X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;h=7a3fbe525352405837e55c1fe7b69350768257ed;hb=af3b84f4cacade7d83221ca0249b546c50ddf340;hp=ac0df0414c7f79ef671d9e8dd62b4fe13286b2b7;hpb=25b75559e2d9bf84e2de35b851d93fefdae36e17;p=talweg.git

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index ac0df04..7a3fbe5 100644
--- 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, ...)
 		{
 			# (re)initialize computed parameters
-			params <<- list("weights"=NA, "indices"=NA, "window"=NA)
+			private$.params <- list("weights"=NA, "indices"=NA, "window"=NA)
+
+			# Determine indices of no-NAs days followed by no-NAs tomorrows
+			fdays = private$.data$getCoupleDays(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(
+					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)
@@ -36,29 +36,43 @@ 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$.predictShapeAux(fdays, i, horizon, h, kernel, simtype, FALSE)
 					if (!is.na(prediction[1]))
 					{
 						nb_jours = nb_jours + 1
-						error = error + mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
+						error = error +
+							mean((private$.data$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$.predictShapeAux(
+					fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
+			}
 			if (simtype == "exo")
-				return (.predictShapeAux(fdays, today, horizon, h_best_exo,  kernel, "exo",  TRUE))
+			{
+				return (private$.predictShapeAux(
+					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(
+					fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
 			}
 		}
 	),
@@ -71,6 +85,8 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 			if (length(fdays) < 3)
 				return (NA)
 
+			data = private$.data #shorthand
+
 			if (simtype != "exo")
 			{
 				h_endo = ifelse(simtype=="mix", h[1], h)
@@ -138,15 +154,15 @@ 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") {