merge devel branch in

GPy/plotting/matplot_dep/dim_reduction_plots.py

@@ -31,7 +31,7 @@ def plot_latent(model, labels=None, which_indices=None,
                 resolution=50, ax=None, marker='o', s=40,
                 fignum=None, plot_inducing=False, legend=True,
                 plot_limits=None, 
-                aspect='auto', updates=False, **kwargs):
+                aspect='auto', updates=False, predict_kwargs={}, imshow_kwargs={}):
     """
     :param labels: a np.array of size model.num_data containing labels for the points (can be number, strings, etc)
     :param resolution: the resolution of the grid on which to evaluate the predictive variance
@@ -60,7 +60,7 @@ def plot_latent(model, labels=None, which_indices=None,
     def plot_function(x):
         Xtest_full = np.zeros((x.shape[0], model.X.shape[1]))
         Xtest_full[:, [input_1, input_2]] = x
-        _, var = model.predict(Xtest_full)
+        _, var = model.predict(Xtest_full, **predict_kwargs)
         var = var[:, :1]
         return np.log(var)
 
@@ -81,7 +81,7 @@ def plot_latent(model, labels=None, which_indices=None,
     view = ImshowController(ax, plot_function,
                             (xmin, ymin, xmax, ymax),
                             resolution, aspect=aspect, interpolation='bilinear',
-                            cmap=pb.cm.binary, **kwargs)
+                            cmap=pb.cm.binary, **imshow_kwargs)
 
     # make sure labels are in order of input:
     ulabels = []

GPy/plotting/matplot_dep/models_plots.py

@@ -97,7 +97,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
 
         for d in which_data_ycols:
             plots['gpplot'] = gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], ax=ax, edgecol=linecol, fillcol=fillcol)
-            plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], data_symbol, mew=1.5)
+            if not plot_raw: plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], data_symbol, mew=1.5)
 
         #optionally plot some samples
         if samples: #NOTE not tested with fixed_inputs
@@ -151,7 +151,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
         for d in which_data_ycols:
             m_d = m[:,d].reshape(resolution, resolution).T
             plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
-            plots['dataplot'] = ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
+            if not plot_raw: plots['dataplot'] = ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
 
         #set the limits of the plot to some sensible values
         ax.set_xlim(xmin[0], xmax[0])

GPy/plotting/matplot_dep/visualize.py

@@ -88,7 +88,6 @@ class vector_show(matplotlib_show):
 
 
 class lvm(matplotlib_show):
-    
     def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0,1], disable_drag=False):
         """Visualize a latent variable model
 
@@ -150,7 +149,6 @@ class lvm(matplotlib_show):
         pass
 
     def on_click(self, event):
-        print 'click!'
         if event.inaxes!=self.latent_axes: return
         if self.disable_drag:
             self.move_on = True
@@ -228,11 +226,10 @@ class lvm_dimselect(lvm):
         self.labels = labels
         lvm.__init__(self,vals,model,data_visualize,latent_axes,sense_axes,latent_index)
         self.show_sensitivities()
-        print "use left and right mouse butons to select dimensions"
+        print "use left and right mouse buttons to select dimensions"
 
 
     def on_click(self, event):
-
         if event.inaxes==self.sense_axes:
             new_index = max(0,min(int(np.round(event.xdata-0.5)),self.model.input_dim-1))
             if event.button == 1: