Merge branch 'release/v0.1.0'

.gitignore

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+.Rproj.user
+.Rhistory
+.RData

DESCRIPTION

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+Package: grnn
+Title: General regression neural network
+Description: The program GRNN implements the algorithm proposed by Specht
+    (1991).
+URL: http://flow.chasset.net/r-grnn/
+Version: 0.1.0
+Author: Pierre-Olivier Chasset
+Maintainer: Pierre-Olivier Chasset <[email protected]>
+License: AGPL
+Collate:
+    'create.R'
+    'grnn-package.r'
+    'guess.r'
+    'kernel.R'
+    'learn.R'
+    'smooth.R'

NAMESPACE

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+export(guess)
+export(learn)
+export(smooth)

NEWS

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+grnn 0.1
+========
+
+* Package structure implemented.

R/create.R

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+create.grnn <- function() {
+    nn <- list(
+        model="General regression neural network",
+        set=NULL
+    )
+    return(nn)
+}

R/grnn-package.r

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+#' GRNN
+#' 
+#' General regression neural network.
+#' 
+#' The program GRNN implements the algorithm proposed by Specht (1991).
+#' 
+#' @author Pierre-Olivier Chasset
+#' @docType package
+#' @keywords Neural network, Regression
+#' @references Specht D.F. (1991). A general regression neural network. IEEE Transactions on Neural Networks, 2(6):568-576.
+#' @aliases grnn
+#' @name grnn-package
+NULL

R/guess.r

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+#' Guess
+#' 
+#' Infers the value of a new observation.
+#' 
+#' @examples
+#' n <- 100
+#' set.seed(1)
+#' x <- runif(n, -2, 2)
+#' y0 <- x^3
+#' epsilon <- rnorm(n, 0, .1)
+#' y <- y0 + epsilon
+#' grnn <- learn(data.frame(y,x))
+#' grnn <- smooth(grnn, sigma=0.1)
+#' guess(grnn, -2)
+#' guess(grnn, -1)
+#' guess(grnn, -0.2)
+#' guess(grnn, -0.1)
+#' guess(grnn, 0)
+#' guess(grnn, 0.1)
+#' guess(grnn, 0.2)
+#' guess(grnn, 1)
+#' guess(grnn, 2)
+#' @param nn A trained and smoothed General regression neural network.
+#' @param X A vector describing a new observation.
+#' @seealso \code{\link{grnn-package}}
+#' @export
+guess <- function(nn, X) {
+    results <- Y(nn$Xa, nn$Ya, X, nn$sigma)
+    return(results)
+}

R/kernel.R

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+ds <- function(Xa, X) {
+    value <- (X - Xa) %*% t(X - Xa)
+    return(as.numeric(value))
+}
+
+pattern <- function(Xa, X, sigma) {
+    res <- exp( - ds(Xa, X) / (2 * sigma ^ 2) )
+    return(as.numeric(res))
+}
+
+patterns <- function(Xa, X, sigma)
+    apply(Xa, 1, pattern, X, sigma)
+
+Y <- function(Xa, Ya, X, sigma) {
+    p <- length(X) # Dimensionality of measurement space
+    m <- length(Xa[,1]) # Total number of training patterns from category A
+    patterns1 <- patterns(Xa, X, sigma)
+    f <- sum(Ya * patterns1) / sum(patterns1)
+    return(f)
+}

R/learn.R

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+#' Learn
+#' 
+#' Create or update a General regression neural network.
+#' 
+#' @param set Data frame representing the training set. The first column is used to define the category of each observation (set \code{category.column} if it is not the case).
+#' @param nn A General regression neural network with or without training.
+#' @param variable.column The field number of the variable (1 by default).
+#' @seealso \code{\link{grnn-package}}
+#' @export
+learn <- function(set, nn, variable.column=1) {
+    if(missing(set)) stop("Set is missing!")
+    if(missing(nn)) nn <- create.grnn()
+    if(is.null(nn$set)) {
+        nn$variable.column <- variable.column
+        nn$set <- set
+    } else {
+        nn$set <- rbind(nn$set, set)
+    }
+    nn$Xa <- as.matrix(nn$set[,-nn$variable.column])
+    nn$Ya <- as.matrix(nn$set[,nn$variable.column])
+    nn$k <- length(nn$set[1,]) - 1
+    nn$n <- length(nn$set[,1])
+    return(nn)
+}

R/smooth.R

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+#' Smooth
+#' 
+#' Smooth a General regression neural network.
+#' 
+#' @param nn A trained General regression neural network.
+#' @param sigma A scalar.
+#' @seealso \code{\link{grnn-package}}
+#' @export
+smooth <- function(nn, sigma) {
+    if(!missing(sigma)) {
+        nn$sigma <- sigma
+        return(nn)
+    }
+    stop()
+}

inst/CITATION

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+bibentry("Manual",
+   title = "GRNN: General regression neural network for the statistical software R",
+   author = person("P.-O. Chasset"),
+   organization = "Independant scientist",
+   address      = "Nancy, France",
+   year   = 2013,
+   note   = "Software",
+   url    = "http://flow.chasset.net/r-grnn/",
+
+   mheader = "To cite the library GRNN in publications use:",
+
+   mfooter = 
+     paste("We have invested a lot of time and effort in creating GRNN,",
+      "please cite it when using it for data analysis.", sep = " ")
+)

man/grnn-package.Rd

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+\docType{package}
+\name{grnn-package}
+\alias{grnn}
+\alias{grnn-package}
+\title{GRNN}
+\description{
+  General regression neural network.
+}
+\details{
+  The program GRNN implements the algorithm proposed by
+  Specht (1991).
+}
+\author{
+  Pierre-Olivier Chasset
+}
+\references{
+  Specht D.F. (1991). A general regression neural network.
+  IEEE Transactions on Neural Networks, 2(6):568-576.
+}
+\keyword{Neural}
+\keyword{Regression}
+\keyword{network,}
+

man/guess.Rd

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+\name{guess}
+\alias{guess}
+\title{Guess}
+\usage{
+  guess(nn, X)
+}
+\arguments{
+  \item{nn}{A trained and smoothed General regression
+  neural network.}
+
+  \item{X}{A vector describing a new observation.}
+}
+\description{
+  Infers the value of a new observation.
+}
+\examples{
+n <- 100
+set.seed(1)
+x <- runif(n, -2, 2)
+y0 <- x^3
+epsilon <- rnorm(n, 0, .1)
+y <- y0 + epsilon
+grnn <- learn(data.frame(y,x))
+grnn <- smooth(grnn, sigma=0.1)
+guess(grnn, -2)
+guess(grnn, -1)
+guess(grnn, -0.2)
+guess(grnn, -0.1)
+guess(grnn, 0)
+guess(grnn, 0.1)
+guess(grnn, 0.2)
+guess(grnn, 1)
+guess(grnn, 2)
+}
+\seealso{
+  \code{\link{grnn-package}}
+}
+

man/learn.Rd

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+\name{learn}
+\alias{learn}
+\title{Learn}
+\usage{
+  learn(set, nn, variable.column = 1)
+}
+\arguments{
+  \item{set}{Data frame representing the training set. The
+  first column is used to define the category of each
+  observation (set \code{category.column} if it is not the
+  case).}
+
+  \item{nn}{A General regression neural network with or
+  without training.}
+
+  \item{variable.column}{The field number of the variable
+  (1 by default).}
+}
+\description{
+  Create or update a General regression neural network.
+}
+\seealso{
+  \code{\link{grnn-package}}
+}
+

man/smooth.Rd

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+\name{smooth}
+\alias{smooth}
+\title{Smooth}
+\usage{
+  smooth(nn, sigma)
+}
+\arguments{
+  \item{nn}{A trained General regression neural network.}
+
+  \item{sigma}{A scalar.}
+}
+\description{
+  Smooth a General regression neural network.
+}
+\seealso{
+  \code{\link{grnn-package}}
+}
+