From: Benjamin Auder <benjamin.auder@somewhere>
Date: Mon, 27 Mar 2017 10:34:47 +0000 (+0200)
Subject: 'update'
X-Git-Url: https://git.auder.net/doc/html/assets/scripts/getGraph_%22%20%20%20this.image%20%20%20%22.php?a=commitdiff_plain;h=2ae382665c51cdebbe3f29d58da0e69c494c4c91;p=talweg.git

'update'
---

diff --git a/pkg/R/F_Neighbors2.R b/pkg/R/F_Neighbors2.R
index e6addde..bf9422a 100644
--- a/pkg/R/F_Neighbors2.R
+++ b/pkg/R/F_Neighbors2.R
@@ -19,7 +19,10 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 				return (NA)
 
 			# Determine indices of no-NAs days followed by no-NAs tomorrows
-			fdays = getNoNA2(data, max(today-memory,1), today-1)
+			# Indices of similar days for cross-validation; TODO: 45 = magic number
+			fdays = intersect(
+				getNoNA2(data, max(today-memory,1), today-1)
+				getSimilarDaysIndices(today, limit=45, same_season=TRUE) )
 
 			# Get optional args
 			kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
@@ -29,21 +32,15 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 					fdays, today, horizon, list(...)$h_window, kernel, 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)
-
-
 			# Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
 			errorOnLastNdays = function(h, kernel)
 			{
 				error = 0
 				nb_jours = 0
-				for (i in intersect(fdays,sdays))
+				for (day in fdays)
 				{
 					# 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,day,horizon,h,kernel,FALSE)
 					if (!is.na(prediction[1]))
 					{
 						nb_jours = nb_jours + 1
@@ -55,9 +52,8 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 			}
 
 			# h :: only for endo in this variation
-			h_best_endo = optimize(errorOnLastNdays, c(0,10), kernel=kernel)$minimum
-
-			return (private$.predictShapeAux(data, fdays, today, horizon, h_best, kernel, TRUE))
+			h_best = optimize(errorOnLastNdays, c(0,10), kernel=kernel)$minimum
+			return (private$.predictShapeAux(data,fdays,today,horizon,h_best,kernel,TRUE))
 		}
 	),
 	private = list(
@@ -96,6 +92,7 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 					u
 				}
 
+
 			# EXOGENS: distances computations are enough
 			# TODO: search among similar concentrations....... at this stage ?!
 			M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
@@ -117,6 +114,7 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 			ppv <- sort(distances2, index.return=TRUE)$ix[1:10] #..............
 #PPV pour endo ?
 
+
 			similarities =
 				if (simtype == "exo")
 					simils_exo
@@ -134,13 +132,7 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 			{
 				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)
+				private$.params$window <- h
 			}
 
 			return (prediction)