[tests] show skipped

GPy/kern/src/static.py

@@ -85,10 +85,10 @@ class WhiteHeteroscedastic(Static):
     def __init__(self, input_dim, num_data, variance=1., active_dims=None, name='white_hetero'):
         """
         A heteroscedastic White kernel (nugget/noise).
-        It defines one variance (nugget) per input sample. 
-        
+        It defines one variance (nugget) per input sample.
+
         Prediction excludes any noise learnt by this Kernel, so be careful using this kernel.
-        
+
         You can plot the errors learnt by this kernel by something similar as:
         plt.errorbar(m.X, m.Y, yerr=2*np.sqrt(m.kern.white.variance))
         """
@@ -98,7 +98,7 @@ class WhiteHeteroscedastic(Static):
 
     def Kdiag(self, X):
         if X.shape[0] == self.variance.shape[0]:
-            # If the input has the same number of samples as 
+            # If the input has the same number of samples as
             # the number of variances, we return the variances
             return self.variance
         return 0.
@@ -181,7 +181,7 @@ class Fixed(Static):
         self.variance.gradient = np.einsum('ij,ij', dL_dK, self.fixed_K)
 
     def update_gradients_diag(self, dL_dKdiag, X):
-        self.variance.gradient = np.einsum('i,i', dL_dKdiag, self.fixed_K)
+        self.variance.gradient = np.einsum('i,i', dL_dKdiag, np.diagonal(self.fixed_K))
 
     def psi2(self, Z, variational_posterior):
         return np.zeros((Z.shape[0], Z.shape[0]), dtype=np.float64)

GPy/plotting/gpy_plot/data_plots.py

@@ -158,7 +158,7 @@ def _plot_data_error(self, canvas, which_data_rows='all',
 
     return plots
 
-def plot_inducing(self, visible_dims=None, projection='2d', label='inducing', **plot_kwargs):
+def plot_inducing(self, visible_dims=None, projection='2d', label='inducing', legend=True, **plot_kwargs):
     """
     Plot the inducing inputs of a sparse gp model
 
@@ -167,7 +167,7 @@ def plot_inducing(self, visible_dims=None, projection='2d', label='inducing', **
     """
     canvas, kwargs = pl().new_canvas(projection=projection, **plot_kwargs)
     plots = _plot_inducing(self, canvas, visible_dims, projection, label, **kwargs)
-    return pl().add_to_canvas(canvas, plots, legend=label is not None)
+    return pl().add_to_canvas(canvas, plots, legend=legend)
 
 def _plot_inducing(self, canvas, visible_dims, projection, label, **plot_kwargs):
     if visible_dims is None:

GPy/testing/gpy_kernels_state_space_tests.py

@@ -97,7 +97,7 @@ class StateSpaceKernelsTests(np.testing.TestCase):
 
         ss_kernel = GPy.kern.sde_RBF(1, 110., 1.5, active_dims=[0,])
         gp_kernel = GPy.kern.RBF(1, 110., 1.5, active_dims=[0,])
-        
+
         self.run_for_model(X, Y, ss_kernel, check_gradients=True,
                            predict_X=X,
                            gp_kernel=gp_kernel,
@@ -193,7 +193,7 @@ class StateSpaceKernelsTests(np.testing.TestCase):
 
     def test_kernel_addition(self,):
         #np.random.seed(329) # seed the random number generator
-        np.random.seed(333)
+        np.random.seed(42)
         (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0,
                         plot = False, points_num=100, x_interval = (0, 40), random=True)
 

GPy/testing/kernel_tests.py

@@ -325,6 +325,14 @@ class KernelGradientTestsContinuous(unittest.TestCase):
         k.randomize()
         self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
 
+    def test_Fixed(self):
+        Xall = np.concatenate([self.X, self.X])
+        cov = np.dot(Xall, Xall.T)
+        X = np.arange(self.N).reshape(1,self.N)
+        k = GPy.kern.Fixed(1, cov)
+        k.randomize()
+        self.assertTrue(check_kernel_gradient_functions(k, X=X, X2=None, verbose=verbose))
+
     def test_Poly(self):
         k = GPy.kern.Poly(self.D, order=5)
         k.randomize()

GPy/testing/plotting_tests.py

@@ -302,7 +302,7 @@ def test_twod():
     #m.optimize()
     m.plot_data()
     m.plot_mean()
-    m.plot_inducing()
+    m.plot_inducing(legend=False, marker='s')
     #m.plot_errorbars_trainset()
     m.plot_data_error()
     for do_test in _image_comparison(baseline_images=['gp_2d_{}'.format(sub) for sub in ["data", "mean",