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27 lines
1.0 KiB
27 lines
1.0 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, 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.problems.knapsack import MultiKnapsackGenerator
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def test_knapsack_generator():
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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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p_sum = sum(instance.prices 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(np.mean(w_sum), -1) == 500.0
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# assert round(np.mean(p_sum), -1) == 1200. # flaky
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assert round(np.mean(b_sum), -3) == 25000.0
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