Class: RBFSampler

Approximate a RBF kernel feature map using random Fourier features.

It implements a variant of Random Kitchen Sinks.[1]

Constructors

new RBFSampler()

new RBFSampler(opts?): RBFSampler

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.gamma`?	`number` \| `"scale"`	Parameter of RBF kernel: exp(-gamma * x^2). If `gamma='scale'` is passed then it uses 1 / (n_features * X.var()) as value of gamma.
`opts.n_components`?	`number`	Number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space.
`opts.random_state`?	`number`	Pseudo-random number generator to control the generation of the random weights and random offset when fitting the training data. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns RBFSampler

Defined in generated/kernel_approximation/RBFSampler.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/kernel_approximation/RBFSampler.ts:23
`_isInitialized`	`boolean`	`false`	generated/kernel_approximation/RBFSampler.ts:22
`_py`	`PythonBridge`	`undefined`	generated/kernel_approximation/RBFSampler.ts:21
`id`	`string`	`undefined`	generated/kernel_approximation/RBFSampler.ts:18
`opts`	`any`	`undefined`	generated/kernel_approximation/RBFSampler.ts:19

Accessors

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/RBFSampler.ts:414

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/RBFSampler.ts:389

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/RBFSampler.ts:49

random_offset_

Get Signature

get random_offset_(): Promise<ArrayLike>

Random offset used to compute the projection in the n_components dimensions of the feature space.

Returns Promise<ArrayLike>

Defined in generated/kernel_approximation/RBFSampler.ts:339

random_weights_

Get Signature

get random_weights_(): Promise<ArrayLike[]>

Random projection directions drawn from the Fourier transform of the RBF kernel.

Returns Promise<ArrayLike[]>

Defined in generated/kernel_approximation/RBFSampler.ts:364

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/RBFSampler.ts:101

fit()

fit(opts): Promise<any>

Fit the model with X.

Samples random projection according to n_features.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`	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/RBFSampler.ts:120

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/RBFSampler.ts:159

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