print(encoder_values)
encoder_labels = train[0].copy()
dev_encoder_values = dev[0].copy()
dev_encoder_labels = dev[0].copy()
x, y = encoder_values.shape
for i in range(0, x):
encoder_values[i][random.randint(0, y - 1)] = 0
x, y = dev_encoder_values.shape
for i in range(0, x):
dev_encoder_values[i][random.randint(0, y - 1)] = 0
Autoencoder = ANN.AutoEncoder()
Autoencoder.useNetwork((encoder_values, encoder_labels),
(dev_encoder_values, dev_encoder_labels))
SVM = ANN.SVM()
SVM.useNetwork(train, dev, patience=100)
mlp = ANN.MLP()
mlp.useNetwork(train, dev)
GAN = ANN.GAN()
GAN.useNetwork(train, dev)
normalized_dataset = Data.normalize(dataset)
train, dev, val = Data.dataSeparation(normalized_dataset)
train = Data.to_numpy(train)
dev = Data.to_numpy(dev)
val = Data.to_numpy(val)
