sklearn.decomposition.FastICA
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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)
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FastICA: a fast algorithm for Independent Component Analysis.
Read more in the User Guide.
Parameters: |
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n_components : int, optional -
Number of components to use. If none is passed, all are used. -
algorithm : {‘parallel’, ‘deflation’} -
Apply parallel or deflational algorithm for FastICA. -
whiten : boolean, optional -
If whiten is false, the data is already considered to be whitened, and no whitening is performed. -
fun : string or function, optional. 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):
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return x ** 3, 3 * x ** 2 -
fun_args : dictionary, optional -
Arguments to send to the functional form. If empty and if fun=’logcosh’, fun_args will take value {‘alpha’ : 1.0}. -
max_iter : int, optional -
Maximum number of iterations during fit. -
tol : float, optional -
Tolerance on update at each iteration. -
w_init : None of an (n_components, n_components) ndarray -
The mixing matrix to be used to initialize the algorithm. -
random_state : int, RandomState instance or None, optional (default=None) -
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 . |
Attributes: |
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components_ : 2D array, shape (n_components, n_features) -
The unmixing matrix. -
mixing_ : array, shape (n_features, n_components) -
The mixing matrix. -
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. |
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[, y, copy]) | Recover the sources from X (apply the unmixing matrix). |
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__init__(n_components=None, algorithm=’parallel’, whiten=True, fun=’logcosh’, fun_args=None, max_iter=200, tol=0.0001, w_init=None, random_state=None)
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fit(X, y=None)
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Fit the model to X.
Parameters: |
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X : array-like, shape (n_samples, n_features) -
Training data, where n_samples is the number of samples and n_features is the number of features. -
y : Ignored |
Returns: |
- self
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fit_transform(X, y=None)
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Fit the model and recover the sources from X.
Parameters: |
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X : array-like, shape (n_samples, n_features) -
Training data, where n_samples is the number of samples and n_features is the number of features. -
y : Ignored |
Returns: |
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X_new : array-like, shape (n_samples, n_components) |
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get_params(deep=True)
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Get parameters for this estimator.
Parameters: |
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deep : boolean, optional -
If True, will return the parameters for this estimator and contained subobjects that are estimators. |
Returns: |
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params : mapping of string to any -
Parameter names mapped to their values. |
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inverse_transform(X, copy=True)
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Transform the sources back to the mixed data (apply mixing matrix).
Parameters: |
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X : array-like, shape (n_samples, n_components) -
Sources, where n_samples is the number of samples and n_components is the number of components. -
copy : bool (optional) -
If False, data passed to fit are overwritten. Defaults to True. |
Returns: |
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X_new : array-like, shape (n_samples, n_features) |
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set_params(**params)
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Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
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transform(X, y=’deprecated’, copy=True)
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Recover the sources from X (apply the unmixing matrix).
Parameters: |
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X : array-like, shape (n_samples, n_features) -
Data to transform, where n_samples is the number of samples and n_features is the number of features. -
y : (ignored) -
Deprecated since version 0.19: This parameter will be removed in 0.21. -
copy : bool (optional) -
If False, data passed to fit are overwritten. Defaults to True. |
Returns: |
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X_new : array-like, shape (n_samples, n_components) |
Examples using sklearn.decomposition.FastICA