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39 lines
1.1 KiB
39 lines
1.1 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 numpy.linalg import norm
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from miplearn.classifiers.counting import CountingClassifier
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E = 0.1
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def test_counting() -> None:
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clf = CountingClassifier()
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n_features = 25
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x_train = np.zeros((8, n_features))
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y_train = np.array(
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[
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[True, False, False],
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[True, False, False],
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[False, True, False],
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[True, False, False],
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[False, True, False],
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[False, True, False],
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[False, True, False],
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[False, False, True],
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]
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)
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x_test = np.zeros((2, n_features))
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y_expected = np.array(
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[
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[3 / 8.0, 4 / 8.0, 1 / 8.0],
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[3 / 8.0, 4 / 8.0, 1 / 8.0],
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]
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)
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clf.fit(x_train, y_train)
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y_actual = clf.predict_proba(x_test)
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assert norm(y_actual - y_expected) < E
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