Permalink
Switch branches/tags
Nothing to show
Find file
Fetching contributors…
Cannot retrieve contributors at this time
67 lines (54 sloc) 2.82 KB
# Copyright (C) 2009, Brad Beattie
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# This class implements Schulze STV, a proportional representation system
from abstract_classes import MultipleWinnerVotingSystem
from schulze_helper import SchulzeHelper
from pygraph.classes.digraph import digraph
import itertools
class SchulzeSTV(MultipleWinnerVotingSystem, SchulzeHelper):
def __init__(self, ballots, tie_breaker=None, required_winners=1, ballot_notation=None):
self.standardize_ballots(ballots, ballot_notation)
super(SchulzeSTV, self).__init__(self.ballots, tie_breaker=tie_breaker, required_winners=required_winners)
def calculate_results(self):
# Don't bother if everyone's going to win
super(SchulzeSTV, self).calculate_results()
if hasattr(self, 'winners'):
return
# Generate the list of patterns we need to complete
self.generate_completed_patterns()
self.generate_vote_management_graph()
# Build the graph of possible winners
self.graph = digraph()
for candidate_set in itertools.combinations(self.candidates, self.required_winners):
self.graph.add_nodes([tuple(sorted(list(candidate_set)))])
# Generate the edges between nodes
for candidate_set in itertools.combinations(self.candidates, self.required_winners + 1):
for candidate in candidate_set:
other_candidates = sorted(set(candidate_set) - set([candidate]))
completed = self.proportional_completion(candidate, other_candidates)
weight = self.strength_of_vote_management(completed)
if weight > 0:
for subset in itertools.combinations(other_candidates, len(other_candidates) - 1):
self.graph.add_edge((tuple(other_candidates), tuple(sorted(list(subset) + [candidate]))), weight)
# Determine the winner through the Schwartz set heuristic
self.graph_winner()
# Split the "winner" into its candidate components
self.winners = set(self.winner)
del self.winner
def as_dict(self):
data = super(SchulzeSTV, self).as_dict()
if hasattr(self, 'actions'):
data['actions'] = self.actions
return data