X-Git-Url: https://git.auder.net/?p=talweg.git;a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;fp=pkg%2FR%2FF_Neighbors.R;h=9d1e3fbb84c8fb6e451e1ba08a3c57b7d6906d1f;hp=d63c177f952b0e7aad8172ddcca5dd8d71513477;hb=4f3fdbb8e2ac4bd57a4e27539a58ef0e7ec2304c;hpb=5037d6d061b86f7bc4baaf5c4614c6da9c9eaa1b

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index d63c177..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
@@ -152,12 +148,6 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 					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