From f46c72b79b752def2883143bcb90e1cb0394f0ee Mon Sep 17 00:00:00 2001
From: Neil Lawrence <lawrennd@gmail.com>
Date: Tue, 19 Nov 2013 06:50:25 +0000
Subject: [PATCH] Bug fix for single output sympy kernel.

---
 GPy/kern/parts/__init__.py  |  2 +-
 GPy/kern/parts/sympykern.py | 15 +++++++++++----
 GPy/util/datasets.py        | 15 +++++++++++++--
 3 files changed, 25 insertions(+), 7 deletions(-)

diff --git a/GPy/kern/parts/__init__.py b/GPy/kern/parts/__init__.py
index d8e7f8e6..f278941a 100644
--- a/GPy/kern/parts/__init__.py
+++ b/GPy/kern/parts/__init__.py
@@ -14,7 +14,7 @@ import Matern32
 import Matern52
 import mlp
 import ODE_1
-import ODE_UY
+#import ODE_UY
 import periodic_exponential
 import periodic_Matern32
 import periodic_Matern52
diff --git a/GPy/kern/parts/sympykern.py b/GPy/kern/parts/sympykern.py
index 88c179aa..7f7fba11 100644
--- a/GPy/kern/parts/sympykern.py
+++ b/GPy/kern/parts/sympykern.py
@@ -177,8 +177,15 @@ class spkern(Kernpart):
         # Code to compute argument string when only diagonal is required.
         diag_arg_string = re.sub('int jj','//int jj',X_arg_string)
         diag_arg_string = re.sub('j','i',diag_arg_string)
-        diag_precompute_string = precompute_list[0]
-
+        if precompute_string == '':
+            # if it's not multioutput, the precompute strings are set to zero
+            diag_precompute_string = ''
+            diag_precompute_replace = ''
+        else:
+            # for multioutput we need to extract the index of the output form the input.
+            diag_precompute_string = precompute_list[0]
+            diag_precompute_replace = precompute_list[1]
+        
 
         # Here's the code to do the looping for K
         self._K_code =\
@@ -215,13 +222,13 @@ class spkern(Kernpart):
             TARGET2(i, i) += k(%s);
             for (j=0;j<i;j++){
               %s //int jj=(int)X2(j, 1);
-              double kval = k(%s); //double kval = k(X2(i, 0), X2(j, 0), shared_lengthscale, LENGTHSCALE1(ii), SCALE1(ii), LENGTHSCALE1(jj), SCALE1(jj));
+              double kval = k(%s); //double kval = k(X2(i, 0), shared_lengthscale, LENGTHSCALE1(ii), SCALE1(ii));
               TARGET2(i, j) += kval;
               TARGET2(j, i) += kval;
             }
         }
         /*%s*/
-        """%(diag_precompute_string, diag_arg_string, re.sub('Z2', 'X2', precompute_list[1]), X_arg_string,str(self._sp_k)) #adding a string representation forces recompile when needed
+        """%(diag_precompute_string, diag_arg_string, re.sub('Z2', 'X2', diag_precompute_replace), X_arg_string,str(self._sp_k)) #adding a string representation forces recompile when needed
 
         # Code to do the looping for Kdiag
         self._Kdiag_code =\
diff --git a/GPy/util/datasets.py b/GPy/util/datasets.py
index 565f8e76..69f010f9 100644
--- a/GPy/util/datasets.py
+++ b/GPy/util/datasets.py
@@ -689,7 +689,7 @@ def olympic_marathon_men(data_set='olympic_marathon_men'):
     Y = olympics[:, 1:2]
     return data_details_return({'X': X, 'Y': Y}, data_set)
 
-def olympics():
+def olympic_sprints(data_set='rogers_girolami_data'):
     """All olympics sprint winning times for multiple output prediction."""
     X = np.zeros((0, 2))
     Y = np.zeros((0, 1))
@@ -707,7 +707,18 @@ def olympics():
     data['X'] = X
     data['Y'] = Y
     data['info'] = "Olympics sprint event winning for men and women to 2008. Data is from Rogers and Girolami's First Course in Machine Learning."
-    return data
+    return data_details_return({
+        'X': X,
+        'Y': Y,
+        'info': "Olympics sprint event winning for men and women to 2008. Data is from Rogers and Girolami's First Course in Machine Learning.",
+        'output_info': {
+          0:'100m Men', 
+          1:'100m Women', 
+          2:'200m Men', 
+          3:'200m Women', 
+          4:'400m Men', 
+          5:'400m Women'}
+        }, data_set)
 
 # def movielens_small(partNo=1,seed=default_seed):
 #     np.random.seed(seed=seed)