# Cluster one full task (nb_curves / ntasks series)
clusteringTask = function(indices,getSeries,getSeriesForSynchrones,synchrones_file,
- getCoefs,K1,K2,nb_series_per_chunk,ncores,to_file)
+ getCoefs,K1,K2,nb_series_per_chunk,ncores,to_file,ftype)
{
cl = parallel::makeCluster(ncores)
repeat
parallel::stopCluster(cl)
if (K2 == 0)
return (indices)
- computeClusters2(indices, K2, getSeries, getSeriesForSynchrones, to_file)
+ computeClusters2(indices, K2, getSeries, getSeriesForSynchrones, to_file,
+ nb_series_per_chunk,ftype)
vector("integer",0)
}
}
# Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-computeClusters2 = function(indices, K2, getSeries, getSeriesForSynchrones, to_file)
+computeClusters2 = function(indices, K2, getSeries, getSeriesForSynchrones, to_file,
+ nb_series_per_chunk, ftype)
{
- curves = computeSynchrones(indices, getSeries, getSeriesForSynchrones)
+ curves = computeSynchrones(indices, getSeries, getSeriesForSynchrones, nb_series_per_chunk)
dists = computeWerDists(curves)
medoids = cluster::pam(dists, K2, diss=TRUE)$medoids
if (to_file)
{
- serialize(medoids, synchrones_file)
+ serialize(medoids, synchrones_file, ftype, nb_series_per_chunk)
return (NULL)
}
medoids
}
# Compute the synchrones curves (sum of clusters elements) from a clustering result
-computeSynchrones = function(indices, getSeries, getSeriesForSynchrones)
+computeSynchrones = function(indices, getSeries, getSeriesForSynchrones, nb_series_per_chunk)
{
#les getSeries(indices) sont les medoides --> init vect nul pour chacun, puis incr avec les
#courbes (getSeriesForSynchrones) les plus proches... --> au sens de la norme L2 ?
- series = getSeries(indices)
- #...........
- #sapply(seq_along(inds), colMeans(getSeries(inds[[i]]$indices,inds[[i]]$ids)))
+ medoids = getSeries(indices)
+ K = nrow(medoids)
+ synchrones = matrix(0, nrow=K, ncol=ncol(medoids))
+ counts = rep(0,K)
+ index = 1
+ repeat
+ {
+ series = getSeriesForSynchrones((index-1)+seq_len(nb_series_per_chunk))
+ if (is.null(series))
+ break
+ #get medoids indices for this chunk of series
+ index = which.min( rowSums( sweep(medoids, 2, series[i,], '-')^2 ) )
+ synchrones[index,] = synchrones[index,] + series[i,]
+ counts[index] = counts[index] + 1
+ }
+ #NOTE: odds for some clusters to be empty? (when series already come from stage 2)
+ synchrones = sweep(synchrones, 1, counts, '/')
}
# Compute the WER distance between the synchrones curves (in rows)