merge with remote

[aggexp.git] / scripts / make_summaries / modele.Rnw

\documentclass[12pt]{article}

\usepackage{geometry}
\geometry{
	a4paper,
	total={210mm,297mm},
	left=10mm,
	right=10mm,
	top=15mm,
	bottom=15mm,
}
\setlength{\parindent}{0pt}
\usepackage[utf8]{inputenc}
\usepackage[french]{babel}
\usepackage{helvet}
\renewcommand{\familydefault}{\sfdefault}
\usepackage{amsmath,amsfonts,amssymb}
\usepackage{float}
\usepackage{graphicx}
\usepackage{wrapfig}
\usepackage{xcolor}
\usepackage{cprotect}
%\setcounter{section}{9}

\begin{document}

<<setup, include=FALSE, cache=FALSE, echo=FALSE>>=
opts_chunk$set(fig.path="figs/", fig.align="center", fig.show="hold", echo=FALSE)
options(replace.assign=TRUE,width=90)
@

<<initialize>>=
library(aggexp)
station = "STATIONNAME"
experts = aggexp:::expertsArray[EXPERTSINDICES]
K = length(experts)
@

\cprotect\section{\verb|STATIONNAME| - Résumé des données}

\vspace*{-0.5cm}
<<plotCurves, out.width="12cm", out.height="9cm">>=
mock_r = list(data = getData(experts, station), experts=experts, stations=station)
plotCurves(mock_r)
@

\vspace*{-0.8cm}
\cprotect\section{\verb|STATIONNAME| - Performances en prédiction}

\vspace*{-0.5cm}
<<computePredictions>>=
r_ml = runAlgorithm("ml", experts=experts, stations=station, alpha=0.2, H=183, grad=TRUE)
r_rr = runAlgorithm("rr", experts=experts, stations=station, H=183)
r_be = r_ml
be = which.max(getBestExpert(r_ml))
r_be$data[,"Prediction"] = r_be$data[,experts[be]]
@

<<plotPredictionClouds, out.width="6cm", out.height="6cm">>=
plotCloud(r_be, 30, c(30,50,80), main=paste("Nuage Meilleur Expert (",experts[be],")",sep=""))
plotCloud(r_ml, 30, c(30,50,80), main="Nuage ML")
plotCloud(r_rr, 30, c(30,50,80), main="Nuage RR")
@

\vspace*{-0.8cm}
\cprotect\section{\verb|STATIONNAME| - Indicateurs d'alertes}

\vspace*{-0.5cm}
<<computeAlertIndicators>>=
indicators = matrix(nrow=5, ncol=4+K)
indicators[,1] = as.numeric(getIndicators(r_ml, thresh=30))
indicators[,2] = as.numeric(getIndicators(r_rr, thresh=30))
for (i in 1:K) {
	r_be$data[,"Prediction"] = r_be$data[,experts[i]]
	indicators[,i+2] = as.numeric(getIndicators(r_be, thresh=30))
}
indicators = as.data.frame(indicators, row.names=c("TS","FA","MA","RMSE","EV"))
names(indicators) = c("ML","RR", experts)
@

<<plotAlertIndicators, results="asis">>=
library(xtable)
splitIndex = ifelse(length(experts) < 10, 7, 8)
xtable(indicators[,1:splitIndex], caption="Indicateurs d'alertes ; partie 1")
xtable(indicators[,(splitIndex+1):(2+K)], caption="Indicateurs d'alertes ; partie 2")
@

\end{document}