mappings, including tests

GPy/mappings/__init__.py

@@ -4,4 +4,5 @@
 from kernel import Kernel
 from linear import Linear
 from mlp import MLP
-#from rbf import RBF
+from additive import Additive
+from compound import Compound

GPy/mappings/additive.py

@@ -2,8 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from ..core.mapping import Mapping
+from ..core import Mapping
-import GPy
 
 class Additive(Mapping):
     """
@@ -27,8 +26,6 @@ class Additive(Mapping):
         Mapping.__init__(self, input_dim=input_dim, output_dim=output_dim)
         self.mapping1 = mapping1
         self.mapping2 = mapping2
-        self.num_params = self.mapping1.num_params + self.mapping2.num_params
-        self.name = self.mapping1.name + '+' + self.mapping2.name
 
     def f(self, X):
         return self.mapping1.f(X) + self.mapping2.f(X)

GPy/mappings/compound.py

@@ -0,0 +1,39 @@
+# Copyright (c) 2015, James Hensman and Alan Saul
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+from ..core import Mapping
+
+class Compound(Mapping):
+    """
+    Mapping based on passing one mapping through another
+
+    .. math::
+
+       f(\mathbf{x}) = f_2(f_1(\mathbf{x}))
+
+    :param mapping1: first mapping
+    :type mapping1: GPy.mappings.Mapping
+    :param mapping2: second mapping
+    :type mapping2: GPy.mappings.Mapping
+
+    """
+
+    def __init__(self, mapping1, mapping2):
+        assert(mapping1.output_dim==mapping2.input_dim)
+        input_dim, output_dim = mapping1.input_dim, mapping2.output_dim
+        Mapping.__init__(self, input_dim=input_dim, output_dim=output_dim)
+        self.mapping1 = mapping1
+        self.mapping2 = mapping2
+        self.link_parameters(self.mapping1, self.mapping2)
+
+    def f(self, X):
+        return self.mapping2.f(self.mapping1.f(X))
+
+    def update_gradients(self, dL_dF, X):
+        hidden = self.mapping1.f(X)
+        self.mapping2.update_gradients(dL_dF, hidden)
+        self.mapping1.update_gradients(self.mapping2.gradients_X(dL_dF, hidden), X)
+
+    def gradients_X(self, dL_dF, X):
+        hidden = self.mapping1.f(X)
+        return self.mapping1.gradients_X(self.mapping2.gradients_X(dL_dF, hidden), X)

GPy/mappings/kernel.py

@@ -36,16 +36,16 @@ class Kernel(Mapping):
         Mapping.__init__(self, input_dim=input_dim, output_dim=output_dim, name=name)
         self.kern = kernel
         self.Z = Z
-        self.num_bases, Zdim = X.shape
+        self.num_bases, Zdim = Z.shape
         assert Zdim == self.input_dim
-        self.A = GPy.core.Param('A', np.random.randn(self.num_bases, self.output_dim))
+        self.A = Param('A', np.random.randn(self.num_bases, self.output_dim))
-        self.add_parameter(self.A)
+        self.link_parameter(self.A)
 
     def f(self, X):
         return np.dot(self.kern.K(X, self.Z), self.A)
 
     def update_gradients(self, dL_dF, X):
-        self.kern.update_gradients_full(np.dot(dL_dF, self.A.T))
+        self.kern.update_gradients_full(np.dot(dL_dF, self.A.T), X, self.Z)
         self.A.gradient = np.dot( self.kern.K(self.Z, X), dL_dF)
 
     def gradients_X(self, dL_dF, X):

GPy/mappings/mlp.py

@@ -11,32 +11,45 @@ class MLP(Mapping):
     """
 
     def __init__(self, input_dim=1, output_dim=1, hidden_dim=3, name='mlpmap'):
-        super(MLP).__init__(self, input_dim=input_dim, output_dim=output_dim, name=name)
+        super(MLP, self).__init__(input_dim=input_dim, output_dim=output_dim, name=name)
         self.hidden_dim = hidden_dim
         self.W1 = Param('W1', np.random.randn(self.input_dim, self.hidden_dim))
         self.b1 = Param('b1', np.random.randn(self.hidden_dim))
         self.W2 = Param('W2', np.random.randn(self.hidden_dim, self.output_dim))
         self.b2 = Param('b2', np.random.randn(self.output_dim))
+        self.link_parameters(self.W1, self.b1, self.W2, self.b2)
 
 
     def f(self, X):
-        N, D = X.shape
+        layer1 = np.dot(X, self.W1) + self.b1
-        activations = np.tanh(np.dot(X,self.W1) + self.b1)
+        activations = np.tanh(layer1)
-        self.out = np.dot(self.activations,self.W2) + self.b2
+        return  np.dot(activations, self.W2) + self.b2
-        return self.output_fn(self.out)
 
     def update_gradients(self, dL_dF, X):
-        activations = np.tanh(np.dot(X,self.W1) + self.b1)
+        layer1 = np.dot(X,self.W1) + self.b1
-
+        activations = np.tanh(layer1)
 
         #Evaluate second-layer gradients.
         self.W2.gradient = np.dot(activations.T, dL_dF)
         self.b2.gradient = np.sum(dL_dF, 0)
 
         # Backpropagation to hidden layer.
-        delta_hid = np.dot(dL_dF, self.W2.T) * (1.0 - activations**2)
+        dL_dact = np.dot(dL_dF, self.W2.T)
+        dL_dlayer1 = dL_dact / np.square(np.cosh(layer1))
 
         # Finally, evaluate the first-layer gradients.
-        self.W1.gradients = np.dot(X.T,delta_hid)
+        self.W1.gradient = np.dot(X.T,dL_dlayer1)
-        self.b1.gradients = np.sum(delta_hid, 0)
+        self.b1.gradient = np.sum(dL_dlayer1, 0)
+
+    def gradients_X(self, dL_dF, X):
+        layer1 = np.dot(X,self.W1) + self.b1
+        activations = np.tanh(layer1)
+
+        # Backpropagation to hidden layer.
+        dL_dact = np.dot(dL_dF, self.W2.T)
+        dL_dlayer1 = dL_dact / np.square(np.cosh(layer1))
+        
+        return np.dot(dL_dlayer1, self.W1.T)
+
+
 

GPy/testing/mapping_tests.py

@@ -0,0 +1,72 @@
+# Copyright (c) 2012, 2013 GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+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 a weighted 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='map_grad_check'):
+        super(MappingGradChecker, self).__init__(name)
+        self.mapping = mapping
+        self.link_parameter(self.mapping)
+        self.X = GPy.core.Param('X',X)
+        self.link_parameter(self.X)
+        self.dL_dY = np.random.randn(self.X.shape[0], self.mapping.output_dim)
+    def log_likelihood(self):
+        return np.sum(self.mapping.f(self.X) * self.dL_dY)
+    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):
+
+    def test_kernelmapping(self):
+        X = np.random.randn(100,3)
+        Z = np.random.randn(10,3)
+        mapping = GPy.mappings.Kernel(3, 2, Z, GPy.kern.RBF(3))
+        self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
+
+    def test_linearmapping(self):
+        mapping = GPy.mappings.Linear(3, 2)
+        X = np.random.randn(100,3)
+        self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
+
+    def test_mlpmapping(self):
+        mapping = GPy.mappings.MLP(input_dim=3, hidden_dim=5, output_dim=2)
+        X = np.random.randn(100,3)
+        self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
+
+    def test_addmapping(self):
+        m1 = GPy.mappings.MLP(input_dim=3, hidden_dim=5, output_dim=2)
+        m2 = GPy.mappings.Linear(input_dim=3, output_dim=2)
+        mapping = GPy.mappings.Additive(m1, m2)
+        X = np.random.randn(100,3)
+        self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
+
+    def test_compoundmapping(self):
+        m1 = GPy.mappings.MLP(input_dim=3, hidden_dim=5, output_dim=2)
+        Z = np.random.randn(10,2)
+        m2 = GPy.mappings.Kernel(2, 4, Z, GPy.kern.RBF(2))
+        mapping = GPy.mappings.Compound(m1, m2)
+        X = np.random.randn(100,3)
+        self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
+
+
+
+
+if __name__ == "__main__":
+    print "Running unit tests, please be (very) patient..."
+    unittest.main()