rename pkg --> aggexp

[aggexp.git] / pkg / R / z_plotHelper.R

diff --git a/pkg/R/z_plotHelper.R b/pkg/R/z_plotHelper.R
deleted file mode 100644 (file)
index f522f0f..0000000
--- a/pkg/R/z_plotHelper.R
+++ /dev/null
@@ -1,100 +0,0 @@
-#' @include z_runAlgorithm.R
-
-#' @title Get best expert index
-#'
-#' @description Return the weights corresponding to the best expert (...0,1,0...)
-#'
-#' @param r Output of \code{\link{runAlgorithm}}
-#'
-#' @export
-getBestExpert = function(r)
-{
-       X = as.matrix(r$data[,names(r$data) %in% r$experts])
-       Y = r$data[,"Measure"]
-
-       bestIndex = which.min(colMeans(abs(X - Y)^2, na.rm=TRUE))
-       res = rep(0.0, length(r$experts))
-       res[bestIndex] = 1.0
-       return (res)
-}
-
-#' @title Get best convex combination
-#'
-#' @description Return the weights p minimizing the quadratic error ||X*p-Y||^2 under convexity contraint.
-#'
-#' @param r Output of \code{\link{runAlgorithm}}
-#'
-#' @export
-getBestConvexCombination = function(r)
-{
-       X = r$data[,r$experts]
-       Y = as.double(r$data[,"Measure"])
-       indices = getNoNAindices(X) & !is.na(Y)
-       X = as.matrix(X[indices,])
-       Y = Y[indices]
-
-       K = length(r$experts)
-       return (constrOptim(theta=rep(1.0/K,K),
-               method="Nelder-Mead", #TODO: others not better... why?
-               f=function(p){return(sum((X%*%p-Y)^2))}, 
-               grad=NULL, #function(p){return(2.*t(X)%*%(X%*%p-Y))}, 
-               ui=rbind(rep(1.,K),rep(-1.,K),diag(K)), ci=c(0.99999,-1.00001, rep(0.,K)), 
-               control=list(ndeps=1e-3,maxit=10000))$par)
-}
-
-#' @title Get best linear combination
-#'
-#' @description Return the weights u minimizing the quadratic error ||r$X*u-r$Y||^2
-#'
-#' @param r Output of \code{\link{runAlgorithm}}
-#'
-#' @export
-getBestLinearCombination = function(r)
-{
-       X = r$data[,r$experts]
-       Y = r$data[,"Measure"]
-       indices = getNoNAindices(X) & !is.na(Y)
-       X = as.matrix(X[indices,])
-       Y = Y[indices]
-
-       return (mpPsInv(X) %*% Y)
-}
-
-#' @title Get statistical indicators
-#'
-#' @description Return respectively the TS, FA, MA, RMSE, EV indicators in a list.
-#'
-#' @param r Output of \code{\link{runAlgorithm}}
-#' @param thresh Threshold to compute alerts indicators.
-#' @param station Name or index of the station to consider. Default: the first one
-#' @param noNA TRUE to show only errors associated with full lines (old behavior)
-#'
-#' @export
-getIndicators = function(r, thresh, station=1, noNA=TRUE)
-{
-       if (is.character(station))
-               station = match(station, r$stations)
-
-       #TODO: duplicated block (same in plotCloud())
-       XY = subset(r$data, subset = (Station == station), select = c(r$experts,"Measure","Prediction"))
-       Y = XY[,"Measure"]
-       hatY = XY[,"Prediction"]
-       indices = !is.na(Y) & !is.na(hatY)
-       if (noNA)
-       {
-               X = XY[,names(XY) %in% r$experts]
-               indices = indices & getNoNAindices(X)
-       }
-       Y = Y[indices]
-       hatY = hatY[indices]
-
-       RMSE = round(sqrt(sum((Y - hatY)^2) / length(Y)),2)
-       EV = round(1 - var(Y-hatY) / var(Y), 2)
-       A = sum(hatY >= thresh & Y >= thresh, na.rm=TRUE) #right alarm
-       B = sum(hatY >= thresh & Y < thresh, na.rm=TRUE) #false alarm
-       C = sum(hatY < thresh & Y >= thresh, na.rm=TRUE) #missed alert
-       TS = round(A/(A+B+C),2)
-       FA = B/(A+B)
-       MA = C/(A+C)
-       return (list("TS"=TS, "FA"=FA, "MA"=MA, "RMSE"=RMSE, "EV"=EV))
-}