fix ipynb_generator.py

diff --git a/.gitignore b/.gitignore
index c4370d2..6bf4539 100644 (file)
--- a/.gitignore
+++ b/.gitignore
@@ -12,3 +12,4 @@ data/*.csv
 NAMESPACE
 /reports/*.html
 /pkg/vignettes/*.html
+nohup.out

diff --git a/reports/ipynb_generator.py b/reports/ipynb_generator.py
index 339f96a..ce546ad 100755 (executable)
--- a/reports/ipynb_generator.py
+++ b/reports/ipynb_generator.py
@@ -63,9 +63,7 @@ def read(text, argv=sys.argv[3:]):
                 if shortname:
                     # Check if code is to be typeset as static
                     # Markdown code (e.g., shortname=py-t)
-                        .format(shortname))
                     astext = shortname[-2:] == '-t'
-                        .format(astext, shortname))
                     if astext:
                         # Markdown
                         shortname = shortname[:-2]
@@ -123,7 +121,7 @@ def driver():
     """Compile a document and its variables."""
     try:
         inputfile = sys.argv[1]
-        with open(filename, 'r') as f:
+        with open(inputfile, 'r') as f:
             text = f.read()
         outputfile = '-' if len(sys.argv) <= 2 else sys.argv[2]
     except (IndexError, IOError) as e:
@@ -134,7 +132,7 @@ def driver():
     filestr = write(cells)
     # Assuming file extension .gj (generate Jupyter); TODO: less strict
     outputfile = inputfile[:-3]+'.ipynb' if outputfile == '-' else outputfile
-    with open(filename, 'w') as f:
+    with open(outputfile, 'w') as f:
         f.write(filestr)
 
 if __name__ == '__main__':

diff --git a/reports/report_7h_H3.ipynb b/reports/report_7h_H3.ipynb
new file mode 100644 (file)
index 0000000..5cb789a
--- /dev/null
+++ b/reports/report_7h_H3.ipynb
@@ -0,0 +1,548 @@
+{
+ "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\",\n",
+    "\tpredict_at=7) #predict from P+1 to P+H included\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\",\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e_nn_exo = computeError(data, p_nn_exo, 3)\n",
+    "e_nn_mix = computeError(data, p_nn_mix, 3)\n",
+    "e_az = computeError(data, p_az, 3)\n",
+    "e_pz = computeError(data, p_pz, 3)\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\",\n",
+    "\thorizon=3, simtype=\"exo\")\n",
+    "p_nn_mix = computeForecast(data, indices_ep, \"Neighbors\", \"Neighbors\",\n",
+    "\thorizon=3, simtype=\"mix\")\n",
+    "p_az = computeForecast(data, indices_ep, \"Average\", \"Zero\",\n",
+    "\thorizon=3)\n",
+    "p_pz = computeForecast(data, indices_ep, \"Persistence\", \"Zero\",\n",
+    "\thorizon=3, same_day=TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e_nn_exo = computeError(data, p_nn_exo, 3)\n",
+    "e_nn_mix = computeError(data, p_nn_mix, 3)\n",
+    "e_az = computeError(data, p_az, 3)\n",
+    "e_pz = computeError(data, p_pz, 3)\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\",\n",
+    "\thorizon=3, simtype=\"exo\")\n",
+    "p_nn_mix = computeForecast(data, indices_np, \"Neighbors\", \"Neighbors\",\n",
+    "\thorizon=3, simtype=\"mix\")\n",
+    "p_az = computeForecast(data, indices_np, \"Average\", \"Zero\",\n",
+    "\thorizon=3)\n",
+    "p_pz = computeForecast(data, indices_np, \"Persistence\", \"Zero\",\n",
+    "\thorizon=3, same_day=TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e_nn_exo = computeError(data, p_nn_exo, 3)\n",
+    "e_nn_mix = computeError(data, p_nn_mix, 3)\n",
+    "e_az = computeError(data, p_az, 3)\n",
+    "e_pz = computeError(data, p_pz, 3)\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
+}