--- /dev/null
+#' @include b_Algorithm.R
+
+algoNameDictionary = list(
+ ew = "ExponentialWeights",
+ kn = "KnearestNeighbors",
+ ga = "GeneralizedAdditive",
+ ml = "MLpoly",
+ rt = "RegressionTree",
+ rr = "RidgeRegression",
+ sv = "SVMclassif"
+)
+
+#' @title Simulate real-time predict
+#'
+#' @description Run the algorithm coded by \code{shortAlgoName} on data specified by the \code{stations} argument.
+#'
+#' @param shortAlgoName Short name of the algorithm.
+#' \itemize{
+#' \item ew : Exponential Weights
+#' \item ga : Generalized Additive Model
+#' \item kn : K Nearest Neighbors
+#' \item ml : MLpoly
+#' \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 ... 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{experts : character vector of experts for this run.}
+#' \item{stations : character vector of stations for this run.}
+#' }
+#'
+#' @export
+runAlgorithm = function(shortAlgoName, experts=expertsArray, stations=stationsArray, ...)
+{
+ #check, sanitize and format provided arguments
+ 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")
+ 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)
+
+ #simulate incremental forecasts acquisition + prediction + get measure
+ algoData = as.data.frame(matrix(nrow=0, ncol=ncol(oracleData)))
+ names(algoData) = names(oracleData)
+ algorithm = new(algoNameDictionary[[shortAlgoName]], data=algoData, ...)
+ predictions = c()
+ T = oracleData[nrow(oracleData),"Date"]
+ for (t in 1:T)
+ {
+ #NOTE: bet that subset extract rows in the order they appear
+ tData = subset(oracleData, subset = (Date==t))
+ algorithm$inputNextForecasts(tData[,names(tData) != "Measure"])
+ predictions = c(predictions, algorithm$predict_withNA())
+ algorithm$inputNextObservations(tData[,"Measure"])
+ }
+
+ oracleData = cbind(oracleData, Prediction = predictions)
+ return (list(data = oracleData, algo = algorithm, experts = experts, stations = stations))
+}