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Classes
Nystroem

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 (opens in a new tab)

Constructors

constructor()

Signature

new Nystroem(opts?: object): Nystroem;

Parameters

NameTypeDescription
opts?object-
opts.coef0?numberZero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.
opts.degree?numberDegree of the polynomial kernel. Ignored by other kernels.
opts.gamma?numberGamma 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?stringKernel 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?anyAdditional parameters (keyword arguments) for kernel function passed as callable object.
opts.n_components?numberNumber of features to construct. How many data points will be used to construct the mapping. Default Value 100
opts.n_jobs?numberThe 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 (opens in a new tab) context. \-1 means using all processors. See Glossary for more details.
opts.random_state?numberPseudo-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 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/kernel_approximation/Nystroem.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/kernel_approximation/Nystroem.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/kernel_approximation/Nystroem.ts:21 (opens in a new tab)

id

string

Defined in: generated/kernel_approximation/Nystroem.ts:18 (opens in a new tab)

opts

any

Defined in: generated/kernel_approximation/Nystroem.ts:19 (opens in a new tab)

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:372 (opens in a new tab)

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:349 (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/kernel_approximation/Nystroem.ts:447 (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/kernel_approximation/Nystroem.ts:422 (opens in a new tab)

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:397 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/kernel_approximation/Nystroem.ts:76 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/kernel_approximation/Nystroem.ts:80 (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/kernel_approximation/Nystroem.ts:136 (opens in a new tab)

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

NameTypeDescription
optsobject-
opts.X?ArrayLikeTraining data, where n\_samples is the number of samples and n\_features is the number of features.
opts.y?ArrayLikeTarget values (undefined for unsupervised transformations).

Returns

Promise<any>

Defined in: generated/kernel_approximation/Nystroem.ts:155 (opens in a new tab)

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

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Input samples.
opts.fit_params?anyAdditional fit parameters.
opts.y?ArrayLikeTarget values (undefined for unsupervised transformations).

Returns

Promise<any[]>

Defined in: generated/kernel_approximation/Nystroem.ts:195 (opens in a new tab)

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

NameTypeDescription
optsobject-
opts.input_features?anyOnly used to validate feature names with the names seen in fit.

Returns

Promise<any>

Defined in: generated/kernel_approximation/Nystroem.ts:244 (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

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/kernel_approximation/Nystroem.ts:89 (opens in a new tab)

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

NameTypeDescription
optsobject-
opts.transform?"default" | "pandas"Configure output of transform and fit\_transform.

Returns

Promise<any>

Defined in: generated/kernel_approximation/Nystroem.ts:281 (opens in a new tab)

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

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Data to transform.

Returns

Promise<ArrayLike[]>

Defined in: generated/kernel_approximation/Nystroem.ts:316 (opens in a new tab)