X-Git-Url: https://git.auder.net/?p=talweg.git;a=blobdiff_plain;f=pkg%2FR%2FForecaster.R;fp=pkg%2FR%2FForecaster.R;h=2b259fc9bacdfd78e0ca073b28396ab9b1733716;hp=52587602ee9b6c3533c70fc1245b3bb0dc4616e4;hb=4f3fdbb8e2ac4bd57a4e27539a58ef0e7ec2304c;hpb=5037d6d061b86f7bc4baaf5c4614c6da9c9eaa1b

diff --git a/pkg/R/Forecaster.R b/pkg/R/Forecaster.R
index 5258760..2b259fc 100644
--- a/pkg/R/Forecaster.R
+++ b/pkg/R/Forecaster.R
@@ -6,8 +6,9 @@
 #' example "Neighbors" method stores informations about the considered neighborhood for
 #' the current prediction task) and one main function: \code{predictSerie()}. This last
 #' function (by default) calls \code{predictShape()} to get a forecast of a centered
-#' serie, and then calls the "jump prediction" function -- see "field" section -- to
-#' adjust it based on the last observed values.
+#' serie, and then calls the "jump prediction" function if it's provided -- see "field"
+#' section -- to adjust it based on the last observed values. The main method in derived
+#' forecasters is \code{predictShape()}; see 'Methods' section.
 #'
 #' @usage # Forecaster$new(pjump) #warning: predictShape() is unimplemented
 #'
@@ -15,25 +16,27 @@
 #' @field .pjump Function: how to predict the jump at day interface? The arguments of
 #'   this function are -- in this order:
 #'   \itemize{
-#'     \item data : object output of \code{getData()},
-#'     \item today : index (integer or date) of the last known day in data,
-#'     \item memory : number of days to use in the past (including today),
-#'     \item horizon : number of time steps to predict,
-#'     \item params : optimized parameters in the main method \code{predictShape()},
-#'     \item ... : additional arguments.
+#'     \item data: object output of \code{getData()},
+#'     \item today: index of the current day in data (known until predict_from-1),
+#'     \item memory: number of days to use in the past (including today),
+#'     \item predict_from: first time step to predict (in [1,24])
+#'     \item horizon: last time step to predict (in [predict_from,24]),
+#'     \item params: optimized parameters in the main method \code{predictShape()},
+#'     \item ...: additional arguments.
 #'   }
 #'   .pjump returns an estimation of the jump after the last observed value.
 #'
 #' @section Methods:
 #' \describe{
 #' \item{\code{initialize(data, pjump)}}{
-#'   Initialize a Forecaster object with a Data object and a jump prediction function.}
-#' \item{\code{predictSerie(today,memory,horizon,...)}}{
-#'   Predict a new serie of \code{horizon} values at day index \code{today} using
-#'   \code{memory} days in the past.}
-#' \item{\code{predictShape(today,memory,horizon,...)}}{
-#'   Predict a new shape of \code{horizon} values at day index \code{today} using
+#'   Initialize a Forecaster object with a Data object and a jump prediction function,
+#'   or NULL if \code{predictShape()} returns an adjusted curve.}
+#' \item{\code{predictSerie(data,today,memory,predict_from,horizon,...)}}{
+#'   Predict the next curve (at index today) from predict_from to horizon (hours), using
 #'   \code{memory} days in the past.}
+#' \item{\code{predictShape(data,today,memory,predict_from,horizon,...)}}{
+#'   Predict the shape of the next curve (at index today) from predict_from to horizon
+#'   (hours), using \code{memory} days in the past.}
 #' \item{\code{getParameters()}}{
 #'   Return (internal) parameters.}
 #' }
@@ -55,15 +58,20 @@ Forecaster = R6::R6Class("Forecaster",
 		predictSerie = function(data, today, memory, predict_from, horizon, ...)
 		{
 			# Parameters (potentially) computed during shape prediction stage
-			predicted_shape = self$predictShape(data,today,memory,predict_from,horizon,...)
-			predicted_delta = private$.pjump(data, today, memory, predict_from, horizon,
-				private$.params, ...)
+			predicted_shape <- self$predictShape(data,today,memory,predict_from,horizon,...)
+			predicted_delta <-
+				if (is.null(private$.pjump))
+					NULL
+				else
+					private$.pjump(data,today,memory,predict_from,horizon,private$.params,...)
 
-			# Predicted shape is aligned on the end of current day + jump
+			# Predicted shape is aligned on the end of current day + jump (if jump!=NULL)
 			c( data$getSerie(today)[if (predict_from>=2) 1:(predict_from-1) else c()],
-				predicted_shape - predicted_shape[1] + predicted_delta +
-					ifelse(predict_from>=2,
-						data$getSerie(today)[predict_from-1], tail(data$getSerie(today-1),1)) )
+				predicted_shape + ifelse( is.null(private$.pjump),
+					0,
+					predicted_delta - predicted_shape[1] +
+						ifelse(predict_from>=2,
+							data$getSerie(today)[predict_from-1], tail(data$getSerie(today-1),1)) ) )
 		},
 		predictShape = function(data, today, memory, predict_from, horizon, ...)
 			NULL #empty default implementation: to implement in inherited classes