ica#
- diffsptk.ICA#
alias of
IndependentComponentAnalysis
- class diffsptk.IndependentComponentAnalysis(order: int, n_comp: int, *, func: str = 'logcosh', n_iter: int = 100, eps: float = 0.0001, batch_size: int | None = None, seed: int | None = None, verbose: bool = False)[source]#
Independent component analysis module. Note that the forward method is not differentiable.
- Parameters:
- orderint >= 0
The order of the vector, \(M\).
- n_compint >= 1
The number of components, \(K\).
- func[‘logcosh’, ‘gauss’]
The nonquadratic function used in the approximation of negentropy.
- n_iterint >= 1
The number of iterations.
- epsfloat >= 0
The convergence threshold.
- batch_sizeint >= 1 or None
The batch size.
- seedint or None
The random seed.
- verbosebool
If True, shows progress bars.
References
[1]A. Hyvarinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Networks, vol. 13, pp. 411-430, 2000.
See also