9.5.3. sklearn.mixture.VBGMM

class sklearn.mixture.VBGMM(n_components=1, cvtype='diag', alpha=1.0, random_state=None, thresh=0.01, verbose=False, min_covar=None)

Variational Inference for the Gaussian Mixture Model

Variational inference for a Gaussian mixture model probability distribution. This class allows for easy and efficient inference of an approximate posterior distribution over the parameters of a gaussian mixture model with a fixed number of components.

Initialization is with normally-distributed means and identity covariance, for proper convergence.

Parameters :

n_components: int, optional :

Number of mixture components. Defaults to 1.

cvtype: string (read-only), optional :

String describing the type of covariance parameters to use. Must be one of ‘spherical’, ‘tied’, ‘diag’, ‘full’. Defaults to ‘diag’.

alpha: float, optional :

Real number representing the concentration parameter of the dirichlet distribution. Intuitively, the higher the value of alpha the more likely the variational mixture of gaussians model will use all components it can. Defaults to 1.

See also

GMM

Finite gaussian mixture model fit with EM

DPGMM

Ininite gaussian mixture model, using the dirichlet

process, fit

Attributes

cvtype	Covariance type of the model.
weights	Mixing weights for each mixture component.
means	Mean parameters for each mixture component.
precisions	Return precisions as a full matrix.

n_features	int	Dimensionality of the Gaussians.
n_components	int (read-only)	Number of mixture components.
converged_	bool	True when convergence was reached in fit(), False otherwise.

Methods

decode(X)	Find most likely mixture components for each point in X.
eval(X)	Compute a lower-bound of the log likelihood of X under the model and an approximate posterior distribution over mixture components.
fit(X)	Estimate the posterior of themodel parameters from X using the variational mean-field algorithm.
predict(X)	Like decode, find most likely mixtures components for each observation in X.
rvs(n=1)	Generate n samples from the posterior for the model.
score(X)	Compute the log likelihood of X under the model.

__init__(n_components=1, cvtype='diag', alpha=1.0, random_state=None, thresh=0.01, verbose=False, min_covar=None)

cvtype

Covariance type of the model.

Must be one of ‘spherical’, ‘tied’, ‘diag’, ‘full’.

decode(obs)

Find most likely mixture components for each point in obs.

Parameters :

obs : array_like, shape (n, n_features)

List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns :

logprobs : array_like, shape (n_samples,)

Log probability of each point in obs under the model.

components : array_like, shape (n_samples,)

Index of the most likelihod mixture components for each observation

eval(obs=None)

Evaluate the model on data

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

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

Parameters :

obs : 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 obs

posteriors: array_like, shape (n_samples, n_components) :

Posterior probabilities of each mixture component for each observation

fit(X, n_iter=10, params='wmc', init_params='wmc')

Estimate model parameters with the variational algorithm.

For a full derivation and description of the algorithm see doc/dp-derivation/dp-derivation.tex

A initialization step is performed before entering the em algorithm. If you want to avoid this step, set the keyword argument init_params to the empty string ‘’. Likewise, if you would like just to do an initialization, call this method with 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.

n_iter : int, optional

Maximum number of iterations to perform before convergence.

params : string, optional

Controls which parameters are updated in the training process. Can contain any combination of ‘w’ for weights, ‘m’ for means, and ‘c’ for covars. Defaults to ‘wmc’.

init_params : string, optional

Controls which parameters are updated in the initialization process. Can contain any combination of ‘w’ for weights, ‘m’ for means, and ‘c’ for covars. Defaults to ‘wmc’.

means

Mean parameters for each mixture component.

precisions

Return precisions as a full matrix.

predict(X)

Predict label for data.

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

predict_proba(X)

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

Parameters :

X : array-like, shape = [n_samples, n_features]

Returns :

T : array-like, shape = (n_samples, n_components)

Returns the probability of the sample for each Gaussian (state) in the model.

rvs(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 :

obs : array_like, shape (n_samples, n_features)

List of samples

score(obs)

Compute the log probability under the model.

Parameters :

obs : 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 obs

set_params(**params)

Set the parameters of the estimator.

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

Returns :self :

weights

Mixing weights for each mixture component.