#test gradient g = Gradient() M_ini = g.generate_I(newX.shape[1]) M = g.sgd_metric_learning(newX, newY, 0.002, 50000, 0, M_ini) # Calculate distance m = Metrics() X = u.select_features(train_facile['X'],feat_idx) X -= X.mean(axis=0) X /= X.std(axis=0) X[np.isnan(X)] = 0. dist = m.mahalanobis_dist(X, pairs_idx,M) #dist[np.isnan(dist)] = 50. ## Evaluate model e = Evaluate() e.evaluation(pairs_label,dist) ## display results e.display_roc() e.easy_score() # Evaluate test dataset and save it test_facile = u.load_matrix('data/data_test_facile.mat') #X2 = u.select_features(test_facile['X'],feat_idx) #X2 -= X2.mean(axis=0) #X2 /= X2.std(axis=0) #X2[np.isnan(X2)] = 0. #dist_test = m.mahalanobis_dist(X2, test_facile['pairs'],M) #dist_test[np.isnan(dist_test)] = 1. #u.save_test(dist_test)