From dc138aa97b54ddc9c2da6c610f25f4ab7af12907 Mon Sep 17 00:00:00 2001 From: Max Zwiessele Date: Mon, 11 May 2015 11:21:45 +0100 Subject: [PATCH] [predict] documentation --- GPy/core/gp.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/GPy/core/gp.py b/GPy/core/gp.py index 4b6231af..3acea54b 100644 --- a/GPy/core/gp.py +++ b/GPy/core/gp.py @@ -229,13 +229,14 @@ class GP(Model): :param Y_metadata: metadata about the predicting point to pass to the likelihood :param kern: The kernel to use for prediction (defaults to the model kern). this is useful for examining e.g. subprocesses. - :returns: (mean, var, lower_upper): + :returns: (mean, var): mean: posterior mean, a Numpy array, Nnew x self.input_dim var: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise - lower_upper: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.input_dim If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim. If self.input_dim == 1, the return shape is Nnew x Nnew. This is to allow for different normalizations of the output dimensions. + + Note: If you want the predictive quantiles (e.g. 95% confidence interval) use :py:func:~GPy.core.gp.GP.predict_quantiles. """ #predict the latent function values mu, var = self._raw_predict(Xnew, full_cov=full_cov, kern=kern) @@ -255,7 +256,7 @@ class GP(Model): :param quantiles: tuple of quantiles, default is (2.5, 97.5) which is the 95% interval :type quantiles: tuple :returns: list of quantiles for each X and predictive quantiles for interval combination - :rtype: [np.ndarray (Xnew x self.input_dim), np.ndarray (Xnew x self.input_dim)] + :rtype: [np.ndarray (Xnew x self.output_dim), np.ndarray (Xnew x self.output_dim)] """ m, v = self._raw_predict(X, full_cov=False) if self.normalizer is not None: