Added binomial derivative and test

GPy/testing/likelihood_tests.py

@@ -117,7 +117,10 @@ class TestNoiseModels(object):
         self.positive_Y = np.exp(self.Y.copy())
         tmp = np.round(self.X[:, 0]*3-3)[:, None] + np.random.randint(0,3, self.X.shape[0])[:, None]
         self.integer_Y = np.where(tmp > 0, tmp, 0)
-
+        self.ns = np.random.poisson(50, size=self.N)[:, None]
+        p = np.abs(np.cos(2*np.pi*self.X + np.random.normal(scale=.2, size=(self.N, self.D)))).mean(1)
+        self.binomial_Y = np.array([np.random.binomial(self.ns[i], p[i]) for i in range(p.shape[0])])[:, None]
+        
         self.var = 0.2
         self.deg_free = 4.0
 
@@ -204,15 +207,6 @@ class TestNoiseModels(object):
                 },
                 "laplace": True
             },
-            #"Student_t_log": {
-            #"model": GPy.likelihoods.StudentT(gp_link=link_functions.Log(), deg_free=5, sigma2=self.var),
-            #"grad_params": {
-            #"names": [".*t_noise"],
-            #"vals": [self.var],
-            #"constraints": [(".*t_noise", self.constrain_positive), (".*deg_free", self.constrain_fixed)]
-            #},
-            #"laplace": True
-            #},
             "Gaussian_default": {
                 "model": GPy.likelihoods.Gaussian(variance=self.var),
                 "grad_params": {
@@ -273,6 +267,13 @@ class TestNoiseModels(object):
                 "laplace": True,
                 "ep": False #Should work though...
             },
+            "Binomial_default": {
+                "model": GPy.likelihoods.Binomial(),
+                "link_f_constraints": [partial(self.constrain_bounded, lower=0, upper=1)],
+                "Y": self.binomial_Y,
+                "Y_metadata": {'trials': self.ns},
+                "laplace": True,
+            },
             #,
             #GAMMA needs some work!"Gamma_default": {
             #"model": GPy.likelihoods.Gamma(),