[pypy-svn] r70805 - pypy/benchmarks/own

jacob at codespeak.net jacob at codespeak.net
Sun Jan 24 20:08:10 CET 2010

```Author: jacob
Date: Sun Jan 24 20:08:10 2010
New Revision: 70805

pypy/benchmarks/own/schulze.py
Log:
Added Schulze voting resolution as benchmark. The data is live data from the primary elections of the Swedish Pirate Party.

==============================================================================
--- (empty file)
+++ pypy/benchmarks/own/schulze.py	Sun Jan 24 20:08:10 2010
@@ -0,0 +1,88 @@
+fp.close()
+
+# Strip superfluous information
+lines = [line[14:].rstrip() for line in lines]
+
+votes = [line.split(' ') for line in lines]
+
+# Make a canonical set of all the candidates
+candidates = {}
+    for memberId in line:
+        candidates[memberId] = 1
+
+# Go from member number to an index
+for i, k in enumerate(candidates.keys()):
+    candidates[k] = i
+
+# And vice versa
+reverseCandidates = {}
+for k, v in candidates.items():
+    reverseCandidates[v] = k
+
+# Turn the votes in to an index number
+numbers = [[candidates[memberId] for memberId in line] for line in votes]
+
+size = len(candidates)
+
+# Initialize the d and p matrixes
+row = []
+for i in range(size):
+    row.append(0)
+
+d = []
+p = []
+for i in range(size):
+    d.append(row[:])
+    p.append(row[:])
+
+# Fill in the preferences in the d matrix
+for i in range(size):
+    for line in numbers:
+        for entry in line:
+            if entry == i:
+                break
+            d[entry][i] += 1
+
+# Calculate the p matrix. Algorithm copied straight from wikipedia
+# article http://en.wikipedia.org/wiki/Schulze_method
+for i in range(size):
+    for j in range(size):
+        if i != j:
+            if d[i][j] > d[j][i]:
+                p[i][j] = d[i][j]
+            else:
+                p[i][j] = 0
+
+for i in range(size):
+    for j in range(size):
+        if i != j:
+            for k in range(size):
+                if i != k:
+                    if j != k:
+                        p[j][k] = max(p[j][k], min(p[j][i], p[i][k]))
+
+# Find the best candidate (p[candidate, X] >= p[X, candidate])
+# Put the candidate on the final list, remove the candidate from p and
+# repeat
+order = []
+cl = range(size)
+
+while cl:
+    for c in cl:
+        for n in cl:
+            if c != n:
+                if p[c][n] < p[n][c]:
+                    break # Found a better candidate
+        else:
+            order.append(c)
+            cl.remove(c)
+
+# Display the results
+j = 0
+for i in order:
+    j += 1
+    print '%3d %s' % (j, reverseCandidates[i])

==============================================================================
--- (empty file)
+++ pypy/benchmarks/own/schulze.votes	Sun Jan 24 20:08:10 2010
@@ -0,0 +1,1300 @@
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```