plotting debug

GPy/kern/kern.py

@@ -52,12 +52,14 @@ class kern(parameterised):
         parameterised.__init__(self)
 
 
-    def plot_ARD(self, ax=pb.gca()):
+    def plot_ARD(self, ax=None):
         """
         If an ARD kernel is present, it bar-plots the ARD parameters
 
         
         """
+        if ax is None:
+            ax = pb.gca()
         for p in self.parts:
             if hasattr(p, 'ARD') and p.ARD:
                 ax.set_title('ARD parameters, %s kernel' % p.name)

GPy/models/GPLVM.py

@@ -60,12 +60,13 @@ class GPLVM(GP):
         mu, var, upper, lower = self.predict(Xnew)
         pb.plot(mu[:,0], mu[:,1],'k',linewidth=1.5)
 
-    def plot_latent(self, labels=None, which_indices=None, resolution=50, ax=pb.gca()):
+    def plot_latent(self, labels=None, which_indices=None, resolution=50, ax=None):
         """
         :param labels: a np.array of size self.N containing labels for the points (can be number, strings, etc)
         :param resolution: the resolution of the grid on which to evaluate the predictive variance
         """
-
+        if ax is None:
+            ax = pb.gca()
         util.plot.Tango.reset()
 
         if labels is None:

GPy/models/mrd.py

@@ -225,6 +225,29 @@ class MRD(model):
         self.X = X
         return X
 
+    def plot_X_1d(self, colors=None):
+        if colors is None:
+            colors = pylab.gca()._get_lines.color_cycle
+        fig = pylab.figure(num="MRD X 1d", figsize=(4 * len(self.bgplvms), (2 * self.X.shape[1])))
+        fig.clf()
+        ax1 = fig.add_subplot(self.X.shape[1], 1, 1)
+        ax1.plot(self.X, c='k', alpha=.3)
+        plots = ax1.plot(self.X.T[0], c=colors.next())
+        for i in range(self.X.shape[1] - 1):
+            ax = fig.add_subplot(self.X.shape[1], 1, i + 2)
+            ax.plot(self.X, c='k', alpha=.3)
+            plots.extend(ax.plot(self.X.T[i + 1], c=colors.next()))
+            if i < self.X.shape[1] - 2:
+                ax.set_xticklabels('')
+        ax1.set_xticklabels('')
+        ax1.legend(plots, [r"$\mathbf{{X_{}}}$".format(i + 1) for i in range(self.X.shape[1])],
+                   bbox_to_anchor=(0., 1 + .01 * self.X.shape[1],
+                                   1., 1. + .01 * self.X.shape[1]), loc=3,
+                   ncol=self.X.shape[1], mode="expand", borderaxespad=0.)
+        pylab.draw()
+        fig.tight_layout(h_pad=.01, rect=(0, 0, 1, .95))
+        return fig
+
     def plot_X(self):
         fig = pylab.figure("MRD X", figsize=(4 * len(self.bgplvms), 3))
         fig.clf()
@@ -267,6 +290,7 @@ class MRD(model):
 
     def _debug_plot(self):
         self.plot_X()
+        self.plot_X_1d()
         self.plot_latent()
         self.plot_scales()