merged last devel

GPy/plotting/gpy_plot/gp_plots.py

@@ -235,8 +235,6 @@ def plot_density(self, plot_limits=None, fixed_inputs=None,
 
     Give the Y_metadata in the predict_kw if you need it.
 
-
-
     :param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
     :type plot_limits: np.array
     :param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input dimension i should be set to value v.
@@ -420,4 +418,4 @@ def _plot(self, canvas, plots, helper_data, helper_prediction, levels, plot_indu
 
         if helper_prediction[2] is not None:
             plots.update(_plot_samples(self, canvas, helper_data, helper_prediction, projection, "Samples"))
-        return plots
+        return plots

GPy/plotting/gpy_plot/kernel_plots.py

@@ -140,4 +140,4 @@ def plot_covariance(kernel, x=None, label=None,
         return pl().add_to_canvas(canvas, plots)
 
     else:
-        raise NotImplementedError("Cannot plot a kernel with more than two input dimensions")
+        raise NotImplementedError("Cannot plot a kernel with more than two input dimensions")

GPy/plotting/gpy_plot/latent_plots.py

@@ -131,7 +131,9 @@ def plot_latent_inducing(self,
 
     Z = self.Z.values
     labels = np.array(['inducing'] * Z.shape[0])
-    scatters = _plot_latent_scatter(canvas, Z, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
+    kwargs['marker'] = marker
+    update_not_existing_kwargs(kwargs, pl().defaults.inducing_2d)  # @UndefinedVariable
+    scatters = _plot_latent_scatter(canvas, Z, sig_dims, labels, num_samples=num_samples, projection=projection, **kwargs)
     return pl().add_to_canvas(canvas, dict(scatter=scatters), legend=legend)
 
 
@@ -147,6 +149,7 @@ def _plot_magnification(self, canvas, which_indices, Xgrid,
     def plot_function(x):
         Xtest_full = np.zeros((x.shape[0], Xgrid.shape[1]))
         Xtest_full[:, which_indices] = x
+
         mf = self.predict_magnification(Xtest_full, kern=kern, mean=mean, covariance=covariance)
         return mf.reshape(resolution, resolution).T
     imshow_kwargs = update_not_existing_kwargs(imshow_kwargs, pl().defaults.magnification)
@@ -215,7 +218,12 @@ def _plot_latent(self, canvas, which_indices, Xgrid,
     def plot_function(x):
         Xtest_full = np.zeros((x.shape[0], Xgrid.shape[1]))
         Xtest_full[:, which_indices] = x
-        mf = np.log(self.predict(Xtest_full, kern=kern)[1])
+        mf = self.predict(Xtest_full, kern=kern)[1]
+        if mf.shape[1]==self.output_dim:
+            mf = mf.sum(-1)
+        else:
+            mf *= self.output_dim
+        mf = np.log(mf)
         return mf.reshape(resolution, resolution).T
 
     imshow_kwargs = update_not_existing_kwargs(imshow_kwargs, pl().defaults.latent)

GPy/plotting/gpy_plot/plot_util.py

@@ -194,6 +194,7 @@ def scatter_label_generator(labels, X, visible_dims, marker=None):
             x = X[index, input_1]
             y = X[index, input_2]
             z = X[index, input_3]
+
         yield x, y, z, this_label, index, m
 
 def subsample_X(X, labels, num_samples=1000):
@@ -385,5 +386,5 @@ def x_frame2D(X,plot_limits=None,resolution=None):
 
     resolution = resolution or 50
     xx, yy = np.mgrid[xmin[0]:xmax[0]:1j*resolution,xmin[1]:xmax[1]:1j*resolution]
-    Xnew = np.vstack((xx.flatten(),yy.flatten())).T
+    Xnew = np.c_[xx.flat, yy.flat]
     return Xnew, xx, yy, xmin, xmax