# Copyright (c) 2022 AIRBUS and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Thanks to Leuven university for the cpmyp library.
from typing import Any, Optional
from cpmpy import Model, boolvar
from discrete_optimization.generic_tools.cpmpy_tools import (
CpmpySolver,
MetaCpmpyConstraint,
)
from discrete_optimization.generic_tools.do_problem import (
ParamsObjectiveFunction,
Solution,
)
from discrete_optimization.knapsack.problem import KnapsackProblem, KnapsackSolution
from discrete_optimization.knapsack.solvers import KnapsackSolver
[docs]
class CpmpyKnapsackSolver(CpmpySolver, KnapsackSolver):
def __init__(
self,
problem: KnapsackProblem,
params_objective_function: Optional[ParamsObjectiveFunction] = None,
**kwargs
):
super().__init__(
problem=problem, params_objective_function=params_objective_function
)
self.variables: dict[str, Any] = {}
[docs]
def init_model(self, **kwargs: Any) -> None:
values = [
self.problem.list_items[i].value for i in range(self.problem.nb_items)
]
weights = [
self.problem.list_items[i].weight for i in range(self.problem.nb_items)
]
capacity = self.problem.max_capacity
# Construct the model.
x = boolvar(shape=self.problem.nb_items, name="x")
self.model = Model(sum(x * weights) <= capacity, maximize=sum(x * values))
self.variables["x"] = x
[docs]
def retrieve_current_solution(self) -> Solution:
list_taken = self.variables["x"].value()
return KnapsackSolution(problem=self.problem, list_taken=list_taken)