FastICA
FastICA: a fast algorithm for Independent Component Analysis.
The implementation is based on [1].
Read more in the User Guide.
Python Reference (opens in a new tab)
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 'warn' |
opts.whiten_solver? | "svd" | "eigh" | The solver to use for whitening. Default Value 'svd' |
Returns
Defined in: generated/decomposition/FastICA.ts:25 (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:401 (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:494 (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:447 (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:424 (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:469 (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.
Signature
n_iter_(): Promise<number>;Returns
Promise<number>
Defined in: generated/decomposition/FastICA.ts:519 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/decomposition/FastICA.ts:92 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/decomposition/FastICA.ts:96 (opens in a new tab)
whitening_
Only set if whiten is ‘true’. This is the pre-whitening matrix that projects data onto the first n\_components principal components.
Signature
whitening_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/FastICA.ts:542 (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)
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:282 (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:326 (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:359 (opens in a new tab)