X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=pkg%2Fman%2Fclustering.Rd;fp=pkg%2Fman%2Fclustering.Rd;h=1768cab8979dbf5eb36be9b2f15ffb173eac35dc;hp=0000000000000000000000000000000000000000;hb=e906736ea27105237e84c904dce6170353726292;hpb=57f337af19cd6251815bb1ff2d62f4c58e8b6078

diff --git a/pkg/man/clustering.Rd b/pkg/man/clustering.Rd
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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/clustering.R
+\name{clustering}
+\alias{clustering}
+\alias{clusteringTask1}
+\alias{computeClusters1}
+\alias{computeClusters2}
+\alias{clusteringTask1}
+\alias{clusteringTask2}
+\alias{computeClusters1}
+\alias{computeClusters2}
+\title{Two-stage clustering, withing one task (see \code{claws()})}
+\usage{
+clusteringTask1(indices, getContribs, K1, nb_series_per_chunk,
+  ncores_clust = 1, verbose = FALSE, parll = TRUE)
+
+clusteringTask2(medoids, K2, getRefSeries, nb_ref_curves, nb_series_per_chunk,
+  ncores_clust = 1, verbose = FALSE, parll = TRUE)
+
+computeClusters1(contribs, K1, verbose = FALSE)
+
+computeClusters2(distances, K2, verbose = FALSE)
+}
+\arguments{
+\item{indices}{Range of series indices to cluster in parallel (initial data)}
+
+\item{getContribs}{Function to retrieve contributions from initial series indices:
+\code{getContribs(indices)} outpus a contributions matrix}
+
+\item{K1}{Number of super-consumers to be found after stage 1 (K1 << N)}
+
+\item{nb_series_per_chunk}{(~Maximum) number of series in each group, inside a task}
+
+\item{ncores_clust}{"OpenMP" number of parallel clusterings in one task}
+
+\item{verbose}{Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage)}
+
+\item{parll}{TRUE to fully parallelize; otherwise run sequentially (debug, comparison)}
+
+\item{medoids}{big.matrix of medoids (curves of same length as initial series)}
+
+\item{K2}{Number of clusters to be found after stage 2 (K2 << K1)}
+
+\item{getRefSeries}{Function to retrieve initial series (e.g. in stage 2 after series
+have been replaced by stage-1 medoids)}
+
+\item{nb_ref_curves}{How many reference series? (This number is known at this stage)}
+
+\item{contribs}{matrix of contributions (e.g. output of \code{curvesToContribs()})}
+
+\item{distances}{matrix of K1 x K1 (WER) distances between synchrones}
+}
+\value{
+For \code{clusteringTask1()} and \code{computeClusters1()}, the indices of the
+  computed (K1) medoids. Indices are irrelevant for stage 2 clustering, thus
+  \code{computeClusters2()} outputs a big.matrix of medoids
+  (of size limited by nb_series_per_chunk)
+}
+\description{
+\code{clusteringTask1()} runs one full stage-1 task, which consists in
+  iterated stage 1 clustering (on nb_curves / ntasks energy contributions, computed
+  through discrete wavelets coefficients).
+  \code{clusteringTask2()} runs a full stage-2 task, which consists in synchrones
+  and then WER distances computations, before applying the clustering algorithm.
+  \code{computeClusters1()} and \code{computeClusters2()} correspond to the atomic
+  clustering procedures respectively for stage 1 and 2. The former applies the
+  clustering algorithm (PAM) on a contributions matrix, while the latter clusters
+  a chunk of series inside one task (~max nb_series_per_chunk)
+}