Added tests and fixed some naming

GPy/likelihoods/likelihood_functions.py

@@ -507,7 +507,7 @@ class Gaussian(LikelihoodFunction):
         d3lik_d3f = np.diagonal(0*self.I)[:, None] # FIXME: CAREFUL THIS MAY NOT WORK WITH MULTIDIMENSIONS?
         return d3lik_d3f
 
-    def lik_dvar(self, y, f, extra_data=None):
+    def dlik_dvar(self, y, f, extra_data=None):
         """
         Gradient of the likelihood (lik) w.r.t sigma parameter (standard deviation)
         """
@@ -538,7 +538,7 @@ class Gaussian(LikelihoodFunction):
 
     def _gradients(self, y, f, extra_data=None):
         #must be listed in same order as 'get_param_names'
-        derivs = ([self.lik_dvar(y, f, extra_data=extra_data)],
+        derivs = ([self.dlik_dvar(y, f, extra_data=extra_data)],
                   [self.dlik_df_dvar(y, f, extra_data=extra_data)],
                   [self.d2lik_d2f_dvar(y, f, extra_data=extra_data)]
                  ) # lists as we might learn many parameters

GPy/testing/laplace_tests.py

@@ -0,0 +1,84 @@
+import numpy as np
+import unittest
+import GPy
+from GPy.models import GradientChecker
+import functools
+
+class LaplaceTests(unittest.TestCase):
+    def setUp(self):
+        self.N = 5
+        self.D = 1
+        self.X = np.linspace(0, 1, self.N)[:, None]
+
+        self.real_std = 0.2
+        noise = np.random.randn(*self.X.shape)*self.real_std
+        self.Y = np.sin(self.X*2*np.pi) + noise
+
+        self.f = np.random.rand(self.N, 1)
+
+    def test_gaussian_dlik_df(self):
+        var = 0.1
+        gauss = GPy.likelihoods.functions.Gaussian(var, self.D, self.N)
+        link = functools.partial(gauss.link_function, self.Y)
+        dlik_df = functools.partial(gauss.dlik_df, self.Y)
+        grad = GradientChecker(link, dlik_df, self.f.copy(), 'f')
+        grad.randomize()
+        grad.checkgrad(verbose=1)
+
+    def test_gaussian_d2lik_d2f(self):
+        var = 0.1
+        gauss = GPy.likelihoods.functions.Gaussian(var, self.D, self.N)
+        dlik_df = functools.partial(gauss.dlik_df, self.Y)
+        d2lik_d2f = functools.partial(gauss.d2lik_d2f, self.Y)
+        grad = GradientChecker(dlik_df, d2lik_d2f, self.f.copy(), 'f')
+        grad.randomize()
+        grad.checkgrad(verbose=1)
+
+    def test_gaussian_d3lik_d3f(self):
+        var = 0.1
+        gauss = GPy.likelihoods.functions.Gaussian(var, self.D, self.N)
+        d2lik_d2f = functools.partial(gauss.d2lik_d2f, self.Y)
+        d3lik_d3f = functools.partial(gauss.d3lik_d3f, self.Y)
+        grad = GradientChecker(d2lik_d2f, d3lik_d3f, self.f.copy(), 'f')
+        grad.randomize()
+        grad.checkgrad(verbose=1)
+
+    def test_gaussian_dlik_dvar(self):
+        var = 0.1
+        gauss = GPy.likelihoods.functions.Gaussian(var, self.D, self.N)
+        #Since the function we are checking does not directly accept the variable we wish to tweak
+        #We make function which makes the change (set params) then calls the function
+        def p_link_var(var, likelihood, f, Y):
+            likelihood._set_params(var)
+            return likelihood.link_function(f, Y)
+
+        def p_dlik_dvar(var, likelihood, f, Y):
+            likelihood._set_params(var)
+            return likelihood.dlik_dvar(f, Y)
+
+        link = functools.partial(p_link_var, likelihood=gauss, f=self.f, Y=self.Y)
+        dlik_dvar = functools.partial(p_dlik_dvar, likelihood=gauss, f=self.f, Y=self.Y)
+        grad = GradientChecker(link, dlik_dvar, var, 'v')
+        grad.randomize()
+        grad.checkgrad(verbose=1)
+
+    def test_gaussian_dlik_df_dvar(self):
+        var = 0.1
+        gauss = GPy.likelihoods.functions.Gaussian(var, self.D, self.N)
+        def p_dlik_df(var, likelihood, f, Y):
+            likelihood._set_params(var)
+            return likelihood.dlik_df(f, Y)
+
+        def p_dlik_df_dstd(var, likelihood, f, Y):
+            likelihood._set_params(var)
+            return likelihood.dlik_df_dvar(f, Y)
+
+        dlik_df = functools.partial(p_dlik_df, likelihood=gauss, f=self.f, Y=self.Y)
+        dlik_df_dstd = functools.partial(p_dlik_df_dstd, likelihood=gauss, f=self.f, Y=self.Y)
+        grad = GradientChecker(dlik_df, dlik_df_dstd, var, 'v')
+        grad.randomize()
+        grad.checkgrad(verbose=1)
+
+if __name__ == "__main__":
+    print "Running unit tests"
+    unittest.main()