0b1b36a5a83e7f36faad969ee036ebee84698b2c,data/generate_arabic_voice_dataset.py,,,#,4

Before Change

    var_count = X[i].shape[-1]
    X_train[i, :, :var_count] = X[i]

X_train = (X_train - X_train.mean(axis=0)) / (X_train.std(axis=0) + 1e-8)

""" Load test set """

After Change

print("max number variables : ", max_nb_variables)
print("min number variables : ", min_nb_variables)

X_train = np.zeros((X.shape[0], X[0].shape[0], max_nb_variables))
y_train = y_data[(np.array(trainind)-1)] //////Train Labels

// pad ending with zeros to get numpy arrays
for i in range(X_train.shape[0]):
    var_count = X[i].shape[-1]
    X_train[i, :, :var_count] = X[i]

X_train_mean = X_train.mean()
X_train_std = X_train.std()
print("Train Mean +- std : ", X_train_mean, X_train_std)
X_train = (X_train - X_train_mean) / (X_train_std + 1e-8)

""" Load test set """

X = X_data[(np.array(testind)-1)] ////////Test Data

X_test = np.zeros((X.shape[0], X[0].shape[0], max_nb_variables))
y_test = y_data[(np.array(testind)-1)] ////////Test Labels​

// pad ending with zeros to get numpy arrays
for i in range(X_test.shape[0]):
    var_count = X[i].shape[-1]
    X_test[i, :, :var_count] = X[i]

X_test = (X_test - X_train_mean) / (X_train_std + 1e-8)

""" Save the datasets """
print("Train dataset : ", X_train.shape, y_train.shape)
print("Test dataset : ", X_test.shape, y_test.shape)

Instances