You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
54 lines
1.9 KiB
54 lines
1.9 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
|
# Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
|
|
# Released under the modified BSD license. See COPYING.md for more details.
|
|
|
|
import numpy as np
|
|
from scipy.stats import uniform, randint
|
|
|
|
from miplearn.problems.pmedian import PMedianGenerator, build_pmedian_model
|
|
|
|
|
|
def test_pmedian() -> None:
|
|
np.random.seed(42)
|
|
gen = PMedianGenerator(
|
|
x=uniform(loc=0.0, scale=100.0),
|
|
y=uniform(loc=0.0, scale=100.0),
|
|
n=randint(low=5, high=6),
|
|
p=randint(low=2, high=3),
|
|
demands=uniform(loc=0, scale=20),
|
|
capacities=uniform(loc=0, scale=100),
|
|
distances_jitter=uniform(loc=0.95, scale=0.1),
|
|
demands_jitter=uniform(loc=0.95, scale=0.1),
|
|
capacities_jitter=uniform(loc=0.95, scale=0.1),
|
|
fixed=True,
|
|
)
|
|
data = gen.generate(2)
|
|
|
|
assert data[0].p == 2
|
|
assert data[0].demands.tolist() == [0.41, 19.4, 16.65, 4.25, 3.64]
|
|
assert data[0].capacities.tolist() == [18.34, 30.42, 52.48, 43.19, 29.12]
|
|
assert data[0].distances.tolist() == [
|
|
[0.0, 50.17, 82.42, 32.76, 33.2],
|
|
[50.17, 0.0, 72.64, 72.51, 17.06],
|
|
[82.42, 72.64, 0.0, 71.69, 70.92],
|
|
[32.76, 72.51, 71.69, 0.0, 56.56],
|
|
[33.2, 17.06, 70.92, 56.56, 0.0],
|
|
]
|
|
|
|
assert data[1].p == 2
|
|
assert data[1].demands.tolist() == [0.42, 19.03, 16.68, 4.27, 3.53]
|
|
assert data[1].capacities.tolist() == [19.2, 31.26, 54.79, 44.9, 29.41]
|
|
assert data[1].distances.tolist() == [
|
|
[0.0, 51.6, 83.31, 33.77, 31.95],
|
|
[51.6, 0.0, 70.25, 71.09, 17.05],
|
|
[83.31, 70.25, 0.0, 68.81, 67.62],
|
|
[33.77, 71.09, 68.81, 0.0, 58.88],
|
|
[31.95, 17.05, 67.62, 58.88, 0.0],
|
|
]
|
|
|
|
model = build_pmedian_model(data[0])
|
|
assert model.inner.numVars == 30
|
|
assert model.inner.numConstrs == 11
|
|
model.optimize()
|
|
assert round(model.inner.objVal) == 107
|