X-Git-Url: https://git.auder.net/doc/current/git-logo.png?a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;h=9d1e3fbb84c8fb6e451e1ba08a3c57b7d6906d1f;hb=4f3fdbb8e2ac4bd57a4e27539a58ef0e7ec2304c;hp=02536ebf164c9634c6bb7a6f23eeb3cf27fbbea9;hpb=638f27f4296727aff62b56643beb9f42aa5b57ef;p=talweg.git

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 02536eb..9d1e3fb 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -1,27 +1,23 @@
 #' Neighbors 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.
+#' Predict next serie as a weighted combination of curves observed on "similar" days in
+#' the past (and future if 'opera'=FALSE); the nature of the similarity is controlled by
+#' the options 'simtype' and 'local' (see below).
 #'
-#' The main method is \code{predictShape()}, taking arguments data, today, memory,
-#' predict_from, horizon respectively for the dataset (object output of
-#' \code{getData()}), the current index, the data depth (in days), the first predicted
-#' hour and the last predicted hour.
-#' In addition, optional arguments can be passed:
+#' Optional arguments:
 #' \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>
+#'   \item local: TRUE (default) to constrain neighbors to be "same days in same season"
+#'   \item simtype: 'endo' for a similarity based on the series only,<cr>
 #'             'exo' for a similarity 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
+#'   \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 Determine N (=20) recent days without missing values, and preceded by a
+#'     curve 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
@@ -61,8 +57,8 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 			if (!opera)
 				tdays = setdiff(tdays, today) #always exclude current day
 
-			# Shortcut if window is known
-			if (hasArg("window"))
+			# Shortcut if window is known or local==TRUE && simtype==none
+			if (hasArg("window") || (local && simtype=="none"))
 			{
 				return ( private$.predictShapeAux(data, tdays, today, predict_from, horizon,
 					local, list(...)$window, simtype, opera, TRUE) )
@@ -129,8 +125,8 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 
 			if (local)
 			{
-				# TODO: 60 == magic number
-				tdays = getSimilarDaysIndices(today, data, limit=60, same_season=TRUE,
+				# limit=Inf to not censor any day (TODO: finite limit? 60?)
+				tdays = getSimilarDaysIndices(today, data, limit=Inf, same_season=TRUE,
 					days_in=tdays_cut, operational=opera)
 #				if (length(tdays) <= 1)
 #					return (NA)
@@ -146,6 +142,13 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 					}
 					return ( data$getSerie(tdays[1])[predict_from:horizon] )
 				}
+				max_neighbs = 10 #TODO: 12 = arbitrary number
+				if (length(tdays) > max_neighbs)
+				{
+					distances2 <- .computeDistsEndo(data, today, tdays, predict_from)
+					ordering <- order(distances2)
+					tdays <- tdays[ ordering[1:max_neighbs] ]
+				}
 			}
 			else
 				tdays = tdays_cut #no conditioning
@@ -156,19 +159,9 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 				window_endo = ifelse(simtype=="mix", window[1], window)
 
 				# Distances from last observed day to selected days in the past
+				# TODO: redundant computation if local==TRUE
 				distances2 <- .computeDistsEndo(data, today, tdays, predict_from)
 
-				if (local)
-				{
-					max_neighbs = 12 #TODO: 12 = arbitrary number
-					if (length(distances2) > max_neighbs)
-					{
-						ordering <- order(distances2)
-						tdays <- tdays[ ordering[1:max_neighbs] ]
-						distances2 <- distances2[ ordering[1:max_neighbs] ]
-					}
-				}
-
 				simils_endo <- .computeSimils(distances2, window_endo)
 			}
 
@@ -284,7 +277,8 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 	sapply(tdays, function(i) {
 		delta = lastSerie - c(data$getSerie(i-1),
 			data$getSerie(i)[if (predict_from>=2) 1:(predict_from-1) else c()])
-		sqrt(mean(delta^2))
+#		sqrt(mean(delta^2))
+		sqrt(sum(delta^2))
 	})
 }