Numerous ex8 fixes

- Formatting
- More consistent display of plots

ex8/estimateGaussian.py

@@ -17,7 +17,6 @@ def estimateGaussian(X):
     #               In particular, mu(i) should contain the mean of
     #               the data for the i-th feature and sigma2(i)
     #               should contain variance of the i-th feature.
-    #
     # =============================================================
 
     return mu, sigma2

ex8/ex8.py

@@ -52,7 +52,6 @@
 show()
 input('Program paused. Press Enter to continue...')
 
-
 # ================== Part 2: Estimate the dataset statistics ===================
 #  For this exercise, we assume a Gaussian distribution for the dataset.
 #
@@ -71,6 +70,7 @@
 p = multivariateGaussian(X, mu, sigma2)
 
 # Visualize the fit
+plt.figure()
 visualizeFit(X,  mu, sigma2)
 plt.xlabel('Latency (ms)')
 plt.ylabel('Throughput (mb/s)')
@@ -93,6 +93,10 @@
 outliers = np.where(p < epsilon, True, False)
 
 # Draw a red circle around those outliers
+plt.figure()
+visualizeFit(X,  mu, sigma2)
+plt.xlabel('Latency (ms)')
+plt.ylabel('Throughput (mb/s)')
 plt.plot(X[outliers, 0], X[outliers, 1], 'ro', lw=2,
          markersize=10, fillstyle='none', markeredgewidth=1)
 show()

ex8/ex8_cofi.py

@@ -49,7 +49,8 @@
 # We can "visualize" the ratings matrix by plotting it with imagesc
 
 plt.figure()
-plt.imshow(Y, aspect='equal', origin='upper', extent=(0, Y.shape[1], 0, Y.shape[0]/2.0))
+plt.imshow(Y, aspect='equal', origin='upper',
+           extent=(0, Y.shape[1], 0, Y.shape[0]/2.0))
 plt.ylabel('Movies')
 plt.xlabel('Users')
 show()

ex8/visualizeFit.py

@@ -22,4 +22,3 @@ def visualizeFit(X, mu, sigma2):
     # Do not plot if there are infinities
     if not isinf(np.sum(Z)):
         plt.contour(X1[0], X1[1], Z, 10.0 ** np.arange(-20, 0, 3).T)
-        show()