From a2cf7677558e3701e7fc53d5cd6e7893f313ccb3 Mon Sep 17 00:00:00 2001
From: Pete Shadbolt <pete.shadbolt@gmail.com>
Date: Mon, 19 Jan 2015 21:26:20 +0000
Subject: [PATCH] Tidy

---
 run-tests.py | 76 +++++++++++++++++++++++++---------------------------
 1 file changed, 36 insertions(+), 40 deletions(-)

diff --git a/run-tests.py b/run-tests.py
index 6079cb5..8c65608 100644
--- a/run-tests.py
+++ b/run-tests.py
@@ -1,46 +1,42 @@
 import os, sys
-import time
-import multiprocessing as mp
 import numpy as np
 import time
 from matplotlib import pyplot as plt
 from permanent import permanent
+import itertools as it
+
+
+def permanent(a):
+    """ Slow way to compute the permanent """
+    r = range(len(a))
+    return sum([np.prod(a[r, p]) for p in it.permutations(r)])
+
+
+if __name__ == '__main__':
+    maxtime=1
+    dimensions=range(1,11)
+
+    for (function, label) in zip((permanent, perm_ryser), ("C", "Python")):
+        counts=[]
+        for dimension in dimensions:
+            print dimension
+            real=np.random.uniform(-1, 1, dimension*dimension).reshape((dimension, dimension))
+            imag=np.random.uniform(-1, 1, dimension*dimension).reshape((dimension, dimension))
+            submatrix=real+1j*imag
+
+            t=time.clock()
+            n=0
+            while time.clock()-t < maxtime:
+                for i in range(5):
+                    function(submatrix)
+                n+=5
+            counts.append(n)
+
+        plt.plot(dimensions, counts, ".-", label=label)
 
-def perm_ryser(a):
-    ''' the permanent calculated using the ryser formula. much faster than the naive approach '''
-    n,n2=a.shape
-    z=np.arange(n)
-    irange=xrange(2**n)
-    get_index=lambda i: (i & (1 << z)) != 0 
-    get_term=lambda index: ((-1)**np.sum(index))*np.prod(np.sum(a[index,:], 0))
-    indeces=map(get_index, irange)
-    terms=map(get_term, indeces) 
-    return np.sum(terms)*((-1)**n)
-
-maxtime=1
-dimensions=range(1,11)
-
-for (function, label) in zip((permanent, perm_ryser), ("C", "Python")):
-    counts=[]
-    for dimension in dimensions:
-        print dimension
-        real=np.random.uniform(-1, 1, dimension*dimension).reshape((dimension, dimension))
-        imag=np.random.uniform(-1, 1, dimension*dimension).reshape((dimension, dimension))
-        submatrix=real+1j*imag
-
-        t=time.clock()
-        n=0
-        while time.clock()-t < maxtime:
-            for i in range(5):
-                function(submatrix)
-            n+=5
-        counts.append(n)
-
-    plt.plot(dimensions, counts, '.-', label=label)
-
-plt.ylabel('Number of permanents per second')
-plt.xlabel('Dimension')
-plt.xlim(min(dimensions), max(dimensions))
-plt.legend()
-plt.semilogy()
-plt.savefig('out.pdf')
+    plt.ylabel("Number of permanents per second")
+    plt.xlabel("Dimension")
+    plt.xlim(min(dimensions), max(dimensions))
+    plt.legend()
+    plt.semilogy()
+    plt.savefig("out.pdf")