Parameters for Gaussian Mixture Model clustering.
| Name | Type | Description | Notes |
|---|---|---|---|
| n_components | int | Number of mixture components | [optional] [default to 1] |
| covariance_type | str | Type of covariance parameters ('full', 'tied', 'diag', 'spherical') | [optional] [default to 'full'] |
| tol | float | Convergence threshold | [optional] [default to 0.001] |
| reg_covar | float | Regularization added to the diagonal of covariance | [optional] [default to 1.0E-6] |
| max_iter | int | Maximum number of EM iterations | [optional] [default to 100] |
| n_init | int | Number of initializations to perform | [optional] [default to 1] |
| init_params | str | Method used to initialize weights, means and covariances ('kmeans' or 'random') | [optional] [default to 'kmeans'] |
| weights_init | List[object] | Initial weights | [optional] |
| means_init | List[object] | Initial means | [optional] |
| precisions_init | List[object] | Initial precisions | [optional] |
| random_state | int | Random seed for reproducibility | [optional] [default to 42] |
| warm_start | bool | If True, use the solution of the last fit as initialization | [optional] [default to False] |
| verbose | int | Enable verbose output | [optional] [default to 0] |
| verbose_interval | int | Number of iterations between each verbose message | [optional] [default to 10] |
from mixpeek.models.gaussian_mixture_params import GaussianMixtureParams
# TODO update the JSON string below
json = "{}"
# create an instance of GaussianMixtureParams from a JSON string
gaussian_mixture_params_instance = GaussianMixtureParams.from_json(json)
# print the JSON string representation of the object
print(GaussianMixtureParams.to_json())
# convert the object into a dict
gaussian_mixture_params_dict = gaussian_mixture_params_instance.to_dict()
# create an instance of GaussianMixtureParams from a dict
gaussian_mixture_params_from_dict = GaussianMixtureParams.from_dict(gaussian_mixture_params_dict)