X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;h=a3d44a3916cc8b27195ddd693fc35f6dbcceb2b1;hb=546b0cb65870355a2a2c3705c91418570499d3a6;hp=7a3fbe525352405837e55c1fe7b69350768257ed;hpb=af3b84f4cacade7d83221ca0249b546c50ddf340;p=talweg.git

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 7a3fbe5..a3d44a3 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -4,24 +4,29 @@
 #'
 #' Predict tomorrow as a weighted combination of "futures of the past" days.
 #' Inherits \code{\link{Forecaster}}
+#'
 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)
 
+			# Do not forecast on days with NAs (TODO: softer condition...)
+			if (any(is.na(data$getCenteredSerie(today))))
+				return (NA)
+
 			# Determine indices of no-NAs days followed by no-NAs tomorrows
-			fdays = private$.data$getCoupleDays(max(today-memory,1), today-1)
+			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) )
 			}
 
@@ -36,12 +41,13 @@ 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((private$.data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
+							mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
 					}
 				}
 				return (error / nb_jours)
@@ -60,33 +66,31 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 
 			if (simtype == "endo")
 			{
-				return (private$.predictShapeAux(
+				return (private$.predictShapeAux(data,
 					fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
 			}
 			if (simtype == "exo")
 			{
-				return (private$.predictShapeAux(
+				return (private$.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$.predictShapeAux(
+				return(private$.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
 			if (length(fdays) < 3)
 				return (NA)
 
-			data = private$.data #shorthand
-
 			if (simtype != "exo")
 			{
 				h_endo = ifelse(simtype=="mix", h[1], h)
@@ -97,17 +101,22 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 				{
 					delta = data$getCenteredSerie(today) - data$getCenteredSerie(fdays[i])
 					# Require at least half of non-NA common values to compute the distance
-					if (sum(is.na(delta)) <= 0) #length(delta)/2)
-						distances2[i] = mean(delta^2) #, na.rm=TRUE)
+					if ( !any( is.na(delta) ) )
+						distances2[i] = mean(delta^2)
 				}
 
 				sd_dist = sd(distances2)
 				if (sd_dist < .Machine$double.eps)
+				{
+					warning("All computed distances are very close: stdev too small")
 					sd_dist = 1 #mostly for tests... FIXME:
+				}
 				simils_endo =
 					if (kernel=="Gauss")
 						exp(-distances2/(sd_dist*h_endo^2))
-					else { #Epanechnikov
+					else
+					{
+						# Epanechnikov
 						u = 1 - distances2/(sd_dist*h_endo^2)
 						u[abs(u)>1] = 0.
 						u
@@ -135,10 +144,17 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 				}
 
 				sd_dist = sd(distances2)
+				if (sd_dist < .Machine$double.eps)
+				{
+					warning("All computed distances are very close: stdev too small")
+					sd_dist = 1 #mostly for tests... FIXME:
+				}
 				simils_exo =
 					if (kernel=="Gauss")
 						exp(-distances2/(sd_dist*h_exo^2))
-					else { #Epanechnikov
+					else
+					{
+						# Epanechnikov
 						u = 1 - distances2/(sd_dist*h_exo^2)
 						u[abs(u)>1] = 0.
 						u
@@ -163,13 +179,12 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 				private$.params$weights <- similarities
 				private$.params$indices <- fdays
 				private$.params$window <-
-					if (simtype=="endo") {
+					if (simtype=="endo")
 						h_endo
-					} else if (simtype=="exo") {
+					else if (simtype=="exo")
 						h_exo
-					} else { #mix
+					else #mix
 						c(h_endo,h_exo)
-					}
 			}
 
 			return (prediction)