# Module `miplearn.classifiers.tests.test_counting`

Expand source code

    #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
    #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
    #  Released under the modified BSD license. See COPYING.md for more details.

    import numpy as np
    from numpy.linalg import norm

    from miplearn.classifiers.counting import CountingClassifier

    E = 0.1

    def test_counting():
        clf = CountingClassifier()
        clf.fit(np.zeros((8, 25)), [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])
        expected_proba = np.array([[0.375, 0.625], [0.375, 0.625]])
        actual_proba = clf.predict_proba(np.zeros((2, 25)))
        assert norm(actual_proba - expected_proba) < E

## Functions

`def test_counting()`
  Expand source code

      def test_counting():
          clf = CountingClassifier()
          clf.fit(np.zeros((8, 25)), [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])
          expected_proba = np.array([[0.375, 0.625], [0.375, 0.625]])
          actual_proba = clf.predict_proba(np.zeros((2, 25)))
          assert norm(actual_proba - expected_proba) < E