def predictive_values(self, mu, var, full_cov=False, Y_metadata=None):
try:<EOL><INDENT>pred_mean = self.predictive_mean(mu, var, Y_metadata=Y_metadata)<EOL>pred_var = self.predictive_variance(mu, var, pred_mean, Y_metadata=Y_metadata)<EOL><DEDENT>except NotImplementedError:<EOL><INDENT>print("<STR_LIT>")<EOL>Nf_samp = <NUM_LIT><EOL>Ny_samp = <NUM_LIT:1><EOL>s = np.random.randn(mu.shape[<NUM_LIT:0>], Nf_samp)*np.sqrt(var) + mu<EOL>ss_y = self.samples(s, Y_metadata, samples=Ny_samp)<EOL>pred_mean = np.mean(ss_y, axis=<NUM_LIT:1>)[:, None]<EOL>pred_var = np.var(ss_y, axis=<NUM_LIT:1>)[:, None]<EOL><DEDENT>return pred_mean, pred_var<EOL>
Compute  mean, variance of the  predictive distibution.

:param mu: mean of the latent variable, f, of posterior
:param var: variance of the latent variable, f, of posterior
:param full_cov: whether to use the full covariance or just the diagonal
:type full_cov: Boolean