X-Git-Url: https://git.auder.net/?p=aggexp.git;a=blobdiff_plain;f=pkg%2FR%2Fz_runAlgorithm.R;fp=pkg%2FR%2Fz_runAlgorithm.R;h=ed75454c0862de8e04ced1b96dfd443441912e53;hp=f1c6ea46713e870ac001f9fc0321663c9183860b;hb=d1ae093753e81f18ffeaced56ea74446c5922bd6;hpb=ac475607b3776ed6fdbdc1a26a86f6c0d8498011

diff --git a/pkg/R/z_runAlgorithm.R b/pkg/R/z_runAlgorithm.R
index f1c6ea4..ed75454 100644
--- a/pkg/R/z_runAlgorithm.R
+++ b/pkg/R/z_runAlgorithm.R
@@ -23,59 +23,34 @@ algoNameDictionary = list(
 #'   \item rt : Regression Tree
 #'   \item rr : Ridge Regression
 #' }
-#' @param experts Vector of experts to consider (names or indices). Default: all of them.
-#' @param stations Vector of stations to consider (names or indices). Default: all of them.
+#' @param stations List of stations dataframes to consider.
+#' @param experts Vector of experts to consider (names).
 #' @param ... Additional arguments to be passed to the Algorithm object.
 #'
 #' @return A list with the following slots
 #' \itemize{
 #'   \item{data : data frame of all forecasts + measures (may contain NAs) + predictions, with date and station indices.}
 #'   \item{algo : object of class \code{Algorithm} (or sub-class).}
+#'   \item{stations : list of dataframes of stations for this run.}
 #'   \item{experts : character vector of experts for this run.}
-#'   \item{stations : character vector of stations for this run.}
 #' }
 #'
+#' @examples
+#' data(stations)
+#' r = runAlgorithm("ew", list(st[[1]]), c("P","MA3"))
+#' plotCurves(r)
+#' r2 = runAlgorithm("ml", st[c(1,2)], c("MA3","MA10"))
+#' plotError(r2)
 #' @export
-runAlgorithm = function(shortAlgoName, experts=expertsArray, stations=stationsArray, ...)
+runAlgorithm = function(shortAlgoName, stations, experts, ...)
 {
-	#check, sanitize and format provided arguments
+	#very basic input checks
 	if (! shortAlgoName %in% names(algoNameDictionary))
-		stop(paste("Typo in short algo name:", shortAlgoName))
-	if (!is.character(experts) && !is.numeric(experts))
-		stop("Wrong argument type: experts should be character or integer")
-	if (!is.character(stations) && !is.numeric(stations))
-		stop("Wrong argument type: stations should be character or integer")
+		stop("Unknown algorithm:")
 	experts = unique(experts)
-	stations = unique(stations)
-	Ka = length(expertsArray)
-	Sa = length(stationsArray)
-	if (length(experts) > Ka)
-		stop("Too many experts specified: at least one of them does not exist")
-	if (length(stations) > Sa)
-		stop("Too many stations specified: at least one of them does not exist")
-	if (is.numeric(experts) && any(experts > Ka))
-		stop(paste("Some experts indices are higher than the maximum which is", Ka))
-	if (is.numeric(stations) && any(stations > Sa))
-		stop(paste("Some stations indices are higher than the maximum which is", Sa))
-	if (is.character(experts))
-	{
-		expertsMismatch = (1:Ka)[! experts %in% expertsArray]
-		if (length(expertsMismatch) > 0)
-			stop(cat(paste("Typo in experts names:", experts[expertsMismatch]), sep="\n"))
-	}
-	if (is.character(stations))
-	{
-		stationsMismatch = (1:Sa)[! stations %in% stationsArray]
-		if (length(stationsMismatch) > 0)
-			stop(cat(paste("Typo in stations names:", stations[stationsMismatch]), sep="\n"))
-	}
-	if (!is.character(experts))
-		experts = expertsArray[experts]
-	if (!is.character(stations))
-		stations = stationsArray[stations]
 
 	#get data == ordered date indices + forecasts + measures + stations indices (would be DB in prod)
-	oracleData = getData(experts, stations)
+	oracleData = getData(stations, experts)
 
 	#simulate incremental forecasts acquisition + prediction + get measure
 	algoData = as.data.frame(matrix(nrow=0, ncol=ncol(oracleData)))