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from ortools.sat.python import cp_model | |
import numpy as np | |
import random as rd | |
class VarArraySolutionPrinter(cp_model.CpSolverSolutionCallback): | |
#print intermediate solution | |
def __init__(self,variables): | |
cp_model.CpSolverSolutionCallback.__init__(self) | |
self.__variables = variables | |
self.__solution_count = 0 | |
def on_solution_callback(self): | |
self.__solution_count += 1 | |
for v in self.__variables: | |
print(f'{v}={self.Value(v)}', end = ' ') | |
print() | |
def solution_count(self): | |
return self.__solution_count | |
def decrease(x): return x - 1 | |
def file_inp(name=r'BACP\bacp-n9-m4.inp'): | |
with open(name, 'r') as f: | |
n, m = map(int, f.readline().split()) | |
gamma, lamda = map(int, f.readline().split()) | |
alpha, beta = map(int, f.readline().split()) | |
c = list(map(int, f.readline().split())) | |
k = int(f.readline()) | |
prerequisites = [] | |
for _ in range(k): | |
prerequisites.append(list(map(decrease, map(int, f.readline().split())))) | |
return n, m, gamma, lamda, alpha, beta, c, k, prerequisites | |
n, m, gamma, lamda, alpha, beta, c, k, prerequisites = file_inp() | |
print(n, m, gamma, lamda, alpha, beta, c, k, prerequisites, sep='\n') | |
model = cp_model.CpModel() | |
x = [model.NewIntVar(0,m-1,f'x[{i}]') for i in range(n)] | |
# prerequisites | |
for i, j in prerequisites: | |
model.Add(x[i] < x[j]) | |
# z | |
z = [[model.NewIntVar(0,1,f'z[{i},{j}]') for j in range(m)] for i in range(n)] | |
for i in range(n): | |
for j in range(m): | |
b = model.NewBoolVar('b') | |
model.Add(x[i] == j).OnlyEnforceIf(b) | |
model.Add(x[i] != j).OnlyEnforceIf(b.Not()) | |
model.Add(z[i][j] == 1).OnlyEnforceIf(b) | |
b = model.NewBoolVar('b') | |
model.Add(x[i] != j).OnlyEnforceIf(b) | |
model.Add(x[i] == j).OnlyEnforceIf(b.Not()) | |
model.Add(z[i][j] == 0).OnlyEnforceIf(b) | |
for j in range(m): | |
sum_i = sum([z[i][j] for i in range(n)]) | |
model.Add(alpha <= sum_i) | |
model.Add(sum_i <= beta) | |
sum_i_c = sum([z[i][j]*c[i] for i in range(n)]) | |
model.Add(gamma <= sum_i_c) | |
model.Add(sum_i_c <= lamda) | |
for i in range(n): | |
sum_j = sum([z[i][j] for j in range(m)]) | |
model.Add(sum_j == 1) | |
solver = cp_model.CpSolver() | |
solution_printer = VarArraySolutionPrinter(x) | |
solver.SearchForAllSolutions(model, solution_printer) |
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we are f**ked by optimization :)