From 73f44ea0d82a2076dc1d4ec17362ee1801f0cd53 Mon Sep 17 00:00:00 2001
From: Max Zwiessele <ibinbei@gmail.com>
Date: Mon, 14 Oct 2013 18:00:11 +0100
Subject: [PATCH] more testing

---
 GPy/testing/parameter_testing.py | 48 +++++++++++++++++++-------------
 1 file changed, 29 insertions(+), 19 deletions(-)

diff --git a/GPy/testing/parameter_testing.py b/GPy/testing/parameter_testing.py
index 919920a9..46adb3ad 100644
--- a/GPy/testing/parameter_testing.py
+++ b/GPy/testing/parameter_testing.py
@@ -13,7 +13,7 @@ from GPy.likelihoods.gaussian import Gaussian
 
 
 class Test(unittest.TestCase):
-    N, D, Q = 100, 1, 2
+    N, D, Q = 10, 6, 4
     def setUp(self):
         self.rbf_variance = numpy.random.rand()
         self.rbf_lengthscale = numpy.random.rand(self.Q)
@@ -30,10 +30,9 @@ class Test(unittest.TestCase):
         self.Y = numpy.random.multivariate_normal(numpy.zeros(self.N), K + numpy.eye(self.N) * .2, self.D).T
         
         self.bgplvm = BayesianGPLVM(Gaussian(self.Y, variance=self.noise_variance), self.Q, self.X, self.X_variance, kernel=self.kern)
-        self.bgplvm.ensure_default_constraints()
+        self.bgplvm.ensure_default_constraints(warning=False)
         self.bgplvm.tie_params("noise_variance|white_variance")
-        self.bgplvm.constrain_fixed("rbf_var")
-        
+        self.bgplvm.constrain_fixed("rbf_var", warning=False)
         self.parameter = Parameterized([
                                     Parameterized([
                                                 Param('X', self.X),
@@ -48,15 +47,15 @@ class Test(unittest.TestCase):
                                     Param('white_variance', self.noise_variance),
                                     Param('noise_variance', self.noise_variance),
                                      ])
-        self.parameter['.*variance'].constrain_positive()
-        self.parameter['.*length'].constrain_positive()
+        
+        self.parameter['.*variance'].constrain_positive(False)
+        self.parameter['.*length'].constrain_positive(False)
         self.parameter.white.tie_to(self.parameter.noise)
-        self.parameter.rbf_var.constrain_fixed()
+        self.parameter.rbf_var.constrain_fixed(False)
 
     def tearDown(self):
         pass
 
-
 #     def testGrepParamNamesTest(self):
 #         assert(self.bgplvm.grep_param_names('X_\d') == self.parameter.grep_param_names('X_\d'))
 #         assert(self.bgplvm.grep_param_names('X_\d+_1') == self.parameter.grep_param_names('X_\d+_1'))
@@ -85,25 +84,35 @@ class Test(unittest.TestCase):
         assert(numpy.allclose(self.bgplvm._get_params_transformed(), self.parameter._get_params_transformed()))
 
     def testSlicing(self):
-        assert(numpy.alltrue(self.parameter.X[:,1] == self.X[:,1]))
-        assert(numpy.alltrue(self.parameter.X[:,1] == self.X[:,1]))
-        assert(numpy.alltrue(self.parameter.X_variance[1,1] == self.X_variance[1,1]))
-        import ipdb;ipdb.set_trace()
-        assert(numpy.alltrue(self.parameter.X_variance[:] == self.X_variance[:]))
-        assert(numpy.alltrue(self.parameter.X[:,:][:,0:2][:,1] == self.X[:,1]))
-        assert(numpy.alltrue(self.parameter.X[:,1] == self.X[:,1]))
-        assert(numpy.alltrue(self.parameter.X_variance[1,1] == self.X_variance[1,1]))
-        assert(numpy.alltrue(self.parameter.X_variance[:] == self.X_variance[:]))
+        assert(numpy.allclose(self.parameter.X[:,1], self.X[:,1]))
+        assert(numpy.allclose(self.parameter.X[:,1], self.X[:,1]))
+        assert(numpy.allclose(self.parameter.X_variance[1,1], self.X_variance[1,1]))
+        assert(numpy.allclose(self.parameter.X_variance[:], self.X_variance[:]))
+        assert(numpy.allclose(self.parameter.X[:,:][:,0:2][:,1], self.X[:,1]))
+        assert(numpy.allclose(self.parameter.X[:,1], self.X[:,1]))
+        assert(numpy.allclose(self.parameter.X_variance[1,1], self.X_variance[1,1]))
+        assert(numpy.allclose(self.parameter.X_variance[:], self.X_variance[:]))
 
+    def testSlicingSet(self):
+        self.parameter['.*variance'] = 1.
+        assert(numpy.alltrue(self.parameter['.*variance'] == 1.))
+        self.parameter.X[0,:3] = 2
+        assert(numpy.alltrue(self.parameter.X[0,:3] == 2))
+        X = self.parameter.X.copy()
+        self.parameter.X[[0,4,9],[0,1,3]] -= 1
+        assert(numpy.alltrue((X[[0,4,9],[0,1,3]] - 1) == self.parameter.X[[0,4,9],[0,1,3]]))
+        self.parameter[''] = 10
+        assert(numpy.alltrue(self.parameter[''] == 10))
+            
     def testConstraints(self):
         self.parameter[''].unconstrain()
         self.parameter.X.constrain_positive()
-        self.parameter.X[:,0].unconstrain_positive()
+        self.parameter.X[:,numpy.s_[0::2]].unconstrain_positive()
         assert(numpy.alltrue(self.parameter._constraints.indices()[0] == numpy.r_[1:self.N*self.Q:2]))
 
     def testNdarrayFunc(self):
         assert(numpy.alltrue(self.parameter.X * self.parameter.X == self.X * self.X))
-        assert(numpy.alltrue(self.parameter.X * self.parameter.X == self.X * self.X))
+        assert(numpy.alltrue(self.parameter.X[0,:] * self.parameter.X[1,:] == self.X[0,:] * self.X[1,:]))
         
     
 
@@ -115,5 +124,6 @@ if __name__ == "__main__":
                            'Test.testNdarrayFunc',
                            'Test.testSetParams',
                            'Test.testConstraints',
+                           'Test.testSlicingSet',
                            ]
     unittest.main()