GaussianProcess.py 文件源码

def predict(self, Xstar, return_std=False):
        """
        Returns mean and covariances for the posterior Gaussian 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)
        kstar = self.covfunc.K(self.X, Xstar).T
        fmean = self.mprior + np.dot(kstar, self.alpha)
        v = solve(self.L, kstar.T)
        fcov = self.covfunc.K(Xstar, Xstar) - np.dot(v.T, v)
        if return_std:
            fcov = np.diag(fcov)
        return fmean, fcov