Class: PLSSVD

Partial Least Square SVD.

This transformer simply performs a SVD on the cross-covariance matrix X'Y. It is able to project both the training data X and the targets Y. The training data X is projected on the left singular vectors, while the targets are projected on the right singular vectors.

Constructors

new PLSSVD()

new PLSSVD(opts?): PLSSVD

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.copy`?	`boolean`	Whether to copy `X` and `Y` in fit before applying centering, and potentially scaling. If `false`, these operations will be done inplace, modifying both arrays.
`opts.n_components`?	`number`	The number of components to keep. Should be in `\[1, min(n_samples, n_features, n_targets)\]`.
`opts.scale`?	`boolean`	Whether to scale `X` and `Y`.

Returns PLSSVD

Defined in generated/cross_decomposition/PLSSVD.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/cross_decomposition/PLSSVD.ts:23
`_isInitialized`	`boolean`	`false`	generated/cross_decomposition/PLSSVD.ts:22
`_py`	`PythonBridge`	`undefined`	generated/cross_decomposition/PLSSVD.ts:21
`id`	`string`	`undefined`	generated/cross_decomposition/PLSSVD.ts:18
`opts`	`any`	`undefined`	generated/cross_decomposition/PLSSVD.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/cross_decomposition/PLSSVD.ts:411

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/cross_decomposition/PLSSVD.ts:388

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/cross_decomposition/PLSSVD.ts:51

x_weights_

Get Signature

get x_weights_(): Promise<ArrayLike[]>

The left singular vectors of the SVD of the cross-covariance matrix. Used to project X in transform.

Returns Promise<ArrayLike[]>

Defined in generated/cross_decomposition/PLSSVD.ts:342

y_weights_

Get Signature

get y_weights_(): Promise<any>

The right singular vectors of the SVD of the cross-covariance matrix. Used to project X in transform.

Returns Promise<any>

Defined in generated/cross_decomposition/PLSSVD.ts:365

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/cross_decomposition/PLSSVD.ts:102

fit()

fit(opts): Promise<any>

Fit model to data.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	Training samples.
`opts.y`?	`ArrayLike`	Targets.
`opts.Y`?	`ArrayLike`	Targets.

Returns Promise<any>

Defined in generated/cross_decomposition/PLSSVD.ts:119

fit_transform()

fit_transform(opts): Promise<ArrayLike>

Learn and apply the dimensionality reduction.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	Training samples.
`opts.y`?	`ArrayLike`	Targets.

Returns Promise<ArrayLike>

Defined in generated/cross_decomposition/PLSSVD.ts:161

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