X-Git-Url: https://git.auder.net/?p=talweg.git;a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;h=9ba72b8f308fcf00ef43e99c5e968c731eac1fbe;hp=c55291aa996838a2a02535725f72c4fb8709b922;hb=445e7bbc18aa739ec0b3caba4d8710a9d9e1a43c;hpb=21c70378d89863afeb124d86989f7f956e280808

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index c55291a..9ba72b8 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -22,8 +22,8 @@ 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"
+			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,
@@ -34,7 +34,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 			cv_days = getSimilarDaysIndices(today, data, limit=20, same_season=FALSE,
 				days_in=fdays)
 
-			# Optimize h : h |--> sum of prediction errors on last 45 "similar" days
+			# Optimize h : h |--> sum of prediction errors on last N "similar" days
 			errorOnLastNdays = function(window, simtype)
 			{
 				error = 0
@@ -54,6 +54,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 				return (error / nb_jours)
 			}
 
+			# TODO: 7 == magic number
 			if (simtype != "endo")
 			{
 				best_window_exo = optimize(
@@ -100,9 +101,9 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 					return (NA)
 				levelToday = data$getLevel(today)
 				distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
-				#TODO: 2, 3, 5, 10 magic numbers here...
+				#TODO: 2, 10, 3, 12 magic numbers here...
 				dist_thresh = 2
-				min_neighbs = min(3,length(fdays))
+				min_neighbs = min(10,length(fdays))
 				repeat
 				{
 					same_pollution = (distances <= dist_thresh)
@@ -112,7 +113,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 					dist_thresh = dist_thresh + 3
 				}
 				fdays = fdays[same_pollution]
-				max_neighbs = 10
+				max_neighbs = 12
 				if (nb_neighbs > max_neighbs)
 				{
 					# Keep only max_neighbs closest neighbors
@@ -133,7 +134,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 			else
 				fdays = fdays_cut #no conditioning
 
-			if (simtype != "exo")
+			if (simtype == "endo" || simtype == "mix")
 			{
 				# Compute endogen similarities using given window
 				window_endo = ifelse(simtype=="mix", window[1], window)
@@ -154,10 +155,10 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 				simils_endo = exp(-distances2/(sd_dist*window_endo^2))
 			}
 
-			if (simtype != "endo")
+			if (simtype == "exo" || simtype == "mix")
 			{
 				# Compute exogen similarities using given window
-				h_exo = ifelse(simtype=="mix", window[2], 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)) )
@@ -192,8 +193,10 @@ 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)