icalcc 0.1.3

Locally centered contrast functions for scikit-learn FastICA

icalcc

Locally centered contrast functions for scikit-learn FastICA. Drop-in replacement for sklearn.decomposition.FastICA with bounded LCC-tanh and LCC-exp contrasts, plus polynomial LCC contrasts of order 4, 6, and 8.

from icalcc import ICALCC
ica = ICALCC(n_components=4, K='ltanh', random_state=0)
S_hat = ica.fit_transform(X)

Installation

pip install icalcc

Supported K Values

K	Description
'ltanh'	Bounded LCC-tanh (robust to heavy tails and skewness)
'lexp'	Bounded LCC-exp (maximizes Rényi-2 entropy)
4	Polynomial LCC order 4
6	Polynomial LCC order 6, couples m₃, m₄, m₆
8	Polynomial LCC order 8, couples moments up to order 8
'tanh'	Classical logcosh contrast (scikit-learn default)
'exp'	Classical Gaussian contrast
'skew'	Classical cube contrast

Usage

from icalcc import ICALCC

# Bounded LCC-tanh (recommended for heavy-tailed or skewed sources)
ica = ICALCC(n_components=4, K='ltanh', random_state=0)
S_hat = ica.fit_transform(X)

# Bounded LCC-exp (Rényi-2 entropy interpretation)
ica = ICALCC(n_components=4, K='lexp', random_state=0)
S_hat = ica.fit_transform(X)

# Polynomial LCC order 8 (near-Gaussian sources)
ica = ICALCC(n_components=4, K=8, random_state=0)
S_hat = ica.fit_transform(X)

# Classical FastICA baseline
ica = ICALCC(n_components=4, K='tanh', random_state=0)
S_hat = ica.fit_transform(X)

Convergence tracking

ica.fit(X)
print(ica.converged_)  # True if all components converged

See Also

• gpuicalcc — PyTorch GPU-accelerated extension (40–48× speedup for bounded contrasts)
• Experiment code

Requirements

• Python ≥ 3.9
• numpy ≥ 1.24
• scikit-learn ≥ 1.3

Citation

If you use this package, please cite both the software paper and the underlying LCC kernel paper:

@article{saito2026icalcc,
  author  = {Saito, Tetsuya},
  title   = {{ICALCC}: Locally Centered Contrast Functions for
             {FastICA} with {GPU} Acceleration},
  journal = {TechRxiv},
  year    = {2026}
}

@article{saito2026lcc,
  author  = {Saito, Tetsuya},
  title   = {Locally Centered Cyclic Kernels for Higher-Order
             Independent Component Analysis},
  journal = {TechRxiv},
  year    = {2026}
}

License

CC BY 4.0