From 64e65b846d8b7eafc1abe66d735a4dbf2dfa540c Mon Sep 17 00:00:00 2001
From: Alan Saul <alan.daniel.saul@gmail.com>
Date: Wed, 11 Sep 2013 11:54:47 +0100
Subject: [PATCH] Modified gradient_checker to allow for variable 'f'

---
 GPy/models/gradient_checker.py | 30 +++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/GPy/models/gradient_checker.py b/GPy/models/gradient_checker.py
index 5afcd7c4..face9589 100644
--- a/GPy/models/gradient_checker.py
+++ b/GPy/models/gradient_checker.py
@@ -26,40 +26,40 @@ class GradientChecker(Model):
         """
         :param f: Function to check gradient for
         :param df: Gradient of function to check
-        :param x0: 
+        :param x0:
             Initial guess for inputs x (if it has a shape (a,b) this will be reflected in the parameter names).
-            Can be a list of arrays, if takes a list of arrays. This list will be passed 
+            Can be a list of arrays, if takes a list of arrays. This list will be passed
             to f and df in the same order as given here.
             If only one argument, make sure not to pass a list!!!
-            
+
         :type x0: [array-like] | array-like | float | int
         :param names:
             Names to print, when performing gradcheck. If a list was passed to x0
             a list of names with the same length is expected.
         :param args: Arguments passed as f(x, *args, **kwargs) and df(x, *args, **kwargs)
-        
+
         Examples:
         ---------
             from GPy.models import GradientChecker
             N, M, Q = 10, 5, 3
-        
+
             Sinusoid:
-            
+
                 X = numpy.random.rand(N, Q)
                 grad = GradientChecker(numpy.sin,numpy.cos,X,'x')
                 grad.checkgrad(verbose=1)
-    
+
             Using GPy:
-            
+
                 X, Z = numpy.random.randn(N,Q), numpy.random.randn(M,Q)
                 kern = GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True)
-                grad = GradientChecker(kern.K, 
+                grad = GradientChecker(kern.K,
                                        lambda x: 2*kern.dK_dX(numpy.ones((1,1)), x),
                                        x0 = X.copy(),
-                                       names='X')  
+                                       names='X')
                 grad.checkgrad(verbose=1)
                 grad.randomize()
-                grad.checkgrad(verbose=1)      
+                grad.checkgrad(verbose=1)
         """
         Model.__init__(self)
         if isinstance(x0, (list, tuple)) and names is None:
@@ -81,8 +81,8 @@ class GradientChecker(Model):
 #             self._param_names.extend(map(lambda nameshape: ('_'.join(nameshape)).strip('_'), itertools.izip(itertools.repeat(name), itertools.imap(lambda t: '_'.join(map(str, t)), itertools.product(*map(lambda xi: range(xi), shape))))))
         self.args = args
         self.kwargs = kwargs
-        self.f = f
-        self.df = df
+        self._f = f
+        self._df = df
 
     def _get_x(self):
         if len(self.names) > 1:
@@ -90,10 +90,10 @@ class GradientChecker(Model):
         return [self.__getattribute__(self.names[0])] + list(self.args)
 
     def log_likelihood(self):
-        return float(numpy.sum(self.f(*self._get_x(), **self.kwargs)))
+        return float(numpy.sum(self._f(*self._get_x(), **self.kwargs)))
 
     def _log_likelihood_gradients(self):
-        return numpy.atleast_1d(self.df(*self._get_x(), **self.kwargs)).flatten()
+        return numpy.atleast_1d(self._df(*self._get_x(), **self.kwargs)).flatten()
 
 
     def _get_params(self):