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LedoitWolf

LedoitWolf

LedoitWolf Estimator.

Ledoit-Wolf is a particular form of shrinkage, where the shrinkage coefficient is computed using O. Ledoit and M. Wolf’s formula as described in “A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices”, Ledoit and Wolf, Journal of Multivariate Analysis, Volume 88, Issue 2, February 2004, pages 365-411.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new LedoitWolf(opts?: object): LedoitWolf;

Parameters

NameTypeDescription
opts?object-
opts.assume_centered?booleanIf true, data will not be centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If false (default), data will be centered before computation. Default Value false
opts.block_size?numberSize of blocks into which the covariance matrix will be split during its Ledoit-Wolf estimation. This is purely a memory optimization and does not affect results. Default Value 1000
opts.store_precision?booleanSpecify if the estimated precision is stored. Default Value true

Returns

LedoitWolf

Defined in: generated/covariance/LedoitWolf.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/covariance/LedoitWolf.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/covariance/LedoitWolf.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/covariance/LedoitWolf.ts:21 (opens in a new tab)

id

string

Defined in: generated/covariance/LedoitWolf.ts:18 (opens in a new tab)

opts

any

Defined in: generated/covariance/LedoitWolf.ts:19 (opens in a new tab)

Accessors

covariance_

Estimated covariance matrix.

Signature

covariance_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/covariance/LedoitWolf.ts:327 (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/covariance/LedoitWolf.ts:446 (opens in a new tab)

location_

Estimated location, i.e. the estimated mean.

Signature

location_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/covariance/LedoitWolf.ts:352 (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/covariance/LedoitWolf.ts:421 (opens in a new tab)

precision_

Estimated pseudo inverse matrix. (stored only if store_precision is true)

Signature

precision_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/covariance/LedoitWolf.ts:375 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/covariance/LedoitWolf.ts:51 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/covariance/LedoitWolf.ts:55 (opens in a new tab)

shrinkage_

Coefficient in the convex combination used for the computation of the shrunk estimate. Range is [0, 1].

Signature

shrinkage_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/covariance/LedoitWolf.ts:398 (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/covariance/LedoitWolf.ts:106 (opens in a new tab)

error_norm()

Compute the Mean Squared Error between two covariance estimators.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.comp_cov?ArrayLike[]The covariance to compare with.
opts.norm?"frobenius" | "spectral"The type of norm used to compute the error. Available error types: - ‘frobenius’ (default): sqrt(tr(A^t.A)) - ‘spectral’: sqrt(max(eigenvalues(A^t.A)) where A is the error (comp\_cov \- self.covariance\_). Default Value 'frobenius'
opts.scaling?booleanIf true (default), the squared error norm is divided by n_features. If false, the squared error norm is not rescaled. Default Value true
opts.squared?booleanWhether to compute the squared error norm or the error norm. If true (default), the squared error norm is returned. If false, the error norm is returned. Default Value true

Returns

Promise<number>

Defined in: generated/covariance/LedoitWolf.ts:123 (opens in a new tab)

fit()

Fit the Ledoit-Wolf shrunk covariance model to X.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Training data, where n\_samples is the number of samples and n\_features is the number of features.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/covariance/LedoitWolf.ts:181 (opens in a new tab)

get_precision()

Getter for the precision matrix.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.precision_?ArrayLike[]The precision matrix associated to the current covariance object.

Returns

Promise<any>

Defined in: generated/covariance/LedoitWolf.ts:219 (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/covariance/LedoitWolf.ts:64 (opens in a new tab)

mahalanobis()

Compute the squared Mahalanobis distances of given observations.

Signature

mahalanobis(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit.

Returns

Promise<ArrayLike>

Defined in: generated/covariance/LedoitWolf.ts:252 (opens in a new tab)

score()

Compute the log-likelihood of X\_test under the estimated Gaussian model.

The Gaussian model is defined by its mean and covariance matrix which are represented respectively by self.location\_ and self.covariance\_.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X_test?ArrayLike[]Test data of which we compute the likelihood, where n\_samples is the number of samples and n\_features is the number of features. X\_test is assumed to be drawn from the same distribution than the data used in fit (including centering).
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<number>

Defined in: generated/covariance/LedoitWolf.ts:287 (opens in a new tab)