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Add comments to easily read code
[epclust.git]
/
epclust
/
R
/
clustering.R
diff --git
a/epclust/R/clustering.R
b/epclust/R/clustering.R
index
a4c273a
..
70d263e
100644
(file)
--- a/
epclust/R/clustering.R
+++ b/
epclust/R/clustering.R
@@
-1,6
+1,6
@@
#' @name clustering
#' @rdname clustering
#' @name clustering
#' @rdname clustering
-#' @aliases clusteringTask1 computeClusters1 computeClusters2
+#' @aliases clusteringTask1 c
lusteringTask2 c
omputeClusters1 computeClusters2
#'
#' @title Two-stage clustering, withing one task (see \code{claws()})
#'
#'
#' @title Two-stage clustering, withing one task (see \code{claws()})
#'
@@
-11,8
+11,8
@@
#' 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
#' 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
)
+#'
first clustering algorithm
on a contributions matrix, while the latter clusters
+#' a
set of series inside one task (~nb_items_clust
)
#'
#' @param indices Range of series indices to cluster in parallel (initial data)
#' @param getContribs Function to retrieve contributions from initial series indices:
#'
#' @param indices Range of series indices to cluster in parallel (initial data)
#' @param getContribs Function to retrieve contributions from initial series indices:
@@
-31,11
+31,23
@@
NULL
#' @rdname clustering
#' @export
clusteringTask1 = function(
#' @rdname clustering
#' @export
clusteringTask1 = function(
- indices, getContribs, K1, nb_series_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
+ indices, getContribs, K1, nb_per_chunk, nb_items_clust, ncores_clust=1,
+ verbose=FALSE, parll=TRUE)
{
if (verbose)
cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep=""))
{
if (verbose)
cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep=""))
+
+
+
+
+
+##TODO: reviser le spreadIndices, tenant compte de nb_items_clust
+
+ ##TODO: reviser / harmoniser avec getContribs qui en récupère pt'et + pt'et - !!
+
+
+
if (parll)
{
cl = parallel::makeCluster(ncores_clust)
if (parll)
{
cl = parallel::makeCluster(ncores_clust)
@@
-87,7
+99,7
@@
computeClusters1 = function(contribs, K1, verbose=FALSE)
{
if (verbose)
cat(paste(" computeClusters1() on ",nrow(contribs)," lines\n", sep=""))
{
if (verbose)
cat(paste(" computeClusters1() on ",nrow(contribs)," lines\n", sep=""))
- cluster::pam(
contribs
, K1, diss=FALSE)$id.med
+ cluster::pam(
t(contribs)
, K1, diss=FALSE)$id.med
}
#' @rdname clustering
}
#' @rdname clustering
@@
-96,7
+108,7
@@
computeClusters2 = function(distances, K2, verbose=FALSE)
{
if (verbose)
cat(paste(" computeClusters2() on ",nrow(distances)," lines\n", sep=""))
{
if (verbose)
cat(paste(" computeClusters2() on ",nrow(distances)," lines\n", sep=""))
- cluster::pam(
distances
, K2, diss=TRUE)$id.med
+ cluster::pam(
distances
, K2, diss=TRUE)$id.med
}
#' computeSynchrones
}
#' computeSynchrones
@@
-110,7
+122,7
@@
computeClusters2 = function(distances, K2, verbose=FALSE)
#' @param nb_ref_curves How many reference series? (This number is known at this stage)
#' @inheritParams claws
#'
#' @param nb_ref_curves How many reference series? (This number is known at this stage)
#' @inheritParams claws
#'
-#' @return A big.matrix of size
K1 x L where L = data_length
+#' @return A big.matrix of size
L x K1 where L = length of a serie
#'
#' @export
computeSynchrones = function(medoids, getRefSeries,
#'
#' @export
computeSynchrones = function(medoids, getRefSeries,
@@
-142,8
+154,8
@@
computeSynchrones = function(medoids, getRefSeries,
{
if (parll)
synchronicity::lock(m)
{
if (parll)
synchronicity::lock(m)
- synchrones[
mi[i], ] = synchrones[ mi[i], ] + ref_series[i,
]
- counts[ mi[i] ] = counts[ mi[i] ] + 1 #TODO: remove counts?
+ synchrones[
, mi[i] ] = synchrones[, mi[i] ] + ref_series[,i
]
+ counts[ mi[i] ] = counts[ mi[i] ] + 1 #TODO: remove counts?
...or as arg?!
if (parll)
synchronicity::unlock(m)
}
if (parll)
synchronicity::unlock(m)
}
@@
-152,7
+164,7
@@
computeSynchrones = function(medoids, getRefSeries,
K = nrow(medoids) ; L = ncol(medoids)
# Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
# TODO: if size > RAM (not our case), use file-backed big.matrix
K = nrow(medoids) ; L = ncol(medoids)
# Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
# TODO: if size > RAM (not our case), use file-backed big.matrix
- synchrones = bigmemory::big.matrix(nrow=
K, ncol=L
, type="double", init=0.)
+ synchrones = bigmemory::big.matrix(nrow=
L, ncol=K
, type="double", init=0.)
counts = bigmemory::big.matrix(nrow=K, ncol=1, type="double", init=0)
# synchronicity is only for Linux & MacOS; on Windows: run sequentially
parll = (requireNamespace("synchronicity",quietly=TRUE)
counts = bigmemory::big.matrix(nrow=K, ncol=1, type="double", init=0)
# synchronicity is only for Linux & MacOS; on Windows: run sequentially
parll = (requireNamespace("synchronicity",quietly=TRUE)
@@
-181,14
+193,14
@@
computeSynchrones = function(medoids, getRefSeries,
#TODO: can we avoid this loop? ( synchrones = sweep(synchrones, 1, counts, '/') )
for (i in seq_len(K))
#TODO: can we avoid this loop? ( synchrones = sweep(synchrones, 1, counts, '/') )
for (i in seq_len(K))
- synchrones[
i,] = synchrones[i,] / counts[i,1
]
+ synchrones[
,i] = synchrones[,i] / counts[i
]
#NOTE: odds for some clusters to be empty? (when series already come from stage 2)
# ...maybe; but let's hope resulting K1' be still quite bigger than K2
#NOTE: odds for some clusters to be empty? (when series already come from stage 2)
# ...maybe; but let's hope resulting K1' be still quite bigger than K2
- noNA_rows = sapply(seq_len(K), function(i) all(!is.nan(synchrones[
i,
])))
+ noNA_rows = sapply(seq_len(K), function(i) all(!is.nan(synchrones[
,i
])))
if (all(noNA_rows))
return (synchrones)
# Else: some clusters are empty, need to slice synchrones
if (all(noNA_rows))
return (synchrones)
# Else: some clusters are empty, need to slice synchrones
- synchrones[noNA_rows,]
+ bigmemory::as.big.matrix(synchrones[,noNA_rows])
}
#' computeWerDists
}
#' computeWerDists
@@
-272,7
+284,7
@@
computeWerDists = function(synchrones, nbytes,endian,ncores_clust=1,verbose=FALS
{
#from cwt_file ...
res <- getDataInFile(c(2*index-1,2*index), cwt_file, nbytes, endian)
{
#from cwt_file ...
res <- getDataInFile(c(2*index-1,2*index), cwt_file, nbytes, endian)
- ###############TODO:
+ ###############TODO:
}
# Distance between rows i and j
}
# Distance between rows i and j