OrthogonalMatchingPursuitCV

Cross-validated Orthogonal Matching Pursuit model (OMP).

See glossary entry for cross-validation estimator.

Constructors

constructor()

Signature

new OrthogonalMatchingPursuitCV(opts?: object): OrthogonalMatchingPursuitCV;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.copy?`	`boolean`	Whether 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?`	`number`	Determines the cross-validation splitting strategy. Possible inputs for cv are:
`opts.fit_intercept?`	`boolean`	Whether 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?`	`number`	Maximum numbers of iterations to perform, therefore maximum features to include. 10% of `n\_features` but at least 5 if available.
`opts.n_jobs?`	`number`	Number 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?`	`boolean`	This 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` \| `boolean`	Sets 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

Name	Type
`pythonBridge`	`PythonBridge`

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

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Training data.
`opts.y?`	`ArrayLike`	Target 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

Name	Type
`py`	`PythonBridge`

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

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`	Samples.

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

Name	Type	Description
`opts`	`object`	-
`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?`	`ArrayLike`	Sample weights.
`opts.y?`	`ArrayLike`	True values for `X`.

Returns

Promise<number>

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

OrthogonalMatchingPursuit OutputCodeClassifier