def _check_parameters(self, X):
        if self.covariance_type not in ['spherical', 'tied', 'diag', 'full']:
            raise ValueError("Invalid value for 'covariance_type': %s "
                             "'covariance_type' should be in "
                             "['spherical', 'tied', 'diag', 'full']"
                             % self.covariance_type)

        if self.weights_init is not None:
            self.weights_init = _check_weights(self.weights_init,
                                               self.n_components)

        if self.means_init is not None:
            self.means_init = _check_means(self.means_init,
                                           self.n_components, X.shape[1])

        if self.covariances_init is not None:
            self.covariances_init = _check_covariances(self.covariances_init,
                                                       self.covariance_type,
                                                       self.n_components,
                                                       X.shape[1])
Test smoothing part of ExponentialSmoothing a second time.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/mixture/gaussian_mixture.py/GaussianMixture._check_parameters--crossover with--dataset/ETHPy150Open T-002/pycast/pycast/tests/methodtest.py/ExponentialSmoothingTest.second_smoothing_test