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32 lines
1.2 KiB
32 lines
1.2 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.stab import MaxStableSetInstance, MaxStableSetGenerator
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import networkx as nx
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import numpy as np
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def test_stab():
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graph = nx.cycle_graph(5)
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weights = [1.0, 2.0, 3.0, 4.0, 5.0]
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instance = MaxStableSetInstance(graph, weights)
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solver = LearningSolver()
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solver.solve(instance)
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assert instance.model.OBJ() == 8.0
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def test_stab_generator():
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graph = nx.cycle_graph(5)
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base_weights = [1.0, 2.0, 3.0, 4.0, 5.0]
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instances = MaxStableSetGenerator(graph=graph,
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base_weights=base_weights,
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perturbation_scale=1.0,
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).generate(100_000)
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weights = np.array([instance.weights for instance in instances])
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weights_avg = np.round(np.average(weights, axis=0), 2)
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weights_std = np.round(np.std(weights, axis=0), 2)
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assert list(weights_avg) == [1.50, 2.50, 3.50, 4.50, 5.50]
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assert list(weights_std) == [0.29] * 5
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