almost finished debug
[talweg.git] / pkg / R / Forecaster.R
index d5d5280..2955479 100644 (file)
@@ -1,50 +1,52 @@
-#' @title Forecaster (abstract class)
+#' Forecaster
 #'
-#' @description Abstract class to represent a forecaster (they all inherit this)
+#' Forecaster (abstract class, implemented by all forecasters)
+#'
+#' @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 ?
-Forecaster = setRefClass(
-       Class = "Forecaster",
-
-       fields = list(
-               params = "list",
-               data = "Data",
-               pjump = "function"
+#'
+#' @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
        ),
-
-       methods = list(
-               initialize = function(...)
+       public = list(
+               initialize = function(data, pjump)
                {
-                       "Initialize (generic) Forecaster object"
-
-                       callSuper(...)
-                       if (!hasArg(data))
-                               stop("Forecaster must be initialized with a Data object")
-                       params <<- list()
+                       private$.data <- data
+                       private$.pjump <- pjump
+                       invisible(self)
                },
-               predict = function(today, memory, horizon, ...)
+               predictSerie = function(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$predictShape(today, memory, horizon, ...)
+                       predicted_delta = private$.pjump(
+                               private$.data, today, memory, horizon, private$.params, ...)
                        # Predicted shape is aligned it on the end of current day + jump
-                       predicted_shape + tail(data$getSerie(today),1) - predicted_shape[1] + predicted_delta
+                       predicted_shape + tail(private$.data$getSerie(today),1) -
+                               predicted_shape[1] + predicted_delta
                },
                predictShape = function(today, memory, horizon, ...)
-               {
-                       "Shape prediction (centered curve)"
-
-                       #empty default implementation: to implement in inherited classes
-               },
+                       NULL #empty default implementation: to implement in inherited classes
+               ,
                getParameters = function()
-               {
-                       "Get parameters list"
-
-                       params
-               }
+                       private$.params
        )
 )