def run_ica_EM(data, principal_components=1):
clf = FastICA(n_components=principal_components)
data = clf.fit_transform(data)
scores = []
for i in range(1, len(data[0]) * 2):
print(f'EM and ICA: with K = {i}')
clf = GaussianMixture(n_components=i)
clf.fit(data)
scores.append(clf.score(data))
return scores
epoch = 0
while epoch < N_EPOCHS:
print('epoch: ', epoch)
# SHUFFLING
random_perm = np.random.permutation(X_train.shape[0])
mini_batch_index = 0
while True:
# MINI-BATCH
indices = random_perm[mini_batch_index:mini_batch_index + N_BATCH]
clf.partial_fit(X_train[indices], y_train[indices], classes=N_CLASSES)
mini_batch_index += N_BATCH
if mini_batch_index >= N_TRAIN_SAMPLES:
break
# SCORE TRAIN
scores_train.append(clf.score(X_train, y_train))
# SCORE TEST
scores_test.append(clf.score(X_test, y_test))
epoch += 1
plt.figure()
plt.plot(scores_train, color='b', alpha=0.8, label='Train')
plt.plot(scores_test, color='r', alpha=0.8, label='Test')
plt.title("Credit Cards Neural Network Accuracy over epochs", fontsize=14)
plt.xlabel('Epochs')
plt.ylabel("Accuracy")
plt.legend(loc='upper left')
plt.savefig('charts/creditcards.neuralnetwork.ICA.accuracy.png')
