Class: GaussianMixture

Gaussian Mixture.

Representation of a Gaussian mixture model probability distribution. This class allows to estimate the parameters of a Gaussian mixture distribution.

Constructors

new GaussianMixture()

new GaussianMixture(opts?): GaussianMixture

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.covariance_type`?	`"full"` \| `"tied"` \| `"diag"` \| `"spherical"`	String describing the type of covariance parameters to use. Must be one of:
`opts.init_params`?	`"k-means++"` \| `"random"` \| `"kmeans"` \| `"random_from_data"`	The method used to initialize the weights, the means and the precisions. String must be one of:
`opts.max_iter`?	`number`	The number of EM iterations to perform.
`opts.means_init`?	`ArrayLike`[]	The user-provided initial means, If it is `undefined`, means are initialized using the `init_params` method.
`opts.n_components`?	`number`	The number of mixture components.
`opts.n_init`?	`number`	The number of initializations to perform. The best results are kept.
`opts.precisions_init`?	`ArrayLike`	The user-provided initial precisions (inverse of the covariance matrices). If it is `undefined`, precisions are initialized using the ‘init_params’ method. The shape depends on ‘covariance_type’:
`opts.random_state`?	`number`	Controls 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`?	`number`	Non-negative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive.
`opts.tol`?	`number`	The convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold.
`opts.verbose`?	`number`	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.
`opts.verbose_interval`?	`number`	Number of iteration done before the next print.
`opts.warm_start`?	`boolean`	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. In that case, ‘n_init’ is ignored and only a single initialization occurs upon the first call. See the Glossary.
`opts.weights_init`?	`ArrayLike`	The user-provided initial weights. If it is `undefined`, weights are initialized using the `init_params` method.

Returns GaussianMixture

Defined in generated/mixture/GaussianMixture.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/mixture/GaussianMixture.ts:23
`_isInitialized`	`boolean`	`false`	generated/mixture/GaussianMixture.ts:22
`_py`	`PythonBridge`	`undefined`	generated/mixture/GaussianMixture.ts:21
`id`	`string`	`undefined`	generated/mixture/GaussianMixture.ts:18
`opts`	`any`	`undefined`	generated/mixture/GaussianMixture.ts:19

Accessors

converged_

Get Signature

get converged_(): Promise<boolean>

True when convergence of the best fit of EM was reached, false otherwise.

Returns Promise<boolean>

Defined in generated/mixture/GaussianMixture.ts:663

covariances_

Get Signature

get covariances_(): Promise<ArrayLike>

The covariance of each mixture component. The shape depends on covariance_type:

Returns Promise<ArrayLike>

Defined in generated/mixture/GaussianMixture.ts:588

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

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

Returns Promise<ArrayLike>

Defined in generated/mixture/GaussianMixture.ts:763

lower_bound_

Get Signature

get lower_bound_(): Promise<number>

Lower bound value on the log-likelihood (of the training data with respect to the model) of the best fit of EM.

Returns Promise<number>

Defined in generated/mixture/GaussianMixture.ts:713

means_

Get Signature

get means_(): Promise<ArrayLike[]>

The mean of each mixture component.

Returns Promise<ArrayLike[]>

Defined in generated/mixture/GaussianMixture.ts:563

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/mixture/GaussianMixture.ts:738

n_iter_

Get Signature

get n_iter_(): Promise<number>

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

Returns Promise<number>

Defined in generated/mixture/GaussianMixture.ts:688

precisions_

Get Signature

get precisions_(): Promise<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 log-likelihood of new samples at test time. The shape depends on covariance_type:

Returns Promise<ArrayLike>

Defined in generated/mixture/GaussianMixture.ts:613

precisions_cholesky_

Get Signature

get precisions_cholesky_(): Promise<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 log-likelihood of new samples at test time. The shape depends on covariance_type:

Returns Promise<ArrayLike>

Defined in generated/mixture/GaussianMixture.ts:638

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/mixture/GaussianMixture.ts:120

weights_

Get Signature

get weights_(): Promise<ArrayLike>

The weights of each mixture components.

Returns Promise<ArrayLike>

Defined in generated/mixture/GaussianMixture.ts:538

Methods

aic()

aic(opts): Promise<number>

Akaike information criterion for the current model on the input X.

You can refer to this mathematical section for more details regarding the formulation of the AIC used.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`[]	The input samples.

Returns Promise<number>

Defined in generated/mixture/GaussianMixture.ts:191

bic()

bic(opts): Promise<number>

Bayesian information criterion for the current model on the input X.

You can refer to this mathematical section for more details regarding the formulation of the BIC used.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`[]	The input samples.

Returns Promise<number>

Defined in generated/mixture/GaussianMixture.ts:225

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

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

Returns Promise<void>

Defined in generated/mixture/GaussianMixture.ts:172

fit()

fit(opts): Promise<any>

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.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	List of n_features-dimensional data points. Each row corresponds to a single data point.
`opts.y`?	`any`	Not used, present for API consistency by convention.

Returns Promise<any>

Defined in generated/mixture/GaussianMixture.ts:259

fit_predict()

fit_predict(opts): Promise<any>

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.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	List of n_features-dimensional data points. Each row corresponds to a single data point.
`opts.y`?	`any`	Not used, present for API consistency by convention.

Returns Promise<any>

Defined in generated/mixture/GaussianMixture.ts:298

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.routing`?	`any`	A `MetadataRequest` encapsulating routing information.

Returns Promise<any>

Defined in generated/mixture/GaussianMixture.ts:337

init()

init(py): Promise<void>

Initializes the underlying Python resources.

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

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/mixture/GaussianMixture.ts:133

predict()

predict(opts): Promise<any>

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

Parameters

Parameter	Type	Description
`opts`	`object`	-
`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/GaussianMixture.ts:371

predict_proba()

predict_proba(opts): Promise<any>

Evaluate the components’ density for each sample.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`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/GaussianMixture.ts:403

sample()

sample(opts): Promise<any>

Generate random samples from the fitted Gaussian distribution.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.n_samples`?	`number`	Number of samples to generate.

Returns Promise<any>

Defined in generated/mixture/GaussianMixture.ts:435

score()

score(opts): Promise<number>

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

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	List of n_features-dimensional data points. Each row corresponds to a single data point.
`opts.y`?	`any`	Not used, present for API consistency by convention.

Returns Promise<number>

Defined in generated/mixture/GaussianMixture.ts:469

score_samples()

score_samples(opts): Promise<any>

Compute the log-likelihood of each sample.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`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/GaussianMixture.ts:506

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