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Nystroem

Approximate a kernel map using a subset of the training data.

Constructs an approximate feature map for an arbitrary kernel using a subset of the data as basis.

Read more in the User Guide.

Python Reference

Constructors

constructor()

Signature

new Nystroem(opts?: object): Nystroem;

Parameters

Name Type Description
opts? object -
opts.coef0? number Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.
opts.degree? number Degree of the polynomial kernel. Ignored by other kernels.
opts.gamma? number Gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left to the kernel; see the documentation for sklearn.metrics.pairwise. Ignored by other kernels.
opts.kernel? string Kernel map to be approximated. A callable should accept two arguments and the keyword arguments passed to this object as kernel\_params, and should return a floating point number. Default Value 'rbf'
opts.kernel_params? any Additional parameters (keyword arguments) for kernel function passed as callable object.
opts.n_components? number Number of features to construct. How many data points will be used to construct the mapping. Default Value 100
opts.n_jobs? number The number of jobs to use for the computation. This works by breaking down the kernel matrix into n\_jobs even slices and computing them in parallel. undefined means 1 unless in a joblib.parallel\_backend context. \-1 means using all processors. See Glossary for more details.
opts.random_state? number Pseudo-random number generator to control the uniform sampling without replacement of n\_components of the training data to construct the basis kernel. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns

Nystroem

Defined in: generated/kernel_approximation/Nystroem.ts:25

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/kernel_approximation/Nystroem.ts:136

fit()

Fit estimator to data.

Samples a subset of training points, computes kernel on these and computes normalization matrix.

Signature

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

Parameters

Name 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/Nystroem.ts:155

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/kernel_approximation/Nystroem.ts:195

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/kernel_approximation/Nystroem.ts:244

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/kernel_approximation/Nystroem.ts:281

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/kernel_approximation/Nystroem.ts:89

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/kernel_approximation/Nystroem.ts:316

transform()

Apply feature map to X.

Computes an approximate feature map using the kernel between some training points and X.

Signature

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

Parameters

Name Type Description
opts object -
opts.X? ArrayLike[] Data to transform.

Returns

Promise<ArrayLike[]>

Defined in: generated/kernel_approximation/Nystroem.ts:351

Properties

_isDisposed

boolean = false

Defined in: generated/kernel_approximation/Nystroem.ts:23

_isInitialized

boolean = false

Defined in: generated/kernel_approximation/Nystroem.ts:22

_py

PythonBridge

Defined in: generated/kernel_approximation/Nystroem.ts:21

id

string

Defined in: generated/kernel_approximation/Nystroem.ts:18

opts

any

Defined in: generated/kernel_approximation/Nystroem.ts:19

Accessors

component_indices_

Indices of components\_ in the training set.

Signature

component_indices_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/kernel_approximation/Nystroem.ts:407

components_

Subset of training points used to construct the feature map.

Signature

components_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/kernel_approximation/Nystroem.ts:384

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/kernel_approximation/Nystroem.ts:482

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/kernel_approximation/Nystroem.ts:457

normalization_

Normalization matrix needed for embedding. Square root of the kernel matrix on components\_.

Signature

normalization_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/kernel_approximation/Nystroem.ts:432

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/kernel_approximation/Nystroem.ts:76

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name Type
pythonBridge PythonBridge

Returns

void

Defined in: generated/kernel_approximation/Nystroem.ts:80