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sklearn.decomposition._fastica.FastICA
sklearn.FastICA
sklearn.decomposition._fastica.FastICA
1
openml==0.10.2,sklearn==0.22.1
FastICA: a fast algorithm for Independent Component Analysis.
2020-05-18T23:43:57
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
algorithm
"deflation"
fun
string or function
"exp"
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_args
dictionary
null
Arguments to send to the functional form
If empty and if fun='logcosh', fun_args will take value
{'alpha' : 1.0}
max_iter
int
297
Maximum number of iterations during fit
n_components
int
4
Number of components to use. If none is passed, all are used
algorithm : {'parallel', 'deflation'}
Apply parallel or deflational algorithm for FastICA
random_state
int
42
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
tol
float
0.051729889556082674
Tolerance on update at each iteration
w_init
None of an
null
The mixing matrix to be used to initialize the algorithm
whiten
boolean
false
If whiten is false, the data is already considered to be
whitened, and no whitening is performed
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1