sklearn.mixture
.BayesianGaussianMixture¶

class
sklearn.mixture.
BayesianGaussianMixture
(n_components=1, covariance_type='full', tol=0.001, reg_covar=1e06, max_iter=100, n_init=1, init_params='kmeans', weight_concentration_prior_type='dirichlet_process', weight_concentration_prior=None, mean_precision_prior=None, mean_prior=None, degrees_of_freedom_prior=None, covariance_prior=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10)[source]¶ Variational Bayesian estimation of a Gaussian mixture.
This class allows to infer an approximate posterior distribution over the parameters of a Gaussian mixture distribution. The effective number of components can be inferred from the data.
This class implements two types of prior for the weights distribution: a finite mixture model with Dirichlet distribution and an infinite mixture model with the Dirichlet Process. In practice Dirichlet Process inference algorithm is approximated and uses a truncated distribution with a fixed maximum number of components (called the Stickbreaking representation). The number of components actually used almost always depends on the data.
New in version 0.18.
Read more in the User Guide.
 Parameters
 n_componentsint, defaults to 1.
The number of mixture components. Depending on the data and the value of the
weight_concentration_prior
the model can decide to not use all the components by setting some componentweights_
to values very close to zero. The number of effective components is therefore smaller than n_components. covariance_type{‘full’, ‘tied’, ‘diag’, ‘spherical’}, defaults to ‘full’
String describing the type of covariance parameters to use. Must be one of:
'full' (each component has its own general covariance matrix), 'tied' (all components share the same general covariance matrix), 'diag' (each component has its own diagonal covariance matrix), 'spherical' (each component has its own single variance).
 tolfloat, defaults to 1e3.
The convergence threshold. EM iterations will stop when the lower bound average gain on the likelihood (of the training data with respect to the model) is below this threshold.
 reg_covarfloat, defaults to 1e6.
Nonnegative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive.
 max_iterint, defaults to 100.
The number of EM iterations to perform.
 n_initint, defaults to 1.
The number of initializations to perform. The result with the highest lower bound value on the likelihood is kept.
 init_params{‘kmeans’, ‘random’}, defaults to ‘kmeans’.
The method used to initialize the weights, the means and the covariances. Must be one of:
'kmeans' : responsibilities are initialized using kmeans. 'random' : responsibilities are initialized randomly.
 weight_concentration_prior_typestr, defaults to ‘dirichlet_process’.
String describing the type of the weight concentration prior. Must be one of:
'dirichlet_process' (using the Stickbreaking representation), 'dirichlet_distribution' (can favor more uniform weights).
 weight_concentration_priorfloat  None, optional.
The dirichlet concentration of each component on the weight distribution (Dirichlet). This is commonly called gamma in the literature. The higher concentration puts more mass in the center and will lead to more components being active, while a lower concentration parameter will lead to more mass at the edge of the mixture weights simplex. The value of the parameter must be greater than 0. If it is None, it’s set to
1. / n_components
. mean_precision_priorfloat  None, optional.
The precision prior on the mean distribution (Gaussian). Controls the extent of where means can be placed. Larger values concentrate the cluster means around
mean_prior
. The value of the parameter must be greater than 0. If it is None, it is set to 1. mean_priorarraylike, shape (n_features,), optional
The prior on the mean distribution (Gaussian). If it is None, it is set to the mean of X.
 degrees_of_freedom_priorfloat  None, optional.
The prior of the number of degrees of freedom on the covariance distributions (Wishart). If it is None, it’s set to
n_features
. covariance_priorfloat or arraylike, optional
The prior on the covariance distribution (Wishart). If it is None, the emiprical covariance prior is initialized using the covariance of X. The shape depends on
covariance_type
:(n_features, n_features) if 'full', (n_features, n_features) if 'tied', (n_features) if 'diag', float if 'spherical'
 random_stateint, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by
np.random
. warm_startbool, default to False.
If ‘warm_start’ is True, the solution of the last fitting is used as initialization for the next call of fit(). This can speed up convergence when fit is called several times on similar problems. See the Glossary.
 verboseint, default to 0.
Enable verbose output. If 1 then it prints the current initialization and each iteration step. If greater than 1 then it prints also the log probability and the time needed for each step.
 verbose_intervalint, default to 10.
Number of iteration done before the next print.
 Attributes
 weights_arraylike, shape (n_components,)
The weights of each mixture components.
 means_arraylike, shape (n_components, n_features)
The mean of each mixture component.
 covariances_arraylike
The covariance of each mixture component. The shape depends on
covariance_type
:(n_components,) if 'spherical', (n_features, n_features) if 'tied', (n_components, n_features) if 'diag', (n_components, n_features, n_features) if 'full'
 precisions_arraylike
The precision matrices for each component in the mixture. A precision matrix is the inverse of a covariance matrix. A covariance matrix is symmetric positive definite so the mixture of Gaussian can be equivalently parameterized by the precision matrices. Storing the precision matrices instead of the covariance matrices makes it more efficient to compute the loglikelihood of new samples at test time. The shape depends on
covariance_type
:(n_components,) if 'spherical', (n_features, n_features) if 'tied', (n_components, n_features) if 'diag', (n_components, n_features, n_features) if 'full'
 precisions_cholesky_arraylike
The cholesky decomposition of the precision matrices of each mixture component. A precision matrix is the inverse of a covariance matrix. A covariance matrix is symmetric positive definite so the mixture of Gaussian can be equivalently parameterized by the precision matrices. Storing the precision matrices instead of the covariance matrices makes it more efficient to compute the loglikelihood of new samples at test time. The shape depends on
covariance_type
:(n_components,) if 'spherical', (n_features, n_features) if 'tied', (n_components, n_features) if 'diag', (n_components, n_features, n_features) if 'full'
 converged_bool
True when convergence was reached in fit(), False otherwise.
 n_iter_int
Number of step used by the best fit of inference to reach the convergence.
 lower_bound_float
Lower bound value on the likelihood (of the training data with respect to the model) of the best fit of inference.
 weight_concentration_prior_tuple or float
The dirichlet concentration of each component on the weight distribution (Dirichlet). The type depends on
weight_concentration_prior_type
:(float, float) if 'dirichlet_process' (Beta parameters), float if 'dirichlet_distribution' (Dirichlet parameters).
The higher concentration puts more mass in the center and will lead to more components being active, while a lower concentration parameter will lead to more mass at the edge of the simplex.
 weight_concentration_arraylike, shape (n_components,)
The dirichlet concentration of each component on the weight distribution (Dirichlet).
 mean_precision_prior_float
The precision prior on the mean distribution (Gaussian). Controls the extent of where means can be placed. Larger values concentrate the cluster means around
mean_prior
. If mean_precision_prior is set to None,mean_precision_prior_
is set to 1. mean_precision_arraylike, shape (n_components,)
The precision of each components on the mean distribution (Gaussian).
 mean_prior_arraylike, shape (n_features,)
The prior on the mean distribution (Gaussian).
 degrees_of_freedom_prior_float
The prior of the number of degrees of freedom on the covariance distributions (Wishart).
 degrees_of_freedom_arraylike, shape (n_components,)
The number of degrees of freedom of each components in the model.
 covariance_prior_float or arraylike
The prior on the covariance distribution (Wishart). The shape depends on
covariance_type
:(n_features, n_features) if 'full', (n_features, n_features) if 'tied', (n_features) if 'diag', float if 'spherical'
See also
GaussianMixture
Finite Gaussian mixture fit with EM.
References
 R16529824bff21
 R16529824bff22
 R16529824bff23
Methods
fit
(self, X[, y])Estimate model parameters with the EM algorithm.
fit_predict
(self, X[, y])Estimate model parameters using X and predict the labels for X.
get_params
(self[, deep])Get parameters for this estimator.
predict
(self, X)Predict the labels for the data samples in X using trained model.
predict_proba
(self, X)Predict posterior probability of each component given the data.
sample
(self[, n_samples])Generate random samples from the fitted Gaussian distribution.
score
(self, X[, y])Compute the persample average loglikelihood of the given data X.
score_samples
(self, X)Compute the weighted log probabilities for each sample.
set_params
(self, \*\*params)Set the parameters of this estimator.

__init__
(self, n_components=1, covariance_type='full', tol=0.001, reg_covar=1e06, max_iter=100, n_init=1, init_params='kmeans', weight_concentration_prior_type='dirichlet_process', weight_concentration_prior=None, mean_precision_prior=None, mean_prior=None, degrees_of_freedom_prior=None, covariance_prior=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(self, X, y=None)[source]¶ Estimate model parameters with the EM algorithm.
The method fits the model
n_init
times and sets the parameters with which the model has the largest likelihood or lower bound. Within each trial, the method iterates between Estep and Mstep formax_iter
times until the change of likelihood or lower bound is less thantol
, otherwise, aConvergenceWarning
is raised. Ifwarm_start
isTrue
, thenn_init
is ignored and a single initialization is performed upon the first call. Upon consecutive calls, training starts where it left off. Parameters
 Xarraylike, shape (n_samples, n_features)
List of n_featuresdimensional data points. Each row corresponds to a single data point.
 Returns
 self

fit_predict
(self, X, y=None)[source]¶ Estimate model parameters using X and predict the labels for X.
The method fits the model n_init times and sets the parameters with which the model has the largest likelihood or lower bound. Within each trial, the method iterates between Estep and Mstep for
max_iter
times until the change of likelihood or lower bound is less thantol
, otherwise, aConvergenceWarning
is raised. After fitting, it predicts the most probable label for the input data points.New in version 0.20.
 Parameters
 Xarraylike, shape (n_samples, n_features)
List of n_featuresdimensional data points. Each row corresponds to a single data point.
 Returns
 labelsarray, shape (n_samples,)
Component labels.

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(self, X)[source]¶ Predict the labels for the data samples in X using trained model.
 Parameters
 Xarraylike, shape (n_samples, n_features)
List of n_featuresdimensional data points. Each row corresponds to a single data point.
 Returns
 labelsarray, shape (n_samples,)
Component labels.

predict_proba
(self, X)[source]¶ Predict posterior probability of each component given the data.
 Parameters
 Xarraylike, shape (n_samples, n_features)
List of n_featuresdimensional data points. Each row corresponds to a single data point.
 Returns
 resparray, shape (n_samples, n_components)
Returns the probability each Gaussian (state) in the model given each sample.

sample
(self, n_samples=1)[source]¶ Generate random samples from the fitted Gaussian distribution.
 Parameters
 n_samplesint, optional
Number of samples to generate. Defaults to 1.
 Returns
 Xarray, shape (n_samples, n_features)
Randomly generated sample
 yarray, shape (nsamples,)
Component labels

score
(self, X, y=None)[source]¶ Compute the persample average loglikelihood of the given data X.
 Parameters
 Xarraylike, shape (n_samples, n_dimensions)
List of n_featuresdimensional data points. Each row corresponds to a single data point.
 Returns
 log_likelihoodfloat
Log likelihood of the Gaussian mixture given X.

score_samples
(self, X)[source]¶ Compute the weighted log probabilities for each sample.
 Parameters
 Xarraylike, shape (n_samples, n_features)
List of n_featuresdimensional data points. Each row corresponds to a single data point.
 Returns
 log_probarray, shape (n_samples,)
Log probabilities of each data point in X.

set_params
(self, **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. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.