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# Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import numpy as np import pytest from sklearn.mixture import BayesianGaussianMixture, GaussianMixture @pytest.mark.parametrize("estimator", [GaussianMixture(), BayesianGaussianMixture()]) def test_gaussian_mixture_n_iter(estimator): # check that n_iter is the number of iteration performed. rng = np.random.RandomState(0) X = rng.rand(10, 5) max_iter = 1 estimator.set_params(max_iter=max_iter) estimator.fit(X) assert estimator.n_iter_ == max_iter @pytest.mark.parametrize("estimator", [GaussianMixture(), BayesianGaussianMixture()]) def test_mixture_n_components_greater_than_n_samples_error(estimator): """Check error when n_components <= n_samples""" rng = np.random.RandomState(0) X = rng.rand(10, 5) estimator.set_params(n_components=12) msg = "Expected n_samples >= n_components" with pytest.raises(ValueError, match=msg): estimator.fit(X)
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