From 976b27b654f991f396d76e88d117da1e1d55bbbf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Nicol=C3=B2=20Fusi?= <nicolo.fusi@gmail.com>
Date: Sun, 3 Feb 2013 15:00:53 +0000
Subject: [PATCH] working on linear kernel

---
 GPy/examples/BGPLVM_demo.py |  2 +-
 GPy/kern/kern.py            | 38 ++++++++++++++++++-------------------
 GPy/kern/linear.py          |  2 +-
 3 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/GPy/examples/BGPLVM_demo.py b/GPy/examples/BGPLVM_demo.py
index a5912462..18a96a47 100644
--- a/GPy/examples/BGPLVM_demo.py
+++ b/GPy/examples/BGPLVM_demo.py
@@ -17,7 +17,7 @@ K = k.K(X)
 Y = np.random.multivariate_normal(np.zeros(N),K,D).T
 
 # k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
-k = GPy.kern.linear(Q, ARD = False)  + GPy.kern.white(Q, 0.00001)
+k = GPy.kern.linear(Q, ARD = True)  + GPy.kern.white(Q)
 m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k,  M=M)
 m.constrain_positive('(rbf|bias|noise|white|S)')
 # m.constrain_fixed('S', 1)
diff --git a/GPy/kern/kern.py b/GPy/kern/kern.py
index 0433d1f4..6bfd224f 100644
--- a/GPy/kern/kern.py
+++ b/GPy/kern/kern.py
@@ -6,7 +6,7 @@ import numpy as np
 from ..core.parameterised import parameterised
 from kernpart import kernpart
 import itertools
-from product_orthogonal import product_orthogonal 
+from product_orthogonal import product_orthogonal
 
 class kern(parameterised):
     def __init__(self,D,parts=[], input_slices=None):
@@ -325,11 +325,11 @@ class kern(parameterised):
 
         # MASSIVE TODO: do something smart for white
         # "crossterms"
-        psi1_matrices = [np.zeros((mu.shape[0], Z.shape[0])) for p in self.parts]
-        [p.psi1(Z[s2],mu[s1],S[s1],psi1_target[s1,s2]) for p,s1,s2,psi1_target in zip(self.parts,slices1,slices2, psi1_matrices)]
-        for a,b in itertools.combinations(psi1_matrices, 2):
-            tmp = np.multiply(a,b)
-            target += tmp[:,None,:] + tmp[:, :,None]
+        # psi1_matrices = [np.zeros((mu.shape[0], Z.shape[0])) for p in self.parts]
+        # [p.psi1(Z[s2],mu[s1],S[s1],psi1_target[s1,s2]) for p,s1,s2,psi1_target in zip(self.parts,slices1,slices2, psi1_matrices)]
+        # for a,b in itertools.combinations(psi1_matrices, 2):
+        #     tmp = np.multiply(a,b)
+        #     target += tmp[:,None,:] + tmp[:, :,None]
 
         return target
 
@@ -340,21 +340,21 @@ class kern(parameterised):
         [p.dpsi2_dtheta(partial[s1,s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[ps]) for p,i_s,s1,s2,ps in zip(self.parts,self.input_slices,slices1,slices2,self.param_slices)]
 
 
-        # "crossterms"
-        # 1. get all the psi1 statistics
-        psi1_matrices = [np.zeros((mu.shape[0], Z.shape[0])) for p in self.parts]
-        [p.psi1(Z[s2],mu[s1],S[s1],psi1_target[s1,s2]) for p,s1,s2,psi1_target in zip(self.parts,slices1,slices2, psi1_matrices)]
-        partial1 = np.zeros_like(partial1)
+        # # "crossterms"
+        # # 1. get all the psi1 statistics
+        # psi1_matrices = [np.zeros((mu.shape[0], Z.shape[0])) for p in self.parts]
+        # [p.psi1(Z[s2],mu[s1],S[s1],psi1_target[s1,s2]) for p,s1,s2,psi1_target in zip(self.parts,slices1,slices2, psi1_matrices)]
+        # partial1 = np.zeros_like(partial1)
 
-        # 2. get all the dpsi1/dtheta gradients
-        psi1_gradients = [np.zeros(self.Nparam) for p in self.parts]
-        [p.dpsi1_dtheta(partial1[s2,s1],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],psi1g_target[ps]) for p,ps,s1,s2,i_s,psi1g_target in zip(self.parts, self.param_slices,slices1,slices2,self.input_slices,psi1_gradients)]
+        # # 2. get all the dpsi1/dtheta gradients
+        # psi1_gradients = [np.zeros(self.Nparam) for p in self.parts]
+        # [p.dpsi1_dtheta(partial1[s2,s1],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],psi1g_target[ps]) for p,ps,s1,s2,i_s,psi1g_target in zip(self.parts, self.param_slices,slices1,slices2,self.input_slices,psi1_gradients)]
 
-        # 3. multiply them somehow
-        for a,b in itertools.combinations(range(len(psi1_matrices)), 2):
-            gne = (psi1_gradients[a][None]*psi1_matrices[b].sum(0)[:,None]).sum(0)
-
-            target += (gne[None] + gne[:, None]).sum(0)
+        # # 3. multiply them somehow
+        # for a,b in itertools.combinations(range(len(psi1_matrices)), 2):
+        #     gne = (psi1_gradients[a][None]*psi1_matrices[b].sum(0)[:,None]).sum(0)
+        #     target += (gne[None] + gne[:, None]).sum(0)
+            
         return target
 
     def dpsi2_dZ(self,partial,Z,mu,S,slices1=None,slices2=None):
diff --git a/GPy/kern/linear.py b/GPy/kern/linear.py
index 52bc1757..7c8ba398 100644
--- a/GPy/kern/linear.py
+++ b/GPy/kern/linear.py
@@ -30,7 +30,7 @@ class linear(kernpart):
             if variances is not None:
                 if isinstance(variances, float):
                     variances = np.array([variances])
-                    
+
                 assert variances.shape == (1,)
             else:
                 variances = np.ones(1)