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35 lines
1.2 KiB
35 lines
1.2 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, 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 scipy.stats import uniform, randint
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from miplearn import LearningSolver
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from miplearn.problems.knapsack import MultiKnapsackGenerator
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def test_knapsack_generator() -> None:
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gen = MultiKnapsackGenerator(
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n=randint(low=100, high=101),
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m=randint(low=30, high=31),
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w=randint(low=0, high=1000),
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K=randint(low=500, high=501),
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u=uniform(loc=1.0, scale=1.0),
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alpha=uniform(loc=0.50, scale=0.0),
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)
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instances = gen.generate(100)
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w_sum = sum(instance.weights for instance in instances) / len(instances)
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b_sum = sum(instance.capacities for instance in instances) / len(instances)
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assert round(float(np.mean(w_sum)), -1) == 500.0
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assert round(float(np.mean(b_sum)), -3) == 25000.0
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def test_knapsack() -> None:
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instance = MultiKnapsackGenerator(
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n=randint(low=5, high=6),
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m=randint(low=5, high=6),
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).generate(1)[0]
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solver = LearningSolver()
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solver.solve(instance)
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