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47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
# 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_gurobipy,
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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=5, high=6),
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gamma=uniform(loc=1.0, scale=0.25),
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round=True,
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)
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data = gen.generate(1)
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assert data[0].distances.tolist() == [
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[0.0, 525.0, 950.0, 392.0, 382.0],
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[525.0, 0.0, 752.0, 761.0, 178.0],
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[950.0, 752.0, 0.0, 809.0, 721.0],
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[392.0, 761.0, 809.0, 0.0, 700.0],
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[382.0, 178.0, 721.0, 700.0, 0.0],
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]
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model = build_tsp_model_gurobipy(data[0])
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model.optimize()
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assert model.inner.getAttr("x", model.inner.getVars()) == [
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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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1.0,
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0.0,
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1.0,
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1.0,
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0.0,
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0.0,
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]
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