kerns

GPy/kern/bias.py

@@ -0,0 +1,47 @@
+from kernpart import kernpart
+import numpy as np
+import hashlib
+
+class bias(kernpart):
+    def __init__(self,D,variance=1.):
+        """
+        Arguments
+        ----------
+        D: int - the number of input dimensions
+        variance: float
+        """
+        self.D = D
+        self.Nparam = 1
+        self.name = 'bias'
+        self.set_param(np.array([variance]).flatten())
+
+    def get_param(self):
+        return self.variance
+
+    def set_param(self,x):
+        assert x.shape==(1,)
+        self.variance = x
+
+    def get_param_names(self):
+        return ['variance']
+
+    def K(self,X,X2,target):
+        if X2 is None: X2 = X
+        np.add(self.variance, target,target)
+
+    def Kdiag(self,X,target):
+        np.add(target,self.variance,target)
+
+    def dK_dtheta(self,X,X2,target):
+        """Return shape is NxMx(Ntheta)"""
+        if X2 is None: X2 = X
+        np.add(target[:,:,0],1., target[:,:,0])
+
+    def dKdiag_dtheta(self,X,target):
+        np.add(target[:,0],1.,target[:,0])
+
+    def dK_dX(self, X, X2, target):
+        pass
+
+    def dKdiag_dX(self,X,target):
+        pass