# sklearn.mixture.GMM¶

Warning

DEPRECATED

class sklearn.mixture.GMM(*args, **kwargs)[source]

Legacy Gaussian Mixture Model

Deprecated since version 0.18: This class will be removed in 0.20. Use sklearn.mixture.GaussianMixture instead.

Methods

 aic(X) Akaike information criterion for the current model fit and the proposed data. bic(X) Bayesian information criterion for the current model fit and the proposed data. fit(X[, y]) Estimate model parameters with the EM algorithm. fit_predict(X[, y]) Fit and then predict labels for data. get_params([deep]) Get parameters for this estimator. predict(X) Predict label for data. predict_proba(X) Predict posterior probability of data under each Gaussian in the model. sample([n_samples, random_state]) Generate random samples from the model. score(X[, y]) Compute the log probability under the model. score_samples(X) Return the per-sample likelihood of the data under the model. set_params(**params) Set the parameters of this estimator.
__init__(*args, **kwargs)[source]

DEPRECATED: The class GMM is deprecated in 0.18 and will be removed in 0.20. Use class GaussianMixture instead.

aic(X)[source]

Akaike information criterion for the current model fit and the proposed data.

Parameters: X : array of shape(n_samples, n_dimensions) aic : float (the lower the better)
bic(X)[source]

Bayesian information criterion for the current model fit and the proposed data.

Parameters: X : array of shape(n_samples, n_dimensions) bic : float (the lower the better)
fit(X, y=None)[source]

Estimate model parameters with the EM algorithm.

A initialization step is performed before entering the expectation-maximization (EM) algorithm. If you want to avoid this step, set the keyword argument init_params to the empty string ‘’ when creating the GMM object. Likewise, if you would like just to do an initialization, set n_iter=0.

Parameters: X : array_like, shape (n, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. self
fit_predict(X, y=None)[source]

Fit and then predict labels for data.

Warning: Due to the final maximization step in the EM algorithm, with low iterations the prediction may not be 100% accurate.

New in version 0.17: fit_predict method in Gaussian Mixture Model.

Parameters: X : array-like, shape = [n_samples, n_features] C : array, shape = (n_samples,) component memberships
get_params(deep=True)[source]

Get parameters for this estimator.

Parameters: deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. params : mapping of string to any Parameter names mapped to their values.
predict(X)[source]

Predict label for data.

Parameters: X : array-like, shape = [n_samples, n_features] C : array, shape = (n_samples,) component memberships
predict_proba(X)[source]

Predict posterior probability of data under each Gaussian in the model.

Parameters: X : array-like, shape = [n_samples, n_features] responsibilities : array-like, shape = (n_samples, n_components) Returns the probability of the sample for each Gaussian (state) in the model.
sample(n_samples=1, random_state=None)[source]

Generate random samples from the model.

Parameters: n_samples : int, optional Number of samples to generate. Defaults to 1. X : array_like, shape (n_samples, n_features) List of samples
score(X, y=None)[source]

Compute the log probability under the model.

Parameters: X : array_like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. logprob : array_like, shape (n_samples,) Log probabilities of each data point in X
score_samples(X)[source]

Return the per-sample likelihood of the data under the model.

Compute the log probability of X under the model and return the posterior distribution (responsibilities) of each mixture component for each element of X.

Parameters: X : array_like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. logprob : array_like, shape (n_samples,) Log probabilities of each data point in X. responsibilities : array_like, shape (n_samples, n_components) Posterior probabilities of each mixture component for each observation
set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns: self