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44 lines
1.7 KiB
44 lines
1.7 KiB
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
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# Written by Alinson S. Xavier <axavier@anl.gov>
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from miplearn import LearningSolver
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from miplearn.problems.knapsack import MultiKnapsackGenerator, MultiKnapsackInstance
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from scipy.stats import uniform, randint
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import numpy as np
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def test_knapsack_generator():
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gen = MultiKnapsackGenerator(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.
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assert round(np.mean(p_sum), -1) == 1250.
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assert round(np.mean(b_sum), -3) == 25000.
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def test_knapsack_instance():
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instance = MultiKnapsackInstance(
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prices=np.array([5.0, 10.0, 15.0]),
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capacities=np.array([20.0, 30.0]),
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weights=np.array([
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[5.0, 5.0, 5.0],
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[5.0, 10.0, 15.0],
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])
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)
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assert (instance.get_instance_features() == np.array([
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5.0, 10.0, 15.0, 20.0, 30.0, 5.0, 5.0, 5.0, 5.0, 10.0, 15.0
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])).all()
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solver = LearningSolver()
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results = solver.solve(instance)
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assert results["Problem"][0]["Lower bound"] == 30.0 |