manual merge of tests

GPy/core/gp.py

@@ -148,7 +148,7 @@ class GP(Model):
 
         return Ysim
 
-    def posterior_samples(self,X,size=10, full_cov=True, Y_metadata=None):
+    def posterior_samples(self, X, size=10, full_cov=False, Y_metadata=None):
         """
         Samples the posterior GP at the points X.
 
@@ -163,7 +163,7 @@ class GP(Model):
         :returns: Ysim: set of simulations, a Numpy array (N x samples).
         """
         Ysim = self.posterior_samples_f(X, size, full_cov=full_cov)
-        Ysim = self.likelihood.noise_model.samples(Ysim, Y_metadata)
+        Ysim = self.likelihood.samples(Ysim, Y_metadata)
 
         return Ysim
 

GPy/likelihoods/gaussian.py

@@ -94,7 +94,7 @@ class Gaussian(Likelihood):
         return self.variance + sigma**2
 
     def predictive_quantiles(self, mu, var, quantiles, Y_metadata):
-        return  [stats.norm.ppf(q)*np.sqrt(var) + mu for q in quantiles]
+        return  [stats.norm.ppf(q/100.)*np.sqrt(var) + mu for q in quantiles]
 
     def pdf_link(self, link_f, y, extra_data=None):
         """

GPy/likelihoods/likelihood.py

@@ -397,7 +397,7 @@ class Likelihood(Parameterized):
 
         return [np.percentile(ss_y ,q, axis=1)[:,None] for q in quantiles]
 
-    def samples(self, gp):
+    def samples(self, gp, Y_metadata=None):
         """
         Returns a set of samples of observations based on a given value of the latent variable.
 

GPy/likelihoods/student_t.py

@@ -263,7 +263,7 @@ class StudentT(Likelihood):
     def conditional_variance(self, gp):
         return self.deg_free/(self.deg_free - 2.)
 
-    def samples(self, gp):
+    def samples(self, gp, Y_metadata=None):
         """
         Returns a set of samples of observations based on a given value of the latent variable.
 

GPy/plotting/matplot_dep/models_plots.py

@@ -86,8 +86,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
             upper = m + 2*np.sqrt(v)
         else:
             m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata)
-
-        lower, upper = model.predict_quantiles(Xgrid, Y_metadata=Y_metadata)
+            lower, upper = model.predict_quantiles(Xgrid, Y_metadata=Y_metadata)
 
 
         for d in which_data_ycols:

GPy/testing/kernel_tests.py

@@ -298,6 +298,24 @@ class KernelTestsMiscellaneous(unittest.TestCase):
         self.assertTrue(np.allclose(self.sumkern.K(self.X, which_parts=[self.linear, self.rbf]), self.linear.K(self.X)+self.rbf.K(self.X)))
         self.assertTrue(np.allclose(self.sumkern.K(self.X, which_parts=self.sumkern.parts[0]), self.rbf.K(self.X)))
 
+class KernelTestsNonContinuous(unittest.TestCase):
+    def setUp(self):
+        N = 100
+        N1 = 110
+        self.D = 2
+        D = self.D
+        self.X = np.random.randn(N,D)
+        self.X2 = np.random.randn(N1,D)
+        self.X_block = np.zeros((N+N1, D+D+1))
+        self.X_block[0:N, 0:D] = self.X
+        self.X_block[N:N+N1, D:D+D] = self.X2
+        self.X_block[0:N, -1] = 1
+        self.X_block[N:N+1, -1] = 2
+
+    def test_IndependantOutputs(self):
+        k = GPy.kern.RBF(self.D)
+        kern = GPy.kern.IndependentOutputs(self.D+self.D,k)
+        self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose))
 
 if __name__ == "__main__":
     print "Running unit tests, please be (very) patient..."