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comparison python/ml.py @ 916:7345f0d51faa
added display of paths
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Tue, 04 Jul 2017 17:36:24 -0400 |
parents | 13434f5017dd |
children | 89cc05867c4c |
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915:13434f5017dd | 916:7345f0d51faa |
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264 similarities[l][k] = similarities[k][l] | 264 similarities[l][k] = similarities[k][l] |
265 print('Mean similarity to prototype: {}'.format((similarities[prototypeIndices[i]][cluster].sum()+1)/(n-1))) | 265 print('Mean similarity to prototype: {}'.format((similarities[prototypeIndices[i]][cluster].sum()+1)/(n-1))) |
266 print('Mean overall similarity: {}'.format((similarities[cluster][:,cluster].sum()+n)/(n*(n-1)))) | 266 print('Mean overall similarity: {}'.format((similarities[cluster][:,cluster].sum()+n)/(n*(n-1)))) |
267 | 267 |
268 # Gaussian Mixture Models | 268 # Gaussian Mixture Models |
269 def plotGMM(mean, covariance, num, fig, color, alpha = 0.3): | |
270 v, w = np.linalg.eigh(covariance) | |
271 angle = 180*np.arctan2(w[0][1], w[0][0])/np.pi | |
272 v *= 4 | |
273 ell = mpl.patches.Ellipse(mean, v[0], v[1], 180+angle, color=color) | |
274 ell.set_clip_box(fig.bbox) | |
275 ell.set_alpha(alpha) | |
276 fig.axes[0].add_artist(ell) | |
277 plt.plot([mean[0]], [mean[1]], 'x'+color) | |
278 plt.annotate(str(num), xy=(mean[0]+1, mean[1]+1)) | |
279 | |
269 def plotGMMClusters(model, labels = None, dataset = None, fig = None, colors = utils.colors, nUnitsPerPixel = 1., alpha = 0.3): | 280 def plotGMMClusters(model, labels = None, dataset = None, fig = None, colors = utils.colors, nUnitsPerPixel = 1., alpha = 0.3): |
270 '''plot the ellipse corresponding to the Gaussians | 281 '''plot the ellipse corresponding to the Gaussians |
271 and the predicted classes of the instances in the dataset''' | 282 and the predicted classes of the instances in the dataset''' |
272 if fig is None: | 283 if fig is None: |
273 fig = plt.figure() | 284 fig = plt.figure() |
274 axes = fig.get_axes() | 285 if len(fig.get_axes()) == 0: |
275 if len(axes) == 0: | 286 fig.add_subplot(111) |
276 axes = [fig.add_subplot(111)] | |
277 for i in xrange(model.n_components): | 287 for i in xrange(model.n_components): |
278 mean = model.means_[i]/nUnitsPerPixel | 288 mean = model.means_[i]/nUnitsPerPixel |
279 covariance = model.covariances_[i]/nUnitsPerPixel | 289 covariance = model.covariances_[i]/nUnitsPerPixel |
280 # plot points | 290 # plot points |
281 if dataset is not None: | 291 if dataset is not None: |
282 tmpDataset = dataset/nUnitsPerPixel | 292 tmpDataset = dataset/nUnitsPerPixel |
283 plt.scatter(tmpDataset[labels == i, 0], tmpDataset[labels == i, 1], .8, color=colors[i]) | 293 plt.scatter(tmpDataset[labels == i, 0], tmpDataset[labels == i, 1], .8, color=colors[i]) |
284 # plot an ellipse to show the Gaussian component | 294 # plot an ellipse to show the Gaussian component |
285 v, w = np.linalg.eigh(covariance) | 295 plotGMM(mean, covariance, i, fig, colors[i], alpha) |
286 angle = 180*np.arctan2(w[0][1], w[0][0])/np.pi | |
287 v *= 4 | |
288 ell = mpl.patches.Ellipse(mean, v[0], v[1], 180+angle, color=colors[i]) | |
289 ell.set_clip_box(fig.bbox) | |
290 ell.set_alpha(alpha) | |
291 axes[0].add_artist(ell) | |
292 plt.plot([mean[0]], [mean[1]], 'x'+colors[i]) | |
293 plt.annotate(str(i), xy=(mean[0]+1, mean[1]+1)) | |
294 if dataset is None: # to address issues without points, the axes limits are not redrawn | 296 if dataset is None: # to address issues without points, the axes limits are not redrawn |
295 minima = model.means_.min(0) | 297 minima = model.means_.min(0) |
296 maxima = model.means_.max(0) | 298 maxima = model.means_.max(0) |
297 xwidth = 0.5*(maxima[0]-minima[0]) | 299 xwidth = 0.5*(maxima[0]-minima[0]) |
298 ywidth = 0.5*(maxima[1]-minima[1]) | 300 ywidth = 0.5*(maxima[1]-minima[1]) |