version 1.1.1

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: evclass
 Type: Package
 Title: Evidential Distance-Based Classification
-Version: 1.1.0
-Date: 2016-07-01
+Version: 1.1.1
+Date: 2017-03-19
 Author: Thierry Denoeux
 Maintainer: Thierry Denoeux <tdenoeux@utc.fr>
 Description: Different evidential distance-based classifiers, which provide
@@ -12,10 +12,10 @@ License: GPL-3
 Depends: R (>= 3.1.0)
 Imports: FNN
 LazyData: TRUE
-RoxygenNote: 5.0.1
+RoxygenNote: 6.0.1
 VignetteBuilder: knitr
 Suggests: knitr,rmarkdown,datasets
 NeedsCompilation: no
-Packaged: 2016-07-01 08:58:55 UTC; Thierry
+Packaged: 2017-03-19 10:12:39 UTC; Thierry
 Repository: CRAN
-Date/Publication: 2016-07-01 13:54:48
+Date/Publication: 2017-03-20 05:22:18 UTC

MD5

@@ -1,8 +1,8 @@
-527ced758fbb3daf73e3d5cf8bc6a476 *DESCRIPTION
+2ac5725c9684565bc95032c62073ad98 *DESCRIPTION
 a5c2724892a74189c1da78b052dd85f5 *NAMESPACE
-b1177ebc5f71a8b36341eb7ed2df0baf *R/EkNNfit.R
-4a492283408257cfb90af408e81dd804 *R/EkNNinit.R
-4ee80bfa2fcaef4a9742f1eb81c32225 *R/EkNNval.R
+9eba1012e5420f0fc8e99b040b630cf2 *R/EkNNfit.R
+54f63d0437bfb9db30002a0c68807569 *R/EkNNinit.R
+5c687ee192237eda2c5247078ae8b688 *R/EkNNval.R
 52a431a233d5075e603d3b52f9c9782c *R/classds.R
 bba8fad3584d23527015bd66decb3bb5 *R/decision.R
 b4b6d2da30c068ca1ebc7483aeab92c8 *R/evclass.R
@@ -12,27 +12,27 @@ aeaad4cb6952aa9e16d979f5b01f0950 *R/glass-data.R
 20a8bb25740f8f2445eda7ea0460ec20 *R/harris.R
 7c919b97eb312a99911bba45aa026664 *R/ionosphere-data.R
 848b2e2c11ce455a332dd8b0e7306f75 *R/optimds.R
-07349727a3fdec8e7adab2d787fb6bf9 *R/proDSfit.R
-d290f1445c20f34e6c5f725d2088363f *R/proDSinit.R
-0fd8bba09664d173f876e9336c328a12 *R/proDSval.R
+9304dd06f9f4b678894aacd75ac0b33c *R/proDSfit.R
+d17526659096bd88b364d14ab6a00a9f *R/proDSinit.R
+0270969814396a980ecbf472d21b2492 *R/proDSval.R
 a988fc277f3bfdb7f16cd8807d7bec4e *R/vehicles-data.R
-66d6b013c497c3521de3206e5855209e *build/vignette.rds
+b0638d895e5d86904eba4944778227e9 *build/vignette.rds
 988e27d0f83a7ff327e4f38f089aeefd *data/glass.RData
 b7fa49999b8679c81898cc5262ee0c81 *data/ionosphere.RData
 1665c19c5134e390d23a6f938557c3b5 *data/vehicles.RData
-533bbd19c4a779c4752827530d2c8ea5 *inst/doc/Introduction.R
-0dfd3d1fa0aa29cdb381bc5545958d4b *inst/doc/Introduction.Rmd
-4a4cdd01ee8bf578eb467911dadf5d18 *inst/doc/Introduction.html
-692b0234dd3ae533733ef4382a3109b6 *man/EkNNfit.Rd
-df9e6f51bbe7a511a71c62669b0369e9 *man/EkNNinit.Rd
-5c6c7e59a33b0c199ea37c15e93b6f1a *man/EkNNval.Rd
-1321ba273ed3af3992038c683ae4d242 *man/decision.Rd
-f9877cda81848dcb74cf78427486b2e5 *man/evclass.Rd
-ecf423e10f75e6dab5a7ced8a253575b *man/glass.Rd
-144a8069c347be152abc7543780db1c2 *man/ionosphere.Rd
-4f20dd9c17a3c66b6eda7e1c24012814 *man/proDSfit.Rd
-19dc1316032dc586a3b83fd4d9b31945 *man/proDSinit.Rd
-5d2098b37a40c37c063e2f93e705331f *man/proDSval.Rd
-6d00db39a18df54421981daa1b761cdc *man/vehicles.Rd
-0dfd3d1fa0aa29cdb381bc5545958d4b *vignettes/Introduction.Rmd
+00ba0472a892302912cad89f89bee419 *inst/doc/Introduction.R
+c2c200db2be802ed9dfab1407f4daf83 *inst/doc/Introduction.Rmd
+fed14e726d8730357eb82981f07c9b30 *inst/doc/Introduction.html
+e53ed775b823c9dc60c78956a46b22db *man/EkNNfit.Rd
+a1b2eb0b85044b846f7970d78dc2e7a6 *man/EkNNinit.Rd
+25aa6fbec299c876bffb0615065853ab *man/EkNNval.Rd
+9db266f40815c32197786c75d1644512 *man/decision.Rd
+3aaa9454280722f6c2912c40f9b5b7a9 *man/evclass.Rd
+f9927ec9566b8148fac14075e694f06f *man/glass.Rd
+ca25346bfe637ddd51b2217a7db2429c *man/ionosphere.Rd
+e56e66f1b6c9344529db700bf4ddb5f5 *man/proDSfit.Rd
+605f413649e45a313c05ff79a26f8da7 *man/proDSinit.Rd
+cec96b0831ec4355fbccbdb83a5d5a55 *man/proDSval.Rd
+822d0b76d472aaf0665a9c0d1118dd64 *man/vehicles.Rd
+c2c200db2be802ed9dfab1407f4daf83 *vignettes/Introduction.Rmd
 7a59c365f0124c990e37d0f2e8c9768c *vignettes/tdenoeux.bib

R/EkNNfit.R

@@ -7,7 +7,7 @@
 #' @param x Input matrix of size n x d, where n is the number of objects and d the number of
 #' attributes.
 #' @param y Vector of class labels (of length n). May be a factor, or a vector of
-#' integers.
+#' integers from 1 to M (number of classes).
 #' @param K Number of neighbors.
 #' @param param Initial parameters (default: NULL).
 #' @param alpha Parameter \eqn{\alpha} (default: 0.95)
@@ -26,7 +26,7 @@
 #'   \item{param}{The optimized parameters.}
 #'   \item{cost}{Final value of the cost function.}
 #'   \item{err}{Leave-one-out error rate.}
-#'   \item{ypred}{Leave-one-out predicted class labels.}
+#'   \item{ypred}{Leave-one-out predicted class labels (coded as integers from 1 to M).}
 #'   \item{m}{Leave-one-out predicted mass functions. The first M columns correspond
 #'   to the mass assigned to each class. The last column corresponds to the mass
 #'   assigned to the whole set of classes.}
@@ -55,7 +55,7 @@
 #' fit<-EkNNfit(x,y,K=5)
 EkNNfit<-function(x,y,K,param=NULL,alpha=0.95,lambda=1/max(as.numeric(y)),optimize=TRUE,
                   options=list(maxiter=300,eta=0.1,gain_min=1e-6,disp=TRUE)){
-  y<-as.numeric(y)
+  y<-as.integer(as.factor(y))
   x<-as.matrix(x)
   if(is.null(param)) param<-EkNNinit(x,y,alpha)
   knn<-get.knn(x,k=K)

R/EkNNinit.R

@@ -11,7 +11,7 @@
 #' @param x Input matrix of size n x d, where n is the number of objects and d the number of
 #' attributes.
 #' @param y Vector of class lables (of length n). May be a factor, or a vector of
-#' integers.
+#' integers from 1 to M (number of classes).
 #' @param alpha Parameter \eqn{\alpha}.
 #'
 #' @return A list with two elements:
@@ -43,7 +43,7 @@
 #' param<-EkNNinit(x,y)
 #' param
 EkNNinit<-function(x,y,alpha=0.95){
-  y<-as.numeric(y)
+  y<-as.integer(as.factor(y))
   x<-as.matrix(x)
   M<-max(y)
   gamm<-rep(0,M)

R/EkNNval.R

@@ -9,20 +9,20 @@
 #' @param xtrain Matrix of size ntrain x d, containing the values of the d attributes for the
 #' training data.
 #' @param ytrain Vector of class labels for the training data (of length ntrain). May
-#' be a factor, or a vector of integers.
+#' be a factor, or a vector of integers from 1 to M (number of classes).
 #' @param xtst Matrix of size ntst x d, containing the values of the d attributes for the
 #' test data.
 #' @param K Number of neighbors.
 #' @param ytst Vector of class labels for the test data (optional). May
-#' be a factor, or a vector of integers.
+#' be a factor, or a vector of integers from 1 to M (number of classes).
 #' @param param Parameters, as returned by \code{\link{EkNNfit}}.
 #'
 #' @return A list with three elements:
 #'   \describe{
 #'   \item{m}{Predicted mass functions for the test data. The first M columns correspond
 #'   to the mass assigned to each class. The last column corresponds to the mass
 #'   assigned to the whole set of classes.}
-#'   \item{ypred}{Predicted class labels for the test data.}
+#'   \item{ypred}{Predicted class labels for the test data (coded as integers from 1 to M).}
 #'   \item{err}{Test error rate.}
 #'  }
 #'
@@ -54,11 +54,11 @@
 #' fit<-EkNNfit(xtrain,ytrain,K)
 #' test<-EkNNval(xtrain,ytrain,xtst,K,ytst,fit$param)
 EkNNval <- function(xtrain,ytrain,xtst,K,ytst=NULL,param=NULL){
-  
+
   xtst<-as.matrix(xtst)
   xtrain<-as.matrix(xtrain)
-  ytrain<-as.numeric(ytrain)
-  if(!is.null(ytst)) ytst<-as.numeric(ytst)
+  ytrain<-y<-as.integer(as.factor(ytrain))
+  if(!is.null(ytst)) ytst<-y<-as.integer(as.factor(ytst))
 
   if(is.null(param)) param<-EkNNinit(xtrain,ytrain)
 

R/proDSfit.R

@@ -10,7 +10,7 @@
 #' @param x Input matrix of size n x d, where n is the number of objects and d the number of
 #' attributes.
 #' @param y Vector of class lables (of length n). May be a factor, or a vector of
-#' integers.
+#' integers from 1 to M (number of classes).
 #' @param param Initial parameters (see \code{link{proDSinit}}).
 #' @param lambda Parameter of the cost function. If \code{lambda=1}, the
 #' cost function measures the error between the plausibilities and the 0-1 target values.
@@ -55,7 +55,7 @@
 proDSfit <- function(x,y,param,lambda=1/max(as.numeric(y)),mu=0,optimProto=TRUE,
                      options=list(maxiter=500,eta=0.1,gain_min=1e-4,disp=10)){
   x<-as.matrix(x)
-  y<-as.numeric(y)
+  y<-as.integer(as.factor(y))
   M<-max(y)
   n<-nrow(param$W)
   p<-ncol(param$W)

R/proDSinit.R

@@ -10,7 +10,7 @@
 #' @param x Input matrix of size n x d, where n is the number of objects and d the number of
 #' attributes.
 #' @param y Vector of class lables (of length n). May be a factor, or a vector of
-#' integers.
+#' integers from 1 to M (number of classes).
 #' @param nproto Number of prototypes.
 #' @param nprotoPerClass Boolean. If TRUE, there are \code{nproto} prototypes per class. If
 #' FALSE (default), the total number of prototypes is equal to \code{nproto}.
@@ -44,7 +44,7 @@
 #' param0<-proDSinit(xapp,yapp,nproto=7)
 #' param0
 proDSinit<- function(x,y,nproto,nprotoPerClass=FALSE,crisp=FALSE){
-  y<-as.numeric(y)
+  y<-as.integer(as.factor(y))
   x<-as.matrix(x)
   M <- max(y)
   N <- nrow(x)

R/proDSval.R

@@ -7,7 +7,7 @@
 #' @param x Matrix of size n x d, containing the values of the d attributes for the test data.
 #' @param param Neural network parameters, as provided by \code{\link{proDSfit}}.
 #' @param y Optional vector of class labels for the test data. May be a factor, or a vector of
-#' integers.
+#' integers from 1 to M (number of classes).
 #'
 #' @return A list with three elements:
 #'   \describe{
@@ -48,6 +48,7 @@ proDSval<-function(x,param,y=NULL){
   n<-nrow(param$W)
   p<-ncol(param$W)
   x<-as.matrix(x)
+  y<-as.integer(as.factor(y))
   N <- nrow(x)
   M <- ncol(param$beta)
   x<-t(x)
@@ -71,7 +72,7 @@ proDSval<-function(x,param,y=NULL){
   ypred<-max.col(mk[,1:M])
 
   if(!is.null(y)){
-    err<-length(which(as.numeric(y)!=ypred))/N
+    err<-length(which(y!=ypred))/N
   } else err<-NULL
 
   return(list(m=mk,ypred=ypred,err=err))

inst/doc/Introduction.R

@@ -81,7 +81,7 @@ Dupper<-Dlower
 Dpig<-Dlower
 for(i in 1:nx){
   X<-matrix(c(rep(xx[i],ny),yy),ny,2)
-  val<-proDSval(X,fit$param,rep(0,ny))
+  val<-proDSval(X,fit$param)
   Dupper[i,]<-decision(val$m,L=L,rule='upper')
   Dlower[i,]<-decision(val$m,L=L,rule='lower')
   Dpig[i,]<-decision(val$m,L=L,rule='pignistic')

inst/doc/Introduction.Rmd

@@ -154,7 +154,7 @@ Dupper<-Dlower
 Dpig<-Dlower
 for(i in 1:nx){
   X<-matrix(c(rep(xx[i],ny),yy),ny,2)
-  val<-proDSval(X,fit$param,rep(0,ny))
+  val<-proDSval(X,fit$param)
   Dupper[i,]<-decision(val$m,L=L,rule='upper')
   Dlower[i,]<-decision(val$m,L=L,rule='lower')
   Dpig[i,]<-decision(val$m,L=L,rule='pignistic')