update documention, fix package to compete with 'method Bruno'

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

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 60eb7b7..9d1e3fb 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
@@ -146,7 +142,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                        }
                                        return ( data$getSerie(tdays[1])[predict_from:horizon] )
                                }
-                               max_neighbs = 12 #TODO: 12 = arbitrary number
+                               max_neighbs = 10 #TODO: 12 = arbitrary number
                                if (length(tdays) > max_neighbs)
                                {
                                        distances2 <- .computeDistsEndo(data, today, tdays, predict_from)
@@ -281,7 +277,8 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
        sapply(tdays, function(i) {
                delta = lastSerie - c(data$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))
        })
 }