NuSVR

Nu Support Vector Regression.

Similar to NuSVC, for regression, uses a parameter nu to control the number of support vectors. However, unlike NuSVC, where nu replaces C, here nu replaces the parameter epsilon of epsilon-SVR.

The implementation is based on libsvm.

Constructors

constructor()

Signature

new NuSVR(opts?: object): NuSVR;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.C?`	`number`	Penalty parameter C of the error term. `Default Value` `1`
`opts.cache_size?`	`number`	Specify the size of the kernel cache (in MB). `Default Value` `200`
`opts.coef0?`	`number`	Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’. `Default Value` `0`
`opts.degree?`	`number`	Degree of the polynomial kernel function (‘poly’). Must be non-negative. Ignored by all other kernels. `Default Value` `3`
`opts.gamma?`	`number` \| `"auto"` \| `"scale"`	Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. `Default Value` `'scale'`
`opts.kernel?`	`"sigmoid"` \| `"precomputed"` \| `"linear"` \| `"poly"` \| `"rbf"`	Specifies the kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix. `Default Value` `'rbf'`
`opts.max_iter?`	`number`	Hard limit on iterations within solver, or -1 for no limit. `Default Value` `-1`
`opts.nu?`	`number`	An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1]. By default 0.5 will be taken. `Default Value` `0.5`
`opts.shrinking?`	`boolean`	Whether to use the shrinking heuristic. See the User Guide. `Default Value` `true`
`opts.tol?`	`number`	Tolerance for stopping criterion. `Default Value` `0.001`
`opts.verbose?`	`boolean`	Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context. `Default Value` `false`

Returns

NuSVR

Defined in: generated/svm/NuSVR.ts:27 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/svm/NuSVR.ts:25 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/svm/NuSVR.ts:24 (opens in a new tab)

_py

PythonBridge

Defined in: generated/svm/NuSVR.ts:23 (opens in a new tab)

id

string

Defined in: generated/svm/NuSVR.ts:20 (opens in a new tab)

opts

any

Defined in: generated/svm/NuSVR.ts:21 (opens in a new tab)

Accessors

class_weight_

Multipliers of parameter C for each class. Computed based on the class\_weight parameter.

Signature

class_weight_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/svm/NuSVR.ts:319 (opens in a new tab)

dual_coef_

Coefficients of the support vector in the decision function.

Signature

dual_coef_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/svm/NuSVR.ts:342 (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/svm/NuSVR.ts:434 (opens in a new tab)

fit_status_

0 if correctly fitted, 1 otherwise (will raise warning)

Signature

fit_status_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/svm/NuSVR.ts:365 (opens in a new tab)

intercept_

Constants in decision function.

Signature

intercept_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/svm/NuSVR.ts:388 (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/svm/NuSVR.ts:411 (opens in a new tab)

n_iter_

Number of iterations run by the optimization routine to fit the model.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/svm/NuSVR.ts:459 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/svm/NuSVR.ts:109 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/svm/NuSVR.ts:113 (opens in a new tab)

shape_fit_

Array dimensions of training vector X.

Signature

shape_fit_(): Promise<any[]>;

Returns

Promise<any[]>

Defined in: generated/svm/NuSVR.ts:481 (opens in a new tab)

support_

Indices of support vectors.

Signature

support_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/svm/NuSVR.ts:504 (opens in a new tab)

support_vectors_

Support vectors.

Signature

support_vectors_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/svm/NuSVR.ts:526 (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/svm/NuSVR.ts:171 (opens in a new tab)

fit()

Fit the SVM model according to the given training data.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	Training vectors, where `n\_samples` is the number of samples and `n\_features` is the number of features. For kernel=”precomputed”, the expected shape of X is (n_samples, n_samples).
`opts.sample_weight?`	`ArrayLike`	Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.
`opts.y?`	`ArrayLike`	Target values (class labels in classification, real numbers in regression).

Returns

Promise<any>

Defined in: generated/svm/NuSVR.ts:188 (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/svm/NuSVR.ts:122 (opens in a new tab)

predict()

Perform regression on samples in X.

For an one-class model, +1 (inlier) or -1 (outlier) is returned.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).

Returns

Promise<ArrayLike>

Defined in: generated/svm/NuSVR.ts:237 (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/svm/NuSVR.ts:272 (opens in a new tab)

NuSVC Nystroem