sklearn.decomposition.FastICA  
class sklearn.decomposition.FastICA(n_components=None, *, algorithm='parallel', whiten=True, fun='logcosh', fun_args=None, max_iter=200, tol=0.0001, w_init=None, random_state=None) [source]
 
FastICA: a fast algorithm for Independent Component Analysis. Read more in the User Guide.  Parameters 
 
n_componentsint, default=None 

Number of components to use. If None is passed, all are used.  
algorithm{‘parallel’, ‘deflation’}, default=’parallel’ 

Apply parallel or deflational algorithm for FastICA.  
whitenbool, default=True 

If whiten is false, the data is already considered to be whitened, and no whitening is performed.  
fun{‘logcosh’, ‘exp’, ‘cube’} or callable, default=’logcosh’ 

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. Example: def my_g(x):
    return x ** 3, (3 * x ** 2).mean(axis=-1)
  
fun_argsdict, default=None 

Arguments to send to the functional form. If empty and if fun=’logcosh’, fun_args will take value {‘alpha’ : 1.0}.  
max_iterint, default=200 

Maximum number of iterations during fit.  
tolfloat, default=1e-4 

Tolerance on update at each iteration.  
w_initndarray of shape (n_components, n_components), default=None 

The mixing matrix to be used to initialize the algorithm.  
random_stateint, RandomState instance or None, default=None 

Used to initialize w_init when not specified, with a normal distribution. Pass an int, for reproducible results across multiple function calls. See Glossary.    Attributes 
 
components_ndarray of shape (n_components, n_features) 

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.  
mixing_ndarray of shape (n_features, n_components) 

The pseudo-inverse of components_. It is the linear operator that maps independent sources to the data.  
mean_ndarray of shape(n_features,) 

The mean over features. Only set if self.whiten is True.  
n_iter_int 

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.  
whitening_ndarray of shape (n_components, n_features) 

Only set if whiten is ‘True’. This is the pre-whitening matrix that projects data onto the first n_components principal components.     Notes Implementation based on A. Hyvarinen and E. Oja, Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5), 2000, pp. 411-430 Examples >>> from sklearn.datasets import load_digits
>>> from sklearn.decomposition import FastICA
>>> X, _ = load_digits(return_X_y=True)
>>> transformer = FastICA(n_components=7,
...         random_state=0)
>>> X_transformed = transformer.fit_transform(X)
>>> X_transformed.shape
(1797, 7)
 Methods  
fit(X[, y]) Fit the model to X.  
fit_transform(X[, y]) Fit the model and recover the sources from X.  
get_params([deep]) Get parameters for this estimator.  
inverse_transform(X[, copy]) Transform the sources back to the mixed data (apply mixing matrix).  
set_params(**params) Set the parameters of this estimator.  
transform(X[, copy]) Recover the sources from X (apply the unmixing matrix).    
fit(X, y=None) [source]
 
Fit the model to X.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Training data, where n_samples is the number of samples and n_features is the number of features.  
yIgnored 
  Returns 
 self
   
  
fit_transform(X, y=None) [source]
 
Fit the model and recover the sources from X.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Training data, where n_samples is the number of samples and n_features is the number of features.  
yIgnored 
  Returns 
 
X_newndarray of shape (n_samples, n_components) 
   
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

If True, will return the parameters for this estimator and contained subobjects that are estimators.    Returns 
 
paramsdict 

Parameter names mapped to their values.     
  
inverse_transform(X, copy=True) [source]
 
Transform the sources back to the mixed data (apply mixing matrix).  Parameters 
 
Xarray-like of shape (n_samples, n_components) 

Sources, where n_samples is the number of samples and n_components is the number of components.  
copybool, default=True 

If False, data passed to fit are overwritten. Defaults to True.    Returns 
 
X_newndarray of shape (n_samples, n_features) 
   
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
  
transform(X, copy=True) [source]
 
Recover the sources from X (apply the unmixing matrix).  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Data to transform, where n_samples is the number of samples and n_features is the number of features.  
copybool, default=True 

If False, data passed to fit can be overwritten. Defaults to True.    Returns 
 
X_newndarray of shape (n_samples, n_components) 
   
 
 Examples using sklearn.decomposition.FastICA
 
  Blind source separation using FastICA  

  FastICA on 2D point clouds  

  Faces dataset decompositions