'update'

[talweg.git] / pkg / R / Forecaster.R

diff --git a/pkg/R/Forecaster.R b/pkg/R/Forecaster.R
index 2bd2e4e..ce1bb35 100644 (file)
--- a/pkg/R/Forecaster.R
+++ b/pkg/R/Forecaster.R
@@ -1,48 +1,67 @@
 #' 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 -- see "field" section -- to
+#' adjust it based on the last observed values.
+#'
+#' @usage f <- 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 (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.
+#'   }
+#'   .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(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.}
+#' }
+#'
+#' @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$.pjump <- pjump
                        invisible(self)
                },
-               predictSerie = function(today, memory, horizon, ...)
+               predictSerie = function(data, today, memory, horizon, ...)
                {
                        # Parameters (potentially) computed during shape prediction stage
-                       predicted_shape = o$predictShape(today, memory, horizon, ...)
-                       predicted_delta = private$.pjump(private$.data,today,memory,horizon,private$.params,...)
+                       predicted_shape = self$predictShape(data, today, memory, horizon, ...)
+                       predicted_delta = private$.pjump(data,today,memory,horizon,private$.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+tail(data$getSerie(today),1)-predicted_shape[1]+predicted_delta
                },
-               predictShape = function(today, memory, horizon, ...)
-                       #empty default implementation: to implement in inherited classes
+               predictShape = function(data, today, memory, horizon, ...)
+                       NULL #empty default implementation: to implement in inherited classes
                ,
                getParameters = function()
                        private$.params