Add Gaussian Mixture Model (GMM) Algorithm for Clustering #13637
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| Gaussian Mixture Model implemented using the Expectation-Maximization algorithm. | ||
| """ | ||
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| def __init__(self, n_components=2, max_iter=100, tol=1e-4, seed=None): |
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Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide type hint for the parameter: n_components
Please provide type hint for the parameter: max_iter
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| self.covariances_ = None | ||
| self.log_likelihoods_ = [] | ||
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| def _initialize_parameters(self, X): |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/gaussian_mixture_model.py, please provide doctest for the function _initialize_parameters
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| ) | ||
| self.weights_ = np.ones(self.n_components) / self.n_components | ||
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| def _e_step(self, X): |
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| responsibilities /= responsibilities.sum(axis=1, keepdims=True) | ||
| return responsibilities | ||
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| def _m_step(self, X, responsibilities): |
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| def _m_step(self, X, responsibilities): | ||
| """Update weights, means, and covariances""" | ||
| n_samples, n_features = X.shape | ||
| Nk = responsibilities.sum(axis=0) |
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: Nk
| # Add small regularization term for numerical stability | ||
| self.covariances_[k] += np.eye(n_features) * 1e-6 | ||
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| def _compute_log_likelihood(self, X): |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/gaussian_mixture_model.py, please provide doctest for the function _compute_log_likelihood
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| log_likelihood = np.sum(np.log(np.sum(total_pdf, axis=1) + 1e-12)) | ||
| return log_likelihood | ||
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| def fit(self, X): |
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| print(f"{TAG}Training complete. Final log-likelihood: {log_likelihood:.4f}") | ||
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| def predict(self, X): |
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| responsibilities = self._e_step(X) | ||
| return np.argmax(responsibilities, axis=1) | ||
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| def plot_results(self, X): |
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| warnings.filterwarnings("ignore") | ||
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| TAG = "GAUSSIAN-MIXTURE/ " | ||
| FloatArray: TypeAlias = NDArray[np.float64] |
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Describe your change:
This pull request adds a new implementation of the Gaussian Mixture Model (GMM) algorithm under the
machine_learning/directory.The GMM is an unsupervised learning algorithm that represents data as a mixture of multiple Gaussian distributions.
It uses the Expectation–Maximization (EM) algorithm to iteratively estimate each component’s mean, covariance, and mixing probability.
Key Highlights:
References:
Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1–22.
Checklist: