metrics.py 文件源码

def quadratic_loss(covariance, precision):
    """Computes ...

    Parameters
    ----------
    covariance : 2D ndarray (n_features, n_features)
        Maximum Likelihood Estimator of covariance

    precision : 2D ndarray (n_features, n_features)
        The precision matrix of the model to be tested

    Returns
    -------
    Quadratic loss
    """
    assert covariance.shape == precision.shape
    dim, _ = precision.shape
    return np.trace((np.dot(covariance, precision) - np.eye(dim))**2)