codes.py 文件源码

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

    Return 
    --------
    The mean squared error values are returned. 
    """
    # Transformed to array if they are list, np.mat
    X = np.array( X)
    y = np.array( y)
    # Later, assert can be used to define the size of X and y

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

    kf = cross_validation.LeaveOneOut( y.shape[0])
    # flatterned error vectors for each point are stored in this vector.
    errors_l = list()
    for tr, te in kf:
        lm.fit( X[tr,:], y[tr,:])
        yp = lm.predict( X[te, :])
        errors_l.extend( (y[te,:] - yp).flatten().tolist())

    return errors_l