#' @docType class
#' @importFrom R6 R6Class
#'
-#' @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 ?
+#' @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}
+#' @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}
+#' \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.}}
+#' \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 = self$predictShape(today, memory, horizon, ...)
- predicted_delta = private$.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, ...)
+ predictShape = function(data, today, memory, horizon, ...)
NULL #empty default implementation: to implement in inherited classes
,
getParameters = function()