def plot_mean_activity_3d(self, precision=20, axes=(0, 1), specific_neurons=None, weight_deform=0.5): ''' Plot the mean activity of the network on a sphere/torus ''' (mean_activity, feature_space1, feature_space2) = self.get_mean_activity(precision=precision, specific_neurons=specific_neurons, return_axes_vect=True, axes=axes) utils.plot_torus(feature_space1, feature_space2, mean_activity, weight_deform=weight_deform)
def plot_neuron_activity_3d(self, neuron_index=0, precision=20, axes=(0, 1), weight_deform=0.5, draw_colorbar=True): ''' Plot the activity of a neuron on the sphere/torus ''' coverage_1D = self.init_feature_space(precision) activity = self.get_neuron_activity(neuron_index, precision=precision, axes=axes) utils.plot_torus(coverage_1D, coverage_1D, activity, weight_deform=weight_deform, draw_colorbar=draw_colorbar)
n_neighbors=10, geod_n_neighbors=10, embedding_dim=3, verbose=True).fit() ptu_time = round(time() - t, 2) print('ptu time: ', ptu_time) # Perform Isomap t = time() iso = Isomap(n_neighbors=10, n_components=3).fit_transform(exact) isomap_time = round(time() - t, 2) print('isomap time: ', isomap_time) # Align PTU and Isomap to exact parametrization via best isometric # transformation, and compute errors ptu = align(ptu, exact) iso = align(iso, exact) ptu_error = relative_error(ptu, exact) iso_error = relative_error(iso, exact) print('ptu relative error: {}%'.format(ptu_error)) print('isomap relative error: {}%'.format(iso_error)) # Plot results f = plot_torus(exact, ptu, iso, ptu_time, isomap_time, hue='normalized_poinwise_error') plt.show()