RSNNS 0.4-3: see ChangeLog

git-svn-id: svn+ssh://atolon.ugr.es/home/ts/code/trunk/Rpackages/RSNNS@464 4da2758a-f866-4958-9045-a385b76b148e

ChangeLog

@@ -1,3 +1,11 @@
+version 0.4-3 (09-1-2012):
+
+- included a citation to the JSS article about RSNNS in the documentation and the CITATION file
+- recompressed example data to fix a warning in CRAN checks
+- fixed the jordan and elman examples: "decay=0.1" was given as parameter, instead of the correct "learnFuncParams=c(0.1)" 
+- added an example for training an MLP for the iris data, using the low-level api (mlp_irisSnnsR.R)
+- fixed the decodeClassLables() function, which didn't correctly translate factor level names to column names  
+
 version 0.4-2 (29-9-2011):
 
 - removed original SNNS manual, to avoid license confusions, as it states an outdated license
@@ -13,7 +21,7 @@ version 0.4-0 (21-6-2011):
 - The art2 demo was fixed
 - changed package encoding to UTF-8
 - wrapping of some of the missing functions was added
-- artmap was added in the high-level interfact
+- artmap was added in the high-level interface
 - initialization to zero was added for all former static variables 
   (this was not always performed before, but it turned out that some
   SNNS functions rely on this and crash otherwise, e.g. dlvq and artmap)

DESCRIPTION

@@ -6,7 +6,7 @@ Title: Neural Networks in R using the Stuttgart Neural Network
 LinkingTo: Rcpp
 Type: Package
 LazyLoad: yes
-Author: Christoph Bergmeir <c.bergmeir@decsai.ugr.es>
+Author: Christoph Bergmeir and José M. Benítez
 Description: The Stuttgart Neural Network Simulator (SNNS) is a library
     containing many standard implementations of neural networks. This
     package wraps the SNNS functionality to make it available from
@@ -15,9 +15,9 @@ Description: The Stuttgart Neural Network Simulator (SNNS) is a library
     Furthermore, the package contains a convenient high-level
     interface, so that the most common neural network topologies and
     learning algorithms integrate seamlessly into R.
-Version: 0.4-2
+Version: 0.4-3
 URL: http://sci2s.ugr.es/dicits/software/RSNNS
-Date: 2011-09-29
+Date: 2012-01-09
 Depends: R (>= 2.10.0), methods, Rcpp (>= 0.8.5)
 Suggests: scatterplot3d
 Encoding: UTF-8

R/RSNNS-package.R

@@ -40,6 +40,10 @@
 #' If you have problems using RSNNS, find a bug, or have suggestions, please contact the package maintainer by email, instead of writing to the general R lists or contacting the 
 #' authors of the original SNNS software. 
 #' 
+#' If you use the package, please cite the following work in your publications:
+#'
+#' Bergmeir, C. and Benítez, J.M. (2012), Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS. Journal of Statistical Software, 46(7), 1-26. \url{http://www.jstatsoft.org/v46/i07/}
+#'
 #' The package has a hierarchical architecture with three levels:
 #' \itemize{
 #' \item RSNNS high-level api (rsnns)
@@ -96,14 +100,17 @@
 # @encoding Latin-1
 #' @author Christoph Bergmeir \email{c.bergmeir@@decsai.ugr.es} 
 #' 
-#' with José M. Benítez \email{j.m.benitez@@decsai.ugr.es}
+#' and José M. Benítez \email{j.m.benitez@@decsai.ugr.es}
 #' 
 #' DiCITS Lab, Sci2s group, DECSAI, University of Granada.
 #' 
 #' \url{http://dicits.ugr.es}, \url{http://sci2s.ugr.es}
 #' 
 #' @references 
 #' 
+#' Bergmeir, C. and Benítez, J.M. (2012), 'Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS', Journal of Statistical Software, 46(7), 1-26.
+#' \url{http://www.jstatsoft.org/v46/i07/}
+#'
 #' \emph{General neural network literature:}
 #' 
 #' Bishop, C. M. (2003), Neural networks for pattern recognition, University Press, Oxford.

R/elman.R

@@ -88,9 +88,9 @@ elman <- function(x, ...) UseMethod("elman")
 #' 
 #' par(mfrow=c(1,2))
 #' 
-#' modelElman <- elman(inputs, outputs, size=8, decay=0.1, maxit=1000)
+#' modelElman <- elman(inputs, outputs, size=8, learnFuncParams=c(0.1), maxit=1000)
 #' modelElman
-#' modelJordan <- jordan(inputs, outputs, size=8, decay=0.1, maxit=1000)
+#' modelJordan <- jordan(inputs, outputs, size=8, learnFuncParams=c(0.1), maxit=1000)
 #' modelJordan
 #' 
 #' plotIterativeError(modelElman)

R/reg_class.R

@@ -183,41 +183,32 @@ plotIterativeError.rsnns <- function(object, ...)
 #' 100 001 010 001
 #' 
 #' @title Decode class labels to a binary matrix
+#' @references
+#' Venables, W. N. and Ripley, B. D. (2002), 'Modern Applied Statistics with S', Springer-Verlag.
 #' @param x class label vector
 #' @param valTrue see Details paragraph
 #' @param valFalse see Details paragraph
 #' @return a matrix containing the decoded class labels
 #' @export
+#' @author The implementation is a slightly modified version of the function \code{class.ind} from the \code{nnet} package of Brian Ripley.
 #' @examples
 #' decodeClassLabels(c(1,3,2,3))
+#' decodeClassLabels(c("r","b","b","r", "g", "g"))
 #' 
 #' data(iris)
 #' decodeClassLabels(iris[,5])
-decodeClassLabels <- function(x, valTrue=1, valFalse=0) {
-
-  #y <- gl(2, 4, 8)
-  #levels(y) <- c("low", "high")
-  #if(length(levels(y))!=0) {}
-
-  classes <- unique(x)
-  numClasses <- 1:length(classes)
-  names(numClasses) <- classes
-
-  targets <- matrix(nrow=length(x), ncol=length(classes))
-  for(i in 1:length(x))
-    for(j in numClasses)  {
-      if(j == numClasses[x[i]]) {
-        targets[i,j] <- valTrue  
-      }
-      else {
-        targets[i,j] <- valFalse
-      }
-    }
+decodeClassLabels <- function(x, valTrue=1, valFalse=0)
+{
+  n <- length(x)
+  x <- as.factor(x)
+  res <- matrix(valFalse, n, length(levels(x)) )
+  res[(1:n) + n*(unclass(x)-1)] <- valTrue
+  dimnames(res) <- list(names(x), levels(x))
 
-  colnames(targets) <- classes
-  targets
+  res
 }
 
+
 #' Applies \code{analyzeClassification} row-wise to a matrix.
 #' 
 #' @title Encode a matrix of (decoded) class labels

demo/00Index

@@ -9,6 +9,7 @@ dlvq_ziff	the original SNNS dlvq ziff example, using the RSNNS high-level api (r
 eight_elman	the original SNNS eight elman example, using the RSNNS high-level api (rsnns)
 rbf_sin	a rbf network to approximante a a noisy sin function, using the RSNNS high-level api (rsnns)
 mlp_iris_tuning training of an mlp for iris, with grid search of the hyper-parameters (rsnns)
+mlp_irisSnnsR	a mlp for classification of the iris data, using the RSNNS low-level api (SnnsR)
 encoderSnnsCLib	the original SNNS encoder example, using the RSNNS low-level api (SnnsR), and only functions directly mapped to SnnsCLib functions
 art1_lettersSnnsR	the original art1 letters example, using the RSNNS low-level api (SnnsR)
 art2_tetraSnnsR	the original art2 tetra example, using the RSNNS low-level api (SnnsR)

demo/eight_elman.R

@@ -7,10 +7,10 @@ outputs <- snnsData$eight_016.pat[,outputColumns(snnsData$eight_016.pat)]
 
 par(mfrow=c(1,2))
 
-modelElman <- elman(inputs, outputs, size=8, decay=0.1, maxit=1000)
+modelElman <- elman(inputs, outputs, size=8, learnFuncParams=c(0.1), maxit=1000)
 modelElman
 
-modelJordan <- jordan(inputs, outputs, size=8, decay=0.1, maxit=1000)
+modelJordan <- jordan(inputs, outputs, size=8, learnFuncParams=c(0.1), maxit=1000)
 modelJordan
 
 plotIterativeError(modelElman)

demo/mlp_irisSnnsR.R

@@ -0,0 +1,57 @@
+library(RSNNS)
+
+basePath <- ("./")
+
+data(iris)
+
+seed <- 2
+set.seed(seed)
+setSnnsRSeedValue(seed)
+
+#shuffle the vector
+#iris <- iris[sample(1:nrow(iris),length(1:nrow(iris))),1:ncol(iris)]
+
+#normalize data
+inputs <- normalizeData(iris[,1:4], "norm")
+
+#outputs <- decodeClassLabels(iris[,5])
+outputs <- decodeClassLabels(iris[,5], valTrue=0.9, valFalse=0.1)
+
+numHiddenUnits <- 10
+
+snnsObject <- SnnsRObjectFactory()
+
+snnsObject$setLearnFunc('Quickprop')
+snnsObject$setUpdateFunc('Topological_Order')
+snnsObject$setUnitDefaults(1,0,1,0,1,'Act_Logistic','Out_Identity')
+
+snnsObject$createNet(c(ncol(inputs),numHiddenUnits,ncol(outputs)), TRUE)
+
+patset <- snnsObject$createPatSet(inputs, outputs)
+snnsObject$setCurrPatSet(patset$set_no)
+
+snnsObject$shufflePatterns(TRUE)
+snnsObject$DefTrainSubPat()
+
+snnsObject$initializeNet(-1)
+
+snnsObject$saveNet(paste(basePath,"mlp_irisSnnsR_untrained.net",sep=""),"mlp_irisSnnsR_untrained.net")
+
+parameters <- c(0.2, 0, 0, 0, 0)
+maxit <- 100
+
+error <- vector()
+for(i in 1:maxit) {
+  res <- snnsObject$learnAllPatterns(parameters)
+  error[i] <- res[[2]]
+}
+
+plot(error, type="l")
+
+predictions <- snnsObject$predictCurrPatSet("output", c(0))
+
+confusionMatrix(outputs,predictions)
+
+snnsObject$saveNet(paste(basePath,"mlp_irisSnnsR.net",sep=""),"mlp_irisSnnsR")
+snnsObject$saveNewPatterns(paste(basePath,"mlp_irisSnnsR.pat",sep=""), patset$set_no)
+

demo/rbf_irisSnnsR.R

@@ -63,5 +63,5 @@ predictions <- snnsObject$predictCurrPatSet("output", c(0))
 confusionMatrix(outputs,predictions)
 
 snnsObject$saveNet(paste(basePath,"rbf_irisSnnsR.net",sep=""),"rbf_irisSnnsR")
-snnsObject$saveNewPatterns(paste(basePath,"rbf_irisSnnsR.pat",sep=""), patset$set_no);
+snnsObject$saveNewPatterns(paste(basePath,"rbf_irisSnnsR.pat",sep=""), patset$set_no)
 

inst/CITATION

@@ -1,8 +1,20 @@
-citHeader("To cite package 'RSNNS' in publications use:")
-
-citEntry(entry="Manual",
-	title = "Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS",
-	author = "Christoph Bergmeir, Jos\\'e M. Ben\\'itez",
-	year = 2010,
-	note = "R package version 0.4-2",
-	textVersion = "Christoph Bergmeir, Jose M. Benitez (2010). Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS")
+citHeader("To cite RSNNS in publications use:")
+
+citEntry(entry = "Article",
+  title        = "Neural Networks in {R} Using the Stuttgart Neural Network Simulator: {RSNNS}",
+  author       = personList(as.person("Christoph Bergmeir"),
+                   as.person(" Jos\\'e M. Ben\\'itez")),
+  journal      = "Journal of Statistical Software",
+  year         = "2012",
+  volume       = "46",
+  number       = "7",
+  pages        = "1--26",
+  url          = "http://www.jstatsoft.org/v46/i07/",
+
+  textVersion  =
+  paste("Christoph Bergmeir,  Jose M. Benitez (2012).",
+        "Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS.",
+        "Journal of Statistical Software, 46(7), 1-26.",
+        "URL http://www.jstatsoft.org/v46/i07/.")
+)
+