merged

GPy/examples/dimensionality_reduction.py

@@ -176,7 +176,7 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
     if plot:
         y = m.Y
         fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
-        m.plot_latent(ax=latent_axes)
+        m.plot_latent(ax=latent_axes, labels=m.data_labels)
         data_show = GPy.plotting.matplot_dep.visualize.vector_show(y)
         lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(param_to_array(m.X.mean), # @UnusedVariable
             m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)

GPy/kern/_src/add.py

@@ -167,4 +167,10 @@ class Add(CombinationKernel):
         else:
             self.add_parameter(other)
         self.input_dim, self.active_dims = self.get_input_dim_active_dims(self.parts)
-        return self
+        return self
+
+    def input_sensitivity(self):
+        in_sen = np.zeros(self.input_dim)
+        for i, p in enumerate(self.parts):
+            in_sen[p.active_dims] += p.input_sensitivity()
+        return in_sen

GPy/kern/_src/kern.py

@@ -238,7 +238,4 @@ class CombinationKernel(Kern):
         return input_dim, active_dims
 
     def input_sensitivity(self):
-        in_sen = np.zeros((self.num_params, self.input_dim))
+        raise NotImplementedError("Choose the kernel you want to get the sensitivity for. You need to override the default behaviour for getting the input sensitivity to be able to get the input sensitivity. For sum kernel it is the sum of all sensitivities, TODO: product kernel? Other kernels?, also TODO: shall we return all the sensitivities here in the combination kernel? So we can combine them however we want? This could lead to just plot all the sensitivities here...")
-        for i, p in enumerate(self.parts):
-            in_sen[i, p.active_dims] = p.input_sensitivity()
-        return in_sen

GPy/kern/_src/kernel_slice_operations.py

@@ -42,6 +42,7 @@ class _Slice_wrap(object):
             self.X2 = self.k._slice_X(X2) if X2 is not None else X2
             self.ret = True
         else:
+            assert X.shape[1] == self.k.input_dim, "You did not specify active_dims and X has wrong shape: X_dim={} -- input_dim={}".format(X.shape[1], self.input_dim)
             self.X = X
             self.X2 = X2
             self.ret = False

GPy/plotting/matplot_dep/visualize.py

@@ -131,10 +131,10 @@ class lvm(matplotlib_show):
 
     def modify(self, vals):
         """When latent values are modified update the latent representation and ulso update the output visualization."""
-        self.vals = vals[None,:].copy()
+        self.vals = vals.copy()
         y = self.model.predict(self.vals)[0]
         self.data_visualize.modify(y)
-        self.latent_handle.set_data(self.vals[:,self.latent_index[0]], self.vals[:,self.latent_index[1]])
+        self.latent_handle.set_data(self.vals[0,self.latent_index[0]], self.vals[0,self.latent_index[1]])
         self.axes.figure.canvas.draw()
 
 
@@ -153,8 +153,8 @@ class lvm(matplotlib_show):
         if event.inaxes!=self.latent_axes: return
         if self.called and self.move_on:
             # Call modify code on move
-            self.latent_values[self.latent_index[0]]=event.xdata
+            self.latent_values[:, self.latent_index[0]]=event.xdata
-            self.latent_values[self.latent_index[1]]=event.ydata
+            self.latent_values[:, self.latent_index[1]]=event.ydata
             self.modify(self.latent_values)
 
     def show_sensitivities(self):