From af20bed7479b38a631ca8bb4f8da7ab3dbb56b43 Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Wed, 22 Jul 2015 18:32:12 +0100 Subject: [PATCH] Passing metadata --- GPy/core/gp.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/GPy/core/gp.py b/GPy/core/gp.py index 898c7b58..4bcf1957 100644 --- a/GPy/core/gp.py +++ b/GPy/core/gp.py @@ -244,7 +244,7 @@ class GP(Model): mu, var = self.normalizer.inverse_mean(mu), self.normalizer.inverse_variance(var) # now push through likelihood - mean, var = self.likelihood.predictive_values(mu, var, full_cov, Y_metadata) + mean, var = self.likelihood.predictive_values(mu, var, full_cov, Y_metadata=Y_metadata) return mean, var def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None): @@ -261,7 +261,7 @@ class GP(Model): m, v = self._raw_predict(X, full_cov=False) if self.normalizer is not None: m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v) - return self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata) + return self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata=Y_metadata) def predictive_gradients(self, Xnew): """ @@ -331,7 +331,7 @@ class GP(Model): :returns: Ysim: set of simulations, a Numpy array (N x samples). """ fsim = self.posterior_samples_f(X, size, full_cov=full_cov) - Ysim = self.likelihood.samples(fsim, Y_metadata) + Ysim = self.likelihood.samples(fsim, Y_metadata=Y_metadata) return Ysim def plot_f(self, plot_limits=None, which_data_rows='all',