baysian gplvm and example changes

GPy/core/model.py

@@ -13,7 +13,6 @@ import priors
 from ..util.linalg import jitchol
 from ..inference import optimization
 from .. import likelihoods
-import re
 
 class model(parameterised):
     def __init__(self):
@@ -215,7 +214,7 @@ class model(parameterised):
         for s in positive_strings:
             for i in self.grep_param_names(s):
                 if not (i in currently_constrained):
-                    to_make_positive.append(re.escape(param_names[i]))
+                    to_make_positive.append(param_names[i])
                     if warn:
                         print "Warning! constraining %s postive"%name
         if len(to_make_positive):

GPy/models/Bayesian_GPLVM.py

@@ -161,13 +161,26 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
         ax.plot(self.Z[:, input_1], self.Z[:, input_2], '^w')
         return ax
 
-    def plot_X_1d(self, fig_num="MRD X 1d", axes=None, colors=None):
-        import pylab
+    def plot_X_1d(self, fig=None, axes=None, fig_num="MRD X 1d", colors=None):
+        """
+        Plot latent space X in 1D:
         
-        fig = pylab.figure(num=fig_num, figsize=(min(8, (3 * len(self.bgplvms))), min(12, (2 * self.X.shape[1]))))
+            -if fig is given, create Q subplots in fig and plot in these
+            -if axes is given plot Q 1D latent space plots of X into each `axis`
+            -if neither fig nor axes is given create a figure with fig_num and plot in there
+            
+        colors:
+            
+            colors of different latent space dimensions Q
+        """
+        import pylab
+        if fig is None and axes is None:
+            fig = pylab.figure(num=fig_num, figsize=(8, min(12, (2 * self.X.shape[1]))))
         if colors is None:
             colors = pylab.gca()._get_lines.color_cycle
             pylab.clf()
+        else:
+            colors = iter(colors)
         plots = []
         for i in range(self.X.shape[1]):
             if axes is None: