m = 2.5
max_iteration = 1000
num_of_clusters = 2
gama = 0.2
num_of_classes = 2
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: " +
