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73 lines
1.7 KiB
73 lines
1.7 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import numpy as np
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from miplearn.problems.tsp import (
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TravelingSalesmanData,
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TravelingSalesmanGenerator,
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build_tsp_model,
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)
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from scipy.spatial.distance import pdist, squareform
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from scipy.stats import randint, uniform
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def test_tsp_generator() -> None:
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np.random.seed(42)
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gen = TravelingSalesmanGenerator(
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x=uniform(loc=0.0, scale=1000.0),
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y=uniform(loc=0.0, scale=1000.0),
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n=randint(low=3, high=4),
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gamma=uniform(loc=1.0, scale=0.25),
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fix_cities=True,
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round=True,
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)
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data = gen.generate(2)
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assert data[0].distances.tolist() == [
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[0.0, 591.0, 996.0],
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[591.0, 0.0, 765.0],
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[996.0, 765.0, 0.0],
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]
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assert data[1].distances.tolist() == [
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[0.0, 556.0, 853.0],
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[556.0, 0.0, 779.0],
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[853.0, 779.0, 0.0],
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]
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def test_tsp() -> None:
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data = TravelingSalesmanData(
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n_cities=6,
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distances=squareform(
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pdist(
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[
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[0.0, 0.0],
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[1.0, 0.0],
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[2.0, 0.0],
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[3.0, 0.0],
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[0.0, 1.0],
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[3.0, 1.0],
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]
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)
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),
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)
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model = build_tsp_model(data)
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model.optimize()
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assert model.inner.getAttr("x", model.inner.getVars()) == [
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1.0,
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0.0,
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0.0,
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1.0,
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0.0,
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1.0,
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0.0,
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0.0,
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0.0,
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1.0,
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0.0,
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0.0,
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0.0,
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1.0,
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1.0,
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]
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