name instead of year; ipynb generator debugged, with logging

diff --git a/NOTES b/NOTES
index f9f9f26..96967a5 100644 (file)
--- a/NOTES
+++ b/NOTES
@@ -50,3 +50,5 @@ http://hplgit.github.io/doconce/doc/pub/ipynb/ipynb_generator.html
 
 Essayer ::
 juste PM10, PM10 et PA ...
+
+use subtree here for pkgs...

diff --git a/talweg/DESCRIPTION b/pkg/DESCRIPTION
similarity index 100%
rename from talweg/DESCRIPTION
rename to pkg/DESCRIPTION

diff --git a/talweg/LICENSE b/pkg/LICENSE
similarity index 100%
rename from talweg/LICENSE
rename to pkg/LICENSE

diff --git a/talweg/R/Data.R b/pkg/R/Data.R
similarity index 100%
rename from talweg/R/Data.R
rename to pkg/R/Data.R

diff --git a/talweg/R/F_Average.R b/pkg/R/F_Average.R
similarity index 100%
rename from talweg/R/F_Average.R
rename to pkg/R/F_Average.R

diff --git a/talweg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
similarity index 100%
rename from talweg/R/F_Neighbors.R
rename to pkg/R/F_Neighbors.R

diff --git a/talweg/R/F_Persistence.R b/pkg/R/F_Persistence.R
similarity index 100%
rename from talweg/R/F_Persistence.R
rename to pkg/R/F_Persistence.R

diff --git a/talweg/R/F_Zero.R b/pkg/R/F_Zero.R
similarity index 100%
rename from talweg/R/F_Zero.R
rename to pkg/R/F_Zero.R

diff --git a/talweg/R/Forecast.R b/pkg/R/Forecast.R
similarity index 100%
rename from talweg/R/Forecast.R
rename to pkg/R/Forecast.R

diff --git a/talweg/R/Forecaster.R b/pkg/R/Forecaster.R
similarity index 100%
rename from talweg/R/Forecaster.R
rename to pkg/R/Forecaster.R

diff --git a/talweg/R/J_Neighbors.R b/pkg/R/J_Neighbors.R
similarity index 100%
rename from talweg/R/J_Neighbors.R
rename to pkg/R/J_Neighbors.R

diff --git a/talweg/R/J_Persistence.R b/pkg/R/J_Persistence.R
similarity index 100%
rename from talweg/R/J_Persistence.R
rename to pkg/R/J_Persistence.R

diff --git a/talweg/R/J_Zero.R b/pkg/R/J_Zero.R
similarity index 100%
rename from talweg/R/J_Zero.R
rename to pkg/R/J_Zero.R

diff --git a/talweg/R/computeError.R b/pkg/R/computeError.R
similarity index 100%
rename from talweg/R/computeError.R
rename to pkg/R/computeError.R

diff --git a/talweg/R/computeForecast.R b/pkg/R/computeForecast.R
similarity index 100%
rename from talweg/R/computeForecast.R
rename to pkg/R/computeForecast.R

diff --git a/talweg/R/getData.R b/pkg/R/getData.R
similarity index 100%
rename from talweg/R/getData.R
rename to pkg/R/getData.R

diff --git a/talweg/R/plot.R b/pkg/R/plot.R
similarity index 100%
rename from talweg/R/plot.R
rename to pkg/R/plot.R

diff --git a/talweg/R/utils.R b/pkg/R/utils.R
similarity index 100%
rename from talweg/R/utils.R
rename to pkg/R/utils.R

diff --git a/talweg/inst/extdata/meteo_extra_noNAs.csv b/pkg/inst/extdata/meteo_extra_noNAs.csv
similarity index 100%
rename from talweg/inst/extdata/meteo_extra_noNAs.csv
rename to pkg/inst/extdata/meteo_extra_noNAs.csv

diff --git a/talweg/inst/extdata/pm10_mesures_H_loc.csv b/pkg/inst/extdata/pm10_mesures_H_loc.csv
similarity index 100%
rename from talweg/inst/extdata/pm10_mesures_H_loc.csv
rename to pkg/inst/extdata/pm10_mesures_H_loc.csv

diff --git a/talweg/inst/extdata/pm10_mesures_H_loc_report.csv b/pkg/inst/extdata/pm10_mesures_H_loc_report.csv
similarity index 100%
rename from talweg/inst/extdata/pm10_mesures_H_loc_report.csv
rename to pkg/inst/extdata/pm10_mesures_H_loc_report.csv

diff --git a/talweg/inst/testdata/exo_test.csv b/pkg/inst/testdata/exo_test.csv
similarity index 100%
rename from talweg/inst/testdata/exo_test.csv
rename to pkg/inst/testdata/exo_test.csv

diff --git a/talweg/inst/testdata/ts_test.csv b/pkg/inst/testdata/ts_test.csv
similarity index 100%
rename from talweg/inst/testdata/ts_test.csv
rename to pkg/inst/testdata/ts_test.csv

diff --git a/talweg/man/talweg-package.Rd b/pkg/man/talweg-package.Rd
similarity index 100%
rename from talweg/man/talweg-package.Rd
rename to pkg/man/talweg-package.Rd

diff --git a/talweg/tests/testthat.R b/pkg/tests/testthat.R
similarity index 100%
rename from talweg/tests/testthat.R
rename to pkg/tests/testthat.R

diff --git a/talweg/tests/testthat/test.Forecaster.R b/pkg/tests/testthat/test.Forecaster.R
similarity index 100%
rename from talweg/tests/testthat/test.Forecaster.R
rename to pkg/tests/testthat/test.Forecaster.R

diff --git a/talweg/tests/testthat/test.computeFilaments.R b/pkg/tests/testthat/test.computeFilaments.R
similarity index 100%
rename from talweg/tests/testthat/test.computeFilaments.R
rename to pkg/tests/testthat/test.computeFilaments.R

diff --git a/talweg/tests/testthat/test.dateIndexToInteger.R b/pkg/tests/testthat/test.dateIndexToInteger.R
similarity index 100%
rename from talweg/tests/testthat/test.dateIndexToInteger.R
rename to pkg/tests/testthat/test.dateIndexToInteger.R

diff --git a/talweg/vignettes/talweg.Rmd b/pkg/vignettes/talweg.Rmd
similarity index 100%
rename from talweg/vignettes/talweg.Rmd
rename to pkg/vignettes/talweg.Rmd

diff --git a/reports/ipynb_generator.py b/reports/ipynb_generator.py
old mode 100644 (file)
new mode 100755 (executable)
index a89ec40..4e47063
--- a/reports/ipynb_generator.py
+++ b/reports/ipynb_generator.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env python
+
 import sys, os, re, logging
 
 # Languages mapping as used by markdown/pandoc
@@ -44,7 +46,7 @@ def read(text, argv=sys.argv[2:]):
         from mako.template import Template
         from mako.lookup import TemplateLookup
         lookup = TemplateLookup(directories=[os.curdir])
-        text = text.encode('utf-8')
+#        text = text.encode('utf-8')
         temp = Template(text=text, lookup=lookup, strict_undefined=True)
         logging.info('******* mako_kwargs: {}'.format(str(mako_kwargs)))
         text = temp.render(**mako_kwargs)
@@ -146,7 +148,7 @@ def driver():
 
 if __name__ == '__main__':
     logfile = 'tmp.log'
-    if os.path.isfile:
+    if os.path.isfile(logfile):
         os.remove(logfile)
     logging.basicConfig(format='%(message)s', level=logging.DEBUG,
                         filename=logfile)

diff --git a/reports/report.gj b/reports/report.gj
index a9f10d0..b901075 100644 (file)
--- a/reports/report.gj
+++ b/reports/report.gj
@@ -1,6 +1,5 @@
 -----
-
-## Introduction
+<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
@@ -22,9 +21,7 @@ lendemains : ce sont donc les voisinages tels qu'utilisés dans l'algorithme.
 list_titles = ['Pollution par chauffage', 'Pollution par épandage', 'Semaine non polluée']
 list_indices = ['indices_ch', 'indices_ep', 'indices_np']
 %>
-
 -----r
-
 library(talweg)
 
 ts_data = read.csv(system.file("extdata","pm10_mesures_H_loc_report.csv",package="talweg"))
@@ -34,19 +31,14 @@ data = getData(ts_data, exo_data, input_tz = "Europe/Paris", working_tz="Europe/
 indices_ch = seq(as.Date("2015-01-18"),as.Date("2015-01-24"),"days")
 indices_ep = seq(as.Date("2015-03-15"),as.Date("2015-03-21"),"days")
 indices_np = seq(as.Date("2015-04-26"),as.Date("2015-05-02"),"days")
-
-% for loop in range(3):
-
+% for i in range(3):
 -----
-
-<h2 style="color:blue;font-size:2em">${list_titles[loop]}</h2>
-
+<h2 style="color:blue;font-size:2em">${list_titles[i]}</h2>
 -----r
-p_nn_exo = computeForecast(data, ${list_indices[loop]}, "Neighbors", "Neighbors", simtype="exo", horizon=H)
-p_nn_mix = computeForecast(data, ${list_indices[loop]}, "Neighbors", "Neighbors", simtype="mix", horizon=H)
-p_az = computeForecast(data, ${list_indices[loop]}, "Average", "Zero", horizon=H) #, memory=183)
-p_pz = computeForecast(data, ${list_indices[loop]}, "Persistence", "Zero", horizon=H, same_day=TRUE)
-
+p_nn_exo = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", simtype="exo", horizon=H)
+p_nn_mix = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", simtype="mix", horizon=H)
+p_az = computeForecast(data, ${list_indices[i]}, "Average", "Zero", horizon=H) #, memory=183)
+p_pz = computeForecast(data, ${list_indices[i]}, "Persistence", "Zero", horizon=H, same_day=TRUE)
 -----r
 e_nn_exo = computeError(data, p_nn_exo)
 e_nn_mix = computeError(data, p_nn_mix)
@@ -55,11 +47,10 @@ e_pz = computeError(data, p_pz)
 options(repr.plot.width=9, repr.plot.height=7)
 plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))
 
-#Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence
+# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence
 
 i_np = which.min(e_nn_exo$abs$indices)
 i_p = which.max(e_nn_exo$abs$indices)
-
 -----r
 options(repr.plot.width=9, repr.plot.height=4)
 par(mfrow=c(1,2))
@@ -73,8 +64,7 @@ plotPredReal(data, p_nn_mix, i_p); title(paste("PredReal nn mix day",i_p))
 plotPredReal(data, p_az, i_np); title(paste("PredReal az day",i_np))
 plotPredReal(data, p_az, i_p); title(paste("PredReal az day",i_p))
 
-#Bleu: prévue, noir: réalisée
-
+# Bleu: prévue, noir: réalisée
 -----r
 par(mfrow=c(1,2))
 f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste("Filaments nn exo day",i_np))
@@ -82,7 +72,6 @@ f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste("Filamen
 
 f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste("Filaments nn mix day",i_np))
 f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste("Filaments nn mix day",i_p))
-
 -----r
 par(mfrow=c(1,2))
 plotFilamentsBox(data, f_np_exo); title(paste("FilBox nn exo day",i_np))
@@ -90,7 +79,6 @@ plotFilamentsBox(data, f_p_exo); title(paste("FilBox nn exo day",i_p))
 
 plotFilamentsBox(data, f_np_mix); title(paste("FilBox nn mix day",i_np))
 plotFilamentsBox(data, f_p_mix); title(paste("FilBox nn mix day",i_p))
-
 -----r
 par(mfrow=c(1,2))
 plotRelVar(data, f_np_exo); title(paste("StdDev nn exo day",i_np))
@@ -99,8 +87,7 @@ plotRelVar(data, f_p_exo); title(paste("StdDev nn exo day",i_p))
 plotRelVar(data, f_np_mix); title(paste("StdDev nn mix day",i_np))
 plotRelVar(data, f_p_mix); title(paste("StdDev nn mix day",i_p))
 
-#Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir
-
+# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir
 -----r
 par(mfrow=c(1,2))
 plotSimils(p_nn_exo, i_np); title(paste("Weights nn exo day",i_np))
@@ -109,20 +96,17 @@ plotSimils(p_nn_exo, i_p); title(paste("Weights nn exo day",i_p))
 plotSimils(p_nn_mix, i_np); title(paste("Weights nn mix day",i_np))
 plotSimils(p_nn_mix, i_p); title(paste("Weights nn mix day",i_p)
 
-#- pollué à gauche, + pollué à droite
-
+# - pollué à gauche, + pollué à droite
 -----r
-#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite
+# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite
 p_nn_exo$getParams(i_np)$window
 p_nn_exo$getParams(i_p)$window
 
 p_nn_mix$getParams(i_np)$window
 p_nn_mix$getParams(i_p)$window
-
 % endfor
-
 -----
-## Bilan
+<h2>Bilan</h2>
 
 Problème difficile : on ne fait guère mieux qu'une naïve moyenne des lendemains des jours
 similaires dans le passé, ce qui n'est pas loin de prédire une série constante égale à la

diff --git a/reports/report.ipynb b/reports/report.ipynb
new file mode 100644 (file)
index 0000000..05f51de
--- /dev/null
+++ b/reports/report.ipynb
@@ -0,0 +1,535 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "<h2>Introduction</h2>\n",
+    "\n",
+    "J'ai fait quelques essais dans différentes configurations pour la méthode \"Neighbors\"\n",
+    "(la seule dont on a parlé).<br>Il semble que le mieux soit\n",
+    "\n",
+    " * simtype=\"exo\" ou \"mix\" : similarités exogènes avec/sans endogènes (fenêtre optimisée par VC)\n",
+    " * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons\n",
+    " * mix_strategy=\"mult\" : on multiplie les poids (au lieu d'en éteindre)\n",
+    "\n",
+    "J'ai systématiquement comparé à une approche naïve : la moyennes des lendemains des jours\n",
+    "\"similaires\" dans tout le passé ; à chaque fois sans prédiction du saut (sauf pour Neighbors :\n",
+    "prédiction basée sur les poids calculés).\n",
+    "\n",
+    "Ensuite j'affiche les erreurs, quelques courbes prévues/mesurées, quelques filaments puis les\n",
+    "histogrammes de quelques poids. Concernant les graphes de filaments, la moitié gauche du graphe\n",
+    "correspond aux jours similaires au jour courant, tandis que la moitié droite affiche les\n",
+    "lendemains : ce sont donc les voisinages tels qu'utilisés dans l'algorithme.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "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\", predict_at=13)\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\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "<h2 style=\"color:blue;font-size:2em\">Pollution par chauffage</h2>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "p_nn_exo = computeForecast(data, indices_ch, \"Neighbors\", \"Neighbors\", simtype=\"exo\", horizon=H)\n",
+    "p_nn_mix = computeForecast(data, indices_ch, \"Neighbors\", \"Neighbors\", simtype=\"mix\", horizon=H)\n",
+    "p_az = computeForecast(data, indices_ch, \"Average\", \"Zero\", horizon=H) #, memory=183)\n",
+    "p_pz = computeForecast(data, indices_ch, \"Persistence\", \"Zero\", horizon=H, same_day=TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e_nn_exo = computeError(data, p_nn_exo)\n",
+    "e_nn_mix = computeError(data, p_nn_mix)\n",
+    "e_az = computeError(data, p_az)\n",
+    "e_pz = computeError(data, p_pz)\n",
+    "options(repr.plot.width=9, repr.plot.height=7)\n",
+    "plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))\n",
+    "\n",
+    "# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence\n",
+    "\n",
+    "i_np = which.min(e_nn_exo$abs$indices)\n",
+    "i_p = which.max(e_nn_exo$abs$indices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "options(repr.plot.width=9, repr.plot.height=4)\n",
+    "par(mfrow=c(1,2))\n",
+    "\n",
+    "plotPredReal(data, p_nn_exo, i_np); title(paste(\"PredReal nn exo day\",i_np))\n",
+    "plotPredReal(data, p_nn_exo, i_p); title(paste(\"PredReal nn exo day\",i_p))\n",
+    "\n",
+    "plotPredReal(data, p_nn_mix, i_np); title(paste(\"PredReal nn mix day\",i_np))\n",
+    "plotPredReal(data, p_nn_mix, i_p); title(paste(\"PredReal nn mix day\",i_p))\n",
+    "\n",
+    "plotPredReal(data, p_az, i_np); title(paste(\"PredReal az day\",i_np))\n",
+    "plotPredReal(data, p_az, i_p); title(paste(\"PredReal az day\",i_p))\n",
+    "\n",
+    "# Bleu: prévue, noir: réalisée"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste(\"Filaments nn exo day\",i_np))\n",
+    "f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste(\"Filaments nn exo day\",i_p))\n",
+    "\n",
+    "f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste(\"Filaments nn mix day\",i_np))\n",
+    "f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste(\"Filaments nn mix day\",i_p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "plotFilamentsBox(data, f_np_exo); title(paste(\"FilBox nn exo day\",i_np))\n",
+    "plotFilamentsBox(data, f_p_exo); title(paste(\"FilBox nn exo day\",i_p))\n",
+    "\n",
+    "plotFilamentsBox(data, f_np_mix); title(paste(\"FilBox nn mix day\",i_np))\n",
+    "plotFilamentsBox(data, f_p_mix); title(paste(\"FilBox nn mix day\",i_p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "plotRelVar(data, f_np_exo); title(paste(\"StdDev nn exo day\",i_np))\n",
+    "plotRelVar(data, f_p_exo); title(paste(\"StdDev nn exo day\",i_p))\n",
+    "\n",
+    "plotRelVar(data, f_np_mix); title(paste(\"StdDev nn mix day\",i_np))\n",
+    "plotRelVar(data, f_p_mix); title(paste(\"StdDev nn mix day\",i_p))\n",
+    "\n",
+    "# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "plotSimils(p_nn_exo, i_np); title(paste(\"Weights nn exo day\",i_np))\n",
+    "plotSimils(p_nn_exo, i_p); title(paste(\"Weights nn exo day\",i_p))\n",
+    "\n",
+    "plotSimils(p_nn_mix, i_np); title(paste(\"Weights nn mix day\",i_np))\n",
+    "plotSimils(p_nn_mix, i_p); title(paste(\"Weights nn mix day\",i_p)\n",
+    "\n",
+    "# - pollué à gauche, + pollué à droite"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
+    "p_nn_exo$getParams(i_np)$window\n",
+    "p_nn_exo$getParams(i_p)$window\n",
+    "\n",
+    "p_nn_mix$getParams(i_np)$window\n",
+    "p_nn_mix$getParams(i_p)$window"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "<h2 style=\"color:blue;font-size:2em\">Pollution par épandage</h2>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "p_nn_exo = computeForecast(data, indices_ep, \"Neighbors\", \"Neighbors\", simtype=\"exo\", horizon=H)\n",
+    "p_nn_mix = computeForecast(data, indices_ep, \"Neighbors\", \"Neighbors\", simtype=\"mix\", horizon=H)\n",
+    "p_az = computeForecast(data, indices_ep, \"Average\", \"Zero\", horizon=H) #, memory=183)\n",
+    "p_pz = computeForecast(data, indices_ep, \"Persistence\", \"Zero\", horizon=H, same_day=TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e_nn_exo = computeError(data, p_nn_exo)\n",
+    "e_nn_mix = computeError(data, p_nn_mix)\n",
+    "e_az = computeError(data, p_az)\n",
+    "e_pz = computeError(data, p_pz)\n",
+    "options(repr.plot.width=9, repr.plot.height=7)\n",
+    "plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))\n",
+    "\n",
+    "# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence\n",
+    "\n",
+    "i_np = which.min(e_nn_exo$abs$indices)\n",
+    "i_p = which.max(e_nn_exo$abs$indices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "options(repr.plot.width=9, repr.plot.height=4)\n",
+    "par(mfrow=c(1,2))\n",
+    "\n",
+    "plotPredReal(data, p_nn_exo, i_np); title(paste(\"PredReal nn exo day\",i_np))\n",
+    "plotPredReal(data, p_nn_exo, i_p); title(paste(\"PredReal nn exo day\",i_p))\n",
+    "\n",
+    "plotPredReal(data, p_nn_mix, i_np); title(paste(\"PredReal nn mix day\",i_np))\n",
+    "plotPredReal(data, p_nn_mix, i_p); title(paste(\"PredReal nn mix day\",i_p))\n",
+    "\n",
+    "plotPredReal(data, p_az, i_np); title(paste(\"PredReal az day\",i_np))\n",
+    "plotPredReal(data, p_az, i_p); title(paste(\"PredReal az day\",i_p))\n",
+    "\n",
+    "# Bleu: prévue, noir: réalisée"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste(\"Filaments nn exo day\",i_np))\n",
+    "f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste(\"Filaments nn exo day\",i_p))\n",
+    "\n",
+    "f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste(\"Filaments nn mix day\",i_np))\n",
+    "f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste(\"Filaments nn mix day\",i_p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "plotFilamentsBox(data, f_np_exo); title(paste(\"FilBox nn exo day\",i_np))\n",
+    "plotFilamentsBox(data, f_p_exo); title(paste(\"FilBox nn exo day\",i_p))\n",
+    "\n",
+    "plotFilamentsBox(data, f_np_mix); title(paste(\"FilBox nn mix day\",i_np))\n",
+    "plotFilamentsBox(data, f_p_mix); title(paste(\"FilBox nn mix day\",i_p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "plotRelVar(data, f_np_exo); title(paste(\"StdDev nn exo day\",i_np))\n",
+    "plotRelVar(data, f_p_exo); title(paste(\"StdDev nn exo day\",i_p))\n",
+    "\n",
+    "plotRelVar(data, f_np_mix); title(paste(\"StdDev nn mix day\",i_np))\n",
+    "plotRelVar(data, f_p_mix); title(paste(\"StdDev nn mix day\",i_p))\n",
+    "\n",
+    "# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "plotSimils(p_nn_exo, i_np); title(paste(\"Weights nn exo day\",i_np))\n",
+    "plotSimils(p_nn_exo, i_p); title(paste(\"Weights nn exo day\",i_p))\n",
+    "\n",
+    "plotSimils(p_nn_mix, i_np); title(paste(\"Weights nn mix day\",i_np))\n",
+    "plotSimils(p_nn_mix, i_p); title(paste(\"Weights nn mix day\",i_p)\n",
+    "\n",
+    "# - pollué à gauche, + pollué à droite"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
+    "p_nn_exo$getParams(i_np)$window\n",
+    "p_nn_exo$getParams(i_p)$window\n",
+    "\n",
+    "p_nn_mix$getParams(i_np)$window\n",
+    "p_nn_mix$getParams(i_p)$window"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "<h2 style=\"color:blue;font-size:2em\">Semaine non polluée</h2>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "p_nn_exo = computeForecast(data, indices_np, \"Neighbors\", \"Neighbors\", simtype=\"exo\", horizon=H)\n",
+    "p_nn_mix = computeForecast(data, indices_np, \"Neighbors\", \"Neighbors\", simtype=\"mix\", horizon=H)\n",
+    "p_az = computeForecast(data, indices_np, \"Average\", \"Zero\", horizon=H) #, memory=183)\n",
+    "p_pz = computeForecast(data, indices_np, \"Persistence\", \"Zero\", horizon=H, same_day=TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e_nn_exo = computeError(data, p_nn_exo)\n",
+    "e_nn_mix = computeError(data, p_nn_mix)\n",
+    "e_az = computeError(data, p_az)\n",
+    "e_pz = computeError(data, p_pz)\n",
+    "options(repr.plot.width=9, repr.plot.height=7)\n",
+    "plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))\n",
+    "\n",
+    "# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence\n",
+    "\n",
+    "i_np = which.min(e_nn_exo$abs$indices)\n",
+    "i_p = which.max(e_nn_exo$abs$indices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "options(repr.plot.width=9, repr.plot.height=4)\n",
+    "par(mfrow=c(1,2))\n",
+    "\n",
+    "plotPredReal(data, p_nn_exo, i_np); title(paste(\"PredReal nn exo day\",i_np))\n",
+    "plotPredReal(data, p_nn_exo, i_p); title(paste(\"PredReal nn exo day\",i_p))\n",
+    "\n",
+    "plotPredReal(data, p_nn_mix, i_np); title(paste(\"PredReal nn mix day\",i_np))\n",
+    "plotPredReal(data, p_nn_mix, i_p); title(paste(\"PredReal nn mix day\",i_p))\n",
+    "\n",
+    "plotPredReal(data, p_az, i_np); title(paste(\"PredReal az day\",i_np))\n",
+    "plotPredReal(data, p_az, i_p); title(paste(\"PredReal az day\",i_p))\n",
+    "\n",
+    "# Bleu: prévue, noir: réalisée"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste(\"Filaments nn exo day\",i_np))\n",
+    "f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste(\"Filaments nn exo day\",i_p))\n",
+    "\n",
+    "f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste(\"Filaments nn mix day\",i_np))\n",
+    "f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste(\"Filaments nn mix day\",i_p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "plotFilamentsBox(data, f_np_exo); title(paste(\"FilBox nn exo day\",i_np))\n",
+    "plotFilamentsBox(data, f_p_exo); title(paste(\"FilBox nn exo day\",i_p))\n",
+    "\n",
+    "plotFilamentsBox(data, f_np_mix); title(paste(\"FilBox nn mix day\",i_np))\n",
+    "plotFilamentsBox(data, f_p_mix); title(paste(\"FilBox nn mix day\",i_p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "plotRelVar(data, f_np_exo); title(paste(\"StdDev nn exo day\",i_np))\n",
+    "plotRelVar(data, f_p_exo); title(paste(\"StdDev nn exo day\",i_p))\n",
+    "\n",
+    "plotRelVar(data, f_np_mix); title(paste(\"StdDev nn mix day\",i_np))\n",
+    "plotRelVar(data, f_p_mix); title(paste(\"StdDev nn mix day\",i_p))\n",
+    "\n",
+    "# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "plotSimils(p_nn_exo, i_np); title(paste(\"Weights nn exo day\",i_np))\n",
+    "plotSimils(p_nn_exo, i_p); title(paste(\"Weights nn exo day\",i_p))\n",
+    "\n",
+    "plotSimils(p_nn_mix, i_np); title(paste(\"Weights nn mix day\",i_np))\n",
+    "plotSimils(p_nn_mix, i_p); title(paste(\"Weights nn mix day\",i_p)\n",
+    "\n",
+    "# - pollué à gauche, + pollué à droite"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
+    "p_nn_exo$getParams(i_np)$window\n",
+    "p_nn_exo$getParams(i_p)$window\n",
+    "\n",
+    "p_nn_mix$getParams(i_np)$window\n",
+    "p_nn_mix$getParams(i_p)$window"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "<h2>Bilan</h2>\n",
+    "\n",
+    "Problème difficile : on ne fait guère mieux qu'une naïve moyenne des lendemains des jours\n",
+    "similaires dans le passé, ce qui n'est pas loin de prédire une série constante égale à la\n",
+    "dernière valeur observée (méthode \"zéro\"). La persistence donne parfois de bons résultats\n",
+    "mais est trop instable (sensibilité à l'argument <code>same_day</code>).\n",
+    "\n",
+    "Comment améliorer la méthode ?"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.3.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

diff --git a/reports/tmp.log b/reports/tmp.log
new file mode 100644 (file)
index 0000000..39629a7
--- /dev/null
+++ b/reports/tmp.log
@@ -0,0 +1,768 @@
+******* text after include:
+-----
+<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
+
+ * simtype="exo" ou "mix" : similarités exogènes avec/sans endogènes (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)
+
+J'ai systématiquement comparé à une approche naïve : la moyennes des lendemains des jours
+"similaires" dans tout le passé ; à chaque fois sans prédiction du saut (sauf pour Neighbors :
+prédiction basée sur les poids calculés).
+
+Ensuite j'affiche les erreurs, quelques courbes prévues/mesurées, quelques filaments puis les
+histogrammes de quelques poids. Concernant les graphes de filaments, la moitié gauche du graphe
+correspond aux jours similaires au jour courant, tandis que la moitié droite affiche les
+lendemains : ce sont donc les voisinages tels qu'utilisés dans l'algorithme.
+
+<%
+list_titles = ['Pollution par chauffage', 'Pollution par épandage', 'Semaine non polluée']
+list_indices = ['indices_ch', 'indices_ep', 'indices_np']
+%>
+-----r
+library(talweg)
+
+ts_data = read.csv(system.file("extdata","pm10_mesures_H_loc_report.csv",package="talweg"))
+exo_data = read.csv(system.file("extdata","meteo_extra_noNAs.csv",package="talweg"))
+data = getData(ts_data, exo_data, input_tz = "Europe/Paris", working_tz="Europe/Paris", predict_at=13)
+
+indices_ch = seq(as.Date("2015-01-18"),as.Date("2015-01-24"),"days")
+indices_ep = seq(as.Date("2015-03-15"),as.Date("2015-03-21"),"days")
+indices_np = seq(as.Date("2015-04-26"),as.Date("2015-05-02"),"days")
+% for i in range(3):
+-----
+<h2 style="color:blue;font-size:2em">${list_titles[i]}</h2>
+-----r
+p_nn_exo = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", simtype="exo", horizon=H)
+p_nn_mix = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", simtype="mix", horizon=H)
+p_az = computeForecast(data, ${list_indices[i]}, "Average", "Zero", horizon=H) #, memory=183)
+p_pz = computeForecast(data, ${list_indices[i]}, "Persistence", "Zero", horizon=H, same_day=TRUE)
+-----r
+e_nn_exo = computeError(data, p_nn_exo)
+e_nn_mix = computeError(data, p_nn_mix)
+e_az = computeError(data, p_az)
+e_pz = computeError(data, p_pz)
+options(repr.plot.width=9, repr.plot.height=7)
+plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))
+
+# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence
+
+i_np = which.min(e_nn_exo$abs$indices)
+i_p = which.max(e_nn_exo$abs$indices)
+-----r
+options(repr.plot.width=9, repr.plot.height=4)
+par(mfrow=c(1,2))
+
+plotPredReal(data, p_nn_exo, i_np); title(paste("PredReal nn exo day",i_np))
+plotPredReal(data, p_nn_exo, i_p); title(paste("PredReal nn exo day",i_p))
+
+plotPredReal(data, p_nn_mix, i_np); title(paste("PredReal nn mix day",i_np))
+plotPredReal(data, p_nn_mix, i_p); title(paste("PredReal nn mix day",i_p))
+
+plotPredReal(data, p_az, i_np); title(paste("PredReal az day",i_np))
+plotPredReal(data, p_az, i_p); title(paste("PredReal az day",i_p))
+
+# Bleu: prévue, noir: réalisée
+-----r
+par(mfrow=c(1,2))
+f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste("Filaments nn exo day",i_np))
+f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste("Filaments nn exo day",i_p))
+
+f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste("Filaments nn mix day",i_np))
+f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste("Filaments nn mix day",i_p))
+-----r
+par(mfrow=c(1,2))
+plotFilamentsBox(data, f_np_exo); title(paste("FilBox nn exo day",i_np))
+plotFilamentsBox(data, f_p_exo); title(paste("FilBox nn exo day",i_p))
+
+plotFilamentsBox(data, f_np_mix); title(paste("FilBox nn mix day",i_np))
+plotFilamentsBox(data, f_p_mix); title(paste("FilBox nn mix day",i_p))
+-----r
+par(mfrow=c(1,2))
+plotRelVar(data, f_np_exo); title(paste("StdDev nn exo day",i_np))
+plotRelVar(data, f_p_exo); title(paste("StdDev nn exo day",i_p))
+
+plotRelVar(data, f_np_mix); title(paste("StdDev nn mix day",i_np))
+plotRelVar(data, f_p_mix); title(paste("StdDev nn mix day",i_p))
+
+# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir
+-----r
+par(mfrow=c(1,2))
+plotSimils(p_nn_exo, i_np); title(paste("Weights nn exo day",i_np))
+plotSimils(p_nn_exo, i_p); title(paste("Weights nn exo day",i_p))
+
+plotSimils(p_nn_mix, i_np); title(paste("Weights nn mix day",i_np))
+plotSimils(p_nn_mix, i_p); title(paste("Weights nn mix day",i_p)
+
+# - pollué à gauche, + pollué à droite
+-----r
+# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite
+p_nn_exo$getParams(i_np)$window
+p_nn_exo$getParams(i_p)$window
+
+p_nn_mix$getParams(i_np)$window
+p_nn_mix$getParams(i_p)$window
+% endfor
+-----
+<h2>Bilan</h2>
+
+Problème difficile : on ne fait guère mieux qu'une naïve moyenne des lendemains des jours
+similaires dans le passé, ce qui n'est pas loin de prédire une série constante égale à la
+dernière valeur observée (méthode "zéro"). La persistence donne parfois de bons résultats
+mais est trop instable (sensibilité à l'argument <code>same_day</code>).
+
+Comment améliorer la méthode ?
+******* mako_kwargs: {}
+******* text after mako:
+-----
+<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
+
+ * simtype="exo" ou "mix" : similarités exogènes avec/sans endogènes (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)
+
+J'ai systématiquement comparé à une approche naïve : la moyennes des lendemains des jours
+"similaires" dans tout le passé ; à chaque fois sans prédiction du saut (sauf pour Neighbors :
+prédiction basée sur les poids calculés).
+
+Ensuite j'affiche les erreurs, quelques courbes prévues/mesurées, quelques filaments puis les
+histogrammes de quelques poids. Concernant les graphes de filaments, la moitié gauche du graphe
+correspond aux jours similaires au jour courant, tandis que la moitié droite affiche les
+lendemains : ce sont donc les voisinages tels qu'utilisés dans l'algorithme.
+
+
+-----r
+library(talweg)
+
+ts_data = read.csv(system.file("extdata","pm10_mesures_H_loc_report.csv",package="talweg"))
+exo_data = read.csv(system.file("extdata","meteo_extra_noNAs.csv",package="talweg"))
+data = getData(ts_data, exo_data, input_tz = "Europe/Paris", working_tz="Europe/Paris", predict_at=13)
+
+indices_ch = seq(as.Date("2015-01-18"),as.Date("2015-01-24"),"days")
+indices_ep = seq(as.Date("2015-03-15"),as.Date("2015-03-21"),"days")
+indices_np = seq(as.Date("2015-04-26"),as.Date("2015-05-02"),"days")
+-----
+<h2 style="color:blue;font-size:2em">Pollution par chauffage</h2>
+-----r
+p_nn_exo = computeForecast(data, indices_ch, "Neighbors", "Neighbors", simtype="exo", horizon=H)
+p_nn_mix = computeForecast(data, indices_ch, "Neighbors", "Neighbors", simtype="mix", horizon=H)
+p_az = computeForecast(data, indices_ch, "Average", "Zero", horizon=H) #, memory=183)
+p_pz = computeForecast(data, indices_ch, "Persistence", "Zero", horizon=H, same_day=TRUE)
+-----r
+e_nn_exo = computeError(data, p_nn_exo)
+e_nn_mix = computeError(data, p_nn_mix)
+e_az = computeError(data, p_az)
+e_pz = computeError(data, p_pz)
+options(repr.plot.width=9, repr.plot.height=7)
+plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))
+
+# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence
+
+i_np = which.min(e_nn_exo$abs$indices)
+i_p = which.max(e_nn_exo$abs$indices)
+-----r
+options(repr.plot.width=9, repr.plot.height=4)
+par(mfrow=c(1,2))
+
+plotPredReal(data, p_nn_exo, i_np); title(paste("PredReal nn exo day",i_np))
+plotPredReal(data, p_nn_exo, i_p); title(paste("PredReal nn exo day",i_p))
+
+plotPredReal(data, p_nn_mix, i_np); title(paste("PredReal nn mix day",i_np))
+plotPredReal(data, p_nn_mix, i_p); title(paste("PredReal nn mix day",i_p))
+
+plotPredReal(data, p_az, i_np); title(paste("PredReal az day",i_np))
+plotPredReal(data, p_az, i_p); title(paste("PredReal az day",i_p))
+
+# Bleu: prévue, noir: réalisée
+-----r
+par(mfrow=c(1,2))
+f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste("Filaments nn exo day",i_np))
+f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste("Filaments nn exo day",i_p))
+
+f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste("Filaments nn mix day",i_np))
+f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste("Filaments nn mix day",i_p))
+-----r
+par(mfrow=c(1,2))
+plotFilamentsBox(data, f_np_exo); title(paste("FilBox nn exo day",i_np))
+plotFilamentsBox(data, f_p_exo); title(paste("FilBox nn exo day",i_p))
+
+plotFilamentsBox(data, f_np_mix); title(paste("FilBox nn mix day",i_np))
+plotFilamentsBox(data, f_p_mix); title(paste("FilBox nn mix day",i_p))
+-----r
+par(mfrow=c(1,2))
+plotRelVar(data, f_np_exo); title(paste("StdDev nn exo day",i_np))
+plotRelVar(data, f_p_exo); title(paste("StdDev nn exo day",i_p))
+
+plotRelVar(data, f_np_mix); title(paste("StdDev nn mix day",i_np))
+plotRelVar(data, f_p_mix); title(paste("StdDev nn mix day",i_p))
+
+# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir
+-----r
+par(mfrow=c(1,2))
+plotSimils(p_nn_exo, i_np); title(paste("Weights nn exo day",i_np))
+plotSimils(p_nn_exo, i_p); title(paste("Weights nn exo day",i_p))
+
+plotSimils(p_nn_mix, i_np); title(paste("Weights nn mix day",i_np))
+plotSimils(p_nn_mix, i_p); title(paste("Weights nn mix day",i_p)
+
+# - pollué à gauche, + pollué à droite
+-----r
+# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite
+p_nn_exo$getParams(i_np)$window
+p_nn_exo$getParams(i_p)$window
+
+p_nn_mix$getParams(i_np)$window
+p_nn_mix$getParams(i_p)$window
+-----
+<h2 style="color:blue;font-size:2em">Pollution par épandage</h2>
+-----r
+p_nn_exo = computeForecast(data, indices_ep, "Neighbors", "Neighbors", simtype="exo", horizon=H)
+p_nn_mix = computeForecast(data, indices_ep, "Neighbors", "Neighbors", simtype="mix", horizon=H)
+p_az = computeForecast(data, indices_ep, "Average", "Zero", horizon=H) #, memory=183)
+p_pz = computeForecast(data, indices_ep, "Persistence", "Zero", horizon=H, same_day=TRUE)
+-----r
+e_nn_exo = computeError(data, p_nn_exo)
+e_nn_mix = computeError(data, p_nn_mix)
+e_az = computeError(data, p_az)
+e_pz = computeError(data, p_pz)
+options(repr.plot.width=9, repr.plot.height=7)
+plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))
+
+# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence
+
+i_np = which.min(e_nn_exo$abs$indices)
+i_p = which.max(e_nn_exo$abs$indices)
+-----r
+options(repr.plot.width=9, repr.plot.height=4)
+par(mfrow=c(1,2))
+
+plotPredReal(data, p_nn_exo, i_np); title(paste("PredReal nn exo day",i_np))
+plotPredReal(data, p_nn_exo, i_p); title(paste("PredReal nn exo day",i_p))
+
+plotPredReal(data, p_nn_mix, i_np); title(paste("PredReal nn mix day",i_np))
+plotPredReal(data, p_nn_mix, i_p); title(paste("PredReal nn mix day",i_p))
+
+plotPredReal(data, p_az, i_np); title(paste("PredReal az day",i_np))
+plotPredReal(data, p_az, i_p); title(paste("PredReal az day",i_p))
+
+# Bleu: prévue, noir: réalisée
+-----r
+par(mfrow=c(1,2))
+f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste("Filaments nn exo day",i_np))
+f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste("Filaments nn exo day",i_p))
+
+f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste("Filaments nn mix day",i_np))
+f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste("Filaments nn mix day",i_p))
+-----r
+par(mfrow=c(1,2))
+plotFilamentsBox(data, f_np_exo); title(paste("FilBox nn exo day",i_np))
+plotFilamentsBox(data, f_p_exo); title(paste("FilBox nn exo day",i_p))
+
+plotFilamentsBox(data, f_np_mix); title(paste("FilBox nn mix day",i_np))
+plotFilamentsBox(data, f_p_mix); title(paste("FilBox nn mix day",i_p))
+-----r
+par(mfrow=c(1,2))
+plotRelVar(data, f_np_exo); title(paste("StdDev nn exo day",i_np))
+plotRelVar(data, f_p_exo); title(paste("StdDev nn exo day",i_p))
+
+plotRelVar(data, f_np_mix); title(paste("StdDev nn mix day",i_np))
+plotRelVar(data, f_p_mix); title(paste("StdDev nn mix day",i_p))
+
+# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir
+-----r
+par(mfrow=c(1,2))
+plotSimils(p_nn_exo, i_np); title(paste("Weights nn exo day",i_np))
+plotSimils(p_nn_exo, i_p); title(paste("Weights nn exo day",i_p))
+
+plotSimils(p_nn_mix, i_np); title(paste("Weights nn mix day",i_np))
+plotSimils(p_nn_mix, i_p); title(paste("Weights nn mix day",i_p)
+
+# - pollué à gauche, + pollué à droite
+-----r
+# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite
+p_nn_exo$getParams(i_np)$window
+p_nn_exo$getParams(i_p)$window
+
+p_nn_mix$getParams(i_np)$window
+p_nn_mix$getParams(i_p)$window
+-----
+<h2 style="color:blue;font-size:2em">Semaine non polluée</h2>
+-----r
+p_nn_exo = computeForecast(data, indices_np, "Neighbors", "Neighbors", simtype="exo", horizon=H)
+p_nn_mix = computeForecast(data, indices_np, "Neighbors", "Neighbors", simtype="mix", horizon=H)
+p_az = computeForecast(data, indices_np, "Average", "Zero", horizon=H) #, memory=183)
+p_pz = computeForecast(data, indices_np, "Persistence", "Zero", horizon=H, same_day=TRUE)
+-----r
+e_nn_exo = computeError(data, p_nn_exo)
+e_nn_mix = computeError(data, p_nn_mix)
+e_az = computeError(data, p_az)
+e_pz = computeError(data, p_pz)
+options(repr.plot.width=9, repr.plot.height=7)
+plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))
+
+# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence
+
+i_np = which.min(e_nn_exo$abs$indices)
+i_p = which.max(e_nn_exo$abs$indices)
+-----r
+options(repr.plot.width=9, repr.plot.height=4)
+par(mfrow=c(1,2))
+
+plotPredReal(data, p_nn_exo, i_np); title(paste("PredReal nn exo day",i_np))
+plotPredReal(data, p_nn_exo, i_p); title(paste("PredReal nn exo day",i_p))
+
+plotPredReal(data, p_nn_mix, i_np); title(paste("PredReal nn mix day",i_np))
+plotPredReal(data, p_nn_mix, i_p); title(paste("PredReal nn mix day",i_p))
+
+plotPredReal(data, p_az, i_np); title(paste("PredReal az day",i_np))
+plotPredReal(data, p_az, i_p); title(paste("PredReal az day",i_p))
+
+# Bleu: prévue, noir: réalisée
+-----r
+par(mfrow=c(1,2))
+f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste("Filaments nn exo day",i_np))
+f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste("Filaments nn exo day",i_p))
+
+f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste("Filaments nn mix day",i_np))
+f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste("Filaments nn mix day",i_p))
+-----r
+par(mfrow=c(1,2))
+plotFilamentsBox(data, f_np_exo); title(paste("FilBox nn exo day",i_np))
+plotFilamentsBox(data, f_p_exo); title(paste("FilBox nn exo day",i_p))
+
+plotFilamentsBox(data, f_np_mix); title(paste("FilBox nn mix day",i_np))
+plotFilamentsBox(data, f_p_mix); title(paste("FilBox nn mix day",i_p))
+-----r
+par(mfrow=c(1,2))
+plotRelVar(data, f_np_exo); title(paste("StdDev nn exo day",i_np))
+plotRelVar(data, f_p_exo); title(paste("StdDev nn exo day",i_p))
+
+plotRelVar(data, f_np_mix); title(paste("StdDev nn mix day",i_np))
+plotRelVar(data, f_p_mix); title(paste("StdDev nn mix day",i_p))
+
+# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir
+-----r
+par(mfrow=c(1,2))
+plotSimils(p_nn_exo, i_np); title(paste("Weights nn exo day",i_np))
+plotSimils(p_nn_exo, i_p); title(paste("Weights nn exo day",i_p))
+
+plotSimils(p_nn_mix, i_np); title(paste("Weights nn mix day",i_np))
+plotSimils(p_nn_mix, i_p); title(paste("Weights nn mix day",i_p)
+
+# - pollué à gauche, + pollué à droite
+-----r
+# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite
+p_nn_exo$getParams(i_np)$window
+p_nn_exo$getParams(i_p)$window
+
+p_nn_mix$getParams(i_np)$window
+p_nn_mix$getParams(i_p)$window
+-----
+<h2>Bilan</h2>
+
+Problème difficile : on ne fait guère mieux qu'une naïve moyenne des lendemains des jours
+similaires dans le passé, ce qui n'est pas loin de prédire une série constante égale à la
+dernière valeur observée (méthode "zéro"). La persistence donne parfois de bons résultats
+mais est trop instable (sensibilité à l'argument <code>same_day</code>).
+
+Comment améliorer la méthode ?
+******* cell: markdown
+******* found shortname r
+******* cell: astext=False shortname=r
+******* cell: markdown
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* cell: markdown
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* cell: markdown
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* found shortname r
+******* cell: astext=False shortname=r
+******* cell: markdown
+******* cell data structure:\b[['markdown',
+  'text',
+  '\n'
+  '\n'
+  '<h2>Introduction</h2>\n'
+  '\n'
+  "J'ai fait quelques essais dans différentes configurations pour la méthode "
+  '"Neighbors"\n'
+  '(la seule dont on a parlé).<br>Il semble que le mieux soit\n'
+  '\n'
+  ' * simtype="exo" ou "mix" : similarités exogènes avec/sans endogènes '
+  '(fenêtre optimisée par VC)\n'
+  ' * same_season=FALSE : les indices pour la validation croisée ne tiennent '
+  'pas compte des saisons\n'
+  ' * mix_strategy="mult" : on multiplie les poids (au lieu d\'en éteindre)\n'
+  '\n'
+  "J'ai systématiquement comparé à une approche naïve : la moyennes des "
+  'lendemains des jours\n'
+  '"similaires" dans tout le passé ; à chaque fois sans prédiction du saut '
+  '(sauf pour Neighbors :\n'
+  'prédiction basée sur les poids calculés).\n'
+  '\n'
+  "Ensuite j'affiche les erreurs, quelques courbes prévues/mesurées, quelques "
+  'filaments puis les\n'
+  'histogrammes de quelques poids. Concernant les graphes de filaments, la '
+  'moitié gauche du graphe\n'
+  'correspond aux jours similaires au jour courant, tandis que la moitié '
+  'droite affiche les\n'
+  "lendemains : ce sont donc les voisinages tels qu'utilisés dans "
+  "l'algorithme.\n"
+  '\n'],
+ ['codecell',
+  'R',
+  '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", predict_at=13)\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")'],
+ ['markdown',
+  'text',
+  '\n\n<h2 style="color:blue;font-size:2em">Pollution par chauffage</h2>'],
+ ['codecell',
+  'R',
+  'p_nn_exo = computeForecast(data, indices_ch, "Neighbors", "Neighbors", '
+  'simtype="exo", horizon=H)\n'
+  'p_nn_mix = computeForecast(data, indices_ch, "Neighbors", "Neighbors", '
+  'simtype="mix", horizon=H)\n'
+  'p_az = computeForecast(data, indices_ch, "Average", "Zero", horizon=H) #, '
+  'memory=183)\n'
+  'p_pz = computeForecast(data, indices_ch, "Persistence", "Zero", horizon=H, '
+  'same_day=TRUE)'],
+ ['codecell',
+  'R',
+  'e_nn_exo = computeError(data, p_nn_exo)\n'
+  'e_nn_mix = computeError(data, p_nn_mix)\n'
+  'e_az = computeError(data, p_az)\n'
+  'e_pz = computeError(data, p_pz)\n'
+  'options(repr.plot.width=9, repr.plot.height=7)\n'
+  'plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], '
+  '4))\n'
+  '\n'
+  '# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: '
+  'persistence\n'
+  '\n'
+  'i_np = which.min(e_nn_exo$abs$indices)\n'
+  'i_p = which.max(e_nn_exo$abs$indices)'],
+ ['codecell',
+  'R',
+  'options(repr.plot.width=9, repr.plot.height=4)\n'
+  'par(mfrow=c(1,2))\n'
+  '\n'
+  'plotPredReal(data, p_nn_exo, i_np); title(paste("PredReal nn exo '
+  'day",i_np))\n'
+  'plotPredReal(data, p_nn_exo, i_p); title(paste("PredReal nn exo day",i_p))\n'
+  '\n'
+  'plotPredReal(data, p_nn_mix, i_np); title(paste("PredReal nn mix '
+  'day",i_np))\n'
+  'plotPredReal(data, p_nn_mix, i_p); title(paste("PredReal nn mix day",i_p))\n'
+  '\n'
+  'plotPredReal(data, p_az, i_np); title(paste("PredReal az day",i_np))\n'
+  'plotPredReal(data, p_az, i_p); title(paste("PredReal az day",i_p))\n'
+  '\n'
+  '# Bleu: prévue, noir: réalisée'],
+ ['codecell',
+  'R',
+  'par(mfrow=c(1,2))\n'
+  'f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); '
+  'title(paste("Filaments nn exo day",i_np))\n'
+  'f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); '
+  'title(paste("Filaments nn exo day",i_p))\n'
+  '\n'
+  'f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); '
+  'title(paste("Filaments nn mix day",i_np))\n'
+  'f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); '
+  'title(paste("Filaments nn mix day",i_p))'],
+ ['codecell',
+  'R',
+  'par(mfrow=c(1,2))\n'
+  'plotFilamentsBox(data, f_np_exo); title(paste("FilBox nn exo day",i_np))\n'
+  'plotFilamentsBox(data, f_p_exo); title(paste("FilBox nn exo day",i_p))\n'
+  '\n'
+  'plotFilamentsBox(data, f_np_mix); title(paste("FilBox nn mix day",i_np))\n'
+  'plotFilamentsBox(data, f_p_mix); title(paste("FilBox nn mix day",i_p))'],
+ ['codecell',
+  'R',
+  'par(mfrow=c(1,2))\n'
+  'plotRelVar(data, f_np_exo); title(paste("StdDev nn exo day",i_np))\n'
+  'plotRelVar(data, f_p_exo); title(paste("StdDev nn exo day",i_p))\n'
+  '\n'
+  'plotRelVar(data, f_np_mix); title(paste("StdDev nn mix day",i_np))\n'
+  'plotRelVar(data, f_p_mix); title(paste("StdDev nn mix day",i_p))\n'
+  '\n'
+  '# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir'],
+ ['codecell',
+  'R',
+  'par(mfrow=c(1,2))\n'
+  'plotSimils(p_nn_exo, i_np); title(paste("Weights nn exo day",i_np))\n'
+  'plotSimils(p_nn_exo, i_p); title(paste("Weights nn exo day",i_p))\n'
+  '\n'
+  'plotSimils(p_nn_mix, i_np); title(paste("Weights nn mix day",i_np))\n'
+  'plotSimils(p_nn_mix, i_p); title(paste("Weights nn mix day",i_p)\n'
+  '\n'
+  '# - pollué à gauche, + pollué à droite'],
+ ['codecell',
+  'R',
+  '# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n'
+  'p_nn_exo$getParams(i_np)$window\n'
+  'p_nn_exo$getParams(i_p)$window\n'
+  '\n'
+  'p_nn_mix$getParams(i_np)$window\n'
+  'p_nn_mix$getParams(i_p)$window'],
+ ['markdown',
+  'text',
+  '\n\n<h2 style="color:blue;font-size:2em">Pollution par épandage</h2>'],
+ ['codecell',
+  'R',
+  'p_nn_exo = computeForecast(data, indices_ep, "Neighbors", "Neighbors", '
+  'simtype="exo", horizon=H)\n'
+  'p_nn_mix = computeForecast(data, indices_ep, "Neighbors", "Neighbors", '
+  'simtype="mix", horizon=H)\n'
+  'p_az = computeForecast(data, indices_ep, "Average", "Zero", horizon=H) #, '
+  'memory=183)\n'
+  'p_pz = computeForecast(data, indices_ep, "Persistence", "Zero", horizon=H, '
+  'same_day=TRUE)'],
+ ['codecell',
+  'R',
+  'e_nn_exo = computeError(data, p_nn_exo)\n'
+  'e_nn_mix = computeError(data, p_nn_mix)\n'
+  'e_az = computeError(data, p_az)\n'
+  'e_pz = computeError(data, p_pz)\n'
+  'options(repr.plot.width=9, repr.plot.height=7)\n'
+  'plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], '
+  '4))\n'
+  '\n'
+  '# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: '
+  'persistence\n'
+  '\n'
+  'i_np = which.min(e_nn_exo$abs$indices)\n'
+  'i_p = which.max(e_nn_exo$abs$indices)'],
+ ['codecell',
+  'R',
+  'options(repr.plot.width=9, repr.plot.height=4)\n'
+  'par(mfrow=c(1,2))\n'
+  '\n'
+  'plotPredReal(data, p_nn_exo, i_np); title(paste("PredReal nn exo '
+  'day",i_np))\n'
+  'plotPredReal(data, p_nn_exo, i_p); title(paste("PredReal nn exo day",i_p))\n'
+  '\n'
+  'plotPredReal(data, p_nn_mix, i_np); title(paste("PredReal nn mix '
+  'day",i_np))\n'
+  'plotPredReal(data, p_nn_mix, i_p); title(paste("PredReal nn mix day",i_p))\n'
+  '\n'
+  'plotPredReal(data, p_az, i_np); title(paste("PredReal az day",i_np))\n'
+  'plotPredReal(data, p_az, i_p); title(paste("PredReal az day",i_p))\n'
+  '\n'
+  '# Bleu: prévue, noir: réalisée'],
+ ['codecell',
+  'R',
+  'par(mfrow=c(1,2))\n'
+  'f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); '
+  'title(paste("Filaments nn exo day",i_np))\n'
+  'f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); '
+  'title(paste("Filaments nn exo day",i_p))\n'
+  '\n'
+  'f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); '
+  'title(paste("Filaments nn mix day",i_np))\n'
+  'f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); '
+  'title(paste("Filaments nn mix day",i_p))'],
+ ['codecell',
+  'R',
+  'par(mfrow=c(1,2))\n'
+  'plotFilamentsBox(data, f_np_exo); title(paste("FilBox nn exo day",i_np))\n'
+  'plotFilamentsBox(data, f_p_exo); title(paste("FilBox nn exo day",i_p))\n'
+  '\n'
+  'plotFilamentsBox(data, f_np_mix); title(paste("FilBox nn mix day",i_np))\n'
+  'plotFilamentsBox(data, f_p_mix); title(paste("FilBox nn mix day",i_p))'],
+ ['codecell',
+  'R',
+  'par(mfrow=c(1,2))\n'
+  'plotRelVar(data, f_np_exo); title(paste("StdDev nn exo day",i_np))\n'
+  'plotRelVar(data, f_p_exo); title(paste("StdDev nn exo day",i_p))\n'
+  '\n'
+  'plotRelVar(data, f_np_mix); title(paste("StdDev nn mix day",i_np))\n'
+  'plotRelVar(data, f_p_mix); title(paste("StdDev nn mix day",i_p))\n'
+  '\n'
+  '# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir'],
+ ['codecell',
+  'R',
+  'par(mfrow=c(1,2))\n'
+  'plotSimils(p_nn_exo, i_np); title(paste("Weights nn exo day",i_np))\n'
+  'plotSimils(p_nn_exo, i_p); title(paste("Weights nn exo day",i_p))\n'
+  '\n'
+  'plotSimils(p_nn_mix, i_np); title(paste("Weights nn mix day",i_np))\n'
+  'plotSimils(p_nn_mix, i_p); title(paste("Weights nn mix day",i_p)\n'
+  '\n'
+  '# - pollué à gauche, + pollué à droite'],
+ ['codecell',
+  'R',
+  '# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n'
+  'p_nn_exo$getParams(i_np)$window\n'
+  'p_nn_exo$getParams(i_p)$window\n'
+  '\n'
+  'p_nn_mix$getParams(i_np)$window\n'
+  'p_nn_mix$getParams(i_p)$window'],
+ ['markdown',
+  'text',
+  '\n\n<h2 style="color:blue;font-size:2em">Semaine non polluée</h2>'],
+ ['codecell',
+  'R',
+  'p_nn_exo = computeForecast(data, indices_np, "Neighbors", "Neighbors", '
+  'simtype="exo", horizon=H)\n'
+  'p_nn_mix = computeForecast(data, indices_np, "Neighbors", "Neighbors", '
+  'simtype="mix", horizon=H)\n'
+  'p_az = computeForecast(data, indices_np, "Average", "Zero", horizon=H) #, '
+  'memory=183)\n'
+  'p_pz = computeForecast(data, indices_np, "Persistence", "Zero", horizon=H, '
+  'same_day=TRUE)'],
+ ['codecell',
+  'R',
+  'e_nn_exo = computeError(data, p_nn_exo)\n'
+  'e_nn_mix = computeError(data, p_nn_mix)\n'
+  'e_az = computeError(data, p_az)\n'
+  'e_pz = computeError(data, p_pz)\n'
+  'options(repr.plot.width=9, repr.plot.height=7)\n'
+  'plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], '
+  '4))\n'
+  '\n'
+  '# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: '
+  'persistence\n'
+  '\n'
+  'i_np = which.min(e_nn_exo$abs$indices)\n'
+  'i_p = which.max(e_nn_exo$abs$indices)'],
+ ['codecell',
+  'R',
+  'options(repr.plot.width=9, repr.plot.height=4)\n'
+  'par(mfrow=c(1,2))\n'
+  '\n'
+  'plotPredReal(data, p_nn_exo, i_np); title(paste("PredReal nn exo '
+  'day",i_np))\n'
+  'plotPredReal(data, p_nn_exo, i_p); title(paste("PredReal nn exo day",i_p))\n'
+  '\n'
+  'plotPredReal(data, p_nn_mix, i_np); title(paste("PredReal nn mix '
+  'day",i_np))\n'
+  'plotPredReal(data, p_nn_mix, i_p); title(paste("PredReal nn mix day",i_p))\n'
+  '\n'
+  'plotPredReal(data, p_az, i_np); title(paste("PredReal az day",i_np))\n'
+  'plotPredReal(data, p_az, i_p); title(paste("PredReal az day",i_p))\n'
+  '\n'
+  '# Bleu: prévue, noir: réalisée'],
+ ['codecell',
+  'R',
+  'par(mfrow=c(1,2))\n'
+  'f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); '
+  'title(paste("Filaments nn exo day",i_np))\n'
+  'f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); '
+  'title(paste("Filaments nn exo day",i_p))\n'
+  '\n'
+  'f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); '
+  'title(paste("Filaments nn mix day",i_np))\n'
+  'f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); '
+  'title(paste("Filaments nn mix day",i_p))'],
+ ['codecell',
+  'R',
+  'par(mfrow=c(1,2))\n'
+  'plotFilamentsBox(data, f_np_exo); title(paste("FilBox nn exo day",i_np))\n'
+  'plotFilamentsBox(data, f_p_exo); title(paste("FilBox nn exo day",i_p))\n'
+  '\n'
+  'plotFilamentsBox(data, f_np_mix); title(paste("FilBox nn mix day",i_np))\n'
+  'plotFilamentsBox(data, f_p_mix); title(paste("FilBox nn mix day",i_p))'],
+ ['codecell',
+  'R',
+  'par(mfrow=c(1,2))\n'
+  'plotRelVar(data, f_np_exo); title(paste("StdDev nn exo day",i_np))\n'
+  'plotRelVar(data, f_p_exo); title(paste("StdDev nn exo day",i_p))\n'
+  '\n'
+  'plotRelVar(data, f_np_mix); title(paste("StdDev nn mix day",i_np))\n'
+  'plotRelVar(data, f_p_mix); title(paste("StdDev nn mix day",i_p))\n'
+  '\n'
+  '# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir'],
+ ['codecell',
+  'R',
+  'par(mfrow=c(1,2))\n'
+  'plotSimils(p_nn_exo, i_np); title(paste("Weights nn exo day",i_np))\n'
+  'plotSimils(p_nn_exo, i_p); title(paste("Weights nn exo day",i_p))\n'
+  '\n'
+  'plotSimils(p_nn_mix, i_np); title(paste("Weights nn mix day",i_np))\n'
+  'plotSimils(p_nn_mix, i_p); title(paste("Weights nn mix day",i_p)\n'
+  '\n'
+  '# - pollué à gauche, + pollué à droite'],
+ ['codecell',
+  'R',
+  '# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n'
+  'p_nn_exo$getParams(i_np)$window\n'
+  'p_nn_exo$getParams(i_p)$window\n'
+  '\n'
+  'p_nn_mix$getParams(i_np)$window\n'
+  'p_nn_mix$getParams(i_p)$window'],
+ ['markdown',
+  'text',
+  '\n'
+  '\n'
+  '<h2>Bilan</h2>\n'
+  '\n'
+  "Problème difficile : on ne fait guère mieux qu'une naïve moyenne des "
+  'lendemains des jours\n'
+  "similaires dans le passé, ce qui n'est pas loin de prédire une série "
+  'constante égale à la\n'
+  'dernière valeur observée (méthode "zéro"). La persistence donne parfois de '
+  'bons résultats\n'
+  "mais est trop instable (sensibilité à l'argument <code>same_day</code>).\n"
+  '\n'
+  'Comment améliorer la méthode ?']]