From ad61e9c6ad042982b30803117bd948cbfb35b54f Mon Sep 17 00:00:00 2001
From: Joel Lowery <joeltlowery@gmail.com>
Date: Sun, 7 May 2017 22:46:24 -0500
Subject: [PATCH] Numerous ex8 fixes - Formatting - More consistent display of
 plots

---
 ex8/estimateGaussian.py | 1 -
 ex8/ex8.py              | 6 +++++-
 ex8/ex8_cofi.py         | 3 ++-
 ex8/visualizeFit.py     | 1 -
 4 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/ex8/estimateGaussian.py b/ex8/estimateGaussian.py
index 9630620..6b7b31d 100644
--- a/ex8/estimateGaussian.py
+++ b/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
diff --git a/ex8/ex8.py b/ex8/ex8.py
index f702d2a..62936c3 100644
--- a/ex8/ex8.py
+++ b/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()
diff --git a/ex8/ex8_cofi.py b/ex8/ex8_cofi.py
index 4359fab..53b31f7 100644
--- a/ex8/ex8_cofi.py
+++ b/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()
diff --git a/ex8/visualizeFit.py b/ex8/visualizeFit.py
index 3a9bd1f..766a434 100644
--- a/ex8/visualizeFit.py
+++ b/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()