gradient checker implemented

GPy/models/__init__.py

@@ -12,3 +12,4 @@ from sparse_gplvm import SparseGPLVM
 from warped_gp import WarpedGP
 from bayesian_gplvm import BayesianGPLVM
 from mrd import MRD
+from gradient_checker import GradientChecker

GPy/models/gradient_checker.py

@@ -37,10 +37,29 @@ class GradientChecker(Model):
             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:
+        ---------
+        
+            Sinusoid:
+            
+                X = numpy.random.rand(N, Q)
+                f = lambda x: numpy.sin(x)
+                df = lambda x: numpy.cos(x)
+                grad = gc.GradientChecker(f,df,X,'x')
+    
+            Using GPy:
+            
+                N, M, Q = 10, 5, 3
+                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)
+                import GPy.models.gradient_checker as gc
+                grad = gc.GradientChecker(kern.K, 
+                                          lambda x: 2*kern.dK_dX(numpy.ones((1,1)), x),
+                                          x0 = X.copy(),
+                                          names='X')        
         """
         Model.__init__(self)
-        self.f = f
-        self.df = df
         if isinstance(x0, (list, tuple)) and names is None:
             self.shapes = [get_shape(xi) for xi in x0]
             self.names = ['X{i}'.format(i=i) for i in range(len(x0))]
@@ -60,17 +79,19 @@ 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
 
     def _get_x(self):
         if len(self.names) > 1:
-            return [self.__getattribute__(name) for name in self.names]
-        return self.__getattribute__(self.names[0])
+            return [self.__getattribute__(name) for name in self.names] + list(self.args)
+        return [self.__getattribute__(self.names[0])] + list(self.args)
 
     def log_likelihood(self):
-        return numpy.atleast_1d(self.f(self._get_x(), *self.args, **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.args, **self.kwargs))
+        return numpy.atleast_1d(self.df(*self._get_x(), **self.kwargs)).flatten()
 
 
     def _get_params(self):
@@ -86,4 +107,3 @@ class GradientChecker(Model):
 
     def _get_param_names(self):
         return self._param_names
-