# Clustering
## GMM
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### select_n_components
``` python
def select_n_components(
X, ks, n_init:int=5, max_iter:int=100, covariant_reg:float=1e-06, init_params:str='kmeans', random_seed:int=0
):
```
*Try multiple K values and several initializations, return a summary
dict.*
Returns: results: dict keyed by K containing the best model and metrics
(bic, aic, avg_log_lik, silhouette if possible).
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### GaussianMixture
``` python
def GaussianMixture(
n_components, tol:float=1e-06, max_iter:int=100, reg_covar:float=1e-06, init_params:str='kmeans'
):
```
*Gaussian Mixture Model with EM algorithm and K-means initialization.*
Args:
n_components (int): Number of Gaussian components.
tol (float): Convergence tolerance.
max_iter (int): Maximum number of EM iterations.
reg_covar (float): Regularization for covariance matrix.
init_params (str): Initialization method ('kmeans' or 'random').
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### GaussianMixture.fit
``` python
def fit(
X, key:ArrayImpl=Array([0, 0], dtype=uint32)
):
```
*Fit the Gaussian mixture model using EM.*
X : array (N, D) key : PRNGKey for random init