gama += 0.3 cov, C, U, g = FCM.calculate_g_mat(train_data_no_label, num_of_clusters, m, max_iteration, gama) U_space = FCM.calculate_U(random_input, C, num_of_clusters, m, max_iteration) Y = FCM.calculate_Y(train_labels, num_of_classes) W, y = FCM.calculate_W(g, Y) # print(g) Y_test = FCM.calculate_Y(test_labels, num_of_classes) G_prime = FCM.calculate_g_mat_prime(test_data_no_label, C, U, cov, gama) y_test = FCM.calculate_y_test(G_prime, W) # print(U_space) # print(y) # print("Train Accuracy = " + str(FCM.accuracy(Y, y)) + "%" + # " Number of clusters: " + str(num_of_clusters)) print("Test Accuracy = " + str(FCM.accuracy(Y_test, y_test)) + "%" + " Number of clusters: " + str(num_of_clusters) + "Gama: " + str(gama)) # print(len(train_data_no_label), len(U)) FCM.draw_data(U, U_space, train_data_no_label, C, random_input, gama, max_iteration)