From 87af7e252594b49111ad211f537368d77b53e4e0 Mon Sep 17 00:00:00 2001
From: Max Zwiessele <ibinbei@gmail.com>
Date: Thu, 21 Apr 2016 12:31:00 +0100
Subject: [PATCH] [static] added fixed tests

---
 GPy/kern/src/static.py      | 12 ++++++------
 GPy/testing/kernel_tests.py |  9 ++++++---
 README.md                   |  4 ++--
 3 files changed, 14 insertions(+), 11 deletions(-)

diff --git a/GPy/kern/src/static.py b/GPy/kern/src/static.py
index 24099dbb..3ce0dc0a 100644
--- a/GPy/kern/src/static.py
+++ b/GPy/kern/src/static.py
@@ -195,15 +195,15 @@ class Fixed(Static):
 class Precomputed(Fixed):
     def __init__(self, input_dim, covariance_matrix, variance=1., active_dims=None, name='precomputed'):
         """
-        Class for precomputed kernels, indexed by X
-        
+        Class for precomputed kernels, indexed by columns in X
+
         Usage example:
-        
+
         import numpy as np
         from GPy.models import GPClassification
         from GPy.kern import Precomputed
         from sklearn.cross_validation import LeaveOneOut
-        
+
         n = 10
         d = 100
         X = np.arange(n).reshape((n,1))         # column vector of indices
@@ -211,14 +211,14 @@ class Precomputed(Fixed):
         X0 = np.random.randn(n,d)
         k = np.dot(X0,X0.T)
         kern = Precomputed(1,k)                 # k is a n x n covariance matrix
-        
+
         cv = LeaveOneOut(n)
         ypred = y.copy()
         for train, test in cv:
             m = GPClassification(X[train], y[train], kernel=kern)
             m.optimize()
             ypred[test] = 2*(m.predict(X[test])[0]>0.5)-1
-        
+
         :param input_dim: the number of input dimensions
         :type input_dim: int
         :param variance: the variance of the kernel
diff --git a/GPy/testing/kernel_tests.py b/GPy/testing/kernel_tests.py
index fa2cdc28..b834ba9f 100644
--- a/GPy/testing/kernel_tests.py
+++ b/GPy/testing/kernel_tests.py
@@ -2,11 +2,14 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import unittest
-import numpy as np
+from unittest.case import skip
+
 import GPy
 from GPy.core.parameterization.param import Param
+import numpy as np
+
 from ..util.config import config
-from unittest.case import skip
+
 
 verbose = 0
 
@@ -347,7 +350,7 @@ class KernelGradientTestsContinuous(unittest.TestCase):
         k = GPy.kern.StdPeriodic(self.D)
         k.randomize()
         self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
-        
+
     def test_Precomputed(self):
         Xall = np.concatenate([self.X, self.X2])
         cov = np.dot(Xall, Xall.T)
diff --git a/README.md b/README.md
index d0257bab..fceab117 100644
--- a/README.md
+++ b/README.md
@@ -41,10 +41,10 @@ Python 2.7, 3.4 and higher
 ## Citation
 
     @Misc{gpy2014,
-      author =   {{The GPy authors}},
+      author =   {{GPy}},
       title =    {{GPy}: A Gaussian process framework in python},
       howpublished = {\url{http://github.com/SheffieldML/GPy}},
-      year = {2012--2015}
+      year = {since 2012}
     }
 
 ### Pronounciation: