Class: OrthogonalMatchingPursuitCV
Cross-validated Orthogonal Matching Pursuit model (OMP).
See glossary entry for cross-validation estimator.
Read more in the User Guide.
Constructors
new OrthogonalMatchingPursuitCV()
new OrthogonalMatchingPursuitCV(
opts?):OrthogonalMatchingPursuitCV
Parameters
| Parameter | 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. |
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). |
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 context. \-1 means using all processors. See Glossary for more details. |
opts.verbose? | number | boolean | Sets the verbosity amount. |
Returns OrthogonalMatchingPursuitCV
Defined in generated/linear_model/OrthogonalMatchingPursuitCV.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/linear_model/OrthogonalMatchingPursuitCV.ts:23 |
_isInitialized | boolean | false | generated/linear_model/OrthogonalMatchingPursuitCV.ts:22 |
_py | PythonBridge | undefined | generated/linear_model/OrthogonalMatchingPursuitCV.ts:21 |
id | string | undefined | generated/linear_model/OrthogonalMatchingPursuitCV.ts:18 |
opts | any | undefined | generated/linear_model/OrthogonalMatchingPursuitCV.ts:19 |
Accessors
coef_
Get Signature
get coef_():
Promise<ArrayLike>
Parameter vector (w in the problem formulation).
Returns Promise<ArrayLike>
Defined in generated/linear_model/OrthogonalMatchingPursuitCV.ts:374
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/linear_model/OrthogonalMatchingPursuitCV.ts:482
intercept_
Get Signature
get intercept_():
Promise<number|ArrayLike>
Independent term in decision function.
Returns Promise<number | ArrayLike>
Defined in generated/linear_model/OrthogonalMatchingPursuitCV.ts:347
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/linear_model/OrthogonalMatchingPursuitCV.ts:455
n_iter_
Get Signature
get n_iter_():
Promise<number|ArrayLike>
Number of active features across every target for the model refit with the best hyperparameters got by cross-validating across all folds.
Returns Promise<number | ArrayLike>
Defined in generated/linear_model/OrthogonalMatchingPursuitCV.ts:428
n_nonzero_coefs_
Get Signature
get n_nonzero_coefs_():
Promise<number>
Estimated number of non-zero coefficients giving the best mean squared error over the cross-validation folds.
Returns Promise<number>
Defined in generated/linear_model/OrthogonalMatchingPursuitCV.ts:401
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/linear_model/OrthogonalMatchingPursuitCV.ts:66
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/linear_model/OrthogonalMatchingPursuitCV.ts:122
fit()
fit(
opts):Promise<any>
Fit the model using X, y as training data.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.fit_params? | any | Parameters to pass to the underlying splitter. |
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:139
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 MetadataRouter encapsulating routing information. |
Returns Promise<any>
Defined in generated/linear_model/OrthogonalMatchingPursuitCV.ts:187
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/linear_model/OrthogonalMatchingPursuitCV.ts:79
predict()
predict(
opts):Promise<any>
Predict using the linear model.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Samples. |
Returns Promise<any>
Defined in generated/linear_model/OrthogonalMatchingPursuitCV.ts:223
score()
score(
opts):Promise<number>
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.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
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.y? | ArrayLike | True values for X. |
Returns Promise<number>
Defined in generated/linear_model/OrthogonalMatchingPursuitCV.ts:261
set_score_request()
set_score_request(
opts):Promise<any>
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:
Parameters
| Parameter | 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/OrthogonalMatchingPursuitCV.ts:311