8927b03942
Some of these imports are not used. The rest of them only import string to use the string.atoi function. But string.atoi(s) on a string input is identical to just int(s). See the docs: "deprecated since 2.0".
113 lines
2.8 KiB
Python
113 lines
2.8 KiB
Python
# Copyright 2010 Hakan Kjellerstrand hakank@bonetmail.com
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Combinatorial auction in Google CP Solver.
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This is a more general model for the combinatorial example
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in the Numberjack Tutorial, pages 9 and 24 (slides 19/175 and
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51/175).
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The original and more talkative model is here:
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http://www.hakank.org/numberjack/combinatorial_auction.py
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Compare with the following models:
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* MiniZinc: http://hakank.org/minizinc/combinatorial_auction.mzn
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* Gecode: http://hakank.org/gecode/combinatorial_auction.cpp
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This model was created by Hakan Kjellerstrand (hakank@bonetmail.com)
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Also see my other Google CP Solver models:
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http://www.hakank.org/google_or_tools/
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"""
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import sys
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from collections import *
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from ortools.constraint_solver import pywrapcp
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def main():
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# Create the solver.
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solver = pywrapcp.Solver("Problem")
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#
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# data
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#
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N = 5
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# the items for each bid
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items = [
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[0, 1], # A,B
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[0, 2], # A, C
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[1, 3], # B,D
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[1, 2, 3], # B,C,D
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[0] # A
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]
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# collect the bids for each item
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items_t = defaultdict(list)
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# [items_t.setdefault(j,[]).append(i) for i in range(N) for j in items[i] ]
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# nicer:
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[items_t[j].append(i) for i in range(N) for j in items[i]]
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bid_amount = [10, 20, 30, 40, 14]
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#
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# declare variables
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#
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X = [solver.BoolVar("x%i" % i) for i in range(N)]
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obj = solver.IntVar(0, 100, "obj")
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#
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# constraints
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#
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solver.Add(obj == solver.ScalProd(X, bid_amount))
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for item in items_t:
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solver.Add(solver.Sum([X[bid] for bid in items_t[item]]) <= 1)
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# objective
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objective = solver.Maximize(obj, 1)
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#
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# solution and search
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#
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solution = solver.Assignment()
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solution.Add(X)
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solution.Add(obj)
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# db: DecisionBuilder
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db = solver.Phase(X,
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solver.CHOOSE_FIRST_UNBOUND,
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solver.ASSIGN_MIN_VALUE)
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solver.NewSearch(db, [objective])
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num_solutions = 0
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while solver.NextSolution():
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print "X:", [X[i].Value() for i in range(N)]
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print "obj:", obj.Value()
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print
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num_solutions += 1
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solver.EndSearch()
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print
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print "num_solutions:", num_solutions
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print "failures:", solver.Failures()
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print "branches:", solver.Branches()
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print "WallTime:", solver.WallTime()
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if __name__ == "__main__":
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main()
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