X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;h=12595d998f82d4883069f4c02a32d458b21aaa70;hb=102bcfda4afbb5cfee885cbee0f55545624168fd;hp=9ba72b8f308fcf00ef43e99c5e968c731eac1fbe;hpb=445e7bbc18aa739ec0b3caba4d8710a9d9e1a43c;p=talweg.git

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 9ba72b8..12595d9 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -1,9 +1,36 @@
-#' @include Forecaster.R
-#'
 #' Neighbors Forecaster
 #'
-#' Predict tomorrow as a weighted combination of "futures of the past" days.
-#' Inherits \code{\link{Forecaster}}
+#' Predict next serie as a weighted combination of "futures of the past" days,
+#' where days in the past are chosen and weighted according to some similarity measures.
+#'
+#' The main method is \code{predictShape()}, taking arguments data, today, memory,
+#' horizon respectively for the dataset (object output of \code{getData()}), the current
+#' index, the data depth (in days) and the number of time steps to forecast.
+#' In addition, optional arguments can be passed:
+#' \itemize{
+#'   \item local : TRUE (default) to constrain neighbors to be "same days within same
+#'     season"
+#'   \item simtype : 'endo' for a similarity based on the series only,<cr>
+#'             'exo' for a similaruty based on exogenous variables only,<cr>
+#'             'mix' for the product of 'endo' and 'exo',<cr>
+#'             'none' (default) to apply a simple average: no computed weights
+#'   \item window : A window for similarities computations; override cross-validation
+#'     window estimation.
+#' }
+#' The method is summarized as follows:
+#' \enumerate{
+#'   \item Determine N (=20) recent days without missing values, and followed by a
+#'     tomorrow also without missing values.
+#'   \item Optimize the window parameters (if relevant) on the N chosen days.
+#'   \item Considering the optimized window, compute the neighbors (with locality
+#'     constraint or not), compute their similarities -- using a gaussian kernel if
+#'     simtype != "none" -- and average accordingly the "tomorrows of neigbors" to
+#'     obtain the final prediction.
+#' }
+#'
+#' @docType class
+#' @format R6 class, inherits Forecaster
+#' @alias F_Neighbors
 #'
 NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 	inherit = Forecaster,
@@ -55,32 +82,29 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 			}
 
 			# TODO: 7 == magic number
-			if (simtype != "endo")
-			{
-				best_window_exo = optimize(
-					errorOnLastNdays, c(0,7), simtype="exo")$minimum
-			}
-			if (simtype != "exo")
+			if (simtype=="endo" || simtype=="mix")
 			{
 				best_window_endo = optimize(
 					errorOnLastNdays, c(0,7), simtype="endo")$minimum
 			}
-
-			if (simtype == "endo")
+			if (simtype=="exo" || simtype=="mix")
 			{
-				return (private$.predictShapeAux(data, fdays, today, horizon, local,
-					best_window_endo, "endo", TRUE))
-			}
-			if (simtype == "exo")
-			{
-				return (private$.predictShapeAux(data, fdays, today, horizon, local,
-					best_window_exo, "exo", TRUE))
-			}
-			if (simtype == "mix")
-			{
-				return(private$.predictShapeAux(data, fdays, today, horizon, local,
-					c(best_window_endo,best_window_exo), "mix", TRUE))
+				best_window_exo = optimize(
+					errorOnLastNdays, c(0,7), simtype="exo")$minimum
 			}
+
+			best_window =
+				if (simtype == "endo")
+					best_window_endo
+				else if (simtype == "exo")
+					best_window_exo
+				else if (simtype == "mix")
+					c(best_window_endo,best_window_exo)
+				else #none: value doesn't matter
+					1
+
+			return(private$.predictShapeAux(data, fdays, today, horizon, local,
+				best_window, simtype, TRUE))
 		}
 	),
 	private = list(
@@ -128,7 +152,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 						private$.params$indices <- fdays
 						private$.params$window <- 1
 					}
-					return ( data$getSerie(fdays[1])[1:horizon] ) #what else?!
+					return ( data$getSerie(fdays[1])[1:horizon] )
 				}
 			}
 			else
@@ -212,8 +236,10 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 						window_endo
 					else if (simtype=="exo")
 						window_exo
-					else #mix
+					else if (simtype=="mix")
 						c(window_endo,window_exo)
+					else #none
+						1
 			}
 
 			return (prediction)