X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FForecaster.R;h=ef270b18b9847762756d490bb28cbfadec05c5ae;hb=a3344f7591f6f4b3d337a69e4a568e9b16e33415;hp=47160b561647cc80c454de929384f41df412166f;hpb=5d83d8150dc135347d5ef39e5015b88f33fa9ee3;p=talweg.git

diff --git a/pkg/R/Forecaster.R b/pkg/R/Forecaster.R
index 47160b5..ef270b1 100644
--- a/pkg/R/Forecaster.R
+++ b/pkg/R/Forecaster.R
@@ -1,47 +1,78 @@
 #' Forecaster
 #'
-#' Forecaster (abstract class, implemented by all forecasters)
+#' Forecaster (abstract class, implemented by all forecasters).
 #'
-#' @docType class
-#' @importFrom R6 R6Class
+#' A Forecaster object encapsulates parameters (which can be of various kinds, for
+#' 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 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
+#'
+#' @field .params List of computed parameters (if applicable).
+#' @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 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.
 #'
-#' @field params List of computed parameters, for post-run analysis (dev)
-#' @field data Dataset, object of class Data
-#' @field pjump Function: how to predict the jump at day interface ?
+#' @section Methods:
+#' \describe{
+#' \item{\code{initialize(pjump)}}{
+#'   Initialize a Forecaster object with a jump prediction function.}
+#' \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.}
+#' }
+#'
+#' @docType class
+#' @format R6 class
 #'
-#' @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 \code{memory}
-#'   days in the past.}
-#' \item{\code{getParameters()}}
-#'   {Return (internal) parameters.}}
 Forecaster = R6::R6Class("Forecaster",
 	private = list(
 		.params = list(),
-		.data = NULL,
 		.pjump = NULL
 	),
 	public = list(
-		initialize = function(data, pjump)
+		initialize = function(pjump)
 		{
-			private$.data <- data
 			private$.pjump <- pjump
 			invisible(self)
 		},
-		predictSerie = function(today, memory, horizon, ...)
+		predictSerie = function(data, today, memory, predict_from, horizon, ...)
 		{
 			# Parameters (potentially) computed during shape prediction stage
-			predicted_shape = self$predictShape(today, memory, horizon, ...)
-			predicted_delta = private$.pjump(private$.data,today,memory,horizon,private$.params,...)
-			# Predicted shape is aligned it on the end of current day + jump
-			predicted_shape+tail(private$.data$getSerie(today),1)-predicted_shape[1]+predicted_delta
+			predicted_shape <- self$predictShape(data,today,memory,predict_from,horizon,...)
+
+			if (is.na(predicted_shape[1]))
+				return (NA)
+
+			predicted_delta <- private$.pjump(data, today, memory, predict_from,
+					horizon, private$.params, first_pred=predicted_shape[1], ...)
+
+			# Predicted shape is aligned on the end of current day + jump
+			c( data$getSerie(today)[if (predict_from>=2) 1:(predict_from-1) else c()],
+				(predicted_shape - predicted_shape[1]) + #shape with first_pred = 0
+				ifelse(predict_from>=2, #last observed value
+					data$getSerie(today)[predict_from-1], tail(data$getSerie(today-1),1)) +
+				predicted_delta ) #jump
 		},
-		predictShape = function(today, memory, horizon, ...)
+		predictShape = function(data, today, memory, predict_from, horizon, ...)
 			NULL #empty default implementation: to implement in inherited classes
 		,
 		getParameters = function()