160 lines
4.0 KiB
Python
160 lines
4.0 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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Discrete tomography in Google CP Solver.
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Problem from http://eclipse.crosscoreop.com/examples/tomo.ecl.txt
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'''
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This is a little 'tomography' problem, taken from an old issue
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of Scientific American.
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A matrix which contains zeroes and ones gets "x-rayed" vertically and
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horizontally, giving the total number of ones in each row and column.
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The problem is to reconstruct the contents of the matrix from this
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information. Sample run:
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?- go.
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0 0 7 1 6 3 4 5 2 7 0 0
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0
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0
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8 * * * * * * * *
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2 * *
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6 * * * * * *
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4 * * * *
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5 * * * * *
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3 * * *
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7 * * * * * * *
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0
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0
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Eclipse solution by Joachim Schimpf, IC-Parc
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'''
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Compare with the following models:
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* Comet: http://www.hakank.org/comet/discrete_tomography.co
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* Gecode: http://www.hakank.org/gecode/discrete_tomography.cpp
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* MiniZinc: http://www.hakank.org/minizinc/tomography.mzn
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* Tailor/Essence': http://www.hakank.org/tailor/tomography.eprime
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* SICStus: http://hakank.org/sicstus/discrete_tomography.pl
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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 ortools.constraint_solver import pywrapcp
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def main(row_sums="", col_sums=""):
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# Create the solver.
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solver = pywrapcp.Solver("n-queens")
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#
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# data
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#
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if row_sums == "":
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print "Using default problem instance"
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row_sums = [0, 0, 8, 2, 6, 4, 5, 3, 7, 0, 0]
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col_sums = [0, 0, 7, 1, 6, 3, 4, 5, 2, 7, 0, 0]
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r = len(row_sums)
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c = len(col_sums)
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# declare variables
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x = []
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for i in range(r):
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t = []
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for j in range(c):
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t.append(solver.IntVar(0, 1, "x[%i,%i]" % (i, j)))
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x.append(t)
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x_flat = [x[i][j] for i in range(r) for j in range(c)]
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#
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# constraints
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#
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[solver.Add(solver.Sum([x[i][j] for j in range(c)]) == row_sums[i])
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for i in range(r)]
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[solver.Add(solver.Sum([x[i][j] for i in range(r)]) == col_sums[j])
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for j in range(c)]
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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_flat)
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# db: DecisionBuilder
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db = solver.Phase(x_flat,
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solver.INT_VAR_SIMPLE,
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solver.ASSIGN_MIN_VALUE)
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solver.NewSearch(db)
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num_solutions = 0
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while solver.NextSolution():
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print_solution(x, r, c, row_sums, col_sums)
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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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#
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# Print solution
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#
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def print_solution(x, rows, cols, row_sums, col_sums):
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print " ",
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for j in range(cols):
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print col_sums[j],
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print
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for i in range(rows):
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print row_sums[i],
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for j in range(cols):
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if x[i][j].Value() == 1:
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print "#",
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else:
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print ".",
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print ""
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#
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# Read a problem instance from a file
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#
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def read_problem(file):
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f = open(file, "r")
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row_sums = f.readline()
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col_sums = f.readline()
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row_sums = [int(r) for r in (row_sums.rstrip()).split(",")]
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col_sums = [int(c) for c in (col_sums.rstrip()).split(",")]
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return [row_sums, col_sums]
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if __name__ == "__main__":
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if len(sys.argv) > 1:
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file = sys.argv[1]
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print "Problem instance from", file
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[row_sums, col_sums] = read_problem(file)
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main(row_sums, col_sums)
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else:
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main()
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