added precomputation of linear kernel, changed the logic a bit

GPy/kern/linear.py

@@ -21,6 +21,7 @@ class linear(kernpart):
         self.Nparam = 1
         self.name = 'linear'
         self.set_param(variance)
+        self._Xcache, self._X2cache = np.empty(shape=(2,))
 
     def get_param(self):
         return self.variance
@@ -32,7 +33,8 @@ class linear(kernpart):
         return ['variance']
 
     def K(self,X,X2,target):
-        target += self.variance * np.dot(X, X2.T)
+        self._K_computations(X, X2)
+        target += self.variance * self._dot_product
 
     def Kdiag(self,X,target):
         np.add(target,np.sum(self.variance*np.square(X),-1),target)
@@ -42,7 +44,9 @@ class linear(kernpart):
         Computes the derivatives wrt theta
         Return shape is NxMx(Ntheta)
         """
-        product = np.dot(X, X2.T)
+        self._K_computations(X, X2)
+        product = self._dot_product
+        # product = np.dot(X, X2.T)
         target += np.sum(product*partial)
 
     def dK_dX(self,partial,X,X2,target):
@@ -51,6 +55,20 @@ class linear(kernpart):
     def dKdiag_dtheta(self,partial,X,target):
         target += np.sum(partial*np.square(X).sum(1))
 
+    def _K_computations(self,X,X2):
+        # (Nicolo) changed the logic here. If X2 is None, we want to cache
+        # (X,X). In practice X2 should always be passed.
+        if X2 is None:
+            X2 = X
+        if not (np.all(X==self._Xcache) and np.all(X2==self._X2cache)):
+            self._Xcache = X
+            self._X2cache = X2
+            self._dot_product = np.dot(X,X2.T) 
+        else:
+            # print "Cache hit!"
+            pass # TODO: insert debug message here (logging framework)
+
+
     # def psi0(self,Z,mu,S,target):
     #     expected = np.square(mu) + S
     #     np.add(target,np.sum(self.variance*expected),target)