'update'

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

diff --git a/pkg/R/z_plotHelper.R b/pkg/R/z_plotHelper.R
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+#' @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))
+}