Class: PolynomialCountSketch

Polynomial kernel approximation via Tensor Sketch.

Implements Tensor Sketch, which approximates the feature map of the polynomial kernel:

Constructors

new PolynomialCountSketch()

new PolynomialCountSketch(opts?): PolynomialCountSketch

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.coef0`?	`number`	Constant term of the polynomial kernel whose feature map will be approximated.
`opts.degree`?	`number`	Degree of the polynomial kernel whose feature map will be approximated.
`opts.gamma`?	`number`	Parameter of the polynomial kernel whose feature map will be approximated.
`opts.n_components`?	`number`	Dimensionality of the output feature space. Usually, `n_components` should be greater than the number of features in input samples in order to achieve good performance. The optimal score / run time balance is typically achieved around `n_components` = 10 * `n_features`, but this depends on the specific dataset being used.
`opts.random_state`?	`number`	Determines random number generation for indexHash and bitHash initialization. Pass an int for reproducible results across multiple function calls. See Glossary.

Returns PolynomialCountSketch

Defined in generated/kernel_approximation/PolynomialCountSketch.ts:23

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/kernel_approximation/PolynomialCountSketch.ts:21
`_isInitialized`	`boolean`	`false`	generated/kernel_approximation/PolynomialCountSketch.ts:20
`_py`	`PythonBridge`	`undefined`	generated/kernel_approximation/PolynomialCountSketch.ts:19
`id`	`string`	`undefined`	generated/kernel_approximation/PolynomialCountSketch.ts:16
`opts`	`any`	`undefined`	generated/kernel_approximation/PolynomialCountSketch.ts:17

Accessors

bitHash_

Get Signature

get bitHash_(): Promise<ArrayLike[]>

Array with random entries in {+1, -1}, used to represent the 2-wise independent hash functions for Count Sketch computation.

Returns Promise<ArrayLike[]>

Defined in generated/kernel_approximation/PolynomialCountSketch.ts:400

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/kernel_approximation/PolynomialCountSketch.ts:454

indexHash_

Get Signature

get indexHash_(): Promise<ArrayLike[]>

Array of indexes in range \0, n_components) used to represent the 2-wise independent hash functions for Count Sketch computation.

Returns Promise<[ArrayLike[]>

Defined in generated/kernel_approximation/PolynomialCountSketch.ts:373

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/kernel_approximation/PolynomialCountSketch.ts:427

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/kernel_approximation/PolynomialCountSketch.ts:61

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/kernel_approximation/PolynomialCountSketch.ts:117

fit()

fit(opts): Promise<any>

Fit the model with X.

Initializes the internal variables. The method needs no information about the distribution of data, so we only care about n_features in X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	Training data, where `n_samples` is the number of samples and `n_features` is the number of features.
`opts.y`?	`ArrayLike`	Target values (`undefined` for unsupervised transformations).

Returns Promise<any>

Defined in generated/kernel_approximation/PolynomialCountSketch.ts:136

fit_transform()

fit_transform(opts): Promise<any[]>

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.fit_params`?	`any`	Additional fit parameters.
`opts.X`?	`ArrayLike`[]	Input samples.
`opts.y`?	`ArrayLike`	Target values (`undefined` for unsupervised transformations).

Returns Promise<any[]>

Defined in generated/kernel_approximation/PolynomialCountSketch.ts:177

get_feature_names_out()

get_feature_names_out(opts): Promise<any>

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: \["class_name0", "class_name1", "class_name2"\].

Parameters