X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=slides%2F201701_point.tex;fp=slides%2F201701_point.tex;h=0000000000000000000000000000000000000000;hb=d300b49cd63d0539d29bbee120fa8237f7acee9b;hp=eebc740d1711108f3dd3bfe9fb25eec282145f43;hpb=5c6529795907ba1b34d4552cbfd0e0cbb77cac0f;p=epclust.git diff --git a/slides/201701_point.tex b/slides/201701_point.tex deleted file mode 100644 index eebc740..0000000 --- a/slides/201701_point.tex +++ /dev/null @@ -1,621 +0,0 @@ -%-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= -% -% 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} - - -