def visualize_spe(features, labels): from modshogun import StochasticProximityEmbedding, SPE_GLOBAL converter = StochasticProximityEmbedding() converter.set_strategy(SPE_GLOBAL) converter.set_k(10) converter.set_target_dim(2) converter.set_nupdates(40) embedding = converter.embed(features) plot_data(embedding.get_feature_matrix(), labels.get_labels()) pyplot.show()
def visualize_spe(features,labels): from modshogun import StochasticProximityEmbedding, SPE_GLOBAL converter = StochasticProximityEmbedding() converter.set_strategy(SPE_GLOBAL) converter.set_k(10) converter.set_target_dim(2) converter.set_nupdates(40); embedding = converter.embed(features) plot_data(embedding.get_feature_matrix(),labels.get_labels()) pyplot.show()
def converter_stochasticproximityembedding_modular (data_fname, k): try: from modshogun import RealFeatures,StochasticProximityEmbedding, SPE_GLOBAL, SPE_LOCAL, CSVFile features = RealFeatures(CSVFile(data_fname)) converter = StochasticProximityEmbedding() converter.set_target_dim(1) converter.set_nupdates(40) # Embed with local strategy converter.set_k(k) converter.set_strategy(SPE_LOCAL) converter.embed(features) # Embed with global strategy converter.set_strategy(SPE_GLOBAL) converter.embed(features) return features except ImportError: print('No Eigen3 available')
# Compute SPE embedding embedding = converter.embed(features) X = embedding.get_feature_matrix() fig.add_subplot(2, 2, 2) pylab.plot(X[0, y1], X[1, y1], 'ro') pylab.plot(X[0, y2], X[1, y2], 'go') pylab.title('SPE with global strategy') # Compute a second SPE embedding with local strategy converter.set_strategy(SPE_LOCAL) converter.set_k(12) embedding = converter.embed(features) X = embedding.get_feature_matrix() fig.add_subplot(2, 2, 3) pylab.plot(X[0, y1], X[1, y1], 'ro') pylab.plot(X[0, y2], X[1, y2], 'go') pylab.title('SPE with local strategy') # Compute Isomap embedding (for comparison) converter = Isomap() converter.set_target_dim(2) converter.set_k(6)