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OrthogonalMatchingPursuitCV

OrthogonalMatchingPursuitCV

Cross-validated Orthogonal Matching Pursuit model (OMP).

See glossary entry for cross-validation estimator.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new OrthogonalMatchingPursuitCV(opts?: object): OrthogonalMatchingPursuitCV;

Parameters

NameTypeDescription
opts?object-
opts.copy?booleanWhether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortran-ordered, otherwise a copy is made anyway. Default Value true
opts.cv?numberDetermines the cross-validation splitting strategy. Possible inputs for cv are:
opts.fit_intercept?booleanWhether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered). Default Value true
opts.max_iter?numberMaximum numbers of iterations to perform, therefore maximum features to include. 10% of n\_features but at least 5 if available.
opts.n_jobs?numberNumber of CPUs to use during the cross validation. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details.
opts.normalize?booleanThis parameter is ignored when fit\_intercept is set to false. If true, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False. Default Value false
opts.verbose?number | booleanSets the verbosity amount. Default Value false

Returns

OrthogonalMatchingPursuitCV

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:21 (opens in a new tab)

id

string

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:18 (opens in a new tab)

opts

any

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:19 (opens in a new tab)

Accessors

coef_

Parameter vector (w in the problem formulation).

Signature

coef_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:314 (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/linear_model/OrthogonalMatchingPursuitCV.ts:422 (opens in a new tab)

intercept_

Independent term in decision function.

Signature

intercept_(): Promise<number | ArrayLike>;

Returns

Promise<number | ArrayLike>

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:287 (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/linear_model/OrthogonalMatchingPursuitCV.ts:395 (opens in a new tab)

n_iter_

Number of active features across every target for the model refit with the best hyperparameters got by cross-validating across all folds.

Signature

n_iter_(): Promise<number | ArrayLike>;

Returns

Promise<number | ArrayLike>

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:368 (opens in a new tab)

n_nonzero_coefs_

Estimated number of non-zero coefficients giving the best mean squared error over the cross-validation folds.

Signature

n_nonzero_coefs_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:341 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:73 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:77 (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/linear_model/OrthogonalMatchingPursuitCV.ts:136 (opens in a new tab)

fit()

Fit the model using X, y as training data.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Training data.
opts.y?ArrayLikeTarget values. Will be cast to X’s dtype if necessary.

Returns

Promise<any>

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:153 (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/linear_model/OrthogonalMatchingPursuitCV.ts:86 (opens in a new tab)

predict()

Predict using the linear model.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?anySamples.

Returns

Promise<any>

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:197 (opens in a new tab)

score()

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y\_true \- y\_pred)\*\* 2).sum() and \(v\) is the total sum of squares ((y\_true \- y\_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n\_samples, n\_samples\_fitted), where n\_samples\_fitted is the number of samples used in the fitting for the estimator.
opts.sample_weight?ArrayLikeSample weights.
opts.y?ArrayLikeTrue values for X.

Returns

Promise<number>

Defined in: generated/linear_model/OrthogonalMatchingPursuitCV.ts:236 (opens in a new tab)