X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FcomputeForecast.R;h=e1b29b6b9c9a800e946031f663589937963e1cdf;hb=8ab6420267542d34b7428f978aa76ba939b9754b;hp=ca8badd3523d6220d31cfe0e6318e030f64f7a79;hpb=1e8327df4e8abce5c23808be4f98037635bb2714;p=talweg.git

diff --git a/pkg/R/computeForecast.R b/pkg/R/computeForecast.R
index ca8badd..e1b29b6 100644
--- a/pkg/R/computeForecast.R
+++ b/pkg/R/computeForecast.R
@@ -1,29 +1,33 @@
 #' Compute forecast
 #'
-#' 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.
+#' Predict time-series curves ("today" from predict_from to horizon) at the selected days
+#' indices ("today" from 1am to predict_from-1). This function just runs a loop over all
+#' requested indices, and stores the individual forecasts into a Forecast object.
 #'
 #' @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>
-#' \itemize{
-#'   \item Persistence : use last (similar, next) day
-#'   \item Neighbors : weighted tomorrows of similar days
-#'   \item Average : average tomorrow of all same day-in-week
-#'   \item Zero : just output 0 (benchmarking purpose)
-#' }
+#'   \itemize{
+#'     \item Persistence : use last (similar) day
+#'     \item Neighbors : weighted similar days
+#'     \item Average : average curve of all same day-in-week
+#'     \item Zero : just output 0 (benchmarking purpose)
+#'   }
 #' @param pjump Function to predict the jump at the interface between two days;
 #'   more details: ?J_<functionname>
-#' \itemize{
-#'   \item Persistence : use last (similar, next) day
-#'   \item Neighbors: re-use the weights from F_Neighbors
-#'   \item Zero: just output 0 (no adjustment)
-#' }
+#'   \itemize{
+#'     \item Persistence : use last (similar) day
+#'     \item Neighbors: re-use the weights from F_Neighbors
+#'     \item Zero: just output 0 (no adjustment)
+#'   }
+#'   If pjump=NULL, then no adjustment is performed (output of \code{predictShape()} is
+#'   used directly).
+#' @param predict_from First time step to predict.
 #' @param memory Data depth (in days) to be used for prediction.
-#' @param horizon Number of time steps to predict.
+#' @param horizon Last time step to predict.
 #' @param ncores Number of cores for parallel execution (1 to disable).
+#' @param verbose TRUE to print basic traces (runs beginnings)
 #' @param ... Additional parameters for the forecasting models.
 #'
 #' @return An object of class Forecast
@@ -31,48 +35,63 @@
 #' @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, limit=200)
+#' data <- getData(ts_data, exo_data, limit=200)
 #' pred <- computeForecast(data, 100:130, "Persistence", "Zero",
-#'   memory=50, horizon=12, ncores=1)
-#' \dontrun{#Sketch for real-time mode:
+#'   predict_from=8, memory=50, horizon=12, ncores=1)
+#' \dontrun{
+#' #Sketch for real-time mode:
 #' data <- Data$new()
 #' forecaster <- MyForecaster$new(myJumpPredictFunc)
 #' repeat {
-#'   # In the morning 7am+ or afternoon 1pm+:
+#'   # As soon as daily predictions are available:
 #'   data$append(
-#'     times_from_H+1_yersteday_to_Hnow,
-#'     PM10_values_of_last_24h,
-#'     exogenous_measures_of_last_24h,
-#'     exogenous_predictions_for_next_24h)
+#'     level_hat=predicted_level,
+#'     exo_hat=predicted_exogenous)
+#'   # When a day ends:
+#'   data$append(
+#'     level=observed_level,
+#'     exo=observed_exogenous)
+#'   # And, at every hour:
+#'   data$append(
+#'     time=current_hour,
+#'     value=current_PM10)
+#'   # Finally, a bit before predict_from hour:
 #'   pred <- forecaster$predictSerie(data, data$getSize(), ...)
 #'   #do_something_with_pred
-#' }}
+#' } }
 #' @export
 computeForecast = function(data, indices, forecaster, pjump, predict_from,
-	memory=Inf, horizon=length(data$getSerie(1)), ncores=3, ...)
+	memory=Inf, horizon=length(data$getSerie(1)), ncores=3, verbose=FALSE, ...)
 {
 	# (basic) Arguments sanity checks
 	predict_from = as.integer(predict_from)[1]
 	if (! predict_from %in% 1:length(data$getSerie(1)))
 		stop("predict_from in [1,24] (hours)")
+	if (hasArg("opera") && !list(...)$opera && memory < Inf)
+		memory <- Inf #finite memory in training mode makes no sense
 	horizon = as.integer(horizon)[1]
 	if (horizon<=predict_from || horizon>length(data$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")
-	if (!is.character(forecaster) || !is.character(pjump))
-		stop("forecaster (name) and pjump (function) should be of class character")
+	if (!is.character(forecaster))
+		stop("forecaster (name): character")
+	if (!is.null(pjump) && !is.character(pjump))
+		stop("pjump (function): character or NULL")
 
 	pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) )
 	forecaster_class_name = getFromNamespace(
 		paste(forecaster,"Forecaster",sep=""), "talweg")
-	forecaster = forecaster_class_name$new( #.pjump =
-		getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
+
+	if (!is.null(pjump))
+		pjump <- getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg")
+	forecaster = forecaster_class_name$new(pjump)
 
 	computeOneForecast <- function(i)
 	{
+		if (verbose)
+			print(paste("Index",i))
 		list(
 			"forecast" = forecaster$predictSerie(data,i,memory,predict_from,horizon,...),
 			"params" = forecaster$getParameters(),