X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors2.R;h=ee40f61192dd744451e9e447ee1b5d8b62f404d1;hb=9003747badc4416d68cab45ff17de3ecea327942;hp=fb63e4087f1b2aa4b426712d140132a2f2e83193;hpb=5e838b3e17465c376ca075b766cf2543c82e9864;p=talweg.git

diff --git a/pkg/R/F_Neighbors2.R b/pkg/R/F_Neighbors2.R
index fb63e40..ee40f61 100644
--- a/pkg/R/F_Neighbors2.R
+++ b/pkg/R/F_Neighbors2.R
@@ -9,15 +9,6 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 	inherit = Forecaster,
 
 	public = list(
-		predictSerie = function(data, today, memory, horizon, ...)
-		{
-			# Parameters (potentially) computed during shape prediction stage
-			predicted_shape = self$predictShape(data, today, memory, horizon, ...)
-#			predicted_delta = private$.pjump(data,today,memory,horizon,private$.params,...)
-			# Predicted shape is aligned it on the end of current day + jump
-#			predicted_shape+tail(data$getSerie(today),1)-predicted_shape[1]+predicted_delta
-			predicted_shape
-		},
 		predictShape = function(data, today, memory, horizon, ...)
 		{
 			# (re)initialize computed parameters
@@ -39,12 +30,8 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 					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)
-
-			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))]
+			# 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)
@@ -59,8 +46,7 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 					if (!is.na(prediction[1]))
 					{
 						nb_jours = nb_jours + 1
-						error = error +
-							mean((data$getSerie(cv_days[i]+1)[1:horizon] - prediction)^2)
+						error = error + mean((data$getSerie(cv_days[i]+1)[1:horizon] - prediction)^2)
 					}
 				}
 				return (error / nb_jours)
@@ -69,12 +55,12 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 			if (simtype != "endo")
 			{
 				h_best_exo = optimize(
-					errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
+					errorOnLastNdays, c(0,7), kernel=kernel, simtype="exo")$minimum
 			}
 			if (simtype != "exo")
 			{
 				h_best_endo = optimize(
-					errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
+					errorOnLastNdays, c(0,7), kernel=kernel, simtype="endo")$minimum
 			}
 
 			if (simtype == "endo")
@@ -99,24 +85,45 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 		# Precondition: "today" is full (no NAs)
 		.predictShapeAux = function(data, fdays, today, horizon, h, kernel, simtype, final_call)
 		{
-			fdays = fdays[ fdays < today ]
+			fdays_cut = fdays[ fdays < today ]
 			# TODO: 3 = magic number
-			if (length(fdays) < 3)
+			if (length(fdays_cut) < 3)
 				return (NA)
 
-			# Neighbors: days in "same season"
-			sdays = getSimilarDaysIndices(today, limit=45, same_season=TRUE, data)
-			indices = intersect(fdays,sdays)
+			# 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$getLevel(today)
-			distances = sapply(indices, function(i) abs(data$getLevel(i)-levelToday))
-			same_pollution = (distances <= 2)
-			if (sum(same_pollution) < 3) #TODO: 3 == magic number
+			distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
+			#TODO: 2, 3, 5, 10 magic numbers here...
+			dist_thresh = 2
+			min_neighbs = min(3,length(fdays))
+			repeat
 			{
-				same_pollution = (distances <= 5)
-				if (sum(same_pollution) < 3)
-					return (NA)
+				same_pollution = (distances <= dist_thresh)
+				nb_neighbs = sum(same_pollution)
+				if (nb_neighbs >= min_neighbs) #will eventually happen
+					break
+				dist_thresh = dist_thresh + 3
+			}
+			fdays = fdays[same_pollution]
+			max_neighbs = 10
+			if (nb_neighbs > max_neighbs)
+			{
+				# Keep only max_neighbs closest neighbors
+				fdays = fdays[ sort(distances[same_pollution],index.return=TRUE)$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?!
 			}
-			indices = indices[same_pollution]
 
 			if (simtype != "exo")
 			{
@@ -124,7 +131,7 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 
 				# Distances from last observed day to days in the past
 				serieToday = data$getSerie(today)
-				distances2 = sapply(indices, function(i) {
+				distances2 = sapply(fdays, function(i) {
 					delta = serieToday - data$getSerie(i)
 					mean(delta^2)
 				})
@@ -151,17 +158,21 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 			{
 				h_exo = ifelse(simtype=="mix", h[2], h)
 
-				M = matrix( nrow=1+length(indices), ncol=1+length(data$getExo(today)) )
+				M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
 				M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
-				for (i in seq_along(indices))
-					M[i+1,] = c( data$getLevel(indices[i]), as.double(data$getExo(indices[i])) )
+				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
-#				sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
-				sigma_inv = MASS::ginv(sigma)
-#if (final_call) browser()
+				# 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(indices), function(i) {
+				distances2 = sapply(seq_along(fdays), function(i) {
 					delta = M[1,] - M[i+1,]
 					delta %*% sigma_inv %*% delta
 				})
@@ -191,14 +202,15 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 					simils_endo
 				else #mix
 					simils_endo * simils_exo
+			similarities = similarities / sum(similarities)
 
 			prediction = rep(0, horizon)
-			for (i in seq_along(indices))
-				prediction = prediction + similarities[i] * data$getSerie(indices[i]+1)[1:horizon]
-			prediction = prediction / sum(similarities, na.rm=TRUE)
+			for (i in seq_along(fdays))
+				prediction = prediction + similarities[i] * data$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 <-