FastICA

FastICA: a fast algorithm for Independent Component Analysis.

The implementation is based on [1].

Constructors

constructor()

Signature

new FastICA(opts?: object): FastICA;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.algorithm?`	`"parallel"` \| `"deflation"`	Specify which algorithm to use for FastICA. `Default Value` `'parallel'`
`opts.fun?`	`"logcosh"` \| `"exp"` \| `"cube"`	The functional form of the G function used in the approximation to neg-entropy. Could be either ‘logcosh’, ‘exp’, or ‘cube’. You can also provide your own function. It should return a tuple containing the value of the function, and of its derivative, in the point. The derivative should be averaged along its last dimension. Example: `Default Value` `'logcosh'`
`opts.fun_args?`	`any`	Arguments to send to the functional form. If empty or `undefined` and if fun=’logcosh’, fun_args will take value {‘alpha’ : 1.0}.
`opts.max_iter?`	`number`	Maximum number of iterations during fit. `Default Value` `200`
`opts.n_components?`	`number`	Number of components to use. If `undefined` is passed, all are used.
`opts.random_state?`	`number`	Used to initialize `w\_init` when not specified, with a normal distribution. Pass an int, for reproducible results across multiple function calls. See Glossary.
`opts.tol?`	`number`	A positive scalar giving the tolerance at which the un-mixing matrix is considered to have converged. `Default Value` `0.0001`
`opts.w_init?`	`ArrayLike`[]	Initial un-mixing array. If `w\_init=None`, then an array of values drawn from a normal distribution is used.
`opts.whiten?`	`string` \| `boolean`	Specify the whitening strategy to use. `Default Value` `'unit-variance'`
`opts.whiten_solver?`	`"svd"` \| `"eigh"`	The solver to use for whitening. `Default Value` `'svd'`

Returns

FastICA

Defined in: generated/decomposition/FastICA.ts:25 (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/decomposition/FastICA.ts:154 (opens in a new tab)

fit()

Fit the model to X.

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?`	`any`	Not used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/decomposition/FastICA.ts:171 (opens in a new tab)

fit_transform()

Fit the model and recover the sources from X.

Signature

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

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?`	`any`	Not used, present for API consistency by convention.

Returns

Promise<ArrayLike[]>

Defined in: generated/decomposition/FastICA.ts:209 (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

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/decomposition/FastICA.ts:249 (opens in a new tab)

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/decomposition/FastICA.ts:284 (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

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/decomposition/FastICA.ts:105 (opens in a new tab)

inverse_transform()

Transform the sources back to the mixed data (apply mixing matrix).

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Sources, where `n\_samples` is the number of samples and `n\_components` is the number of components.
`opts.copy?`	`boolean`	If `false`, data passed to fit are overwritten. Defaults to `true`. `Default Value` `true`

Returns

Promise<ArrayLike[]>

Defined in: generated/decomposition/FastICA.ts:317 (opens in a new tab)

set_inverse_transform_request()

Request metadata passed to the inverse\_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_inverse_transform_request(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.copy?`	`string` \| `boolean`	Metadata routing for `copy` parameter in `inverse\_transform`.

Returns

Promise<any>

Defined in: generated/decomposition/FastICA.ts:363 (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

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

Returns

Promise<any>

Defined in: generated/decomposition/FastICA.ts:400 (opens in a new tab)

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.copy?`	`string` \| `boolean`	Metadata routing for `copy` parameter in `transform`.

Returns

Promise<any>

Defined in: generated/decomposition/FastICA.ts:437 (opens in a new tab)

transform()

Recover the sources from X (apply the unmixing matrix).

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Data to transform, where `n\_samples` is the number of samples and `n\_features` is the number of features.
`opts.copy?`	`boolean`	If `false`, data passed to fit can be overwritten. Defaults to `true`. `Default Value` `true`

Returns

Promise<ArrayLike[]>

Defined in: generated/decomposition/FastICA.ts:470 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/decomposition/FastICA.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/decomposition/FastICA.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/decomposition/FastICA.ts:21 (opens in a new tab)

id

string

Defined in: generated/decomposition/FastICA.ts:18 (opens in a new tab)

opts

any

Defined in: generated/decomposition/FastICA.ts:19 (opens in a new tab)

Accessors

components_

The linear operator to apply to the data to get the independent sources. This is equal to the unmixing matrix when whiten is false, and equal to np.dot(unmixing\_matrix, self.whitening\_) when whiten is true.

Signature

components_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/decomposition/FastICA.ts:512 (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/decomposition/FastICA.ts:605 (opens in a new tab)

mean_

The mean over features. Only set if self.whiten is true.

Signature

mean_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/decomposition/FastICA.ts:558 (opens in a new tab)

mixing_

The pseudo-inverse of components\_. It is the linear operator that maps independent sources to the data.

Signature

mixing_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/decomposition/FastICA.ts:535 (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/decomposition/FastICA.ts:580 (opens in a new tab)

n_iter_

If the algorithm is “deflation”, n_iter is the maximum number of iterations run across all components. Else they are just the number of iterations taken to converge.