From 5b1f7002389f4fb2fc4c9e75e32cfb26a4e7680d Mon Sep 17 00:00:00 2001
From: James Hensman <james.hensman@gmail.com>
Date: Fri, 22 Nov 2013 08:58:29 +0000
Subject: [PATCH] changed nasty whitespace

---
 GPy/core/mapping.py | 16 +++++++---------
 1 file changed, 7 insertions(+), 9 deletions(-)

diff --git a/GPy/core/mapping.py b/GPy/core/mapping.py
index 0da93c7c..5f517706 100644
--- a/GPy/core/mapping.py
+++ b/GPy/core/mapping.py
@@ -36,7 +36,6 @@ class Mapping(Parameterized):
 
     def df_dtheta(self, dL_df, X):
         """The gradient of the outputs of the multi-layer perceptron with respect to each of the parameters.
-        
         :param dL_df: gradient of the objective with respect to the function.
         :type dL_df: ndarray (num_data x output_dim)
         :param X: input locations where the function is evaluated.
@@ -44,14 +43,13 @@ class Mapping(Parameterized):
         :returns: Matrix containing gradients with respect to parameters of each output for each input data.
         :rtype: ndarray (num_params length)
         """
-        
         raise NotImplementedError
 
     def plot(self, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, samples=0, fignum=None, ax=None, fixed_inputs=[], linecol=Tango.colorsHex['darkBlue']):
         """
 
         Plot the mapping.
-        
+
         Plots the mapping associated with the model.
           - In one dimension, the function is plotted.
           - In two dimsensions, a contour-plot shows the function
@@ -110,7 +108,7 @@ class Mapping(Parameterized):
             for d in range(y.shape[1]):
                 ax.plot(Xnew, f[:, d], edgecol=linecol)
 
-        elif self.X.shape[1] == 2: 
+        elif self.X.shape[1] == 2:
             resolution = resolution or 50
             Xnew, _, _, xmin, xmax = x_frame2D(self.X, plot_limits, resolution)
             x, y = np.linspace(xmin[0], xmax[0], resolution), np.linspace(xmin[1], xmax[1], resolution)
@@ -135,14 +133,14 @@ class Mapping_check_model(Model):
             X = np.random.randn(num_samples, mapping.input_dim)
         if dL_df==None:
             dL_df = np.ones((num_samples, mapping.output_dim))
-        
+
         self.mapping=mapping
         self.X = X
         self.dL_df = dL_df
         self.num_params = self.mapping.num_params
         Model.__init__(self)
 
-        
+
     def _get_params(self):
         return self.mapping._get_params()
 
@@ -157,7 +155,7 @@ class Mapping_check_model(Model):
 
     def _log_likelihood_gradients(self):
         raise NotImplementedError, "This needs to be implemented to use the Mapping_check_model class."
-    
+
 class Mapping_check_df_dtheta(Mapping_check_model):
     """This class allows gradient checks for the gradient of a mapping with respect to parameters. """
     def __init__(self, mapping=None, dL_df=None, X=None):
@@ -175,13 +173,13 @@ class Mapping_check_df_dX(Mapping_check_model):
         if dL_df==None:
             dL_df = np.ones((self.X.shape[0],self.mapping.output_dim))
         self.num_params = self.X.shape[0]*self.mapping.input_dim
-        
+
     def _log_likelihood_gradients(self):
         return self.mapping.df_dX(self.dL_df, self.X).flatten()
 
     def _get_param_names(self):
         return ['X_'  +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])]
-                
+
     def _get_params(self):
         return self.X.flatten()