b84b7df8bf23aee3cb98b0390aa69f7ee31fe30a,GPflow/model.py,GPModel,predict_density,#GPModel#Any#Any#,205

Before Change

        ignoring correlations between them. The result is a matrix the same
        shape as Ynew containing the log densities.
        
        tf_Xnew = tf.placeholder("float64")
        tf_Ynew = tf.placeholder("float64")
        with self.tf_mode():
            pred_f_mean, pred_f_var = self.build_predict(tf_Xnew)
            pred_y_density = self.likelihood.predict_density(pred_f_mean, pred_f_var, tf_Ynew)
        return self._session.run(pred_y_density,
                                 feed_dict={self._free_vars:self.get_free_state(),
                                            tf_Xnew:Xnew,
                                            tf_Ynew:Ynew })



            

After Change

        shape as Ynew containing the log densities.
        
        pred_f_mean, pred_f_var = self.build_predict(Xnew)
        return self.likelihood.predict_density(pred_f_mean, pred_f_var, Ynew)



            

Instances