codes.py 文件源码

def _cross_val_score_loo_r0( lm, X, y):
    """
    mean_square_error metric is used from sklearn.metric.

    Return 
    --------
    The mean squared error values are returned. 
    """

    if len( y.shape) == 1:
        y = np.array( [y]).T

    kf = cross_validation.LeaveOneOut( y.shape[0])
    score_l = list()
    for tr, te in kf:
        lm.fit( X[tr,:], y[tr,:])
        yp = lm.predict( X[te, :])
        score_l.append( metrics.mean_squared_error( y[te,:], yp))

    return score_l