--- a/python/ml.py	Wed Jun 28 23:43:52 2017 -0400
+++ b/python/ml.py	Tue Jul 04 17:03:29 2017 -0400
@@ -264,27 +264,37 @@
                         similarities[l][k] = similarities[k][l]
             print('Mean similarity to prototype: {}'.format((similarities[prototypeIndices[i]][cluster].sum()+1)/(n-1)))
             print('Mean overall similarity: {}'.format((similarities[cluster][:,cluster].sum()+n)/(n*(n-1))))
-                    
+
 # Gaussian Mixture Models
-def plotGMMClusters(model, labels, dataset = None, fig = None, colors = utils.colors, nUnitsPerPixel = 1., alpha = 0.3):
+def plotGMMClusters(model, labels = None, dataset = None, fig = None, colors = utils.colors, nUnitsPerPixel = 1., alpha = 0.3):
     '''plot the ellipse corresponding to the Gaussians
     and the predicted classes of the instances in the dataset'''
     if fig is None:
         fig = plt.figure()
-    tmpDataset = dataset/nUnitsPerPixel
+    axes = fig.get_axes()
+    if len(axes) == 0:
+        axes = [fig.add_subplot(111)]
     for i in xrange(model.n_components):
         mean = model.means_[i]/nUnitsPerPixel
         covariance = model.covariances_[i]/nUnitsPerPixel
+        # plot points
         if dataset is not None:
+            tmpDataset = dataset/nUnitsPerPixel
             plt.scatter(tmpDataset[labels == i, 0], tmpDataset[labels == i, 1], .8, color=colors[i])
-        plt.annotate(str(i), xy=(mean[0]+1, mean[1]+1))
-
-        # Plot an ellipse to show the Gaussian component                                                  
+        # plot an ellipse to show the Gaussian component                                                  
         v, w = np.linalg.eigh(covariance)
-        angle = np.arctan2(w[0][1], w[0][0])
-        angle = 180*angle/np.pi  # convert to degrees                                             
+        angle = 180*np.arctan2(w[0][1], w[0][0])/np.pi
         v *= 4
         ell = mpl.patches.Ellipse(mean, v[0], v[1], 180+angle, color=colors[i])
         ell.set_clip_box(fig.bbox)
         ell.set_alpha(alpha)
-        fig.axes[0].add_artist(ell)
+        axes[0].add_artist(ell)
+        plt.plot([mean[0]], [mean[1]], 'x'+colors[i])
+        plt.annotate(str(i), xy=(mean[0]+1, mean[1]+1))
+    if dataset is None: # to address issues without points, the axes limits are not redrawn
+        minima = model.means_.min(0)
+        maxima = model.means_.max(0)
+        xwidth = 0.5*(maxima[0]-minima[0])
+        ywidth = 0.5*(maxima[1]-minima[1])
+        plt.xlim(minima[0]-xwidth,maxima[0]+xwidth)
+        plt.ylim(minima[1]-ywidth,maxima[1]+ywidth)