--- a/python/ml.py	Tue Jul 04 17:03:29 2017 -0400
+++ b/python/ml.py	Tue Jul 04 17:36:24 2017 -0400
@@ -266,14 +266,24 @@
             print('Mean overall similarity: {}'.format((similarities[cluster][:,cluster].sum()+n)/(n*(n-1))))
 
 # Gaussian Mixture Models
+def plotGMM(mean, covariance, num, fig, color, alpha = 0.3):
+    v, w = np.linalg.eigh(covariance)
+    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=color)
+    ell.set_clip_box(fig.bbox)
+    ell.set_alpha(alpha)
+    fig.axes[0].add_artist(ell)
+    plt.plot([mean[0]], [mean[1]], 'x'+color)
+    plt.annotate(str(num), xy=(mean[0]+1, mean[1]+1))
+
 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()
-    axes = fig.get_axes()
-    if len(axes) == 0:
-        axes = [fig.add_subplot(111)]
+    if len(fig.get_axes()) == 0:
+        fig.add_subplot(111)
     for i in xrange(model.n_components):
         mean = model.means_[i]/nUnitsPerPixel
         covariance = model.covariances_[i]/nUnitsPerPixel
@@ -281,16 +291,8 @@
         if dataset is not None:
             tmpDataset = dataset/nUnitsPerPixel
             plt.scatter(tmpDataset[labels == i, 0], tmpDataset[labels == i, 1], .8, color=colors[i])
-        # plot an ellipse to show the Gaussian component                                                  
-        v, w = np.linalg.eigh(covariance)
-        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)
-        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))
+        # plot an ellipse to show the Gaussian component
+        plotGMM(mean, covariance, i, fig, colors[i], alpha)
     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)