\name{pcaRes}
\docType{class}
\alias{nlpcaNet}
\alias{nlpcaNet-class}

\title{Class for representing a neural network for computing Non-linear PCA}

\description{This is a class representation of a non-linear PCA neural
  network. The \code{nlpcaNet} class is not meant for user-level usage.}

\section{Creating Objects}{
  \code{new("nlpcaNet", net=[the network structure],
    hierarchic=[hierarchic design],
    fct=[the functions at each layer], fkt=[the functions used for
    forward propagation], weightDecay=[incremental decrease of weight
    changes over iterations (between 0 and 1)], featureSorting=[sort
    features or not], dataDist=[represents the present values],
    inverse=[net is inverse mode or not], fCount=[amount of times
    features were sorted], componentLayer=[which layer is the
    'bottleneck' (principal components)],
    erro=[the used error function], gradient=[the used gradient method],
    weights=[the present weights],
    maxIter=[the amount of iterations that was done], scalingFactor=[the
    scale of the original matrix])}
}

\section{Slots}{
  \describe{
    \item{net}{"matrix",  matrix showing the representation of the
      neural network, e.g. (2,4,6) for a network with two features, a
      hidden layer and six output neurons (original variables).}
    \item{hierarchic}{"list",  the hierarchic design of the network,
      holds 'idx' (), 'var' () and layer (which layer is the principal
      component layer).}
    \item{fct}{"character",  a vector naming the functions that will be
      applied on each layer. "linr" is linear (i.e.) standard matrix
      products and "tanh" means that the arcus tangens is applied on the
      result of the matrix product (for non-linearity).}
    \item{fkt}{"character",  same as fct but the functions used during
      back propagation.}
    \item{weightDecay}{"numeric",  the value that is used to
      incrementally decrease the weight changes to ensure convergence.}
    \item{featureSorting}{"logical", indicates if features will be
      sorted or not. This is used to make the NLPCA assume properties
      closer to those of standard PCA were the first component is more
      important for reconstructing the data than the second component.}
    \item{dataDist}{"matrix", a matrix of ones and zeroes indicating
      which values will add to the errror.}
    \item{inverse}{"logical", network is inverse mode (currently only
      inverse is supported) or not. Eg. the case when we have truly
      missing values and wish to impute them.}
    \item{fCount}{"integer", Counter for the amount of times features
      were really sorted.}
    \item{componentLayer}{"numeric", the index of 'net' that is the
      component layer.}
    \item{error}{"function", the used error function. Currently only one
      is provided \code{errorHierarchic}.}
    \item{gradient}{"function", the used gradient function. Currently
      only one is provided  \code{derrorHierarchic}}
    \item{weights}{"list", A list holding managements of the
      weights. The list has two functions, weights$current() and
      weights$set() which access a matrix in the local environment of
      this object.}
    \item{maxIter}{"integer", the amount of iterations used to train
      this network.}
    \item{scalingFactor}{"numeric", training the network is best made
      with 'small' values so the original data is scaled down to a
      suitable range by division with this number.}
  }
}
\section{Methods}{
  \describe{
    \item{vector2matrices}{Returns the weights in a matrix representation.}
  }
}
\seealso{
  \code{\link{nlpca}}
}
\keyword{classes}