Mercurial > hg > nsaunier > traffic-intelligence

comparison python/ml.py @ 913:1cd878812529

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
work in progress
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 28 Jun 2017 17:57:06 -0400
parents b297525b2cbf
children f228fd649644
comparison
equal deleted inserted replaced
912:fd057a6b04db 913:1cd878812529
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 plotGMMClusters(model, dataset = None, fig = None, colors = utils.colors, nUnitsPerPixel = 1., alpha = 0.3): 269 def plotGMMClusters(model, labels, dataset = None, fig = None, colors = utils.colors, nUnitsPerPixel = 1., alpha = 0.3):
270 '''plot the ellipse corresponding to the Gaussians 270 '''plot the ellipse corresponding to the Gaussians
271 and the predicted classes of the instances in the dataset''' 271 and the predicted classes of the instances in the dataset'''
272 if fig is None: 272 if fig is None:
273 fig = plt.figure() 273 fig = plt.figure()
274 labels = model.predict(dataset)
275 tmpDataset = dataset/nUnitsPerPixel 274 tmpDataset = dataset/nUnitsPerPixel
276 for i in xrange(model.n_components): 275 for i in xrange(model.n_components):
277 mean = model.means_[i]/nUnitsPerPixel 276 mean = model.means_[i]/nUnitsPerPixel
278 covariance = model.covars_[i]/nUnitsPerPixel 277 covariance = model.covars_[i]/nUnitsPerPixel
279 if dataset is not None: 278 if dataset is not None:
287 v *= 4 286 v *= 4
288 ell = mpl.patches.Ellipse(mean, v[0], v[1], 180+angle, color=colors[i]) 287 ell = mpl.patches.Ellipse(mean, v[0], v[1], 180+angle, color=colors[i])
289 ell.set_clip_box(fig.bbox) 288 ell.set_clip_box(fig.bbox)
290 ell.set_alpha(alpha) 289 ell.set_alpha(alpha)
291 fig.axes[0].add_artist(ell) 290 fig.axes[0].add_artist(ell)
292 return labels