kerns

GPy/kern/white.py

@@ -0,0 +1,82 @@
+from kernpart import kernpart
+import numpy as np
+class white(kernpart):
+    """
+    White noise kernel.
+
+    :param D: the number of input dimensions
+    :type D: int
+    :param variance:
+    :type variance: float
+    """
+    def __init__(self,D,variance=1.):
+        self.D = D
+        self.Nparam = 1.
+        self.name = 'white'
+        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 X.shape==X2.shape:
+            if np.all(X==X2):
+                np.add(target,np.eye(X.shape[0])*self.variance,target)
+
+    def Kdiag(self,X,target):
+        target += self.variance
+
+    def dK_dtheta(self,X,X2,target):
+        if X.shape==X2.shape:
+            if np.all(X==X2):
+                np.add(target[:,:,0],np.eye(X.shape[0]),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
+        
+    def psi0(self,Z,mu,S,target):
+        target += self.variance
+
+    def dpsi0_dtheta(self,Z,mu,S,target):
+        target += 1.
+
+    def dpsi0_dmuS(self,Z,mu,S,target_mu,target_S):
+        pass
+
+    def psi1(self,Z,mu,S,target):
+        pass
+
+    def dpsi1_dtheta(self,Z,mu,S,target):
+        pass
+
+    def dpsi1_dZ(self,Z,mu,S,target):
+        pass
+
+    def dpsi1_dmuS(self,Z,mu,S,target_mu,target_S):
+        pass
+
+    def psi2(self,Z,mu,S,target):
+        pass
+
+    def dpsi2_dZ(self,Z,mu,S,target):
+        pass
+
+    def dpsi2_dtheta(self,Z,mu,S,target):
+        pass
+
+    def dpsi2_dmuS(self,Z,mu,S,target_mu,target_S):
+        pass
+