mv_gaussian.py 文件源码

def transform_features(x_train, x_test):
    """ Transform features using a boxcox transform. Remove vibrato features.
    Comptes the optimal value of lambda on the training set and applies this
    lambda to the testing set.

    Parameters
    ----------
    x_train : np.array [n_samples, n_features]
        Untransformed training features.
    x_test : np.array [n_samples, n_features]
        Untransformed testing features.

    Returns
    -------
    x_train_boxcox : np.array [n_samples, n_features_trans]
        Transformed training features.
    x_test_boxcox : np.array [n_samples, n_features_trans]
        Transformed testing features.
    """
    x_train = x_train[:, 0:6]
    x_test = x_test[:, 0:6]

    _, n_feats = x_train.shape

    x_train_boxcox = np.zeros(x_train.shape)
    lmbda_opt = np.zeros((n_feats,))

    eps = 1.0  # shift features away from zero
    for i in range(n_feats):
        x_train_boxcox[:, i], lmbda_opt[i] = boxcox(x_train[:, i] + eps)

    x_test_boxcox = np.zeros(x_test.shape)
    for i in range(n_feats):
        x_test_boxcox[:, i] = boxcox(x_test[:, i] + eps, lmbda=lmbda_opt[i])

    return x_train_boxcox, x_test_boxcox