kgrid_r0.py 文件源码

def cv_LinearRegression_Bias( xM, yV):
    """
    N_it times iteration is performed for cross_validation in order to make further average effect. 
    The flag of 'disp' is truned off so each iteration will not shown.  
    """
    #print( "cv_LinearRegression_None", xM.shape, yV.shape)
    X, y = np.array( xM)[:,0], np.array( yV)[:,0]

    # only 1-dim is allowed for both X and y
    assert (X.ndim == 1) or (X.shape[2] == 1) and (yV.ndim == 1) or (yV.shape[2] == 1)

    loo_c = model_selection.LeaveOneOut()
    loo = loo_c.split( X)

    yP = y.copy()
    for train, test in loo:
        bias = np.mean(y[train] - X[train])
        yP[test] = X[test] + bias

    cv_score_le = np.abs( np.array( y - yP)).tolist()

    o_d = {'median_abs_err': np.median( cv_score_le),
           'mean_abs_err': np.mean( cv_score_le),
           'std_abs_err': np.std( cv_score_le), # this can be std(err)
           'list': cv_score_le,
           'ci': "t.b.d",
           'yVp': X.tolist()}

    return o_d