OrthogonalMatchingPursuit
Orthogonal Matching Pursuit model (OMP).
Read more in the User Guide.
Python Reference (opens in a new tab)
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 norm of the residual. If not undefined, overrides n_nonzero_coefs. |
Returns
Defined in: generated/linear_model/OrthogonalMatchingPursuit.ts:23 (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:269 (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:404 (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:296 (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:377 (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:323 (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.
Signature
n_nonzero_coefs_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/OrthogonalMatchingPursuit.ts:350 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/linear_model/OrthogonalMatchingPursuit.ts:59 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/linear_model/OrthogonalMatchingPursuit.ts:63 (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)
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:179 (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:218 (opens in a new tab)