merge the current devel into psi2

GPy/plotting/matplot_dep/dim_reduction_plots.py

@@ -30,7 +30,7 @@ def most_significant_input_dimensions(model, which_indices):
 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, 
+                plot_limits=None,
                 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)
@@ -84,6 +84,7 @@ def plot_latent(model, labels=None, which_indices=None,
                             cmap=pb.cm.binary, **imshow_kwargs)
 
     # make sure labels are in order of input:
+    labels = np.asarray(labels)
     ulabels = []
     for lab in labels:
         if not lab in ulabels:

GPy/plotting/matplot_dep/models_plots.py

@@ -8,7 +8,7 @@ from base_plots import gpplot, x_frame1D, x_frame2D
 from ...util.misc import param_to_array
 from ...models.gp_coregionalized_regression import GPCoregionalizedRegression
 from ...models.sparse_gp_coregionalized_regression import SparseGPCoregionalizedRegression
-
+from scipy import sparse
 
 def plot_fit(model, plot_limits=None, which_data_rows='all',
         which_data_ycols='all', fixed_inputs=[],
@@ -61,11 +61,14 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
 
     if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs():
         X = model.X.mean
-        X_variance = param_to_array(model.X.variance)
+        X_variance = model.X.variance
     else:
         X = model.X
-    X, Y = param_to_array(X, model.Y)
-    if hasattr(model, 'Z'): Z = param_to_array(model.Z)
+    #X, Y = param_to_array(X, model.Y)
+    Y = model.Y
+    if sparse.issparse(Y): Y = Y.todense().view(np.ndarray)
+
+    if hasattr(model, 'Z'): Z = model.Z
 
     #work out what the inputs are for plotting (1D or 2D)
     fixed_dims = np.array([i for i,v in fixed_inputs])
@@ -147,7 +150,11 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
         if plot_raw:
             m, _ = model._raw_predict(Xgrid)
         else:
-            m, _ = model.predict(Xgrid)
+            if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression):
+                meta = {'output_index': Xgrid[:,-1:].astype(np.int)}
+            else:
+                meta = None
+            m, v = model.predict(Xgrid, full_cov=False, Y_metadata=meta)
         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)

GPy/plotting/matplot_dep/visualize.py

@@ -98,9 +98,9 @@ class lvm(matplotlib_show):
         """
         if vals is None:
             if isinstance(model.X, VariationalPosterior):
-                vals = param_to_array(model.X.mean)
+                vals = model.X.mean.values
             else:
-                vals = param_to_array(model.X)
+                vals = model.X.values
         if len(vals.shape)==1:
             vals = vals[None,:]
         matplotlib_show.__init__(self, vals, axes=latent_axes)
@@ -136,7 +136,7 @@ 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.copy()
+        self.vals = vals.view(np.ndarray).copy()
         y = self.model.predict(self.vals)[0]
         self.data_visualize.modify(y)
         self.latent_handle.set_data(self.vals[0,self.latent_index[0]], self.vals[0,self.latent_index[1]])
@@ -226,6 +226,7 @@ class lvm_dimselect(lvm):
         self.labels = labels
         lvm.__init__(self,vals,model,data_visualize,latent_axes,sense_axes,latent_index)
         self.show_sensitivities()
+        print self.latent_values
         print "use left and right mouse buttons to select dimensions"
 
 
@@ -255,6 +256,7 @@ class lvm_dimselect(lvm):
 
 
     def on_leave(self,event):
+        print type(self.latent_values)
         latent_values = self.latent_values.copy()
         y = self.model.predict(latent_values[None,:])[0]
         self.data_visualize.modify(y)