Skip to content

Latest commit

 

History

History
348 lines (207 loc) · 9.49 KB

KernelCenterer.md

File metadata and controls

348 lines (207 loc) · 9.49 KB
Raw

KernelCenterer

Center an arbitrary kernel matrix \(K\).

Let define a kernel \(K\) such that:

Python Reference

Constructors

constructor()

Signature

new KernelCenterer(opts?: object): KernelCenterer;

Parameters

Name Type Description
opts? object -
opts.K_fit_all_? number Average of kernel matrix.
opts.K_fit_rows_? ArrayLike Average of each column of kernel matrix.
opts.feature_names_in_? ArrayLike Names of features seen during fit. Defined only when X has feature names that are all strings.
opts.n_features_in_? number Number of features seen during fit.

Returns

KernelCenterer

Defined in: generated/preprocessing/KernelCenterer.ts:23

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/preprocessing/KernelCenterer.ts:107

fit()

Fit KernelCenterer.

Signature

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

Parameters

Name Type Description
opts object -
opts.K? ArrayLike[] Kernel matrix.
opts.y? any Ignored.

Returns

Promise<any>

Defined in: generated/preprocessing/KernelCenterer.ts:124

fit_transform()

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.

Signature

fit_transform(opts: object): Promise<any[]>;

Parameters

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

Returns

Promise<any[]>

Defined in: generated/preprocessing/KernelCenterer.ts:164

get_feature_names_out()

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: \["class\_name0", "class\_name1", "class\_name2"\].

Signature

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

Parameters

Name Type Description
opts object -
opts.input_features? any Only used to validate feature names with the names seen in fit.

Returns

Promise<any>

Defined in: generated/preprocessing/KernelCenterer.ts:213

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

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

Parameters

Name Type Description
opts object -
opts.routing? any A MetadataRequest encapsulating routing information.

Returns

Promise<any>

Defined in: generated/preprocessing/KernelCenterer.ts:251

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/preprocessing/KernelCenterer.ts:61

set_fit_request()

Request metadata passed to the fit 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:

Signature

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

Parameters

Name Type Description
opts object -
opts.K? string | boolean Metadata routing for K parameter in fit.

Returns

Promise<any>

Defined in: generated/preprocessing/KernelCenterer.ts:290

set_output()

Set output container.

See Introducing the set_output API for an example on how to use the API.

Signature

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

Parameters

Name Type Description
opts object -
opts.transform? "default" | "pandas" Configure output of transform and fit\_transform.

Returns

Promise<any>

Defined in: generated/preprocessing/KernelCenterer.ts:327

set_transform_request()

Request metadata passed to the transform 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:

Signature

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

Parameters

Name Type Description
opts object -
opts.K? string | boolean Metadata routing for K parameter in transform.
opts.copy? string | boolean Metadata routing for copy parameter in transform.

Returns

Promise<any>

Defined in: generated/preprocessing/KernelCenterer.ts:364

transform()

Center kernel matrix.

Signature

transform(opts: object): Promise<ArrayLike[]>;

Parameters

Name Type Description
opts object -
opts.K? ArrayLike[] Kernel matrix.
opts.copy? boolean Set to false to perform inplace computation. Default Value true

Returns

Promise<ArrayLike[]>

Defined in: generated/preprocessing/KernelCenterer.ts:404

Properties

_isDisposed

boolean = false

Defined in: generated/preprocessing/KernelCenterer.ts:21

_isInitialized

boolean = false

Defined in: generated/preprocessing/KernelCenterer.ts:20

_py

PythonBridge

Defined in: generated/preprocessing/KernelCenterer.ts:19

id

string

Defined in: generated/preprocessing/KernelCenterer.ts:16

opts

any

Defined in: generated/preprocessing/KernelCenterer.ts:17

Accessors

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/preprocessing/KernelCenterer.ts:48

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name Type
pythonBridge PythonBridge

Returns

void

Defined in: generated/preprocessing/KernelCenterer.ts:52