tStudentProcess.py 文件源码

def logpdf(x, df, mu, Sigma):
    """
    Marginal log-likelihood of a Student-t Process

    Parameters
    ----------
    x: array-like
        Point to be evaluated
    df: float
        Degrees of freedom (>2.0)
    mu: array-like
        Mean of the process.
    Sigma: array-like
        Covariance matrix of the process.

    Returns
    -------
    logp: float
        log-likelihood 

    """
    d = len(x)
    x = np.atleast_2d(x)
    xm = x - mu
    V = df * Sigma
    V_inv = np.linalg.inv(V)
    _, logdet = slogdet(np.pi * V)

    logz = -gamma(df / 2.0 + d / 2.0) + gamma(df / 2.0) + 0.5 * logdet
    logp = -0.5 * (df + d) * np.log(1 + np.sum(np.dot(xm, V_inv) * xm, axis=1))

    logp = logp - logz

    return logp[0]