Offset Kernel

GPy/kern/src/offset.py

@@ -0,0 +1,71 @@
+# Copyright (c) 2012 - 2014 the GPy Austhors (see AUTHORS.txt)
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+# Written by Mike Smith. michaeltsmith.org.uk
+
+from __future__ import division
+import numpy as np
+from .kern import Kern
+from ...core.parameterization import Param
+from paramz.transformations import Logexp
+import math
+
+class Offset(Kern):
+    """
+    A kernel in which subsets of the data can be shifted.
+    
+    :offsets list of offsets of length of number of subsets
+    """
+    def __init__(self, kernel, input_dim, offsets=[], index_dim=-1, active_dims=None, name='offset'):
+        super(Offset, self).__init__(input_dim, active_dims, name)
+        assert isinstance(index_dim, int), "Offset kernel is only defined with one input dimension being the index"
+        self.kern = kernel
+        
+        self.offsets = Param('offset', offsets)
+        self.link_parameter(self.offsets)
+        
+        self.offset_index_dim = index_dim
+        self.link_parameters(kernel) #maybe whole class should inherit from CombinationKernel?
+
+    def shift(self,X):
+        self.selected = np.array([int(x) for x in X[:,self.offset_index_dim]])
+        offsets = np.hstack([0.0,self.offsets.values])[:,None]
+        return X - offsets[self.selected]
+        
+    def K(self,X ,X2=None):
+        return self.kern.K(self.shift(X),X2)
+
+    def Kdiag(self,X):
+        return self.kern.Kdiag(self.shift(X))
+        
+        
+        
+    def dX_doffset(self,sel,delta):
+        #given the select array (sel) and the offsets (delta)
+        #finds dX/dDelta
+        #returns them as a 2d matrix, one row/col[?] for each offset (delta).
+
+        #a matrix G represents the effect of increasing the offset on the X_i passed to the kernel for each input. For example
+        #what effect will increasing offset 4 have on the kernel output of input 5? Answer: Gs[4,5]... (positive or zero)
+        Gs = []
+        for i,d in enumerate(delta):            
+            G = np.array(sel==(i+1))[:,None]*1
+            Gs.append(G)      
+        return np.array(Gs)*2.0
+            
+    def update_gradients_full(self,dL_dK,X,X2=None):
+        dL_dX = self.kern.gradients_X(dL_dK, self.shift(X), X)
+        dX_doff = self.dX_doffset(self.selected,self.offsets.values)
+        for i in range(len(dX_doff)):
+            dL_doff = dL_dX * dX_doff[i]
+            self.offsets.gradient[i] = -np.sum(dL_doff)
+        self.kern.update_gradients_full(dL_dK,self.shift(X),X2)
+        
+    def gradients_X(self,dL_dK, X, X2=None):
+        return self.kern.gradients_X(dL_dK,self.shift(X),X2)
+
+    def gradients_X_diag(self, dL_dKdiag, X):
+        return self.kern.gradients_X_diag(dL_dKdiag, self.shift(X))
+        
+    def update_gradients_diag(self, dL_dKdiag, X): #TODO What is this?
+        raise NotImplementedError("Not implemented Offset.update_gradients_diag")
+        #self.kern.update_gradients_diag(dL_dKdiag, self.shift(X))