Class: 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.
Constructors
new BayesianGaussianMixture()
new BayesianGaussianMixture(
opts?):BayesianGaussianMixture
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.covariance_prior? | number | ArrayLike | The 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: |
opts.degrees_of_freedom_prior? | number | The 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: |
opts.max_iter? | number | The number of EM iterations to perform. |
opts.mean_precision_prior? | number | 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 undefined, it is set to 1. |
opts.mean_prior? | ArrayLike | The prior on the mean distribution (Gaussian). If it is undefined, it is set to the mean of X. |
opts.n_components? | number | 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 component weights_ to values very close to zero. The number of effective components is therefore smaller than n_components. |
opts.n_init? | number | The number of initializations to perform. The result with the highest lower bound value on the likelihood is kept. |
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 on the likelihood (of the training data with respect to the model) 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. See the Glossary. |
opts.weight_concentration_prior? | number | 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 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. |
Returns BayesianGaussianMixture
Defined in generated/mixture/BayesianGaussianMixture.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/mixture/BayesianGaussianMixture.ts:23 |
_isInitialized | boolean | false | generated/mixture/BayesianGaussianMixture.ts:22 |
_py | PythonBridge | undefined | generated/mixture/BayesianGaussianMixture.ts:21 |
id | string | undefined | generated/mixture/BayesianGaussianMixture.ts:18 |
opts | any | undefined | generated/mixture/BayesianGaussianMixture.ts:19 |
Accessors
converged_
Get Signature
get converged_():
Promise<boolean>
True when convergence of the best fit of inference was reached, false otherwise.
Returns Promise<boolean>
Defined in generated/mixture/BayesianGaussianMixture.ts:654
covariance_prior_
Get Signature
get covariance_prior_():
Promise<number|ArrayLike>
The prior on the covariance distribution (Wishart). The shape depends on covariance_type:
Returns Promise<number | ArrayLike>
Defined in generated/mixture/BayesianGaussianMixture.ts:924
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/BayesianGaussianMixture.ts:573
degrees_of_freedom_
Get Signature
get degrees_of_freedom_():
Promise<ArrayLike>
The number of degrees of freedom of each components in the model.
Returns Promise<ArrayLike>
Defined in generated/mixture/BayesianGaussianMixture.ts:897
degrees_of_freedom_prior_
Get Signature
get degrees_of_freedom_prior_():
Promise<number>
The prior of the number of degrees of freedom on the covariance distributions (Wishart).
Returns Promise<number>
Defined in generated/mixture/BayesianGaussianMixture.ts:870
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/BayesianGaussianMixture.ts:978
lower_bound_
Get Signature
get lower_bound_():
Promise<number>
Lower bound value on the model evidence (of the training data) of the best fit of inference.
Returns Promise<number>
Defined in generated/mixture/BayesianGaussianMixture.ts:708
mean_precision_
Get Signature
get mean_precision_():
Promise<ArrayLike>
The precision of each components on the mean distribution (Gaussian).
Returns Promise<ArrayLike>
Defined in generated/mixture/BayesianGaussianMixture.ts:816
mean_precision_prior_
Get Signature
get mean_precision_prior_():
Promise<number>
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.
Returns Promise<number>
Defined in generated/mixture/BayesianGaussianMixture.ts:789
mean_prior_
Get Signature
get mean_prior_():
Promise<ArrayLike>
The prior on the mean distribution (Gaussian).
Returns Promise<ArrayLike>
Defined in generated/mixture/BayesianGaussianMixture.ts:843
means_
Get Signature
get means_():
Promise<ArrayLike[]>
The mean of each mixture component.
Returns Promise<ArrayLike[]>
Defined in generated/mixture/BayesianGaussianMixture.ts:546
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/mixture/BayesianGaussianMixture.ts:951
n_iter_
Get Signature
get n_iter_():
Promise<number>
Number of step used by the best fit of inference to reach the convergence.
Returns Promise<number>
Defined in generated/mixture/BayesianGaussianMixture.ts:681
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/BayesianGaussianMixture.ts:600
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/BayesianGaussianMixture.ts:627
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/BayesianGaussianMixture.ts:139
weight_concentration_
Get Signature
get weight_concentration_():
Promise<ArrayLike>
The dirichlet concentration of each component on the weight distribution (Dirichlet).
Returns Promise<ArrayLike>
Defined in generated/mixture/BayesianGaussianMixture.ts:762
weight_concentration_prior_
Get Signature
get weight_concentration_prior_():
Promise<number>
The dirichlet concentration of each component on the weight distribution (Dirichlet). The type depends on weight_concentration_prior_type:
Returns Promise<number>
Defined in generated/mixture/BayesianGaussianMixture.ts:735
weights_
Get Signature
get weights_():
Promise<ArrayLike>
The weights of each mixture components.
Returns Promise<ArrayLike>
Defined in generated/mixture/BayesianGaussianMixture.ts:519
Methods
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/BayesianGaussianMixture.ts:195
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/BayesianGaussianMixture.ts:214
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/BayesianGaussianMixture.ts:255
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/BayesianGaussianMixture.ts:298
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/BayesianGaussianMixture.ts:152
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/BayesianGaussianMixture.ts:334
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/BayesianGaussianMixture.ts:370
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/BayesianGaussianMixture.ts:406
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/BayesianGaussianMixture.ts:444
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>