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niharika2999
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Few changes to gqr and region_optimal which has the radius constraints
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‎examples/region_optimal.ipynb‎

Lines changed: 39 additions & 13 deletions
Original file line numberDiff line numberDiff line change
@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 358,
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"execution_count": 11,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-07-10T04:22:04.386599Z",
@@ -26,7 +26,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 359,
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"execution_count": 12,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-07-10T04:22:07.391526Z",
@@ -66,7 +66,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 360,
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"execution_count": 13,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-07-10T04:22:09.785781Z",
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},
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{
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"cell_type": "code",
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"execution_count": 361,
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"execution_count": 14,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-07-10T04:22:10.835009Z",
@@ -117,7 +117,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 362,
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"execution_count": 15,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-07-10T04:22:11.651751Z",
@@ -173,7 +173,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 364,
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -204,7 +204,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 365,
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -221,7 +221,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 366,
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"execution_count": 18,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-07-10T04:22:22.713344Z",
@@ -304,10 +304,14 @@
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"\n",
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" for j in range(k):\n",
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" r = R[j:, j:]\n",
307-
" # Norm of each column\n",
307+
" # Norm of each column \n",
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" dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0))\n",
309-
" dlens_updated = f_region_distance_constraints(self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j,self.nSensors) #Handling constrained region sensor placement problem\n",
310-
" #print(dlens_updated)\n",
309+
" if j != 0:\n",
310+
" dlens_old = dlens_updated\n",
311+
" else:\n",
312+
" dlens_old = dlens\n",
313+
" dlens_updated = f_region_distance_constraints(self.radius,self._nx,self._ny,self.all_sensorloc,dlens_old,p,j,self.nSensors) #Handling constrained region sensor placement problem\n",
314+
" print(dlens_updated)\n",
311315
" # Choose pivot\n",
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" i_piv = np.argmax(dlens_updated)\n",
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" \n",
@@ -398,14 +402,36 @@
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},
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{
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"cell_type": "code",
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"execution_count": 368,
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"execution_count": 19,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-07-10T04:22:27.911973Z",
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"start_time": "2022-07-10T04:22:26.180620Z"
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}
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},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[3.6017013 3.6861243 3.727096 ... 3.9694474 3.9748106 3.9125938]\n"
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]
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},
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{
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"ename": "IndexError",
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"evalue": "boolean index did not match indexed array along dimension 0; dimension is 4096 but corresponding boolean dimension is 4095",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
426+
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 11'\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000010?line=1'>2</a>\u001b[0m optimizer1 \u001b[39m=\u001b[39m AQR(n_sensors,all_sensors,r,nx,ny)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000010?line=2'>3</a>\u001b[0m model1 \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39mSSPOR(optimizer \u001b[39m=\u001b[39m optimizer1, n_sensors \u001b[39m=\u001b[39m n_sensors)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000010?line=3'>4</a>\u001b[0m model1\u001b[39m.\u001b[39;49mfit(X)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000010?line=4'>5</a>\u001b[0m all_sensors1 \u001b[39m=\u001b[39m model1\u001b[39m.\u001b[39mget_all_sensors()\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000010?line=6'>7</a>\u001b[0m top_sensors \u001b[39m=\u001b[39m model1\u001b[39m.\u001b[39mget_selected_sensors()\n",
428+
"File \u001b[0;32m~/projects/pysensors/pysensors/reconstruction/_sspor.py:156\u001b[0m, in \u001b[0;36mSSPOR.fit\u001b[0;34m(self, x, quiet, prefit_basis, seed, **optimizer_kws)\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_n_sensors()\n\u001b[1;32m 155\u001b[0m \u001b[39m# Find sparse sensor locations\u001b[39;00m\n\u001b[0;32m--> 156\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mranked_sensors_ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptimizer\u001b[39m.\u001b[39;49mfit(\n\u001b[1;32m 157\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbasis_matrix_, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49moptimizer_kws\n\u001b[1;32m 158\u001b[0m )\u001b[39m.\u001b[39mget_sensors()\n\u001b[1;32m 160\u001b[0m \u001b[39m# Randomly shuffle sensors after self.basis.n_basis_modes\u001b[39;00m\n\u001b[1;32m 161\u001b[0m rng \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mrandom\u001b[39m.\u001b[39mdefault_rng(seed)\n",
429+
"\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 9'\u001b[0m in \u001b[0;36mAQR.fit\u001b[0;34m(self, basis_matrix)\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000008?line=78'>79</a>\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000008?line=79'>80</a>\u001b[0m dlens_old \u001b[39m=\u001b[39m dlens\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000008?line=80'>81</a>\u001b[0m dlens_updated \u001b[39m=\u001b[39m f_region_distance_constraints(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mradius,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_nx,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_ny,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mall_sensorloc,dlens_old,p,j,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnSensors) \u001b[39m#Handling constrained region sensor placement problem\u001b[39;00m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000008?line=81'>82</a>\u001b[0m \u001b[39mprint\u001b[39m(dlens_updated)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000008?line=82'>83</a>\u001b[0m \u001b[39m# Choose pivot\u001b[39;00m\n",
430+
"\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 8'\u001b[0m in \u001b[0;36mf_region_distance_constraints\u001b[0;34m(r, nx, ny, all_sensors, dlens, piv, j, n_sensors)\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000007?line=5'>6</a>\u001b[0m idx_constrained \u001b[39m=\u001b[39m distance_constraints_indices(r,nx,ny,piv,n_sensors,j)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000007?line=6'>7</a>\u001b[0m didx \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39misin(piv[j:],idx_constrained,invert\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000007?line=7'>8</a>\u001b[0m dlens[didx] \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/karnn/projects/pysensors/examples/region_optimal.ipynb#ch0000007?line=8'>9</a>\u001b[0m \u001b[39mreturn\u001b[39;00m dlens\n",
431+
"\u001b[0;31mIndexError\u001b[0m: boolean index did not match indexed array along dimension 0; dimension is 4096 but corresponding boolean dimension is 4095"
432+
]
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}
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],
409435
"source": [
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"r = 7\n",
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"optimizer1 = AQR(n_sensors,all_sensors,r,nx,ny)\n",

‎pysensors/optimizers/_gqr.py‎

Lines changed: 11 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -241,22 +241,23 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):
241241
# plot_gallery("First few centered faces", X[:n_components])
242242

243243
#Find all sensor locations using built in QR optimizer
244-
max_const_sensors = 230
245-
n_const_sensors = 2
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n_sensors = 200
244+
#max_const_sensors = 230
245+
n_const_sensors = 3
246+
n_sensors = 10
247+
n_modes = 11
248+
basis = ps.basis.SVD(n_basis_modes=n_modes)
247249
optimizer = ps.optimizers.QR()
248-
model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors)
250+
model = ps.SSPOR(basis = basis, optimizer=optimizer, n_sensors=n_sensors)
249251
model.fit(X)
250252

251253
all_sensors = model.get_all_sensors()
252254

253255
##Constrained sensor location on the grid:
254-
xmin = 20
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xmax = 40
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ymin = 25
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ymax = 45
256+
xmin = 0
257+
xmax = 64
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ymin = 10
259+
ymax = 30
258260
sensors_constrained = ps.utils._constraints.get_constraind_sensors_indices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices
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# didx = np.isin(all_sensors,sensors_constrained,invert=False)
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# const_index = np.nonzero(didx)
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# j =
@@ -277,7 +278,7 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):
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## Fit the dataset with the optimizer GQR
279280
optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors, constraint_option = "exact_n_const_sensors")
280-
model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors)
281+
model1 = ps.SSPOR(basis = basis, optimizer = optimizer1, n_sensors = n_sensors)
281282
model1.fit(X)
282283
all_sensors1 = model1.get_all_sensors()
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