gpmc.py 文件源码

def _build_likelihood(self):
        """
        Construct a tf function to compute the likelihood of a general GP
        model.

            \log p(Y, V | theta).

        """
        K = self.kern.K(self.X)
        L = tf.cholesky(
            K + tf.eye(tf.shape(self.X)[0], dtype=settings.float_type) * settings.numerics.jitter_level)
        F = tf.matmul(L, self.V) + self.mean_function(self.X)

        return tf.reduce_sum(self.likelihood.logp(F, self.Y))