diff options
Diffstat (limited to 'Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src')
-rw-r--r-- | Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb | 58 |
1 files changed, 37 insertions, 21 deletions
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb index 78f408fc..329a46f6 100644 --- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb +++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb | |||
@@ -16,7 +16,7 @@ | |||
16 | }, | 16 | }, |
17 | { | 17 | { |
18 | "cell_type": "code", | 18 | "cell_type": "code", |
19 | "execution_count": 1, | 19 | "execution_count": 11, |
20 | "metadata": {}, | 20 | "metadata": {}, |
21 | "outputs": [], | 21 | "outputs": [], |
22 | "source": [ | 22 | "source": [ |
@@ -30,7 +30,8 @@ | |||
30 | "import ipywidgets as widgets\n", | 30 | "import ipywidgets as widgets\n", |
31 | "from pyclustering.cluster.kmedoids import kmedoids\n", | 31 | "from pyclustering.cluster.kmedoids import kmedoids\n", |
32 | "from pyclustering.utils.metric import distance_metric, type_metric\n", | 32 | "from pyclustering.utils.metric import distance_metric, type_metric\n", |
33 | "import random" | 33 | "import random\n", |
34 | "import numpy as np" | ||
34 | ] | 35 | ] |
35 | }, | 36 | }, |
36 | { | 37 | { |
@@ -176,23 +177,28 @@ | |||
176 | }, | 177 | }, |
177 | { | 178 | { |
178 | "cell_type": "code", | 179 | "cell_type": "code", |
179 | "execution_count": 6, | 180 | "execution_count": 15, |
180 | "metadata": {}, | 181 | "metadata": {}, |
181 | "outputs": [ | 182 | "outputs": [ |
182 | { | 183 | { |
183 | "name": "stdout", | 184 | "name": "stdout", |
184 | "output_type": "stream", | 185 | "output_type": "stream", |
185 | "text": [ | 186 | "text": [ |
186 | "0.046150929558524685\n" | 187 | "average distance: 0.04615092955852465\n", |
188 | "std: 0.017305709419913242\n", | ||
189 | "max: 0.1411706837186424\n", | ||
190 | "min: 0.0\n" | ||
187 | ] | 191 | ] |
188 | } | 192 | } |
189 | ], | 193 | ], |
190 | "source": [ | 194 | "source": [ |
191 | "total_distance = 0\n", | 195 | "distances = []\n", |
192 | "count = 0\n", | ||
193 | "for model in models:\n", | 196 | "for model in models:\n", |
194 | " total_distance += ks_value(od_rep_model.out_d, model.out_d)\n", | 197 | " distances.append(ks_value(od_rep_model.out_d, model.out_d))\n", |
195 | "print(total_distance / len(models))" | 198 | "print('average distance: ', np.mean(distances))\n", |
199 | "print('std: ', np.std(distances))\n", | ||
200 | "print('max:', max(distances))\n", | ||
201 | "print('min:', min(distances))" | ||
196 | ] | 202 | ] |
197 | }, | 203 | }, |
198 | { | 204 | { |
@@ -217,7 +223,7 @@ | |||
217 | }, | 223 | }, |
218 | { | 224 | { |
219 | "cell_type": "code", | 225 | "cell_type": "code", |
220 | "execution_count": 13, | 226 | "execution_count": 7, |
221 | "metadata": {}, | 227 | "metadata": {}, |
222 | "outputs": [ | 228 | "outputs": [ |
223 | { | 229 | { |
@@ -245,16 +251,21 @@ | |||
245 | "name": "stdout", | 251 | "name": "stdout", |
246 | "output_type": "stream", | 252 | "output_type": "stream", |
247 | "text": [ | 253 | "text": [ |
248 | "0.04679429311806747\n" | 254 | "average distance: 0.046794293118067494\n", |
255 | "std: 0.02880119213919405\n", | ||
256 | "max: 0.18702970297029703\n", | ||
257 | "min: 0.0\n" | ||
249 | ] | 258 | ] |
250 | } | 259 | } |
251 | ], | 260 | ], |
252 | "source": [ | 261 | "source": [ |
253 | "total_distance = 0\n", | 262 | "distances = []\n", |
254 | "count = 0\n", | ||
255 | "for model in models:\n", | 263 | "for model in models:\n", |
256 | " total_distance += ks_value(na_rep_model.na, model.na)\n", | 264 | " distances.append(ks_value(na_rep_model.na, model.na))\n", |
257 | "print(total_distance / len(models))" | 265 | "print('average distance: ', np.mean(distances))\n", |
266 | "print('std: ', np.std(distances))\n", | ||
267 | "print('max:', max(distances))\n", | ||
268 | "print('min:', min(distances))" | ||
258 | ] | 269 | ] |
259 | }, | 270 | }, |
260 | { | 271 | { |
@@ -279,7 +290,7 @@ | |||
279 | }, | 290 | }, |
280 | { | 291 | { |
281 | "cell_type": "code", | 292 | "cell_type": "code", |
282 | "execution_count": 16, | 293 | "execution_count": 9, |
283 | "metadata": {}, | 294 | "metadata": {}, |
284 | "outputs": [ | 295 | "outputs": [ |
285 | { | 296 | { |
@@ -300,23 +311,28 @@ | |||
300 | }, | 311 | }, |
301 | { | 312 | { |
302 | "cell_type": "code", | 313 | "cell_type": "code", |
303 | "execution_count": 18, | 314 | "execution_count": 16, |
304 | "metadata": {}, | 315 | "metadata": {}, |
305 | "outputs": [ | 316 | "outputs": [ |
306 | { | 317 | { |
307 | "name": "stdout", | 318 | "name": "stdout", |
308 | "output_type": "stream", | 319 | "output_type": "stream", |
309 | "text": [ | 320 | "text": [ |
310 | "0.07028909225833631\n" | 321 | "average distance: 0.07028909225833632\n", |
322 | "std: 0.03728189051222417\n", | ||
323 | "max: 0.21961550993809065\n", | ||
324 | "min: 0.0\n" | ||
311 | ] | 325 | ] |
312 | } | 326 | } |
313 | ], | 327 | ], |
314 | "source": [ | 328 | "source": [ |
315 | "total_distance = 0\n", | 329 | "distances = []\n", |
316 | "count = 0\n", | ||
317 | "for model in models:\n", | 330 | "for model in models:\n", |
318 | " total_distance += ks_value(mpc_rep_model.mpc, model.mpc)\n", | 331 | " distances.append(ks_value(mpc_rep_model.mpc, model.mpc))\n", |
319 | "print(total_distance / len(models))" | 332 | "print('average distance: ', np.mean(distances))\n", |
333 | "print('std: ', np.std(distances))\n", | ||
334 | "print('max:', max(distances))\n", | ||
335 | "print('min:', min(distances))" | ||
320 | ] | 336 | ] |
321 | }, | 337 | }, |
322 | { | 338 | { |