a28284b9bd72fb5f180d489327e6e5571d9aa8d8,gpflow/models/gpr.py,GPR,log_likelihood,#GPR#,53

Before Change

        
        K = self.kernel(self.X)
        S = tf.eye(self.X.shape[0], dtype=self.X.dtype) * self.likelihood.variance
        L = tf.linalg.cholesky(K + S)
        m = self.mean_function(self.X)
        logpdf = multivariate_normal(self.Y, m, L)  // (R,) log-likelihoods for each independent dimension of Y

After Change

        k_diag = tf.linalg.diag_part(K)
        s_diag = tf.fill([num_data], self.likelihood.variance)
        ks = tf.linalg.set_diag(K, k_diag + s_diag)
        L = tf.linalg.cholesky(ks)
        m = self.mean_function(x)

        // [R,] log-likelihoods for each independent dimension of Y
        distr = tfp.distributions.MultivariateNormalTriL(loc=m, scale_tril=L)

Instances