reorganize folder

[aggexp.git] / R / z_runAlgorithm.R

diff --git a/R/z_runAlgorithm.R b/R/z_runAlgorithm.R
deleted file mode 100644 (file)
index f1c6ea4..0000000
--- a/R/z_runAlgorithm.R
+++ /dev/null
@@ -1,97 +0,0 @@
-#' @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))
-}