From 0332fa14f89b6389d284c6cb2b1abb5371084a2c Mon Sep 17 00:00:00 2001
From: Max Zwiessele <ibinbei@gmail.com>
Date: Fri, 26 Apr 2013 17:17:36 +0100
Subject: [PATCH] tests ignored my nosetests (__test__ = False)

---
 GPy/testing/kern_psi_stat_tests.py | 91 ++++++++++++++++--------------
 1 file changed, 48 insertions(+), 43 deletions(-)

diff --git a/GPy/testing/kern_psi_stat_tests.py b/GPy/testing/kern_psi_stat_tests.py
index 6e79e50d..581de9be 100644
--- a/GPy/testing/kern_psi_stat_tests.py
+++ b/GPy/testing/kern_psi_stat_tests.py
@@ -8,6 +8,8 @@ import GPy
 import numpy as np
 import pylab
 
+__test__ = False
+
 class Test(unittest.TestCase):
     D = 9
     M = 5
@@ -30,49 +32,52 @@ class Test(unittest.TestCase):
         self.q_x_mean.shape = (1, self.D)
         self.q_x_variance.shape = (1, self.D)
 
-#     def test_psi0(self):
-#         for kern in self.kerns:
-#             psi0 = kern.psi0(self.Z, self.q_x_mean, self.q_x_variance)
-#             Kdiag = kern.Kdiag(self.q_x_samples)
-#             self.assertAlmostEqual(psi0, np.mean(Kdiag), 1)
-#             # print kern.parts[0].name, np.allclose(psi0, np.mean(Kdiag))
-#
-#     def test_psi1(self):
-#         for kern in self.kerns:
-#             Nsamples = 100
-#             psi1 = kern.psi1(self.Z, self.q_x_mean, self.q_x_variance)
-#             K_ = np.zeros((self.N, self.M))
-#             diffs = []
-#             for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
-#                 K = kern.K(q_x_sample_stripe, self.Z)
-#                 K_ += K
-#                 diffs.append(((psi1 - (K_ / (i + 1))) ** 2).mean())
-#             K_ /= self.Nsamples / Nsamples
-# #             pylab.figure("+".join([p.name for p in kern.parts]) + "psi1")
-# #             pylab.plot(diffs)
-#             self.assertTrue(np.allclose(psi1.flatten() , K.mean(0), rtol=1e-1))
-#
-#     def test_psi2(self):
-#         for kern in self.kerns:
-#             Nsamples = 100
-#             psi2 = kern.psi2(self.Z, self.q_x_mean, self.q_x_variance)
-#             K_ = np.zeros((self.M, self.M))
-#             diffs = []
-#             for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
-#                 K = kern.K(q_x_sample_stripe, self.Z)
-#                 K = (K[:, :, None] * K[:, None, :]).mean(0)
-#                 K_ += K
-#                 diffs.append(((psi2 - (K_ / (i + 1))) ** 2).mean())
-#             K_ /= self.Nsamples / Nsamples
-#             try:
-# #                 pylab.figure("+".join([p.name for p in kern.parts]) + "psi2")
-# #                 pylab.plot(diffs)
-#                 self.assertTrue(np.allclose(psi2.squeeze(), K_,
-#                                             rtol=1e-1, atol=.1),
-#                                 msg="{}: not matching".format("+".join([p.name for p in kern.parts])))
-#             except:
-#                 print "{}: not matching".format(kern.parts[0].name)
+    def test_psi0(self):
+        for kern in self.kerns:
+            psi0 = kern.psi0(self.Z, self.q_x_mean, self.q_x_variance)
+            Kdiag = kern.Kdiag(self.q_x_samples)
+            self.assertAlmostEqual(psi0, np.mean(Kdiag), 1)
+            # print kern.parts[0].name, np.allclose(psi0, np.mean(Kdiag))
+
+    def test_psi1(self):
+        for kern in self.kerns:
+            Nsamples = 100
+            psi1 = kern.psi1(self.Z, self.q_x_mean, self.q_x_variance)
+            K_ = np.zeros((self.N, self.M))
+            diffs = []
+            for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
+                K = kern.K(q_x_sample_stripe, self.Z)
+                K_ += K
+                diffs.append(((psi1 - (K_ / (i + 1))) ** 2).mean())
+            K_ /= self.Nsamples / Nsamples
+#             pylab.figure("+".join([p.name for p in kern.parts]) + "psi1")
+#             pylab.plot(diffs)
+            self.assertTrue(np.allclose(psi1.flatten() , K.mean(0), rtol=1e-1))
+
+    def test_psi2(self):
+        for kern in self.kerns:
+            Nsamples = 100
+            psi2 = kern.psi2(self.Z, self.q_x_mean, self.q_x_variance)
+            K_ = np.zeros((self.M, self.M))
+            diffs = []
+            for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
+                K = kern.K(q_x_sample_stripe, self.Z)
+                K = (K[:, :, None] * K[:, None, :]).mean(0)
+                K_ += K
+                diffs.append(((psi2 - (K_ / (i + 1))) ** 2).mean())
+            K_ /= self.Nsamples / Nsamples
+            try:
+#                 pylab.figure("+".join([p.name for p in kern.parts]) + "psi2")
+#                 pylab.plot(diffs)
+                self.assertTrue(np.allclose(psi2.squeeze(), K_,
+                                            rtol=1e-1, atol=.1),
+                                msg="{}: not matching".format("+".join([p.name for p in kern.parts])))
+            except:
+                print "{}: not matching".format(kern.parts[0].name)
 
 if __name__ == "__main__":
-    import sys;sys.argv = ['', 'Test.test_psi2']
+    import sys;sys.argv = ['',
+                           'Test.test_psi0',
+                           'Test.test_psi1',
+                           'Test.test_psi2']
     unittest.main()