def test_knapsack_simulated_annealing_low_dimensional_2():
params = open_file("../dataset/low-dimensional/f2_l-d_kp_20_878")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = simulated_annealing(ins_problem,
exp_schedule(2000, 0.00005, 200000))
optimum = int((open(
"../dataset/low-dimensional-optimum/f2_l-d_kp_20_878").readline()))
assert optimum == 1024
assert result.value == optimum
def test_knapsack_simulated_annealing_large_scale_1():
params = open_file("../dataset/large_scale/knapPI_1_100_1000_1")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = simulated_annealing(ins_problem, exp_schedule(1000, 0.0005, 4000))
optimum = int((
open("../dataset/large_scale-optimum/knapPI_1_100_1000_1").readline()))
assert optimum == 9147
assert result.value <= optimum
assert result.value >= 8000
def test_knapsack_simulated_annealing_plot_large_scale_6_4():
params = open_file("../dataset/large_scale/knapPI_1_5000_1000_1")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = simulated_annealing_plot(ins_problem, (800, 0.0005, 4000))
optimum = int((open(
"../dataset/large_scale-optimum/knapPI_1_5000_1000_1").readline()))
assert optimum == 276457
assert result.value <= 230000
assert result.value >= 190000
def test_knapsack_hill_climbing_large_scale_3():
params = open_file("../dataset/large_scale/knapPI_1_500_1000_1")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = hill_climbing(ins_problem)
optimum = int((
open("../dataset/large_scale-optimum/knapPI_1_500_1000_1").readline()))
assert optimum == 28857
assert result.value <= 10000
assert result.value >= 9800
def test_knapsack_hill_climbing_rr_large_scale_5():
params = open_file("../dataset/large_scale/knapPI_1_2000_1000_1")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = hill_climbing_random_restart(ins_problem, 100)
optimum = int((open(
"../dataset/large_scale-optimum/knapPI_1_2000_1000_1").readline()))
assert optimum == 110625
assert result.value <= 30000
assert result.value >= 27000
def test_knapsack_simulated_annealing_large_scale_5():
params = open_file("../dataset/large_scale/knapPI_1_2000_1000_1")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = simulated_annealing(ins_problem)
optimum = int((open(
"../dataset/large_scale-optimum/knapPI_1_2000_1000_1").readline()))
assert optimum == 110625
assert result.value <= 14200
assert result.value >= 8500
def test_knapsack_problem_initial():
problem = KnapsackProblem(20, [(0, 0), (0, 0)])
assert problem.initial.capacity == 20
assert problem.value(problem.initial) == 0
assert problem.initial.weight == 0
def test_knapsack_value():
problem = KnapsackProblem(20, [(25, 30), (5, 9), (20, 200)])
state = KnapsackState(20, 20, 20, [False])
assert problem.value(state) == state.value
def test_knapsack_actions():
problem = KnapsackProblem(20, [(25, 30), (5, 9), (20, 200)])
actions = problem.actions(problem.initial)
assert actions.__contains__((1, (5, 9), 1))
assert actions.__contains__((1, (20, 200), 2))
assert not actions.__contains__((1, (25, 30), 0))