X-Git-Url: https://git.auder.net/?p=talweg.git;a=blobdiff_plain;f=pkg%2FR%2FF_Average.R;h=8f81747fbca9a863a0ac9c882dca87298a6bb63c;hp=ba0f0036441fd8c9c402d263169d3e36053cc1fc;hb=3ddf1c12af0c167fe7d3bb59e63258550270cfc5;hpb=44a9990b6d608ffcd2e99d5193fa8b9e6cbdb436

diff --git a/pkg/R/F_Average.R b/pkg/R/F_Average.R
index ba0f003..8f81747 100644
--- a/pkg/R/F_Average.R
+++ b/pkg/R/F_Average.R
@@ -1,35 +1,21 @@
-#' @include Forecaster.R
+#' Average Forecaster
 #'
-#' @title Average Forecaster
+#' Pointwise average of all the series of the same day of week in the past.
 #'
-#' @description Return the (pointwise) average of the all the (similar) centered day curves
-#'   in the past. Inherits \code{\link{Forecaster}}
-AverageForecaster = setRefClass(
-	Class = "AverageForecaster",
-	contains = "Forecaster",
+#' For example, if the current day (argument "today") is a tuesday, then all series
+#' corresponding to wednesdays in the past (until the beginning or memory limit) are
+#' averaged to provide a smooth prediction. This forecast will most of the time be wrong,
+#' but will also look plausible enough.
+#'
+#' @docType class
+#' @format R6 class, inherits Forecaster
+#' @aliases F_Average
+#'
+AverageForecaster = R6::R6Class("AverageForecaster",
+	inherit = Forecaster,
 
-	methods = list(
-		initialize = function(...)
-		{
-			callSuper(...)
-		},
-		predict = function(today, memory, horizon, ...)
-		{
-			predicted_shape = predictShape(today, memory, horizon, ...)
-			#Take care of never passing same_day==FALSE (when pjump == Persistence)
-			predicted_delta =
-				if (#as.character(substitute(pjump))=="Persistence" && #TODO: doesn't work
-					hasArg("same_day") && list(...)$same_day==FALSE)
-				{
-					args = list(...)
-					args$same_day = TRUE
-					do.call(pjump, append(list("today"=today,"memory"=memory,"horizon"=horizon), args))
-				}
-				else
-					pjump(data, today, memory, horizon, params, ...)
-			predicted_shape + tail(data$getSerie(today),1) - predicted_shape[1] + predicted_delta
-		},
-		predictShape = function(today, memory, horizon, ...)
+	public = list(
+		predictShape = function(data, today, memory, horizon, ...)
 		{
 			avg = rep(0., horizon)
 			first_day = max(1, today-memory)