X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;h=17fdd58d7a19b3e448e92f37e2e8d192a824e261;hb=9b9bb2d490e356ff0393aec66b137cff0a64803d;hp=d63c177f952b0e7aad8172ddcca5dd8d71513477;hpb=5037d6d061b86f7bc4baaf5c4614c6da9c9eaa1b;p=talweg.git

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index d63c177..17fdd58 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
@@ -132,8 +128,6 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 				# 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)
 				# TODO: 10 == magic number
 				tdays = .getConstrainedNeighbs(today, data, tdays, min_neighbs=10)
 				if (length(tdays) == 1)
@@ -146,18 +140,12 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 					}
 					return ( data$getSerie(tdays[1])[predict_from:horizon] )
 				}
-				max_neighbs = 10 #TODO: 12 = arbitrary number
+				max_neighbs = 10 #TODO: 10 or 12 or... ?
 				if (length(tdays) > max_neighbs)
 				{
 					distances2 <- .computeDistsEndo(data, today, tdays, predict_from)
 					ordering <- order(distances2)
 					tdays <- tdays[ ordering[1:max_neighbs] ]
-
-					print("VVVVV")
-					print(sort(distances2)[1:max_neighbs])
-					print(integerIndexToDate(today,data))
-					print(lapply(tdays,function(i) integerIndexToDate(i,data)))
-					print(rbind(data$getSeries(tdays-1), data$getSeries(tdays)))
 				}
 			}
 			else
@@ -236,11 +224,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 .getConstrainedNeighbs = function(today, data, tdays, min_neighbs=10)
 {
 	levelToday = data$getLevelHat(today)
-#	levelYersteday = data$getLevel(today-1)
-	distances = sapply(tdays, function(i) {
-#		sqrt((data$getLevel(i-1)-levelYersteday)^2 + (data$getLevel(i)-levelToday)^2)
-		abs(data$getLevel(i)-levelToday)
-	})
+	distances = sapply( tdays, function(i) abs(data$getLevel(i) - levelToday) )
 	#TODO: 1, +1, +3 : magic numbers
 	dist_thresh = 1
 	min_neighbs = min(min_neighbs,length(tdays))
@@ -252,14 +236,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 			break
 		dist_thresh = dist_thresh + ifelse(dist_thresh>1,3,1)
 	}
-	tdays = tdays[same_pollution]
-#	max_neighbs = 12
-#	if (nb_neighbs > max_neighbs)
-#	{
-#		# Keep only max_neighbs closest neighbors
-#		tdays = tdays[ order(distances[same_pollution])[1:max_neighbs] ]
-#	}
-	tdays
+	tdays[same_pollution]
 }
 
 # compute similarities
@@ -287,8 +264,7 @@ 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(sum(delta^2))
+		sqrt(mean(delta^2))
 	})
 }