CauchyMixture

noloox.mixture.CauchyMixture

Bases: StudentsTMixture

Cauchy Mixture Model. This class allows you to estimate a multivariate mixture of Cauchys over your data. Equivalent to StudentsTMixture, except the degrees of freedom is fixed to 1.

Parameters:

Name	Type	Description	Default
n_components	int	The number of mixture components.	required
tol	float	The convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold.	1e-05
reg_covar	float	Non-negative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive.	1e-06
max_iter	int	The number of EM iterations to perform.	1000

Attributes:

Name	Type	Description
weights_	array-like of shape (n_components,)	The weights of each mixture components.
means_	array-like of shape (n_components, n_features)	The mean of each mixture component.
n_iter_	int	Number of step used by the best fit of EM to reach the convergence.
converged_	bool	True when convergence of the best fit of EM was reached, False otherwise.

Source code in noloox/mixture/tmm.py

class CauchyMixture(StudentsTMixture):
    """Cauchy Mixture Model.
    This class allows you to estimate a multivariate mixture of Cauchys over your data.
    Equivalent to StudentsTMixture, except the degrees of freedom is fixed to 1.

    Parameters
    ----------
    n_components: int
        The number of mixture components.
    tol: float, default=1e-5
        The convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold.
    reg_covar: float, default=1e-6
        Non-negative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive.
    max_iter: int, default=1000
        The number of EM iterations to perform.

    Attributes
    ----------
    weights_: array-like of shape (n_components,)
        The weights of each mixture components.
    means_: array-like of shape (n_components, n_features)
        The mean of each mixture component.
    n_iter_: int
        Number of step used by the best fit of EM to reach the convergence.
    converged_: bool
        True when convergence of the best fit of EM was reached, False otherwise.
    """

    def __init__(
        self,
        n_components: int,
        tol: float = 1e-5,
        reg_covar: float = 1e-06,
        max_iter: int = 1000,
        random_state=None,
    ):
        super().__init__(
            n_components=n_components,
            tol=tol,
            reg_covar=reg_covar,
            max_iter=max_iter,
            random_state=random_state,
            df=1.0,
        )