From 283b0745aac0653ac18bc00821f4c8e7e59e6fa3 Mon Sep 17 00:00:00 2001
From: James Hensman <james.hensman@gmail.com>
Date: Thu, 27 Feb 2014 09:16:36 +0000
Subject: [PATCH] added some caching

---
 GPy/kern/_src/linear.py | 20 ++++++++++----------
 GPy/kern/_src/rbf.py    |  1 +
 2 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/GPy/kern/_src/linear.py b/GPy/kern/_src/linear.py
index 2c82eb7a..ab5030eb 100644
--- a/GPy/kern/_src/linear.py
+++ b/GPy/kern/_src/linear.py
@@ -49,13 +49,8 @@ class Linear(Kern):
 
         self.variances = Param('variances', variances, Logexp())
         self.add_parameter(self.variances)
-        self.variances.add_observer(self, self._on_changed)
 
-    def _on_changed(self, obj):
-        #TODO: move this to base class? isnt it jst for the caching?
-        self._notify_observers()
-
-    #@cache_this(limit=3, reset_on_self=True)
+    @Cache_this(limit=2)
     def K(self, X, X2=None):
         if self.ARD:
             if X2 is None:
@@ -66,7 +61,7 @@ class Linear(Kern):
         else:
             return self._dot_product(X, X2) * self.variances
 
-    #@cache_this(limit=3, reset_on_self=False)
+    @Cache_this(limit=1, ignore_args(0,))
     def _dot_product(self, X, X2=None):
         if X2 is None:
             return tdot(X)
@@ -113,6 +108,7 @@ class Linear(Kern):
     def psi1(self, Z, variational_posterior):
         return self.K(variational_posterior.mean, Z) #the variance, it does nothing
 
+    @Cache_this(limit=1)
     def psi2(self, Z, variational_posterior):
         ZA = Z * self.variances
         ZAinner = self._ZAinner(variational_posterior, Z)
@@ -126,9 +122,11 @@ class Linear(Kern):
         if self.ARD: self.variances.gradient += tmp.sum(0)
         else: self.variances.gradient += tmp.sum()
         #psi2
-        tmp = dL_dpsi2[:, :, :, None] * (self._ZAinner(variational_posterior, Z)[:, :, None, :] * (2. * Z)[None, None, :, :])
-        if self.ARD: self.variances.gradient += tmp.sum(0).sum(0).sum(0)
-        else: self.variances.gradient += tmp.sum()
+        if self.ARD:
+            tmp = dL_dpsi2[:, :, :, None] * (self._ZAinner(variational_posterior, Z)[:, :, None, :] * Z[None, None, :, :])
+            self.variances.gradient += 2.*tmp.sum(0).sum(0).sum(0)
+        else:
+            self.variances.gradient += 2.*np.sum(dL_dpsi2 * self.psi2(Z, variational_posterior))/self.variances
 
     def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
         #psi1
@@ -234,9 +232,11 @@ class Linear(Kern):
                      type_converters=weave.converters.blitz,**weave_options)
 
 
+    @Cache_this(limit=1, ignore_args=(0,))
     def _mu2S(self, vp):
         return np.square(vp.mean) + vp.variance
 
+    @Cache_this(limit=1)
     def _ZAinner(self, vp, Z):
         ZA = Z*self.variances
         inner = (vp.mean[:, None, :] * vp.mean[:, :, None])
diff --git a/GPy/kern/_src/rbf.py b/GPy/kern/_src/rbf.py
index 342b065e..cf5ea0c4 100644
--- a/GPy/kern/_src/rbf.py
+++ b/GPy/kern/_src/rbf.py
@@ -113,6 +113,7 @@ class RBF(Stationary):
     #            Precomputations            #
     #---------------------------------------#
 
+    #TODO: this function is unused, but it will be useful in the stationary class
     def _dL_dlengthscales_via_K(self, dL_dK, X, X2):
         """
         A helper function for update_gradients_* methods