X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors2.R;h=787dd2b62b69af25c01cfca2de541a6939955f30;hb=ee8b1b4e3c13f8dcf13a2c8da6a3bef1520c8252;hp=e6adddea7a03cd5d0f21cf6ee84dfc26b40a1ab8;hpb=5c49f6cecd547358b327e9363e62bcc8219e9e33;p=talweg.git

diff --git a/pkg/R/F_Neighbors2.R b/pkg/R/F_Neighbors2.R
index e6addde..787dd2b 100644
--- a/pkg/R/F_Neighbors2.R
+++ b/pkg/R/F_Neighbors2.R
@@ -9,6 +9,11 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 	inherit = Forecaster,
 
 	public = list(
+		predictSerie = function(data, today, memory, horizon, ...)
+		{
+			# This method predict shape + level at the same time, all in next call
+			self$predictShape(data, today, memory, horizon, ...)
+		},
 		predictShape = function(data, today, memory, horizon, ...)
 		{
 			# (re)initialize computed parameters
@@ -22,100 +27,175 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 			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, TRUE) )
+					fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
 			}
 
-
 			# Indices of similar days for cross-validation; TODO: 45 = magic number
-			# TODO: ici faut une sorte de "same_season==TRUE" --> mois similaires epandage
-			sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
+			sdays = getSimilarDaysIndices(today, data, limit=45, same_season=FALSE)
 
+			cv_days = intersect(fdays,sdays)
+			# Limit to 20 most recent matching days (TODO: 20 == magic number)
+			cv_days = sort(cv_days,decreasing=TRUE)[1:min(20,length(cv_days))]
 
 			# Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
-			errorOnLastNdays = function(h, kernel)
+			errorOnLastNdays = function(h, kernel, simtype)
 			{
 				error = 0
 				nb_jours = 0
-				for (i in intersect(fdays,sdays))
+				for (i in seq_along(cv_days))
 				{
 					# mix_strategy is never used here (simtype != "mix"), therefore left blank
-					prediction = private$.predictShapeAux(data, fdays, i, horizon, h, kernel, FALSE)
+					prediction = private$.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$getSerie(i+1)[1:horizon] - prediction)^2)
+							mean((data$getSerie(cv_days[i]+1)[1:horizon] - prediction)^2)
 					}
 				}
 				return (error / nb_jours)
 			}
 
-			# h :: only for endo in this variation
-			h_best_endo = optimize(errorOnLastNdays, c(0,10), kernel=kernel)$minimum
+			if (simtype != "endo")
+			{
+				h_best_exo = optimize(
+					errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
+			}
+			if (simtype != "exo")
+			{
+				h_best_endo = optimize(
+					errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
+			}
 
-			return (private$.predictShapeAux(data, fdays, today, horizon, h_best, kernel, TRUE))
+			if (simtype == "endo")
+			{
+				return (private$.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, 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)
 
-			# ENDO:: Distances from last observed day to days in the past
-			distances2 = rep(NA, length(fdays))
-			for (i in seq_along(fdays))
+			# Neighbors: days in "same season"
+			sdays = getSimilarDaysIndices(today, data, limit=45, same_season=TRUE)
+			indices = intersect(fdays,sdays)
+			if (length(indices) <= 1)
+				return (NA)
+			levelToday = data$getLevel(today)
+			distances = sapply(indices, function(i) abs(data$getLevel(i)-levelToday))
+			# 2 and 5 below == magic numbers (determined by Bruno & Michel)
+			same_pollution = (distances <= 2)
+			if (sum(same_pollution) == 0)
 			{
-				delta = data$getSerie(today) - data$getSerie(fdays[i])
-				# Require at least half of non-NA common values to compute the distance
-				if ( !any( is.na(delta) ) )
-					distances2[i] = mean(delta^2)
+				same_pollution = (distances <= 5)
+				if (sum(same_pollution) == 0)
+					return (NA)
 			}
-
-			sd_dist = sd(distances2)
-			if (sd_dist < .Machine$double.eps)
+			indices = indices[same_pollution]
+			if (length(indices) == 1)
 			{
-#					warning("All computed distances are very close: stdev too small")
-				sd_dist = 1 #mostly for tests... FIXME:
-			}
-			simils_endo =
-				if (kernel=="Gauss")
-					exp(-distances2/(sd_dist*h_endo^2))
-				else
+				if (final_call)
 				{
-					# Epanechnikov
-					u = 1 - distances2/(sd_dist*h_endo^2)
-					u[abs(u)>1] = 0.
-					u
+					private$.params$weights <- 1
+					private$.params$indices <- indices
+					private$.params$window <- 1
 				}
+				return ( data$getSerie(indices[1])[1:horizon] ) #what else?!
+			}
 
-			# EXOGENS: distances computations are enough
-			# TODO: search among similar concentrations....... at this stage ?!
-			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])) )
+			if (simtype != "exo")
+			{
+				h_endo = ifelse(simtype=="mix", h[1], h)
 
-			sigma = cov(M) #NOTE: robust covariance is way too slow
-			sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
+				# Distances from last observed day to days in the past
+				serieToday = data$getSerie(today)
+				distances2 = sapply(indices, function(i) {
+					delta = serieToday - data$getSerie(i)
+					mean(delta^2)
+				})
 
-			# Distances from last observed day to days in the past
-			distances2 = rep(NA, nrow(M)-1)
-			for (i in 2:nrow(M))
-			{
-				delta = M[1,] - M[i,]
-				distances2[i-1] = delta %*% sigma_inv %*% delta
+				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
+					}
 			}
 
-			ppv <- sort(distances2, index.return=TRUE)$ix[1:10] #..............
-#PPV pour endo ?
+			if (simtype != "endo")
+			{
+				h_exo = ifelse(simtype=="mix", h[2], h)
+
+				M = matrix( nrow=1+length(indices), ncol=1+length(data$getExo(today)) )
+				M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
+				for (i in seq_along(indices))
+					M[i+1,] = c( data$getLevel(indices[i]), as.double(data$getExo(indices[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(indices) > 10)
+						solve(sigma)
+					else
+						MASS::ginv(sigma)
+
+				# Distances from last observed day to days in the past
+				distances2 = sapply(seq_along(indices), 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")
@@ -126,14 +206,14 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 					simils_endo * simils_exo
 
 			prediction = rep(0, horizon)
-			for (i in seq_along(fdays))
-				prediction = prediction + similarities[i] * data$getSerie(fdays[i]+1)[1:horizon]
+			for (i in seq_along(indices))
+				prediction = prediction + similarities[i] * data$getSerie(indices[i]+1)[1:horizon]
 			prediction = prediction / sum(similarities, na.rm=TRUE)
 
 			if (final_call)
 			{
 				private$.params$weights <- similarities
-				private$.params$indices <- fdays
+				private$.params$indices <- indices
 				private$.params$window <-
 					if (simtype=="endo")
 						h_endo