0DHtE2u5eQbN8yPGTI5rVOc3ittbOdbchBI9vN7j1CaqcEXSFKx6w9uC6q2ivuo1FUZWPITiSRPK97+wzGOZc/ctPudoaOhtHUKZtWC+vOF0bmrZju645v1qy3NRYZ9E6Jc1KhrvfIR/27XYlST4HIBe3UQxGI4JMsSiq4aivmAVSKdJEUZUJw8GAshySplcxhxHld71/c/ftNyNhhyjjKdFsDTjnwRq00j2HN4mScolCKU0iJXkPTlJakqYhtr5CR/BdbJUnGqTr57MBjwYShNziXKD1bazepccSLTWdlDgX6Jyn8Z7QkxqENNFHV8SppO/pWUmWEjpDZzpckEjkTcImRIqDD75HpBuCByVTgleEELB0URhGa4wRJFqQJAoj4wWt0ixyxu2W9vKItH1GYZ7y4G3FvZlif2opBylJWSLzMaRD0CWuTug6WG88C1HSoTE6Q7k+SasoIqPxFBISCYX2pNl1Za0j5SKugnrjCRe/F33rUXmCVD06SsW2bZLGajxNe6vNeFHTm369eNp6vI2ggLm0LC/PWJ2f0nSe9vhj2naLdk0cexSR5icF0S4ztAizQGUFXifIYo9ueYzttsirM8qwJYhLkAqdzpkMDnnvO/cZz6Y8O11jnyxou4bOWGrTYlzLxdERbd3SmKidHrxDyJR8OGYwn3P4zrtkozmUkKeWXAeq3KIImM6yXS0IrqZIBEoKtJakeY6UBUKkJPkA3YYoeZqlJFnGYDBiUA4oixKlVC+Q0i8Qe233a/Gpfqz922J1/cbhzBbdHnF3VrCoLZt2zfLoL29tkw33Gd75IQCGjEX2nZvfRW17/4rk6Dh9wZ++Rsx/pspZdxoxGWGPum5YLFev3+4NR6o1RSa5XK3IEh0TtnhplIbH24bN8x+RyEvyZMXhg8D9oeK9cUYQAqkT8umdPsFey9LGON7OWXevsX4U3JyvQrfcHx6jRw/jc+ENh8gGqOw2p/zOLdbd2zhrsf/8z7h8+jc0y2e8OIJ+QRpCLMa6DW55FIsNVcDwEZtFQ2cCZbKlqzeYdktaDG6sdn/wvbvs3xnx8cdnfHC84Xzd4QM0bUPdNLz/bM22i/OK5XJF29YIIcjyksFwznj/e/3+h9deP9fn+/oaU1oznM5pmhxlJMPx9KZbAJAkMjqljUcMBmOy7CTmlpfP2Q1V8zdbNH4jEraxgecXm1ihyuuVaN9ilh6lXG/YQUzSSqGlRClBqjx7yYqRXJH6NatljdaQZoJhkSC1pgk5nYtI8GEa8DZgWk/qN3FO1UmSAoTQXJx3GGGxOBKXgBP4TuJcrxNtHLpHSnedQjjfS51KothpD3zzAeM9zvfynj1QQ8kEnceKtGvinKtzEh8kXmi8ykjUhEQMyVyCWBwj3P/H3ZvsSnJmeX6/8w02+HznGMggmWQO1VmNbkEtCYKgRkMvoI3QG20EvYQaegJttddGS72BNgIESIKmklRV6urKoZlJMoKM4cYdfLThm7T4zO8QjGByyCox+wAO8nqYm5u7udn5zjn/oeGs+YLTUc/jg8jPHnQczjXFZIwdHaHrU6R4iOcI389Zd4atg6uU6EVAC7U1lDZiSVQxYFWkUJHa5CStba7qRalcdsNAyobsFuUZiNGZnqWLPJfQ/a2SmbP5JqPNnht2H9ks+mZbpQarTT1iejhmPHvMdPyQ17/+K774nxXsnmYVueOHeO/olq+Y1jNUWaKlx45PiLZmelxjbGBzfcDmPODcNe12xdQ9w4y3mBg5mD2hrg/4Z//RL/jLv/ic3/7qOZ/+5vd0bUffdxjJi6oehaon2Lrm4cMHzI6PmR0fMz0YobUnpGusmaCMsN012AqsdvjeE4IiphGh35CCYIo8T0cV+GgQHdEm0HqgD/g2MhqPmR/MKeoK33qiizetuZQ0euhupKxc/6cywgbgYutYtZ7um7ww3xKV7vhk/JSz6gUH5cU72+GX7QltqHk4eoqSt7+H956vXrzKBcD/jzGfH2MHd6j187/CaOGxecGDueO9w8S/d1YxG8B2/cl7MD+hFMFMTkjlEX/zaWQYCdOHt6Ovj6srHk2y5rcpa+zRz9+pUf73EVprfvlP/hHnR8Lv+i+4PD8HEocnp0S3YX3+BfPTj1Dm9hjnE+Hf/jPFv/78Y5ZXZzxfw8wumRUbli9/TzmaMz58iFm8z+kM/tN/rvif/tff8dd/+4Lfvtyy3W3Zbpd0Pt50U0ajGQfzA372YExbfURTf8wvDi4o9UsAJmbOjQUYGav08uWGcSF0h2f4cEUXFC+bx8znEw7q3LFYbXqul/fBiB8c1fjNmE8/594owRiNHuf3CCHStt+Phgc/koSdUsK5PltWmgz8YpCQSwhRYl7tRMlo7JhBUgU9hURGusGKQ6WY54gxd19VjGjxWHqM5IQoAsTsq63I7feUehQ1EiPbtiepmAFwKQwI8AGlK4ISnV8TEylmrrE1glWZq5wGsZSEyrraAwI7G2AoMJki5ZOw7Rytg/5GTzonfiMlRgwmtJR+SRk2LPSawypyNE2My0RVmMHpakFUCzo/pvMlXVIsO2h8dggSnbAiVARqlSgUlCSMZHk/m3F32fbypqqWW8hn2rv33E0Xnlyuu5yclc4PG0CZAVw1tNOT3L4+utvkbszQ7sttQqUNo8mcenZEOT+h6y8QEymmC1IKhGbNaHKGNja38ekzct3W1OMJ0R/SXh3hvCduWoqxI8kOsdcUxSGlGXF2POG99xa0TcdXX17ivMe3KmsLa4WyJXoyxoxqytkEUxcoLTjXEKNDlMn66EkhSYPzJNdl45NIVjULOjPmVAXa5lm/KpHkkQA6CJIg+UBhLaOqojSWpBPJ30GJk7sbd6urbzV7/JGEDwkf3r3EMHFD5V/RmSPSoGRWhCtqdtSqo9QdVr3bcMMoj03uG1vkKWUp0neFUopqNMZ1HcH/MEnJEGHZJbZ9wPnMatmHVho1oMJNWFFGh7WOUmfJ3LFVlIXFV2PU7Bg1PUQQdn3mKO9c5O7haRWYFjvEVshADZuXhvEgUiO2RL7BAexexEDsB2CdCKqY3C7Yf0iIUI1qyvEcMz5DLq6BhBmfgRiid2hbDPeAROo3iI3U1ZjFPOOZ1ktL10d2oaWqEqrL9FE1btC24ujBQz76cEvfe843z2k7hYv5Prv/BPX0lNHsCD2t0fYUXczo0xabImPd3l/shR30G5zz9Fpw0dL6Ea0PuFCglMHavOe7Ou4hJLa7rMdXWU1pLZ1zhJsWWQYcxxCGVvn3jx9FwiYFcDtUYZBg801fa0BB0niJSATlIfSZB92jmZqOse2Z2i0mdnmuKnX+ghAIOyQ5SukI4jCqyBWzj8ReSEaDRER6VAoQFFebJqOzjcZJboVrFQk6ooCirOh8T3CORIcxibIQKiOQFM7JcNyJFCwxRHyIKFNiyG0WW5R4J3y17LloIltHHgkIWG0oVI1FU/TnzMJrJmnDo+mOB/PIw8PIqJxh7QhdHhPtY6KcsdrM2YSCrU+sgssAixiYGxhF4dBoxpXOc+oiDAjwQY84p0ySfK13nelZKc/i97aaJH/7elMM56uAsgRjwZb79siQZGKu0J3LKH8lYExO9naSF2EpokOgrEdMHn2IW34J2jN68AC1fYVfr6hOfoKUFckK+AuELUYC80lFoU/ZLj9kd63YrALKrgb50pcobTDlAY+OPsD+w8c8fDDj9VXDV19u8M+2JJ/11YuDA/Siwo41egohdmyX57hdQVGUjEdjtLNoC1ZZVLsjtSvEO1KKdGLofEWIGvQErTK3P0mFxB4tkUrrvOjrHaOiZDaZMC5K6CONBOIwZSDKjSj1oCj+JwM6+zYxcl9x3Cx5Mf6neJW5r/P+N0xlCTz6g6+fF193lvquYazl6OyM69ev2a5WP2hfrU/86jyw3nU0/btpYAd6w1R/vcIK1Zju0U8GX+ncX355kXi++foNvjYdP1t8hj14HzXaJ+bF8PhukUKHv/x9/kNpitNf/HES9j7sHCa/AP0FkPL/m0vg/mLML79ElVPM0ce8/0A4mQr/zzPYbHbsugsePXqA77asXm0z2n56gj3+Gf/4n1g++XDO69cbPrMK/4bk6cHjf4t68SE3v5YEn+0ecWBX/HT89N620r5AbZ4OqnG58r9oz2i7b9ar77rAi1db2s6jRZiNRqx2DbuufWO7+xr23yd+FAk7JQh9dtdKJqF1xIrgUk+vQILGAZ0ItoVRqTiYWE5GjnkdsTqiTL4RGmOwkihVxCCoGCG1lEQ0Pds2EfuMZnbRo01iXiS6zZbOazarlrJUxEpTakuUSECQpLOFmiTEJHSRq6R9XlJaE2PCxY6oStAKay1N37LddjnBKkXwsNwI103kYtfTBYsiK18ZKbFxjKVjlNY84ikz3TM2npMpzCuhUBqxU4I9Zqt/wnZ3QteMWbqGQEMUoSg0hVbZi7nW1IVmMs6zVaXVMFNOtxVbyjQ1Br47Pt7YitLvMu8ykT1BY4TQD69VYA1JaUTMkLBN9qjUw6JLG1CSQWn7ojsJ4l02HukduMGERDQljsPFlNfakLQwm59kEFkveWGgsn6dqGFRkLaUxRRlSg5PT0hhR7vbsbze0TlHZMNMzsF3KEomhUKfCP/hf/CEz545fvWp59WFoosKX2mwiaQim75BVF4ZjxMY79i5FaOmp7YWPSqwpqdWHTpmveUuJvpAnt+5a5StEVtBqeiDZE5/EkQ0qig5PHuEjCsOjn5NShd4l1kMESHqTJtPAEHdaA/8mxIXG8e2X9Pp/41xVfBoUXE4UUyrKQ9Hzyj0D0fVBu+5vrgYqHHC4uh4MH/4/nG5Ea7W8Ogo0XTw4iqflBAjq+0WFwIKYVLXjEZjptMF2lgK5Tg2a0pxGA1PThJ1eVuFd6Hg5e6Ei+cRpXPF2/inSHhNmn4Ckmfh709eMBsF7NFHiP0D4iYAweGXz25HVG9ESncSSIz4qy+4+0MTW6Fnf3gB9a6YTSw//XDM+rO8CPjpB2PCbp3HanfCzN+793mKquQX//iXvHo65uLLMReXX1JXWUFve/WCvtkyVYbkO7TW/PwXHyAHhtX0/ndi64O3HtcmjPjN9gmf/7qlLp/yQf2cee2phw590ziev4r0d0Y616uOzS7PJvb69wBlqTlcVBwfPOboes5fPV3Sx/OvJeyyLLOS3rfg9b8rfhQJm5QIzpNUFhQF0E7lKlGlTHcZOuK2SBQopqVnWgbGRXbVEWUyUMtqjAQKIioN8o4pYsSjiGwHhS9Sto00KSOkt01P02YpOlEKpTzGAJKIEjEqCwvEAeEoWqOCylxrBETlWWOKhJRb+EpnAFrvIkrnZONF2DSe661n5xJ+T+dRKn8ONEVqqeOOWVwxsTC2wqhQWJPb7kFGdExpwoLr3tDGRBcatMko75EqqY1hXBimtaK0klHze4fGLKPF0LsfHvH24fcz6wBdd9vG2X93vuOmEvd6sMfU4Iqhau5ypa3N8N/8faGzuYjsXXEgI8m7Nidto9FuR6nzNkkpCmMxRbZUFdGDUUl2BMv9YofWCbQwmo7YLCeYckq7LkkSMLalrDZIShhzhTU1qiz44P0pGEWbNKlSbHpodcTHnojDyyBXC6gwqN1Fh+kcRguu14QqEK1HfB4BBMKAWQj44BEMQoGYzM2OcQDaqPwY1TOCgvF4wma9Quk8exGyPWvMggPE/drqTyVhpwyyhExNe9uBty7S+UhpXlFqw5GumNdHjGvFyK4RsbBX20r+W3s87yN4j+t72t3uhsvupj3a3CZsEZXpit8h2j4bfZzME/tdNX1k2wV65wcFO8FaQ2FLiqLGiqcSx1i1WEN2jhtnQyAQejQhGq6bApqASMAahTWB2vQk3YIRRBsWY8dkBKqavfV7Tb7jbjs+hZ7Yru499+5IxG5NitnbXpkCFSco//0XT4aeSRUpCwMizGeWXSxw7d3zKZkuemfmrrTm4OSIZrNmc72ivXyB9InaeXq3wvY95XiONhalFQ/OFqzUhOfpfoJuunAvue7DRcN1nFKsGjrdcjJ+SZlKzGjw8fYJP8whsimUJyaD6r/efRABYxTV6IBkRyymE5brryuf7Z3CoMs4re8x4vpRJOwUPW2zBFMgeEIwuM5lxLICYtYJNzby5LTkbFHwkweW0sSB+2zB1KBKgnIgDVY6VPBZ2lEZrERSChQiJJ2Idm+zmXWIXbOj3XpiirjOk1wgaEsSRRDNfKTRWuHIAgRKkSv0CIImxuwuRNR0nSMQmEzGxKRwvaBsoI/Cyit+d7Hh9bqncXq4EBO6sGCEJB2H4YqFahnrwLjS1LUCDUkVRKl5HU7pdkdcby1df45WgSfHFYfVmPlkzOKgxJQFtizA7BP1wDVLg+/1IO5BDLfPD0BwfBYEITjoB8tMCeBC3j60t4l+LwEX06D5qjIYzWT3NIpiqLT10AZXDHJvOenGAN2W1DVEn5XSVN9S65StR5eXlMWIqpqhghm2z5aeqSxgUkPq0CoxX4xI/hij4bPfNGy3r1lf7/DNkslsg3rYYqoFqpzx+OgB89kB7z055m+ejbjeGVa94nKzYdM2XG5e4fuG0Lfs4iVlchQhMMEziZFCeqITWgW6E0QlMJEUuuyx3oGoCpQn7bbZ4jUlbG0QI/R+x6w4RauC48UR6+srrvYUupSTtlFqOE1ZqDHlCr8AACAASURBVIF3mCX82MIHx+XFc0A4ODy7tVd8I0TgZ2djHh8lfvGkRfhqQFQD1SOoBlnQ7hXsfvedjmF5cUEzJGuANJh73I2iLDl59G7p0XdFTPCvn9+ei7/8csP5xhHfkhRFEmf2mmqYxz86ShxO0s2pTMAXdkbnK158lZHs1ireezjl7LjkwXFJks/R9QHm4AnCB/s9f/3AUsJffUZybybY7wZX7LZLNpdfsXjwcabaDpzq7xPd5or166dM51NMOUKA8eJsqBn2nyHhLz9DVVPM4U/uvf70qGSh5/yr3/6S3fYVzfNPASgKi6TA5OgR1fSIX3w8YfHgiJMP7ncD/vd/+YqnL7bvPL4PRs+Zmw1CrqCXq+5rzQjnei5fvWB+cEz9puMa0HaBZ1+tOf3k59jDkl8+/pfsVgXPz9/9vRRFibUWeP3ujd4SP46EnSIptkjMNoMS/WCoLqQBHW60UBfCfJxbvc4lOpdnpMYW4DMVSaWepHqsynrWSjJXTilBS2RcR6yPdAGit4jk9oZRUBrYOclmDT4S1Q5EE6WiSeB1xFrBxIgQiSqjejOLS+crOaSBoJ/R7UoNamc6EoJjuenZdD4DwqQgDoS2Pggm5UVGaRusHhYMOldjbSyJvqbtx3RLRdI9ylxwNE5MRob3TgvGk5pqMqIYFbmi12rgcQ0JeU8e3QNYBvrZDbIp3n0u5ifSkKSThzCodd1YbwKhA58V3nLGZwCcDYhzW5K0ym1zO4DTjL2hdqTgwLUk1+H9DudaXLvDhh1RFHH5HKYLpBoT/RUiAcRDUNAXkKYwSVAktC2oxyUxjFkcHbC68lydrzl/cclm2aJwjBc91bTB2AW1jRwXhg86w3xneb6pqMYjWh+ZtWO6bkfbbuk2QtEuma3PGUmkTAndeaKAk0QK2bJVXLZVjVFQUkAKpNBlzvfwNfc7ULbAMGa33tK6HueyuYxSKhvDJMiwSsVerjTn8j+NhD0ItAGJ3W59oz5VVaOvJW8l4FPJVbeg2W6IMTKeziAUMBg0VHjGfLdItwdx58n7f3vvWF5e0XffrYIUgdNFwnm4XMsgbnOnqk2waztgk01tFv5mLCzcrrvaOKYJU9rzCrGGVMDDg575VHP2sGI2yV1DMzlFivHXZEpjc/2GB/ZwPX0PPkG/W+EGgRPft5Aizep1vo/8gEjrS+zrL1E+Eo1le/UiA9HKN6lpieRawuorVH1w0x7X1RQ7B3RDH2rWzQm6/YLSrKnKgiiWGDz17ITFVPhknn9rTZd48TrxwcMpB9Nbx7iU4DdfXBPbJWP3jGKUsT7r9YqyqrFFwXa9Ylx5jmaBF5eGzikm8wPMu4xghtvn+vUz+gDPLhvW7bcAMX6Py/lHkbAhkWKPipJlNEUjPt2MWLVWGGUorWFUKIwSmi7ReCGKpqzsYP+YMNGRxGG0wxQDMX6QMVUCZREyqDkkOmUhpmzeoaA0MoinQHCJqFrAghiakHBaKKNgJaElzzr3RiE3yPGhZZuEbGqh82pa62yWsWkcjQt0gYw6HhTcXMgXm2ZLUUSMFoIuCQqCCG0oaX0N/ZidlyyOMLri+KjmeKF4eFyhxzWqrjMQbN9DTXuEdhja3BHCntQrtwkahjbz7Tm5aZHHoeL2Q7Wd7iRs74dZtAff5plYircgNpstOZNWwwLC5ONLgRQDwfe53RkcPmzwXYPbbdGxRydNWr/OFkjGEvsWISIq5Da0s0CfFwBKocopVW0QqZgfzuhdj3t5TXN5QaFbKrsFOrRqMeP3KYyjGgmPF8Ko1HQUjNWYXjQTP6Ztt+yaNdevthjpmC4basm+vtrnesqTQHUIkSSDExvZnSmmSAw9rusBBaJxfUDbgDYlzWbDtmuzfnXKCZsgZJ/TW9vTP5lW+FuibW6rG2PM0CLfV1dCSInGWc53B1xdOFIInJgDaAXI4Kyp7aneAD3f4Bm/Z8QEwQU219/StEEY7jH5dBxOE00nXL6F3p1INH2HAIWGONu3Qge8ztChbXzNtT/i+hLqkeboNPHgIHJ2qJifVPn9tMm63/coWhlLEtslsdlz0yV/Id8GhTyMCe9G32zoNpf3nu+2V3cW78N73NFFz0IB37w4MKsriquXiFkQgmO3PEeZAm2/bruaYkN0DdZUiCnyor+YoHWN1p/jqVi5Y/T6UwpZU47qrM+BpxwfMB5H5vPsfbBcCy9eJx4fj3h8fLs4CDHxxfMlvlkz7T9Fh1MCmu1qmXUfjKHbrZjojoPS8aytcaHm8OSMuxdiIt3IjebJlWJ39YJN0/Jq1bHr4+AW+UOZ1298n3/Eff2gUIONZZ8yfL5UAecHWlW0tE2JisLfftphtBtQyllQpSw1WoPSCZ36zDVWFlMoshdEYlYGahsZWYNVgUJFgqlJMeDbbDepxVOXQjIWSkvqwqBL3nHVtMSUKMuCSVVQWZ2dvYZur+8zvzt/mKx4ZVQJuKzz6/PNXBtNwmSFM7KlpUQoVMdUEg8kUitDoYRCJzqxtNGw24zRRUVRGt4/ajk9EH7+/pjjxwtGsyl6NgNT5oNRwJ4GB0MlnJHGKAU2I9pJakjC7Llnd85IImvC9hlk1jW5gE4pV9ti89zaljdI8OQTKXhwu0x9EgWuYJAoz4ua4XDAk1LEp6H1Hh0u7Qjek1qPNgXKGqalRtoNru9Qvs82n6JINoIB1ZxDdwWjGRz3aFtQVYaHDyylnUJ6wm9/0/J6dc76bz7j0dUFp6fCA1GUszUFmnl1hlIV182WNadomaKKI46qGXp6yLOr3xFTR9FdUas8mRZnUFrlebwNuWwSg3gFmGy8EgRCQoVISIGYfMb1hUgsWra7Lbu+ZWoMm6pkV9c43+BjZl5HkRu1v3wq/5iX/t9/bNbXbDZLAEajKaPxjF+92KJe7lC/uyDGQGUV/yDFe/O9C4k8lfsZ+6MHjuP590PcJrJBR0rw8cNvZ1qqteH44UOWbk28WPGvv1R/8Gwcj4VfPtLYO6OMZ6+Fry7z38VI2OuPHIw9//7PthyePaYaT3LymJwOyfr+bTq5Bn/5+xs7SgA1OkBPH+AvPh3m2O+OvlmzubivPlbPT5g//ITli9+R7nhrq25H+fwzIJGMpX380wHsmai++hRx3/xeEr++gNhdv6BZvnrna6Z9RzE7wR59DKJQAj//yYSjGRRW8evrJ3SbC9KzZ/RNy3a3A4Tx9ppxv8UcfgS8ndYmJH42+Zyte8l6B8vLC8ifju16Sbdb8csPdpQ2H/f86IQq1Ly5am42GzarvNgrqorF4QkApVH8+eMpNmxw/Y7lZkv4I7r2/CgS9oCtgbRvJ4fMhR4GpSFm16a2jyy3uc2MgNJZaMWG/gaUbNmbeBQYlxN50SXWXfZKHRWGUiVKHRFT5mq4D6gQSVERiMPKVgb95jDQmjKgqOuF0iisBkwWSiEJwcfsfUwGpaHy8ykpQlI4B73PiVIGxHRMYcCBKUyKVAqmBVjNQAey9LHER4sTQ200B7Xi0UHByWHF4nBMNZ2gxxMoskgHKsuP3qucZUBYp3gzIyVlnrioYZV8o7O4r6z3LcUhxQ6c9KylHthbjGZa1zCfFpX1wltu/Lazi1m62W9KkRhDNhAgkJIjhVxlh9Thfcr4s+kEOx1jxzOk60ndlhTcYJxibubwUUD0dXYEK8YwmqCqMWVVMpnDUQvnr49IKbE9X7JaX2NUy+z4FVCj7QIZV1iJzMsRXd/QeGGz7TH9FtutSMtL2G6IIeGDIyVQPqCSRmmFCuRfnbEDSCwDJnMbPKJuFMsyApzY07ZbvEDyDqs1hc7IfqVStkCNt0IpIn84OfwpRLxz804D88D5O78x8pjk5bL5g4CcUQXxDbOLu7FpJ4SgqfXmrZIzPoDzuUIeV4nqD2iMpJTo2vaGr+2+Ya2QDSEMpdWUb8hehnhbYc+KxOkiUI9qTo9GzBfzrJBnSlQ1R4rxG5X1/g00UkzvfS5VThFl+TbtmJQiMdwH8fmuQURlStnd0YG2xDqvKtIAIJSuRbdbVN8h4duBAeu6Io0nVJMD4vIVaZdbE7GoiNX99ni3vSbGgIuCKUcYW1KM58yC4awNXJwcsTTCarNEbzxKrpFaE1KiNQV1Nccw5fRwwnID213ky/PtcEsLdJtrYp87P/HOoifGSACWW0VVFtiiBMlt8GaXkfsiQlmP0EZnim+7w/c9zW5DUVaICIURjJZ7WvL7EBGstfcc3L5L/CgS9h45nIbqAzJ9Kg1tM58AF4nR03s9AHDINzidkBgxhUYbRYUlogmUKNE3dOHSeKxKVBoqnag1WKPQEjGpQEWFpqUuerRRaCUoZQfUrhtk/yJ91+MKIdgEUiIYCJrg+nwxqgiib/ShQ1S4qFn7bHYfo8kLAZU7CpLAJChwjI3icGTRVkBrvIzZxoreW6xVzMeKjw+Ff/D+hNnhjOnDI9LkEMpx5jPvk/UNXzoMl/QgRJNi1vhOQBIkSp5xE7NtdhgAaOS2drrRwE0Z+BRDtudMHjFCEg22Glrdgox6cD2yqYjtOouK9D4n+qGVHqMnugZ8T4qeRJ/lQIMjpIDzmsYXjOYn1EcHlAcz/MunxGZFvpwsSIEEwOQFhI4dtCtS7GB2ArNTytkxqqixRcl211GUNb++9lytFO32nMXsS5L3GJ2wZoY1iZNxwbVb4ZstL58mZPUCs/oKlp+h+hXRKVLoUdGjo4JgEWtRqsiLLlPcgNed67MrGoJNZvg9RxDog2O77fN8P0GhNaXRVEYNmAeIoobZWEL2MhB/RC3sH0cMXZ870fvE786/hRJUqrlaf5Pn8glWeh7Xn7LvTLz57XUOPnspPDnNyO1vihgC1+fnNNv7cP23vUoEJnVNZb+ZQnY4Dfzsfcfjh4eMZwdMT97Pr9cFZvE+7+JDiykxB0/uPLMfaX3P30fK4LC+WXHw+Gc3Ii+I4NoNy3BfCMZslxQXX32X3bOYz1CLY6bH79G/+oLw8nMA3OKUvhzd+xm069e069fw6veMFmeMDh5iT37OrNwxMi3OPeL5iwl/+ZctcfkVNK+wsiX1DWsi2hYU01N+/uGUX/0+cbkM/MXfnmdHMQIPtueY+HYgWojwu+eW0XjC/DAb0XjfcX2REWRaG47LirKqKcqa1y+/wrme64tzDo5PM57qG0KUUL453/kO8aNI2BmjJIPlYqbzaGSoAnOlm5LktqLps2qYGNqYrfOsI98QyXrgeUgtNzzaSKQPBhcTbRCsyspkonWmYCiFhBqFo/IenQIqBnQHmg6rW6JYMD1athgTMEZRmgKVFCEIDkWQRFQKoyxaaTqn6Z2i98K2T7Qe+pBFSPaXfRo0tx8a4VgJlY20lLhY0IQx2irGpfBnDxIfnJb85OyAw8dnFJM5aXwCxQx0BdgBNLavlhNpr8WcYgZG5T9yYo4yzLKHOfVdgFlwgxF4zLaXvSf0HukaxDvENVmKVTTsZmDLDBKp6szBPpogvkV8B5slqW1I7Y7U9xB7cA3RdcTY4902LyxSwLmOTkY0ZsyiXFDXx+jpgnj1GpTKZixJ8rEpyQuTAKkNiA+gL/Pnix5MhS3GTA9O+eAnhuniABcLXn9Zsr4a8dUXn9J2K5BPmeoDdL3EVlA4je0MXT8h9W1GrLsW6XuUC4SYFx14aEPC+UjR95TWIH2b+eYIepAWTQgp2bx6j5HCFigxJNG0ojLwzhZ4pemSEFXKVMYAWtIAagz7M/dvTLTNFtf3zOaHtwniO8SX1x2v1hl5fTwpOJ19/Ubpk+VFm1HVKUW211/lcwc03Q8HBizbxBfXkc27BdneGkbDh2eJDx/VPDg7ZfHgQ4oBhKVnD7Ou9rcd0EePv/rihk+dwnc7GOk7yldf5D+UZq0MSWdFyMnRe5iiZv4gI7dj8KxfP8XPDgnViPLlFyj/ze+3VgWv9Yj3j59Qzw+/9rnM5grVNXRnT0jvSnYp4q8+RxUj7PEnfBC+YDHVxPgJL7+q+PLynL+4eM7D/pwnfcsWwezWTEjE7pR3tcf3Eb1je/0V5fiAos6iNcqdU6x+m9/eF8BR3jYGrs5f3nyO4D1FUTJdHGKsvddF+ruIH0XC3ke+uQ2zzsFWk6GaTiRCikjw2ekFQZJCUkJ7RVSaKImgI5LUYJWZ279ZpCvd4DLi0KYmKZRoOmVQKaHxBCIqeiR6tOvR0mGTQSuNiEURCAhpqHrycSWiyq3QJBrRebbu/F6iMdJ7ofcMs2vJiN8BDSzATGtGOkt0hlTgpMQrzcgk5lXi4cJwelCzOJxRzhaoekYqxjlZy9AKSwwz/zst7QEMl4VJhi/6XsLet4T2KLI0tNBlEDxRBDR91EhUSBR0COjo2bOQxJVg8ypcbAFVnXWMRUGZW8iSIim6/EieGB3R94S+vWV4pUTUmlRV2GqMqUaIKVCmIBpLcn0GcciAok7kdvvAlxbnoG+h3UK3zs5J9YzJfEpSlpOHHc1mQ7vrWG2+wpaOyXRDOT+nEI0yh2hfoL2FLkK/RXxLcg68y/P5fbUWI54w8KvzwscS0SaPPLTYfGZlkCJNeX4mgBbBakUQRZLsj21sgTF78GQcFnS5tg6yB7f86aVsHwIiWZ7zboQQiDHiXI9S+Te4//zfJloX2VN5S+sZd1kgCGXx6pZ60wE6Nqi4owk1znlaFyjkjlf0d4wE7PrEukusuoR/w7ZLgEkhlObrSbe0MCrh8UxxPKuZzBdUsxOUySAsVUwQ+xZzj/17+zYvqvd/76VFv8OcNCZFO2iSm5hZD8p1kBJueU4a+MLRlFCOkKIagGIFthwRlCZqk1vZneTXviM8iq1YpBhhioz8TrYkliNU1wzdvojqmgHhDtFWg9IlxODw3Q6dImIKpJgwns0QUTx+UtC0jl0bOW8vKVTgQK8wR1tSsaJfn5PaAvwEEliVVS8V6QbwqofvPe3fq2+G425wqsGqDh0LSjXCp4KQNP0dJTtrC2xZUZR5UZBSwhZlftgCYwwEfytT+gPjx5Ow49CRJSeWPoYsUCKaqAuyMInKIB4RlPIo49EqV+ExaoLTdEZlPmwMJCyKDOpKg9uWKvatYYVK4EXwMVGYEq1qgoBWEZUiuCkxtPhujaFBSU9hpljfYH3H2LdZRyQoUlHnrBMC2pYoUWzWHbuup+k7dr2lH9rjKWZAWEoJJZ5SRR7UBeO6IpQLtmFKUIaihvfmjsezxC8eHTI7PWV8+gQ5fDLMrEcZ+IUMdK1hQBYGNDdpAHSFQVloX9fHAYgmN+pmMjhGZX3UgpQ0KRpStLjQsUKTVIXSHaVO2LhDhxa1a9EJDJLb46ZARjMoZ/lvO0PpEbGeooqStLskteeE0BL6Ft80pLogVRavZ8j4iOroAeXZCeV8hsSAlBPU+IBw+RSURhlNTCEDWpwnmYHX7UB1DmED10/B7UAbRpMz7PiAn+sCJQpd1Dz7y9f4cI1KS4rxM0Z+kw0X2gVlW1O8Tuh+S+E3+KYh9js6t8JYi1YW0R4fPCEluiD4oEk+MKs0RiuMjvikiSgU2f0LXQ6jGkutCwpb4EWwakLbzOm7hqvVhpaOHpdR8knyrF/2mI4/pUhsmh0iwvwt/NWUEsvrWx7q4uCEovgW6l1vxOuN43Lr+PNHU2R0wHn979z790X3K6b9p0wP3+f1pufZ+Y7H9oJavmNpPERM8KvzwcjlLaGV8GcnmtJ+ffHx8DDxaCH8u6c19dkp9sEn2JOfZjwN8M0z6IS/fkrq380r/jbR+Irn2zMASt2j31OU50+xy9dUX316s51/+htCPaF97xOmx08oJwvmZx+xW56zu35B9+Aj9HZJ9fy7ceT7wzN6JdSf/y1+ekB/9Ij66a9RQ7JsH/6EMMkuY+36km67ZPHop+yXfHr+mOkc/uFjGE0nHBxM+L//r8B29YrL7gX/6Elk0jcsn3+Kb65R2ylwynFxzePyJRcbx67fsbl8xvToCbYcMzv+gN3yJavXnwHQjRZ0i494WH1GqXY8rH7P6/4RG38r/yoizI9OMPYWZ6C14fD0AYfbxMEWYoo0TcOquUu/+/7xo0jYKUU619J12e5MFHilMNaidMSQ8txXslB8JIN6JAVCJnKi9paUyoFWJCO4ZAbLwsyLTpLQukBJyrNCZUmiCcpSaoPR+SLTqkBrm+e1aJIZEZNFSSDKhNd9wy527Lo1VnmMOIrCoUVhRLJHNpHWdXTO04dEHwSfVEb+pr0AJYyVcGg15WgC5ZitjDG1praJo0nk/UPL40XBbHFMOTlA6hnoEUlVoMshYQ/zTjXMmSUvSggefEBiGubVDDPrQe4sxRtqRopxqKoH5DcWiRCrgE8lvSvpXQOhpVVCqUus31H5DTH4XH02K0iC2qygGCGmRIoSGWbcKThEDGZykt2t0IS2xWEISUMSirJmdHJGuThCTWZAzO1oNFy+IkZHYJdHJsO9LaGzqli04AVUhKYFrvJYQEDbKdPZlLOzA5Lvef3ZGa5XXFw4Dq9alFqhRs+RfofxNThD6gPRZV/d6DJ1S7UODdQqM+glZYOVEAJbCUhSWK0orCFhSGj6BFEnooYoiRR6hB5jRhhlMXVFPDzCGMXzV68IIeB6l9v/wydMJP6Itrp/pxFTZNPsBvJBRBA2zY6qKDDfwOvd7dZ0b3EyskVJVd1nYrftBu8948n8xpb02XWLrF/RmP/j3rZNuOZ1zPs1SvjoeMRIKcxg/NCpxHkXaVbnTOrI4eT7L4xKWzAuDUrua7+XFs4OEj8/Mjycl4TT9/GTBbe3+r/bk6uqGaqc4tcvwE5J08fI7hk+Ri7aQ2Yjz2jflm82mHUeL6m+pXj5BXF1ST8sKoK1YItcJ5Q13ekT7PX5TcK9G1VZcLw4oDx4hJnmmXAdwYomffFr9G5NEZ8i91rr97//FCO7qxcUrqcWjZ6cZHMTgaODEvV4zO9+f4S0O5KH6+WKkBKz2ZTUvUTtXnLQvES3Havdjs31C1yXFz3t5pK+yTry/s7x+z5vF076zMgkMTXXFNJx5U5vusGb1fUNgGw0mWJtiSA8PF5QWsP/sjyn+SOe2h9Fwo4p0bsO79INfVi0IgpoMlBKKZ3lRwdiakQhKRBFgDCAwgQtDpQiGUWICkkQ5NYlRatyaDnGQSpTE43BqQKrNLE0GDtCmxpQiEqINlkDGgipJriKretwKlGblpGBsQQKHTFaE0KmLzkf8DESYkaleiDpvORIQ5Vfa8XMCLaqiaamk5KyzMjV0wmczgqO5zX1aIau7qDBZbCylAw0ywA9n7nPooAwjKUHzjTZg1tSGihIaRBVGU6CDKYcWsBaRAwJRQqJJIHoa5wxBG/pQ8QnKBB06DKYigSuAR9QPoHe5NFAVaBsgdgi+2cLSDFBbAPOE5XFi8Elg0UwZclkscCMpqh6CiRk0iEhgco0vBj7AYG5d7US9kYxEjOVCudBtiTpoZqgSFTjKYvFmNDPqecHtJcd6+WS3WqDLXeU3RXiHSrUqDAieSH0ibbpiMEhKiJ9yKJxNlEImZOfAh7BE9EJvMkcbEUCibg00OC1IqoISiPiMUWuxm0xIk6niFHUVU3bdTTDhGOPy9pPO/4UIsY0CIfchg9hcK26X3WqO5qrdwVM0o3pCdRxTFHcn0N2XYvrO+p6fNNGf70KQAvcNwbZitxsczi2PJjnBek+dgm2rWe1usLFwKhKGC2IJDSB7xKV1Uyq4t6oVpSmLODsIPJwXvJgNqY7PCPV0++077yvrN1/ty3+lq0GF6w7zxQT1OgI2V2higozfUB0V4TesXYKUx6iRgUkTxEDZuCXS3DY1QWsLm6+ibQ4QQ7Ohg2EMJ5hNldv+nkAYIuC+XxBMT5EjfMcuPQtOjgaWyCux2xXb6V/3Uai22YKVVlPBkS8AWWYTgrM8ZjZYkZ/PSKsDevNFiXCfFQh/RLVdYzdNSFCkxLd7jrrPwCu27z1HYPvCb7HuYQbfrKaLaX0CCfDcCvR7m67HbYo0dqglOJgOmJSF/yf1nzrMc+3iR+UsEVkAfw3wJ+T7yn/OfBr4L8DPgQ+A/55SunqHbsAIIbEdt0SgkepXDklBdrlD98bg9IGpQzamDzbG9rKAF4NnsHDzFophdK56oJEjB4tGkER2UFMeaZaZDUuQWh0tmUrrcm2jcpQplkGV+lE1AmlhWpUobVB65qteoDtHFZ6TsqGsW6Z2yusabO6mbIYbTGmpw89vRjQJT5FUvIoFIdlyXuzCqkOs3G91pyNI4djzccnIx4djTiYjTDjAmoDVoFOuYokgB8sPb3Oq4HsKUYmx0UcjpA83ncD6MtT2x2miNgCGNe3Jh1FlUVIylGejesK62HUB9h0LC9f02w3rPvAaueJW8dhWzFRMNMesQokEn1Eug2SAnrX58WWNiRdkLQh2DzfjcUYNZ4jOqMnBYOpJ4znc7Qu889TC2r2AMo55vwCv76g27xG0hVaQuZC70F8YvNrooJUDmMCD+vPSe4S0Zb5TFOWC558fMi57nh5vWH5Yoc4x2S2RIcVZSyw5mPWIbDbOTrfEP2QtAdswFXnKEVRKp317CWg6LNdpyiUdVTaYJVCa0OvAq0EuqRIyqCKiip4iiowmRusnTI3I84ePgJt2DSOFD2ROCDP1d85SvyPdj3HwG739bZtjBFrb285gjCfTNBvkVzddR3tMCtsuh73xpw041zg8vLlH6xNx+M59Sgnx+ud46+fva2XnUjxiM87+Osr+LOHYw6rltPy6Vu2fXe8N1d8dGTQd+7Rk4PHHCwKzt7r0MeHdPM587to7G8dglk8Ibod/uLTd26lijHm8MM3XpoX8vboY44P4fCx5l99+nOWV2vU9V9z3c9YdjWs/l8O/IpvGkyY5QVm9YZb2jtm6OVozuTxz9B3Rh169hDKGe2T3xB9j6RE9eVvUf23U5zz11+g7Ahz/DF6+oCyOOC9Ryue+VM+e9URn38BILPETAAAIABJREFUyws+ac/RekKWOvp+8fsX91kBSguTo7cD+FdXl+w2a45OH2YgtQiLg1PaICy3b18YfNf4oRX2fw389yml/0RECrIT+H8J/A8ppf9KRP4F8C+A/+KbdpJSIsTsu7w3SCAx3KwyiErp/BBJma6FGVDQA2xrUDoDBti53Jg3xJQVZ0QikcyBVjC01RNaVNYBThGnBCU5oRPbvCsf8eIRLfjQYk2J0ZZYarwSgrZcu0QXIUrNlI5SArFzECNaKTof6AmAH8wchFrBuKwY1ROiTmiTGBcwqw3zWjOvLHVZYqsSKaucUG01OGGpWyOPlIgu4F0ghIgP2TAiROhdNqLo2x6rFdYUFGVCFYlUghTFwBtXN25YN+pGKaCMwWCoJoqojilnU8rxlG6zwm83mMvnpO6Kvk2Y2CCSBU0iEQkRGdTrVPIQsyds9H0G6sWQAXGSkf3F4gA7X6BGkwww0QUUBUmH3EpfnOCjEJpE324x0qJVzBzsKMN+zI30KREkSG7X+waal+hyQWFLzk5q4mbK9tWCGJa4tsE1HUmBShFDg86oNvrQk4LHiMYNdDSFp09CIANljIARISmNVplznWLApUQheSiTf3cqg+u8ou92hBhIKRvHhNBjXYtNHpFwM8bZy17+PXTE/zjXM9yoQN0N79093quI0HYWawyFvc83zuOpARil1TvRtym+TcH7fvR9+43z/6KoBsnUPFoKCV6uI20XSYUwH7/B006Jtu/po1CY+8etBMy+IjMlthrz4EhzPI2c0FMqMg5DaVQ5QZWTXC2+7bP1G0Lf8rpZ5JFRfgdSKIjb3F5WCs6O5J7MvJhyoHi+JZS+8QA6OTRMqhGyeC//W3TEyxVFc4nbXWDWV0j4+uLmhhr6DZFE8NNDGM+yw+D9lgNiLNXilM2mZbPpsUnfeFjroeL20wNMNcYUNd3mjiJdypr9YXOOKqcoYzg7rthtJrx4dQDxKSkM40H1RnsdWIURRMVYtVzsMmj4ZKxYtYnGJU4mij19Przxs4sp0u2u2COYy3p2081IKRK8Z7tZEfqGvtsxUxvW+rb1kGI2FdE6azh81/jeCVtE5sA/Bf6zfLCpB3oR+Y+BfzZs9t8C/yPfImHHGPKPbg9uJmUQlwjBJ5SOaBMwSlBa7o5uSUnyBaDy/FJuKGHD/kTfCvOroZGacms6i5vkVm2KCRcDWiVUTITU5FZscrjU/3/svUmvZUmS3/czdz/jHd8UY0bkVJlZ1cVmdxNEgwQECBC/g7aCIIA7gl9DW2250UobQRsB0kIrSRABqQFJZHezWdVZQ1blFNOb7nwGdzct/Nw3ZERWRuRAJIoy4GVE3nj33HPuGczN7D8ASts4cluRuQId12R5RpSMvhNyK3jjsQhGWnS3Ax+xYmh7pYkBYuJrGzGMM2FUVVSjKd5F8jwwqRwHdc5B5ZiVjqrKyaoSqjrxrfPRlRSnDisPDUpoA92uo20926gEMQRrkzxn39Oud0xmI6qioKozbKZorpgiH5TRdACJR/ApAUoE8uQbbLKKbDJJqPGgtM2Wbrdh8/sxnH1B+6xF+hXGKqYEbROamx7MwOsmRDR4YuwSH1sV4uDghaE8PiE/PMHUM3Al6op0zDEgLscd3UV6i18IYfWMTHryrMOEnisDMOuQrABaJCoaTGIb9C26/RJjDXl+wMO7NXE7Y/Gig9NLfKu02zO0ABMjTtZYNZgIjW8wUcldifc7gu8pXaQbkM4RxYklJ0dKwUnqNPQxYolpEUDiVzvrCCh9ULomQLuj2TT4fofvt7hmRRYajPiUrFGipmva/oAZ+/u8n78u+v62yIYgbKylLAry7LarV5FlFNk3qJm8ZrTtjrb9em73ZHqAu5F4VYUnly0L0+FzIXNpBn0Tpb9tW7wacucGtsfwXLnR4s+KmvHsDo/udtwtW+75DZ36YYwnmGKSqs1boVfJMDYL+uUZn56NaFM9M4RF5F76jEy497ZJjnW3NqM33vCVC0fSf+6fCIny9JPhAz3988hu8Yz1WY1tG6TdXm/rakbzGiGG/vghZnr08ucDRiz1wX1W/YaLsw0H6pKCIEq2PCPu1vjxjLycUM2O6XarG8cF6jvC4guYvYWt5jy8V7LbTvn8WYe9zFDaYZp0k2GRCplzP8IGoc4bnq0jXYDjGi52yvNN5LBOUtavCtXIdjmotImQ5fU1A0ITX391ec5u+Zzd+owZsLLtMLpLi++ubSmK4j9swgbeBV4A/62I/Bnw/wD/Erirqk+G33kK3H29zcnV6nY4JWhMEoVGBA3JotDTg1UkA5clzWa1bqg6HURP0EiIg4pYNOQhI5jUXsz2qlEiWHGgiR4GYajmARLP2zuPmrRq8jEhr716fO9xbAl9S17klHVBnlm8VbpYQik0LuOw7nHaUvqkLNS2SrtzdH2gtIaHByWTWY1MxhQSqXNhXFnGLmOSO2ZjS34wQg7mcPQIsim4CXhF2yFJr1aErqPzLUE9UQImd8Q+EDcd/eoCsY47j99JGtys2a2f0HQXSHPGZD7C5YY8I819g4KUYIfPKk/AFoircK5CTY61FVlmidmM8Z/8GfifQvdPMOsvYXNBfPY7wounhMUl/vycGHpi7EG3QERcRPuA2hw3e0yIO7BK/fN/Sj49TM5r/S4leesgL6AcYe+8haMgC8Jit8I259jNF1RVD9Jj1p9COIA4R3I34BY8spMBQLBCpYf8OZVZcTAO3L/neLGZEDTgtwsMLQZPps9od5azC0PfNhiEnU2gRytCVMVpxMYIzV4vPUnsJvChHewxkyqWiSGZ02QWK4niFruQgGXSYfsOfEcVd1Q0lM7TdsOibLgfXvth+e3ie76fXz/63nOxWjOuK7LvaDbxbWKzWV4pWZXViLpOXNxGMz7rTiiakj5jaI9fn4Pep/3e7rbUmfCffHSHw4MTJpNk8TjONhyWvyWTY0JRs7t/j/rkMfnsDvmdDxH7cqs2LL4gDnNVjR4jkY8OfsfFwvPFs9QyrkrLu4/qYQwE8RzWm0u2iyTukZU148OHbC6eEPqWycnbtypcU05f7XEtFnf0PuODt6ne6jB/0qLtBn/5GZuLJ/jVOcWTT1KF/Q0hNuPoZ/8ZdnSAcdkNFPx+JxzZ0fuc5GeM66d8bv8St3rGg9XfpPf7juqzX+G3ay63C6Lv6eKKiye/urWZUbulGB9BDIzqjLfuT/jV+WN2cc1h5jkMO94LW56VDR+fjfj4bMTz0+c4a/DTE9xYKQmIXPBgKtwZ35aSfZPYLp8RQsfk4K1br+fOMR+PExBToarrVFx+i/gud4cD/hHwL1T1r0TkvyG1y65CVVXk1WRHEfnnwD8HroAoX+2iCdcr1/0DK4ZIIGBMnzBXe7CwDgl+4CGHEIbqO2KivQZGDcgd3fuRSmpzpNH5leg1EDFqiINJhuxFOzQSYp90fCVRbpI6VYFiiMHRhJi0yvPUtrYmMHT2aX3EGCHPLdO6JM+Tn2suSfGqtBl1ZqhygxvlmNEYRjOC1IQGQrskND2h9/RNg7bb9PC3fVpwGBAyjBiywqAx0aCKIsl6ImBtDjYHMkK/QILH9z3aJk11CQZsjdgRpnqIZBOkOIRiBrYacG4WKxZrXaqEywxyoJ4QjUOKKWa2gPJLdLtAtwti011//8YmmVGNmLIiqwvc9BBTTRIjIPj0aOzblLSNQ4oaOxqRzSbEYpoQ3E2O9X0CcWmPsan7IGaUAHQMZieiqO+RboVqi9WOwsGkhoWzRO8IfTKdwXisNkRvaXZDu0ug9R5H8izvQ4+oYDH4wUwliuBDOr9ZYlATibR+GMNIILOBK+5+HORhJaHSDBErgjOGzKSF6jW07puNFr5jfO/38zeFkvjYqqkD0Q+yn19N2jFG/FdAVu4VALZviiwvEn/e3670YwiDih70XUvnGrIsOem1aui0oI8vN9VDiPTRo6o4Y5mWlrK4NrYoyo7pyDAWT0UkFlUCfOUl4iratmO32VeOPfgNtj1Dwu1ZrmVFKTCrBMlqysIycs1VpWzMCHE5Nivp1qfgG9osx599gbYbQvS3hq52fIwMIuYiFslr9i1eycoE4Sz2u7UDJ2RZhdRzbNcR+x2hbzG7zSuTd8wKqCe40QFmECJ5KUSQrCKvxpj5HJ839G7DWnJK9TiNSLcjri9RY6Acpfumu30ddJvLq/m51Z5Z1TOqLRIzGknXVaE9uWnxvbBYp+8sqLBqPFmWIyJc7pQ6g8oJy1bJLNTZNyRVBd/vrsR4fL8jhp6+3Vz5wadDFZy1KY8J36qy3sd3SdifA5+r6l8N//8/kG7wZyJyX1WfiMh94JUq76r6r4B/BeCcS03wK6bO0DJiYCKZlGAjSog+qfqIBxNRXKIIBEW1RxhI8aHHo4gKAT8ocwn9YNuJGEJmwCjReHJyLENrnEDEk/lZeuRqxKgbLD8DPvb02tN2PX3Mib4fgDMFxmW0AbaitIUQJSDS4bAQI20fmc9KppOSk8NDyizHhkhVCmOXMzUjjqrIbGqxJyPk5D46vk/TjNh88Yz1p5+zXW3o+jW7/pTxNFBUQj3JsDbNfdVasvEhozuPsMXDdGGtn2LLGlvW5PMPkqFHt6H77f9Kt7vEb87xzSbJiW4ucPRYInn1IbZ+gJn/CYwfQjYBsxuQ5mkUkczni1SRTw8xxx/gJPlml08+pv/iY/rPfkn/ZUtsGkLXo6ZEMXSXT8nf/5D8nQ+w9QjjioT09mskduiGQbnOQVbgZjNqGzBfntH6jvPVGKNbcAFTedALxK+A+6k1XuaDAA/QKRou0ky8zKiccDwSLp3QhgzfFphijZqejI7QCdu1YTou8FFZNR1TU2EFNt1u6OwYms6TY3A4tiHgTerkBGkJ9KxaB2qwRKomIaPVKN7YpOonDmc0+bbEEmcDlcmvZt97DMYPnLC/v/s5c6+5o0rTNDjnKKuKTdPgjOVgMuZmG7XzntVXeKyTqqb8OrvDV4SIMJsd0XUty8XZ1/7evn1+eHTva3289xFCoGl2VFVNUb5cLZd1xfG9ezzqLyiC8FVI1emTF/zu4wE81l/A+pfcvXNMXb2szFWMD/jw3Udkxz8BhP40VZpiLO7wHZxxjDTy/K//Z/z2guWzTyiefEK2vqT79Je3tmXvvgNFOjbJKrLjD78WICFZhTt6n+nR+2jo6E/eYnvxlOb8S6pPf/lK8w8/O8Yfv8X0NbolppqTFVNw5+zE8btsxmO/ZBrTdt3qHLu5ZPf4Z6h5+Xw0qzOaVTqfBrhXQ3xrS9e9rHHeNFsWl1sOj+7hfc/lxQvmBycEY/nFeeCdueHOWPj4NHBQCT85+sPSsqBsLp+89Orq/M2Aim8S3zphq+pTEflMRD5S1b8H/hnw74ef/wL4r4c//8dv2pZA0vJQHRaC+6Y4gyswMCCBA0oUHVyQBu9XkyfwSUyr8b30pxkKZkSTAIUKSEigHyW1iGOqXoLZz7odLsF9CDIg+2IyIImqBMwgKpI2HfpAHzt832OsxWlyZvISEZuUlyBHpB2WIZHjKuPeuKSeVZiY6GmVGzEpCo5GBaNJoJiM0Pp9VhcjurMWv/s7IFA9NNShAFugxV2yeoTLC1xVI/uEbQokq7HljFTa9/iqJLRLtN8RmgtcVmDrGfLOX6L9Bg1rbLcj9g1h/ZS4OSOuL2guLpD1Ge78bykmH2DLE+zkJ2iWkOTiapLZiBkAcRlSlomnaSzMH+OqI+zDn5N9/gvCxVO6z39NLxU+BPoXv6MqoKoSME1Cm8TXggcN0DdpcdavYXyCNTnUM2Ynx2yNYdl52tUG2a4x/hSXKS4TiD2UI0RnSMyhV2gasBuwHcbMMRqxrqMagdASfHLTUic42WKDQGfJzBRBkdARjaACvhP6zGAyh1hPr4HLtmeqikTFS4dmgWgiu35DDA4JGbZKAKE0JkhUNLVjxFkkEwgdlRju1jnrtqePigTF4IYq+4eJ7/N+ftMIIdLsduR5QURYbm8n51cBzpqupfMvP5T3URclzl4/cFWV5fJ8WHS/WTTrMzahg/LV65C6LJiOJ0wO3iL7Cv1Mgf74Adl4yuTkbbKiIvYtF7/61xTq+MnbA7885uBr6uNHZOXLIjPG5bi8SvxjwB28Q9yeov3NZYAwe+cfX8mT6tHbsE1grXZzSbe5JH/xOfHyOd1f/2/pHaMZ8vNkWvOqkKzETtKcXYzDHbzNaHSX4s6HML+H31xcteKl7yhOv6QYH1CdPLryQQclLJ8mlTbAjo6R4prSJgKP79csyyO+/OIjWP8C4vVCQKJSPP8MX0/xBye39s8tzjDNhu7kYXreDNF3HauLC+6MJAH9viH2YDTtSwLnXDaRX74IvD03VN9UaX9DTAvhoxPD33bCK6jqbxTfdWD0L4D/bkCU/hb4L0n59b8Xkf8K+D3wn7/WlnSPbdDkZsUe43DtO4pCNJpy0ODslZDf8Soh+xDZi68kELmmhB1T0sYMiVcjElK1YwWihgHks2f2KtF2yDDjjoNJfcQMrXFJSGCJBDwh+AHxe+0yxlXb14IxCThnhXHhmJQ5WVaABzOgTcssoy4cpnBEV9L6iu2qoW02SHhKPh+TH87IXYnJcuxoginniKsweQ1DwkYGuVJTgq7R0CbxEu3wTZfs97Ick5UwfyuhQ8MO7RuMb9B6QlxMCVLRLbfQXuB3z6EPuOKUPMSkYZ6NMfnhcBk5cDXiMvAFkldoVqY22qSCyTFGI6Ycods1wRu0a9HCYcqMrMpTQoqDv3ZIFDRij7Sp60F1gBiDyUrK2ZTYB9blGr8Z0YWcvm0h9IiPqDGItgmgKDVYQeMOzBZcC3WFRI8xDXnhCH1P2Ma0WBAw2iExGakYNRhVzCDQkUQTDF4NJgpOkkFN5z2lESzQ9T1GItEqIQwLAW/o8wRqjOpRLwOyNCTwoyQVPCuGyiXfdzMIv7Dnmv+w8f3dz28QqhHvI1mWoyTO9j7i16DA+xCSNOLXRO6yq7mtGcZfN3ne3xQxBKIJGGMJoR94uwm4BGCNwZpEBR0VGZOqIK8mV6Az62xyFaSnLefYckZhHNp1aNzQnH9BNbvL7OjhjU89wM0f3UpmXxemmqP9dliADAlFhGJ+DWILzhKbJArSXT5DswzZrNC+ob94mjQHNgvC/c9Sy1wguhwxFrP3LQ89pjq82qYMGv2ZGLwE4upF6n6G5DUQl2cU9Yxscpz4xxoS0HQoFgAkr1Mnze7dxYTZrELDFF4EvJvQxy0u7IYjU+x2iYoQxrdb7Ha3xm6XdMcP0ljJ++SKFwK7zYZdVuAy+1JzSkSw1g3OienvrTq0T/ThPnjOtpHjkWIN5K+J+FTAq8Wg2EGYp3Ay/BismO9kt/mdEraq/lvgH7/in/7Zm25LTJK33E+KzDBvhUGMC1JCDYke4zWhN8UIuU0KYjGC9wNv9eYbQyQSUJJDVRyq9Kz3qfFuLMH4NP8NPj04jSOKGebCPUb3DIF4xQGFtHDoY6CJDQQoNCczQuksNksP4V4zbG0oMmUWIpP5mHo0gu0YZyyZNUyrmrLKiUXJuR6gl5Hmd/8OI7/C5QsO3v2Q/OgfUb/952TjdDPE2GP2lSxJHCKliwJufJdYg3VjjHlA1h7Sbc+IIXGhrRuBapIPzQYHrWoG04fo3SX27mO6xQt2Tz5h88XvkBdPGX3xf5NlNdaNyE7+BFPdw44eIeUx9A7WPomDGAfVGGyO2gybldjDt3CuYPPbj+n9GcXjh+SP3sPd+yAtNrot0p5D16XzJzHN90Kf5E6LCaacUN87AQPF509g9hhfjFg/f0HOBbmsyf0Fpq2x4QIb7oDLicaj7EB6JFsCHqct49kY0Uj3wiCZoHni7qtmQEW/3SJiGNmcoMmnupxWtG1g13qOixKNPbuwQ2LGLii+99QkG9aJFvSqtLGnaUliPC7JmsZg6FqD6w2ZF6a1I0Sl06STlouyNkqnyU/9h4zv837+tmGNYT4e7/eHi/X6SvToTWK9uy5lRmVJVbwZF3exOCXPS2bzE0bTu0zGCvJbEiRcmI5GqGSIMXx0knHv0F51lY0xHN25z7haAk95sTtCdgqf/GsehhVTieijDzGjQ7I7P739wa9r+gHYyX3sRHklKRiw0wdX4LJxOacczZAHH9JuF2xefJb8rDcLmv/rfwJArWX3+KcUszuMj9L7tN/Sv/j729sdn2An93GH72LqQ5zLWb74PX2M7B59RHb/p2THH4AIcXuBX3x+NWcGCMvUwctOPmIPRHKH72LjOXDBk8k/4Dy8w3vn/wei14syt1liB1vOq69LddA+B7dKHQTjZlf//veLhNCOXwFI5XnJ4fE99np0h0f32awvubx4wcHhXbpux2p5wa9OI5NC+Pnd6/P7h8Kr47PumAO75sDd5l5PqoouZiw262893PpRKJ2pkFy6VAfEdqJ17Strs19naUpDMigXXZmFGK5m1JY9//r6KxHdt8dTlWKQ1MGVZF0oWBgAbE7MoGFuMEktM20DQQxYNQQMQQZ3q0QOw4dA5z1N07OMhq5R2sbSthnbAL4yuFwYq+BtxSoUPGkUZz2ZE+I6MPaBqe8puwW581TmgunBEW5yArO7aO6I/oJ+vb2aI5isRmx6cIhxiGSIqYcKO7vadySAcxgqHIcYN7htkQBZIgNaXgxGhGgsuAIzOiEzRTJVcBW6WeBXZ/h2C90O9+KXuPwzsuJjbHUf46a44i5SjpGsTO3sYQUVjYcQiF0PvsFoh8kajOwGBHmRMAQmh7gB36bWdhi00LNx+jN22Dwnn46ZPpizOw30i0iTPyR2JgFTbIfVDswqte6zCvJEHVKx6G6bpFzpcS7DZYqSDAECDaHriJojZmAOGEUyTfN3koBOH5I/clAlKES1xD5pCUSJeJN070tnsIOFbE/qCBF90sBHwbRpsdkKjS1QApmBIhPyIJhOcXqba/vHHHLVUXi9yKxL9KqviZut8dcNvdHZO9t4Wq9criesd8oyCGUdkSxQ1RM6V3DeFWwuUxvXGkNRLwi7jibL4PKCIovcHQX6aowvC+r5XZwxhPWzW59rqoOrtvc3xg0K2av//UYiH6SbweCKEfXhfXy7JQzoeLtdYNqG7OwJslnSXyaogrocPz0iH02vzDtiuwZ9wm51Rmw3xN0loU8dsezyFDl8RpidYEfHw5f51QWXotET1s+ST0AxBTGUhePh3Yrzy45u63hha8ahYaT91fvkVeJBMZJfPEPaBjRyEHZI9JzCNUX45S8HQWiaDaqRqhoPXd7IbrsiDPzzeI1Tfq0wROZ2TWleln3bV/PfJX4UCRtIkp177i9JeGP4tomDZaFAqoJFMHvTj8GS0GGwYnGklkNiZw0cPE1fVFLTsulCNkJmAoIhqk2UGwOFCNEkJyVjEzI3qiI2JTQXbZpwiwzWmOmC8DEiPtC1PcvOshE4FUtvcnpxhKrAYcjU0otjGSyNKs5GXICNBMrOM2phvN4xrnoenKyo5u9QHR3TjyqcMbjdKRrb5IDkDKaYYVyJcRYxOcbkYHvEDuYg6RtILWXnEJvhbImIx4hn4LGholfDABnGEmosUh3g8hFuNMZXE8J6QfPkS/rz3+O7Z9iLT8gkUNiIK9/ClXeR+Z9hOMEwhtAk8QXviX6ZFlmmRPotJrZkdofVFeIX1/trs5So/S7Ru/ouAd1sBaGB2GGOH5FPxkwfHuB9T+8Dbf4WofN4v8U0SzKNGLNBXImEgJhJOm9i0KZJ4DMbyGyLcxAlQOiI2qbEHENSlRuAFOKUsOtBhSKfsmv7NM9GB+tPiwza7WqHxK1Q5IJxBoKh7TqiDrx0Tf5dYrpBUEHpnGBsJDOGPBPyAHaQxf2js8O+ETeVt1XhVWD0/e8MfndXr2fOUX8Hj+FvivNNz/kG4LpVXVTpB5IYatMBgzKbs8JhfcnGGl7ggHOmo8jxu9BWc5rplIPJCQL0i6fDwaVnmmSj10/Y3zJsXlLnJct2R1+nhC0vYkrYQ6Ley6WEakzz4H2stTibCoC4WxJ2C9ZPfkP0w6xZFdN3ZBdP4fIJYX6Mqea3k/XN8SYRv3yKHZ9g8jGIUBaWB/dqmjbSNpYXdgyqVMGzd7p7VYhGsotrLORhTI5mYpJ97R+6cZpmQwwhJWzSQm37lSr+m0KvHxFYiRy6V6ua7U0Qv0v8KBK2kCpXFSFIYG8NeSVEIFxVvZmTYWGpID5VKH2fZEDF0pmcqJ5AT+byIbGDujRHzmP6RJWEsJQILqS2dojgMKj0qER8l2YfEhS1w74MSmAWCHtYu1WchEQrih2dVARxeM3Akqoz21/Zfbbk9JLRl4bM5DixeA82eqwPWCPYjeHvlmMmX66pCs/RbM582nB4sOXkbsl4OuLg7h1qN8cWNa4YIdKB9CARpSN9+PV8az/IkSxLowAxQ+LoQRsCbbK/9LuE0vZrjJSJglUe4Y5r7KzDHt7Hnx4QLr+ku3iBX+1oFhtYnGKXzxkvf01eHeGKGW7yAbgpuDHie1QM0UY2qxc0uwsqtyVb/p68apHRFokuAcTCCvoNLJ6lWZhxqD+H6UNw70DbYbOC8tGHTMIW4xacP5sg7WNMO8O3U8pwicYzMs6x+QIb1smv2xaopKet2Ij1LVk0jKoZfdfSe8XHNUGFAHSa4XAYsfh4gUYwWlHGFvWGIB4UCmMoM6FwlmpUk2eW3AnORcSk9r5Zdfg+NQoMCW9BFnDRYWOW2nchw/uCCVuQjmfSEGIk/JFmbGcdRVkiIoQYuVivqIvylnCKAJO6BpTVdsuoLK/U0cx3rFq+7whR+cWT9a329rS0eF9hf9vgssjx/S+ZT0vms9Sqnx8d8P7PPxrmuv9hYnz88CqB7soRzXie2uQ31M1Ms6X69BfEz39FM9Do+tkJ/cEd4g3QX/78M9x2iaiyW7xg/eXHzOGWprnvGpYvfn+oio0KAAAgAElEQVRrH8rpOaNujTt8F8lHZCcf8bb5jONDx69+82ec7z5n1XzB2/2C7A1Al3lRcPetIy7Pzmg2349T1tfFslV+fRZ4/9Ayr77+WvzgyHBZGf7PN1sP3IofRcIesGYJ7CNJpjFdR0PFJ2l1PTCJhhj+ooqGiJoELhMrSaZykMRMOlQDQCwqIQ4gNEjSlRFsTC30BBGxJB5QTNLcuv/QBO4I+x0FkNQmN0YGMwpDH5OMaaLmWowOXNpBC1r2bfy9nzOWCPSi+CH/OxVMENrO0UUlbzp2fsPltudsteN8mTMabzg4ixwetNTjEfPjI4pSyQtSNWciKd3cnG+F1N+xAz5AZOj5D2MEDah6oAMJiJWBr24H3jaIdalz4U8wNtHf1JaIFsRNJISWNnrCboltG7LeYLIpks9BKtQU+GzCbtfQ7pKojPY7tL2E7AI0B2+GyrpJP/tVupDoY1mF5BMop5jRjHx8gD/syQ83tNHQ9g4b1qgabN8j3QLwiNuRFjEWYgWakrDRPoFEnNI3QuwNwSd/czE2WY+KGWQKkxoTQYdRSzKJMCZ5XBdWqLOMcTWmqkvyImNUJGOQHk8TM0yzYxPWoInDG0NEYkRiwKtHTIZ1Dh/BazqFaQ37x5mwleR2ZofrMsTrSqzIHD4EfAhJvnj4CkKM+FfIZn59CLlzb9SSjDHQtulhLyKDAck3v18VWn+bhrcW+PIiWQcb4znvThmPcsajdF8dHu7wpiQvClxeUk1PGNdCVX7HxYgGYrO6hSYXm6VKvlujA4fY1bMExl2eJ4YGit2ukr1t3wHd9dGYNM+1gLqMWI243DbIesdR6RKttm9pV6e3EnZstsjl81sAsBgjjcuoyjm2nCD5mHyUgGUHh57LFzPO11vmpqXWnlq/nhkQs4JYVNjtCqfKoYlsUbYKi62w6978/pmVwqxyFOWErCiwN0cvqrRNQ0nLOF/jTJIyvWziraK+cMKkEHY9bL9+918rfhQJmwH1bUUwNnkABw1Dkk7oxdRO0OFk71u3CUTmfSDLAuoCZAq9Il4HwYChAx6Ta0oXI2oi0aZK3ii4cMX/YieKiTEZplepskcMe3xwmlpbBJtm3kYorOBMjmLY+khvOjA9uU2GI1YNGrJhDi4pkcaI9TbN5x14a9GBb14M6m5EQ98BEjjrF8i5Ynwk7yOFtUyqikdvHXJ8POXDP32bo7sT5kc15Xg2GJElD3AYcnNsE/Ja7OCcNRzXnj8XA8lyZ4dYRV2OkCVwjSQ7PaLFumR7quMKzTrMuMWMerrFCXHXsFufoZtTaM/Izj8jy8ZkxYxYv0vIj+jKgsWqwTcNB9FBtyPuADcCSggl7LbQNtC1aL9NoDPfg2+RbplK1Mk9xH5EefAIMzphtmh4YWpWvgLv8b6GziC2Iw+bdFxkIFliCGiBlSlGNhjTYbKWGBTfCH2bPNbFOcwgW9n5Pl2HRhIoTiNiheGrRIxhkmVMiorj8SHTkyOq6YRyUiU9d9+CfZpcocJn+LAjhojfgQZPiJ429uRFzaQYsY2RdfSok7TQ+iNN2CEEQkh8ZnvjAS8ijKuaputY7bZsmuuks+tadm9gZ20QDiaTN0rY3vdXft3WOg6P7n3rGeSui/zq+Y1K78nttunByPH3f/0LDo7vMjm8w8Of/6e8+5bl0T14k3n+V0ODx19+eqs1LfkId/AO/uzX6KCqVk4OyespF32TtPdVKT/7GNu+XJ26zQK3WQDgRzOa6j0+WXX0y5Z/WgwpRSPrsy9uvc/sNlRPPrn1Wt9sWMWAdQX55AR3NMaO71CWM96LG/52c8TTLwz5uOGINfUfoPKFekJ38hblZx9Ttlse+SXn6nkR4HfPhPPVdZfxdePtueFgUjI+fMjs8Jh6dD0WUZTTp1+Sr84pSCDHXa98fBpvzb1PRsKksHy2iJxtvhs188eRsFE0JH60HZK0tSVR49VKO+WUhATfzwL2ds5eFJ9p8q4QTbaGGq8qacWkeWQiZSEKNkhyuUr5HdSnitk64kDgNsEky0lJ2GsFkjOzYvEJ4a2WLDqc5imZa0hylUjCfBkhWkElibdEdWkmGQXtlFYDqkoWAyou0co0JYaYCU4tokLMeqxCFqE1Qotn0a04/6yjfHrKLz7/kqODmsN5zQcfPuLgcMbRvWPqyYQsyzA2S99B6BHTD5V1PowWzLAwMigOtbPUbxBQyqGm1FR1m9Qt0GKMOqGYPcC5JdYsoJgQOsE3b9FfPqdbL3jx9HNM32K3l5Trv6MNJefbX1BOa+p7EyYf/BSXtUTbEJonmOgwoYZuSIq7MwgbiB2whXCBdqeAQLtItK/Dt3G2Ynp0wuasYSk9KwpaX+A3DtMZYqFY2yFZi7FZMgwJETwJXW9A7Q5PTzfMk71XolcKZxJOIXjQNIbxeEQ8GQHUkLucOh9xdHzCdDzh3r17zI/vMJrNmd45JmqkCx3l/DPOT5+hYrlYPGO3W9HrZrgGkzaa71ua5ZJ1G2i80PcRosH88LSuP9pQlOV2S5456uKbZ8ST6SGqkfUq8ZhjDCwuX1BW45e8ub+PWDeBXz7Z4M4+py5f8NOLCz4pDXVl+OCDR5zcv8PDdx+/8XbFZmSH795+cTAGsdMH2EFBLmxOYXfJ9ORt2u2CZnlKd+cRexvPxWLFZrPh4vT0dqfnoic+X2PznNn9hzT3jpJJUYwUzz4lljX9wZ2v3T+7vqTsGjbG0u1WjFHs5B6S1WSH72EqRbnk2e4hl/0znm2f8eGsYJr9sPfCQSU8mBqOj+8xHs84OLnH8fGEyWRMnHxIMrOInBz/HYuzZ/zmV8Ju9QL6lxc4l43y754FNt+iwv9q/EgSNgMgYfCJxmDEXrWpU1KRWwXGFZZgoHqFmEA+e9yk3ph3DKPjBKYyMrwg7HFtcV9lolhNimtRhuI0DqNebiDGB1DMUOenfR3UqHQAEzGA0uLQPFJDatsrST5VIKjijRAFjCYojWIIhJQshVTFkRK+KASSF3jURPVpuoAFVusli4uCs0mFEeH4zoa295zc9dSjmnoyTUCumNCcIu7Guj21IUQsqCPxuAflOc0TyEc9CXk1LJ2MAwpMOYEYUd+RiUFyizpH33doFLpsgXaK9i1tt6brG9bbjtH9A6qTA/KjYyQsUR+JuxcQDBJ6pHepivbbBDTTDrwAg6DK5iydlKyAcopUUNY15aikrB3bC0OIFgkFo1DifEv0LcbHoYJIqz0NA81AAe2Ig3958JI8rAcAHqpXNp4gRGJydTNgjKVwGVVRUo+n1LMZo/kB4/kho/kBk8M7qEBQz65NF9Tl2Qt2zZau80Rph9FNugL6oaXoQ0zMxKGr9MeermOMSNgzQr7fboICffCICN6lJCTCrYr+ZhgZXAD371el61qsyxKrwO05xN9P9EFZ7DzsPOttw9T1CcRkLarJr9sVJVVVkOUZ1aj+xm0C6b7+Gl63ZDVoTLoMg8NhVo6IwdPnG8Ie4wL4LtJ7pY9K6wPtFcfQw6bhwbvvMTs4xMwPicFD32KbNWjEDNam5hWqaMZ3qO/pF6d4oCvH5NkIaxxSjKlGNfNpznIlNF3Faut4Zyw37cyvjyd4TLt7La3zV0XuQAYxtUkhzEthNJpST+ZMpiNGkynlZIZOj8lyS+6UxtxDFerJJd1uAbycsPsAi++Jk/njSdiSVsF9HGREbTLpSNfR7RtDh3l03CMHA4S+x5tku7m/0a4lFxQT90jzbFgcBIJeq6IxJDATAtEowSgSizQ314i3qQQqCIkYpgYjgy6stfTq8YM5yJUIQfSIhmTObktQMBqw+zRubbohvB1Qhh5LT3CaRBdCIE1XhbxP7/IDYG4YPROGo2yC5WnjeXq24zefXzKtMu4dFPzFn7/Lg0d3+cmf/pxMVjg6jCkG0FnyC2c4fqQAsYjx142j2HI17B9m3nvbUhUDxRyswxUWVme4pkFjIB97xDjm3Zz1Ome9LHn+dIGxBdXxXU7+9J9w9Pgh+YEQN4G42REue7TvMHGFbiPSB2RYYKT2hodg04xNXkC7Ab+E0GEm9xif/IzjB8cIHU+/PKdXR+PuMs6VzF0Q+0+wvSKuIYZI9AHTdUlxLAi236FeCN4Q2iFho/RZSIu7XiikHGwyI2ITlW6aF2R5TVaNqeoZ9eiA8fwO9cEx1XSGG82wWYbNLFk+YjKb0vkVvY+EmHO+jXjdobEhjxAIbEjudblJNCFNGn8/zL33I4n2hrBJlWckJ6nvN3rfc7keLFGtu+J8fzUWXyNfutuuaXYbDo/uY38go5LWR/79l9ct87/5/G84mfyS90/+in/wD3/Cw8f3+dO//ItboLZvG+pb+tOPb41binpKXk9YPPkNfpDmms9mHIwqPmxP+WTR8Ovl7eT7+P2fcfetdxBgc/GE3UWiq6X2+fIP7oOglE9/R1idcxkD09BTTE/ITj7iJ+9MeGt2n//lf/+S0/WYRfMeXXwOvJz83foSt17wpm3vfTyYGOZzt98pAGZHxxwcHfPw3hidvo+WdwHhzqHwzgPLv+FnuHDI0d0V2z3i/weMH0nCvlnrDQQuTeVvSg1yjZEaquGoceAOC4GICQFiILMpWRpsUpMaqmEdZrkBP5TISensyjxkUFczDJQwHSp1jfh95S4QB86yMaDW0BMJvqMPIVXMKMbEYdYZhsrVgzaosUSbY1QRSWprRgxCwBvBSvKNjRKRvRMUAIbe6NCKh2DN9X7DAFqLw/fnCb3hUj2t74l//SlfPFkSQs6DByUHhyVlPgjRDwOD9D0nnXToUW2uXpfBFEH3vy+k5K37JA5iJKHoc0E0YPINpt1iXIspPaYTpCiZPJxSz0+498HPOXjvI4r5FLVbtFmjOHyssaQFg2GXqnrtkOiRqMPzKbWiCAo+QNvC8kvot0hWUhrL7HDO4Z0R69ywXXs629AbJTJBQ4f0iVkgZs/xjtBHYoioB/VxsDMUSmsIPoFwovUJ34ChEMG6pANeVSVZUZPXY2aTEbPxiHpUU1YVeZkkMhM4TbBlSTWdc3T/bS4uWjq1LLYbtttIG1pCCHhNGH+iwcd0DapE1P5xJ+yb0fY9m2b3Wu3rN4mbj/IQI+vdjiJLntyvvQ1VNpvFVSFR12k2vt2sKMqKLPuO+6y391NVWe56fvtiw+Kvf8+DZ2vqyZjjg4LxKMdO7vJ14imv92FfSXAifJVVl63OMbsVSQcj/c5kPmc8nXNy/xHzhx/hJgcDr/yrC4nXSaD7/VB2qzO87xjnI8Tm5POHHB4uiDHZ2V4WDbndcBS2r+hxpM/a+Min657lDbOQIi8ZT4obkqlQVWMsgRO3pDLd1RrI5RVFNUtmQrZEJ+9DNsU64fE9w3SUxrKP7hnqbEbf/ild1+LKMYv2N5xtApdN2hcfAk2XABfxO6icwY8mYUN68g8Jew8b3x/cTWA2cC2qYoaUM/CgY8A5d53idZ9kUhtX5dpEQbmWQN0n4z3XW4bPlNQ1J2jExoRW171DmCgYwUdFQ0/nB3ew4U1GFDUg+1UBEdSllKvXLXHR5AUdbGo3GyMEEwcudLjaJ69m37UnDIsGdwWFuz5GISJi6Dth3Xqadcvl+YajyZi6eMB4XFCNzLD62SNZNbWZU7YC2tSKH/ZzYBimNvk+URMHadb0O3KVsBWT7zBui8labBEwhWDanPHxA+YPHvPwz/6cbHqMyRzaKBFLVPAxSx0Pky5M0YjSY2LABB1IjGkogFcwAekalFPwG6hn5MUdxpM5B0c1KoatBgJbgno0jNLoInSYkERcEggioj61xzVGNMSk4Q3kxqTXiOnzBunRXJJtXuYsVVWRlTXFaMS4HjGua8qiJM9LsqzAGnMtk5llZPWI2eF9pseX7Lqe8fPnhH5H16ZZeUDxpOsjDKtHlf05/o8jeu9puu6NFcreJFQjbd/hrL3qsN4UbNqHyJUKxFU0u83V3/O8xBjDdrvCGHPLWzu9fw9q/fax6yK7ruPp4jlnix0/fXxI8c6EWsbY8ckfSNia5tADRuVWxHA1o/6msNslbnUBMEh5WqbzA47uvcV7f/IXZAePwGSE9Su9YV4vVCF4+vUFsd1S1jPc7AG2PuZgXhG8p+1hWyxZGOUofD1dqw3Kp+vbqETrMsqyvtWxrcqSQjwzdzrgeFIyz/KaanJMluUYV6DVw9QVFTieQ56lfHU4E4Sa0+W7dOszLJHli0/pgnLZDPiAGNh1L3cEvk38eBK2XKXiYY0Uua6vh//q7Ys+abLKMPlVrILRgadtkstWkiEdeNciWCxxqNz3kocihkjyz86S7FoSZclSMjKS4D6yn1k6kjuXj/gusGt74lBBGWsJkqRSexmqYvUYD0KyUIzD3DoYRSVpR9uQEcXTmIY8ZFfo+Gg9DKIuqOCjQfxgkmLTscuQqBlmoKKWjMFkQnrW7Ybf/e5Tju/OmR0YxvOI1YESozJQuQKpzdSgg6/QFd99v/jR/cKnSwsRNL1PFIzFkmgzWjfgVxjboqYmmJxoC+79/B8yPn5IdXhCVIN2Df3yGe3F5/QXv4d+MSRql86XBWMSGpsABgexQ0JMD5rWoT5PIJeuBvd73NRjR4HHj+8wGTdkZkXVe6y3xPaIYAxiNmgImBgxWIJXfB/p49AZsZIWSKJY8WRRiGKJUlA5yEXIc5hPZ0zHY8pyTF7UVKM589kh9WhCZsokg+sDvouY3GCtSTamUSnqEUf37yKZsFpc0mrPstmh7bB4ipEuJAELsem6jt/xof//x+2w1jIbjW49wNuuv4VGhyQpmWdfz49eLs+vzsxms/yK8IYwPzj5RuevN4nNZse/+X9/ycH4A+7eeXVLfx8aevzprzGjY+z4NvjLX35K7F4t8vGHYjSZcPdgzM/+4p8yOnxAfvIeiEX9G8D2XxGmWVP//hdAomhdqlLtVlSzCz58XHA0FfLMYuQj8AvozniT9vdut+Z8uWZ+cPdqnHHHLahNMmYqx0eUo+RlXo3GzA5PeHBvQjmpr85v5+Hf/n3k/kmqtP/uN5HNLu3D8WHFSOacPnuPYvcMBobB9xk/noQ9xF6+Tc1Q3g5V4B4mv9f/uJKbG8BfVyi0OICJXt5ySk57atgwCB5SDvsthkFrXJGEqr5mfLGHp6uSgG6DwlYIPrl/SaqQzNDaVknmJGaQ/UzHlqbVqa2ejCPs8MDYV+1meM0ihGGxYtjvdrxaxpirCvl2FZCkSNPCxVqHtQ4vjtW65ex0yfxoTFFV5GU1HNi+TWMHMFp+pTSlDI5oiff1lZ+QOgrGIhQgWXq/EUyeJzR8l5FPpkyqA8bHdylnh4gt0XaNb5Y051/g1ytCFxFTEI0l2gwTFQyE4BIwxgK9w2h6CKFrUIe4CqwBn0GzRtwFiKWUgtr2TGyPiQZLjsQxSIdKQGODhg4Nm7S9GDFRwASwER8dIQ7n0yQpXCdKIYlrXeQZo3rEdDyjrKbkRUVVTSnyInmEEwnRI74ndi1OSHzumK4d73uctZRlyWQ65eKyxrkMb1IDw+y7Svv1EkN75z+iUIWm6wjhhxkFCAz6Cant3PY9ne9falvefM1Z95LUqd543jiX476S3OUNfbv/0A4f1I5ZXZAVFZsdnJ43nIzOscUIyV9Gr4sYTDm9pZ6mvkXbVXLP+gMVdj6aYVxGt10S6gkg2PUFRVkwH88o5/fIxkdgMmKzJDZLmtUZcXmKW53jx/OrdrtbL5Dwh0nIMlTYAGJsAr9tlwhQjGZUeWBebdj1FYQ3T12qCZh8M8lbiTgr5OWcenJANZ4DMBrXzGcVeW6x5oZPXpqmsVzDk1OlaXS/y0mLIXOMpgfU4x1VtaNptlhjqPKCru+/k/EH/JgS9lABm6F9KMak5DtUyfsn11W3KvXNr1LUPllr8FeSkPHqXWZoPcuVcUeq1oeWumpSIwM6jYn8JWmmKUMiTsJgQ6UVQKPSdh0+RLyGweVJELU4kw3J26f5rhGMDEnQJu6uWCFzhgzBiUmVlBissVhJFB5zA2CWNNaTRvXARkNUEtVHUvdBBh1w2FfHkLmcLC/QsuZiuSP78gUndwsmzHB54qbvOxhIDmKwe6UOkmkKmihMiRqnQzJPIitiBTQbvqMCMRkYhy3qhBPYGKrZEZP6EZOTB7hyhpiS2J3Sb87ZPP8tdP8fe2/yY1m2pXn91m5Od1tr3czdIzwi3ovXZSYvQakSBUhIBUioJkwYICSEEKiGSPwFxYAhEkNQSQyYpQAhMWDCBKYlFVUSVZWVmfXyvXovGo8Ib8ytuc1p9l4M9j7XzLwL93APFJWZS3K5NcfOPffcc87a61vf+r4eCRbjZ0TnCUWBNYAXohaoU0wP5qJA+y6zx/ukiuYi+AKxHWwucx+jpbRHTBhYmDW9NRgpMbJI41waiHGdpE870JAWeVYtYnvUBfowSUlbM5NY0nlunKHylqYumE1nLOd7NM0+vqiomklK2C6J/4ShRzvJC0xJzPqYnIS2mw0GoS5Klvt7PH46wRWeYMFExcQR3NAkparwHMD0lz4UfaHafV8xPjd0ZGeostpuXjCJAHb9R3jRtvP5qKqGuvl2t63vEgKcLkr25xNmywPO18LvvrxiMf2cYnEH95KEjXHYxf1bP9J+kww5XvtiQrM4pt9e0a0vGOYHhHpGvTpPLaD9fYrlPUydRE7i6hHd5SOunnxB8fhzivOnbB78DHWJ5GvaDXYndPPmVXG3vqDbXuGrCaVpOajO+Dr66wLqLaYJnr99xqLEGMdkccJsecBknoxD5tOCo4P6lft6dqk8uxyLxuu3JMYyX+4zX7bMzju6bouzjmlteRbCOy8+fzAJW0cpUh37yanHFGPEmAR9q+6eYjuiWIoMDCv0IUHhIUucpA6xyVVpSv1RIyEqgsuFd+5Rk1SWgiTHL2dMroKTAw9AaAc2fUwVNomsFcfq3qSq1niPsxZX1Elm1AjeFhhxGFNgfLb/NEn73IhhwOya50oykwghQDSggolDqmtVsNYjkkhQxD63mX1SJrOChqR/rhrRPtC2PVfrnkeff83+vGIx67j34T3qpsL5Mve2XL4BkrBIIpYpRjwpq21R+kxOM0Qc0OdRI8mTURtC7Gm3U9YXhn5rsTTU8w+Y3vmYYroALKE7Z3v5L+hWX2GnFsMMqyWx3xCHls36AikUW1Y4dw/TbTF9T4g9ui0xW4/pV9BHWK1BTdIbj6TrQ8FaoQrCcqJse0McDHQOiRVGJ4hsUAkMoSX0gTAMKJHtENj0EWtrvCspY0F0SSTHo9jYUkTH4fSEg9k++4s9JpMF1leYsgEDxjl8WeLqGlOV4EtsUWALS1SPGQxD37JZrWi3G+p6ymJvj/3VIV+tnyUZ0rQqZOTZiRgk/hXL2N9TCDCta0QM51crQF+rJNdU1c5cxNzoAxdFxXS2uLWtMd/fI1UVfvN4w2dnLe6LSx4cNZzuN1j5EacfLbi3+PZ9AJhyij/8ya2fhatviNtnr39959ne+zHF8pjJ/l3sjV791dOHtM++BJR+ccww20etw67O8U+/ol8e0/nER/BPv9oJr7wqZOioPv/nDItDhsUhF9/8dtfCPKjOoIhsm0/xZ1/jrl5/3GNU9YQ9O8FaR2NaDtwFhQxAalcs5iUnp6nFYL9F9Pt4T7h7nLa5WMGvP4s8OduwebZCgTuzAnsy5e+fGTbtlvV2+16Qoh9MwpZxdplrbs2104peL6TGDUYdb7i1dErVdfp3U8pUbm0oCUofRVlUdyXpOCalGT7fVTW5yI9DYAiREDQR0CCrlyWRFeMczjm88zhXYmyWrzSeZDxiE61IFQlZBDWj/yKaIPOsaqUxIOIAQ5RA3OHzZFW0BMelxUvEm6Rd7QuXz0Ui4qlGtqs15x3Qb7g4O2f/cEEIHc67vE+D5mo9fQrXM9djrzq1I0ap1xtjRpIPxpQoFX1fs1kL3dYyXxxgq32KySJBwmFA+ws0XoFscU2JoUBigWqPhj6NXFEktTZfYozD2I7YbPKHoMntKgyp0u679Pn5NdqnCl+sYKOjcJYQlGBj6gWLw2gBFCgdGgMxDomBqtB3hrY1WCnxpiDYAjWSWPs64IgURmjKZNlYViVlWWJcgXifpgiswxiDsdlX2FmMTeIsznh8WVJWNVfn58QhSdSWZcVsPuNr79ChIw6ZzMduefpiifDX8Z0j3fpJ3jTGNN5pjL11jo1Itr/N97O/3Ye2zr2Q5EPoCa+Bfq31t1jKr4vKGwr3IpweonJ2taGyEULgg7Mt8+O3kGk1DiluP/rlW0bUuj7J6E4Wh/j5EcXsKFljxgHtt4RuTRg6XDkh2o6Yz4GEAbtd07mCWKXZ8VhP0oIUkif3S+azRRXbronbFaYo0c0lah1SVHjTg4HoGvRbjnvTwSajXKWFWaG0CkYilcnHaISirChLT1nkz8YUYNPxqntx5t17mE3SxdIP6Rro+0jXpWdiXRUs5xNMdqHsX+Pd/jbxg0nYIySeKmB2rl3kyvnWpmN/L2W5bL6VFMzGZB1VU2WCppnZrPVtMKlPyTCi6qCjXWfS5khgtKS+VZpcwmtKjqHtGXJOxQrOOrx1Sa7TOJwrKIsiOdvYAmwmqmniTvXDQOzbtFqMFokGwSSbDqsMVvFDumDVdMmhyniwXRI2wRHp2VkzjgYSGIrSsZg6PjhcYsUSo3Bx/ozNZsvZsxX9duAyGJ4+esLB8Zy+u8QVloTwWcidcsXmXrumn6vPX3uEiOqQdMdzL3t8ZoltUCLrzRHnZ5d0Wzh48CHl4hjfLIBA7C8J689xdoWZAOUB2gZ0O6CdoJ2B6EAXCc5relzZIMOAuBbKAG5AKaDbQHuGdHlW2wAxoMMWcRcYU+JlQk8eY7MxPTCZQFxBUELfEYeOMES6aLi6bLh65ijKGUrqybeSHyybc6aTKYvSM5/VTKY1VV3jyxqxBWI90RjEWoyxWPX+GlgAACAASURBVOtSxZWRGjFCPZ1Q1BXWGdaXl6wvrtheramLmpM7d/nNZ38OsUO7jF5oaukEiePA4V/HO4YCq+1m93XXdQzDwGQy4WbGdtaymKSKyxjLcnmUnyMp2nbN2dO3Y0XPFwdvrJR2PCs4Xb7Ikr/aBv7k4RUPz1vONoG7X11xcPfdCF/fFufnF3SDcvLJLymW1z7bcXvO8PQ3xG6FdQWLk49Zn33N5uLRK/fV792h37sDgD/7huLxF6/c1l88wV+kmfhhtkd78tFbHfdXZ6MkKczshond8ll3dGsbaz37xydUzXVi1mIfnd1GId4mmskMV9YY8yffeR8vix9OwtYEgduUZXerXtWxywSock2GuuEtKon5HRkIcYS8I5rJXZi4m31O+TmT2EbSlkDUkKetMutahdgnUpWaCDYrgxmTFa90VxFHMybNtGLXTUBki0n03tQfjyaPiAnOaKqVjcWoTQsEm+CAmPv3AkQriImICZhsjKBmQCXDc4CIw4ji8t8oiqsds6ZhPmkQnRPDwGa1YbtOoiaHy4am8gmBuNXNG8Pmr695ydefQSRqm5jlIzKh6ecyBCSkOeX54R0wE5an9ykmJWQjEjEOUxQ4N0elRlyJekXLiIlbBruGWBDiAhM9dirQn0O/QXyDui0qK7R1iBZImKHawRDQzTpX3wHqRN5zrsKZAWJ+NzZZqUonRAbQFZIXQF3n2bSeTVsQXUFQS8htmgKYSWBWFcwmE2aLPapmjiuaZEOaE7K1DslaAIkvETD4xKHIC1FrLPV0xv7JCWINq6sVXh1iC6bzCduh52LTEYesdCYRjbc93v8qRRgCQz9WQ4aifHfG9c0z6b3PrOGXQxibtiXEDZt+oPR+5yIW3nAkajJdgCqrbxEQeVmICLPlPpOmYD5N73uIykc/Hji/7BiGyOnxlNnku7l8aegIFw+TXv9r4vjuA2w1ozj4CPOSXnmzOMpon1BOljvv7KGashVDLEqMK5ksj9lePaXfprG4YTJHrcM/+RLzGp3wm+GffoXpErchFjXt8QcUj79EvuXzuIoVV6FmUEORP+pqesBsscfxYUNdffs5dA4+vmeY3ii6pw389GODiz/iyTf7PP3in0FMCO/H+4ZGPJ/RsN62DG94zbzy9d/pr99jJIRKU0WqeahLn5uJ3JVy6WvZYYZjZZ5HueLIBiQnvySykvrMJi8CxsdfSr6qOfHsbuXsApYlUY3JdbeYPNI0JrlI1JBmhmM67hDZyVhej2AmxbIokuBRkbz40ASV72Bpm5jXogmKNWlRIOJQExGjxExySJNdWXEtIxFDiPSqYIW6LpnUFc4ohCnbdUsYAstFTVNXGGN3hLwxJet4gm+0I8azdD23HXI/Kbcmcr+cIc02W+OYLPax1T7VYg9rFaXPyIgkMw2ZoBISY9wBDnS7QoMhbHuGS5+QhaIBCWBzD1cFDQEuc886DSynhNa3YNqE1pQ1UGAMWBNQk5Tkkd0gPUkUJovEYBmCoQ+eLhaouh2KIQpOoTJQl566rqmbKb6sMS4R9TQjPUl7fiQzJjKbcKO7EJOjnC9KJosF/dDjSk8IAwRHM6mpNptsOjPOX/8Vrax1ZPYG+vwwt9Yy9hxf2DxL/u4W8q/pS4+2vUAmuuZtb2wesyNYN/R0w8Cma6mLklCWyQDmDXsUaaTr7RdbQ1S6QVHjcWXFfNGkNosIp1Y4v2hpu8DR4YzJ5DuKtcSIdquda9etX4UejRHjCqb7x5SzI2yzz8tmvn11PV7mqim+maMhcV6G7VUi8OZkPnRbQp8QAa0sgy9xl09zm/DboX3TbrGbKyT0dAcV8Q0Riy46LkONk5A9EqGsZzTTPWbTIl83kuBw8/LkLQLzCRTF9WdfeOF4Hx6fHbDpSy6//nPCMGAiHE0Luq7gm5Wwbd8dBfnBJOzr0aKQUmiGoEeRkTGuWeKJw2xIGtiR8QaLu35zYv8q0SXLzJFHcL0YCIBko/MM7cYMhaN0eS7XqUlQuRGMFTQOxDgQ+khyexMMLaPsytjDtFYwarE2Mb8T/c3l8S128qJIrtYRbExWm2Pv3GhMxx0rDCQC3u5ho8koRRKEv95EVtstV5cPmVYle7MnfHB/xt6y4aMPDji9d8J8WjNfzigms6Tkk/tpSiI5kcVKdr3y3eeSDjad/gSZ7x6G2qP9hn51RuiUenFEffTj5L5T2vQwiEIcrtDYI77E2ip7khtQixagA8TQohcDF998gbUls9NP8bP72MLC6iFiJ5jyAMwZXG3QR88IKwPdNiXDPmJkk5KlsVA02NgSZYCuTT1jSSYwUQJKmx/WBZvW0RUTQtOAlOkzjh1lGCgJTH3JdHnA7PCY2fwIW5ZZCcmm92FI5wUlBgNtlwhwZYUSEpGw6xFnkcIxaWZoVA7u3+fs8ddszjbcOT4gaOCrx2d0oSPqkO4NYxMB8a9UKJvNOiXib9+UbV7oVHVKXqqR9Xr9Yq4UqOs6oVZA23a7Cv757S4uLimra1vFbdfR9j3L6fRbiUljXJx/t3ncr85bvr7okM8vOZwVfHLUsH90h9l8wt07E/7gp0sODhrK45/uHOXeNsSX+OOfMpx/SVzfkGNV5eKb3yFi2Lv7KW7vwSuT9fNhp8fYZo/+0T/P+4JHj5/gyg0H9z6l2btDs0wz4ZvLJ6zPvmJ7+gl2fUn18Nffuv/2zoeYdk39+a/wT7/CP/36jfXDRZS7/imFSQuDxf4he0fH7JKMWOLyX0k2vi+JfoB/9GeRe8fCR3dfsnBxhvunM86erXn6LDI7+JBmeALffAsr/w3jB5OwRx5ZzJlKxz717hu4Qb9J34nJ/+SF1fTN9e+tucpdla4YHU94ZlXnue9ddZz3EFQxu3GwtDCIMTHWb9TjJHjdgCbnMSElaWWURo2JrKW5rs+yqrkZnzvnNyLK2FZOyTTPcxsdk/Y49pWsSROknqrsddsjukG+CJw/W9Oueh7c32c4XrI4OkqsZlNwe656SPDrCHcDaUVyPXedsmqbvs5tAA0tOqwgCsY1VNMPKZo9fFGBBkTzOF5sQVuEASgRk2DgJN4SMYXHlh5XeSiUfug4+/KMxT1Ls5hCsQd0iHYwNSgr9GqLdhuIAxJKkESOSVw5k9jzPgmPaFwRY5/IbaYlaEBJI1MhGLrega3wVcXUKdJDbC3lsKW0Sl3VNNMl9WSJNQ4jLvn9jm5n5gZRMSv1aQjEoU9tFROQziDRIjHQbVuGtsdah7MOawoKN8XZS8j2raPaXlq4/uWHxEUE7z2q0HX9C60AjUrfdvnmTtuON43z137XQz8QQ3h5ha03Hin5By9tN2SU6RbIl37Itmtx1u3g8dfFzWNo27SYrOsp31ah32zbXW56Pnuy5kn7mNnkktBNKewh4jx3T+x3n/UOA2HzFO03L/yqnCwxrsDNTxMMfjNZqxLXT4hdgrZNtURcSnKmnDAqhl2/F6Xdbnj42V+w2D+mmaZxMF9OqBcpeWvZ0G1XuKtnLyWi7cJcq7bJDS2Kl4WvZtSajmu2jej2CicB5zxFNWexmLKYlWnRXuyBX6QK+1VWO7njdn4Jn3+l3DkE724yFR0y/QBWD4HNdX56T/EDSdhjdr5WM4vxWrnr5hY3k7YRk2QfkZREVXdbGGBklcXdH0sif2UY3WZQe4ipRxhjqrxN7o+P7dmUzHPlhOycwXYA8oh5ZgnSdFNHIv7GQNlIgBsSNJ4wWtLAbSSJYozWoGN/PO08JfgA6iAazE4EfRxLS/2S3NQmoHRDIPZb1qs1hTM8/foS7ZOJxoOfe3A+zQVrlx8oASRk1CGAjrB3JsgRM4Q/QNxwDVfk7/sViMe4BeXex7iywDiLhm1ecGhK1rFN+6ACPGKa3JIYsKUnVh7XFJja0F8GnnzxhHI6oWommHofkQ3oCiY1qh6tz9Cth9hjqBDp0/FH0gctDuMtRgV0IA5bNLSI36I67GRRQ7T0vUNsha9qJsUGtjAEg98GKiPUTcNktkcz3cOYlKzF2HytGIyxhHzNGdVk+hIDYehQY1FjoBNkMGAs7XpDt22xxmTyYkFhpzhTg+R2RW733OhX/KUOI4aiKOnalq5/EUKMGmmzzKMxBj9OOQj4kcWtyct6GN6COf0WoSjrtqX0kcK755fZr422XTP0HVU1easH+aoNrNqAOdsyrSy2nSb1NVdz8hN9Sye36xXL2MN+IUSo54eYcrojmD2/j7B6lJy+ANPsYcrZdVKPIbedxvaf0LVbPv/Nn+N8QT2ZImLw1QSfIe2umnDRbzF9+/qE/RZR1guaYpHQWnmCH5K8qnUlzeKYveWUxbxKz9PiAK1f9l5fjIsr5XKtLOcGl7tsAIhHmwdosQa+vobZ31P8QBI2kHuF1uTOsN5QK8u9T7KM56j77X2RXL1krOB0JxwCo6hI+lpG8wUjRFWCKv0Q0KgEyW5MaO4zm1wb516ygHGaZqqtIQ6CDslicRwHG3XGFPLfJcxblKRINvZ8UXQ87ZqqwRBBjRII9PR4RoE1kzhrUVLfPCbhFAsJaheTmOKqDNLn3nc24jDgnWV/6pmUluOp4/TYcLgE7zqMbPOr9MCA0F+fbM3a2SQ2faqqO5SQDDniOonaECBskMFgZEZ58BPEL/FVBbFF+22uNtMiQExim2tUCJnc5z1kSVNbRJgWgGdxt2D9dMujLyJnn3/DcPmYw0+PMK5Gyil05xhvYf9OOr9Fiek0n9NAFAFN/AU1LnmNR9AoaDSYQQCHoSLEnhANQxSmswWlnTE8+5xOHa0r8XXNZDplefcu08UdqmaJKSusLzDOEWPY9a5HTSQRSUz6GJAWcB58wRAsRh1W0nSBFp5pXTJMJoSuJ9LTVJd4X7LZtImPQUKe/rLn66qqEvnrDZ5vZVni3A2imMJ2u8kLfd4MRn/H6PqBZ5dXTJsa/4bOXdPpkrJs3ipZjyECnxw1zKp3U7yL20vCRWZmv0Z4xO09QIo3s/EMzz4n+hq3/3E6UGNwBz+irpe4Mk26XF0+49Hjp6yffc05PYs7HyWE6nuM2XIPp3v87s/+AeGGdGpR1Rye3MOXFdiKuPi9XFm/eajCn/xa2Zsrn3744pJJRNg7usPTbQB+965vBfhBJezrruntoevbW6SUahKZbISBd2zy50Hz/Fc5wZPhCSHJUEYhmzmYXOBm+CtX1+NBiUBmL6XKSgZGDtausL75HvKKMiVwzaIw+UGSx89Srh9HorKJh6Ya3o2A/lhpkyrbqJKgX1I1LtHlSjwR06wYTCreKB1MSjheepbTgruHU+6cLFgeznBekn2nDkCfji0vkHafASM6cQMKz+pmKYl3iA5oHBCZgNvDFEvETdLv6dE4oCa5a6Xz4nb9+mTtKTtIS0UQKTBuiquVcqmEsMF8c8l6fUHotyzunOMbcGWV4GdrEOegKGAoU3KONvez+tTaGDqwmkfRhCFA7DVJhV6fZlQNIp6qLqnKmm3X0G4Vp4qNFbZoKOs51ldpNCzzFEaJWXJ7ht31Y4hG8+ekiXwWEyESk81bCo8YpQ4Tuq6j7zra7RVFUeKrAlmZ7AH+V6O8lrwYD8PwSlej0XjCmBdhYMnXP3AL0dy1sMi+AJn0aUTw1jGYjjfjJ9+OhM6F1+W8F8IYi7GWrtsyikX5orw1Lnb7PSm1dDhncb7iZA/2Zo7ZYp+TewcsD/df+bcvPeL2Cu2udpXxS1/TFoiv0z/7qiQmmGKG3pA8Fetv/V5ciS1nMDtiMgS0mPPoArZtxF48o5pd4IoaV9wmzMWyIYSA3Vzxrtd9UxeUvmaz77i8HLhaBXw5wZdJCjjlEJPGZ9+2ElZoW6Xr0t+Nc9kAYTWjW99he/EY7wvKskbW61EH+zvHOyVsEfmvgP+CdFb/MfCfAafAHwMHwP8D/Ceq+lp6nJLRS1FCFg25JSWeN1JJCmCI4pxNeuOqCfrKiXOXdMzYUx6tOdNDlGzkgUQoUmp0yI6IxhCIcTRayDtNxtdgLdYWGNu/8NFqNiJBkuBCuhCSyEjQmKQ2kVRoh5SEjU2VYBCDiQlOHXTA43OC0+T6JQrSoRpzb9wT1RMx2S9ZcUYoDRTO0PiUrPemwk/uNhwdTfn4p/eZHR5Tzxf4ukiLAE3w9HXtllXUzThoTkIGNNlupv8HVHpMvILYIrFB3RwpPsaUe+kzCGe7ipw43vCSpEtH320SazR5W2t+6BRYVyJ2yeTkGCmuqL7+LWcPe/p1y+HyEdPDiD8s0TzvLDZC4SHWQI/EmPrhQ4Kj6VaItdBHQjRst0rXBhqjiFXE9GnBJwbjGmbzCcV8xtp+yHB+wdX5BZgJtl5QTvZxtsKIT3P2RvJiaeRSuF2nIBHRNF+HIFGQAGpDan8Yoa4nIBPcpEzsXwPbq0uqqmIyb3h2eQ4D2CBJ//x7Zou/r/v5XSJGZbN5sZ86hjGGqn6JZKRAWb2cKNS1LV2WF7XWUmZSmjWG+SQtlrbt+4Fg3yQ0Bs6fPU4JW4T9gxOsfUXCRrnjnzGZTJkuD/j5/Za7p56f/eHv4/c+xDQHb/HCyvDsMzS8/uMz5Qy7/OD1+xLBLu+/fhtAyhmunLHY/wTz7Bx51HCx+hWrq8d4b5kuj3H7t2Hofv8Ow3RJ/bs/3YlbfddYzkvq5ZT7k2P+/C+e8Kd/cc5kcUI93Xun/b4sPji5hr+b6gOkPuGLf/J/44uKxfKQs8sL2pe0ed4mvnPCFpF7wH8J/EJVNyLyPwP/EfC3gf9OVf9YRP4H4D8H/vtv29/IE9qtqOK1h7XekO20maktJiW/sWudBm1ThZooY2a3CDBG0sObyMBNS03d9RiMpORtbBqhEmzymI6abBgFRDWRlmJi7oqQofMboyEmE1MkYk3S9jZmZIjnXmdS68aS7TUjBBt2LmKiY8IXgmqGd8NuARijwRhF2eB9QVNYjo8Llo1n3jhO5g3zecPB4Zzje0dM5nMWx3dwVYX1SVKU3bkuyAozGbZPC4+EGuS+tQhqfHpzu78sECmQ8lPEHoI/SvuNScZUGMfE9MYiOc+42WsY83pBoOwYdgLiwU0s85Mpl6s91p3w+W8fcrj5hpPwGFvtJ6RBIhRlqtgNuafeQ5t7aEOLGRTpAkPX0w6OdphQ4DAakCB5nNzi65K4uSBo4OB4ApMKme5zeW7wRcW2TxRBMZLYDJJEUcTaG72q68VPUtoTsGmxh7FYY9EQ6a6uQGvEGuIwQIwYJfkplzXifF4oggRzSxLz+4j3fT9/l+jewIIwxsh2k657694MTp02E/xsztVmc6teCzFyvlrtRsZuRllWeXSLVyqTeetoqhL3imT7slivLthuzCvHzQAKJ3x0UNNM07jWB41lMS3ZP5hzcu+Y6fIAf/AJ4l+tdf2dQgxu+cEto5D3GVVp+fSjCX/2z+/w8JnHPXrK8PQJ7rf/jO7oPuHGeVbnaU8/zt9Eym8+w26uKL/8i3SoIT0znpqalfHcHS536hG3YvslrFagkcXBXT60P2a9evWC8LvE5Vr5p7+KfHAqVB5+9Zmy2qR7//ig4dnF+7OIfVdI3AG1JKZPAzwE/hbwH+ff/0/Af82b3OD6km+uuWi7MGaEmyWLpbBDcFVHwtao2AUj3Cwj3H2DmLbb+UjHJSVXY1wSPREYZdfUpEowMU/TuFmSJCXDa4zY+7gawI6zuSMkj7nh2pXeT8qHyjg+JWMSy9Q5lZhr0cxcT04gjApnTiylt+wvPMd7NUfzmrsHS+aLKXvHC2aHRxTNNOl4m7zwidmyUhWkSL3vmxiiwghjZwpA+r1ERoczTAVaIP4O2Hkagwgb0I5EYMuc99GTXEfqHYyEqrTfTHJDs/91JsAZi/VCNa/wTY34lvNLR1Vt6GYbKlsipgApkpoBLi0oQocMkiQLNUmzEnskxKzOZoniiZJH+jSimshjvikY2i06BNzJkqqpmEhBH3ucFDfm/JPWvDGSoFlvb1+DuysvnwMzzn+n74Mqoe8JvUPUEkIghkDUiBiDdY7CF+la31384/n6XuP93c9vEUJagMcQd3ftK0OvCalvGt476qKkj2Gnz5B2pXRDT4jXyMWIxDn3auZ16pAZnLUUbzlONXyLOIiTQOOE0z3L/uGUxd6S03lkPinYWy6YHd3HT/Yw1Zz3eUGIceDKRBx7Iz10TXaaIq+BzW+Hc5bl3gxXrunpuVg9pTIblmZNN5mjxY3EZgxhkpjkxEgsa0y3xT0nPrMVy0r8rjzYvR8RXFHQbq+IOtDUnqJqmC6O6Puv33ix9ybR9/D0XNmbC0MJTy8UDT0StjiXUM+6sHhrscak58d3fK3vnLBV9QsR+W9J3fQN8H+SILNnqqPZMp8D975tX2Ouu975zd9c1y2JlJaqFAMEHXbscI2pkrMCSEycr10D2l1Lj8fkczyIItEmUVBriHFIL+vKnGiVyhWZDeyQ2BGGjtXlZXq4EjDiEhEt98MQIVqDcS4pMjkZO8GI+kQUw2IlCaBohuJRKDUx0AcUY/rEgKegFyWaiA2JUG5FsXbAIrhgKOiZOMOPjib8+NP7PHhwyvz0Hq6qcVWdbsRRvUVD/hdvJOxIkiV1u9w6VtmQVNhU0ojRCKGrWsSdIOYIqR6k7eMG2OaETe6zW0QKVNM4F0MaAUnCBNP8f7FDT9B12k47YMAUhtnxAXtPNsTNhs+vTrHrSyZnF9xpIr4I6U+9zxWsRzqXxstsBdqnajt0EAa8sxS1TxC6f4rGDdqvwB7g3JS9/T1++6ePuHr2hGJ6xjD/mHL/Aw7qAekCrAObbYuxBdWBxXqPG009QiR0ycdaR7DACGoMVlzGYYQQIhoDYdjSbxWxlla3rNoVF+2Kdb/FGuHucp/H/ksG2dLFcL3Y+p7ifd7PbxvWWaqqZrPZJAGZ14Sxhrp+MyLUzRAR5pPbAhvDEDhf3faDds5TluW3nGthMZlg35dt5o048ufcmUT+8EclD37UcPLhA4rjfw+xY5vsRqHxHsNM72Anh889iF8TqgxPf4O4MhHN3iDEV/ijn3BworTtwOcPP8bsbdk/bV//usawvfsj3OUZ5de/faPX8mXJnXvH/MN/8piz8wv+7b95mo4hE8G+j/j150nHWiPI5kv06nc8fHhBaAf+4N6My/MZEDm/ensP8jHeBRLfA/4D4GPgGfC/AP/+W/z93wH+DpArVdmxtCHnjVsFayYHZM/pIQwMw0AYyWa7P8g3kd6A1Mf+MpKr2ZQk0zM1Mcd24qImyaOKChqFmJWmrK8xtqAMgShp6ziS2MTspCmNNTmBGzyWKJEgaczHSLzBXM+Huauu0yO9yKuVCFglwbaaSFlKYgubrMCGRGrvmE8KTk4POTjeZ3a4pJhMMK7M0Fa2z9Q8sqVDvjnyCnNMljlBp9VRnr0ehWWAXSUsDuM+BHuC2H2SiUqP0rHjDNyYb08fiQHxqPq0z9iRckJMeuuSdMxHNx40IBKSSE0l+CZQTAPVbMFgPU/XluXmEkOH9ZC0x21e0SmUBVDllnubHMxigqSdU9T0oCtUt2kxlc1a5nXF/OAO6gfarkVo8GWBFUfUjtBv0vxmWeF8gSsLrPfX5iEx+bdhBHGpTz0SK8bPORm9DMnrtyMp75mU7EPbsV2tkBDYn84pnceIQWTAEDMz//uJ93k/m7dMZDFGuq5Nozffuq3SdS3OecxbQNFwfSWPYa1hUtU7C91+tNF8Se4wItRluduHuZYwfKdQVdari101H+Y9zjoO9hZM9o4plqdZGfB9VISCnd4hdlfETRpvEuMx08M8O/0270ew0yM09ISLLzHN/htA6el+mPoN+/Ul62WFLT1P24Z5cYkzr2FkiWQk8HbMY0epIRslX4f3jv29JYenE2IZ+OybSDUvOdhPx9h1gcur22jH/lKYNfD518p38eq4NRKe0V5VxRcF8719nPvdC9fg28a7QOL/LvAbVX0EICL/G/BvAksRcXlVfh94qbK7qv494O8BOOd07BHrCP6OyTb3iTEmi1QkJbIwDAwhq23lmyc9GyXnicw+JiZm9u5E3YBNANltk/9CIqMMWcxypSIR60sEKLQnxtRz7ENknDE0Jrt1mTQbbkVwYgkiICGZk6DXF9bu4htHqJL6lhUl5G6MADYrsoWsHy6qOEmWn94oTZn6W0d3Dlge7jFZztGyupbX28G0QxZtSXC13pwP3P1ndrlbMzs8VdYjnC2olIi7D+4YMTPQ87zvsTJ6Dl4HRGzqeZsuk9na3OxXYEqy9rQojmQ04jGSF0suYMseX3WUzZyhE8620G2vKOyALbp0bJoqepwF60EL6HvikBc7+foxmmfhdYPGNGMu3uIKjysLpvszei90mzMKGorCo0YYumS9aZ3DFyXOeaz3WO/ot8lhLMTMdZCsB7/7iJOufSQQDUQCxEDfZb6Ft8RhIHQ97XqDxMi8nlI4n3gQBjTqG6s5fcd4f/ezd2+F+KWE/WZkHNW0bXJDe8OEne+B50epjBiaqs7ckQRXPz/xMf6NEUNdlC/s411i5Kn03SbzYSJWHGVRs1wuqOcHmMnRt+/ozV8QUy9BDHGboWVXYCfHb5ms876aA+L2guHst0g5e4OErRAjpVkzLy5ZzhqCep5thalfAfG6DaFkTYjXR6M9taa2hgpJQIpELpxNJ+wfNWyN4auvH3JvUnA6T7D7at2/kLDnkyQx+uWj75awXxXOFUxmHuf9O18/75Kwfwf86yLSkMqlfwf4B8D/BfyHJGbpfwr879++q5wQlFEXDGDXWrY29RiNWIZhSL3rrGRlxt7uyGfKbGxUMJoenBbZGXaoXs9LpweoyWIkuTceyQpYinUGKxbBYRhwYinqBYUt6KuW9bOLBCmPFpoiSekmC5mogMXhKBLbmyT8YPLCQzVSKTiUThUjaWtjQlqwSMBLmtsVTaNaTSH84v4hs4ljMoHZ3py9wwV3P7nHxwcRrwAAIABJREFU9OAIJsucvITRcGP0iMbkXutI8JJRRg12Smbj3IGQMPi4Sd/IBIqfgZ2BnefCPMmBJjJaQWp9akq+2R1tl0jFpd8Zn/eXX2+4AONRcWjcJASAgYgjdC3bx7+mf/Ibwtkj2kdKyyHBnfL0cg5W2T8qQGqIFjbJgSvdbRvIEqsxBOKgDKFiswls24jRHms1qYzNG/AFna5SoV6WBD3E1HtUzYzQRVxvKGY9zhlUAkEiEnIbZUQmEskg+Y+0A8akay5onv2PSrTXQijdtqUPkWCFtu2JYoiiqFpctBwu5gzDht897HB4zPc7hfke7+cfVmzalrbvWUwmt6p/5zzLvSOKizNWPsGUN3vozjrmo4OT8F6TNSR/5ul0wc9OJuxVGw6LLzg+OWD/8Ij9ez+heN9MZo30T/4CcRXF8c/Sz3b8ne8Wppymfb3BPLX2G4anv+HpV7/j8VdfEvVrLrs9nnWHHFeRyWTC7DCx08PQcf71bzKf59XxyDY8xfPom4ecloZPFykhd23HF19+zXpzirEzDu6c0sxeT9L77KvIl98k+dHvI+bzAxabyNnlJW/DwbgZ79LD/vsi8r8C/5A0ePuPSCvs/wP4YxH5b/LP/sc32+H1lztTj5xgU3KDMBJ+skj/tQra+D3XamdjiaqRODolad42V5dRxno+u2cxqorBWOsn+rrCEFFrCNZgfIE3QlG1hGEgxIFBsyRlHFK7MYMEo6uXM0nUxYqkWVGBEFMrIC0syMzxdByJyKZUzlJ7y9GiYH+/4eBgxo8/uUfTeMpKqOqKejahWuxjyhkqTX43eREk47mRa3KZhpfgDewqfSRkv2sFKjAlapcpWZsqn5ceiUOCz3Wcsy5Bbiy4dnuOaTxDADXZbWxkvYcbffNEOtIQibElDi0aFOs9vqnw7hlDf0XXnnG1tVSdYT9m4oI1UNTQt2kiIPMakgSs5iquZ3UVWa8HnLWUdU1Tl1R796HYJ4Z94kVkCIqfzannSyazJZvLNbFUdNpQzRvKadahtlm9bMgXW67kr997OocaI2GIDENqj6gARhLxLASGaJI5QiQtEPMoYlPNmFQbjJyjDFnG9vuJ934/f88RXlICOedemnxiruyeD1Wla7c452maKW3f0w8Dfe6jqyrda9TSrDF493aP0HklNB6WUzg+WXJ6/2M+PD1mUgbm/iOm8wWT2Qy/OHmpK9a7himniSRm344s98oQM1ZJLw0dWjTLl+rQoqHHO0tVFqw3G+jO0NWGdXdE0TnK7SW+mmJ9STVZ0ncbQvdqVvem7TgferZDpM+GHPboA4qiYWkbHg81phdm05pquodWaRFkdcVsOmDrfbRYAlBXQl3Ck2f6gqXzG0X/LBUcxctH7fanJdt1xVdFQT8M38m5652W7Kr6d4G/+9yPfw38je+wtySeMYLXsmsDACZVKGHY9blGiPtamCT9LN6EqbMYSIyjrneCp9HEBNcMURsZ243KAGmsCq79soeYvbEjgzEUhcOJpRp62tWW7mpgSyTmqtEai4ghmCH3t9O8dGktU++wJiWvABDTckFsStwuKmPGVwOzynI4Kfmjny/55Kf3+eTnD9j/9EdJPASTkqUYpFoipiFS535XADqgQwgJElab10GjVviINuQF0ihRqj06aoWbJdglFHfJ1lhI3CZd8JjhaCS9pkxyb31USAs5oY/CKzFX7u5GpR9zwk/nKkQYhkjs1sShw5iCYrZHg2H6zTnh2SWbyw3nmw8pto7YdUiVFO+op2mffbubUMuiagwhstlsOD9Tzp8FyqZg7huaesLk9PeRap+4rglffknbbTj84IDZ8TF7h3fQ9itCBbIH06N96smEGB14l4RhunwOjCTy2zgFMC4a+kgcIkM3JHU9SYBD6LeEGAjWokNAApRSMJgksDKvD9g2AWse0utACN/vHPb7vZ+/3+j7nv6WYYfQNBZjX0zYYzz/DA5h4OLiKbP5PvNmxtC3rDabXcIeQuBy/Zzt5I3dl96/VcIWgZOZ4d5C+MWHys9/ecov/uhfwx/99Hsbpbp9AOYVMqPfR6Tni3Yrhme3Vb6m0wkS9th82WL6R/jVGc82f5voPEX8nNnRA8rJgunhfVZnX7F5RcJWYH15ybPVqIuRCLbuk19SzA+YqfLw6jHn6zVHBw0yuYNOPgKgKJ9wLFfExYOkIQ4c7wl3DoTzy5Bu6bcM2XyJDGvi/v6LvwNOFyVuaPhtU3O53jB0/z8n7PcVqiR5TgJGLCMBzVqLyQ9xjTE9eW/No45lNIzJZxSu0JxEJOtr7xgBkkhfkYA3Re6ZyK4yv+43WsIQUjVsDC0t6IC6gkiZzBrKEgk90inSdRBiFnvJdp6B3MtMvezWBOIw0EwKrDM4F3FR8Co4MThRCglMK8/+suGXv3+fH//8AXfuHbF/ekI1m1JPp7imzg5jAtkFTKTI0PQAo40kNQmuTdC4EkAiCfXMpIhxZTRWb0rWAZ6Cm4M9zRVxhJts793stEPkBoFNBCTPdmdN8uvPKFf8kjOWRrJFV9qXm8DQwfoqvQVncXtTquoCOxVO9WOmT1qm37Q4PwFX0qpQOIP1IGEDNqQ+tjeMCsuhG2hjx5NVz1drx9nGciBHNOxRL07BFAyxZRN6tn3HEC3zo1Oa6QIrnrqZoqYE1yC+YlDDsO4wJhHjREGMYqwQYpa7HQYIEY2RYQj0faAPA13YEmIkbgJd6BNpsqzS9ecdpq4w3cCwHZgvlnTa4lzHECD5kP51fJdQlMv1isJ5mupbkqNC22536mg3w/vUiwToh4FnV1dM6wr3GmnSHx8YTvZL/uBn+/zox59ydOeU+cmHNNM5fjp/47Gof1kirh4Rtxe4vQcv/X2zOKKoZ4QYqauCoqpw3uHLhsXJj7D+2+eWV0Pkn55t+c1j5fGV8PEdJUwWbO5/iC9KYrvh6umXnH19ztmZ497P/43biIVfEJe/BHc9cfDFI+XRmdK/cR5V5PLPkZC8uQlr0IB59v9CaJ9bIArzvQM6tcBfvOkLvBA/yCfAdR8pV9Eab7j2jJX1dZVN/nqn5y03//zmVtcJfySlpSooy5RmAphk0ZXcVt+R0hCFYImE1As3DmM91jmky0c+Fo2S+e6aRsRSb1PoSTrmFk1qpwhWhcIKTeFZ1CV3jpccHy/4vd//EQ9+9oCD0yOKxUHSo76htZzemSPv5UYJEVEde9Rjb2lkgKcxrfGdjdvvxrwUoAQzRc0CzCTD6mtS1dyjccso9Zr+WcY5410zV0fyYPp6h1QqiGRG+FjVj6QCRsa9R2zIKmaC1fSZ1osJoTcMm8jQJdOStitwKplsknTRMSF9ZvlfVE0VdhdZB2GNY+nmUBxiqmPwDVGUtl/Rx0gQRzVpcEUJkmat1Sm2tKgKcVBiyHPjarKejGauXYJfNaTErSGmHnocCKFnGPrUQukDXRgIueMSUAIRk9XbVJSqKKnLmsJ7+n5g+B4h8X+ZI0mSSn5O8Mr56cQhUfow4MwodpMq7WHocK7A+wHfd3TtdSvlZtgbiTmqEsPwAnwqAhMvVKVh0nh+9sGCe3dm/MHv3ePeRz9mcXiaktn3LIbzgwjjEN+gw3bXj7a+RIxlOtsjRhiC0ukWggOZ3wQxXhkhKs+6wFVf0AZPcJ7O77Hx+8zEEWPkaj2w3Xb0naJuxi2Pa3Hg57f22bbKGwvexS75KPTnxH5D19+8ThIDX0ftjxzeF/iyxPsCY7Zv+EK34weRsEUSaSvmcaA0ImVyzzqX3zm53OwQpgd8SlxmNxo2ZjNNFaVKEkFhVA8zuRDUxPYWAXxyYhRAAmpTAjJ4iErQAUhaZWU/4IakYiblHCdCXVjabUeMHYGYc1DusOf+OYZs8AESk5qZybaMHuG0Fj6+t88f/OwTfvm3/oi9uydMTj9IGre556uM41PhxkXtEZKBxm4US4e8VhmTdk7cu0HrmwQRZdT+JuR+sv0A3CIJouzIW12eae4hbMHWiClSz1zcddImQmjJwrKAZxRc2bHHVRDa1DMXAfG745Niiiv2UL5CdUXQFvE9VgN2Yim2lmotXH39K/q1cHk2p2ju4F2Dhh7iFmWDhjU69MQhMLRKtzWsthUbaejqBnf3Q8zRITo5wcwXxH7F2dU/ZqMD0Rc0swZbOgaj9CFgFYqiIG6SPWeSEk0OXVtNPXqDYob0XAqa+f9GGbRjCB1D19L1LX0IdH1PFxJUv312sZuCKJsStQOYntqULKo5B7MjYn9O1z0Hz/51AGmEpyhK1uvVK6VLBWHeTBiGgfOrKxaT6Q7OTupjlv3DE4pNlQyIFNquY7N5+3NuBX5+bPj0wZxf/uKAn//h32R2eA9/+GPY8XPeL4HthxRmcoiZHAKCqeaYckb/5Fe7XjaAsY7FnY8o6illYXn48Df0wXJeHDI7fkDZLN7oterpAaE5ZFjc49I1PFzX7EVHCMpXq2M2w4Z3FvB+SUj7CLn6NaBs28DDb1Yv3/C5xZxznr39O2y6gdX27RXXfhAJG7JqVGTn/asxwYm6a2TLjev8Btksl2dRU8K2xiY4Va7dutSM2+UaPDPSEkHS5lVzzOxum1dGPUNWMDMSUXUQlE46BjEYNbg++TArw3W9GkelNbK0oSZyW5ZTdSJY5ym956gx3D+Yc3K44A//1V9weHLKnfsP2L93StFMwCYLvvRub1505hbCsFMHIyA77+qRtjweWZ7/zuNaO8vMYcOorIZbglSpb20c0KFsER2Q2GdRkHFfuVLfCbOQ4W9SAh73n6VHVV1WWAtJdWy0IRVJixANqHhEphi7RPHEKMiQXh+NxLgGO2Arg68DVgJRntFe9tCXlE2Nxi0a1kTWRO0ZQse279gMsKFByzlFs8fs5CMwypPPnnC1+hd0sWf1pMdITT2d4auavh/YrFZ4BOdMcmrTrAefF5RD6tfsHOC0H9CQhFG6EBhiYLvZMPQDQz/Qx54hDnR9x7YL9CGwbTuGricMA7YsoLBo5fC+xMaavcMDLrcbLjeX736j/SUKEaEoSqxNC9AyIyIvCwVW280tpbMQI+t2S+ULCi9cXZwRwvDSVPr8az0fpYMPFob7JxNO7kz55S9+xv7xEUd3T5kcHGPL5/yk/xJH3DwjtulaNcUE0xxgp8dotyZcfQ2AxsDq7CuGbrM736bdUHz9OzoxdJP090O3AVWKJw8x2+ukOGjB43bJJjQYazjcb0AM3zxeM5GHxCh8/WSB8Q37h55PHxjO18KTs+/AJgtbZH27Dy9hBShPn23ZtuGFxLzdrGizJr4vS5rJDEiqZ58cNVydO56ev/2h/GASNuR7Tdkl6VEC9GZFfRMtH9O2yPU2Y89a5Fp/OUg2mshVdmLhaoY7k8RgzCQoYzTbMEaiDddTD5lhHmNIZh0YYmiREJNrFSOcnBJk6iunQ42iOVmDE6EqPfNpxf29ip98fMLHH97hb/xbf0RzeEp5cBcoGXW1U00+SjbePBckslp6BVI3Om2b/j0/tmWu/37neT0keFsFkQo1M7BTkJpra80u95lHohqAvV1V5zcqOwOUjGiMvQm1iNr8ucbsnJWPRjQhB9oRs9mJFZ8/KyW5goUM17dgwBSCLZJgS6Sl3WzQweOKQzS0aNwSaIn0DLGjC4E2GDopkGKCL+c0yyNsv2J7ecVq+yVd7Fm3M6wtcPUE60vaTU+7WlP6EmtN7jCYXa9+5wKV/dFjVGLfo2EgdB3d0P9/7L1JrCRXlqb3nXtt8PGNMTLIZJLMZGVVV3WVqqpnFKo3DUGCgEZLQGspCQJ6pb16p21vBQgQ0Auh1RsNOwmCVhIgSAu1tBC6VIMqK5mZZCaHmN/gz91tuPceLc41c38xkBFkZDWrsg8QDIYP5ubmZnbu+c9//p8uBLbbhtgHYh9JEggaCaGn7zr6EOnanm67pW8aKEr8fEJVHpBqh6tqDg4OKOtyNBL5ZY/92eiyLMdLwnTyXxyK0maS2tid0UTbdaae6B1Ns8mL9d2f4fPMe7vcv/wM2ROhLjyLacUHdyb86oenvP/eDX7wm3+F+vDOnyPJ61sQqpACqb0ibZ4irsjtN0vcA2is2SO+XZ+je0xpCT1lc0E7mRNCZ+2/vF2/OsNle8yI0FFyFY4hM/UXyzldr2y7LY8fPSEEOL8qKYqSg8MFt08dka+ZsLVDmgcM99IYc+ZR5Wrd0+/B4UOO6JqGzdoWHZOUqCc2WVI64eaiYlJ+vcXbtyJhK3YQBFv1Wjtzvys95IT9kSFH4d0IKw+ZdfDTHZnPYtWRqAmweO9x3kQX0jBWJWBWHGrVZJ77LpwiAtEJIsPJk0hY9V+Ejhjs5huDVa1uaOvmXdoVukrhhXkNv/eDm/zg+/f4u//m3+TgznvMT+7iyiNECpQS86bO/VgkV6NZfWw8YpKf25+dHv4/YhCzLRzA7eaiczUusUVji7hDE0Ap3so3QkVpgM5O1LjOHtgh94Bqu5DcFKTe6wsl8+FW6+GOi4fcTkAm1ptVg9c1bNHY0DdPGUly9QlRHaoXIFcGidOSiAhKWQpaOlLp2HoIbcv52RM2vqCqSvDJyIBO6J0nSKDXwDo6tlqT6gPq6TF+dsrh0YKD45ssjj7kT/6PDeePH/Hk4oJb777L8ekda6NERdvIpt+Yd3UxoXBlnuXNN/WoaDBEiBRJ0cb8GiJ96Onbjm51Rds0dNuGVBhMnpwS+kiIkdD3qFPcpCR1W+KqpVlvmdy5iy+F24cnfFJNBpPSX+rw3jPdh7zfwCHZNA1t13G0WND2Hettw3qzIeXRsdF7+5nPqoqC5WzG73z4Lj94/zb//j/4LSYnb1Mubxqv5Zekoh5CY0t4/JElYXEUN76XCXVKOPuElCHx9dkD2vXZtWS9H9WjTykuHtO8/X14AR/h02LJwzhBgeXBEYujEzj9bb57u+bdu8r/8j+VPHjykCePPuN73/8Od2/P39gMfYzKp19cGUlUd2PE4/Mh8PThF9e4D22z4fH9huObt64xqr5OfCsS9lhVkxv1WEK2Svn5FZEMdPCcpMXlIWYRVNR0uMWPSVsztG1dVCvNNY3dbpIJP+btZaEUddbPzprQBu0agcoNIHQ0OcoUE8M8925JkbefR3wKhJPFhL/y3VN+63d/jQ8+eJvD01Omi0PKaoGKwXkDVD32gCXuKtfxT4bBx7mz4SAm6xdjqdySZZ4wV8dILksRqBA/BXeQk69joD6ptgg9ol3uiRsMrrInkegKhun1/KvsHE33YZBRUlGBqZHjnI2iJYXY9Wjs0JTwbpllTreos965JMWpI4nHld4E3GqgmJDaLe020tPRevvNJ9Mpk+nE9FNw9BS0FPQypZ4dkiYLXF1x+eTnpNbTbzxN29FGRxOEcrFgenJICAFxUE8qXFKrwsrSPNcShNCRQkLN7xUVI4qpA8ThKYjeFn/GzxPwbmeRmXajiMP4V0yKunzeomzWVxSlZ1LOsurZL2fCNhQsK/0NIgevEaUvKLyn6Tq8c1RF9jIHpnVN1wdiSmy7lhCya57uruRh5rsorlfYi2nNB2/d5Hf/+m/w4Qf3WN58Gz87Qqo/hxGtb2OoVc99c0XsW9zsdPTI1tCOpLNyMht/wtBt6bYmWrONiY+vrIouisjNxaNcuEFcHpPaLcXm0jQhvWe+PKCsjVF++ehTpiEyb9Z0bWtiRSlBfUya3OXzx3B59TURKlehs3tId4bGqxFNe1mMWiHDYclqldv1Gl94ZssDZrMZs+r1Xby+HQkbRha4+T1nM43ds2Pys78G6UfJN0NnferM0hURvNisrwrGAJe99JLsBum8oDlxjOitZEMQ8Tkp2iLP/JWFxJD4lKAxs4D3EmweHxtvx3m3KhHuHC34O7/1Hn/j936HO++8hfYR56emsZ3HnKy63kHblsBS1j/ZScVYpDF927N7cDgDmSy/QrHtZyEYcXPwh2bCId4IYBqsAtasODaYhYDtn6vs72t66LIHle89rAPKYZWJrbuG93aM8q9dILYrUtjiJkcYAVBRsYTtkpLU4aTAVQUugg+KlDMiW5pNIoUtQiC0PQfHhyQU56dE9fRS0uqE3s2ZLE/Qagm+4skXH7EuWqZ1x2Zb0yVHEwvKgwNmp0d0oUecMJtP0dZm631V40NEUyS0rZ2zCVxRoKIkryacokLhbI7XpYQrPJLMzlNTzDP9mQuQI+bxL6oi/5LC1WpFVRYc3TyiKkrK4pczYXvvqV7idf0qUZUFk6qmCz2F98z3KvT5ZIrqhtBF1s2Oubt/pPsscFNkZ65hoXW0mPJX37/H7/3+X+PdD77DOPHwKvEl9prXd+Iv0G+eEc1uu6K5OqOaLk0H/Zmo50fUcxMr2V4+pmvWILAJys8uzJNg5nvuVJ9ZHnCO5js/wLUb/HYFYmz95fEJ4NCUOP/8h6RHZ3ST+7Sb79ruiMDkNn31HT7+DIb7pgzt1FfN326Czt+H9GfQXu2Q0/3Y25Y8d5+22FxdUlY1p7fvslwsmc9e3x71W5Gwx0SdyWLZPHNXp4nLbHBFGJSgvDHDnTM3rUGaFOsvA8S9ojSneoPBxSjhJroCSPYuRiB41CniYnawNn1aKWS8kWoIaAx07dZGSZRcfRVZpMUAgMpBVEUUfutXbvHbv/0hv/8P/h6Hh4fEbQehQ+uewS5zIINp2lhPN3aEviGFlu78nGHDUllPfICjJS9IJNs3OuczgW7opQvOV4hfgJsjxS2gxtjZ+XPpQDeIdog2uwrezRgVbFKWTHUD3A67RcEA0Q/3mWG+2rFjiMtuW5S4YsHs1vdQ3aCYSYeR/xJRB6vOGhFDGERKfCWUItQntwkyIV02NGefEzfnbM/us764YLaYc3DrLXw5w08PkOkhwoLUTZktD6kWB7RPAuvL+zx58AlNbKCecfe99ylnM/oUqUKP4HDFhOXxBI1Ks+pGBKSqJ7ZEExm5EgmbPIgp0vWtGcEUnsl8RjmbEkUIMdL1Hav1Jd12SxcTUpSUvsCpEvswqLbTp5bUBZrLjnm94O7NO/wBP33zF+C3KJxzTCbXb2TfFM7cth1d37Oczl7NYUtgMp1cy6n7u+BE+OvfXfI7v/E2/96/+7uc3j5AQ0M4+wQ/v4WbfYWkaOzpn/50rDhfFn5xEzd7sWrWtzHEV5Q3PuTg+D0WsUMvP98t+F8S9fyIsp6jeBbzGc57Lp48oWm2/F+PjKHvRfhN9xF+ccD2nR+Qnl7Y8ApwdPd7zI9v4y7+mKePWv7go5o+PcaXNae336J+9lxy8GvvO7Yt/OTnrz8mWXjhrTuLa49tm8DjJ7ZDvig4vX2X9epy7GG/KGbzA05O7gB/8Hqf/9p7/AuL6xUHA4lLxunq3VUjQwbekdAGgtPgNZ0Xe1k9jfH1Q+4QJ3mTAk7GhG3VumTTEZ89j3fjSE6xHrba/OawqlZvjlPiJBtM7KpeEbh584AbNw+ZHSxor67oFSbLORIj2rek2JNiZ+zi5owQGkK7pd2u6bZbVo8e2/fzHleDeIcrqvFYGFJoY0ZlUeC9UHi1nm5RUk+PKCYTG+WuxHSuJSEuZOi8Y9c3H9jb3vrquTq3g2qLCxkBw2HQbvfbmBjLnoY5+f2q6NAPJ2WYuM79biXompHtrgOXIS8BxOOkMj/ZwlPWQjkJFLNj5OoSdS19f0GzMYSgmm8oZ8bGp5xDnJNCmVXPIm0Utp2wbiCKUE8rDk5vMJ0vqMrK9OfzDHZRl6SsSqR5Aea8EetS5icMEOrgl23tEkNdXGnH0Dy0Tf++KCt8CIaFaLDzJUPjIwpCJkb1LYUvmM+u3yj+UobCzl3PquvXKTKdGIn02TBPgh0R9UWfG2McDXzEuZd+rIhw+8ZNbt+6wY3TOc1mQ9v0TF1hF6Iq2l0xEFHXm0iMPYQr1o3QtoHm4Y+u9XCd9/jyenLxswY3vaKqawoXqX0LxSKjXDBbzL8R8vDGQxxSTuxeqpGQb9tfFs4XOOeZLY9QhG3T0jcNa1Wu2gbUFkhPV2tm5ZSynqClQp+LstgTugYXIm0H61aAwLSacXDjHkdHMw4Od7+kCCxmvJ70qAboV6buCFTPEMZC2P1bEIo8b11nxbwUI31vUL+mRNdsmRaOGwevbxP7rUjYQ3JlD1FK7OVl9hLC0IyWNMqWEnUs4IYR45yPM/ThcvvZYHAr+gRJPgt1eBMyEaDweThK8NgYhxQlpIiq4iN0RKJqhsI1a4MXiPcUvsSnhCRFgzGtnVfe+t5tju8c0qwbnv7pjxCFd//23yL0HVycsd2c0Wyu2F6ec/74C5r1mvXFJRePzrm6uOKzn99HcThfUNSCKwqTJ3WGFoiHovT4smA5mVBVnunUM1tMmcwOOL79KyyPJkwXE+bLc4qyNInVEsRFkA7JtpoqBSrZkGP8hcCSeTLW9nglDkYje7c3MZ3tgTVKzL7UanPSpM34lqQFiCXZlM4gE8zsF3fAoBjkcW6OUCFSMptcwkLY3rhHaDpS8nSbNW3XEfoWV54xjSV+WsLkEHVL+q4irLemXXx+RrduaeMEP6mYLI658fa73Lh1h/liQbe5RAYo3tsCTlJCo4n4UJTjtw6pMU3w3siHMQS6znTmVROUlemJx0BEEF9QT2fMvcP3Pet1g6Qen4l92iuuSahzBA1ctpf4quDQv2EziG9hJDUJWbDEOJu9HmGo9P453+tXCVWlacw+tf6KJCjO8dYHf5Wb98xX+ZM/+wlRan7zb/+u7WsKhLNP0GTTED/+8Yr15VNY/TF/9mnF/SfKxcMfo3sVdjlZsDx557nPcs5x4849jqZX3Jp+Dstfh8JGhL736z/g9tt3X/u7/qIjrZ8Q149e/Q0iLE7vUc0O8AQbobuY8vCzT028RpU/etpws+z4EGD2LlDAxRWrBx+zevAxAJurnepJNTvg9od/k++/57h58qLz5zUydtzgLv7otd7X3LD4AAAgAElEQVQzmy/HUa7t5orzJ3Y8Quh5+ugBRxUc3Xn98/RbkbDJ1Qyq7JTCZcB6d7D2UCUDqmKa89mvegfJau4m2w1v3AyCqt14VU0YYeCtFTDaa5JS9smGIB5Niao3mDalyHbb0PVb+tCZCeagW05mrPvCbP8UkvQIBYVX/t8/+ZwH99d8/MPH9NsVVVXwfi90m0S7jTw9f8JmteXqfMuTi3ParqPtEl0b6PvIpm/GkTTvM2mmaHBS2srWY3rkhTDxBaUTai+Uk5KqrFkuz1kuj5jP59y8dcjyYMLB0ZTDkzmTWcliOWU2nZgKT71kl4AHuH73W+165LsYofDhfXvjeNaC8Ca4QkBjB9Lb7+gqkGjogA6jbHk76izZZ0Zm9r4CTRQ11KlkcbCgO5iiaQHpbWK/JcSOKy0IfY003nrY1LQp0G2v6NuGsLkiNGv69opbb33Ayd273H77bSZVZSS4sMFXM4qqpDlfE7Yd/WaLU2s7hL43pStVQuiNHR56QgykGImdKZqllEgSR/zIvqEDV+IKxeMQ30G2VPUkUhHRMuFDBerpYp+JU1+TNPOv44URk/Wt7ZruqevJdTcv50cZU9XE1bahKgtm9YQ//OgzHj9+yM9+2LPaBMp6ztkqst2sadYrrh59xLr3XMQZX9x/TNtsIFxytXU0HfRtRrJyOLemePTJc/soznH6tKcuErUPUPwkayTA+x/d587pAQfHpyzmFcuFSZzOpp63bk3wi1vIL8BAZD/2Z6vHx/qvp+K1H957Tm7dYrO6Yrs2dnnTtDx89IS+PGZIXZv1apx3jsHc+Q6Ojrl9+5hfe9+xnEMflB//XDk5FG4eCz/+NHH1Ep2TZ0PWP0X6L3fXqivPnVszzi5a2vbNi7Tsx7ckYVvIs/8YSGXjnx0UPhCw7D07MdOBZc7Y8x6eGN5qidvIaVx73rZhCVtUiC7i8uyweAUNhLCl71tiDHbw8n7acsHIcoOyGkktkbvEF19ccnXWcP7FCimVyaRgo0pz2bJdd5xdnrO56lmf9zxdX9HFRNAs1IGQSvK2zcVLxCE+4rKUqhSSBc2UShyFQIXgSk/hC2Z1w3z2hNm05vajAw4OJxyfTjm9dch8OeXGzQOOjo6ZzhZMpMaNlqH7hoPKjrS/n5jzAdyjW1x/bnh9VmJjMANRzH7TkphQ2G8rRvAjS3+O2xEZoWcpBZ88k0lFNZsS+jkpOrp+S+q3hJToU0UTvI14IYQYaNot3WZNarakviVqZLqcszgylySJvSmmZaMS8UK3bQnbjtT3xnoVZ+IoahB4jDFbeNrfw79jjCSNmRpoMOtO014M2fEJ7/1AMURTZ0fQKc574/3RZynNl105f3lDNUGyVtMrvR41X/K9cOJeWKWrKl3f04eeGANVVV37HBEZR0dVFe86Sl9QFgWfP3rK6ixw9nNTv6imC7ZywtXqks3qgs3Zp1yGiidhycX5Y/rQkVJ66b4wjnJeDxGhkxnODbfq8/G5ZnXFw+WEk1uXHC4nHB8ZMnCwKJmkBdNbE6q5oyoNRcS9+du9pp7UDAoggvjypeNaXxUiDlfUlEVJVVWmLBgTffZKV2Cz2cJyV0D0XUez3WVfQZgdHHNwfMSNYzvOXadsGlhmOs75JWybr1j8aoTUId0ZBGOxx6jjGJdzMk5teC/MZyWrq552T+BKUbsvvEFz7W9RwrYk6mGnBT6yrXMyELv4VPYmktVetUvYMVe8kJLdKL1TnHqDWDO5DRFcsqqmSx1kbWFJxib3AikFkghdIUh0xJBou36sqFzh80JizxqqBwqP855JXZJCTwwdD7/Y8oA1f8YjCmcYQPwXH1lyEqgXSzyeUj1d9ESF3sV8gQ/jiA4ha3aPjHkjl2UvT0Bw3kw2OmdoAiFwlVa4ZoOI54efPqJ0UHplsShYLirevrPgvffe5uatU979/vscHh+xPDykrBeI+L3f4dmEPCTxgbE+9CUyuWwQXtGAamPQu9PcHxegtkpaBeQElQZY2etTAgpwtX1vctJyirgSj6OaRw5u36Y+OGRzFei7DX27oTlbgdS0oWTrerbdlu1mnQVKOmLbI+Lx9ZJbd+9w4+bJqMomJKrZMarQbVvaJqABXFEhzmbJuxhG/pwO7O9kgikxBGJ2kwrJlN0QD84TnGRSo3mUiCs4WC7p+o6u77h6vEZDpNSElA7xDq+e0hUUv2xzvapsN1uK8qth6iH6EDhbXV17bD6ZMK2//P32WRvKqqTKrw3RtrWczajLkqPlwpadKfGzTz8mpjQuUEXOkT/8YrxjHS0WOOlQ1qgqIUQu1muWsyl1+WbMPj49a/jsvEE+vWBnSQyH04If3F3wKz94xFtvnfBr31tSHtzBL2+/kc99WUhhpLO4uv96kHiOsp5xfO/7+LKi3VwCMJtNmC5eg7shws0Pfocb7+y+a1UJv/Urlj9ehZwPQPcYt/qIfWLg0/OGVR47OzyoOT3+8vE9VeXpw/tmBPSG4luSsAUZdKiHqliGvvWgTMbQkM7dzSE9DGSn8Wkb28J61mDzrQyMaUDU4bN7lTHFQ6b6m0hJEiGSBVUyAc0SbzBRkJEhLeNN2wRfAqotngRakKoC9YqoI4Zg1ZhLJLX9jpkBJwKajLCiIvSatahVbL9VcNFmzCPRIGQcPjnrP5Osv4qg6ghSWKUgJmIq5kZCGslihYnuA2kbaUNH311xefUZhwfnPH684u5bN7jz1k1u3btLPZlRTxemWjTMRIzHIN+w9uVjCdaSyG5cgsm3Qu7pDsQ0PM7XRiZPgLO+sGrDIK+qeaHC0NIY7ThNP94XnqKuSThq7fG1p5zUSCroW6FplE1o2XYKQXBq89ohKmVdMFtOzdwjJsJ2jXMDzuIJXaJvgv0YOpDABkF/MTW8lIixH1fSKZkVa4qBFHpSJpa5UmyB540fkfIi1AlImX8lBe9LYlJiF+w9DirvbKzslyxhw4Cd2W8Suh6E0S3rxa/f/XeILvQoyrSqx+q26brRRhPszC2r8pq5x7CttrfqeFpXDIVF0my5e+3Ftj0ng/ufvbbpWnqzW6PtelJSJnW1u3d9RWzWK8qqpq6vk5TGgYtnFtLrJvDxozVX+hk/+fyM88dLTu9sOLm94dZpTTmZ4qa/CD6EQaFucmAtMED77V4F/lVvzxNC4sax3ul0OrYpuq5nvdmyuupo4vPnQFnVTGZzyv4x3Ur45PM73DgW5lO7ji5Wytml0ocvy9oJ2X4O/QXPMtx3x/vFsZiX1LV97822Z9sos+UBXdvQbF4Rg/+K+FYkbKtSPfvg68h+HlniZNh6B4/Ks6f8KLpies+aXbIGZygbc7Jq2ylEGRJ2JJfvkHvgiFCKMZ0TSgid+XHnH3G48C13GWyekuJJiESgIMkka7GM3mB523YCqgjOkxmpxjJHTJM6DDTGvN+aHAkbN4saMwpgM4hJLClbj94RnM8nvlg1ChTGmsIgZ6N2RYXUK00XWJ0nHjzcMKmEs4ePeO+9W2wvzygrODg6pqw84mcgxS5RXzt58+1tEMAZ/LAHi81hrjsz7G1G2+FcjSQ7xiJFhqIKe72kzLrNHzFoyWQA3XylhaKqSOIpVfCxIKUJKTriZU973rHZtLS9Qiwy16BAY6IoSmYHB4bahEi/uaKYVIgvzFmtjTTrHheMuT3O6ttq0Agx0SxTB7GEgSWeYiT1AQ096s0C1fsCX5hIT8iCK0lMY97Y60pRlhluN/REnFAV5sbm5Hn28y9NqJr/tTDOQ79ivqMPkRATk7Iaq6ym6+j3Kx8xNyUZh3R322/7nhAjdVW+cltC9+5Vtjiw+0YbbFuTqnql/VdVNpsVkxSpqkm+73z5G5s+8dlZy2dnD6hLx+rRgu+82/DuuxsWHx4yOzqhnhywc9d7syH1El8b4Sptnr56wh7eL3ZvQBOTuqKuKhDHer1ltW65uGppghGG96Oqaw4Oj/DhKe0l/ExuM5sI80y+v1jDz+9/FRSekM2n5nuwF2k/97wkFvPdIkKT0jSR+eIA59xewpZvNKb4rUjYYCMXaEVMPYwN59zDyieWosQ88uFF823bxEk0pbEqj8kIS4Pjo5hoWUZrhaDmwOV8Xr9rxCVvo2HetuExC8jYR/q+p+87uxm7nNxxVp/vrXKH00eDWSuGHpwrEO/A2chOiiZHCUIpBVGsim7DNvPnlJB6S3wx9zWzMYoTB+pxKeZKcKeDqgy9eYjBemVFtL6pc0ABlXN4J3ShN3U38bhecWonQog9l03ijz+OfPZoyx//fw/49Y8fcO/tW3z4a+9z+90PzCO6mMNY6cY92Gg4EXOCG2DxvFxRcfl4dSQNIIWRsMSZNvlYsEge9xq2Ec14JOTPGhEUSJKMJS9CCj3NZkPTNIQwoU81iZq2jbR9i3OemAKaeuqyZHlwwO2791gu5tSFEDcXlOUNfFGR+kTqAqFp8KnDKTj1hNTn6smDOpx4+oF4KB4XErEzaFzbDg09rRgy4EpYTCoUYbNH5EtDG8YJk2mJk0irPV3Xj0xp1fSVN4y/rBH6QMwKZAJs1msbdfoS7fD9mNYV06rGOaHtw2gCklKiabajaNN2k525BCaTqZFHc6SUOF9dMa1rS7ZfEqrKxdXVUGA/J1/5daJtt/RP7nN4dEpRvPooVxcSf/TZFX/68KfUf/Apv//b3+N7313xu7/ZUBy/+wsnpX2dmJ+8xWR5wsWDj7m8vOBytYXlr3K1FR5cHfHk8RkprTm6ceva+xbzknfeWiInfwUtl29sf9oucv/Rhhi/ub1tWZYc3bj1tZP2tyZhC2o3Xlyu0gblMBmrNoO9rbIzgRLd+U7nGN2kBinRlCvu3OJJGc4dPxMgC44M/akkYlWrCjEG+r4jBPNYxjFaeaaU2C253bhFQ+XNM1kHoD3P5sYYBhCZ6MYOGCEzofMOsqtYTVZ01x828pYdoTi2DYZkrYBL0WQydZhJN7/owjsKL5ACfZ5zHjRxI0qQlP2Cey43iRgCP/3kMds2Is6jfsrJzY7j02IUaRlcuXZYkbDf94GdJ6zmY68aDKaXYcGU0BTMN3wkbci4ABttOJ0zMprLbm5qCdy5ksIXFGXAeWP0o2VWIovZeAPEe2LsUQ1MFlMWh0sOjo+o6grnnS30XIkrKrRr0Big71G1NgbJmMUGi+e58ux57RxUlSN1Do1i41+aRgg9RSWGRN/1qIidG1lm09gVmtsXg0GNM9IZQogpu4W9+WroL0IYCrYDu1V318erRErmgU2Etutp2y4/bmiIdx5xQggxk4n8czdUBaIm+hiQPrfCko6ypTbnPfgY2Gvf5PpKVTPC97rvgzYk2pBo2sBHP39CjB2LquPknZr54TFHp8e8MnTwKp/Zb0yKFEb98NcJ5wugpp4fIltHcDVoRUhKSCUh2Ihks1kTgy1q68mMenaMX9xGy7mpMuaICc4ulPXmJQdPA3TmYS35noKfosUS6c9QjYRw/ffs+sjV+jpJcDIp7P76kqgnU3xR0HctZVXvkKLXiG9NwgbFiaDe2w0yRUh5TEt1hKqGqi0idmPOzW03DF7nhObEja6RWdKCJLIb3xKlIFnvXAobr0HpUxrqOojkmdqGmHOGL71VrcP2wW6uJrUBYilHAAnW21VnF1sMPaHrcqtH6J1d9ES7USesr10X3gpnclc4Aw6S/5NUTZggiRmV6NB3tWOBmla3aZdYv947qCtHVXp86pE8Fzycci7rqatTRALbGOk2gfWPHvPZFysefnFG3yfeee8ei/mcspoZI1Tz2NVe24KcjPcr7l2yTghhDxlwNq8atygJkT73uIcLxIxBEkbAUidZDn1lST4ppZ/gXUVMNSFW9LEmtEDoSF1P6hpim1AfCSGgqixv3eb4zk1u3L1J6W1x16cSyim+mpLWQ8JuMnphI+UJ4yJoND1hu3EHfO2ZzrI3uQTSWX6d2qRACtA1ajKqTogiaFmAd3hX4km40ZXMEJyyromqbLYdB8sJdf36F/i/Dmh6s1gFq9abZ3yIy7LEFwUxrvG++FKCW9v3o+tXjHHclnOe2ezP43b69VcBMSl/+NFnfH6/5MGnM379Nzrufecuv/m3fvuV++mvEmlz9rVIZ/vhfMnyxjtchi10w5jYLkHGEMbZZuc8hyenTI7fQZc/eG5bfVB++EnipeT11OJWP7xWaGh5A11+iJz/S+D5MbXNJrDZXCeT3bk1o5i9/BpdHBwhIjx58AUHxycUi7+gCXuEupNB4wkZ7S7tBRlbQnJa3V91ZzJQcruTTpSU06YAknwmoOVKSTLAHq1CT04wGcxE1ITiSOIQVeJID9K9kzp/rncMUqpeBtev3brBlhXg1F5LcgTncnU46IYLmoTod/PHSQWveYY8q6upI1dhOycucg/VibHahxEsDzgVCFmRXBUpbOER1CPq0NQjqad0HlWx5AOgiovW2w+aEC24XPd88uk5/v/+iC8+e0rlE3feeYvjGydZBlaGL8ywYBhX7Clggim99YV0GJnK9pwZPTDVNSO1GQfhCh1dyOw4a3JoaEn9FkJAksO7CSoT0BJfOcqJo44TGzfRlkknTLcR6NmsOxtXqwtObt9ieXpMNZ2ina3snK/pG9DUQYqIRhyGLqRo40Jd6EnJWOFVZeIzpS+YVI7FRKiKCU6UxwAhmj822d5VPOoKkjhTSIOsahatLSFC7RzqHF0m2ogqMXSkVL3Riu0venRdR9/bDbMsCorqmy1mRITpdPq1oUpNie12g/UoYTKZEGOk63omk9pG+t5ArFbnlGXFcnnM1+0/X24Df/rFGil/xtnZCl947r37Die3b7yRfQRAHMXRO2i/JV49fHPbfUE4J9y5Nac+eH3VN5uzvrBiYvoWWtsxOJq23D34CRw4zs6nfHb/6iu29PKoJlNObt2hKMtvzBj/ViRs2AOBZTgNLUnvXE/yjO8e6WKAx3dbYEca2RtzGl+LQZhDsznlvncaiFGkXIvn1JwZv9d6h3lHNROChn0dBdhQGyGWQaRFGTjRpn3uR5h01DKH61CX5vdJJlxlpowyEJ8GGHzAAtIoKmO1mYzVrKaB5a50LiCaKFJhDmMx4gsbk4sDJK+MyIZBe0rfR9Yx8ODBOaTEZz//jHpWMJkKs8nCEi0DOSwn7KHhMBLOstKZDgnMAX6E8iXDCEYkKTESXsLoaC5X3XmOOZoLmUiB8xMSJaIlRVlRVCVFaGG7Au+Q0lOUJUWhOBdwpbHIF4cHTOdzfOEJrUASpHCkEIn0+fcim41ljCbFPQ/shDjwhaPwmmcycz/dG6Q93vxTQmNEQ7SFkRgcPtImBwQFg8e9czabLWk8d9Avdwj6ZQuTfbWKyDnBxRcT8sY+tO4g8BeGDHKzrx4iBp+njMrt4HEhxvxZ+Vq/1rbD5HG9c9eEWl4lQt+BGgn2RQnbF+VXVst9VC6awIMnV6DKjU8+ZzqbUc8mzBavqiynaN+O0Lf4Cikm1/dJPOJLpJxec+t6tRCknFDVidm0Z9vseDJFWRoXKPR4Xxg7fH5IUU+fW9M2HRQNzKdC2yndwCXTALGxZN3bCBl+CqWZkoh7QpEuwEFROOrK04dEiq9/DXrn8fWUEGwk+Jtcxd+ahJ15XGOiEHyusHKiVUYfKsvlQ+IekthwGHSnCOZ2FbD1a6NZIo7bcAgJ57LurUgeIc6M39AbmW2/PzuwzxlY535M1MN5PlpPZzg+xZjXDg6fiTJp8P0eIP6YhVGcgeOoQ3XobAISc/oCS2i5u5stNEstKDIJD1GSWK84RkWjI0Wha3uSUyrnKLAfv6w9CaWLPZKyg1b02SYSEh1RHQHHk7M1TdNR/J8NzfaKfnPGB7/yAUU5xbmacSVEZDdMH0BbNDWgKwzW6oAlsKsaxUvuIRTABJGQUY8mLzwUjSWaS1NXzIEJmhZocjhK5otbiF+TWPHo4UM2bUMbI64qqWbCTKCeL5gsD7l77y0m0ymFeEIM2KBARNs1RE81r9C6JExK2k1PjIa+ICDO4SVlb/WCoEpoA1fbnjYKXRsp6xoNhhr1zZbUR1LqEadIWSJ+QkFpCxTJPfl8zla+QGvHtrMZ3roqACW8wXnOv0zR55n3Z8M5x2w2H3NIs91eH8X6huELz9TP2G635iuQQ1XZbjeUZcl0NnsuryZNXKyvmNYT5pPXt+IMoefpkwfPPS4iHJ/cpiheYc5b4eMnWx6tOjQE1ldbzh495rf+zl+j/ApSnb1fCWcfo8HgYn94z8a5xtZYIjz9CW52QnnjQ/onP0K7zSt/RzMS+R536oecHNznj390OT53cHxK6DqePPiC+cEhy6NTOPlN9AVkvI8/T0wq4d/4VcfnDxOffJ5vOP0F7uJPXvr555eBy8+Hqlp5++6Ch483rK6eP89eLZTLp0/o2oZvApV9axI2OeENVbPI4KQl7GZ/d6/erSJNjWtQQhv8s4V9stpAErLTyQ0jY/sC/5lAZIYemXmeWamOIYHlhLo3Lyji8wx53sGh8s5KVppX2oP6j3cOX1U5SSeTr1SDRIftJh3Gz5JV6VnPLK86UDG93UKsRy1qpDYzOnGoz0iBi8Ro1WyRCoJkollirNTb3poHfTT9c9E8YJYG8lMCifQqSKqRLnD/0SUnHz+iErhz55TZEupZ/dx5uN9AsGrZtMjNqnMK1MSusdaA5P6t1IhfgkyQ1KLhDLSx1bmbIV4R7e01TCEtKasl+An4JemqZ7tt2KzWrC+3rM4C/UZwruDmvbeYHxwzWx6yqKbUVU1dV9RaE0OkbzsKsTn77iKQQrS56HZL6K06JmFjaCkQmg5R4zX0vbLdNqTkSCGZ1vu0MgU6b1oAI+ExRbTrzOfMe9Q7YjQWfOhs5leSUorHlaClLQnjG1RM+ssY1d6cdd93qCpt2/A8WvcGQ6CqSlKyW2kI/fXf6SXFqpLnw7e2T4V3TL6GP/L1bdq2YuzZbK47RdX1lKq6bi4yENI+frxherRhuljTn3+OXx5+6Zx2ai9J24usCDjEDvl8LkTw81totdnB4+Lwyztotx7HvqSY4OcZls+o3fmq5/HnGx4+3nJ+sebizJTeytLz/gf3KA/fYXJ4hw++U1EUO7Ri0yifP7Jc0gflp58m1ls7SrL5mZl5PHPDkvYxpBadvcuLzpfloqIsPWcXzQvBgstVx6axhdvzEqXCbLlkMrNZ+hgjF2dPXny8viS+MmGLyH8F/DvAQ1X99fzYCfDfAd8FPgb+oaqeiV0t/znwbwMb4D9U1f/nlfZkYH4OKPeAfj+TmHev3Ts9hrEal9/4TKTM6LVkna06ZZA71Ayz2g8UYiDrrWSbSjEHL9mh8QPsbNaV+84+ykA5Gyt7JZOTDGgvnB9hOhMUifbjD19fdwdAGVhzdrMf9iuSjKA3VLR5/neYXVdVEKvHE9YH1ZQ9w/PXVQQc9D1EUWICl9T67eJwgvXOhyQP9Flt5elFy8MHFywqz+ZqRVVPmczyNp+9Kep+2h7aFiVIhWpJCg3iuiwAYwI64mb5HS2wBY1GbnMT8BGhBTdHmCGywNdHiJ/SB08XlGbb0LcdXdPRbAKpNyLR0c0bHB7dYL44ZFbVVGVFVdfU3hG6wDp0phqXEu0m2OJLPLGz5J1EbDww2WJLxAh34mb0fWK76Yc1G845fFkiTkh5LI1oi6oB1o+9g6Qk9aPwSgyBGE17vHAmIBEKoB98179Z/Lldz/8Koij82CuOIRBSeGHl/aajLEpDo3Q3DTKELfqFF+WyMI54QlWUVGU53je+Vig2ihoT2831nqtzfi9h2/3CiaOP8OCy49Zlw82LNf3lA6pSvjRha9+SmnNrd1kvIS+UbDE63oPdblbaTY/QoiJtnhqC6gr87NRYKs2FweflBDe/wWC0FPrAatXx6GnHat2z2bTjPHOxmHHn3l3c4Xeolne4e9NR7GWzyyt4dKaECDHC/cd7CGzzEOJ14iEA/QUS1ujkDoNoysjtAarK453j/KKxaaFBi0OVlGCzDaPt53ic91ow9TTf11Li6uL8a4mpvEqF/c+A/wL453uP/WPgf1XVfyIi/zj/+z8F/i3g+/nP3wD+y/z3V4b1ddIzak7uuQQw2Hy4YUE3JNFMABuSnqI4kxTbs9i0Cpl8coXYW2UabW7ahC9MRcyJG3V/Fas8Zf+zvA3j2EMp/3hC0gpUMqGtz6NclpF93umiKnAOikKgSaQujSe8asAlb3PW+fw3gU4Z+6oeQwAGhTATY1GSdKgofvAMF4emwip08XlsCBIGcaPOvDXyKenVvlHwPjPoE0OnMADqIkmsyjxbBR48bHly/4yynLM4Gn6d/GPuWV1AYZWzW1i/wE9IOiUlSN0jXNEhPkFxBMwRDkhEFA8yAV9ZjZQEqBA5QPzCErs/4OzigqvLz/j0xz+i7bb0oWVycBeKnqArSMJ0OufW3VssD06YzQ6YTmaUk9pIZ0kIXYemnvZ8Tdj2iEuEPhFDhM5UywKKS3kBFSNtH+gcuAjatcimsRn83L7RrJBWFGYLqs4b4QxbdIUQSCEQOxNaiSkStlvavqfpW5Y3byBFQb/ZUpflq0GVXx3/jD+H6/lfRQwuX/D8uvEXGYvZDCfCxfp6kgx9IITAdDq7NtP9ohgkVZezKdXXGPcZ4vz88Vcirm27ZXX5lMOjG5SlQfJXF+c8+qLl6edr8I7D43df+n4/P8VNDwmPP0KKmuL4XRCPhpbw+Mc2bimO8vR7iN+ds1JMKW/tsbjH/r1QnryHZHvRcPkZ28tzfviTFaGPiMBbt+es5iVFaShEPTsknfwO332n5PaN68kaYDkXfufXHD/6mfLk7DVOBo248z8YT6AHjza03b4+uE3eHiwqTo7s2G2bwINHz8P9ozRpXpQd37yFiOPs0X3mB0fcvHvv1fcrx1cmbFX930Xku888/PeBv5v//78G/jfsAv/7wD9Xw5FsKsEAACAASURBVBL+hYgcichdVf3iyz7D2se7GeahChxg8gHSuvb3NQRmqOwG6k5+avjPWLQOwiv5wOeVXIppJPbAgMIPid1WrQbL76B5Ew0bvJ/Hb8HgN2W9ax0FGnbn5lCi26iVE5eZ1sN15sbtyl51mrJusUbyPK5Y8hvJS5gSllNDBjICoNmIZEBj948BChKt522CMvbKQc1N8/C6iqDO+umIUObWQ8IqhBhzn19lPM4ZO85jXzH/liW4ApWptQqi2WpqyiQsLY2xHjtS7NHU2N/27nw8IzFEtk8eEYKjTw9ZXaxYX605e/ipHVvvKOoaL55pPUVVqOsaJ46qKKnrClcYIz2laEx8p5TTAm0r4xKgyLYlNT1FYTajMVkrwkRc0nh+RRIhBUv6rcnXah5H0Gy9ilSI93jns1+w/dohBvo+IBopCLaUSj0EsxkVZ/tcFgVF8c2Vzv48rud/VfELgbyfiRRt5LQoivH+MPBlJlWdf/fr8SokLueEqihf7tf9iqEvIdWFvmO7XY2vqScznNudT5rRgRTDV5t3iENciZudIK7YmYooaMqmPuLzc3vnbArPqZ5ptwaUxw+fENW2s3ryiM3qisePG0MWBRazksmk4OjIkrpZC5d473mRfo4IlIVweghVfv5qA6u1oJNb0K+Q7umLjiCknr5PbJpA1yfiC4hmzsmoi1BVnoODis0m2Lz2bkuZ6BjHB5x3TKZzc0V0r389f90e9u29i/Y+MCit3wN+vve6T/Njz13gIvKPgH8EjH3hMVEPf8acaQnKREiEXTbWMXePcHLWoR0kScZDnWFjTTbXPfSnLRlmRrCws8kcGeA7xS5Log4/wCTO5rqH0Ssr3gd7T0vYA1nJqympSX6fOKHwBV4suVrO31uI5AWCkrep0RYJZLnW7L7jnMMhBmU7sZ3IAjSShMG2MZBFPKLNcA+AexXEVACdEDLbFUkkKSjweE2o80AJ3jY/KxyFB7w5VkV2Ljbo0PsxwRFoGQw1yFA4MiGlDRo3oFvQEpIHrdAIfVyTQoOmBo0blIKkBh/3fU/Ttjz46X02Vy2XVy3tekvXdWy6DdWoe77Ea8FsOiWps/5mTBTeU1cVyTuUROjbTBtUqmmBSxPSpCRERUIkpi2Tuka8J3T2/e1rDa0DR3RKiJFu20DToyGaoEPhofCITChcYedVvtF7sXOAHrapoZRI4QIFPYGAjxFRxYswq6b4PAX3C4o3fD3/BdM8fw0EOoRA33X4+XVxFecci2mGnIfrGHZ1xld8bOH87v2/gGjbLW1rCEQ9mXF4eGPciX2w8uXxTNIS8Ms7zzx//TWDaNT479gRLj59brNJlZ//+OORCX7/4Yb1ph+RP+eE6aSgrjw3T/IxKuo8cHv9AI+0JQBRbp/C7VM7H39+X1ltBJ2/C+3jlyRsi20bePzkBbD5M58DUJWOmydT7scNIaZd7lKutTgUc+A7ODndHbLXjG9MOlNVlbG5/Frv+6fAPwUoykKduDGRjr+BuPyT7Mw2hkpOBrq4Yn1kdpB4yipYw0iWDskc7L250TjstAg4KXMffJi3tmoZ52ylqLbUc74geY9Kdv7K1PU+u1X5Ea8HkhqRK2ke9bHHh0Wwi3n+WXcMc+t5Y/PhCqUDL4KvKpw3SL9w2Xks1+YOMinNsFjrm5iLlsvVdHRKzI9XrsoGJ4ovLClU4ul8QRQQOpw6nHqiz4BAUmpxLCee77435a3jGbcOpyzFU8cEqcXGtBQ0omkL2qESDC6TAgaRlHBB6lZobMDbKJRSoF2PxitidwHSk1Kg3zZIMUWlpulrLs/POXv0kMefPqJrIiF6gvbWw0+gPQQiV+szfDmhni2YLmcURc123bNdbymrNWXhSG0krDtiZm46B345RyYVeGF6Y0l9vOTyi8ew2RBiS7ttCX2ga1qSRluUPFVS25PWHbFrs4RtshnsHqTt8JNA2SnFfIarSmRW46YlZZpQoxzUiWWdOF4ktpuOi4uOVdOSEpy+dcrVds22/eY+w18Vb+p6fuM79gsI5xyTyfSVrTtfJfquY7vdMp1OCCHSdx31ZPJCSLwuq9FF7JvoS3+TmJaOD27O+OBu4u0bLeVLFoXx8j6p3TG1EaE4/u4IeceLz0jtFWMW0kh48pMRhbDHnq/+n5x33H+8pX36Q5pWeNzcoe9N1On88SOmiwWzxZL7D9fMZ+UIQxO3uLN/idx+FzCJ0sdnOmqFz8sN7x1+hj+4i6sPALh9CocLx5/+VGnb3T70IT0HaX+VDOnVuqNpA7dvzigz2e30eMJyXvLg0YaDpfmT3701Zb3peHrWsF5dELsNH374LmeXLev16/Mrvm7CfjBAYyJyFxgm4z8D3tl73dv5sa+MUWD02hJpb055eAi5RkYbT4dhTjkTvFTj2MtO1zf5zFJyl0hl7/93bxjgZ9mDybPUaYbtTQU1ux7vEREG/WeDmYeFw074JWrMlWneKSXPd2dKmwjOu/GPuN3nD59hM9N7qLzT3cpzb+0zXEeDeYBgUDfOxuIGhrmtg54xBZDMU1d7Z+lNt/foeMpsPqGsvCXp8bdIkDpUW3ARlQoGr+uUbFwudpD6zLA3CU6TNNyg/Rm4Hk2R0LZkszG2G2GzalivtrRtbxKFCaIGYor0XYToGKYBvfMUhWMymVCWNaV6Qrtlc56oK4/2SmrUEnayhVpRFIaU+MIWRc5BkW0uPaQ+e2N3bXbnisQ+C6T0nRmB5ISdhnOSPi9GASc4jbhCQErUCUVVUk2EyVTxaYZ3Hg0QGltoeW9z2f4154RfI9749fxtD+/MAtc592oVthqzV18yFmaEVWsRDW2blGL25t6tXwrnx3uEcybDXPjnpVDfdHhf4Ly3Oe69cCLMKs/RwnF65Kimc3z5PFtdY4f2exWnZKgpBbRvSN1mHPEa3xOspRX79trjvqwRV3C1iZxfdjw5a2GbaHuhbaNJQfcdfd9StCVdUaKpphpWE8UC5x3zcktsVlw8tZ5/sxZctOSZ0obLsxVOb+Cmu+P/rIlH10Xa/Ge/4o0xEMKXJ1QR4WBREvJ+VZWnrgumk4JJbYhAXeXRz+hom3Vuw339iYWvm7D/R+A/AP5J/vt/2Hv8PxGR/xYjp1y8Ur8rV5UyUrHt5hkHaccBHlbF7VWqo2NWSuNrTRt7X188q38N0ERuRY9VMMNoFphCmssNagWKcaoMXIa/yZPRkiXLjSg2zF0nXCayJVLqxgVHxHSARQsMjFVi7IhqJ4WqjqPlvihwztyZiqrOvQ6Dn4PazK5ZYAuaHaisvI+2UMmLeQESySwltdgbSbMFgTfEnCg2GV0mKBX6wr5rcuDsiODE+sd951lfeOr3b3Dr3iGn796irGfWpxkWE6qYWEqXeWgJ9YaMpBBJTWOqZ5KQYobpfju69RdI2uB0RXIdIQbabksIjqCei4cdq9WW1SqRyinqTIwkbiN903L59Iy6sh71wcEtSl8xm1WcHB1QT2YUIlze/4KLT54ymZZUkwX1/MgmBIKSWmhjQqoSWcwRtd8xJOvxFyXE1QX95ZUtBPtE6pUuRhtxia0ZtqiZ3QcCSRKRHtcUFFcXhPYAqSe47QI/X+Bqs02sp55yKvhCqOoNM4GygQZP20WqsrZxwF9MvNnr+S9AVHWNfw1OgGqi2W53bblnIqbExXpN25txz7Z5MZw6q2tznwI2bcPFes3xcoH/BTuxTaZzZrMlT5/cf+HzB8sFt28vOLrzHsXy1RXPUrcmPP3pS5/vtiuuHl+HwRenb+MnR3z08RWPzxqePG2AHbx+dXFOszUG9Wa9YrtZXyNopcX7TGY1v3L6ET/7/AEff/QzAG6d1vzqPWNir64Cf/qTFXrQo5NnFll7ufLJWWPs7meibbZcPH38pd/dDH8cRfY3v3NrxnxWcvf2dUOV6aRgemeBAk0T+fzB17fafJWxrv8GI6TcEJFPgf8Mu7D/exH5j4FPgH+YX/4/YyMgH2FjIP/Rq+yEQdHO+n5D71n2+x+6KyzztSIYAWRgdjO8Wve3O8h76vi+XQEvGVZ+rtttj4lilhiSmekp95oL89fWZCIYYqxtzRuPwcZvNKo5ROZZLFHzek4pkqKtCVKwKiyhFK4AL2hhPWnJs9ix70x3OquHSSakaa76kwQcjsL5cWbb7a3Wfe6zlkPFOKAIDtRlVTaEQIF3tuVCzDfaJYd6I1zlrwxRiCFRSMGknOLrKf8/e++xI0m2rel9W5hwFSplqXuuarYiGwQ4IkCAA07JB+CQ4JAA53wGvgPfg09AcNJgg+xu8opz7hGlMjMiPFyY2GpxsLeZe1RmVmXVUXlu9ypkZYaHuWmzpf71/9raopbm80LiIQ3ZYWubT23UOUOJWV5T6QpQxHHMy4tCqQOiBqI4RnfAh4RzLcchMIx7trd7hsFlAQ2qjAmQMhalLfV6Rd1YmqbC0qPGHen+Fnv9nHZpWF6ukeGITh63fWAYE370WF2DaGIQTKgwpqJOGjdGxjGyurxE6ciwFdYvb6ivlrz+6luSD6TkcUPmHRfxUOZxI5naNTGR/ngSHqzKXO6ajLiPnmQv6L1GWcvV5ppUVcQwsqw8lRiapsURCfIDYKAPsD/E8/ynYM45TDTUHyBz6Z0voj3vzor6s1lvyO+WujnNhJ/39HvncIUAJ490CYd+ePTMaq0KocrvNutWSrHeXD/anxQdx+2X+PEzUBfYy88x7QUgxN03SMzZ8VukJyLEh69mgOqPsdd3jjHtiYe/B9cA6/cu+/Qy8vTC0yy+pmqugCWfrV+xuVhTXf+M5+kVy3bLr7/u2R0CP/9VdoaT5rXqv6SKd3z+yYKtu+Suv3y0/qvLhkVrud0OuSIpid32Hu/G7+7KWyYi7Lb31G3LenPJdjcyjJGbq7b4K42s/hJih+q/4uaqfSeA7cfYh6DE//v3/Oq/eceyAvxPP343cs6XebxLyfsRYru41fNjLSc3pXnaeEaaq8dfLA/DyflP3n8qD8/lZDVlpdMHcvb3XNxGTUpgagKaFZS3kMdzYs7MVCl3K/W4eiAxE6LEKKVEU7atdQaTlQw6LxvnfrYuzljKLLkoRQaIkcvY5XhzPz+fA00OhrSdUOe6lN6ZQUyZUW3KqCmMaRotRUK0nPsMmsuKX3Vlqas6g6i0gjBRu0aQMTtrAoiZ576z7GguK6oy4paCZ4q0RHpERlJyOO/wXuG8pT8Gjl3ksL8nBIhBo2wNGJSEElRpTF1RtRV1W2FjBnGZ6KgU1FbTLBra1YI4LvEPByQm4jCgGwVKZ3rWmCBEom8Yx8CxCyxvLmdN83qzRDUa/1U+VpFAcGPuzUssd4gm6Tz/P1V8QBCdB+QUAUNEpYhKAZGED0LvEjfVClJA1RVWMieArjKD/u8iB/vDPM8fp2WsS7YUc8usqqq58vaWFexLLo++n2XOhTAvmx8UMgve3MI6mY8B/x1elRgj527PmLmG96MtV+OmyZQTPa4uba6mWSAkUgpl2YSRDq0FU9VZbtNYJDjS8PBWmTvvdH5PpfFt8pHZRDLi/NFMuiKKYd+PHEdPPH5Dcte8y2ErBKMCmzbx9DKBHtF1oKo118uRzapBL69Y+yOVdLy6G4lROBRBjhks73doDqz1mmNSBFdj6haFAdOyWDi09qTbIkgkwtgff5CkSBf+jeAd1hqsVYSQGDnNbycRtF3PKWFdmYzd/S1U9z4SpjOBJGhMmfvNN74ufdxCwplBZKFMYqvcE0VP0CtmRyRMPvnEWT1lukC+4ZRG6Tz/rLGIyq5JS+5Np6SwTQVKk5LKw7Y5VZxq6hDzuFMGTeXxsOTCPC6WiTey07R2QsNr/JjZz0L0c/k+pIgKCZUS6EJTKbYgvhVGmULkkpHHWgmWmPmylS4ltaneX1oFpNKPJj84Zb2iDGiDzhPbJdsDVWXHpcMiv6gKgCwD5yJNrbhcWf76L5/w6WdLrp4ZkAGJKotl+CNKRuCYx7e0RZRhYmoTPyJpJDEJe6SSmY6IDHi/JfghI1r7Jc417LuG++2O4+GIu9sjaoHSG+yVztxpIeMHtApIGmjqms3lkpvNktXykuvrF1w+vaRaZNKVxeUK21Ssbl4Q+yNxv6NeN7nkue/YdYnDbsR1rzkeO7pjj1nVVNagrxri2DH6gX5/wCSd752QpUhDcDlmMgbdVGXuPYeT1bKl2Sy4eH6NbVqqpkU3K8RYjiHQdwe6nXC90FgVsJXhuO8IIVGlBevNEvV7RBH/YzeFYrlczMF133fEGOmOR5q2wVZvzz7HGOcy+A9ZKuIfU2+y7zpsVX2v8te0ZxerFeY7yPqfSp5yefWUlCIP2zesN9c0Tb5nznvkbhzYPdxydf2M55cL/uv/8lP+6p/+BZfPf0a8/8UpeHhPv96snmDWL/Cv/3bOwL9rIomHb35OjKeeuUsV33TPkeMvSN03vP7y17haw+LTt75vleeT9hds7AWoG1j/C65uNvz5F2uap3+JbtZM7zlrFP/8rzaPkNuHY+Bv/iHPxTuf+Pd/t+fu4Za73b/j03/+X1E1V6Tr/wK1+3fE49e8+eYrVhdXLJYfpg++WK1ZX2VymdWi4uWLHHSUPIn7h5Hd3vE5/ybzbQCv3nQkET57+f6Kwg/ZR+GwhclJT6Xq0qM++/00Zj1lxSewVlmyXC09Rcvq/KY/Q2LPvz4B2pSS+ftSALJanShClVITJQiTAIma++A5w0+hEF/EUMrXWfJTqxOnOeTRsiwikY96Or4Z/Y7k+Wc0yRhsWUciR3TZAacZUDeBYGRiWp/Xkc9PVJl4ICGZxlRppJSulZjppKK0RYVcMogTIxqKSvSMQWtaw2pV8/Rpw3JjMDW5f59yaZd0QIkD5XPWLqr8rfPhpTHLaKYjSeVjiM4TpSemgXE44p1nHBLe1ThXsdv2dLsD4/GIjwI6ovRAOOTz6MYjMfRApF0Y6kZjKghK8GmgH25ZhmfoaDG2RWuDsZYUssTHGBUMk4KYoCuNQeiPebvH3Z6v/v7vWSwWbFYrmoWlWS25evaE/v7IuOsyME1n6c8oHlJAhxx6KgBbYdua5cWKzc0m96JtTdBVvq4iKB1JRLYPB5YtbFZrLi4Hog+klK9J4rcvif+HakLmHDcmX//T59+p3JWFJ4rR7zrrDP6zjxxg8IWd7sxjCOfvp3dbZSyVtbgzcFNTVZifMJ872TAc5/2YyJO+a8ZYlssNl5XjplY8vfn0pCj2AVzryfVwfJOf/fPPY2A43J2qnzHgRkfXD1A/I4gh9V8x7F4xHB+4H68JwZD89tF6GnlD1Ry4vN7kgEcE3Ctid2TcXWLaC2zyeQ682eSxzO6WY+fY7Qs1qDsdR0rCdjfysD2wfzjy1d/9X6w3Fzx9egnVBXZj+eyzB7rec9zvPkxkp7RxL9Y1bWN5a9BA8nYfdj1tY1gtK1ar6lFQ8VPso3DYSHZ6SsmpzwrFD8ujm1/rnLeodHK4p1/LGerz/AyWkqSUm3h2irlYkQu/ZTk93ei5RK/JfeApewcp1WdViLyy04wxC0SEFJiEQJSyRXmplLBFMnd4nKhKT6+DnJDLWZnekIxlBoeVEniWmcwvkkhClC6l1jT3dKdjnpDySmfwtEmUUbUA+pyu1aDEZCCbVoS6CKhQSj+iwJjssNc1T542LFYGbQVJPlOuxRHSASEUBqMJFW8yLSogsTjscCBKJpXx3uNjh08d/eGIH2EcND7VuMGw227pdwf80BH0EqUiip64G4jB49wDSnlMpWiun1C1xWFLZPAd+tizHl5ibIOpbnKpX+WKQAyJwSWSD2idUDbPSWpRdF122N3dluPdPevNBebzL6gXN1SLBdcvnhH6wPF+n0GCKqFUJPmxYO5KO0QrjF1QLxpWlyvWV2t0XeExhJirNyqYLH6ihe12h1wuuH56RWsz8rw7Cr1OBHl/WfY/2g+ZZOxDJRhzeu29VQ4vj49z/kR4cWbGWOqmfrSs9/6R+MeHWmUNy6ZhezgQZq0BM5OnTNMw01viQ7Luc0rS07tzOsSSkdoau665rm+5qYWryw1N8+GARnEHojtMG5m3kYKnu//20XbH0XG/fSCtPs970P2SfveG/a7n3v01SQxwP5eNReBZ85qLdmBz+WnptwsMXxH0ksP9gKmXED314grdbJBqQTjecThGfvPNY7CfCIQo3N737B/2HB627LZ3PHlyydP2L0iX/xKzueGLP3vDL//hS+5ud+XdrUuSp+ZzmWO7k7iUVorLi4a6egfvQLlU24eR9bpitay4WP/2oNGPwmELMM1e6cJdHWXSEC5lcTWhnG05WaUvKhQpu9xDnAg8plt7KlGoqWQ8OTlUpgotZd+pJ2F0BaVva/R0sWLuJarMV65SKY3H3KMMSQh+irBzVKu1yv1dY0jG5Mw6JoILs4I06jTzLUqTpnGukpHbmLN8j2R2z3KyEoIoIcxtcYXSESUJXdD0Uwtfi0AEFTVaBYxSiLWkpIlK4W0B90kmMNASkdFjjcEaTRCLQVNjESsErenGQD+OjGNHTUQRUYQcOCgDqkGocmbtIyn1pNgTfUeKAylGnPcE7+j614x9jxsHhj7h45Ixbdi9ucX1jmH/QHKCJIVdqSzS4RwpHQiup9veYauKqm4J3TWy0Gip8L1H1ZZQLdjvtox9pL1zKB8RH+kORw67gd19h24FKWj57v7AcBy56yJudLjRoepIFwx3xx2v/5+vUQLryytu/uIFn/6zLxi7HeOxo3vY0y4sShTukNh+u2XsRq5fPmX9dMP62QW9C8QhMcQ8DiJakyRSLWqaRc23v/oNIQ1UjXDTRiwJfE/owIWf3vv6D90UinaxQFKi645IyoyAi0X7OFsOHufcB2VZMUbGcXgnu9mH2OAco/ePtnXse45ld5ZNS1NVPByPVNawan9cS+Sw33I8ZGax5WrDYrF5axnvA19+/S1mecPi4scfgx+OHG7zpN+UWD3s9hwOGfw1qaP97d/8CjeOPKt+Q4oRODk5hfC8+TXj6Pjla8VBBdoqAV9xcXXJ6iLvWB9bvjq+5JtfBZaLO/6a/w+FIsTE3/zd9lFWPdnt/cB+3/Pmm68zzXCxfgj8+qs9fPOv84syjlxcXfHiZSY1OXae2/uBl8+WCJmidL+9x40DN89ecHW15MnNEvseytnLi4bVsuLrVz8dEf4u+ygcdjYpHuk7J33Klqfe9ITGUMwUnNYoYtTEGPAplKw3Z4/FheasXJXe81lQnTPfqec7cZVLyT5PlJ6PyvMISMozxTNzWiyHkBmslNZzpCxFrSuj2osABHnFU7Q2Ze/TTa9gRsALgk0nJPrUpweVnXEpYU8R4IRunzjUVfl8UvaeKE9nhjSyAIigZkUpURGmz4CgE2PQHHrhy28cVdshAk82nqqCqgI1a3JGJLmMOYiWFDUxKrwbSGkkRocfBrwfGfqOvveMY2Icarw3DGOg2x/x40gYHEa3aFuDbpDgCH7II3EhoFVVsueGmd0tgjV5NE6UYnBd5gWPCZ0MKsCwPeCOjtCN+booCAnGfY8fRmKftW8FQ/IBN3j2+yPixyyretijUkD5mpRGtBGadUPdZFIbbTVLL9iFZ/XkinrdgLGMg2d0kcMwoJoGZfOseCriHs55FInt9sDiuqE1CiEgLpHG35005Mdo5h3zyDG8XZb+qZZBqvmPKTPY39XHjjG+UzNboeYxsFgAaO9b9kPtBEg8235RFYTML64UxBRREcZ3CJkoBZWdlAa+s/6zCoF340yFaYzB2oYh1TyMiV9/tafd3GLrhkXbYqoaW/9AcCCCG4744UAsmtgpCcMwMI4OX87R6BWH3hCGLYSBiCfZS5Jd06hVJknxI/3gicGxKS3/utZUdVU0yhVUlyR9kVXRnACB+7sDkFXs+sMtozeM8TFmYHQBHxLBT63KbMEH9rs9ddOCUrhhgFRBqqnbFltVXF5olstM1HJ5AVYHgmu4uFyyWtZUVtMP4VHANbGyxSh4n0p7JXHsPG1r5yTQuYgPP/7e+Sgc9ulWS6detjrvZutMKaoTCY9Kef6t0nkEp9U1MUZCCBz9kZh8RlfnFWUnnFk/SkkUmMetyIjHibbURLQk9PSaKE5o6uNGct9WUiSO6fTAqYJU17oQMhg0hSgkBWLR4VZazceIJEypIEyBQEYaK5BEwBMKC4iOmqkraowuY1cFTBcnAZCUx+FUyHPUkiXqAAyRUPraNiWstlQqUUXJfL86QOHyztWfjIBORKI2Gf3Ytfgg/J//+sDrN44vPtnxn/3LCy4uGi4vFxilQCJETwxCioroIzFZYjT045EYB2I4Evo9wQ8cx47DYOjGhuivGI6Bw+0eNz5kBHmA1dWSZnnJSE0I9/i+ZxzHkjU9pb5oqds6j65FQcbA+maDsrmceOwfkLDFdG+o1QqbavpX+wz+8zAMjhiFOORrq1KE0aNUjdYNQzfmbL+7Y/F0hTUG9+0rduOACY6LJ2vqizXN9TUxOrSG+tmKq80aSYaLZxfEFBm9Zz8kDsee29stbbukbls2z1Yc9x3dvsP3HjeMDF3Hov6Mi4WhISDjCMd/3CXxuv7ObLQIx+Pxh1rBH2RCdibn21IKuu7DNJqVVrTtAleYzP4QNnjHUIhOXAjzONi5aaW53my+C0Z/e11DxzDkY83UpA23YcNh70n/5hXjGBh23/LZpy9ZXT1j/eT7hSkE4Xj7JSGcQGchBF69uXvUy98eNH/7Zc0n7Ze0Vd5+bP+c2HzBFXDcP7C7H/n1a8Wqhn/yaf6urTRPXrwoZFUaln8J+uSMRxf5u1+W7FUC7P6G47jhTf/J95+IYs6NuDevuHn2Am0s29tX3L/JLdWnLz/j6mrJy+fLefmXz5dw9jPkV/+bux53ltlXleaLTzccDo77h3xuYox8M3R8+nLFos0u92Hv2O0fk9h8iH0UDhumvrGZa3+EmAAAIABJREFU09lHHKylxKuLG51KwzFkwJOrcjYpVSYIIWS/kXvi2UnPVCpTH3r6QRWgWck0VYmYRak8GiCJ0+yVEIX557kUphRKZ2rTuUIiQios1UYbMFNmnteRM36LkMUjJOkiOqFI8aQMNhEqpClrZpJ8S6SJZEaBlgw4Q8gAslIvmM4ByAk4lzSZJEbwIqiU0MkjJitK6RSQBCGVdkQCbSaAlyLEmn3v+PlvLK9fOV6+qPnii4ZPPmloa4NVBj9CDArvhRASPgRGvyNEh48DzmXRi7639INlGCzDkIhBEU2N3bzMHO1eUMs1vqoZ7reM/QPjuEO3lqpuWW4uMFVG+dbtkoTn2O/hkJnBlFZUNpXefcL1ibFXuNc7Rg99AHXdglWoOuJ6h3cJlapclUgjdlWjjMVUDcdXD0TnUNHNgMJKr/BO0785UHlPVVnWuqVe1qiqYRx7+nHkMIwMeeSc9WKBc57jMLJ7uMP3PaEfWS6WqFpDrfjy61dsW8ufvVygqoitf/9SkX9Mc+OIDoamaR73lX8f2/qAOdt3WVVZjNEMw/DIMf0p2vGw5SgRc3nD4ec1//e3nn/22Z6XT3u+ePkVT66vsO9S1iC/k1+9eoU/A8xNioff3hvePOTvTW2csPwXdCqy396xWKywKetBd7tbut0rPn/iMLbl2/E5q4srVksLbCkvWjj+HbwDQFd2BiQy9j13b77l4uoGWxTPDvsHxr57q5JRNy3ri0uGviMUsptz63vP19/mN2ZdG55ct4/PXefZ7d2jLPmw2xL8yP7+NT4kfJnfq9sF680l//CLr2lqzV/+1Wdc1nesVu/nMn+ffUQOG3IvN5eip6zzvA6upiJ3KXnncpQixIgyuvS7NWka25pQulBKzGfXPCPQzrZfnN9Ucj9Xnirz3sAj7eppx6ce+ST+MaG3k8gpVFDlMB4fdclmp4z71KcWAWWklJmLL1YzNK78nZhon6dKxJRRT/s+OWw5ewGeKGmKzjZgUso9aAGIiGRAlGjyjKZkZamY8qjyOCZ2+0idIl3vSHjqOnCxrljVDW4QgleMTvAh4INj9Adi8rjo8+de6HsYBoUbFcOYj0RpS1Ut0GiUSkSV9aKH/ogbepx3LFYNuqowda6yaFOhrCV4R/IO3Y9ZXMVoqlXWSdaVJRwioY/4vsd78FGhU42yBlUpUpcFW0gZu5BUgqZCGY22BvGJOASSBJSpsJUh6iypig+l3y74wWPrgDKK4dDTDZ7j6OZl67om+IiPkW44ksYRnMesVhhr0LXm4f7A2BtePltmdbf698uG9ce2WMCUKVV5hhlK2fo0DfE729YPzNm+ZcJc/s7vlnPI6B/WFGoeA9Pqxw+AiSRCcDg3kCSxcxuOD8KbY6JSA0MfUW7EElgsGqy1hWr1TIlKhK7vCKUvHEOYf3/oWu4Pj51rqq5IQBcdJmjEOfr9lqHb4ccjdV2hzIKtW9OYG6KtcalHy5B1CtyYNeatzZn2RCKRMsVxCB4/Cq7XyOVZ6ds53Ds4+LU2NO2Soe9KT/2xhSDz7H2IwmYVp/4pIXgOB8duN2JsNbdxMhOkpz+r2lhbFfUz4WG7pbKK8YuXVDpRN3+iJXGYes3FeSmB4uqmz1En5GF2Rmku35I82qpMW6rBaEia8r8JfTjNUddlHTkqUyjsNCfM4542KuVMVgSXCkOXUAhEFNZMo1ZTj70EESUDTz7m8rdi4lmZNbERVRrmJQvWmdQhv5fyMikWiUVFEeGYAgBbMvBYllRzSR5gLCdJk7ehSqau1DQZrGa3HoooR4oGLQ6lJTNwSZadlJRyMEKVZ61FMKKJEvGD4t+Nmt+8Ufzil5ruYcPL5zU/+yLhukRw0A0JlxxeRsbRE1PCx4TrNd4Jh84xevBRo1uL1YpGRUgNUaCLR9ztLX480j3cz3SvDUtEWtxBaJcZB9CNI8E5ogsM45a6MiwWNavNp1SLDbW94LB9Q+juGfqREPO0wNFvUJVhuVkQ9jnzxx8RApFI2q8xjULXsHp6ifg13Zs9mBwESLNEbCY3cRWMKbJ/s4e7eyQFjvdHolgSNfXNNfWypb5csaot9TgS9gFdW0xMrJ9eYdoavWj49d/+G5R3fPb0kuWqor74vVGTfjSWCiisaVqqumKxWOK9Z/wDCJ98735J3q+PwazRXK5Ps7w/1mW7ceDOfTNH/nd337DZXKOWG/7910e+ft3x6188kER4+WzDixfPuN8+cPxO++A8Kd3e3jKU33fuBfD++fPDw5aUAtvXvyjOTPF6/BxjT98JyfKb419QdX+D6TOobblecf3sOSz/CqqCkOt+Qeq+4s3X37AfLoDl2xv8Hru4fkLwjttvvn5vAOZ85NdfZ1R8ipE333w5J4tPX3wyU5Nurm5YrDy3335VqsKa62cv5jHC4/ZrUor85qvPefbkORcXn/+ofYWPxGHP88STMyP7syxEoU7o6wltVnrQaYJRKYA890tMp0iwfD6VvzOqvIhfyrT+PBvNxII0sYiRHXOeIc431VxiV5MQSAaOSJLTyNd0F5fMWauctZcxaySeStNgmFTDFFOPXZWRrwkJn7L/FymqXMwIeZgoTKfTIqBS4RYvUR9SZtNzeVhlFEuWsUxqvgMEIRbKVG0UVlm0tkTR2enHMMURhBQRyY6/SkIKiuCyOMfxoNkfFP3e4QbHse8JEggEXIQoEJLGDZrg4LhzxJSIEqjEFE72xOB2xCSMBLr9A+PxgNsdUEahG8t4HCEq9LJGuQHve8J4BJVHqZq2RRmoSOx39wxdh+UB/9Dhx4EomljuIBMFcYnu4BiTIliTr3myGLGE4NBWY5Jm1S6oV5pnTy4JKQdzm+tVrgjFxKEPeAdDhHgYicPIeBhQtkbVGhWKSMigUZJIWjJLnDGoqmZ0ASOCSQnlE8pDvx9p6xa9+Cge1z+IheBnbef4WwC7/ugmeewLoKorgs+Z6IdQor7fTu3CD7W6aamrlq7b5ffjuW8SGIecaS5XF6QkxCR8+driElTVltE5Ykwcttt3tgLuuiVdv2A43DGkAy4l2vWTnNAAd9/8khgdQ3csFcR0VjUR+v0dtl7SrK7ojwfG/kh/uOWqvuOqmpKusrj7FsI9AP3DN/SHB26HJwzxBJQLwdMd9j9IMarI1a4//8tPy3tUsVit5qz5Yedmjeu+O+DGoahBynmBdl7X9L2mXdIsFmhj5spw9nPng3Y//gb4aN4AM63fPPSk5v9iAYtN5d4p44bM7ysTsExUBpvNaij67JTkvu3pJpk6u8WplYhoKh9n4Fcpq89lp4I3nxwfkh1wejzvOF34CVw25fknic9SqtcnTvBThaGQnFActJyQ3RN5zLSlKWsHSJIdvEbmuEfKwrNzV4WVLUaSKCTlcrdSeUyMdHYFjMHqpmT8EZV87sMrRUo5R1eA1QmjNVoJ3imGQXE4JI57x9B3HLsdUSWiErxkXWsvljAKcRT6o8/z4sqj0CQFUUXc/khMCWcV++0Dw+FI2g+YtqFCMx5HlGiqRsCNKPEM968xdY1tG0xVYSQz53X9ES1HTDDIUcClfPwoks6UnzEKfR8IAsloUtIIGh01ko5IihgxrJoFy2XD5tmC0WU0bL1cZqiDjxxcR1CKLgju4InHgTh6dKuxNmFSJHmPO0aqKtPQphDRlUGsZXQeEyONJEzug+C7QLo6zYT+h2Axxh9ftv4oTQjeg8r97xgzyUr9I4VcsjPgnc7yQ8zamnaxpO/3ObhXeg6IALx3xBjZbC6wRmEM3O01aOHJJstqxhA47Hbz99JpQJWH/jMOo2W3vQc6lBqolxe5Moewu/2KUNS+opycr1b5z9g/kFKgWV3lMnVwbF//A9VVYn2dW45J8h/ttuVlGRkPtxx2Rw7+GUFObHUpRvrj/gdH7pSGprZ8+uLqncC9/WGc0fZj3z+ac59MkPmcTH9XdZ1Z00RK6zK/f3+Ccu0j+ygctiLrTItSTPGIITthUdMSaqqUz/3ufDMAyiDkMauQisNOoFQsmWd2mRO39zSkPF2fjCMTlESM0ijRheGzEF+YGjsxqZ1d1RQjE5eplKgrX6BCJ0o5AJUb2KeSv573wSj9DkaiMosec7avtTojfVHYAsSIKWtpiaLA2/J5weRQxRSHNAULKcYMc9epOHGdUdFkkQ+VU3mSj8SUCMlT2Q1WV9TGkHAIEVGRi0axaRSff6K5uqi4uWpomkTiyK9/+YrRO3yI+KSJGKICUUtS1LheEfuREFxGe6eESkIY90hyhNATQk2M0LkRtxuIfUDZBhsT+JFDr/FKkHbBemWoCl+6pEjoHbHVKLWiXTynqvP1912P7DsSA94dUFVLs9jQPr0iGYM69oxjxEXFUdo87hUSdrmiWrSYVtO83NBerKiqirpcx6RS0ch29F9+zeFhz7DrCMNISgFWFWbRYFYL6mXN2A+8+vuvy0idZIzAYoEtEfnFZsXV5ZrjJ5fgA5dXK+pKQfzxqNL/aH88Cz5zkE9O9ng80jQNdf3jQXV1ZVm3C3YfiGr/rvXdnqE/klKkaRZsLm54eHgzZ6Cr1QXr9YZ/+dkFN43wtBaefeqAkVe/+XbGxJyYFRXfDj8jSA48Hu6/xQ1HTi3IxP7Nr+bjPO8T/2qbeHPM63mx0fzZ5XvAZMCrB8XdQbN58jNW+orD4Rkvn61o7QiH/xfIxFeftL9gH67Z+hcAVE3D05efsbu/Zejff86eP1myaO17Ufbb29cc9jnQeBeRDuQWw8Ptm3LcOVns9jv6Y3bu7WLJxfUN66tPf2ssxkfhsOGUT5cfTlkkE7BiQk7LqfQgcjbTXMrOIvMDkgHnRY6hZJ0TdWnuYZ/pUBbLGtaA5BfxVJpmYkjjjAXMZDep5SQmkMtNJ1gYTLPdZUNnfnWWCZ3xbyr3x1XpgTO1x1MmdlFnQiZnBYnz8tiZOvajM3vqyzODZvI8dqFfFZnp9absOZFI4ohiiKIwOs82VyZxdQlXG8WTp5Zlo7GV4Fyfx9i8x6ciBxqFqHL5PMRE8AnXRdLYE4MneI+SiJIcTIQUMqJ8zMCPcfQkL4jSGK1QSaHGhK8F4zOHd4yZHUrVi5l+lcJjHqWIieYYh1TOa1IKU1fYzSr3ocllfyUJRcT7mNsDSuesvbJoZQg+4lygtRUpBpIItqkISehHlx10iqTRk4peN5CDN5VLdd65rPAV8vasBeVHVH9A6xqTIsP1huXlikrB4rLFNgml/oRLw99jWmmMNYTwfr3p37cpshb69CidO9qfvE6tMMbM68pCIrm9ZpV99N75IUtJcCFgJ1GRH2kycUVAofTtqWyNLaxv15slTy5aPn8iLMq4Z3SZ5jR+Z5zMpZYhLTh2IyHlIDL4sfSjz/f59L27XjK1MHAYBVcW3Q/CK5O4WWpMDIzdlqpZoZSmWVwxJViJmigVMVUcB0W0sALGtOQQMgBNzJpFvZ75OTKF7PuDAYBhzNTNq+VjLnnvE/0YCD6+n8VOKCjzkW7/filOpa6BJzx5eo3WivW6pnoXO9oH2MfjsCXnwan0dJOevLZCK1Nu7oIgp3B9P3LWJbstz9h8S0/OUJUXg5rAYdOyJRDQ+fv5nkol686OUmdPOWeqSmWecGsqik5lfijLzHVKsZRGTqSqqtSvVSllTxodSk0HMJW9dGYbIzuVpLJoR10kTswUjACSpuBBSjAykb2kIgaqQOoCVTuV65XWmKRyoKGymIkujKJa5X53Hj8XQupJGISKlV3QWMu6hafPIs+eJZ7d1EgS3Bg4Ho6kIBhjSIUMJkoilUrAcPR4FxiPPeJ7JAVSAFFZ2SuJxkdhCMJh7wheiE4w1mCsAqORpIgD2FowRgjOE3yD1hbdXubKgtIo25CUYYwJCRlqJ4XoIokQtcUultgnVyRrSN5DcCgJQMANe6ypsVWLaSpsXWOUoT+MxJR1jfuCWL+0NwwusN0fybP1QnKemIQgQMy0sJISQ98x9D0+jIRhgBCotCJKcfayII2O5fUlL15esl7WLBc1OjpU+sc5h62NpmlbpOsJ8Y8UlCio2yKJKZBiR/wt5UyNydKoKXVzed97TwwRayfmxQ8zHwOhD1yu1lT2t3tte+/w/o6r6+fUdYtSwic3Cz6/rvnrz0Z8F7l/A7v7+3d+v4tr7txzDrufE8P7qz5y9o/fPCT249sB0HYQHkbhP28UNowct1+zvv6cerFhdfXyO0vnhGn7MNKYgeUaDuGSNy6fj+X6gsvrJx94FvK6HnYj/aBZLqpHWXY/Bl696b6X3EQQ9g/3+OHAcft+mXhrcqr32efP5znsn2ofjcOeusCTaE3xpaVwW/SupzNaslNTBAeDDyRJmRHoDE8x96RhZvdK6NL4T995XCZ3lk6OrXw/TiCNqbedhCSKKBGlS0mzLj32wriWYiz63nDuLUVRgBgnmUxDnkFPZI6yaRTMEDPzWNIok2XzchY/nQQpDlyRVM4cRSTLc5aqwNT1j+RZbS0ags3ANykydGoa6TLF5cdTu8BYQDCEXA7XikVtcEFxd9RZZU0CEhxgEaMI2hBjIsY8gx1CzEjfnctiH+NQ2OAAI4huiLQ8PARG7xnGwNhVSIKmLtQxsXCea42yCpcSuJFqpzBGkVJNbXUWU0uC6nqsMRxub7l5dkNTNzRYnI+4YSQoEAngBqSoplWLmrgf0YOn0QaRRAwDeqyIURidIDYD+x62mu7QMXQ9/UNmS4vOs2gvsWqBO8Bx34HzNIsVtrVoC3dffUO/P9Ld74tmiEbbhhQKF50MdPt7vv35r1gt/4rKWsYmYVSFNv+4UeJ122BjxTD8YYhJzk1EGLoeW1mquqZpW6Y3yTi6D+YKr6t6nl2OMdL3Hel3FIQIcOj7R1gGrRSb5fIn4xtqHXhut9hecx8M/9Z+AtwQ/Ns8pZICh/svCWlHSD0xvp8XICb4mzcRXw698x9erej3r3DDjvXVp0yjVIf7LxmPS2IIXNw8QamGL48/4+j3ZKn2H2duHLn79ms2109QuuHLbw6P/EFM+U14cf0E70Ye7t6RQYtw2H7FYUx8654CUKnIi+r+0boOD2/4xb/9P0j+X3F1fcPzpz9dde+jcdizlyz/VvPs9el3UzY5CW9Mpd4ZfVfKaWerme082jsvWE8MaHOmDcwkKpzmlU8rOLGSqXRy+trmMszkWDUU7edTyp+39yi1Ph3jvMOl7F5+1BNoTE699lRK2bqA6CYd8GmmmpQyklzlbPs8dBRyeU1TqEpLXz+3D2SaUDv9mYINEqiQWwNGZ03fAQ4pYlREk2YVI0kQgiJGcCN4H/O4lc9EN8YYTF2jjUZXGq8skgz+Yc8YhGF0xFCwlfP1VcSU50+1zYh3UYoQA8F7jNFY1ZSxeSEFh1eKsfc0TYssoW7XpKQIiczvrshiLAXxabTOZUydR/ZiKSNOlZMQHI3Kql5j7/BjzpbccZh7fLqusJWiXS1xoyfFiNWZOjLGwNh3uGGYgWa5fJ974fnKJ4J3HB92uHHEhQUhVbm0/ttVaD8qy9z9+b7U00yx1n/UY8wUoBoTY6bxLI+NMRrEEN/Tw3yXaW1yEFcya6XUO6lQf6yF7+yDVgofwzsddhYS+X5HrhBa7QjB4CLUD26+HqZqQDijHoVjYQOExwGMsTXqXGUswYij8wHnR6x+m3YWoDbQVuqR2tV3s3ZBCH5AJNEdtjSLBbDKoh/y04K7lCLORSqTRVhGF99571VlZKtuWoJ/WxAm+hEfYEgX1DrAOxT1nA8cXcez3rNcl0pLyAnNj7WPxmErlWk9Z0HqM8cxKWjpAkpDFx1qimyjPzUoH3VzJ1i1opCO5Ax6bgCfIfbe5eRn1jVOIh1Qur4CTgJKFEY0lcoPiNUWozTJZB3kGCMSJ8KFAn9TZ8VyUSR1mhdH0onMRJc9EoWEOAPhpjp/fr0z8ZIiRVWMlD83KtO5qpLJU0YpgsllfVNGzqZ9SGfnXIugE1QJlBbEAmYA64l6gR803QjBOOoGmqWiwqAFiIHgFTEojnvwo8e7I+t2RbOsWWxWtIsFtm6w7YZ90OwdfLX9OWN/T+96agIKcDEz3GmlGFLCmApbtyyWDcokAj2DcwgK21yitYAVhkOexw6jw/k7VmuH/eISlyqSamDRIHVFkpBJxFUG3tllixjNohsYvWf0IQ9/SeYMbtY1zaKlu+0RI1QLi3RFaclqtCh0Zbn65JqkAmqXL6YbHWM/0g8DIQZMbbBVgzEW0RB1IqqE1jZXI+5fc9jtaJYNw7Jh6D3e/XYl2o/JbFWdkNI/LTn8vVgImYRjuVyhC21h3TRIleiOHT9EluK8w5fvn5vWmsViyTgMv1P0exJhd3z3fPhmuaJ5h873u2wb1+xji739DaZwOlw++3MkJXa3v/zB7y8vXlC1p9nwmITrYY+/u+X1/S2X6zWVedvdPF1p/vxaf9At4McjfjyCCIvNNU9efBgN6ffZk5sFq9VinrN+l1VVzc3zlzzcvZmBZO9cl92x1m/zBXSp4dtwzc/OUOzbh5Hd4U+VmlTl8rNRFlGlnKspgAFdAFf5wxPwrGS5kueMExqt5slsZhQaao6WT0xpau5tz1FfeQ5Pal8lCxbmUrya1ztVuUufOKmsGFaW1SWYMFWddY51zCjJ+Xsl6S1salkms/SxZTqyElZMQh1G596rTE42L5FFyE6QPaX1nFBPGX8eQwNSBppZZQpZi+RxMfTcMlBK0Ekg5cAoJY9I7qfrEUiBOwFlTN4nk6iDwnmNNSVbT5JZykRTNzWrVUNTP+XJ8xcsVys2V5dZ3ysJXR8Z9iPK9wiBKJExFoIZpVBJSiAhQEJGh0/CpYam0bSLGsESoyYEKQQ6oG1DjAaPo/OZG9wNAe8C0XmssaSY8C5l/WpAG4tZGMxigdcQhxpnErqyJIExBPbbB7xzNIslCUWSRHXRgsrIh6HLlJWmMiw2a0xVM7pIOHr8fkSrmqq2mGa6mwQXhsIGp5CYFdWMNXT7nnbRIU+v2d0f2d69/2XxMZvW+q0xpvMM9mM050aMMVRlv5XSWZuZ/Og5N74XlCYC4zg+AtCllBjH4VGWHkOY2bQmq6p6DhQ+xDSK5aI9r9PNZs33M+N13Q4/KEYToRFUpXkdLlnqgQvT0+1fvzvrbNc0iwvWl9cs2pqrixrbrBE0d9uR/njgeDxy2G/p++8nm9n2ib9PsFpf0trEtXl7eYViefECPx4Zu3v6wy0Q4DsO2w0927vX+dirmvXmkuV6Q91mWtGx7xm+sz/b3cjoz5TDNDy5ahldYn/mUBWK5WpDVdXsH+5xw4Gxe+C1W5BS4kW1pVGnFsEhthxT3q5Ried2i9vfcmgEXq7pjwe2tw/fe27eZR+Hw4bS05xmhSlIyEI3imFW31CQWb0KGvusfH16A5yAWedO+fEtrafJKrI7kEcP4NwHL9n0HCAUQNoEf5selExdmkfLrFZZXnN6YAQkZqc8V5g5vbDzN/Nss54q6IozJqE5VMgO/NH+lGNElcBGFxxAOSuFslVpyeewVARy0JNAGWYEXOF7U2U0LRctMuNZShofEpIUKTm0tRhrEAs+aIJXGH1qL9TWUFnLalmxXi/YXCx5/tnnLNdrNleXdKNncB73ZoceBMWYJUhJRCkEMWfXIh+7EEMkRKEaLOiKZlGTUu7RBx9z6Q2F1hXKKJJO+CT4EDPWIcQC1qOAi0BJzNcmga5sDkZ6iy7FGFEZrOZjoDsckBRZPF9lRHrSVE2TA70UCTGQYsrAvrqmMZYoHSpBGny+l42hspqQAhIDMWQt9ZjyjLyyGqsV4+AYuhFJiu4w8vAn6LBzKdhgPzDT+1hsQnbbiXpScToGycCt96PIM33lo09EZgKVScUvJfmOw1ZYe1qn5kTE8T5TStFW9bxclrNUZ6XwrCao3rEuNw444AisibQ68RBrkgm0CGEeh9LlnaapK0u7vmSxecrN85ds1ou5J+t8YvB7vHOIHBiG4w8Sl3QeupC4qio2klgheVsipBhywqE09WIDCvy4x7sjptekeFLgMsYgIll1q5xvyKXsqmlmTBHfqaAfjyMh6owl0VlYadFapJT801lbs6prjLUcdluiHxn7B/rwBKvgwnZE0YQyedRLwy4usSqy1CNLMxKGI0PfImT1tO/L1t9nH4XDnrhxlQJMHtEx5PL4yTlmO93OiRhDmb1OhbVqWhtzm3i6SScqzjP815mVrFqd+tYlN59/P8XFhV+klKYzPapSmpKG5j6t5EzXlFUrdUZ9Su4FTZWCkyDHNCeuSncZkJwJa5WQIu6hKJFz+X5V2TxeZqosl6lA6Uko5IxXvBygovS6M4l5VgNTZWY8xcJypJlmzWqbz39KmmHIg/96SIgOiI5UWlMrRaMT1lRYY1g0CzYvL7h5suY/+aefc3G9YX25RlUVIhSnm7XBQ3SE5AnJZ7Be6b9bkwrFbK5gnCRQcw/67m7PYa8ZDg2bVUPbWHQM2MUC2y6olaI2FZuLmpTyOIwMRyoDetXSXF8iOo99VVZhLTTLim6MDKPjduiIUpNsTb/bkkIkReHgO/zQsrm5pmmWtLbO++8cw2FgPPZEnxgODqky2j54h4SALkTsQsKFXDaUBASLEUHrhF01eQQuJ/5ISDkICAFxf3oo8bZdYH4g0/tYLcZI1x1p23aml/xdWlVVVG+Ja5zeTIumYdG8n+Jz/saZc94dO7RWXJSSfEzC9nBg0TQsv2ddx+OO43EPwJ0Sfo1wdf1k7uH+xdMFz29W/Kv//J9gC1YlV/N+B2USgYfta3YKvkb4J08NNwvPw+ufs9g8pV1n5HfdrKme/RX7u18TvOP1118BWdf85vknpwSJx+/3FCO33379zjnqh7tbqqri5sUnXF22bFYVX317JMRcq93evp6DjovrJ1mO82wbn1R388Zehwu6klUnFFolPqtvGVIdCU+3AAAgAElEQVTFr9wzFvLbB60fhcOePOgk/JG1nCGTkJzGr6aUOI9wZf3gFHNWe0qDz/rS81UreewM4Hqccauzf00+OmeaqiDKZfKwc0b/yJKQ58WmkvCJGnWazc4IYGanOYO8KFnzdFxzzl2ChiLCpchVB4XOJTMNost2p0o/ee5XTeeN074KJ943mbnXphlxKSPppVuvp7l3jegJsa7mgEYBiIakSKLwSiFJaBGsUSwWDZfXF1w/vWR9dUGzaNHaIokCDszl48y0XjTCJZ9vLblfrVQBgpFIyqIwuV2CQknuFZPAj4nROJCIMYZFo6h1RbNoEAQdI2HMcqneDaBrVNsgOqPfVcpSpAFFHAKHg6MfPP1DjygP2uUISwRViGeSD3TdHlRG76cY8N4zjAMxOGJIRC8Eo0haYSRha8v6akU6HnBuZOh7RPJoj7F2prStWpupWp2nqg11bbOAgAjhe0ZoPlabstMPsUzb+XH16UWkaCnnbDulzMD2kxnHjD1pPE+ZxXvMx4hyH3bNrTFU1szP12RKKdq6xv5Amf3ENFlyD/JEjTGGJ6uaq8tLVhdr6rr5SXPgP2Sn9wLcdgkfFc9XPD7PKlcJmsXFY5Cxym2XGAJu6GlX64L6zwFIjJ5u9xpTtW/pfIvkxO+439FYAZbEmHCjww19AZrl7Yxnyl62amlXN4z9Ay7CQ2pxxSFvTFdcmmCIKCoiH97m+D77OBz2fHgyP+Bp6kEXoJSUknieLU4FgVmc9uRUZ0d31oeGzELG9GjkmeTMDsbsmOY8VJfsUsi0lCqV/WFeZ/GPTK1kKc4dBUpSzgjJpC4TwngiLUCmaWngbK+YeteqdOEnMvUyA54R3BqUQdtcfYg675tAJi0oLQKSzPs2xZxSqg1KqVKaVzO6HEUW/SgDZVlFW2dsgJ4EUtSs+KUll9GnrDcKRCXUSTBas1m33Dy54unzG5YX6wzCi5mzfQqAoiQCCSSeOWyTA5ISHOhSns/7rJkETvT0n0BwiUF7UgqYqqFOGq0blpsNIhHjO4bgUTEyjj1q0WCaPKNN9BA9STWkCOPRs9/1DEdHd9ehrEJXmqqgZXWKkDQpBI77B1RRCFMh4p2jH3pScAU57hkRkoblakW1qFmuLZ6RKCPjQ4dSDUZb6kXLdCHsooIQUMnTtJZmUVNVNSIJH/64Ahg/2c5927ve9eX33vsPHp/6Q5oPnpgi1laEEH+yNCfksvr7JCu/ay54XHj/6NR52rFoaipr5hbfZFopVm37jm9/mLWV4c+etFxfX7C5vDjPgn7Q8vvmx2/z1UHYj8KzVXkvi8wEKILQrK7f+T0/Duwf7qmaFkmR3f0tACl6uv1r2uU1tmrfOoYYs+wnwFimU7wb2W3vHiWAcwlbKapmhW2WeNfhA9yGC0BRq8BTe8jtxvlEnEi1p4rExBvyY+0H7xyl1P8G/LfAKxH5T8tn/yvw3wEO+HvgfxCRbfnd/wL8j2R8+/8sIv/7D+/G5P40SMhl0wRmyhCVApWdds6oIykEUkiZhvSRgz7xtari+KasfV5GnbY6Z4xaz05MlV6sUQYpDlafEb5Po1mPe94y7wGAFoXReXp52kpxVcVpn8mSqLkoD+TtaqXQtlxgpRFlMvhNF8aiKEhIeDXpb6kzcJ46I4XJ885KC7nRkDP0pDQRRQ0oEqLz+BHk2WqDzqpcSZXyeEAk/15EzYplmuysI4LUgl5oljdLmnVL1dZIFCI5mJApkFCU6DhNJKwoFdG1QlUaUZbw/7d3bjG2bWld/31jzMtaq677frr7dNPdpMUgMdAhhAdEExLTErU1+gAxEYIJMcFEYgzB8CAvPKCRBxOVYCCg4aJGif1iAhojLzYK2EBz7aa76e7T+5x9qV2Xtda8jMvnwxhzrlW1b3X2rn2qDqz/SZ29atacc31zzDnmN77b/8u0r5XYFJM3EbQar2laZI72YNL6ykNYdoRpQ3BLot3DECliZDIrUGdoG09YNnRdZGu3oLCGYjKl7Xq6Zc/x3SNcZi+b1AWdb2kWDUz3RwrZGJTQOtzdByyPWqrZjNnWLsEny2txeIBrO4LviaYGW+EnU6qtCZO9ml0NFJM6rbiLErGWSW1xfQoR9K6lntTceO11Xvvg6+zubKO+wTcL+pOX7xb1zszn04gx0LYtdV0/0bUcQqDr2ufyPr8oUm10mb/L03UvrnBfFNZY6snkQq3TWT2hytdlMpXjzpns9IvC9WtTbt7c4rziG2PY37+N0wNOXpBOFaBdPMJ3S97/NR8lBB0V65MwmW1R1hPmR49oFsccP/giAGIsuzc+QN/OOXrwBXauv46xj7unlydHdMsF127dGbfND79CcKvnxVjL9rX3M93ZZWtnF0GpFkcUh/eZ7b5GVe9Tyjbt4oBueQjA/rTk9u0dbt+5zf7eFiLQzh9ydP/zb3s8zmOn/zTwsTPbfhn4OlX988AfAv8EQES+FvgO4M/lY/61iLy9ANa66Tqo08GKVE21zTGOHVNWSA/swLe9/u+oyEb3nIy/n3JCjy7flLRhjEGsSfWFp2Liq2OfJP7wP81x4nX2tcGXAKcXWHLqJ8s9XMdw/QzkGtntlRPQVg70vCzRwRMx7sJ6LfcgwcrSl9FiTuGI1PgidS6z2eYdbO71Ht4poWVYLRoj2NJQVEVK+EJzMlW6XyG7E4OPI9NczPdXjJIp7DKrnEHVEFUYVPtw31KddGpQIDaFKxKRTcR5R+86vE+K1xpDUdiUICc2EewEh/MerxEvlhCFEBTXB/rW07cO1ZjY7Koyt1A1qE0LRt973LyjPVqwODhmfnRMs1wQXJ/7AvucX5E8KyF4Qgx4wJRV8gRUE4oi9esWY9NzVhhMUVLWNdOtLYq6Amto2yWu71LSzMvjp3kH5rMRQ1EUY/JkzO1pB2jMtLLej7kLzyuZelGIkVTzb01KgMsx2HcUkio9XsjkfAqGuVdYMyayWWPGXtkXiRDiM1nouj7Qtv7UHbRFCueURfncevCnQWMg+I7g/WPUp2t70bVN4jwoS0JIVTnGlvn4fiR5sbZk3QPh+xbXznHtnHZxxHJ+mOlGUyjCmAIxJsvQjTXiGlMCrLElZTlhazKhMp6CDlvWqQoCKKoZ09kW+7vb1HWNmIJl4+n7fqxvfzt4roWtqr8iIh88s+2X1n79JPC38+ePA7+gqh3weRH5LPBNwP9+9pcAqtjB10yyUIfuUuk7Y4pv+cRqlmqbZVSyCYPFmq3CUfnmrEyyotHhmJTg5dXnoTBjTzABpMjqO0riuh4tgKHIKO1n1txQY2RYhRhkbEQyXpdA6hqWKqcQyQQow5kHZSspTpzj2zG30krOA5Mt3PwCMIOuy0cP0QQGktXkTU4tOnN0W0BNGK9BgxAsYBRjqpHrrBIQyS1ZNLHERfwwkgzMfcZAXVjqylJUlkDEeU/vXFr8iBKizy9vk5S2h6jJc5AUduovLlbQaJPVrmEMJ5RiKA2UFmyZFzAmEoJBxeAMtK5D5sfsLBumk5KtuiaIx0vElyUaHBodbd/iZUJVlKTAQQEUI90oEqh3Zky3rxG6dB9j9Pi2IfQOOkMQh9oT5osFZV1Q1wXk3uZp0BWRSN81sIRYGMRUSKlMipYudMQQ8M6k/I3KsFXuMt2aMd3eJYqlcQ5/co+mOXmhCX4W78h8Jr2shzIonvCidc6/lGv5RZFobqc0y+WF1kNfBpq+o3OOazs757Z8XxSHxx2BJe9/7y5PWg8cHLYsl4+HM6qyYG/r5ax+VeXo4METrWJIi8HDh/eZbe+ye+06kEhfdm68n/mjN+ibY+YHX2a6fZPt66d7UDcn91Jtd4YYi7ElJteMz/bu4PuG4wdfOHVc2yzGErGimqTvOvgy3eKAvVsfTucSw/a197K1c439G7eAxFF+960Fi+bFFt8XEcP+HuA/5M/vI034AV/O256LHMEd46TWpBd5co+HpKRjyDR/OrbATBbl8L/1rPIhfiJZMabvCDFmvuxBUWrWw0nLRUnK1sZcDiXJmgsaUjw7SCbSiJltbTDaZaU8JSlPYwX1QyjajEuEEOKQnwaDvMaM51qVm40Xl8qBGDLJB/rUpNgljqO3OkRZjU0mW4lDaECH6D0EWUXnY0wNM6ztUhtNEYqYY91GsOO4lTn2HBCJmZ4VyipZVcYWoElB96GnMIZCTFp05cQSjREilFpSSIUxFSIFAUOvKRwCEKNggk2uIBvHMU4Z9ClEILlWv7YlpamwFLTtHGMmTKttEkub4rVnUpWUtqSsZiAloTeINxRFzY333ia8aZifLFg2PcEEonGYcpIsZefSIkwLqOz4/GnvUKNoZakmW8Qios6gWDSY1BM7eHzXUm/vpsWmNfjG0fueWIZVfL8y1JVSlRVColH98ufvsjiap6S3V48Lmc8heNpmSAoyTKdTjLFoVLquu/Tksrqusyv++YuGqqqwTyD9uApQVU6WS56mr40xbD2lTvudQO89x8vFGEdftC3Tqn5pPvR1GDHsXrtBsVbrX5QVO3v79MuH9M3xY8f4vqGdP8S703khGiOLw7uUk20mOVZui2ql6FVZHN2lrLepZ3uPnbf3kc/cW+J9SfT73L/fcUc79jPFed8teOuLf0CzePs12PCSCltEfojEUfezL3Ds9wLfC6yIAoZMb4aXcqbijDHTfA5JS6vdNB832rfDc6lm1H6r/bMTeEiGWLOM153OA6HJkNVtRIjDsnKwlleh8HQ+VlpWBmY2k44b+MZTKVV2IwwsY2vnGKRZucx1dG2v0uJWC5PVkkVGuQfrfjWcef8Vx+jIpCYiRIYWpNkNLRAJqDGoEYKaMelrlaBnGfwLJmdzG5PqbY1NmQdRNbvAPUIqTxsy5TXHsxMPuSBYkCK5nZGxDntwE0jOSMfEMaY/vICMWbnrC1tSFhVlUaVOWiH1vU4ypxpvNQZypm4MQgwRoykbttwpqI/mdG1P7xy+j/SNoywnaVziihxHTMpWB9Doxh7stipT3qLp8xowLVxCcHjfIUWdlLxIGh/vU5Z9SM92QarJNtZiNBJdz9HBCa7pTvUvfhW4sPmcKThTiZ7BFikTPnkpkiv8Vbm/n4T0vMWctJm2DZnaxjjWO/yduiYkd9xKnAMwzGuzOu95E4eUx2S4CCTinadba1YMk6oaDQBrBv/hi3wXOB8oitQ57zyIMdI7NzKu9c5RFSVnbeW6ABB8TpMdWE4Hi/eZMgvYMoU5gnf5PWSYTLcoqwnGlsSQaEWj75OrXGPyWD12/xTXzUcLe5ChmuwkzoTQ49o5ImbMOBcxGFvg1dBFy6POoWqACSwDW91pspzjR2/hfOQZfUWeihdW2CLy3aTklW/T1VP7BvD+td1ez9seg6r+BPATAGVVaqINkVSuk5+uENNLdxW7W5sgIqQ6ocH1m/5d4+7gVIhek4K0Zr0ri00W4nAuIjZnjEcNaDAYFGMMpS2JooTocpa0QfEM/Nsrt3iaEIkdrcRKQIwhis/c3TErv9TOMuZ9UwggX2OWF4loSOe0ElIEezCbR7a3ZKGrpgovK0MleVbwMY5JeINRH1XTomIIIQwLlNiDJDe1zUxnDTl+H6EyKaYdpMaKYKSgLFaUrCJTVCt6F2k6h7FQ2ki0BWptzpuMIAHvU/vM6EO6xlhk/ZxY61xQbFQqAiIWxSK+SxO4SF3XJLvQocBKQT3dZnd/h53dbaI4CluiGIxUGGNQ5ixdR+sje6FIaxuvFNMdpLBoCeWsYOLq1Gmra5kvl8wm2yPvuXepX3ipml76xmJNeln44DFlAYWgpcE3bbLKjSNoJLSpssHaZOVDSrALIbXijF4Rv8R1W+meB0U6z/zgiND0xFdYh32R87koV+wfk+lkjOf1XcoteCeVNUDf9ziX6EJPNc4whtlsi65rR1KTdRhrmU5PlwGl2umkbrz3525UEmJguVhSTybnzhK/CESNHM5TdrMI7G/vnFvZnkXwyht35+zv1VzfP3/muQIny2b8fBYi8GdvWbAT7rrrKFCJB3nAZOsa0+2b6y7HxxBj5ODem8PZuH77tZGhbrp7m0jJ8f3P0y0f0bfH7N38IGW9xd6tD3Ny8KVTLvFnYXlyn645AlW65WH6TKoP377+Ovf9Ho/cbKyeeRa+dBR5a/7258ELPTki8jHgB4C/qKrrKYCfAH5ORH4MeC/wEeD/nPvEKoTsmtbcjnCwqtP3sqaN5dT2wX5MyWYrCywp9MBAczrYnCKKyQlcZrC6h9jxaX9yZunM7EFaIlETE8/Qu3p0wo+i5cNPx8tRRcNq0WFMknT0jmfrbTyNDsQqYSReUYbzD8lf+Z7ka4s6MJWlSzFD6CAP0OA5MHFIvMvbZLDQ04Y4rAbWeN2dJvd3utrEPqeS479S0bvAsu04ns8pqohKRVXWxKIg5gz0tFAJRKcEHxNhSnQE9UR1yc2u0MfUiUyACsVKTLFtSSVhpjQUtmBWVLiQXOVqQ3KsGJvoP60h+OTxUE09iIOSE8E6xCnaB+JkK1u9SlUWxElF27RotECJ7zpsaSmKmqLUtPgK6dkUHxFbpKSyztGYBcYWmKpAW8nJVD4nSwZi6xOZSpEslaKsqE2Fcy7V+wJqAy62LJY97eKE0ieLO9avhoDklc1nUqmWSFpohBjecWU94omaIv1TFOVoNZ/685Os4ZewjpXEgHY6HCCUVfnKkuDWlu6gQtN1pxctIo+Rs5RlRV3PMMbSucCXDlqCPWY/BLZ2dmlaz8Gj065k506bi6rKcnFM1w6K+tn33QiUxnOjSLXTVtaops4xNqMugJxbPLxH5dT1aow08wPKeko13aOe7WOLmnZxOvvcu4bm+D711v5a7FxPW+T5c9MHHh20zKqCreQqwHVz+q7hMKzi98vFCc38GBSuTQ2V1bc3mThfWdfPA38JuCkiXwb+KSmLtAZ+OQ/GJ1X176vq74jIfwR+l+Ra+z7V5zeVXS9JGzpIBZeqgtHTBCDrDuz127i6J4PyNVkBrvpSj3XHDHo1MhRbjfFIHZS+jn9Tcnk2gtUiPUg6sJ9lNTryBq86ba26h+WFwLDrmuBD4nqIqW57pa7TuTQrbUuu6x4eRR0s6zQSNh+jOS6/ynxPJViWNRGGjHBhdElEFVZR9sFrka5rkMmP49QTtUQoUFOiWNQUdM5DCyfzE8o6gp0wmygaStQGDAVD+l90geiywlZHjOlHowfV1MxTU5OSgpS8Rc7Bi5qUd1EWzKopXewT051JeQbRQF2ll1AIIR8EheSWmRoJoUd8gC41Z9EyogJVZaEuKUpIcXKTlI4BM51R+EgQodcecQEJye2rIRI6R1c22LrCTOo8riFli8eUuRqjQ03EB9jZqimrAi1KetfhfEfnImIjIXYslid0ixPKEDG2IF6AZfZOzOd1PMlyvTzkyXfm/W8LO7bqPbv7qRfG2b+9wOLjLHe4IJnt7MwXvAL9rSjNmUS/wlgma7FfEaEoSqaz1Mij9YGvHHZMy2NKdUxnWzQttO3jj4EOHtD80zQL+v5x7oBhv7OLlFIC14uXp99N4akcfhiva/W5b45QDVTTParJDsaWtItHrN/P4Doa11FOtkcX+tOiH50PvNk0fOT2NvtbSbk3Jycs45KFDqVwSrOYs5zPiQh7E8PeC5THnydL/DufsPknn7H/jwA/8nYFCZpqcqNzo/aOKzs5n5zRDTz8f1TkQ4/ocVGc9hyqhYc2jUaSIlUC0croT19x8cJgo6d3lM0KL9F++kKwhWAUCorkOtWUjKYxotkayhyhaE540txKTcq0qlUSbwdZKcnQ81tX8aE0tZO3YN02KYYXyfgEpRj5OovYAIlJlXvRgZOU0WJXGeOiYoQgZRqrPHZWIeBIFVeS2MGiYKLB4fDiCb3H2g6jDU6E3llCVxJdS99OmZV7xKKEokyLChWIhi4oLii9awl9j/iOIosXcmaaKrRCiuV6KAjM1FCJJbiWYJVQTyjsNJOzOFyMtM2S0igxBpquxVbJo6KxI8ZhsWKxBKw4+pMjjJ8wm+2gpcFMCvZ39wh4AoGFU2w9YevaLkdfXBCWLbgmlReimNIgBdhCmOyUqBiOj44JfYtqIHaS49+Orj6BaImNIdQF1pZER2KE26mZHx8zmUwx0XJw0DI/bDhGUdej52S9ehbeqfl81aAoy2VDWRRUk+fTfUKuH29SXfyp+nEl9bm+wJyCEAJd5sEWk5L0XoXSfux7Y+TRyUn+XmFva4u2XY6Ktixrdvdusjy+x2GXrndrd5/tnccTroL3PLr/FjGkMOC1a7dxHHC8PO1yXrYtXd+zt7V94Z4FRTl8eI+qnrB/4zYAxpTs3vrgqf0GBb44vJtd4k9ffAXXcnLw5aeWlU1Nzweq+0xNCTk6P9m6Tj3bY0ctW9srzdxrwRf7Wy+y1gOuDNNZppkbTMDV1tWvo6Y+bWMPXtv1vwyfV/XLIKNPWFkRq6y4wge39fD8rBbWK4GGhh+GTKCiqYvVEANO+yTmNYmaOL0zk1fMMfJhT8kKk8xsts4HTLZ+E2l/YlEbyq/GYdDB75At5Ow8HxYzw7esGOB0zYU+XLmuXBVrskRiuiZZla+ZfA1DQtmQIx0UNArGhRQnjgGjynJhsVZZLkpMXVNUiUkNFaIa+gAuKL4fml+41d0eXP5ZGhdT2ZaowUfog2KdYooUOrE2ub1CzkRPtdjphetdiw519BpyiZ2A8agE1AaCmxONp+vqtOixhmo2wXuH+B4bPTaSFzwrD84qYiDZsZMqC1Qj/XKBBJ9KmmJMRDdKYgSCPA6CD0rXNZjGYKuSaT2jrieURUXf9LTzhj6ExEH+iohF/qSgKIrknQuJplZETlm06+G180A1eXO8D2esK30CD8SLIbnJfa5WyYvnSG4esua6Xm8mdIHQ7MkCkCD0ayGMMjc+adsFc1Gssez4gOtamifIkjgWfLagwfn+icx1qVHO6bE7WCquhr3JaQUeXPvELO/TEKrJ9uhmbRdHeNdST2cE7xFjmG3tE5zDudOL3hgTb8Izx0iVGJ7FOKeUEhDRxN2+9Mwqy7QqqYAih1tct6BtTpg37VOt9efhaihsBc2uy8E6VlatMg3k9o/DD2M9cT4cGV+kw0s0NZHQzMKVFOKgftLviQozKVmz1lwi0ZGuHOWGpGSMmtH1PMCoYOJAYpCPjyQXLok4YyCNEM2sZAOrGjJmQwsFQ0ORqMltHSSmOO5A3cmaW38YitwgZaAsEFitYDAEAgMjWYS8SDCjy3+MW4sh85XlxYFF1BAoMDnDOsZAHL5fBauCJ2Jiiu+rCsGa1A3rKL10dndAZjPKWSpNUxJvt/cGHxTXe1zX411PIKBjJjjZGo90GDqSC7wN+WVpU2tR9UJpIhZwYkAjLnQs+wXESHCeIHUqq9MIMckbrUv3qAjE+SM01vjjGlNUWFsy25kiixa6iHUBIaLLlJQnpQVfpedMh5K7vCjUZGl0x8epXtxI5m5ViOmpxYIpDUETYcujk0e46Am98trrt5jMZtSTKd18yeLomL73eX682izxdzMEoapqVCNN01BVVeaXfvm4uXM9r9Kzf7a0TDXStqddyWVZUr/iJiqKctKkmLOQEtS89xwfPQRu0NttXgPaZknbPJu9TDVycnxAcw6WM1X448PI/lTYm5y+xr49oW9Pnnm8GEtZfzUiFjQlh4kYxCRr1xYFe9dvsjw5xh2d9VJdrIXvgvK5B0vetz9hWq08OQo08wfMDx9yfBhe+Im8GgobACX7q3PcdhULHmLLcbBsh/+GguWzVy9ZcSbbdWW4C6iYkcXMCqnb16ikUvOJAERRCpNj3CHgY0owKwxr35uaV4gll+UElLCy3GPKRLVFcnVrTKt/k+3TMOa2D7ayMBRwGRIt5zACIknph7WLTLXbeSES47BSIdVZnxknXaNCJcVrgzA6wPOgjZb7YI2LSfLF7DnQ3KBj6HYaNaAYglrU54ULHuap5eaDScTv9fg4pyyKxDqHwflACErfBdq+oesbQuxzCR8pfg1YrVNcXpTGKdEIYi2LJscD45zruztMaiHSEiU13IheQT0+dOBdmtSmpqBMjUhMncbYBEpx+N7R3n+IuXETphX90mGCUle5pMQk8pyb168TY6Q9epTaE7oOFxN/vFFBO0cRPdcqzbkCya3vjaXHojG1/Nu+fgOMwXuH75YU29tsT2umO1tghZP5McvFknbZ0IcWMhvaBo+jKFLmtjGGkD0YXddhjGEynSaOcu+pJxPsS45hWZYUxUBzmnjF67rOluiLc71ba6mqod/2SmELciFyvwjmzZLCFmxPpywXR7i+4fetcHu35tZO9dTjusUjFoujZzCTJav1aLGgrspRsc075dNvBT6wZ9idnE+RTrauU013T8WoAYJvOX7wx0x3biKyzaP7bz3R2r/zga9BJHXtahcH57DmE07ClKMwA2B7UvCBaxNsWbPsPYeP7nFjcgORCXvXbxFj4ODem3zmrZYHL6Gs4Sop7MeSQVblSacc05p/kzXFefow8h9zcpeOv8vw4pVEUSiEVQKYDN+5imePTmolq/7c5jIr0RU7WbKMddRyyqAujSR3VgyGweGsI785a773U0Sh5NXF6G4ddl23rgUdz7W2OVnvw45jav2wbVjKrJp5ZJ86p5/5nECSi7ZXdyK3T8lue9HBAZ8JaAIEE3A+ICIsFp6qEupaqSepbM5i8D5Rlnof8cHhg0t0pXFIlsvfqNk3oJEQlYAhSGKk64ksmp5J6RIJS6mJwCQKzgXAEzUAEYkGYxVrcg8dTUl0Nibr3MQAXUPserAVVtPUECPUs2q8AXY6RQWK0NGUgumF2IRsuSsmRkSVaV0SQqJ0dNFnFneL2ETXWFRF6vwTPaUtqKqSMieqOdczX5zgui5xlKuej0T4TxFWvPlJ2a3XSVuzstKstdnDFZOb/Nb6DIMAAAxoSURBVLz1wyGOMWpjVlUn1qy+a+USX3n+XgYDd4NmXoDh1fV25L4oKKlbGIAPYeTDeDRfMisi1ycRW9Sn37+qBN/RdD2Lzp31ep85v+KCx3qDtwFrDS7Aw0Vgf5J4/qfl8+lebFGN9dADDSmaOjj6fonvm7RAX/NBF2v9w6fbexhjcX1ACBhD6tAV/OgGFzEU5SSF7dYWIYrBFlVqb2xSDk30PRU9lYWyqjHG4PqW5ckhx41j/pJpKFdIYQNxsAnXNw5Z2tlSHNieREYFk1ziAEOTD8kUoFmRqcVKgRhBC8VawRpQN0zwTEoqOS4pJg9MJJN+ZtdnUlaDyxoqRAMSfap3znHk1LwjWaFGkkvdaXqpa1A8fpXklmW2Mtj4giXVJPcxUIiMWYYGEgMZymBuG1EGc3dlQQ+hAhkT0GwOIgyxbDSRlpihC1qO7Yox2OyaR5KiA8DkHO9xFRORAJYyLUPEQzBEk26RFSVI4HgJxlaI1uzvuNRwQyzBGWJUfHT0rqf1nuBJSptIYTSH82P2XsS8GEmt5UubXiQHiwXeBbbrmvfd2ackeQWafg4m8Sxb6uSOj4raRGe68A7xLcbP2VObEuu6hub+AXHScfP1O0SvBBfYv3MDo6DzDtmegrVsTytcCInJ6d4BcbFAj0+w0SGlxd65jWuX+K4hHh5SmYItU1Pu3EKLghBamvkRwQdeu/Vedm7doL65y9F8wfz4kAf3vkJ7MkddRMXSq/AuZ9K8UFR1NVq66zDWMp3NVhtkrXb6vDpPoWtbQn45V1VN8Qxmrq57+S5qQ+/t6XSKLQqm09PXcFnwIXA4P2FnNsOI8OjgHlvBsB8Ldm99CFusLO3ge44ffIH7bocDd4PAm884c0Lnejrn2N/eJmrkeLHgM3GLvVnJ17+nOE9F14i+OWZxdPo7m5P7dMtD9m5/OLnJRbh28xY2PzvDG/P6nffAnfegMXL/7hs084csDu8CieZ09+aHWBx9hW6Zaq93bMOObdm99SFicKcaeXzdbcOd1/a4dusOD958g+bkgPmjNwju5Sfw1VHYklzOK1XDWhJ0js+uW9QixAgDL3f6U0psMkh2fQ/nZgiEJ4swKDHm6LhRbNKEedeKoaWnRJvXEck2VhEK0aQkTUqAGkoYjEllZMlJnjizJZrEGIbNJVSDG3+Iv49O62ExzbplbbKLWyUzg8lg6a97HnIim6RPxBWJS6rjHuSK2YofvklBQ/o7yU1Ojt1HHSqmIWrP0EAFQs4lGHjLQdWDJNrSmGMPMUAwIFFxTun6SNcFlkXA2uTliDnu73xk6QJLp/S5CUSBomEIg2SGMlGiGlxMiUCBgBUoFRrnCKrYw2Nm2yWzrYpJfZ2iKEYCBY1K3zpEyhTbCikmoE5wPlJEKAuh8SeEpuH4aIu6qqiLCV3vsApGlUoD1hh0d4I/aZHOs7WzhUxrzO4uZneCGmgWLXU1owqeYmuHEMFHQ5BqdNvbvR1AmW5VRGk4OnIsH81pFguWR8c0MdAbpQ+KwxNeLhT7rkeV72U/ZMs/7WV+dvsT9nP5HGVZPfHvZVVhQsC5Hr/W9rMoiid2HHtbcj8DrnePlX5BsrKHjmNPQvAB7z1VVT3dGlfo+xQqeNa5zhzyGJax5p6bYY4fUqx9l4vKPbdLE1NXvfOeX1CWXTuG4Zq+RzXwuYMpN2aGazPDdPvmSMADqWVmM394ru84+4Xzo8OR739rezdZ3Ahts6BvW7b39pltzdi9doNqskOMyuLkiHq6T1GuE+kkFjxjDFt7r536mhgNxwcPWRy+ycHxgjcOAo17+Ql8ZRS2DC5eYHT+yqC8MrJmTuom97XWvK+SSUc051FJPnwgNEnx7+EJHMq3RgrRnNBjcpwx0Ygmd+yQAGey+9cUydL2uuYuzy53sckLkDIlI2iRlh+DG9usZ24PKnrdrbRyYSerf1WkparjPDjrhSC7xwdX/SniGElXYRiOFwZmN83uCRVFZOjZvVoSRI1ZYQ/nHZna8+lHjjpGMpcA0aYxDgG8U5wLdE4xAaLNMfEI0UPnofOKjykT3WpqKpLWQko0KYYdh20mldKVKCWGPgR8VGS5ROspJQXb5TZVVVNNpsSYuv24FkSKnJyS3foBfEhZ8aU1SL8gBlgsW4wpmdQl3iWWuhLBaFqw6bSERQ8BJtMaKxMKLGZ3io+Bzh8yNCCrt7dxPoUJmtalLFkx1FWBWChmlkXfs1wsWDw6omtaumVDpwEnyaMwlD3+acZg5Z5H8T0Pg1IsyyfEYgWKTHXpXI8PfkweETG8XVrxlL0OqXvpc+Ra+67HzvMMJRtjWlyUZXn6fXL2/M5jnqP8n4culhyFGXvN/cRIltFrwVG4ld9D50+QVKBby+rrc//xN09q6kK4PhPq2d6p5h/BtS+ksBWlWSszm0xnFPkZ6LuOZjnn5mvvw9rdlTx9y+LkiKKeUdSzx84p2Md6dEeF5eKYdnHIyaLjzZOLmbtyEaUJLy2EyH1gATy4bFmegZtcXfmusmywke9lcFa2r1LVW5clzHkgIifAH1y2HM/AVb7fcLXlu8qywbtPvrc1n6+EwgYQkV9T1W+8bDmehqss31WWDTbyvQyusmxPw1WXeSPfi+MqywZ/8uXb5J5usMEGG2ywwbsAG4W9wQYbbLDBBu8CXCWF/ROXLcBzcJXlu8qywUa+l8FVlu1puOoyb+R7cVxl2eBPuHxXJoa9wQYbbLDBBhs8HVfJwt5ggw022GCDDZ6CS1fYIvIxEfkDEfmsiPzgFZDn/SLyP0Xkd0Xkd0TkH+btPywib4jIp/LPt1+ijF8Qkd/Ocvxa3nZdRH5ZRD6T/732vPO8Arm+Zm18PiUixyLy/Zc5diLyUyJyT0Q+vbbtiWMlCf8yP4u/JSIfvST5/rmI/H6W4RdFZD9v/6CINGvj+OOvWr63i6s0nzdz+aVl28znl5ftYueyql7aD2CBPwI+DFTAbwJfe8kyvQf4aP68A/wh8LXADwP/+DJlW5PxC8DNM9v+GfCD+fMPAj96Be7tm8BXXebYAd8KfBT49PPGCvh24L+R6GW+GfjVS5LvLwNF/vyja/J9cH2/q/Zz1ebzZi5f+L3dzOe3L9uFzuXLtrC/Cfisqn5OVXvgF4CPX6ZAqnpXVX8jfz4Bfg9432XKdE58HPiZ/PlngL9xibIAfBvwR6r6x5cphKr+CnBwZvPTxurjwL/ThE8C+yLynndaPlX9JVUdKKQ+Cbz+KmW4QFyp+byZyxeKzXx+Adkuei5ftsJ+H/Cltd+/zBWaUCLyQeAbgF/Nm/5Bdm381GW5qTIU+CUR+XUR+d687Y6q3s2f3wTuXI5oI74D+Pm136/K2MHTx+oqPo/fQ7ISBnxIRP6fiPwvEfkLlyXUU3AVxw/YzOULwGY+vzxeei5ftsK+shCRbeA/A9+vqsfAvwG+Gvh64C7wLy5RvG9R1Y8CfwX4PhH51vU/avK5XFr6v4hUwF8H/lPedJXG7hQue6yeBRH5IcADP5s33QU+oKrfAPwj4OdEZPdpx2+QsJnLL4fNfH55XNRcvmyF/Qbw/rXfX8/bLhUiUpIm+M+q6n8BUNW3VDWoagT+Lcn9dylQ1Tfyv/eAX8yyvDW4e/K/9y5LPtLL5zdU9S24WmOX8bSxujLPo4h8N/BXgb+TX0KoaqeqD/PnXyfFi//MZcj3FFyZ8RuwmcsXgs18fglc5Fy+bIX9f4GPiMiH8iruO4BPXKZAIiLATwK/p6o/trZ9PfbxN4FPnz32nYCIbInIzvCZlNTwadK4fVfe7buA/3oZ8mV8J2vus6sydmt42lh9Avi7Obv0m4GjNVfbOwYR+RjwA8BfV9Xl2vZbImLz5w8DHwE+907L9wxcqfm8mcsXhs18fkFc+Fx+lVlz5/khZfL9IWmF8UNXQJ5vIblUfgv4VP75duDfA7+dt38CeM8lyfdhUvbtbwK/M4wZcAP4H8BngP8OXL8k+baAh8De2rZLGzvSi+Yu4EgxrL/3tLEiZZP+q/ws/jbwjZck32dJsbfh+fvxvO/fyvf8U8BvAH/tMu7xc67nysznzVy+EBk38/nlZLvQubxhOttggw022GCDdwEu2yW+wQYbbLDBBhucAxuFvcEGG2ywwQbvAmwU9gYbbLDBBhu8C7BR2BtssMEGG2zwLsBGYW+wwQYbbLDBuwAbhb3BBhtssMEG7wJsFPYGG2ywwQYbvAuwUdgbbLDBBhts8C7A/wdpVLGeu9azgQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# ======= Experiment with these parameters ================\n", + "# You should try different values for those parameters\n", + "K = 16\n", + "max_iters = 10\n", + "\n", + "# Load an image of a bird\n", + "# Change the file name and path to experiment with your own images\n", + "A = mpl.image.imread(os.path.join('Data', 'bird_small.png'))\n", + "# ==========================================================\n", + "\n", + "# Divide by 255 so that all values are in the range 0 - 1\n", + "A /= 255\n", + "\n", + "# Reshape the image into an Nx3 matrix where N = number of pixels.\n", + "# Each row will contain the Red, Green and Blue pixel values\n", + "# This gives us our dataset matrix X that we will use K-Means on.\n", + "X = A.reshape(-1, 3)\n", + "\n", + "# When using K-Means, it is important to randomly initialize centroids\n", + "# You should complete the code in kMeansInitCentroids above before proceeding\n", + "initial_centroids = kMeansInitCentroids(X, K)\n", + "\n", + "# Run K-Means\n", + "centroids, idx = utils.runkMeans(X, initial_centroids,\n", + " findClosestCentroids,\n", + " computeCentroids,\n", + " max_iters)\n", + "\n", + "# We can now recover the image from the indices (idx) by mapping each pixel\n", + "# (specified by its index in idx) to the centroid value\n", + "# Reshape the recovered image into proper dimensions\n", + "X_recovered = centroids[idx, :].reshape(A.shape)\n", + "\n", + "# Display the original image, rescale back by 255\n", + "fig, ax = pyplot.subplots(1, 2, figsize=(8, 4))\n", + "ax[0].imshow(A*255)\n", + "ax[0].set_title('Original')\n", + "ax[0].grid(False)\n", + "\n", + "# Display compressed image, rescale back by 255\n", + "ax[1].imshow(X_recovered*255)\n", + "ax[1].set_title('Compressed, with %d colors' % K)\n", + "ax[1].grid(False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You do not need to make any submissions for this part of the exercise.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.5 Optional (ungraded) exercise: Use your own image\n", + "\n", + "In this exercise, modify the code we have supplied in the previous cell to run on one of your own images. Note that if your image is very large, then K-means can take a long time to run. Therefore, we recommend that you resize your images to\n", + "manageable sizes before running the code. You can also try to vary $K$ to see the effects on the compression." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 Principal Component Analysis\n", + "\n", + "In this exercise, you will use principal component analysis (PCA) to perform dimensionality reduction. You will first experiment with an example 2D dataset to get intuition on how PCA works, and then use it on a bigger dataset of 5000 face image dataset.\n", + "\n", + "### 2.1 Example Dataset\n", + "\n", + "To help you understand how PCA works, you will first start with a 2D dataset which has one direction of large variation and one of smaller variation. The cell below will plot the training data, also shown in here:\n", + "\n", + "In this part of the exercise, you will visualize what happens when you use PCA to reduce the data from 2D to 1D. In practice, you might want to reduce data from 256 to 50 dimensions, say; but using lower dimensional data in this example allows us to visualize the algorithms better." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load the dataset into the variable X \n", + "data = loadmat(os.path.join('Data', 'ex7data1.mat'))\n", + "X = data['X']\n", + "\n", + "# Visualize the example dataset\n", + "pyplot.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=1)\n", + "pyplot.axis([0.5, 6.5, 2, 8])\n", + "pyplot.gca().set_aspect('equal')\n", + "pyplot.grid(False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 2.2 Implementing PCA\n", + "\n", + "In this part of the exercise, you will implement PCA. PCA consists of two computational steps: \n", + "\n", + "1. Compute the covariance matrix of the data.\n", + "2. Use SVD (in python we use numpy's implementation `np.linalg.svd`) to compute the eigenvectors $U_1$, $U_2$, $\\dots$, $U_n$. These will correspond to the principal components of variation in the data.\n", + "\n", + "First, you should compute the covariance matrix of the data, which is given by:\n", + "\n", + "$$ \\Sigma = \\frac{1}{m} X^T X$$\n", + "\n", + "where $X$ is the data matrix with examples in rows, and $m$ is the number of examples. Note that $\\Sigma$ is a $n \\times n$ matrix and not the summation operator. \n", + "\n", + "After computing the covariance matrix, you can run SVD on it to compute the principal components. In python and `numpy` (or `scipy`), you can run SVD with the following command: `U, S, V = np.linalg.svd(Sigma)`, where `U` will contain the principal components and `S` will contain a diagonal matrix. Note that the `scipy` library also has a similar function to compute SVD `scipy.linalg.svd`. The functions in the two libraries use the same C-based library (LAPACK) for the SVD computation, but the `scipy` version provides more options and arguments to control SVD computation. In this exercise, we will stick with the `numpy` implementation of SVD.\n", + "\n", + "Complete the code in the following cell to implemente PCA.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def pca(X):\n", + " \"\"\"\n", + " Run principal component analysis.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset to be used for computing PCA. It has dimensions (m x n)\n", + " where m is the number of examples (observations) and n is \n", + " the number of features.\n", + " \n", + " Returns\n", + " -------\n", + " U : array_like\n", + " The eigenvectors, representing the computed principal components\n", + " of X. U has dimensions (n x n) where each column is a single \n", + " principal component.\n", + " \n", + " S : array_like\n", + " A vector of size n, contaning the singular values for each\n", + " principal component. Note this is the diagonal of the matrix we \n", + " mentioned in class.\n", + " \n", + " Instructions\n", + " ------------\n", + " You should first compute the covariance matrix. Then, you\n", + " should use the \"svd\" function to compute the eigenvectors\n", + " and eigenvalues of the covariance matrix. \n", + "\n", + " Notes\n", + " -----\n", + " When computing the covariance matrix, remember to divide by m (the\n", + " number of examples).\n", + " \"\"\"\n", + " # Useful values\n", + " m, n = X.shape\n", + "\n", + " # You need to return the following variables correctly.\n", + " U = np.zeros(n)\n", + " S = np.zeros(n)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " sigma = np.dot(X.transpose(), X)/m\n", + " U, S, V = np.linalg.svd(sigma)\n", + " \n", + " # ============================================================\n", + " return U, S" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before using PCA, it is important to first normalize the data by subtracting the mean value of each feature from the dataset, and scaling each dimension so that they are in the same range.\n", + "\n", + "In the next cell, this normalization will be performed for you using the `utils.featureNormalize` function.\n", + "After normalizing the data, you can run PCA to compute the principal components. Your task is to complete the code in the function `pca` to compute the principal components of the dataset. \n", + "\n", + "Once you have completed the function `pca`, the following cell will run PCA on the example dataset and plot the corresponding principal components found similar to the figure below. \n", + "\n", + "![](Figures/pca_components.png)\n", + "\n", + "\n", + "The following cell will also output the top principal component (eigenvector) found, and you should expect to see an output of about `[-0.707 -0.707]`. (It is possible that `numpy` may instead output the negative of this, since $U_1$ and $-U_1$ are equally valid choices for the first principal component.)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top eigenvector: U[:, 0] = [-0.707107 -0.707107]\n", + " (you should expect to see [-0.707107 -0.707107])\n" + ] + }, + { + "data": { + "image/png": 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URemssqJJbYyZAtSIyK/T3/8u8K3AIwtJFOYel7JTZ6n9AaW818GDB3MSuqenJ+d2tfdNVD27IjxzAVdhVbn3A68Bf1vsOdVc/Y6CUpoApfYHeH2vAwdy29A9PWPne1dz30S1QKeJVo9yOry8JqjX93KT0KXGorxxSmod0oqYcqYeet30zst7Fatyu4ull9rap7nqqg1Mm5Ygmex1fG9VGl1PHTHldHh57Q9w+159fQPMmvU7I7eLJXR+LI8++gLPPjvA8eOLuHDhD+jrgwcegIcfrtxJkIVkdw6eP38SqGPSpEvDXw9dJk3qiPGjk8ltT6y79zpIZ+fqkVtuEjo/lvvu+zl9fW2EehJkWjLZy2OP9fPss28xMHAxQ0ONwFHgc2Sf1VXJo2d9Z1cvL+fSNnXpKtnJVPy93Lehnd+jtD4CP9lNEoFHq7Ltj7apq4ebqYcf/vBzfOIT0wN+r4OA+za0nagsTyw8hbYXWEjcVuVpUkfQ+vWLqa9/EOjLu6cf6GJgYHVJ87iTyV7Wrt3BqlW7WLt2B8lkr02Hlj8JDdFYnmi/pPYE0OD43Gpb4w3apo6k1147zqlTtwDngR1Yf4zDwHQyJ1dkShA3iwWcJ5hsZf36xbS3H2fjxh309Q2U1YbOF4WJKPZLasP/wAmCJnUE5f4RFu7w8rKu2+2xs5Mmnffcy11seuny5eWd5uEH+9pC+B84gbBrbJdzaUdZefxc1+22o+qHP3zOU6eYl9VJYU9EsV/sUr1rvNHN/KtLOVXW/JLz7bffIpW6w/F1Uqn3+MpXbh657aaE9nLofNiLZOxrC43AFqCeUo4PiipjJb2/mpqapLu72/fXjav8RLTOlLqmaJV19+4LI9Xv3Hbz6PNqa99gaChz6Fyh8VbvnWLJZC8LF05y7NXOjw9IT4o5kTcppjITPFas2FLwQwjOAc9j/RuMxpr9gRPFcWpjTFLsthWzK8LLubT67Y5TFba+/l88VVmLVXGtzQn8GYeu5Nptv9j9W0+Y0CtXXfWQLFv2A/nqV39YsfXQ5UKr39HkVIUdGFhNff2DnDp1c9Eqq5tdUGE28AajpVFuCf3kk8+67uWOwjCVV85TaP8s7PB8pUkdkuKJ+AFOnbqZRx45yr59fY7zuN3sgmpNgdyBldS5Cb1kyf05bepiojBMVaoobWYQFE3qkLjdjnjfvj7uvdf5cW5LzpqaswwPj03op5/+U1fPz4jCMJWyF5360TjjZxXWbcm5ZMkRshP6iSe6+I//WOO5Iyiyu2gqQJM6NH5WYZcvnz5mDXW+yZO38txza0du9/T0cPvtt7iKoRCva7dV5eiQVkisYaGJnoatnNgP2QD0YHWUpW+VOfUzW5jDVOOZDmlFlJ8zreyGbCZPfqHs5ZMqetAhrWhyM9PK7da9hYZshoaOsmHDn4w8xs8SWkWXVr8joFAV9pOfnFFwhpjbmU5e9xRT1UWr31WonKq5l10/VXVCdz6pLuWck60ltNKkjqBStwDShFagSR1JpUxM0YRWGZrUEeR1YoomtMqmSR1BbmaITZq0k3feOUNLyw80oVUO10ltjKk1xrxqjHkuyIBUsbnV54BnGBqazuOPz2TLltFNApcsuZ9rr9X51uOdl5J6DfB6UIGoXHZzq2tqngKWMjQ0SPbiDOjh+ee/UdLWwSpeXE0+McZcAWwA/hH4SxFxXHyrk0/8kz0xZXDwJJs3NzM4+H/kJ3Tmtpf54qp6OU0+cTtN9H7gLuAihzdpA9oAZszQdbR23E77zMhe1L927Q7HhAZrqOs733mKxx7TpB637GalZC7gZmBd+vvfBp4r9hydUTaWly117Sxduj5nphj0FJxxVlOzxfVrqupEOYfOA/8MnASOAW8D/wc87vQcTeqxyl2RlT/10y6hrWt7RfbTVuFxSuqiHWUi8tcicoWINADLgJ0i8lX/6grxV860Txg7Dp1f5c7VD8wo+poqvnScugJKnfaZTPaycuUjOQm9ZMn9wHU2rzIM7CezY2g1Hu6myudpPbWIvAi8GEgkMVZ82mcvcIKurreBHdx224d58MGjPP10HYODo+uhJ09+nosu+jW33/4MmzfPZmgouzOsHyuhc7cRitI2vaoy9H+8AuynfZ7DOvZlIrCIN95YwX33LWLhwsls3HiMwcGbsh7bw+DgTTz55B9SWzuBZcv2YG35uyv99X2sEzFz11hHcZteFSxN6gqwn/a5FbgVa0/uUcPD54G/yPpJdhvaaivfdNPlJBJXATcCiyh0OqZu0zs+aVJXQOFpn71Aoc6z3H254VnyO8VSqSt59VWj2/SqgnSPsgoZux/ZCawSNlt+QvcApwq+3pkzNWWdJul1EoyqHprUFZK/MWBX19u8kTPaVCihZ2G1mceaOnW4yPlQhY9fzT0dc/RDZd26flpatkT2lEflgd0AdjmXTj4pLvfkSKeJJWNPmCznIPSwD4BX/kD3KIue0c4zuxIaRieSZCu9rVzuJBhVHTSpQzJvXiM33tiFfUIPA6+QfRA6HGXx4sdLPtKm1EkwqrpomzokBw8e5Pnnv5n1k/wSej9W7/gOrM/eCyxe3McLL3g7oTJbNZ4rrbzTpA5B/lzuJ57o4qc/PcUvfvFj9u49Sl9fI+fPj3Z0jfZm/3FZ71vN50or9/SEjgpzs0lgUIfO+X0onwqPH5skKB+43fUze2MEv9XXb+PQoTYKd6dkOuEKD4ep6qCNpwoJcxvfc+fOsWLFFhYunMihQyuBLqx2+yg9Vzo+tKSugLD35V69emve2dVLsaap7gBg5sw36Oj4ou2EFVVdNKkDFnZC249NN5JZBDIwcBXGXKhYTCpYmtQBCiKhvc7Ztsam8+eY57LGpndo51hMaFIHxO+ELnXOto5Njz+a1Gl+rloKooQe2y62pFJXsnFjPdBFR8fYNrGOTY9DdpPCy7mqaUGHH1v3ZgviwPfu7sOSSPQ77B5qv8jDem5fSc9V0YUu6LCXKQHzJ2RYJeCtBY+xOX36NBs2bODll1/O+XlQnWLlzNl2PpcLdDOFGLLL9nKuaimpSy0Bv/zlLwsgiURCdu3aJSLBlNAZd9650zHGzLVq1c6Cz/e7NqLCh0NJPa7b1KX0DIsIu3fvTt+XYsmSJXz729/m61//+shz/B62KrddXMpmCqp6jeukLqVneGBggLfeemvkdiqVCjShwVp7vW5df9E528U2GQxy+qmKjnHdpi6lBMyU0oV897vfDWRiibaLlRfjOqntt+4dlV8COiX1XXfdxYsvvuhXeDnszqvWOdsq37hferlixZaC47+WYVpbc8d/Z8+eTU9Pj+3rJRIJtm3bxsKFC/0PluCWZarqoksvHXjZZvdXv/oVBw4ccHy9VCpFX19fYEmt7WJVTNGkNsbUAS8Bk9OP3ywi9wQdWKV46Rnes2cPTjWbj370ozQ3N7NkyZKgw1bKlpuSehD4ooicNcZMBF4xxrwgInsCjq2i3JSA2e1pYwyzZs2iubl55Kqvr8cYE3SoSjkqmtTpge6z6ZsT05f/DfEqcffdd9Pc3MyCBQu45JJLwg5HqTFctamNMbVAErgGeFBE9gYalY2wj4r51re+VbH3UqpUrpJaRIaAOcaYDwKdxphPicjB7McYY9qANoAZM/w9aVGPilHKPU/j1CLyS6zDncYMiorIwyLSJCJN06ZN8ys+oLRFF0qNV0WT2hgzLV1CY4xJAF8CDgUdWIYeFaOUN25K6suBXcaYHuCnwI9F5LlgwxqlR8Uo5Y2b3u8e4PoKxFKQbsejlDeRzwTdjkcpbyKf1KUsulBqPIt8UuuyQ6W8qYoFHV4WXSg13lVFUut2PEq5VxVJnaHLDpUqLvJtaqWUN5rUSsWMJrVSMaNJrVTMaFIrFTOa1ErFjCa1UjGjSa1UzGhSKxUzmtRKxYwmtVIxo0mtVMxoUisVM5rUSsWMJrVSMaNJrVTMaFIrFTOa1ErFjCa1UjGjSa1UzGhSKxUzmtRKxYybo2ynG2N2GWN+box5zRizphKBKaVK42bf7wtAu4jsM8ZcBCSNMT8WkZ8HHJtSqgRFS2oR+W8R2Zf+/tfA68DHgg5MKVUaTyd0GGMasM6q3lvgvjagLX3zrDHmcLnBAZcCv/Dhdfyi8TiLWjwQvZj8iqfe7g4jIq5ewRjzG8B/Av8oIs/4EJSb9+wWkaZKvJcbGo+zqMUD0YupEvG46v02xkwEngY6KpXQSqnSuOn9NsB64HUR+U7wISmlyuGmpG4G7gC+aIz5WfpaEnBcGQ9X6H3c0nicRS0eiF5Mgcfjuk2tlKoOOqNMqZjRpFYqZiKZ1MaYHxhjThljDkYglshNkzXG1BljfmKM2Z+O6e/DjgnAGFNrjHnVGPNcBGI5Zow5kO4D6o5APB80xmw2xhwyxrxujFkQ2HtFsU1tjPk8cBZ4VEQ+FXIslwOXZ0+TBZaGOU02PSIxRUTOpocbXwHWiMiesGJKx/WXQBNwsYjcHHIsx4AmEYnExBNjzAbgZRF5xBgzCfiAiPwyiPeKZEktIi8B74YdB0RzmqxYzqZvTkxfoX46G2OuAH4feCTMOKLIGHMJ8HmsoWFE5HxQCQ0RTeqocpomW2npqu7PgFPAj0Uk7JjuB+4ChkOOI0OAHxljkukpzGG6EjgN/Gu6efKIMWZKUG+mSe1Seprs08A3ReR/w45HRIZEZA5wBfBZY0xozRRjzM3AKRFJhhVDAZ8TkbnATcDX0k26sEwA5gIPicj1wHvAXwX1ZprULkR5mmy6GrcLWBxiGM3Arel27BNYE5UeDzEeROTN9NdTQCfw2RDDOQmczKpNbcZK8kBoUhcRxWmyxphpxpgPpr9PAF8CDoUVj4j8tYhcISINwDJgp4h8Nax4jDFT0p2apKu5vwuENpIiIm8DJ4wxH0//aBEQWEerp6WXlWKM2QT8NnCpMeYkcI+IrA8pnMw02QPpNizA34jI8yHFA3A5sMEYU4v1wfxDEQl9GClCLgM6rc9jJgAbRWRruCHxDaAj3fPdB9wZ1BtFckhLKVU6rX4rFTOa1ErFjCa1UjGjSa1UzGhSKxUzmtRKxYwmtVIx8/9qI1nglZh7igAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Before running PCA, it is important to first normalize X\n", + "X_norm, mu, sigma = utils.featureNormalize(X)\n", + "\n", + "# Run PCA\n", + "U, S = pca(X_norm)\n", + "\n", + "# Draw the eigenvectors centered at mean of data. These lines show the\n", + "# directions of maximum variations in the dataset.\n", + "fig, ax = pyplot.subplots()\n", + "ax.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=0.25)\n", + "\n", + "for i in range(2):\n", + " ax.arrow(mu[0], mu[1], 1.5 * S[i]*U[0, i], 1.5 * S[i]*U[1, i],\n", + " head_width=0.25, head_length=0.2, fc='k', ec='k', lw=2, zorder=1000)\n", + "\n", + "ax.axis([0.5, 6.5, 2, 8])\n", + "ax.set_aspect('equal')\n", + "ax.grid(False)\n", + "\n", + "print('Top eigenvector: U[:, 0] = [{:.6f} {:.6f}]'.format(U[0, 0], U[1, 0]))\n", + "print(' (you should expect to see [-0.707107 -0.707107])')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'grader' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpca\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mgrader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrade\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'grader' is not defined" + ] + } + ], + "source": [ + "grader[3] = pca\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Dimensionality Reduction with PCA\n", + "\n", + "After computing the principal components, you can use them to reduce the feature dimension of your dataset by projecting each example onto a lower dimensional space, $x^{(i)} \\rightarrow z^{(i)}$ (e.g., projecting the data from 2D to 1D). In this part of the exercise, you will use the eigenvectors returned by PCA and\n", + "project the example dataset into a 1-dimensional space. In practice, if you were using a learning algorithm such as linear regression or perhaps neural networks, you could now use the projected data instead of the original data. By using the projected data, you can train your model faster as there are less dimensions in the input.\n", + "\n", + "\n", + "\n", + "#### 2.3.1 Projecting the data onto the principal components\n", + "\n", + "You should now complete the code in the function `projectData`. Specifically, you are given a dataset `X`, the principal components `U`, and the desired number of dimensions to reduce to `K`. You should project each example in `X` onto the top `K` components in `U`. Note that the top `K` components in `U` are given by\n", + "the first `K` columns of `U`, that is `Ureduce = U[:, :K]`.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def projectData(X, U, K):\n", + " \"\"\"\n", + " Computes the reduced data representation when projecting only \n", + " on to the top K eigenvectors.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The input dataset of shape (m x n). The dataset is assumed to be \n", + " normalized.\n", + " \n", + " U : array_like\n", + " The computed eigenvectors using PCA. This is a matrix of \n", + " shape (n x n). Each column in the matrix represents a single\n", + " eigenvector (or a single principal component).\n", + " \n", + " K : int\n", + " Number of dimensions to project onto. Must be smaller than n.\n", + " \n", + " Returns\n", + " -------\n", + " Z : array_like\n", + " The projects of the dataset onto the top K eigenvectors. \n", + " This will be a matrix of shape (m x k).\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the projection of the data using only the top K \n", + " eigenvectors in U (first K columns). \n", + " For the i-th example X[i,:], the projection on to the k-th \n", + " eigenvector is given as follows:\n", + " \n", + " x = X[i, :]\n", + " projection_k = np.dot(x, U[:, k])\n", + "\n", + " \"\"\"\n", + " # You need to return the following variables correctly.\n", + " Z = np.zeros((X.shape[0], K))\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " Ureduce = U[:, :K]\n", + " Z = np.dot(X, Ureduce)\n", + " \n", + " # =============================================================\n", + " return Z" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have completed the code in `projectData`, the following cell will project the first example onto the first dimension and you should see a value of about 1.481 (or possibly -1.481, if you got $-U_1$ instead of $U_1$)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Projection of the first example: 1.481274\n", + "(this value should be about : 1.481274)\n" + ] + } + ], + "source": [ + "# Project the data onto K = 1 dimension\n", + "K = 1\n", + "Z = projectData(X_norm, U, K)\n", + "print('Projection of the first example: {:.6f}'.format(Z[0, 0]))\n", + "print('(this value should be about : 1.481274)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'grader' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprojectData\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mgrader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrade\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'grader' is not defined" + ] + } + ], + "source": [ + "grader[4] = projectData\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 2.3.2 Reconstructing an approximation of the data\n", + "\n", + "After projecting the data onto the lower dimensional space, you can approximately recover the data by projecting them back onto the original high dimensional space. Your task is to complete the function `recoverData` to project each example in `Z` back onto the original space and return the recovered approximation in `Xrec`.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def recoverData(Z, U, K):\n", + " \"\"\"\n", + " Recovers an approximation of the original data when using the \n", + " projected data.\n", + " \n", + " Parameters\n", + " ----------\n", + " Z : array_like\n", + " The reduced data after applying PCA. This is a matrix\n", + " of shape (m x K).\n", + " \n", + " U : array_like\n", + " The eigenvectors (principal components) computed by PCA.\n", + " This is a matrix of shape (n x n) where each column represents\n", + " a single eigenvector.\n", + " \n", + " K : int\n", + " The number of principal components retained\n", + " (should be less than n).\n", + " \n", + " Returns\n", + " -------\n", + " X_rec : array_like\n", + " The recovered data after transformation back to the original \n", + " dataset space. This is a matrix of shape (m x n), where m is \n", + " the number of examples and n is the dimensions (number of\n", + " features) of original datatset.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the approximation of the data by projecting back\n", + " onto the original space using the top K eigenvectors in U.\n", + " For the i-th example Z[i,:], the (approximate)\n", + " recovered data for dimension j is given as follows:\n", + "\n", + " v = Z[i, :]\n", + " recovered_j = np.dot(v, U[j, :K])\n", + "\n", + " Notice that U[j, :K] is a vector of size K.\n", + " \"\"\"\n", + " # You need to return the following variables correctly.\n", + " X_rec = np.zeros((Z.shape[0], U.shape[0]))\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " Ureduce = U[:, :K]\n", + " X_rec = np.dot(Z, Ureduce.transpose())\n", + "\n", + " # =============================================================\n", + " return X_rec" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have completed the code in `recoverData`, the following cell will recover an approximation of the first example and you should see a value of about `[-1.047 -1.047]`. The code will then plot the data in this reduced dimension space. This will show you what the data looks like when using only the corresponding eigenvectors to reconstruct it. An example of what you should get for PCA projection is shown in this figure: \n", + "\n", + "![](Figures/pca_reconstruction.png)\n", + "\n", + "In the figure above, the original data points are indicated with the blue circles, while the projected data points are indicated with the red circles. The projection effectively only retains the information in the direction given by $U_1$. The dotted lines show the distance from the data points in original space to the projected space. Those dotted lines represent the error measure due to PCA projection." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Approximation of the first example: [-1.047419 -1.047419]\n", + " (this value should be about [-1.047419 -1.047419])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "X_rec = recoverData(Z, U, K)\n", + "print('Approximation of the first example: [{:.6f} {:.6f}]'.format(X_rec[0, 0], X_rec[0, 1]))\n", + "print(' (this value should be about [-1.047419 -1.047419])')\n", + "\n", + "# Plot the normalized dataset (returned from featureNormalize)\n", + "fig, ax = pyplot.subplots(figsize=(5, 5))\n", + "ax.plot(X_norm[:, 0], X_norm[:, 1], 'bo', ms=8, mec='b', mew=0.5)\n", + "ax.set_aspect('equal')\n", + "ax.grid(False)\n", + "pyplot.axis([-3, 2.75, -3, 2.75])\n", + "\n", + "# Draw lines connecting the projected points to the original points\n", + "ax.plot(X_rec[:, 0], X_rec[:, 1], 'ro', mec='r', mew=2, mfc='none')\n", + "for xnorm, xrec in zip(X_norm, X_rec):\n", + " ax.plot([xnorm[0], xrec[0]], [xnorm[1], xrec[1]], '--k', lw=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'grader' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecoverData\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mgrader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrade\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'grader' is not defined" + ] + } + ], + "source": [ + "grader[5] = recoverData\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Face Image Dataset\n", + "\n", + "In this part of the exercise, you will run PCA on face images to see how it can be used in practice for dimension reduction. The dataset `ex7faces.mat` contains a dataset `X` of face images, each $32 \\times 32$ in grayscale. This dataset was based on a [cropped version](http://conradsanderson.id.au/lfwcrop/) of the [labeled faces in the wild](http://vis-www.cs.umass.edu/lfw/) dataset. Each row of `X` corresponds to one face image (a row vector of length 1024). \n", + "\n", + "The next cell will load and visualize the first 100 of these face images similar to what is shown in this figure:\n", + "\n", + "![Faces](Figures/faces.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load Face dataset\n", + "data = loadmat(os.path.join('Data', 'ex7faces.mat'))\n", + "X = data['X']\n", + "\n", + "# Display the first 100 faces in the dataset\n", + "utils.displayData(X[:100, :], figsize=(8, 8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.4.1 PCA on Faces\n", + "\n", + "To run PCA on the face dataset, we first normalize the dataset by subtracting the mean of each feature from the data matrix `X`. After running PCA, you will obtain the principal components of the dataset. Notice that each principal component in `U` (each column) is a vector of length $n$ (where for the face dataset, $n = 1024$). It turns out that we can visualize these principal components by reshaping each of them into a $32 \\times 32$ matrix that corresponds to the pixels in the original dataset. \n", + "\n", + "The following cell will first normalize the dataset for you and then run your PCA code. Then, the first 36 principal components (conveniently called eigenfaces) that describe the largest variations are displayed. If you want, you can also change the code to display more principal components to see how they capture more and more details." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# normalize X by subtracting the mean value from each feature\n", + "X_norm, mu, sigma = utils.featureNormalize(X)\n", + "\n", + "# Run PCA\n", + "U, S = pca(X_norm)\n", + "\n", + "# Visualize the top 36 eigenvectors found\n", + "utils.displayData(U[:, :36].T, figsize=(8, 8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.4.2 Dimensionality Reduction\n", + "\n", + "Now that you have computed the principal components for the face dataset, you can use it to reduce the dimension of the face dataset. This allows you to use your learning algorithm with a smaller input size (e.g., 100 dimensions) instead of the original 1024 dimensions. This can help speed up your learning algorithm.\n", + "\n", + "The next cell will project the face dataset onto only the first 100 principal components. Concretely, each face image is now described by a vector $z^{(i)} \\in \\mathbb{R}^{100}$. To understand what is lost in the dimension reduction, you can recover the data using only the projected dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The projected data Z has a shape of: (5000, 100)\n" + ] + } + ], + "source": [ + "# Project images to the eigen space using the top k eigenvectors \n", + "# If you are applying a machine learning algorithm \n", + "K = 100\n", + "Z = projectData(X_norm, U, K)\n", + "\n", + "print('The projected data Z has a shape of: ', Z.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, an approximate recovery of the data is performed and the original and projected face images\n", + "are displayed similar to what is shown here:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "From the reconstruction, you can observe that the general structure and appearance of the face are kept while the fine details are lost. This is a remarkable reduction (more than 10x) in the dataset size that can help speed up your learning algorithm significantly. For example, if you were training a neural network to perform person recognition (given a face image, predict the identity of the person), you can use the dimension reduced input of only a 100 dimensions instead of the original pixels." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Project images to the eigen space using the top K eigen vectors and \n", + "# visualize only using those K dimensions\n", + "# Compare to the original input, which is also displayed\n", + "K = 100\n", + "X_rec = recoverData(Z, U, K)\n", + "\n", + "# Display normalized data\n", + "utils.displayData(X_norm[:100, :], figsize=(6, 6))\n", + "pyplot.gcf().suptitle('Original faces')\n", + "\n", + "# Display reconstructed data from only k eigenfaces\n", + "utils.displayData(X_rec[:100, :], figsize=(6, 6))\n", + "pyplot.gcf().suptitle('Recovered faces')\n", + "pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.5 Optional (ungraded) exercise: PCA for visualization\n", + "\n", + "In the earlier K-means image compression exercise, you used the K-means algorithm in the 3-dimensional RGB space. We reduced each pixel of the RGB image to be represented by 16 clusters. In the next cell, we have provided code to visualize the final pixel assignments in this 3D space. Each data point is colored according to the cluster it has been assigned to. You can drag your mouse on the figure to rotate and inspect this data in 3 dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
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');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('