From 5e838b3e17465c376ca075b766cf2543c82e9864 Mon Sep 17 00:00:00 2001
From: Benjamin Auder <benjamin.auder@somewhere>
Date: Tue, 28 Mar 2017 17:35:39 +0200
Subject: [PATCH] first tests for Neighbors2 after debug; TODO: some missing
 forecasts

---
 pkg/DESCRIPTION         |  13 +--
 pkg/R/F_Neighbors.R     |  33 +++----
 pkg/R/F_Neighbors2.R    | 175 ++++++++++++++++++++++----------
 pkg/R/computeForecast.R |  31 ++++--
 reports/report.gj       |   4 +-
 reports/report.ipynb    | 214 ++++++++++++++++++++++++++++++++--------
 6 files changed, 344 insertions(+), 126 deletions(-)

diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION
index b932d77..8d3c4c3 100644
--- a/pkg/DESCRIPTION
+++ b/pkg/DESCRIPTION
@@ -1,10 +1,10 @@
 Package: talweg
 Title: Time-Series Samples Forecasted With Exogenous Variables
 Version: 0.1-0
-Description: Forecast a curve sampled within the day (seconds, minutes, hours...),
-    using past measured curves + paste exogenous informations,
-    which could be some aggregated measure on the past curves, the weather...
-    Main starting point: computeForecast().
+Description: Forecast a curve sampled within the day (seconds, minutes,
+    hours...), using past measured curves + paste exogenous informations, which
+    could be some aggregated measure on the past curves, the weather... Main
+    starting point: computeForecast().
 Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre],
     Jean-Michel Poggi <Jean-Michel.Poggi@parisdescartes.fr> [ctb],
     Bruno Portier <Bruno.Portier@insa-rouen.fr>, [ctb]
@@ -22,13 +22,14 @@ Suggests:
 LazyData: yes
 URL: http://git.auder.net/?p=talweg.git
 License: MIT + file LICENSE
-RoxygenNote: 6.0.1
-Collate: 
+RoxygenNote: 5.0.1
+Collate:
     'A_NAMESPACE.R'
     'Data.R'
     'Forecaster.R'
     'F_Average.R'
     'F_Neighbors.R'
+    'F_Neighbors2.R'
     'F_Persistence.R'
     'F_Zero.R'
     'Forecast.R'
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)
diff --git a/pkg/R/F_Neighbors2.R b/pkg/R/F_Neighbors2.R
index 7267661..fb63e40 100644
--- a/pkg/R/F_Neighbors2.R
+++ b/pkg/R/F_Neighbors2.R
@@ -9,6 +9,15 @@ 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
@@ -22,43 +31,73 @@ 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
 			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)
+			errorOnLastNdays = function(h, kernel, simtype)
 			{
 				error = 0
 				nb_jours = 0
-				for (day in intersect(fdays,sdays))
+				for (i in seq_along(cv_days))
 				{
 					# mix_strategy is never used here (simtype != "mix"), therefore left blank
-					prediction = private$.predictShapeAux(data,fdays,day,horizon,h,kernel,FALSE)
+					prediction = private$.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 = optimize(errorOnLastNdays, c(0,7), kernel=kernel)$minimum
-			return (private$.predictShapeAux(data,fdays,today,horizon,h_best,kernel,TRUE))
+			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
+			}
+
+			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$.predictShapeAux(data,
+					fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
+			}
 		}
 	),
 	private = list(
 		# Precondition: "today" is full (no NAs)
-		.predictShapeAux = function(data, fdays, today, horizon, h, kernel, final_call)
+		.predictShapeAux = function(data, fdays, today, horizon, h, kernel, simtype, final_call)
 		{
 			fdays = fdays[ fdays < today ]
 			# TODO: 3 = magic number
@@ -69,7 +108,7 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 			sdays = getSimilarDaysIndices(today, limit=45, same_season=TRUE, data)
 			indices = intersect(fdays,sdays)
 			levelToday = data$getLevel(today)
-			distances = sapply(seq_along(indices), function(i) abs(data$getLevel(i)-levelToday))
+			distances = sapply(indices, function(i) abs(data$getLevel(i)-levelToday))
 			same_pollution = (distances <= 2)
 			if (sum(same_pollution) < 3) #TODO: 3 == magic number
 			{
@@ -79,53 +118,79 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 			}
 			indices = indices[same_pollution]
 
-			# Now OK: indices same season, same pollution level
-			# ...........
-
+			if (simtype != "exo")
+			{
+				h_endo = ifelse(simtype=="mix", h[1], h)
 
-			# ENDO:: Distances from last observed day to days in the past
-			serieToday = data$getSerie(today)
-			distances2 = sapply(indices, function(i) {
-				delta = serieToday - data$getSerie(i)
-				distances2[i] = mean(delta^2)
-			})
+				# 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)
+				})
 
-			sd_dist = sd(distances2)
-			if (sd_dist < .Machine$double.eps)
-			{
+				sd_dist = sd(distances2)
+				if (sd_dist < .Machine$double.eps)
+				{
 #					warning("All computed distances are very close: stdev too small")
-				sd_dist = 1 #mostly for tests... FIXME:
+					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
+					}
 			}
-			simils_endo =
-				if (kernel=="Gauss")
-					exp(-distances2/(sd_dist*h_endo^2))
-				else
+
+			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$getExo(indices[i])) )
+
+				sigma = cov(M) #NOTE: robust covariance is way too slow
+#				sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
+				sigma_inv = MASS::ginv(sigma)
+#if (final_call) browser()
+				# Distances from last observed day to days in the past
+				distances2 = sapply(seq_along(indices), function(i) {
+					delta = M[1,] - M[i+1,]
+					delta %*% sigma_inv %*% delta
+				})
+
+				sd_dist = sd(distances2)
+				if (sd_dist < .25 * sqrt(.Machine$double.eps))
 				{
-					# Epanechnikov
-					u = 1 - distances2/(sd_dist*h_endo^2)
-					u[abs(u)>1] = 0.
-					u
+#					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
+					}
+			}
 
-#			# 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])) )
-#
-#			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
-#			distances2 = rep(NA, nrow(M)-1)
-#			for (i in 2:nrow(M))
-#			{
-#				delta = M[1,] - M[i,]
-#				distances2[i-1] = delta %*% sigma_inv %*% delta
-#			}
-
-			similarities = simils_endo
+			similarities =
+				if (simtype == "exo")
+					simils_exo
+				else if (simtype == "endo")
+					simils_endo
+				else #mix
+					simils_endo * simils_exo
 
 			prediction = rep(0, horizon)
 			for (i in seq_along(indices))
@@ -135,8 +200,14 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 			if (final_call)
 			{
 				private$.params$weights <- similarities
-				private$.params$indices <- indices
-				private$.params$window <- h
+				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)
 			}
 
 			return (prediction)
diff --git a/pkg/R/computeForecast.R b/pkg/R/computeForecast.R
index 8cf8861..3537e8a 100644
--- a/pkg/R/computeForecast.R
+++ b/pkg/R/computeForecast.R
@@ -55,13 +55,28 @@ computeForecast = function(data, indices, forecaster, pjump,
 	forecaster_class_name = getFromNamespace(paste(forecaster,"Forecaster",sep=""), "talweg")
 	forecaster = forecaster_class_name$new( #.pjump =
 		getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
-	for (today in integer_indices)
-	{
-		pred$append(
-			new_serie = forecaster$predictSerie(data, today, memory, horizon, ...),
-			new_params = forecaster$getParameters(),
-			new_index_in_data = today
-		)
-	}
+
+#oo = forecaster$predictSerie(data, integer_indices[1], memory, horizon, ...)
+#browser()
+
+	library(parallel)
+	ppp <- parallel::mclapply(seq_along(integer_indices), function(i) {
+		list(
+			"forecast" = forecaster$predictSerie(data, integer_indices[i], memory, horizon, ...),
+			"params"= forecaster$getParameters(),
+			"index" = integer_indices[i] )
+		}, mc.cores=3)
+
+#browser()
+
+for (i in seq_along(integer_indices))
+{
+	pred$append(
+			new_serie = ppp[[i]]$forecast,
+			new_params = ppp[[i]]$params,
+			new_index_in_data = ppp[[i]]$index
+	)
+}
+
 	pred
 }
diff --git a/reports/report.gj b/reports/report.gj
index 3932639..aee6ad4 100644
--- a/reports/report.gj
+++ b/reports/report.gj
@@ -2,9 +2,9 @@
 <h2>Introduction</h2>
 
 J'ai fait quelques essais dans différentes configurations pour la méthode "Neighbors"
-(la seule dont on a parlé).<br>Il semble que le mieux soit
+(la seule dont on a parlé) et sa variante récente appelée pour l'instant "Neighbors2".<br>
 
- * simtype="exo" ou "mix" : similarités exogènes avec/sans endogènes (fenêtre optimisée par VC)
+ * simtype="exo", "endo" ou "mix" : type de similarités (fenêtre optimisée par VC)
  * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons
  * mix_strategy="mult" : on multiplie les poids (au lieu d'en éteindre)
 
diff --git a/reports/report.ipynb b/reports/report.ipynb
index 74d6880..899fbf6 100644
--- a/reports/report.ipynb
+++ b/reports/report.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "\n",
     "\n",
@@ -29,24 +32,32 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "library(talweg)\n",
     "\n",
     "ts_data = read.csv(system.file(\"extdata\",\"pm10_mesures_H_loc_report.csv\",package=\"talweg\"))\n",
     "exo_data = read.csv(system.file(\"extdata\",\"meteo_extra_noNAs.csv\",package=\"talweg\"))\n",
-    "data = getData(ts_data, exo_data, input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\",\n",
-    "\tpredict_at=7) #predict from P+1 to P+H included\n",
+    "# Predict from P+1 to P+H included\n",
+    "H = 17\n",
+    "data = getData(ts_data, exo_data, input_tz = \"GMT\", working_tz=\"GMT\", predict_at=7)\n",
     "\n",
     "indices_ch = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n",
     "indices_ep = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n",
-    "indices_np = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")\n"
+    "indices_np = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "\n",
     "\n",
@@ -56,23 +67,47 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
-    "p_nn_exo = computeForecast(data, indices_ch, \"Neighbors\", \"Neighbors\",\n",
-    "\thorizon=3, simtype=\"exo\")\n",
-    "p_nn_mix = computeForecast(data, indices_ch, \"Neighbors\", \"Neighbors\",\n",
-    "\thorizon=3, simtype=\"mix\")\n",
-    "p_az = computeForecast(data, indices_ch, \"Average\", \"Zero\",\n",
-    "\thorizon=3)\n",
-    "p_pz = computeForecast(data, indices_ch, \"Persistence\", \"Zero\",\n",
-    "\thorizon=3, same_day=TRUE)"
+    "reload(\"../pkg\")\n",
+    "p1 = computeForecast(data, indices_ch, \"Neighbors\", \"Zero\", horizon=H, simtype=\"exo\")\n",
+    "p2 = computeForecast(data, indices_ch, \"Neighbors\", \"Zero\", horizon=H, simtype=\"endo\")\n",
+    "p3 = computeForecast(data, indices_ch, \"Neighbors\", \"Zero\", horizon=H, simtype=\"mix\")\n",
+    "p4 = computeForecast(data, indices_ch, \"Neighbors2\", \"Zero\", horizon=H, simtype=\"exo\")\n",
+    "p5 = computeForecast(data, indices_ch, \"Neighbors2\", \"Zero\", horizon=H, simtype=\"endo\")\n",
+    "p6 = computeForecast(data, indices_ch, \"Neighbors2\", \"Zero\", horizon=H, simtype=\"mix\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e1 = computeError(data, p1, H)\n",
+    "e2 = computeError(data, p2, H)\n",
+    "e3 = computeError(data, p3, H)\n",
+    "e4 = computeError(data, p4, H)\n",
+    "e5 = computeError(data, p5, H)\n",
+    "e6 = computeError(data, p6, H)\n",
+    "plotError(list(e1,e2,e3,e4,e5,e6), cols=c(1,2,colors()[258], 4,5,6))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "e_nn_exo = computeError(data, p_nn_exo, 3)\n",
@@ -91,7 +126,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "options(repr.plot.width=9, repr.plot.height=4)\n",
@@ -112,7 +151,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "par(mfrow=c(1,2))\n",
@@ -126,7 +169,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "par(mfrow=c(1,2))\n",
@@ -140,7 +187,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "par(mfrow=c(1,2))\n",
@@ -156,7 +207,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "par(mfrow=c(1,2))\n",
@@ -172,7 +227,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
@@ -185,7 +244,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "\n",
     "\n",
@@ -195,7 +257,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "p_nn_exo = computeForecast(data, indices_ep, \"Neighbors\", \"Neighbors\",\n",
@@ -211,7 +277,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "e_nn_exo = computeError(data, p_nn_exo, 3)\n",
@@ -230,7 +300,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "options(repr.plot.width=9, repr.plot.height=4)\n",
@@ -251,7 +325,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "par(mfrow=c(1,2))\n",
@@ -265,7 +343,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "par(mfrow=c(1,2))\n",
@@ -279,7 +361,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "par(mfrow=c(1,2))\n",
@@ -295,7 +381,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "par(mfrow=c(1,2))\n",
@@ -311,7 +401,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
@@ -324,7 +418,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "\n",
     "\n",
@@ -334,7 +431,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "p_nn_exo = computeForecast(data, indices_np, \"Neighbors\", \"Neighbors\",\n",
@@ -350,7 +451,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "e_nn_exo = computeError(data, p_nn_exo, 3)\n",
@@ -369,7 +474,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "options(repr.plot.width=9, repr.plot.height=4)\n",
@@ -390,7 +499,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "par(mfrow=c(1,2))\n",
@@ -404,7 +517,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "par(mfrow=c(1,2))\n",
@@ -418,7 +535,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "par(mfrow=c(1,2))\n",
@@ -434,7 +555,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "par(mfrow=c(1,2))\n",
@@ -450,7 +575,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
@@ -463,7 +592,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "\n",
     "\n",
-- 
2.44.0