# Copyright 2010 Hakan Kjellerstrand hakank@bonetmail.com
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""

  Simple diet problem in Google CP Solver.

  Standard Operations Research example in Minizinc


  Minimize the cost for the products:
  Type of                        Calories   Chocolate    Sugar    Fat
  Food                                      (ounces)     (ounces) (ounces)
  Chocolate Cake (1 slice)       400           3            2      2
  Chocolate ice cream (1 scoop)  200           2            2      4
  Cola (1 bottle)                150           0            4      1
  Pineapple cheesecake (1 piece) 500           0            4      5

  Compare with the following models:
  * Tailor/Essence': http://hakank.org/tailor/diet1.eprime
  * MiniZinc: http://hakank.org/minizinc/diet1.mzn
  * SICStus: http://hakank.org/sicstus/diet1.pl
  * Zinc: http://hakank.org/minizinc/diet1.zinc
  * Choco: http://hakank.org/choco/Diet.java
  * Comet: http://hakank.org/comet/diet.co
  * ECLiPSe: http://hakank.org/eclipse/diet.ecl
  * Gecode: http://hakank.org/gecode/diet.cpp
  * Gecode/R: http://hakank.org/gecode_r/diet.rb
  * JaCoP: http://hakank.org/JaCoP/Diet.java

  This version use ScalProd() instead of Sum().


  This model was created by Hakan Kjellerstrand (hakank@bonetmail.com)
  Also see my other Google CP Solver models: http://www.hakank.org/google_or_tools/
"""

from constraint_solver import pywrapcp



def main(unused_argv):
  # Create the solver.
  solver = pywrapcp.Solver('Diet')


  #
  # data
  #
  n = 4
  price  = [ 50, 20, 30, 80] # in cents
  limits = [500,  6, 10,  8] # requirements for each nutrition type

  # nutritions for each product
  calories  = [400, 200, 150, 500]
  chocolate = [3,2,0,0]
  sugar     = [2,2,4,4]
  fat       = [2,4,1,5]


  #
  # declare variables
  #
  x = [solver.IntVar(0, 100, 'x%d' % i) for i in range(n)]
  cost = solver.IntVar(0,10000, 'cost')

  #
  # constraints
  #
  solver.Add(solver.ScalProd(x,calories)  >= limits[0])
  solver.Add(solver.ScalProd(x,chocolate) >= limits[1])
  solver.Add(solver.ScalProd(x,sugar)     >= limits[2])
  solver.Add(solver.ScalProd(x,fat)       >= limits[3])

  # objective
  objective = solver.Minimize(cost, 1)

  #
  # solution
  #
  solution = solver.Assignment()
  solution.AddObjective(cost)
  solution.Add(x)

  # last solution since it's a minimization problem
  collector = solver.LastSolutionCollector(solution)
  search_log = solver.SearchLog(100, cost)
  solver.Solve(solver.Phase(x + [cost],
                            solver.INT_VAR_SIMPLE,
                            solver.ASSIGN_MIN_VALUE),
                            [objective, search_log, collector])

  # get the first (and only) solution
  print "cost:", collector.ObjectiveValue(0)
  print [("abcdefghij"[i], collector.Value(0, x[i])) for i in range(n)]
  print
  print "failures:", solver.Failures()
  print "branches:", solver.Branches()
  print "WallTime:", solver.WallTime()
  print


if __name__ == '__main__':
  main("cp sample")