From 534e0df6066c50893991f94ebc8f71b8e7fe81de Mon Sep 17 00:00:00 2001
From: James Hensman <james.hensman@gmail.com>
Date: Tue, 24 Mar 2015 14:11:50 +0000
Subject: [PATCH] some tests for the svgp, and some changes to the likelihoods

---
 GPy/likelihoods/bernoulli.py |  2 +-
 GPy/likelihoods/student_t.py |  4 ++--
 GPy/testing/svgp_tests.py    | 34 ++++++++++++++++++++++++++++++++++
 3 files changed, 37 insertions(+), 3 deletions(-)
 create mode 100644 GPy/testing/svgp_tests.py

diff --git a/GPy/likelihoods/bernoulli.py b/GPy/likelihoods/bernoulli.py
index 26de274b..f5690aa4 100644
--- a/GPy/likelihoods/bernoulli.py
+++ b/GPy/likelihoods/bernoulli.py
@@ -77,7 +77,7 @@ class Bernoulli(Likelihood):
 
         return Z_hat, mu_hat, sigma2_hat
 
-    def variational_expectations(self, Y, m, v, gh_points=None):
+    def variational_expectations(self, Y, m, v, gh_points=None, Y_metadata=None):
         if isinstance(self.gp_link, link_functions.Probit):
 
             if gh_points is None:
diff --git a/GPy/likelihoods/student_t.py b/GPy/likelihoods/student_t.py
index c805d1dd..97c2286e 100644
--- a/GPy/likelihoods/student_t.py
+++ b/GPy/likelihoods/student_t.py
@@ -35,8 +35,8 @@ class StudentT(Likelihood):
 
         self.log_concave = False
 
-    def parameters_changed(self):
-        self.variance = (self.v / float(self.v - 2)) * self.sigma2
+    #def parameters_changed(self):
+        #self.variance = (self.v / float(self.v - 2)) * self.sigma2
 
     def update_gradients(self, grads):
         """
diff --git a/GPy/testing/svgp_tests.py b/GPy/testing/svgp_tests.py
new file mode 100644
index 00000000..6dc0fa56
--- /dev/null
+++ b/GPy/testing/svgp_tests.py
@@ -0,0 +1,34 @@
+import numpy as np
+import scipy as sp
+import GPy
+
+class SVGP_nonconvex(np.testing.TestCase):
+    """
+    Inference in the SVGP with a student-T likelihood
+    """
+    def setUp(self):
+        X = np.linspace(0,10,100).reshape(-1,1)
+        Z = np.linspace(0,10,10).reshape(-1,1)
+        Y = np.sin(X) + np.random.randn(*X.shape)*0.1
+        Y[50] += 3
+
+        lik = GPy.likelihoods.StudentT(deg_free=2)
+        k = GPy.kern.RBF(1, lengthscale=5.) + GPy.kern.White(1, 1e-6)
+        self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k)
+    def test_grad(self):
+        assert self.m.checkgrad(step=1e-4)
+
+class SVGP_classification(np.testing.TestCase):
+    """
+    Inference in the SVGP with a Bernoulli likelihood
+    """
+    def setUp(self):
+        X = np.linspace(0,10,100).reshape(-1,1)
+        Z = np.linspace(0,10,10).reshape(-1,1)
+        Y = np.where((np.sin(X) + np.random.randn(*X.shape)*0.1)>0, 1,0)
+
+        lik = GPy.likelihoods.Bernoulli()
+        k = GPy.kern.RBF(1, lengthscale=5.) + GPy.kern.White(1, 1e-6)
+        self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k)
+    def test_grad(self):
+        assert self.m.checkgrad(step=1e-4)