hstatistics-0.2.2.5: Statistics

Portabilityportable
Stabilityprovisional
Maintainerhaskell.vivian.mcphail <at> gmail <dot> com

Numeric.Statistics.ICA

Description

Independent Components Analysis

implements the FastICA algorithm found in:

  • Aapo Hyvrinen and Erkki Oja, Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5):411-430, 2000

http://www.google.com/url?sa=t&source=web&cd=2&ved=0CBgQFjAB&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.79.7003%26rep%3Drep1%26type%3Dpdf&ei=RQozTJb6L4_fcbCV6cMD&usg=AFQjCNGClLIB9MAvbrEj45SyUx9cYubLyA&sig2=hg5Wnfy3dLPkoIc1hqSfjg

Synopsis

Documentation

sigmoid :: Double -> DoubleSource

sigmoid transfer function

sigmoid' :: Double -> DoubleSource

derivative of sigmoid transfer function

demeanSource

Arguments

:: Array Int (Vector Double)

the data

-> (Array Int (Vector Double), Vector Double)

(demeaned data,mean)

remove the mean from data

whitenSource

Arguments

:: Array Int (Vector Double)

the data

-> Double

eigenvalue threshold

-> (Array Int (Vector Double), Matrix Double)

(whitened data,transform)

whiten data

icaSource

Arguments

:: Int

random seed

-> (Double -> Double)

transfer function (tanh,u exp(u^2/2), etc...)

-> (Double -> Double)

derivative of transfer function

-> NormType

type of normalisation: Infinity, PNorm1, PNorm2

-> Double

convergence tolerance for feature vectors -> Int -- ^ output dimensions

-> Int

sampling size (must be smaller than length of data)

-> Array Int (Vector Double)

data

-> (Array Int (Vector Double), Matrix Double)

transformed data, ica transform

perform an ICA transform

icaDefaultsSource

Arguments

:: Int

random seed

-> Array Int (Vector Double)

data

-> (Array Int (Vector Double), Matrix Double)

transformed data, ica transform

ICA with default values: no dimension reduction, euclidean norms, 16 sample groups, sigmoid