mixture.GMM()

Legacy Gaussian Mixture Model

Methods

__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)
Returns:	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)
Returns:	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.
Returns:	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]
Returns:	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.
Returns:	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]
Returns:	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]
Returns:	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.
Returns:	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.
Returns:	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.
Returns:	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 :