From: Benjamin Auder <benjamin.auder@somewhere>
Date: Wed, 29 Mar 2017 17:22:21 +0000 (+0200)
Subject: finished merging F_Neighbors.R; TODO: test
X-Git-Url: https://git.auder.net/variants/%24%7Bvname%7D/current/mini-custom.min.css?a=commitdiff_plain;h=aa059de77cbcd28a3a66c7ff29ebe0346882867b;p=talweg.git

finished merging F_Neighbors.R; TODO: test
---

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 27cd23a..c55291a 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -22,32 +22,33 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 			fdays = getNoNA2(data, max(today-memory,1), today-1)
 
 			# Get optional args
+			local = ifelse(hasArg("local"), list(...)$local, FALSE) #same level + season?
 			simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix") #or "endo", or "exo"
-			kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
-			if (hasArg(h_window))
+			if (hasArg("window"))
 			{
 				return ( private$.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 45 "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)
@@ -55,45 +56,87 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 
 			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 (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, 3, 5, 10 magic numbers here...
+				dist_thresh = 2
+				min_neighbs = min(3,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 = 10
+				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 != "exo")
 			{
-				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 +146,18 @@ 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")
 			{
-				h_exo = ifelse(simtype=="mix", h[2], h)
+				# Compute exogen similarities using given window
+				h_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 +184,7 @@ 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 =
@@ -172,7 +198,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 
 			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 +206,11 @@ 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)
diff --git a/pkg/R/F_Neighbors2.R b/pkg/R/F_Neighbors2.R
deleted file mode 100644
index ee40f61..0000000
--- a/pkg/R/F_Neighbors2.R
+++ /dev/null
@@ -1,228 +0,0 @@
-#' @include Forecaster.R
-#'
-#' Neighbors2 Forecaster
-#'
-#' Predict tomorrow as a weighted combination of "futures of the past" days.
-#' Inherits \code{\link{Forecaster}}
-#'
-Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
-	inherit = Forecaster,
-
-	public = list(
-		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 = 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$.predictShapeAux(data,
-					fdays, today, horizon, list(...)$h_window, kernel, 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)
-
-			# Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
-			errorOnLastNdays = function(h, kernel, 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$.predictShapeAux(data,
-						fdays, cv_days[i], horizon, h, kernel, simtype, FALSE)
-					if (!is.na(prediction[1]))
-					{
-						nb_jours = nb_jours + 1
-						error = error + mean((data$getSerie(cv_days[i]+1)[1:horizon] - prediction)^2)
-					}
-				}
-				return (error / nb_jours)
-			}
-
-			if (simtype != "endo")
-			{
-				h_best_exo = optimize(
-					errorOnLastNdays, c(0,7), kernel=kernel, simtype="exo")$minimum
-			}
-			if (simtype != "exo")
-			{
-				h_best_endo = optimize(
-					errorOnLastNdays, c(0,7), kernel=kernel, simtype="endo")$minimum
-			}
-
-			if (simtype == "endo")
-			{
-				return (private$.predictShapeAux(data,
-					fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
-			}
-			if (simtype == "exo")
-			{
-				return (private$.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$.predictShapeAux(data,
-					fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
-			}
-		}
-	),
-	private = list(
-		# Precondition: "today" is full (no NAs)
-		.predictShapeAux = function(data, fdays, today, horizon, h, kernel, simtype, final_call)
-		{
-			fdays_cut = fdays[ fdays < today ]
-			# TODO: 3 = magic number
-			if (length(fdays_cut) < 3)
-				return (NA)
-
-			# Neighbors: days in "same season"; TODO: 60 == magic number...
-			fdays = getSimilarDaysIndices(today, data, limit=60, same_season=TRUE, days_in=fdays_cut)
-			if (length(fdays) <= 1)
-				return (NA)
-			levelToday = data$getLevel(today)
-			distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
-			#TODO: 2, 3, 5, 10 magic numbers here...
-			dist_thresh = 2
-			min_neighbs = min(3,length(fdays))
-			repeat
-			{
-				same_pollution = (distances <= dist_thresh)
-				nb_neighbs = sum(same_pollution)
-				if (nb_neighbs >= min_neighbs) #will eventually happen
-					break
-				dist_thresh = dist_thresh + 3
-			}
-			fdays = fdays[same_pollution]
-			max_neighbs = 10
-			if (nb_neighbs > max_neighbs)
-			{
-				# Keep only max_neighbs closest neighbors
-				fdays = fdays[ sort(distances[same_pollution],index.return=TRUE)$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$getSerie(fdays[1])[1:horizon] ) #what else?!
-			}
-
-			if (simtype != "exo")
-			{
-				h_endo = ifelse(simtype=="mix", h[1], h)
-
-				# Distances from last observed day to days in the past
-				serieToday = data$getSerie(today)
-				distances2 = sapply(fdays, function(i) {
-					delta = serieToday - data$getSerie(i)
-					mean(delta^2)
-				})
-
-				sd_dist = sd(distances2)
-				if (sd_dist < .Machine$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
-						u = 1 - distances2/(sd_dist*h_endo^2)
-						u[abs(u)>1] = 0.
-						u
-					}
-			}
-
-			if (simtype != "endo")
-			{
-				h_exo = ifelse(simtype=="mix", h[2], h)
-
-				M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
-				M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
-				for (i in seq_along(fdays))
-					M[i+1,] = c( data$getLevel(fdays[i]), as.double(data$getExo(fdays[i])) )
-
-				sigma = cov(M) #NOTE: robust covariance is way too slow
-				# TODO: 10 == magic number; more robust way == det, or always ginv()
-				sigma_inv =
-					if (length(fdays) > 10)
-						solve(sigma)
-					else
-						MASS::ginv(sigma)
-
-				# Distances from last observed day to days in the past
-				distances2 = sapply(seq_along(fdays), function(i) {
-					delta = M[1,] - M[i+1,]
-					delta %*% sigma_inv %*% delta
-				})
-
-				sd_dist = sd(distances2)
-				if (sd_dist < .25 * sqrt(.Machine$double.eps))
-				{
-#					warning("All computed distances are very close: stdev too small")
-					sd_dist = 1 #mostly for tests... FIXME:
-				}
-				simils_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
-					}
-			}
-
-			similarities =
-				if (simtype == "exo")
-					simils_exo
-				else if (simtype == "endo")
-					simils_endo
-				else #mix
-					simils_endo * simils_exo
-			similarities = similarities / sum(similarities)
-
-			prediction = rep(0, horizon)
-			for (i in seq_along(fdays))
-				prediction = prediction + similarities[i] * data$getSerie(fdays[i]+1)[1:horizon]
-
-			if (final_call)
-			{
-				prediction = prediction - mean(prediction) #predict centered serie (artificial...)
-				private$.params$weights <- similarities
-				private$.params$indices <- fdays
-				private$.params$window <-
-					if (simtype=="endo")
-						h_endo
-					else if (simtype=="exo")
-						h_exo
-					else #mix
-						c(h_endo,h_exo)
-			}
-
-			return (prediction)
-		}
-	)
-)
diff --git a/reports/report.gj b/reports/report.gj
index 1a6b8d9..5e57660 100644
--- a/reports/report.gj
+++ b/reports/report.gj
@@ -1,11 +1,14 @@
 -----
 <h2>Introduction</h2>
 
-J'ai fait quelques essais dans différentes configurations pour la méthode "Neighbors"
-(la seule dont on a parlé) et sa variante récente appelée pour l'instant "Neighbors2",
-avec simtype="mix" : deux types de similarités prises en compte, puis multiplication des poids.
-Pour Neighbors on prédit le saut (par la moyenne pondérée des sauts passés), et pour Neighbors2
-on n'effectue aucun raccordement (prévision directe).
+J'ai fait quelques essais dans deux configurations pour la méthode "Neighbors"
+(la seule dont on a parlé, incorporant désormais la "variante Bruno/Michel").
+
+ * avec simtype="mix" et raccordement simple ("Zero") dans le cas "non local", i.e. on va
+   chercher des voisins n'importe où du moment qu'ils correspondent à deux jours consécutifs sans
+   valeurs manquantes.
+ * avec simtype="endo" et raccordement "Neighbor" dans le cas "local" : voisins de même niveau de
+   pollution et même saison.
 
 J'ai systématiquement comparé à une approche naïve : la moyenne des lendemains des jours
 "similaires" dans tout le passé, ainsi qu'à la persistence -- reproduisant le jour courant ou
@@ -40,74 +43,77 @@ indices_np = seq(as.Date("2015-04-26"),as.Date("2015-05-02"),"days")
 -----
 <h2 style="color:blue;font-size:2em">${list_titles[i]}</h2>
 -----r
-p_nn = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", horizon=H)
-p_nn2 = computeForecast(data, ${list_indices[i]}, "Neighbors2", "Zero", horizon=H)
-p_az = computeForecast(data, ${list_indices[i]}, "Average", "Zero", horizon=H)
-p_pz = computeForecast(data, ${list_indices[i]}, "Persistence", "Zero", horizon=H, same_day=${'TRUE' if loop.index < 2 else 'FALSE'})
+p_n = computeForecast(data, ${list_indices[i]}, "Neighbors", "Zero", horizon=H,
+	simtype="mix", local=FALSE)
+p_l = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", horizon=H,
+	simtype="endo", local=TRUE)
+p_a = computeForecast(data, ${list_indices[i]}, "Average", "Zero", horizon=H)
+p_p = computeForecast(data, ${list_indices[i]}, "Persistence", "Zero", horizon=H,
+	same_day=${'TRUE' if loop.index < 2 else 'FALSE'})
 -----r
-e_nn = computeError(data, p_nn, H)
-e_nn2 = computeError(data, p_nn2, H)
-e_az = computeError(data, p_az, H)
-e_pz = computeError(data, p_pz, H)
+e_n = computeError(data, p_n, H)
+e_l = computeError(data, p_nl, H)
+e_a = computeError(data, p_a, H)
+e_p = computeError(data, p_p, H)
 options(repr.plot.width=9, repr.plot.height=7)
-plotError(list(e_nn, e_pz, e_az, e_nn2), cols=c(1,2,colors()[258], 4))
+plotError(list(e_n, e_p, e_a, e_l), cols=c(1,2,colors()[258], 4))
 
-# Noir: Neighbors, bleu: Neighbors2, vert: moyenne, rouge: persistence
+# Noir: Neighbors non-local, bleu: Neighbors local, vert: moyenne, rouge: persistence
 
-i_np = which.min(e_nn$abs$indices)
-i_p = which.max(e_nn$abs$indices)
+i_np = which.min(e_n$abs$indices)
+i_p = which.max(e_n$abs$indices)
 -----r
 options(repr.plot.width=9, repr.plot.height=4)
 par(mfrow=c(1,2))
 
-plotPredReal(data, p_nn, i_np); title(paste("PredReal nn day",i_np))
-plotPredReal(data, p_nn2, i_p); title(paste("PredReal nn day",i_p))
+plotPredReal(data, p_n, i_np); title(paste("PredReal non-loc day",i_np))
+plotPredReal(data, p_n, i_p); title(paste("PredReal non-loc day",i_p))
 
-plotPredReal(data, p_nn2, i_np); title(paste("PredReal nn2 day",i_np))
-plotPredReal(data, p_nn2, i_p); title(paste("PredReal nn2 day",i_p))
+plotPredReal(data, p_l, i_np); title(paste("PredReal loc day",i_np))
+plotPredReal(data, p_l, i_p); title(paste("PredReal loc day",i_p))
 
-plotPredReal(data, p_az, i_np); title(paste("PredReal az day",i_np))
-plotPredReal(data, p_az, i_p); title(paste("PredReal az day",i_p))
+plotPredReal(data, p_a, i_np); title(paste("PredReal avg day",i_np))
+plotPredReal(data, p_a, i_p); title(paste("PredReal avg day",i_p))
 
 # Bleu: prévue, noir: réalisée
 -----r
 par(mfrow=c(1,2))
-f_np = computeFilaments(data, p_nn, i_np, plot=TRUE); title(paste("Filaments nn day",i_np))
-f_p = computeFilaments(data, p_nn, i_p, plot=TRUE); title(paste("Filaments nn day",i_p))
+f_np_n = computeFilaments(data, p_n, i_np, plot=TRUE); title(paste("Filaments non-loc day",i_np))
+f_p_n = computeFilaments(data, p_n, i_p, plot=TRUE); title(paste("Filaments non-loc day",i_p))
 
-f_np2 = computeFilaments(data, p_nn2, i_np, plot=TRUE); title(paste("Filaments nn2 day",i_np))
-f_p2 = computeFilaments(data, p_nn2, i_p, plot=TRUE); title(paste("Filaments nn2 day",i_p))
+f_np_l = computeFilaments(data, p_l, i_np, plot=TRUE); title(paste("Filaments loc day",i_np))
+f_p_l = computeFilaments(data, p_l, i_p, plot=TRUE); title(paste("Filaments loc day",i_p))
 -----r
 par(mfrow=c(1,2))
-plotFilamentsBox(data, f_np); title(paste("FilBox nn day",i_np))
-plotFilamentsBox(data, f_p); title(paste("FilBox nn day",i_p))
+plotFilamentsBox(data, f_np_n); title(paste("FilBox non-loc day",i_np))
+plotFilamentsBox(data, f_p_n); title(paste("FilBox non-loc day",i_p))
 
 # Generally too few neighbors:
-#plotFilamentsBox(data, f_np2); title(paste("FilBox nn2 day",i_np))
-#plotFilamentsBox(data, f_p2); title(paste("FilBox nn2 day",i_p))
+#plotFilamentsBox(data, f_np_l); title(paste("FilBox loc day",i_np))
+#plotFilamentsBox(data, f_p_l); title(paste("FilBox loc day",i_p))
 -----r
 par(mfrow=c(1,2))
-plotRelVar(data, f_np); title(paste("StdDev nn day",i_np))
-plotRelVar(data, f_p); title(paste("StdDev nn day",i_p))
+plotRelVar(data, f_np_n); title(paste("StdDev non-loc day",i_np))
+plotRelVar(data, f_p_n); title(paste("StdDev non-loc day",i_p))
 
-plotRelVar(data, f_np2); title(paste("StdDev nn2 day",i_np))
-plotRelVar(data, f_p2); title(paste("StdDev nn2 day",i_p))
+plotRelVar(data, f_np_l); title(paste("StdDev loc day",i_np))
+plotRelVar(data, f_p_l); title(paste("StdDev loc day",i_p))
 
 # Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir
 -----r
 par(mfrow=c(1,2))
-plotSimils(p_nn, i_np); title(paste("Weights nn day",i_np))
-plotSimils(p_nn, i_p); title(paste("Weights nn day",i_p))
+plotSimils(p_n, i_np); title(paste("Weights non-loc day",i_np))
+plotSimils(p_n, i_p); title(paste("Weights non-loc day",i_p))
 
-plotSimils(p_nn2, i_np); title(paste("Weights nn2 day",i_np))
-plotSimils(p_nn2, i_p); title(paste("Weights nn2 day",i_p))
+plotSimils(p_l, i_np); title(paste("Weights loc day",i_np))
+plotSimils(p_l, i_p); title(paste("Weights loc day",i_p))
 
 # - pollué à gauche, + pollué à droite
 -----r
-# Fenêtres sélectionnées dans ]0,7] / nn à gauche, nn2 à droite
-p_nn$getParams(i_np)$window
-p_nn$getParams(i_p)$window
+# Fenêtres sélectionnées dans ]0,7] / non-loc à gauche, loc à droite
+p_n$getParams(i_np)$window
+p_n$getParams(i_p)$window
 
-p_nn2$getParams(i_np)$window
-p_nn2$getParams(i_p)$window
+p_l$getParams(i_np)$window
+p_l$getParams(i_p)$window
 % endfor