def update(self,i=0):
# Regenerate GMs
merged_gauss_2d = GaussianMixture(weights, means, covariances,
max_num_mixands=self.num_mixands)
matlab_merged_gauss_2d = self.parent.matlab_gm(weights,
means,
covariances,
max_num_mixands=self.num_mixands)
# Replot GMs
title = 'Python Merged GM ({} mixands)'.format(merged_gauss_2d.weights.size)
if hasattr(self,'old_contour'):
merged_gauss_2d.contourf = self.old_contour
merged_gauss_2d.plot_remove()
self.old_contour = merged_gauss_2d.plot(ax=self.axes[1], levels=self.levels, title=title)
title = 'Matlab Merged GM ({} mixands)'.format(matlab_merged_gauss_2d.weights.size)
if hasattr(self,'old_matlab_contour'):
matlab_merged_gauss_2d.contourf = self.old_matlab_contour
matlab_merged_gauss_2d.plot_remove()
self.old_matlab_contour = matlab_merged_gauss_2d.plot(ax=self.axes[2], levels=self.levels, title=title)
# Decrement mixands (with wrapping)
if self.num_mixands == self.max_num_mixands:
self.num_mixands = 1
elif np.int(self.num_mixands * self.mixand_rate) < self.max_num_mixands:
self.num_mixands = np.int(self.num_mixands * self.mixand_rate)
else:
self.num_mixands = self.max_num_mixands
def update(self, i=0):
# Regenerate GMs
merged_gauss_2d = GaussianMixture(
weights,
means,
covariances,
max_num_mixands=self.num_mixands)
matlab_merged_gauss_2d = self.parent.matlab_gm(
weights,
means,
covariances,
max_num_mixands=self.num_mixands)
# Replot GMs
title = 'Python Merged GM ({} mixands)'.format(
merged_gauss_2d.weights.size)
if hasattr(self, 'old_contour'):
merged_gauss_2d.contourf = self.old_contour
merged_gauss_2d.plot_remove()
self.old_contour = merged_gauss_2d.plot(ax=self.axes[1],
levels=self.levels,
title=title)
title = 'Matlab Merged GM ({} mixands)'.format(
matlab_merged_gauss_2d.weights.size)
if hasattr(self, 'old_matlab_contour'):
matlab_merged_gauss_2d.contourf = self.old_matlab_contour
matlab_merged_gauss_2d.plot_remove()
self.old_matlab_contour = matlab_merged_gauss_2d.plot(
ax=self.axes[2], levels=self.levels, title=title)
# Decrement mixands (with wrapping)
if self.num_mixands == self.max_num_mixands:
self.num_mixands = 1
elif np.int(self.num_mixands *
self.mixand_rate) < self.max_num_mixands:
self.num_mixands = np.int(self.num_mixands *
self.mixand_rate)
else:
self.num_mixands = self.max_num_mixands
