X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FcomputeForecast.R;h=ef46dd3b7f516c23ef91430a923652224c924e1f;hb=638f27f4296727aff62b56643beb9f42aa5b57ef;hp=35372b3f10302bc32a2cf32e901d895b5b015651;hpb=e169b5d568110a86282877de4fc44384dc6d6cb0;p=talweg.git

diff --git a/pkg/R/computeForecast.R b/pkg/R/computeForecast.R
index 35372b3..ef46dd3 100644
--- a/pkg/R/computeForecast.R
+++ b/pkg/R/computeForecast.R
@@ -1,8 +1,10 @@
 #' Compute forecast
 #'
-#' Predict time-series curves for the selected days indices (lines in data).
+#' Predict time-series curves ("tomorrows") at the selected days indices ("todays").
+#' This function just runs a loop over all requested indices, and stores the individual
+#' forecasts into a list which is then turned into a Forecast object.
 #'
-#' @param data Object of type \code{Data}, output of \code{getData()}.
+#' @param data Object of class Data, output of \code{getData()}.
 #' @param indices Indices where to forecast (the day after); integers relative to the
 #'   beginning of data, or (convertible to) Date objects.
 #' @param forecaster Name of the main forecaster; more details: ?F_<forecastername>
@@ -29,34 +31,36 @@
 #' @examples
 #' ts_data <- system.file("extdata","pm10_mesures_H_loc.csv",package="talweg")
 #' exo_data <- system.file("extdata","meteo_extra_noNAs.csv",package="talweg")
-#' data <- getData(ts_data, exo_data, input_tz="GMT", working_tz="GMT", predict_at=7)
-#' pred <- computeForecast(data, 2200:2230, "Persistence", "Zero",
-#'   memory=500, horizon=12, ncores=1)
+#' data <- getData(ts_data, exo_data, input_tz="GMT", working_tz="GMT",
+#'   predict_at=7, limit=200)
+#' pred <- computeForecast(data, 100:130, "Persistence", "Zero",
+#'   memory=50, horizon=12, ncores=1)
 #' \dontrun{#Sketch for real-time mode:
 #' data <- Data$new()
-#' # Initialize: first day has no predictions attached
-#' data$initialize()
 #' forecaster <- MyForecaster$new(myJumpPredictFunc)
 #' repeat {
-#'   # During the night between days j and j+1:
-#'   data$appendExoHat(exogenous_predictions)
 #'   # In the morning 7am+ or afternoon 1pm+:
-#'   data$setMeasures(
-#'     data$getSize()-1,
+#'   data$append(
 #'     times_from_H+1_yersteday_to_Hnow,
 #'     PM10_values_of_last_24h,
-#'     exogenous_measures_for_yersteday)
-#'   pred <- forecaster$predictSerie(data, data$getSize()-1, ...)
+#'     exogenous_measures_of_last_24h,
+#'     exogenous_predictions_for_next_24h)
+#'   pred <- forecaster$predictSerie(data, data$getSize(), ...)
 #'   #do_something_with_pred
 #' }}
 #' @export
-computeForecast = function(data, indices, forecaster, pjump,
-	memory=Inf, horizon=data$getStdHorizon(), ncores=3, ...)
+computeForecast = function(data, indices, forecaster, pjump, predict_from,
+	memory=Inf, horizon=length(data$getSerie(1)), ncores=3, ...)
 {
 	# (basic) Arguments sanity checks
+	predict_from = as.integer(predict_from)[1]
+	if (! predict_from %in% 1:length(data$getSerie(1)))
+		stop("predict_from in [1,24] (hours)")
+	if (hasArg("opera") && !list(...)$opera && memory < Inf)
+		memory <- Inf #finite memory in training mode makes no sense
 	horizon = as.integer(horizon)[1]
-	if (horizon<=0 || horizon>length(data$getCenteredSerie(1)))
-		stop("Horizon too short or too long")
+	if (horizon<=predict_from || horizon>length(data$getSerie(1)))
+		stop("Horizon in [predict_from+1,24] (hours)")
 	integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data))
 	if (any(integer_indices<=0 | integer_indices>data$getSize()))
 		stop("Indices out of range")
@@ -69,27 +73,20 @@ computeForecast = function(data, indices, forecaster, pjump,
 	forecaster = forecaster_class_name$new( #.pjump =
 		getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
 
-	if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
+	computeOneForecast <- function(i)
 	{
-		p <- parallel::mclapply(seq_along(integer_indices), function(i) {
-			list(
-				"forecast" = forecaster$predictSerie(
-					data, integer_indices[i], memory, horizon, ...),
-				"params"= forecaster$getParameters(),
-				"index" = integer_indices[i] )
-			}, mc.cores=ncores)
-	}
-	else
-	{
-		p <- lapply(seq_along(integer_indices), function(i) {
-			list(
-				"forecast" = forecaster$predictSerie(
-					data, integer_indices[i], memory, horizon, ...),
-				"params"= forecaster$getParameters(),
-				"index" = integer_indices[i] )
-			})
+		list(
+			"forecast" = forecaster$predictSerie(data,i,memory,predict_from,horizon,...),
+			"params" = forecaster$getParameters(),
+			"index" = i )
 	}
 
+	p <-
+		if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
+			parallel::mclapply(integer_indices, computeOneForecast, mc.cores=ncores)
+		else
+			lapply(integer_indices, computeOneForecast)
+
 	# TODO: find a way to fill pred in //...
 	for (i in seq_along(integer_indices))
 	{