sklearn.mixture.DPGMM

Warning

DEPRECATED

class sklearn.mixture.DPGMM(*args, **kwargs)

Dirichlet Process Gaussian Mixture Models

Deprecated since version 0.18: This class will be removed in 0.20. Use sklearn.mixture.BayesianGaussianMixture with parameter weight_concentration_prior_type='dirichlet_process' 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.
lower_bound(X, z)	returns a lower bound on model evidence based on X and membership
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 likelihood of the data under the model.
set_params(**params)	Set the parameters of this estimator.

__init__(*args, **kwargs)

DEPRECATED: The DPGMM class is not working correctly and it’s better to use sklearn.mixture.BayesianGaussianMixture class with parameter weight_concentration_prior_type=’dirichlet_process’ instead. DPGMM is deprecated in 0.18 and will be removed in 0.20.

aic(X)

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)

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)

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)

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)

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.

lower_bound(X, z)

returns a lower bound on model evidence based on X and membership

predict(X)

Predict label for data.

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

predict_proba(X)

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)

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)

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)

Return the likelihood of the data under the model.

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

This is done by computing the parameters for the mean-field of z for each observation.

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)

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 :