OrthogonalMatchingPursuit

Orthogonal Matching Pursuit model (OMP).

Constructors

constructor()

Signature

new OrthogonalMatchingPursuit(opts?: object): OrthogonalMatchingPursuit;

Parameters

Name	Type	Description
`opts?`	`object`	-
`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.n_nonzero_coefs?`	`number`	Desired number of non-zero entries in the solution. If `undefined` (by default) this value is set to 10% of n_features.
`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.precompute?`	`boolean` \| `"auto"`	Whether to use a precomputed Gram and Xy matrix to speed up calculations. Improves performance when n_targets or n_samples is very large. Note that if you already have such matrices, you can pass them directly to the fit method. `Default Value` `'auto'`
`opts.tol?`	`number`	Maximum squared norm of the residual. If not `undefined`, overrides n_nonzero_coefs.

Returns

OrthogonalMatchingPursuit

Defined in: generated/linear_model/OrthogonalMatchingPursuit.ts:23 (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/OrthogonalMatchingPursuit.ts:120 (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/OrthogonalMatchingPursuit.ts:137 (opens in a new tab)

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

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

Parameters

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

Returns

Promise<any>

Defined in: generated/linear_model/OrthogonalMatchingPursuit.ts:181 (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/OrthogonalMatchingPursuit.ts:72 (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/OrthogonalMatchingPursuit.ts:219 (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/OrthogonalMatchingPursuit.ts:258 (opens in a new tab)

set_score_request()

Request metadata passed to the score method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.sample_weight?`	`string` \| `boolean`	Metadata routing for `sample\_weight` parameter in `score`.

Returns

Promise<any>

Defined in: generated/linear_model/OrthogonalMatchingPursuit.ts:313 (opens in a new tab)

Properties

_isDisposed

boolean = false

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

_isInitialized

boolean = false

Defined in: generated/linear_model/OrthogonalMatchingPursuit.ts:20 (opens in a new tab)

_py

PythonBridge

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

id

string

Defined in: generated/linear_model/OrthogonalMatchingPursuit.ts:16 (opens in a new tab)

opts

any

Defined in: generated/linear_model/OrthogonalMatchingPursuit.ts:17 (opens in a new tab)

Accessors

coef_

Parameter vector (w in the formula).

Signature

coef_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/OrthogonalMatchingPursuit.ts:351 (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/OrthogonalMatchingPursuit.ts:486 (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/OrthogonalMatchingPursuit.ts:378 (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/OrthogonalMatchingPursuit.ts:459 (opens in a new tab)

n_iter_

Number of active features across every target.

Signature

n_iter_(): Promise<number | ArrayLike>;

Returns

Promise<number | ArrayLike>

Defined in: generated/linear_model/OrthogonalMatchingPursuit.ts:405 (opens in a new tab)

n_nonzero_coefs_

The number of non-zero coefficients in the solution. If n\_nonzero\_coefs is undefined and tol is undefined this value is either set to 10% of n\_features or 1, whichever is greater.