[Merge] merge devel

GPy/plotting/gpy_plot/gp_plots.py

@@ -420,4 +420,4 @@ 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

@@ -33,7 +33,7 @@ from .. import Tango
 from .plot_util import update_not_existing_kwargs, helper_for_plot_data
 from ...kern.src.kern import Kern, CombinationKernel
 
-def plot_ARD(kernel, filtering=None, legend=False, **kwargs):
+def plot_ARD(kernel, filtering=None, legend=False, canvas=None, **kwargs):
     """
     If an ARD kernel is present, plot a bar representation using matplotlib
 
@@ -62,7 +62,11 @@ def plot_ARD(kernel, filtering=None, legend=False, **kwargs):
 
     bars = []
     kwargs = update_not_existing_kwargs(kwargs, pl().defaults.ard)
-    canvas, kwargs = pl().new_canvas(xlim=(-.5, kernel._effective_input_dim-.5), xlabel='input dimension', ylabel='sensitivity', **kwargs)
+
+
+    if canvas is None:
+        canvas, kwargs = pl().new_canvas(xlim=(-.5, kernel._effective_input_dim-.5), xlabel='input dimension', ylabel='sensitivity', **kwargs)
+
     for i in range(ard_params.shape[0]):
         if parts[i].name in filtering:
             c = Tango.nextMedium()
@@ -96,7 +100,7 @@ def plot_covariance(kernel, x=None, label=None,
     """
     X = np.ones((2, kernel._effective_input_dim)) * [[-3], [3]]
     _, free_dims, Xgrid, xx, yy, _, _, resolution = helper_for_plot_data(kernel, X, plot_limits, visible_dims, None, resolution)
-    
+
     from numbers import Number
     if x is None:
         from ...kern.src.stationary import Stationary
@@ -104,7 +108,7 @@ def plot_covariance(kernel, x=None, label=None,
     elif isinstance(x, Number):
         x = np.ones((1, kernel._effective_input_dim))*x
     K = kernel.K(Xgrid, x)
-    
+
     if projection == '3d':
         xlabel = 'X[:,0]'
         ylabel = 'X[:,1]'
@@ -136,4 +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

@@ -147,6 +147,7 @@ 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 +216,12 @@ 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)