kcellml.py 文件源码

def get_train_test( X, pca_order = 10):
    X = X.astype('float32')

    scaler = MinMaxScaler(feature_range=(0, 1))
    X = scaler.fit_transform(X.reshape(-1,1)).reshape( X.shape)

    if pca_order > 0:
        pca = PCA(3)
        X = pca.fit_transform(X)
        X = pca.inverse_transform(X)   

    n_samples = X.shape[0]
    train_size = int(n_samples * 0.67)
    test_size = n_samples - train_size
    train, test = X[0:train_size,:], X[train_size:n_samples,:]
    return train, test, scaler