added polynomial kernel

GPy/kern/__init__.py

@@ -10,6 +10,7 @@ from _src.independent_outputs import IndependentOutputs, Hierarchical
 from _src.coregionalize import Coregionalize
 from _src.ssrbf import SSRBF # TODO: ZD: did you remove this?
 from _src.ODE_UY import ODE_UY
+from _src.poly import Poly
 #from _src.ODE_UYC import ODE_UYC ADD THIS FILE TO THE REPO!!
 #from _src.ODE_st import ODE_st
 # TODO: put this in an init file somewhere

GPy/kern/_src/poly.py

@@ -0,0 +1,42 @@
+# Copyright (c) 2014, James Hensman
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import numpy as np
+from kern import Kern
+from ...util.misc import param_to_array
+from ...core.parameterization import Param
+from ...core.parameterization.transformations import Logexp
+class Poly(Kern):
+    """
+    Polynomial kernel
+    """
+
+    def __init__(self, input_dim, variance=1., order=3., active_dims=None, name='poly'):
+        super(Poly, self).__init__(input_dim, active_dims, name)
+        self.variance = Param('variance', variance, Logexp())
+        self.add_parameter(self.variance)
+        self.order=order
+
+    def K(self, X, X2=None):
+        return (self._dot_product(X, X2) + 1.)**self.order * self.variance
+
+    def _dot_product(self, X, X2=None):
+        if X2 is None:
+            return np.dot(X, X.T)
+        else:
+            return np.dot(X, X2.T)
+
+    def Kdiag(self, X):
+        return self.variance*(np.square(X).sum(1) + 1.)**self.order
+
+    def update_gradients_full(self, dL_dK, X, X2=None):
+        self.variance.gradient = np.sum(dL_dK * (self._dot_product(X, X2) + 1.)**self.order)
+
+    def update_gradients_diag(self, dL_dKdiag, X):
+        raise NotImplementedError
+
+    def gradients_X(self, dL_dK, X, X2=None):
+        raise NotImplementedError
+
+    def gradients_X_diag(self, dL_dKdiag, X):
+        raise NotImplementedError