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 ac0df04..238274b 100644 (file)
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -5,25 +5,25 @@
 #' Predict tomorrow as a weighted combination of "futures of the past" days.
 #' Inherits \code{\link{Forecaster}}
 NeighborsForecaster = R6::R6Class("NeighborsForecaster",
-       inherit = "Forecaster",
+       inherit = Forecaster,
 
        public = list(
-               predictShape = function(today, memory, horizon, ...)
+               predictShape = function(data, today, memory, horizon, ...)
                {
                        # (re)initialize computed parameters
-                       params <<- list("weights"=NA, "indices"=NA, "window"=NA)
+                       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 (.predictShapeAux(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$getSerie(i+1)))
-                       }) ]
+                       {
+                               return ( private$.predictShapeAux(data,
+                                       fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
+                       }
 
                        # Indices of similar days for cross-validation; TODO: 45 = magic number
                        sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
@@ -36,35 +36,50 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                for (i in intersect(fdays,sdays))
                                {
                                        # mix_strategy is never used here (simtype != "mix"), therefore left blank
-                                       prediction = .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 (.predictShapeAux(fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
+                       {
+                               return (private$.predictShapeAux(data,
+                                       fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
+                       }
                        if (simtype == "exo")
-                               return (.predictShapeAux(fdays, today, horizon, h_best_exo,  kernel, "exo",  TRUE))
+                       {
+                               return (private$.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 (.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
@@ -138,15 +153,15 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                        simils_endo * simils_exo
 
                        prediction = rep(0, horizon)
-                       for (i in seq_along(fdays_indices))
-                               prediction = prediction + similarities[i] * data$getSerie(fdays_indices[i]+1)[1:horizon]
+                       for (i in seq_along(fdays))
+                               prediction = prediction + similarities[i] * data$getSerie(fdays[i]+1)[1:horizon]
                        prediction = prediction / sum(similarities, na.rm=TRUE)
 
                        if (final_call)
                        {
-                               params$weights <<- similarities
-                               params$indices <<- fdays_indices
-                               params$window <<-
+                               private$.params$weights <- similarities
+                               private$.params$indices <- fdays
+                               private$.params$window <-
                                        if (simtype=="endo") {
                                                h_endo
                                        } else if (simtype=="exo") {