src/main/python/tests/algorithms/test_gmm.py

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import unittest

from systemds.context import SystemDSContext
from systemds.operator.algorithm import gmm, gmmPredict


class TestGMM(unittest.TestCase):

    sds: SystemDSContext = None

    @classmethod
    def setUpClass(cls):
        cls.sds = SystemDSContext(capture_stdout=True, logging_level=50)

    @classmethod
    def tearDownClass(cls):
        cls.sds.close()

    def test_lm_simple(self):
        a = self.sds.rand(500, 10, -100, 100, pdf="normal", seed=10)
        features = a  # training data all not outliers

        notOutliers = self.sds.rand(10, 10, -1, 1, seed=10)  # inside a
        outliers = self.sds.rand(10, 10, 1150, 1200, seed=10)  # outliers

        test = outliers.rbind(notOutliers)  # testing data half outliers

        n_gaussian = 4

        [_, _, _, _, mu, precision_cholesky, weight] = gmm(
            features, n_components=n_gaussian, seed=10
        )

        [_, pp] = gmmPredict(
            test, weight, mu, precision_cholesky, model=self.sds.scalar("VVV")
        )

        outliers = pp.max(axis=1) < 0.99
        ret = outliers.compute()

        self.assertTrue(ret.sum() == 10)


if __name__ == "__main__":
    unittest.main(exit=False)