tStudentProcess.py 文件源码

def predict(self, Xstar, return_std=False):
        """
        Returns mean and covariances for the posterior t-Student process.

        Parameters
        ----------
        Xstar: np.ndarray, shape=((nsamples, nfeatures))
            Testing instances to predict.
        return_std: bool
            Whether to return the standard deviation of the posterior process. Otherwise,
            it returns the whole covariance matrix of the posterior process.

        Returns
        -------
        np.ndarray
            Mean of the posterior process for testing instances.
        np.ndarray
            Covariance of the posterior process for testing instances.
        """
        Xstar = np.atleast_2d(Xstar)
        self.K21 = self.covfunc.K(Xstar, self.X)
        self.K22 = self.covfunc.K(Xstar, Xstar)
        self.K12 = self.covfunc.K(self.X, Xstar)
        self.K22_tilde = self.K22 - np.dot(np.dot(self.K21, inv(self.K11)), self.K12)

        phi2 = np.dot(np.dot(self.K21, inv(self.K11)), self.y)
        cov = (self.nu + self.beta1 - 2) / (self.nu + self.n1 - 2) * self.K22_tilde
        if return_std:
            return phi2, np.diag(cov)
        return phi2, cov