Fix PEP8 violations in examples/src/main/python.

Author: Reynold Xin <rxin@apache.org>

Closes #870 from rxin/examples-python-pep8 and squashes the following commits:

2829e84 [Reynold Xin] Fix PEP8 violations in examples/src/main/python.

1 files changed, 12 insertions, 8 deletions

diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py
index f0b46cd28b..1a7c4c51f4 100755
--- a/examples/src/main/python/als.py
+++ b/examples/src/main/python/als.py
@@ -29,22 +29,25 @@ from pyspark import SparkContext
 LAMBDA = 0.01   # regularization
 np.random.seed(42)
 
+
 def rmse(R, ms, us):
     diff = R - ms * us.T
     return np.sqrt(np.sum(np.power(diff, 2)) / M * U)
 
+
 def update(i, vec, mat, ratings):
     uu = mat.shape[0]
     ff = mat.shape[1]
-    
+
     XtX = mat.T * mat
     Xty = mat.T * ratings[i, :].T
-    
+
     for j in range(ff):
-        XtX[j,j] += LAMBDA * uu
-    
+        XtX[j, j] += LAMBDA * uu
+
     return np.linalg.solve(XtX, Xty)
 
+
 if __name__ == "__main__":
     """
     Usage: als [M] [U] [F] [iterations] [slices]"
@@ -57,10 +60,10 @@ if __name__ == "__main__":
     slices = int(sys.argv[5]) if len(sys.argv) > 5 else 2
 
     print "Running ALS with M=%d, U=%d, F=%d, iters=%d, slices=%d\n" % \
-            (M, U, F, ITERATIONS, slices)
+        (M, U, F, ITERATIONS, slices)
 
     R = matrix(rand(M, F)) * matrix(rand(U, F).T)
-    ms = matrix(rand(M ,F))
+    ms = matrix(rand(M, F))
     us = matrix(rand(U, F))
 
     Rb = sc.broadcast(R)
@@ -71,8 +74,9 @@ if __name__ == "__main__":
         ms = sc.parallelize(range(M), slices) \
                .map(lambda x: update(x, msb.value[x, :], usb.value, Rb.value)) \
                .collect()
-        ms = matrix(np.array(ms)[:, :, 0])      # collect() returns a list, so array ends up being
-                                                # a 3-d array, we take the first 2 dims for the matrix
+        # collect() returns a list, so array ends up being
+        # a 3-d array, we take the first 2 dims for the matrix
+        ms = matrix(np.array(ms)[:, :, 0])
         msb = sc.broadcast(ms)
 
         us = sc.parallelize(range(U), slices) \