X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;h=9ba72b8f308fcf00ef43e99c5e968c731eac1fbe;hb=445e7bbc18aa739ec0b3caba4d8710a9d9e1a43c;hp=27cd23a7c31a5ad5691434568afee26c96499410;hpb=ea5c7e56ca05a51ce4f0535ffa08cda4c14bff4a;p=talweg.git

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 27cd23a..9ba72b8 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -22,78 +22,122 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 			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))
+			local = ifelse(hasArg("local"), list(...)$local, TRUE) #same level + season?
+			simtype = ifelse(hasArg("simtype"), list(...)$simtype, "none") #or "endo", or "exo"
+			if (hasArg("window"))
 			{
 				return ( private$.predictShapeAux(data,
-					fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
+					fdays, today, horizon, local, list(...)$window, simtype, TRUE) )
 			}
 
 			# Indices of similar days for cross-validation; TODO: 20 = magic number
-			cv_days = getSimilarDaysIndices(today, data, limit=20, same_season=FALSE, days_in=fdays)
+			cv_days = getSimilarDaysIndices(today, data, limit=20, same_season=FALSE,
+				days_in=fdays)
 
-			# Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
-			errorOnLastNdays = function(h, kernel, simtype)
+			# Optimize h : h |--> sum of prediction errors on last N "similar" days
+			errorOnLastNdays = function(window, simtype)
 			{
 				error = 0
 				nb_jours = 0
 				for (i in seq_along(cv_days))
 				{
 					# mix_strategy is never used here (simtype != "mix"), therefore left blank
-					prediction = private$.predictShapeAux(data,
-						fdays, cv_days[i], horizon, h, kernel, simtype, FALSE)
+					prediction = private$.predictShapeAux(data, fdays, cv_days[i], horizon, local,
+						window, simtype, FALSE)
 					if (!is.na(prediction[1]))
 					{
 						nb_jours = nb_jours + 1
 						error = error +
-							mean((data$getCenteredSerie(cv_days[i]+1)[1:horizon] - prediction)^2)
+							mean((data$getSerie(cv_days[i]+1)[1:horizon] - prediction)^2)
 					}
 				}
 				return (error / nb_jours)
 			}
 
+			# TODO: 7 == magic number
 			if (simtype != "endo")
 			{
-				h_best_exo = optimize(
-					errorOnLastNdays, c(0,7), kernel=kernel, simtype="exo")$minimum
+				best_window_exo = optimize(
+					errorOnLastNdays, c(0,7), simtype="exo")$minimum
 			}
 			if (simtype != "exo")
 			{
-				h_best_endo = optimize(
-					errorOnLastNdays, c(0,7), kernel=kernel, simtype="endo")$minimum
+				best_window_endo = optimize(
+					errorOnLastNdays, c(0,7), simtype="endo")$minimum
 			}
 
 			if (simtype == "endo")
 			{
-				return (private$.predictShapeAux(data,
-					fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
+				return (private$.predictShapeAux(data, fdays, today, horizon, local,
+					best_window_endo, "endo", TRUE))
 			}
 			if (simtype == "exo")
 			{
-				return (private$.predictShapeAux(data,
-					fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
+				return (private$.predictShapeAux(data, fdays, today, horizon, local,
+					best_window_exo, "exo", TRUE))
 			}
 			if (simtype == "mix")
 			{
-				h_best_mix = c(h_best_endo,h_best_exo)
-				return(private$.predictShapeAux(data,
-					fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
+				return(private$.predictShapeAux(data, fdays, today, horizon, local,
+					c(best_window_endo,best_window_exo), "mix", TRUE))
 			}
 		}
 	),
 	private = list(
 		# Precondition: "today" is full (no NAs)
-		.predictShapeAux = function(data, fdays, today, horizon, h, kernel, simtype, final_call)
+		.predictShapeAux = function(data, fdays, today, horizon, local, window, simtype,
+			final_call)
 		{
-			fdays = fdays[ fdays < today ]
-			# TODO: 3 = magic number
-			if (length(fdays) < 3)
+			fdays_cut = fdays[ fdays < today ]
+			if (length(fdays_cut) <= 1)
 				return (NA)
 
-			if (simtype != "exo")
+			if (local)
+			{
+				# Neighbors: days in "same season"; TODO: 60 == magic number...
+				fdays = getSimilarDaysIndices(today, data, limit=60, same_season=TRUE,
+					days_in=fdays_cut)
+				if (length(fdays) <= 1)
+					return (NA)
+				levelToday = data$getLevel(today)
+				distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
+				#TODO: 2, 10, 3, 12 magic numbers here...
+				dist_thresh = 2
+				min_neighbs = min(10,length(fdays))
+				repeat
+				{
+					same_pollution = (distances <= dist_thresh)
+					nb_neighbs = sum(same_pollution)
+					if (nb_neighbs >= min_neighbs) #will eventually happen
+						break
+					dist_thresh = dist_thresh + 3
+				}
+				fdays = fdays[same_pollution]
+				max_neighbs = 12
+				if (nb_neighbs > max_neighbs)
+				{
+					# Keep only max_neighbs closest neighbors
+					fdays = fdays[
+						sort(distances[same_pollution],index.return=TRUE)$ix[1:max_neighbs] ]
+				}
+				if (length(fdays) == 1) #the other extreme...
+				{
+					if (final_call)
+					{
+						private$.params$weights <- 1
+						private$.params$indices <- fdays
+						private$.params$window <- 1
+					}
+					return ( data$getSerie(fdays[1])[1:horizon] ) #what else?!
+				}
+			}
+			else
+				fdays = fdays_cut #no conditioning
+
+			if (simtype == "endo" || simtype == "mix")
 			{
-				h_endo = ifelse(simtype=="mix", h[1], h)
+				# Compute endogen similarities using given window
+				window_endo = ifelse(simtype=="mix", window[1], window)
 
 				# Distances from last observed day to days in the past
 				serieToday = data$getSerie(today)
@@ -103,26 +147,18 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 				})
 
 				sd_dist = sd(distances2)
-				if (sd_dist < .Machine$double.eps)
+				if (sd_dist < .25 * sqrt(.Machine$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
-						u = 1 - distances2/(sd_dist*h_endo^2)
-						u[abs(u)>1] = 0.
-						u
-					}
+				simils_endo = exp(-distances2/(sd_dist*window_endo^2))
 			}
 
-			if (simtype != "endo")
+			if (simtype == "exo" || simtype == "mix")
 			{
-				h_exo = ifelse(simtype=="mix", h[2], h)
+				# Compute exogen similarities using given window
+				window_exo = ifelse(simtype=="mix", window[2], window)
 
 				M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
 				M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
@@ -149,16 +185,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 #					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
-						u = 1 - distances2/(sd_dist*h_exo^2)
-						u[abs(u)>1] = 0.
-						u
-					}
+				simils_exo = exp(-distances2/(sd_dist*window_exo^2))
 			}
 
 			similarities =
@@ -166,13 +193,15 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 					simils_exo
 				else if (simtype == "endo")
 					simils_endo
-				else #mix
+				else if (simtype == "mix")
 					simils_endo * simils_exo
+				else #none
+					rep(1, length(fdays))
 			similarities = similarities / sum(similarities)
 
 			prediction = rep(0, horizon)
 			for (i in seq_along(fdays))
-				prediction = prediction + similarities[i] * data$getCenteredSerie(fdays[i]+1)[1:horizon]
+				prediction = prediction + similarities[i] * data$getSerie(fdays[i]+1)[1:horizon]
 
 			if (final_call)
 			{
@@ -180,11 +209,11 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 				private$.params$indices <- fdays
 				private$.params$window <-
 					if (simtype=="endo")
-						h_endo
+						window_endo
 					else if (simtype=="exo")
-						h_exo
+						window_exo
 					else #mix
-						c(h_endo,h_exo)
+						c(window_endo,window_exo)
 			}
 
 			return (prediction)