update reports, last fixes, cleanup

[talweg.git] / pkg / R / F_Neighbors.R

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 60eb7b7..17fdd58 100644 (file)
--- 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)
@@ -146,7 +140,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                        }
                                        return ( data$getSerie(tdays[1])[predict_from:horizon] )
                                }
-                               max_neighbs = 12 #TODO: 12 = arbitrary number
+                               max_neighbs = 10 #TODO: 10 or 12 or... ?
                                if (length(tdays) > max_neighbs)
                                {
                                        distances2 <- .computeDistsEndo(data, today, tdays, predict_from)
@@ -230,11 +224,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 .getConstrainedNeighbs = function(today, data, tdays, min_neighbs=10)
 {
        levelToday = data$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))
@@ -246,14 +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