X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FcomputeForecast.R;h=e967cc7e8364bf7fe7a46a6411283e06bd1b2c50;hb=d2ab47a744d8fb29c03a76a7ca2368dae53f9a57;hp=1d697776c723d5202934d1d6420491681abba782;hpb=c4c329f65e6e842917cdfbabff36fbca6a617d02;p=talweg.git

diff --git a/pkg/R/computeForecast.R b/pkg/R/computeForecast.R
index 1d69777..e967cc7 100644
--- a/pkg/R/computeForecast.R
+++ b/pkg/R/computeForecast.R
@@ -1,10 +1,10 @@
 #' Compute forecast
 #'
-#' Predict time-series curves for the selected days indices.
+#' 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.
 #'
-#' TODO: details
-#'
-#' @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>
@@ -31,9 +31,10 @@
 #' @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$new()
 #' forecaster <- MyForecaster$new(myJumpPredictFunc)
@@ -44,17 +45,20 @@
 #'     PM10_values_of_last_24h,
 #'     exogenous_measures_of_last_24h,
 #'     exogenous_predictions_for_next_24h)
-#'   pred <- forecaster$predictSerie(data, data$getSize()-1, ...)
+#'   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$getSerie(1)))
+		stop("predict_from in [1,24] (hours)")
 	horizon = as.integer(horizon)[1]
-	if (horizon<=0 || horizon>length(data$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")
@@ -72,7 +76,7 @@ computeForecast = function(data, indices, forecaster, pjump,
 		p <- parallel::mclapply(seq_along(integer_indices), function(i) {
 			list(
 				"forecast" = forecaster$predictSerie(
-					data, integer_indices[i], memory, horizon, ...),
+					data, integer_indices[i], memory, predict_from, horizon, ...),
 				"params"= forecaster$getParameters(),
 				"index" = integer_indices[i] )
 			}, mc.cores=ncores)
@@ -82,7 +86,7 @@ computeForecast = function(data, indices, forecaster, pjump,
 		p <- lapply(seq_along(integer_indices), function(i) {
 			list(
 				"forecast" = forecaster$predictSerie(
-					data, integer_indices[i], memory, horizon, ...),
+					data, integer_indices[i], memory, predict_from, horizon, ...),
 				"params"= forecaster$getParameters(),
 				"index" = integer_indices[i] )
 			})