Solutions

Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/exercise1.ipynb

@@ -1294,7 +1294,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,

Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Tanmay_190106065_Week_2/data1.txt

@@ -0,0 +1,97 @@
+6.1101,17.592
+5.5277,9.1302
+8.5186,13.662
+7.0032,11.854
+5.8598,6.8233
+8.3829,11.886
+7.4764,4.3483
+8.5781,12
+6.4862,6.5987
+5.0546,3.8166
+5.7107,3.2522
+14.164,15.505
+5.734,3.1551
+8.4084,7.2258
+5.6407,0.71618
+5.3794,3.5129
+6.3654,5.3048
+5.1301,0.56077
+6.4296,3.6518
+7.0708,5.3893
+6.1891,3.1386
+20.27,21.767
+5.4901,4.263
+6.3261,5.1875
+5.5649,3.0825
+18.945,22.638
+12.828,13.501
+10.957,7.0467
+13.176,14.692
+22.203,24.147
+5.2524,-1.22
+6.5894,5.9966
+9.2482,12.134
+5.8918,1.8495
+8.2111,6.5426
+7.9334,4.5623
+8.0959,4.1164
+5.6063,3.3928
+12.836,10.117
+6.3534,5.4974
+5.4069,0.55657
+6.8825,3.9115
+11.708,5.3854
+5.7737,2.4406
+7.8247,6.7318
+7.0931,1.0463
+5.0702,5.1337
+5.8014,1.844
+11.7,8.0043
+5.5416,1.0179
+7.5402,6.7504
+5.3077,1.8396
+7.4239,4.2885
+7.6031,4.9981
+6.3328,1.4233
+6.3589,-1.4211
+6.2742,2.4756
+5.6397,4.6042
+9.3102,3.9624
+9.4536,5.4141
+8.8254,5.1694
+5.1793,-0.74279
+21.279,17.929
+14.908,12.054
+18.959,17.054
+7.2182,4.8852
+8.2951,5.7442
+10.236,7.7754
+5.4994,1.0173
+20.341,20.992
+10.136,6.6799
+7.3345,4.0259
+6.0062,1.2784
+7.2259,3.3411
+5.0269,-2.6807
+6.5479,0.29678
+7.5386,3.8845
+5.0365,5.7014
+10.274,6.7526
+5.1077,2.0576
+5.7292,0.47953
+5.1884,0.20421
+6.3557,0.67861
+9.7687,7.5435
+6.5159,5.3436
+8.5172,4.2415
+9.1802,6.7981
+6.002,0.92695
+5.5204,0.152
+5.0594,2.8214
+5.7077,1.8451
+7.6366,4.2959
+5.8707,7.2029
+5.3054,1.9869
+8.2934,0.14454
+13.394,9.0551
+5.4369,0.61705

Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Tanmay_190106065_Week_2/data2.txt

@@ -0,0 +1,47 @@
+2104,3,399900
+1600,3,329900
+2400,3,369000
+1416,2,232000
+3000,4,539900
+1985,4,299900
+1534,3,314900
+1427,3,198999
+1380,3,212000
+1494,3,242500
+1940,4,239999
+2000,3,347000
+1890,3,329999
+4478,5,699900
+1268,3,259900
+2300,4,449900
+1320,2,299900
+1236,3,199900
+2609,4,499998
+3031,4,599000
+1767,3,252900
+1888,2,255000
+1604,3,242900
+1962,4,259900
+3890,3,573900
+1100,3,249900
+1458,3,464500
+2526,3,469000
+2200,3,475000
+2637,3,299900
+1839,2,349900
+1000,1,169900
+2040,4,314900
+3137,3,579900
+1811,4,285900
+1437,3,249900
+1239,3,229900
+2132,4,345000
+4215,4,549000
+2162,4,287000
+1664,2,368500
+2238,3,329900
+2567,4,314000
+1200,3,299000
+852,2,179900
+1852,4,299900
+1203,3,239500

Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/exercise3.ipynb

@@ -33,7 +33,7 @@
     "from matplotlib import pyplot\n",
     "\n",
     "# Optimization module in scipy\n",
-    "from scipy import optimize\n",
+    "from scipy import optimize as opt\n",
     "\n",
     "# will be used to load MATLAB mat datafile format\n",
     "from scipy.io import loadmat\n",
@@ -42,7 +42,7 @@
     "import utils\n",
     "\n",
     "# define the submission/grader object for this exercise\n",
-    "grader = utils.Grader()\n",
+    "#grader = utils.Grader()\n",
     "\n",
     "# tells matplotlib to embed plots within the notebook\n",
     "%matplotlib inline"
@@ -348,7 +348,14 @@
     "    grad = np.zeros(theta.shape)\n",
     "    \n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
+    "    hyp = utils.sigmoid(X@theta)\n",
+    "    reg = (lambda_/(2*m)) * (theta[1:].T @ theta[1:])\n",
+    "    J = (-1/m) * (y.T @ np.log(hyp) + (1-y.T) @ np.log(1 - hyp)) + reg\n",
+    "    #J=J+reg\n",
+    "    grad = (1/m)*(hyp-y)@X\n",
+    "    temp = theta\n",
+    "    temp[0] = 0\n",
+    "    grad[1:] = grad[1:] + (lambda_/m)*temp[1:]\n",
     "\n",
     "        \n",
     "    # =============================================================\n",
@@ -422,10 +429,10 @@
    "outputs": [],
    "source": [
     "# appends the implemented function in part 1 to the grader object\n",
-    "grader[1] = lrCostFunction\n",
+    "# grader[1] = lrCostFunction\n",
     "\n",
     "# send the added functions to coursera grader for getting a grade on this part\n",
-    "grader.grade()"
+    "# grader.grade()"
    ]
   },
   {
@@ -452,6 +459,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "def GraDes(X,y,theta,alpha,num_iters,Lambda):\n",
+    "    m=len(y)\n",
+    "    J_his=[]\n",
+    "    \n",
+    "    for i in range(num_iters):\n",
+    "        cost,grad = lrCostFunction(theta,X,y,Lambda)\n",
+    "        theta = theta - (alpha*grad)\n",
+    "        J_his.append(J)\n",
+    "    return theta, J_history\n",
+    "\n",
     "def oneVsAll(X, y, num_labels, lambda_):\n",
     "    \"\"\"\n",
     "    Trains num_labels logistic regression classifiers and returns\n",
@@ -526,7 +543,17 @@
     "    X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "   \n",
+    "    Options = {'maxiter': 50}\n",
+    "    for c in range(num_labels):\n",
+    "        initial_theta = np.zeros((n+1,1))\n",
+    "        res = opt.minimize(lrCostFunction, \n",
+    "                               initial_theta,                               \n",
+    "                               (X, (y == c),lambda_), \n",
+    "                                jac=True, \n",
+    "                                method='CG',\n",
+    "                                options=Options) \n",
+    "        all_theta[c,:] = res[\"x\"]\n",
+    "        \n",
     "\n",
     "\n",
     "    # ============================================================\n",
@@ -563,8 +590,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "grader[2] = oneVsAll\n",
-    "grader.grade()"
+    "# grader[2] = oneVsAll\n",
+    "# grader.grade()"
    ]
   },
   {
@@ -625,7 +652,6 @@
     "    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",
@@ -634,9 +660,7 @@
     "    X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
-    "\n",
-    "    \n",
+    "    p = np.argmax(utils.sigmoid( np.dot(X,all_theta.T) ), axis=1)   \n",
     "    # ============================================================\n",
     "    return p"
    ]
@@ -671,8 +695,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "grader[3] = predictOneVsAll\n",
-    "grader.grade()"
+    "# grader[3] = predictOneVsAll\n",
+    "# grader.grade()"
    ]
   },
   {
@@ -828,13 +852,22 @@
     "    # useful variables\n",
     "    m = X.shape[0]\n",
     "    num_labels = Theta2.shape[0]\n",
+    "    X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "\n",
     "\n",
     "    # You need to return the following variables correctly \n",
     "    p = np.zeros(X.shape[0])\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
     "\n",
-    "\n",
+    "    a2 = utils.sigmoid(np.dot(X,Theta1.T))\n",
+    "    \n",
+    "    #Adding bias unit\n",
+    "    a2 = np.column_stack((np.ones((a2.shape[0],1)), a2))\n",
+    "    \n",
+    "    a3 = utils.sigmoid(np.dot(a2,Theta2.T))\n",
+    "    \n",
+    "    p = np.argmax(a3, axis=1)\n",
     "\n",
     "    # =============================================================\n",
     "    return p"
@@ -897,6 +930,13 @@
     "grader[4] = predict\n",
     "grader.grade()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -915,7 +955,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,