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Classes
BayesianGaussianMixture

BayesianGaussianMixture

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 Stick-breaking representation). The number of components actually used almost always depends on the data.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new BayesianGaussianMixture(opts?: object): BayesianGaussianMixture;

Parameters

NameTypeDescription
opts?object-
opts.covariance_prior?number | ArrayLikeThe prior on the covariance distribution (Wishart). If it is undefined, the emiprical covariance prior is initialized using the covariance of X. The shape depends on covariance\_type:
opts.covariance_type?"full" | "tied" | "diag" | "spherical"String describing the type of covariance parameters to use. Must be one of: Default Value 'full'
opts.degrees_of_freedom_prior?numberThe prior of the number of degrees of freedom on the covariance distributions (Wishart). If it is undefined, it’s set to n\_features.
opts.init_params?"k-means++" | "random" | "kmeans" | "random_from_data"The method used to initialize the weights, the means and the covariances. String must be one of: Default Value 'kmeans'
opts.max_iter?numberThe number of EM iterations to perform. Default Value 100
opts.mean_precision_prior?numberThe 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 undefined, it is set to 1.
opts.mean_prior?ArrayLikeThe prior on the mean distribution (Gaussian). If it is undefined, it is set to the mean of X.
opts.n_components?numberThe 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 component weights\_ to values very close to zero. The number of effective components is therefore smaller than n_components. Default Value 1
opts.n_init?numberThe number of initializations to perform. The result with the highest lower bound value on the likelihood is kept. Default Value 1
opts.random_state?numberControls the random seed given to the method chosen to initialize the parameters (see init\_params). In addition, it controls the generation of random samples from the fitted distribution (see the method sample). Pass an int for reproducible output across multiple function calls. See Glossary.
opts.reg_covar?numberNon-negative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive. Default Value 0.000001
opts.tol?numberThe 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. Default Value 0.001
opts.verbose?numberEnable 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. Default Value 0
opts.verbose_interval?numberNumber of iteration done before the next print. Default Value 10
opts.warm_start?booleanIf ‘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. Default Value false
opts.weight_concentration_prior?numberThe 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 undefined, it’s set to 1. / n\_components.
opts.weight_concentration_prior_type?"dirichlet_process" | "dirichlet_distribution"String describing the type of the weight concentration prior. Default Value 'dirichlet_process'

Returns

BayesianGaussianMixture

Defined in: generated/mixture/BayesianGaussianMixture.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/mixture/BayesianGaussianMixture.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/mixture/BayesianGaussianMixture.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/mixture/BayesianGaussianMixture.ts:21 (opens in a new tab)

id

string

Defined in: generated/mixture/BayesianGaussianMixture.ts:18 (opens in a new tab)

opts

any

Defined in: generated/mixture/BayesianGaussianMixture.ts:19 (opens in a new tab)

Accessors

converged_

True when convergence was reached in fit(), false otherwise.

Signature

converged_(): Promise<boolean>;

Returns

Promise<boolean>

Defined in: generated/mixture/BayesianGaussianMixture.ts:651 (opens in a new tab)

covariance_prior_

The prior on the covariance distribution (Wishart). The shape depends on covariance\_type:

Signature

covariance_prior_(): Promise<number | ArrayLike>;

Returns

Promise<number | ArrayLike>

Defined in: generated/mixture/BayesianGaussianMixture.ts:921 (opens in a new tab)

covariances_

The covariance of each mixture component. The shape depends on covariance\_type:

Signature

covariances_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/mixture/BayesianGaussianMixture.ts:570 (opens in a new tab)

degrees_of_freedom_

The number of degrees of freedom of each components in the model.

Signature

degrees_of_freedom_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/mixture/BayesianGaussianMixture.ts:894 (opens in a new tab)

degrees_of_freedom_prior_

The prior of the number of degrees of freedom on the covariance distributions (Wishart).

Signature

degrees_of_freedom_prior_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/mixture/BayesianGaussianMixture.ts:867 (opens in a new tab)

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/mixture/BayesianGaussianMixture.ts:975 (opens in a new tab)

lower_bound_

Lower bound value on the model evidence (of the training data) of the best fit of inference.

Signature

lower_bound_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/mixture/BayesianGaussianMixture.ts:705 (opens in a new tab)

mean_precision_

The precision of each components on the mean distribution (Gaussian).

Signature

mean_precision_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/mixture/BayesianGaussianMixture.ts:813 (opens in a new tab)

mean_precision_prior_

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 undefined, mean\_precision\_prior\_ is set to 1.

Signature

mean_precision_prior_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/mixture/BayesianGaussianMixture.ts:786 (opens in a new tab)

mean_prior_

The prior on the mean distribution (Gaussian).

Signature

mean_prior_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/mixture/BayesianGaussianMixture.ts:840 (opens in a new tab)

means_

The mean of each mixture component.

Signature

means_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/mixture/BayesianGaussianMixture.ts:543 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/mixture/BayesianGaussianMixture.ts:948 (opens in a new tab)

n_iter_

Number of step used by the best fit of inference to reach the convergence.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/mixture/BayesianGaussianMixture.ts:678 (opens in a new tab)

precisions_

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 log-likelihood of new samples at test time. The shape depends on covariance\_type:

Signature

precisions_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/mixture/BayesianGaussianMixture.ts:597 (opens in a new tab)

precisions_cholesky_

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 log-likelihood of new samples at test time. The shape depends on covariance\_type:

Signature

precisions_cholesky_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/mixture/BayesianGaussianMixture.ts:624 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/mixture/BayesianGaussianMixture.ts:139 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/mixture/BayesianGaussianMixture.ts:143 (opens in a new tab)

weight_concentration_

The dirichlet concentration of each component on the weight distribution (Dirichlet).

Signature

weight_concentration_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/mixture/BayesianGaussianMixture.ts:759 (opens in a new tab)

weight_concentration_prior_

The dirichlet concentration of each component on the weight distribution (Dirichlet). The type depends on weight\_concentration\_prior\_type:

Signature

weight_concentration_prior_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/mixture/BayesianGaussianMixture.ts:732 (opens in a new tab)

weights_

The weights of each mixture components.

Signature

weights_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/mixture/BayesianGaussianMixture.ts:516 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/mixture/BayesianGaussianMixture.ts:220 (opens in a new tab)

fit()

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 E-step and M-step for max\_iter times until the change of likelihood or lower bound is less than tol, otherwise, a ConvergenceWarning is raised. If warm\_start is true, then n\_init is ignored and a single initialization is performed upon the first call. Upon consecutive calls, training starts where it left off.

Signature

fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]List of n_features-dimensional data points. Each row corresponds to a single data point.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/mixture/BayesianGaussianMixture.ts:239 (opens in a new tab)

fit_predict()

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 E-step and M-step for max\_iter times until the change of likelihood or lower bound is less than tol, otherwise, a ConvergenceWarning is raised. After fitting, it predicts the most probable label for the input data points.

Signature

fit_predict(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]List of n_features-dimensional data points. Each row corresponds to a single data point.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/mixture/BayesianGaussianMixture.ts:281 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/mixture/BayesianGaussianMixture.ts:152 (opens in a new tab)

predict()

Predict the labels for the data samples in X using trained model.

Signature

predict(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

Promise<any>

Defined in: generated/mixture/BayesianGaussianMixture.ts:324 (opens in a new tab)

predict_proba()

Evaluate the components’ density for each sample.

Signature

predict_proba(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

Promise<any>

Defined in: generated/mixture/BayesianGaussianMixture.ts:361 (opens in a new tab)

sample()

Generate random samples from the fitted Gaussian distribution.

Signature

sample(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.n_samples?numberNumber of samples to generate. Default Value 1

Returns

Promise<any>

Defined in: generated/mixture/BayesianGaussianMixture.ts:399 (opens in a new tab)

score()

Compute the per-sample average log-likelihood of the given data X.

Signature

score(opts: object): Promise<number>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]List of n_features-dimensional data points. Each row corresponds to a single data point.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<number>

Defined in: generated/mixture/BayesianGaussianMixture.ts:438 (opens in a new tab)

score_samples()

Compute the log-likelihood of each sample.

Signature

score_samples(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

Promise<any>

Defined in: generated/mixture/BayesianGaussianMixture.ts:478 (opens in a new tab)