From 10608a45656ad61aa34ecd3197c716c11640cb67 Mon Sep 17 00:00:00 2001 From: Max Zwiessele Date: Tue, 11 Mar 2014 10:25:21 +0000 Subject: [PATCH] empty spaces --- GPy/models/sparse_gp_regression.py | 4 ++-- GPy/plotting/matplot_dep/models_plots.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/GPy/models/sparse_gp_regression.py b/GPy/models/sparse_gp_regression.py index 99176601..7edb93e4 100644 --- a/GPy/models/sparse_gp_regression.py +++ b/GPy/models/sparse_gp_regression.py @@ -45,10 +45,10 @@ class SparseGPRegression(SparseGP): assert Z.shape[1] == input_dim likelihood = likelihoods.Gaussian() - + if not (X_variance is None): X = NormalPosterior(X,X_variance) - + SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC()) def _getstate(self): diff --git a/GPy/plotting/matplot_dep/models_plots.py b/GPy/plotting/matplot_dep/models_plots.py index 4ca4441e..86777527 100644 --- a/GPy/plotting/matplot_dep/models_plots.py +++ b/GPy/plotting/matplot_dep/models_plots.py @@ -56,7 +56,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all', if ax is None: fig = pb.figure(num=fignum) ax = fig.add_subplot(111) - + if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs(): X = model.X.mean X_variance = param_to_array(model.X.variance)