projects / aggexp.git / blame
| Commit | Line | Data |
|---|---|---|
| a961f8a1 BA |
1 | \documentclass[12pt]{article} |
| 2 | ||
| 3 | \usepackage{geometry} | |
| 4 | \geometry{ | |
| 5 | a4paper, | |
| 6 | total={210mm,297mm}, | |
| 7 | left=10mm, | |
| 8 | right=10mm, | |
| 9 | top=15mm, | |
| 10 | bottom=15mm, | |
| 11 | } | |
| 12 | \setlength{\parindent}{0pt} | |
| 13 | \usepackage[utf8]{inputenc} | |
| 14 | \usepackage[french]{babel} | |
| 15 | \usepackage{helvet} | |
| 16 | \renewcommand{\familydefault}{\sfdefault} | |
| 17 | \usepackage{amsmath,amsfonts,amssymb} | |
| 18 | \usepackage{float} | |
| 19 | \usepackage{graphicx} | |
| 20 | \usepackage{wrapfig} | |
| 21 | \usepackage{xcolor} | |
| 22 | \usepackage{cprotect} | |
| 23 | %\setcounter{section}{9} | |
| 24 | ||
| 25 | \begin{document} | |
| 26 | ||
| 27 | <<setup, include=FALSE, cache=FALSE, echo=FALSE>>= | |
| 28 | opts_chunk$set(fig.path="figs/", fig.align="center", fig.show="hold", echo=FALSE) | |
| 29 | options(replace.assign=TRUE,width=90) | |
| 30 | @ | |
| 31 | ||
| 32 | <<initialize>>= | |
| 33 | library(aggexp) | |
| 34 | station = "STATIONNAME" | |
| 35 | experts = aggexp:::expertsArray[EXPERTSINDICES] | |
| 36 | K = length(experts) | |
| 37 | @ | |
| 38 | ||
| 39 | \cprotect\section{\verb|STATIONNAME| - Résumé des données} | |
| 40 | ||
| 41 | \vspace*{-0.5cm} | |
| 42 | <<plotCurves, out.width="12cm", out.height="9cm">>= | |
| 43 | mock_r = list(data = getData(experts, station), experts=experts, stations=station) | |
| 44 | plotCurves(mock_r) | |
| 45 | @ | |
| 46 | ||
| 47 | \vspace*{-0.8cm} | |
| 48 | \cprotect\section{\verb|STATIONNAME| - Performances en prédiction} | |
| 49 | ||
| 50 | \vspace*{-0.5cm} | |
| 51 | <<computePredictions>>= | |
| 52 | r_ml = runAlgorithm("ml", experts=experts, stations=station, alpha=0.2, H=183, grad=TRUE) | |
| 53 | r_rr = runAlgorithm("rr", experts=experts, stations=station, H=183) | |
| 54 | r_be = r_ml | |
| 55 | be = which.max(getBestExpert(r_ml)) | |
| 56 | r_be$data[,"Prediction"] = r_be$data[,experts[be]] | |
| 57 | @ | |
| 58 | ||
| 59 | <<plotPredictionClouds, out.width="6cm", out.height="6cm">>= | |
| 60 | plotCloud(r_be, 30, c(30,50,80), main=paste("Nuage Meilleur Expert (",experts[be],")",sep="")) | |
| 61 | plotCloud(r_ml, 30, c(30,50,80), main="Nuage ML") | |
| 62 | plotCloud(r_rr, 30, c(30,50,80), main="Nuage RR") | |
| 63 | @ | |
| 64 | ||
| 65 | \vspace*{-0.8cm} | |
| 66 | \cprotect\section{\verb|STATIONNAME| - Indicateurs d'alertes} | |
| 67 | ||
| 68 | \vspace*{-0.5cm} | |
| 69 | <<computeAlertIndicators>>= | |
| 70 | indicators = matrix(nrow=5, ncol=4+K) | |
| 71 | indicators[,1] = as.numeric(getIndicators(r_ml, thresh=30)) | |
| 72 | indicators[,2] = as.numeric(getIndicators(r_rr, thresh=30)) | |
| 73 | for (i in 1:K) { | |
| 74 | r_be$data[,"Prediction"] = r_be$data[,experts[i]] | |
| 75 | indicators[,i+2] = as.numeric(getIndicators(r_be, thresh=30)) | |
| 76 | } | |
| 77 | indicators = as.data.frame(indicators, row.names=c("TS","FA","MA","RMSE","EV")) | |
| 78 | names(indicators) = c("ML","RR", experts) | |
| 79 | @ | |
| 80 | ||
| 81 | <<plotAlertIndicators, results="asis">>= | |
| 82 | library(xtable) | |
| 83 | splitIndex = ifelse(length(experts) < 10, 7, 8) | |
| 84 | xtable(indicators[,1:splitIndex], caption="Indicateurs d'alertes ; partie 1") | |
| 85 | xtable(indicators[,(splitIndex+1):(2+K)], caption="Indicateurs d'alertes ; partie 2") | |
| 86 | @ | |
| 87 | ||
| 88 | \end{document} |