From: Benjamin Auder <benjamin.auder@somewhere>
Date: Mon, 30 Jan 2017 00:15:57 +0000 (+0100)
Subject: add PDF files through git-fat filters
X-Git-Url: https://git.auder.net/variants/Chakart/css/assets/current/pieces/mini-custom.min.css?a=commitdiff_plain;h=67058ab80c383ec7369c89e2cbbf5ab38b36fcb2;p=epclust.git

add PDF files through git-fat filters
---

diff --git a/biblio/ours/ENERGYCON_leuven_GOUDE.pdf b/biblio/ours/ENERGYCON_leuven_GOUDE.pdf
new file mode 100644
index 0000000..8943a9a
--- /dev/null
+++ b/biblio/ours/ENERGYCON_leuven_GOUDE.pdf
@@ -0,0 +1 @@
+#$# git-fat 5b47ab0da075bababc52c7a140f2e791fb425306              1486805
diff --git a/biblio/ours/clust-publie.pdf b/biblio/ours/clust-publie.pdf
new file mode 100644
index 0000000..5801a64
--- /dev/null
+++ b/biblio/ours/clust-publie.pdf
@@ -0,0 +1 @@
+#$# git-fat 7571c6c3b737bf2f57eab62fea99eb24a756eded               895937
diff --git a/biblio/ours/cugliari-mathamsud2016.pdf b/biblio/ours/cugliari-mathamsud2016.pdf
new file mode 100644
index 0000000..b3a5e8a
--- /dev/null
+++ b/biblio/ours/cugliari-mathamsud2016.pdf
@@ -0,0 +1 @@
+#$# git-fat 96fc0307b651a06a05f81c1f5d0f79a1fbd2bd45              2760474
diff --git a/slides/.gitignore b/slides/.gitignore
deleted file mode 100644
index 53d89fc..0000000
--- a/slides/.gitignore
+++ /dev/null
@@ -1,4 +0,0 @@
-#ignore all but this file, and source file
-*
-!.gitignore
-!*.ipynb
diff --git a/slides/201701_IRSDIfollowup.pdf b/slides/201701_IRSDIfollowup.pdf
new file mode 100644
index 0000000..a1120a0
--- /dev/null
+++ b/slides/201701_IRSDIfollowup.pdf
@@ -0,0 +1 @@
+#$# git-fat 84fb4f1732c0e17b4f3f2dfb4424a032f10a9000               912206
diff --git a/slides/201701_point.tex b/slides/201701_point.tex
new file mode 100644
index 0000000..eebc740
--- /dev/null
+++ b/slides/201701_point.tex
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+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%
+%        LOADING DOCUMENT
+%
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+\documentclass[10pt]{beamer}
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%        LOADING PACKAGES
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage[francais]{babel}
+\usepackage{amsmath}
+\usepackage{amsfonts}
+\usepackage{amssymb}
+\usepackage{graphicx}
+\usepackage{booktabs}
+\usetheme{default}
+\usepackage{tikz}
+
+\colorlet{darkred}{red!80!black}
+\colorlet{darkblue}{blue!80!black}
+\colorlet{darkgreen}{green!60!black}
+
+\usetikzlibrary{calc,decorations.pathmorphing,patterns}
+\pgfdeclaredecoration{penciline}{initial}{
+  \state{initial}[width=+\pgfdecoratedinputsegmentremainingdistance,
+  auto corner on length=1mm,]{
+    \pgfpathcurveto%
+     {% From
+       \pgfqpoint{\pgfdecoratedinputsegmentremainingdistance}
+                 {\pgfdecorationsegmentamplitude}
+     }
+     {%  Control 1
+      \pgfmathrand
+      \pgfpointadd{\pgfqpoint{\pgfdecoratedinputsegmentremainingdistance}{0pt}}
+                  {\pgfqpoint{-\pgfdecorationsegmentaspect
+                   \pgfdecoratedinputsegmentremainingdistance}%
+                             {\pgfmathresult\pgfdecorationsegmentamplitude}
+                 }
+      }
+      {%TO 
+      \pgfpointadd{\pgfpointdecoratedinputsegmentlast}{\pgfpoint{1pt}{1pt}}
+      }
+  }
+  \state{final}{}
+}
+\tikzstyle{block} = [draw,rectangle,thick,minimum height=2em,minimum width=2em]
+
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%        BEAMER OPTIONS
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%\setbeameroption{show notes}
+\setbeamertemplate{navigation symbols}{}
+%\setbeamercovered{transparent}
+%\AtBeginSection[]{
+%\begin{frame}{Outline}
+%	\tableofcontents[currentsection]               
+%\end{frame}
+%}
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%
+%	PRESENTATION INFORMATION
+%
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\author{B. Auder \and
+	    J. Cugliari \and
+	    Y. Goude   \and
+    	J.-M. Poggi
+}
+\title{Disaggregated Electricity Forecasting using Clustering of 
+	   Individual Consumers}
+\subtitle{Réunion mi parcours}
+%\logo{}
+\institute{IRSDI - RESEARCH INITIATIVE IN INDUSTRIAL DATA SCIENCE}
+\date{19 janvier 2017}
+%\subject{tito}
+
+\begin{document}
+	
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%
+%	TITLE PAGE
+%
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\frame[plain]{\maketitle} 
+%\maketitle
+
+
+\section{IRSDI follow up meeting}	
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}{The project in a nutshell}
+\begin{block}{Context}
+\begin{itemize}
+\item 
+Industrial : Electricity load forecasting \& smart grids infrastructure 
+\item 
+Academic : curve's shape \& nonparametric function-valued forecast 
+\item 
+Past work : clustering with wavelets (RC, Wer), KWF, Enercon 
+\end{itemize}
+\end{block}
+
+\begin{block}{Aims}
+\begin{itemize}
+\item evaluate the upscaling capacity of the Energycon strategy 
+\item adapt KWF to an exogenous variable (e.g. meteorological)
+\end{itemize}
+\end{block}
+\end{frame}
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}{Clients hierarchical structure and prediction}
+
+\begin{columns}
+\column{.6\textwidth}
+\begin{figure}[!ht]\centering
+ \includegraphics[width = \textwidth]{pics/schema.png} 
+\caption{Hierarchical structure of $N$ individual clients among $K$ 
+groups.}\label{fig:schema-hier}
+\end{figure}
+ 
+\column{.4\textwidth}
+\begin{tikzpicture}[decoration=penciline, decorate]
+  \node[block, decorate] at (0, 0){$Z_t$} ;
+  \node[block, decorate] at (3, 0) {$Z_{t + 1}$} ;
+
+  \node[block, decorate] at (0, -2.5) {$\begin{pmatrix}
+                              Z_{t, 1} \\ Z_{t, 2} \\ \vdots \\ Z_{t, K}
+                               \end{pmatrix}$ };
+
+  \node[block, decorate] at (3, -2.5) {$\begin{pmatrix}
+                           Z_{t+1, 1} \\ Z_{t+1, 2} \\ \vdots \\ Z_{t+1, k}
+                               \end{pmatrix} $};
+
+  \draw[decorate, darkblue,  line width = 2mm, ->] (1, 0) -- (2, 0);
+  \draw[decorate, darkgreen, line width = 2mm, ->] (1, -2.5) -- (2, -2.5);
+  \draw[decorate, black,     line width = 2mm, ->] (3, -1.3) -- (3, -0.4);
+  \draw[decorate, darkred,   line width = 2mm, ->] (1, -1.5) -- (2, -0.75);
+ \end{tikzpicture}
+\end{columns}
+
+\begin{itemize}
+ \item $Z_t$: aggregate demand at $t$
+ \hfill $Z_{t, k}$:demand of group $k$ at moment $t$
+ \item Groups can express tariffs, geographical dispersion, client class ...
+ \item Profiling vs Prediction 
+ \item We follow Misiti \textit{et al}. (2010) to construct classes of customers to better predict the aggregate.
+\end{itemize}
+\end{frame}
+
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}{Expected result}
+
+\includegraphics[width = \textwidth]{pics/perf.pdf}
+\end{frame}
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}{Energy decomposition of the DWT}
+
+%\begin{block}{ }
+\begin{itemize}
+\item Energy conservation of the signal
+%
+  \begin{equation*}\label{eq:energy}  
+     \| z\|^2_H    \approx     \| \widetilde{z_J} \|_2^2 
+        = c_{0,0}^2 + \sum_{j=0}^{J-1} \sum_{k=0}^{2^j-1} d_{j,k} ^2  = 
+                     c_{0,0}^2 + \sum_{j=0}^{J-1} \| \mathbf{d}_{j} \|_2^2.
+  \end{equation*}
+%  \item characterization by the set of channel variances estimated at the output of the corresponding filter bank
+ \item For each $j=0,1,\ldots,J-1$, we compute the \textcolor{blue}{absolute} and 
+ \textcolor{orange}{relative} contribution representations by
+%      
+   \[ \underbrace{\hbox{cont}_j = ||\mathbf{d_j}||^2}_{\fbox{\textcolor{blue}{AC}}}  
+      \qquad  \text{and}  \qquad
+       \underbrace{\hbox{rel}_j  = 
+     \frac{||\mathbf{d_j}||^2}
+          {\sum_j ||\mathbf{d_j}||^2 }}_{\fbox{\textcolor{orange}{RC}}} .\]
+ %\item They quantify the relative importance of the scales to the global dynamic.
+% \item Only the wavelet coefficients $\set{d_{j,k}}$ are used.
+% \item RC normalizes the energy of each signal to 1.
+\end{itemize}
+%\end{block}
+%\end{frame}
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+%\begin{frame} 
+%  \frametitle{Schema of procedure}
+  \begin{center}
+   \includegraphics[width = 7cm, height = 2cm]{./pics/Diagramme1.png}
+   % Diagramme1.png: 751x260 pixel, 72dpi, 26.49x9.17 cm, bb=0 0 751 260
+  \end{center}
+      
+        \begin{footnotesize}
+       \begin{description}
+ \item [0. Data preprocessing.] Approximate sample paths of $z_1(t),\ldots,z_n(t)$ %by the truncated wavelet series at the scale $J$ from sampled data $\mathbf{z}_1, \ldots, \mathbf{z}_n$.
+ \item [1. Feature extraction.] Compute either of the energetic components using absolute contribution (AC) or relative contribution (RC).
+ \item [2. Feature selection.] Screen irrelevant variables. \begin{tiny} [Steinley \& Brusco ('06)]\end{tiny}
+ %\item [3. Determine the number of clusters.] Detecting significant jumps %in the transformed distortion curve.
+ %\begin{tiny} [Sugar \& James ('03)]\end{tiny}
+ %\item [4. Clustering.] Obtain the $K$ clusters using PAM algorithm.
+      \end{description}       \end{footnotesize}
+    
+ \end{frame}
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame} 
+  \frametitle{A function-based distance}
+
+\begin{columns}
+\column{0.6\textwidth}
+  \begin{itemize}
+    \item Distance based on wavelet-correlation between two time series
+    \item Can be used to measure relationship between two functions
+    %variables, i.e. temperature and load.
+    \item The strength of the relation is hierarchically decomposed across
+          scales without losing of time location
+   \end{itemize}    
+
+   Drawback: needs more computation time and storage (complex values) 
+\column{0.4\textwidth}
+  \includegraphics[width  = \textwidth]{pics/conso-week.png} 
+                   
+  \includegraphics[width  = .96\textwidth]{pics/wsp-week.png} 
+
+\end{columns} 
+ \end{frame}
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}{A 2-stages strategy (Energycon)}
+
+\includegraphics[width = \textwidth]{pics/2-stage_strategy.png}
+
+\footnotetext[1]{
+	J. Cugliari, Y. Goude and J. M. Poggi, "Disaggregated electricity forecasting using wavelet-based clustering of individual consumers," 2016 IEEE International Energy Conference (ENERGYCON), Leuven, 2016, pp. 1-6.
+	}
+
+\end{frame}
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}{Data description}
+
+\begin{columns}
+\column{0.45\textwidth}
+\begin{block}{Available}
+\begin{itemize}
+\item 
+EDF : 25K professional clients, sampled @ 30min, 5 semesters 
+\item 
+external open data 
+\end{itemize}
+\end{block}
+
+\begin{block}{Accesible}
+	\begin{itemize}
+		\item simulated (very large) data
+	\end{itemize}
+\end{block}
+\column{0.55\textwidth}
+\includegraphics[width = \columnwidth]{pics/indiv.jpg}
+%\textcolor{red}{A picture here?}
+\end{columns}
+
+\end{frame}
+
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}{Computing resources}
+\begin{block}{2 academic testing architectures}
+\begin{itemize}
+\item Orsay's cluster (500Gb RAM, 80 cores)
+\item \texttt{pulpito} : Lyon 2's box with 2 quadricores (HT x 2), 72Gb RAM
+\end{itemize}
+\end{block}
+
+\begin{block}{1 industrial real-scale architecture}
+\begin{itemize}
+\item mini cluster @ EDF labs
+\end{itemize}
+\end{block}
+
+\end{frame}
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}{Strategies for upscaling}
+
+\begin{itemize}
+\item From 25K to 25M: in 1000 chunks of 25K
+\item Reference values: 
+\begin{itemize}
+\item $K'=200$ super consumers (SC)
+\item  $K\ast=15$ final clusters
+\end{itemize}
+\end{itemize}
+
+
+
+\begin{block}{1st strategy}
+\begin{itemize}
+\item Do 1000 times ONLY Energycon's 1st-step strategy on 25K clients 
+
+\item With the $1000 \times K'$ SC perform a 2-step run 
+      leading to $K^\ast$ clusters
+\end{itemize}
+\end{block}
+
+\begin{block}{2nd strategy}
+\begin{itemize}
+\item Do 1000 times Energycon's 2-step strategy on 25K clients
+      leading to $1000\times K^\ast$ intermediate clusters
+\item Treat the intermediate clusters as individual curves and perform
+      a single 2-step run to get $K^\ast$ final clusters
+\end{itemize}
+\end{block}
+
+\end{frame}
+
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}
+\frametitle{Course + Workshop}
+	
+\begin{block}{1-day IRSDI-ECAS Course}
+\begin{itemize}
+\item 
+GAM : from classical to distributed environments
+\item
+October 19, 2017 @ EDF Labs, Paris-Saclay, France
+\item 
+Simon Wood \& Matteo Fasiolo (University Walk, Bristol, UK)
+\end{itemize}
+\end{block}
+
+
+\begin{block}{1-day Worshop}
+\begin{itemize}
+\item 
+Individual Electricity Consumers, Data, Packages and Methods
+\item
+October 20, 2017 @ EDF Labs, Paris-Saclay, France
+\item 
+5 keynote speakers 
+\begin{itemize}
+\item
+Souhaib Ben Taieb, Monash University, Melbourne, Australia
+\item
+Ram Rajagopal, Stanford Univ., USA
+\item
+Gavin Shaddick, University of Bath, UK
+\item
+Bei Chen, IBM Research, Ireland
+\item
+Jack Kelly, University of London, Imperial College of Science, UK
+\end{itemize}
+\end{itemize}
+\end{block}
+\end{frame}
+
+
+\section{Point sur les codes}
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}{Résumé point sur le code (BA, JC @ Lyon déc 2016)}
+	
+Nous avons réussi à 
+\begin{itemize}
+\item
+faire une fresh installation du code de BA sur une nouvelle machine
+(problèmes divers liés à la compilation, configuration, libraries exotiques)
+\item 
+conduire des expériences sur les données pour mesurer le temps de calcul (le code est blazing fast: 30sec pour obtenir 500 groupes sur 4 procs)
+\item
+identifier de problèmes : manque un installateur et une interface de pretraitement indépendant du calcul
+\end{itemize}
+	
+A faire:
+\begin{itemize}
+\item
+finir les experiences (sur nb de classes, nb de curves / chunk, nb de procs) et sur d'autres architectures
+\item
+interface matrice -> binaire
+\item 
+obtenir les courbes synchrones
+\end{itemize}
+	
+Piste à explorer pour les comparaisons: \texttt{h2o} 
+\end{frame}
+
+\section{Expériences numériques}
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}
+\frametitle{Code C/MPI}
+	
+\begin{enumerate}
+\item [0]] Sérialisation des données : on écrit d'abord la longueur de la série	puis les puissances sont codées sur 3 octets, permettant une excellente compression et une lecture facile (accès en O(1) à n'importe quelle série)
+		
+\item [1]] Algorithme PAM appliqué en parallèle via la librairie MPI.
+		
+\item [2]] Agrégation des médoïdes obtenus, (re-)sérialisation, puis on ré-applique l'algorithme PAM.
+
+\end{enumerate}
+
+\begin{itemize}
+\item 
+		%Plusieurs astuces : sérialisation des données, calcul en parallèle
+\item 
+		Très rapide : environ 5 minutes from raw to 1st stage clustering
+		\item 
+		Divergences par rapport à Energycon (moyennes au lieu d'aggrégation)
+	\end{itemize}
+	
+	%\begin{verbatim}
+	%> time ./ppam.exe serialize 2009.csv 2009.bin 1 0
+	%real	7m34.182s
+	%\end{verbatim}
+\end{frame}
+
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}
+\frametitle{R Code}
+
+\begin{itemize}
+\item Enercon's code update 
+\item \texttt{data.table} is used for readings and writtings \footnote{\texttt{tidyverse} toolbox is much slower}
+\item Disk spaces of the plain text associated object
+\item Timmings on \texttt{pulpito}  (16 cores, 64Gb RAM, SSD) 
+\end{itemize}
+\begin{center}
+\begin{tabular}{lccc}\toprule
+Task & Time & Memory & Disk \\ \midrule
+Raw (15Gb) to matrix  &   7 min  &
+  30 Gb\footnote{\texttt{ff} is a promising alternative if needed} &
+  2.7 Gb \\
+Compute contributions  &   7 min  & <1Gb & 7 Mb \\
+1st stage clustering   &   3 min  & <1Gb & -- \\
+Aggregation            &   1 min  &  6Gb & 30 Mb \\
+Wer distance matrix    &  40 min  & 64Gb\footnote{Embarransgly parallel but still too slow} & 150 Kb \\
+Forecasts              &  10 min  & <1Gb & --\\ 
+\bottomrule
+\end{tabular}
+\end{center}
+\end{frame}
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}
+\frametitle{Why wer distance is so slow ?}
+
+
+\begin{block}{2nd step strategy (Enercon way)}
+% We proceed as follows:
+ \begin{itemize}
+    \item Transform data $z_1(t), \ldots, z_n(t)$ using the CWT and Morlet wavelet to obtain $n$ matrices of size $J\times N$.
+    \item Compute the wer-based dissimilarity matrix 
+    \item Obtain the PAM-based clustering.
+\end{itemize}
+
+\begin{block}{Current choices on the computation}
+\begin{itemize}
+\item From (\texttt{Rwave} \& \texttt{sowas}) to \texttt{biwavelt}
+\item About 1 sec to compute \texttt{werd(x, y)} with current 
+      filtering ($J \sim 52$ with 13 octaves, 4 voices )
+\item Need to compute $n (n - 1) / 2$ pairwise distances 
+     (20K, 130K, 500K entries for $n = 200, 500, 1000$)
+\item Need an efficient \texttt{werd} function (maybe in 
+      RcppParallel ?)
+\end{itemize}
+
+\end{block}
+
+  
+\end{block}
+
+\end{frame}
+
+
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}{CWT}
+
+\begin{block}{Continuous WT}
+Starting with a mother wavelet $\psi$ consider $\psi_{a, \tau} = a^{-1/2} \psi\left(\frac{t-\tau}{a}\right)$.
+
+The CWT of a function $z\in L^2 (\mathbb{R})$ is,
+$$ W_z(a, \tau) = \int_{-\infty}^{\infty} z(t) \psi_{a, \tau}^* (t) dt$$
+
+As for Fourier transform, a spectral approach is possible.
+
+
+\begin{eqnarray*}
+S_z(a, \tau)  &=& |W_z(a, \tau)|^2 \qquad\qquad \hbox{wavelet spectrum} \\
+\mathcal{W}_{z, x}(a, \tau)  &=& W_z(a, \tau)W_x^*(a, \tau) \qquad \hbox{cross-wavelet transform}
+\end{eqnarray*}
+
+%$$ S_z(a, \tau) = |W_z(a, \tau)|^2 \qquad \hbox{wavelet spectrum}$$
+
+%$$ \mathcal{W}_{z, x}(a, \tau) = W_z(a, \tau)W_x(a, \tau)^* \qquad \hbox{cross-wavelet transform}$$
+\end{block}
+
+\end{frame}
+
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}{Wavelet coherence}
+\begin{block}{ }
+\begin{equation*} \label{coherence}
+R_{z,x}^2(a,\tau) = \frac{ |\tilde{\mathcal{W}}_{x,y}(a, \tau)|^2 }{|\tilde{\mathcal{W}}_{x,x}(a, \tau)| |\tilde{\mathcal{W}}_{y,y}(a, \tau) | },
+\end{equation*}
+
+Based on the extended $R^2$ coefficient, we can construct an coefficient of determination between two wavelet spectrums
+ 
+\begin{equation*}\label{eq:wer}
+  WER_{z, x}^2 = \frac{ 
+ \int_0^\infty  \left( \int_{-\infty}^\infty |\tilde{\mathcal{W}}_{z, x}(a, \tau)|  d\tau \right)^2 da} { \int_0^\infty \left( \int_{-\infty}^\infty |\tilde{\mathcal{W}}_{z, z}(a, \tau)| d\tau \int_{-\infty}^\infty |\tilde{\mathcal{W}}_{x, x}(a, \tau)| d\tau\right) da}.
+ \end{equation*}
+
+And obtain a dissimilarity based on it
+ 
+\begin{equation*}\label{eq:dist-wer}
+    d(z, x) = \sqrt{ JN(1 - \widehat{WER}_{z, x}^2)} 
+\end{equation*}
+\end{block}
+\end{frame}
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+%\begin{frame} \frametitle{Wavelet coherence}
+%\begin{block}{ }
+% We proceed as follows:
+% \begin{itemize}
+%    \item Transform data $z_1(t), \ldots, z_n(t)$ using the  CWT and Morlet wavelet to obtain $n$ matrices of size $J\times N$.
+%    \item Compute a dissimilarity matrix with the coherency based dissimilarity.
+%    \item Using PAM obtain clusters $k=8$ clusters.
+%   \end{itemize}
+%
+%  Rand Index (AC, WER) = 0.26
+%  
+%\end{block}
+%
+%\end{frame}
+
+
+\end{document}
+
+
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+%	FRAME:
+%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+\begin{frame}
+\frametitle{Misc jc}
+	
+\begin{itemize}
+\item simulated dataset : howto ?
+\item temperature
+\item Rcpp
+\end{itemize}
+	
+
+
diff --git a/slides/201701_reunion.pdf b/slides/201701_reunion.pdf
new file mode 100644
index 0000000..c0e3067
--- /dev/null
+++ b/slides/201701_reunion.pdf
@@ -0,0 +1 @@
+#$# git-fat 9ee0bf244bef3ab34c2a737d94c8f48a9d1f3f86                95206
diff --git a/slides/presentation.ipynb b/slides/presentation.ipynb
deleted file mode 100644
index 2340782..0000000
--- a/slides/presentation.ipynb
+++ /dev/null
@@ -1,44 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ERROR",
-     "evalue": "Error in library(epclust): there is no package called ‘epclust’\n",
-     "output_type": "error",
-     "traceback": [
-      "Error in library(epclust): there is no package called ‘epclust’\nTraceback:\n",
-      "1. library(epclust)",
-      "2. stop(txt, domain = NA)"
-     ]
-    }
-   ],
-   "source": [
-    "library(epclust)\n",
-    "#TODO..."
-   ]
-  }
- ],
- "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": "3.3.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}