Simplify plots: version OK with R6 classes

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

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 4b6b7e7..238274b 100644 (file)
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -8,26 +8,23 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
        inherit = Forecaster,
 
        public = list(
-               predictShape = function(today, memory, horizon, ...)
+               predictShape = function(data, today, memory, horizon, ...)
                {
                        # (re)initialize computed parameters
                        private$.params <- list("weights"=NA, "indices"=NA, "window"=NA)
 
+                       # Determine indices of no-NAs days followed by no-NAs tomorrows
+                       fdays = getNoNA2(data, max(today-memory,1), today-1)
+
                        # Get optional args
                        simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix") #or "endo", or "exo"
                        kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
                        if (hasArg(h_window))
                        {
-                               return ( private$.predictShapeAux(
+                               return ( private$.predictShapeAux(data,
                                        fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
                        }
 
-                       # Determine indices of no-NAs days followed by no-NAs tomorrows
-                       first_day = max(today - memory, 1)
-                       fdays = (first_day:(today-1))[ sapply(first_day:(today-1), function(i) {
-                               !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1)))
-                       }) ]
-
                        # Indices of similar days for cross-validation; TODO: 45 = magic number
                        sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
 
@@ -39,35 +36,50 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                for (i in intersect(fdays,sdays))
                                {
                                        # mix_strategy is never used here (simtype != "mix"), therefore left blank
-                                       prediction = private$.predictShapeAux(fdays, i, horizon, h, kernel, simtype, FALSE)
+                                       prediction = private$.predictShapeAux(data,
+                                               fdays, i, horizon, h, kernel, simtype, FALSE)
                                        if (!is.na(prediction[1]))
                                        {
                                                nb_jours = nb_jours + 1
-                                               error = error + mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
+                                               error = error +
+                                                       mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
                                        }
                                }
                                return (error / nb_jours)
                        }
 
                        if (simtype != "endo")
-                               h_best_exo = optimize(errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
+                       {
+                               h_best_exo = optimize(
+                                       errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
+                       }
                        if (simtype != "exo")
-                               h_best_endo = optimize(errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
+                       {
+                               h_best_endo = optimize(
+                                       errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
+                       }
 
                        if (simtype == "endo")
-                               return(private$.predictShapeAux(fdays,today,horizon,h_best_endo,kernel,"endo",TRUE))
+                       {
+                               return (private$.predictShapeAux(data,
+                                       fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
+                       }
                        if (simtype == "exo")
-                               return(private$.predictShapeAux(fdays,today,horizon,h_best_exo,kernel,"exo",TRUE))
+                       {
+                               return (private$.predictShapeAux(data,
+                                       fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
+                       }
                        if (simtype == "mix")
                        {
                                h_best_mix = c(h_best_endo,h_best_exo)
-                               return(private$.predictShapeAux(fdays,today,horizon,h_best_mix,kernel,"mix",TRUE))
+                               return(private$.predictShapeAux(data,
+                                       fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
                        }
                }
        ),
        private = list(
                # Precondition: "today" is full (no NAs)
-               .predictShapeAux = function(fdays, today, horizon, h, kernel, simtype, final_call)
+               .predictShapeAux = function(data, fdays, today, horizon, h, kernel, simtype, final_call)
                {
                        fdays = fdays[ fdays < today ]
                        # TODO: 3 = magic number