-#' @title Forecaster (abstract class)
+#' Forecaster
#'
-#' @description Abstract class to represent a forecaster (they all inherit this)
+#' Forecaster (abstract class, implemented by all forecasters)
#'
-#' @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 ?
-Forecaster = setRefClass(
- Class = "Forecaster",
-
- fields = list(
- params = "list",
- data = "Data",
- pjump = "function"
+#' @docType class
+#' @importFrom R6 R6Class
+#'
+#' @field .params List of computed parameters, for post-run analysis (dev)
+#' @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.}
+#' }
+#'
+Forecaster = R6::R6Class("Forecaster",
+ private = list(
+ .params = list(),
+ .pjump = NULL
),
-
- methods = list(
- initialize = function(...)
+ public = list(
+ initialize = function(pjump)
{
- "Initialize (generic) Forecaster object"
-
- callSuper(...)
- if (!hasArg(data))
- stop("Forecaster must be initialized with a Data object")
- params <<- list()
+ private$.pjump <- pjump
+ invisible(self)
},
- predict = function(today, memory, horizon, ...)
+ predictSerie = function(data, today, memory, horizon, ...)
{
- "Obtain a new forecasted time-serie"
-
# Parameters (potentially) computed during shape prediction stage
- predicted_shape = predictShape(today, memory, horizon, ...)
- predicted_delta = pjump(data, today, memory, horizon, params, ...)
+ predicted_shape = self$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(data$getSerie(today),1) - predicted_shape[1] + predicted_delta
- },
- predictShape = function(today, memory, horizon, ...)
- {
- "Shape prediction (centered curve)"
-
- #empty default implementation: to implement in inherited classes
+ predicted_shape+tail(data$getSerie(today),1)-predicted_shape[1]+predicted_delta
},
+ predictShape = function(data, today, memory, horizon, ...)
+ NULL #empty default implementation: to implement in inherited classes
+ ,
getParameters = function()
- {
- params
- }
+ private$.params
)
)