#' @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))
}