sfaStep
(Package: rSFA) :
Update a step of the SFA algorithm.
sfaStep() updates the current step of the SFA algorithm. Depending on sfaList$deg it calls either sfa1Step or sfa2Step to do the main work. See further documentation there
● Data Source:
CranContrib
● Keywords:
● Alias: sfaStep
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sfaSave
(Package: rSFA) :
Save a SFA object.
Save a SFA object.
● Data Source:
CranContrib
● Keywords: internal
● Alias: sfaSave
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sfaGetIntRange
(Package: rSFA) :
Helper Function of SFA.
Helper Function of SFA.
● Data Source:
CranContrib
● Keywords: internal
● Alias: sfaGetIntRange
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xpDim
(Package: rSFA) :
Degree 2 Dimension Calculation
Compute the dimension of a vector expanded in the space of polynomials of 2nd degree.
● Data Source:
CranContrib
● Keywords:
● Alias: xpDim
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lcovPca2
(Package: rSFA) :
Improved Principal Component Analysis on a covariance object
Performs PCA _and_ whitening on the covariance object referenced by lcov.
Difference to LCOV_PCA: null the rows of W (columns of DW) where the corresponding eigenvalue in D is close to zero (more precisely: if lam/lam_max < EPS = 1e-7). This is numerically stable in the case where the covariance matrix is singular.
- Author: Wolfgang Konen, Cologne Univ., May'2009
● Data Source:
CranContrib
● Keywords: internal
● Alias: lcovPca2
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sfaGetHf
(Package: rSFA) :
Return a SFA function as a quadratic form.
sfaGetHf returns function number NR in the sfa object referenced by HDL in the form of a quadratic form
q(x) = 1/2*x'*H*x + f'*x + c
Of course, this only works if a quadratic expansion was used during training. The quadratic form can lie in different spaces, i.e. it can receive as input preprocessed or non-preprocessed vectors. This is specified by setting the argument WHERE. The quadratic form lies - in the preprocessed space for WHERE==0 (e.g. the whitened space if the preprocessing type is PCA) - in the PCA space (i.e. projected on the principal components but not whitened, works only if PCA was used for preprocessing) for WHERE==1 - in the input, mean-free space for WHERE==2 - in the input space for WHERE==3 In general you will need to set WHERE to 2 or 3, but working in the preprocessed spaces can often drastically improve the speed of analysis.
● Data Source:
CranContrib
● Keywords: internal
● Alias: sfaGetHf
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sfaClassify
(Package: rSFA) :
Predict Class for SFA classification
Create a SFA classification mode, predict & evaluate on new data (xtst,realc_tst).
Author of orig. matlab version: Wolfgang Konen, May 2009 - Jan 2010
See also [Berkes05] Pietro Berkes: Pattern recognition with Slow Feature Analysis. Cognitive Sciences EPrint Archive (CogPrint) 4104, http://cogprints.org/4104/ (2005)
● Data Source:
CranContrib
● Keywords:
● Alias: sfaClassify
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lcovPca
(Package: rSFA) :
Principal Component Analysis on a covariance object
Performs PCA _and_ whitening on the covariance object referenced by lcov. CAUTION: can be numerically instable if covariance matrix is singular, better use LCOV_PCA2 instead /W. Konen/
● Data Source:
CranContrib
● Keywords: internal
● Alias: lcovPca
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sfa2Create
(Package: rSFA) :
Create structured list for expanded SFA
'Expanded' SFA means that the input data are expanded into a higher-dimensional space with the function sfaExpandFun. See sfaExpand for the default expansion function.
● Data Source:
CranContrib
● Keywords:
● Alias: sfa2Create
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sfaCheckCondition
(Package: rSFA) :
Check Condition of a matrix for SFA
Creates warnings with recommendations for different settings, if given matrix is ill-conditioned.
● Data Source:
CranContrib
● Keywords: internal
● Alias: sfaCheckCondition
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providing 
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