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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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