X-Git-Url: https://git.auder.net/?p=talweg.git;a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;h=5b2c8990a850d2dff0d75c23d67815e7393ccd07;hp=600c5c8ceeaae272a824a259c36f5075662c4b78;hb=5e838b3e17465c376ca075b766cf2543c82e9864;hpb=9db234c56c330bb3f652718c5ee1eb16bc1f6fc7

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 600c5c8..5b2c899 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -33,21 +33,25 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 			# 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))]
+
 			# 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 intersect(fdays,sdays))
+				for (i in seq_along(cv_days))
 				{
 					# mix_strategy is never used here (simtype != "mix"), therefore left blank
 					prediction = private$.predictShapeAux(data,
-						fdays, i, horizon, h, kernel, simtype, FALSE)
+						fdays, cv_days[i], horizon, h, kernel, simtype, FALSE)
 					if (!is.na(prediction[1]))
 					{
 						nb_jours = nb_jours + 1
 						error = error +
-							mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
+							mean((data$getCenteredSerie(cv_days[i]+1)[1:horizon] - prediction)^2)
 					}
 				}
 				return (error / nb_jours)
@@ -96,14 +100,11 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 				h_endo = ifelse(simtype=="mix", h[1], h)
 
 				# Distances from last observed day to days in the past
-				distances2 = rep(NA, length(fdays))
-				for (i in seq_along(fdays))
-				{
-					delta = data$getCenteredSerie(today) - data$getCenteredSerie(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)
-				Centered}
+				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)
@@ -136,12 +137,10 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 				sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
 
 				# 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
-				}
+				distances2 = sapply(seq_along(fdays), function(i) {
+					delta = M[1,] - M[i+1,]
+					delta %*% sigma_inv %*% delta
+				})
 
 				sd_dist = sd(distances2)
 				if (sd_dist < .Machine$double.eps)