Added gpx dataset.

GPy/kern/__init__.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Copyright (c) 2012, 2013 GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 from constructors import *
@@ -6,4 +6,4 @@ try:
     from constructors import rbf_sympy, sympykern # these depend on sympy
 except:
     pass
-from kern import kern
+from kern import *

GPy/kern/kern.py

@@ -536,3 +536,86 @@ class kern(Parameterized):
         else:
             raise NotImplementedError, "Cannot plot a kernel with more than two input dimensions"
 
+from GPy.core.model import Model
+
+class Kern_check_model(Model):
+    """This is a dummy model class used as a base class for checking that the gradients of a given kernel are implemented correctly. It enables checkgradient() to be called independently on a kernel."""
+    def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
+        num_samples = 20
+        num_samples2 = 10
+        if kernel==None:
+            kernel = GPy.kern.rbf(1)
+        if X==None:
+            X = np.random.randn(num_samples, kernel.input_dim)
+        if X2==None:
+            X2 = np.random.randn(num_samples2, kernel.input_dim)
+        if dL_dK==None:
+            dL_dK = np.ones((X.shape[0], X2.shape[0]))
+        
+        self.kernel=kernel
+        self.X = X
+        self.X2 = X2
+        self.dL_dK = dL_dK
+        #self.constrained_indices=[]
+        #self.constraints=[]
+        Model.__init__(self)
+
+    def is_positive_definite(self):
+        v = np.linalg.eig(self.kernel.K(self.X))[0]
+        if any(v<0):
+            return False
+        else:
+            return True
+        
+    def _get_params(self):
+        return self.kernel._get_params()
+
+    def _get_param_names(self):
+        return self.kernel._get_param_names()
+
+    def _set_params(self, x):
+        self.kernel._set_params(x)
+
+    def log_likelihood(self):
+        return (self.dL_dK*self.kernel.K(self.X, self.X2)).sum()
+
+    def _log_likelihood_gradients(self):
+        raise NotImplementedError, "This needs to be implemented to use the kern_check_model class."
+    
+class Kern_check_dK_dtheta(Kern_check_model):
+    """This class allows gradient checks for the gradient of a kernel with respect to parameters. """
+    def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
+        Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
+
+    def _log_likelihood_gradients(self):
+        return self.kernel.dK_dtheta(self.dL_dK, self.X, self.X2)
+
+class Kern_check_dKdiag_dtheta(Kern_check_model):
+    """This class allows gradient checks of the gradient of the diagonal of a kernel with respect to the parameters."""
+    def __init__(self, kernel=None, dL_dK=None, X=None):
+        Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
+        if dL_dK==None:
+            self.dL_dK = np.ones((self.X.shape[0]))
+        
+    def log_likelihood(self):
+        return (self.dL_dK*self.kernel.Kdiag(self.X)).sum()
+
+    def _log_likelihood_gradients(self):
+        return self.kernel.dKdiag_dtheta(self.dL_dK, self.X)
+
+class Kern_check_dK_dX(Kern_check_model):
+    """This class allows gradient checks for the gradient of a kernel with respect to X. """
+    def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
+        Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
+
+    def _log_likelihood_gradients(self):
+        return self.kernel.dK_dX(self.dL_dK, self.X, self.X2).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)