a little work on mappings

GPy/core/mapping.py

@@ -74,72 +74,4 @@ class Bijective_mapping(Mapping):
         """Inverse mapping from output domain of the function to the inputs."""
         raise NotImplementedError
 
-from model import Model
-
-class Mapping_check_model(Model):
-    """
-    This is a dummy model class used as a base class for checking that the
-    gradients of a given mapping are implemented correctly. It enables
-    checkgradient() to be called independently on each mapping.
-    """
-    def __init__(self, mapping=None, dL_df=None, X=None):
-        num_samples = 20
-        if mapping==None:
-            mapping = GPy.mapping.linear(1, 1)
-        if X==None:
-            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()
-
-    def _get_param_names(self):
-        return self.mapping._get_param_names()
-
-    def _set_params(self, x):
-        self.mapping._set_params(x)
-
-    def log_likelihood(self):
-        return (self.dL_df*self.mapping.f(self.X)).sum()
-
-    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):
-        Mapping_check_model.__init__(self,mapping=mapping,dL_df=dL_df, X=X)
-
-    def _log_likelihood_gradients(self):
-        return self.mapping.df_dtheta(self.dL_df, self.X)
-
-
-class Mapping_check_df_dX(Mapping_check_model):
-    """This class allows gradient checks for the gradient of a mapping with respect to X. """
-    def __init__(self, mapping=None, dL_df=None, X=None):
-        Mapping_check_model.__init__(self,mapping=mapping,dL_df=dL_df, X=X)
-
-        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()
-
-    def _set_params(self, x):
-        self.X=x.reshape(self.X.shape)
 

GPy/old_tests/mapping_tests.py

@@ -5,6 +5,30 @@ import unittest
 import numpy as np
 import GPy
 
+class MappingGradChecker(GPy.core.Model):
+    """
+    This class has everything we need to check the gradient of a mapping. It
+    implement a simple likelihood which is the sum of the outputs of the
+    mapping. the gradients are checked against the parameters of the mapping
+    and the input.
+    """
+    def __init__(self, mapping, X, name):
+        super(MappingChecker).__init__(self, name)
+        self.mapping = mapping
+        self.add_parameter(self.mapping)
+        self.X = GPy.core.Param('X',X)
+        self.add_parameter(self.X)
+        self.dL_dY = np.ones((self.X.shape[0]. self.mapping.output_dim))
+    def log_likelihood(self):
+        return np.sum(self.mapping.f(X))
+    def parameters_changed(self):
+        self.X.gradient = self.mapping.gradients_X(self.dL_dY, self.X)
+        self.mapping.update_gradients(self.dL_dY, self.X)
+
+
+
+
+
 
 
 class MappingTests(unittest.TestCase):