# x <- x / sqrt(rowSums(x^2))
# x %*% t(x)
#}
+
+#TODO: soften condition clustering.R line 37 ?
+#regarder mapply et mcmapply pour le // (pas OK pour Windows ou GUI... mais ?)
+#TODO: map-reduce more appropriate R/clustering.R ligne 88
+#Alternative: use bigmemory to share series when CSV or matrix(...)
+
+#' @importFrom synchronicity boost.mutex lock unlock
parallel,
cluster,
wavelets,
+ bigmemory,
Rwave
Suggests:
+ synchronicity,
devtools,
testthat,
MASS,
#' @importFrom stats filter spline
#' @importFrom utils tail
#' @importFrom methods is
+#' @importFrom bigmemory as.big.matrix is.big.matrix
NULL
-
#' @name clustering
#' @rdname clustering
-#' @aliases clusteringTask computeClusters1 computeClusters2
+#' @aliases clusteringTask1 computeClusters1 computeClusters2
#'
-#' @title Two-stages clustering, withing one task (see \code{claws()})
+#' @title Two-stage clustering, withing one task (see \code{claws()})
#'
-#' @description \code{clusteringTask()} runs one full task, which consists in iterated stage 1
-#' clustering (on nb_curves / ntasks energy contributions, computed through discrete
-#' wavelets coefficients). \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)
+#' @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{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)
#'
#' @param indices Range of series indices to cluster in parallel (initial data)
#' @param getContribs Function to retrieve contributions from initial series indices:
#' @inheritParams computeSynchrones
#' @inheritParams claws
#'
-#' @return For \code{clusteringTask()} and \code{computeClusters1()}, the indices of the
+#' @return 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 matrix of medoids
#' (of size limited by nb_series_per_chunk)
#' @rdname clustering
#' @export
-clusteringTask = function(indices, getContribs, K1, nb_series_per_chunk, ncores_clust)
+clusteringTask1 = function(
+ indices, getContribs, K1, nb_series_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
{
+ if (verbose)
+ cat(paste("*** Clustering task on ",length(indices)," lines\n", sep=""))
-#NOTE: comment out parallel sections for debugging
-#propagate verbose arg ?!
+ wrapComputeClusters1 = function(inds) {
+ if (parll)
+ require("epclust", quietly=TRUE)
+ if (verbose)
+ cat(paste(" computeClusters1() on ",length(inds)," lines\n", sep=""))
+ inds[ computeClusters1(getContribs(inds), K1) ]
+ }
-# cl = parallel::makeCluster(ncores_clust)
-# parallel::clusterExport(cl, varlist=c("getContribs","K1"), envir=environment())
- repeat
+ if (parll)
{
-
-print(length(indices))
-
- nb_workers = max( 1, floor( length(indices) / nb_series_per_chunk ) )
- indices_workers = lapply( seq_len(nb_workers), function(i)
- indices[(nb_series_per_chunk*(i-1)+1):(nb_series_per_chunk*i)] )
- # Spread the remaining load among the workers
- rem = length(indices) %% nb_series_per_chunk
- while (rem > 0)
- {
- index = rem%%nb_workers + 1
- indices_workers[[index]] = c(indices_workers[[index]], tail(indices,rem))
- rem = rem - 1
- }
-# indices = unlist( parallel::parLapply( cl, indices_workers, function(inds) {
- indices = unlist( lapply( indices_workers, function(inds) {
-# require("epclust", quietly=TRUE)
-
-print(paste(" ",length(inds))) ## PROBLEME ICI : 21104 ??!
-
- inds[ computeClusters1(getContribs(inds), K1) ]
- } ) )
- if (length(indices) == K1)
- break
+ cl = parallel::makeCluster(ncores_clust)
+ parallel::clusterExport(cl, varlist=c("getContribs","K1","verbose"), envir=environment())
}
-# parallel::stopCluster(cl)
+ while (length(indices) > K1)
+ {
+ indices_workers = .spreadIndices(indices, nb_series_per_chunk)
+ if (parll)
+ indices = unlist( parallel::parLapply(cl, indices_workers, wrapComputeClusters1) )
+ else
+ indices = unlist( lapply(indices_workers, wrapComputeClusters1) )
+ }
+ if (parll)
+ parallel::stopCluster(cl)
+
indices #medoids
}
#' @rdname clustering
#' @export
-computeClusters2 = function(medoids, K2, getRefSeries, nb_series_per_chunk)
+computeClusters2 = function(medoids, K2,
+ getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
- synchrones = computeSynchrones(medoids, getRefSeries, nb_series_per_chunk)
- medoids[ cluster::pam(computeWerDists(synchrones), K2, diss=TRUE)$medoids , ]
+ synchrones = computeSynchrones(medoids,
+ getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust, verbose, parll)
+ distances = computeWerDists(synchrones, ncores_clust, verbose, parll)
+ medoids[ cluster::pam(distances, K2, diss=TRUE)$medoids , ]
}
#' computeSynchrones
#' @param medoids Matrix of medoids (curves of same legnth as initial series)
#' @param getRefSeries Function to retrieve initial series (e.g. in stage 2 after series
#' have been replaced by stage-1 medoids)
+#' @param nb_ref_curves How many reference series? (This number is known at this stage)
#' @inheritParams claws
#'
#' @export
-computeSynchrones = function(medoids, getRefSeries, nb_series_per_chunk)
+computeSynchrones = function(medoids, getRefSeries,
+ nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
- K = nrow(medoids)
- synchrones = matrix(0, nrow=K, ncol=ncol(medoids))
- counts = rep(0,K)
- index = 1
- repeat
+ computeSynchronesChunk = function(indices)
{
- range = (index-1) + seq_len(nb_series_per_chunk)
- ref_series = getRefSeries(range)
- if (is.null(ref_series))
- break
+ ref_series = getRefSeries(indices)
#get medoids indices for this chunk of series
for (i in seq_len(nrow(ref_series)))
{
j = which.min( rowSums( sweep(medoids, 2, ref_series[i,], '-')^2 ) )
+ if (parll)
+ synchronicity::lock(m)
synchrones[j,] = synchrones[j,] + ref_series[i,]
- counts[j] = counts[j] + 1
+ counts[j,1] = counts[j,1] + 1
+ if (parll)
+ synchronicity::unlock(m)
+ }
+ }
+
+ K = nrow(medoids)
+ # Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
+ synchrones = bigmemory::big.matrix(nrow=K,ncol=ncol(medoids),type="double",init=0.)
+ counts = bigmemory::big.matrix(nrow=K,ncol=1,type="double",init=0)
+ # Fork (// run) only on Linux & MacOS; on Windows: run sequentially
+ parll = (requireNamespace("synchronicity",quietly=TRUE)
+ && parll && Sys.info()['sysname'] != "Windows")
+ if (parll)
+ m <- synchronicity::boost.mutex()
+
+ indices_workers = .spreadIndices(seq_len(nb_ref_curves), nb_series_per_chunk)
+ for (inds in indices_workers)
+ {
+ if (verbose)
+ {
+ cat(paste("--- Compute synchrones for indices range ",
+ min(inds)," -> ",max(inds),"\n", sep=""))
}
- index = index + nb_series_per_chunk
+ if (parll)
+ ignored <- parallel::mcparallel(computeSynchronesChunk(inds))
+ else
+ computeSynchronesChunk(inds)
+ }
+ if (parll)
+ parallel::mccollect()
+
+ mat_syncs = matrix(nrow=K, ncol=ncol(medoids))
+ vec_count = rep(NA, K)
+ #TODO: can we avoid this loop?
+ for (i in seq_len(K))
+ {
+ mat_syncs[i,] = synchrones[i,]
+ vec_count[i] = counts[i,1]
}
#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
- synchrones = sweep(synchrones, 1, counts, '/')
- synchrones[ sapply(seq_len(K), function(i) all(!is.nan(synchrones[i,]))) , ]
+ mat_syncs = sweep(mat_syncs, 1, vec_count, '/')
+ mat_syncs[ sapply(seq_len(K), function(i) all(!is.nan(mat_syncs[i,]))) , ]
}
#' computeWerDists
#' returned (e.g.) by \code{computeSynchrones()}
#'
#' @param synchrones A matrix of synchrones, in rows. The series have same length as the
-#' series in the initial dataset
+#' series in the initial dataset
+#' @inheritParams claws
#'
#' @export
-computeWerDists = function(synchrones)
+computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
n <- nrow(synchrones)
delta <- ncol(synchrones)
s0log = as.integer( (log2( s0*w0/(2*pi) ) - 1) * nvoice + 1.5 )
totnoct = noctave + as.integer(s0log/nvoice) + 1
- # (normalized) observations node with CWT
- Xcwt4 <- lapply(seq_len(n), function(i) {
+ computeCWT = function(i)
+ {
+ if (verbose)
+ cat(paste("+++ Compute Rwave::cwt() on serie ",i,"\n", sep=""))
ts <- scale(ts(synchrones[i,]), center=TRUE, scale=scaled)
totts.cwt = Rwave::cwt(ts,totnoct,nvoice,w0,plot=0)
ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)]
sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0)
sqres <- sweep(ts.cwt,MARGIN=2,sqs,'*')
sqres / max(Mod(sqres))
- })
+ }
- Xwer_dist <- matrix(0., n, n)
+ if (parll)
+ {
+ cl = parallel::makeCluster(ncores_clust)
+ parallel::clusterExport(cl, varlist=c("getContribs","K1","verbose"), envir=environment())
+ }
+
+ # (normalized) observations node with CWT
+ Xcwt4 <-
+ if (parll)
+ parallel::parLapply(cl, seq_len(n), computeCWT)
+ else
+ lapply(seq_len(n), computeCWT)
+
+ if (parll)
+ parallel::stopCluster(cl)
+
+ Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
fcoefs = rep(1/3, 3) #moving average on 3 values (TODO: very slow! correct?!)
- for (i in 1:(n-1))
+ if (verbose)
+ cat("*** Compute WER distances from CWT\n")
+
+ computeDistancesLineI = function(i)
{
+ if (verbose)
+ cat(paste(" Line ",i,"\n", sep=""))
for (j in (i+1):n)
{
- #TODO: later, compute CWT here (because not enough storage space for 200k series)
- # 'circular=TRUE' is wrong, should just take values on the sides; to rewrite in C
+ #TODO: 'circular=TRUE' is wrong, should just take values on the sides; to rewrite in C
num <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE)
WX <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[i]])), fcoefs, circular=TRUE)
WY <- filter(Mod(Xcwt4[[j]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE)
wer2 <- sum(colSums(num)^2) / sum( sum(colSums(WX) * colSums(WY)) )
+ if (parll)
+ synchronicity::lock(m)
Xwer_dist[i,j] <- sqrt(delta * ncol(Xcwt4[[1]]) * (1 - wer2))
Xwer_dist[j,i] <- Xwer_dist[i,j]
+ if (parll)
+ synchronicity::unlock(m)
+ }
+ Xwer_dist[i,i] = 0.
+ }
+
+ parll = (requireNamespace("synchronicity",quietly=TRUE)
+ && parll && Sys.info()['sysname'] != "Windows")
+ if (parll)
+ m <- synchronicity::boost.mutex()
+
+ for (i in 1:(n-1))
+ {
+ if (parll)
+ ignored <- parallel::mcparallel(computeDistancesLineI(i))
+ else
+ computeDistancesLineI(i)
+ }
+ Xwer_dist[n,n] = 0.
+
+ if (parll)
+ parallel::mccollect()
+
+ mat_dists = matrix(nrow=n, ncol=n)
+ #TODO: avoid this loop?
+ for (i in 1:n)
+ mat_dists[i,] = Xwer_dist[i,]
+ mat_dists
+}
+
+# Helper function to divide indices into balanced sets
+.spreadIndices = function(indices, nb_per_chunk)
+{
+ L = length(indices)
+ nb_workers = floor( L / nb_per_chunk )
+ if (nb_workers == 0)
+ {
+ # L < nb_series_per_chunk, simple case
+ indices_workers = list(indices)
+ }
+ else
+ {
+ indices_workers = lapply( seq_len(nb_workers), function(i)
+ indices[(nb_per_chunk*(i-1)+1):(nb_per_chunk*i)] )
+ # Spread the remaining load among the workers
+ rem = L %% nb_per_chunk
+ while (rem > 0)
+ {
+ index = rem%%nb_workers + 1
+ indices_workers[[index]] = c(indices_workers[[index]], indices[L-rem+1])
+ rem = rem - 1
}
}
- diag(Xwer_dist) <- numeric(n)
- Xwer_dist
+ indices_workers
}
#' @name de_serialize
#' @rdname de_serialize
-#' @aliases binarize getDataInFile
+#' @aliases binarize binarizeTransform getDataInFile
#'
-#' @title (De)Serialization of a matrix
+#' @title (De)Serialization of a [big]matrix or data stream
#'
#' @description \code{binarize()} serializes a matrix or CSV file with minimal overhead,
#' into a binary file. \code{getDataInFile()} achieves the inverse task: she retrieves
-#' (ASCII) data rows from indices in the binary file
+#' (ASCII) data rows from indices in the binary file. Finally,
+#' \code{binarizeTransform()} serialize transformations of all data chunks; to use it,
+#' a data-retrieval function must be provided, thus \code{binarize} will most likely be
+#' used first (and then a function defined to seek in generated binary file)
#'
#' @param data_ascii Either a matrix or CSV file, with items in rows
#' @param indices Indices of the lines to retrieve
#' or intput (\code{getDataInFile})
#' @param nb_per_chunk Number of lines to process in one batch
#' @inheritParams claws
+#' @param getData Function to retrieve data chunks
+#' @param transform Transformation function to apply on data chunks
#'
#' @return For \code{getDataInFile()}, the matrix with rows corresponding to the
-#' requested indices
+#' requested indices. \code{binarizeTransform} returns the number of processed lines.
+#' \code{binarize} is designed to serialize in several calls, thus returns nothing.
NULL
#' @rdname de_serialize
data_ascii = file(data_ascii, open="r")
else if (methods::is(data_ascii,"connection") && !isOpen(data_ascii))
open(data_ascii)
+ is_matrix = !methods::is(data_ascii,"connection")
first_write = (!file.exists(data_bin_file) || file.info(data_bin_file)$size == 0)
data_bin = file(data_bin_file, open=ifelse(first_write,"wb","ab"))
{
#number of items always on 8 bytes
writeBin(0L, data_bin, size=8, endian=endian)
- if (is.matrix(data_ascii))
+ if ( is_matrix )
data_length = ncol(data_ascii)
- else #if (is(data, "connection"))
+ else #connection
{
data_line = scan(data_ascii, double(), sep=sep, nlines=1, quiet=TRUE)
writeBin(data_line, data_bin, size=nbytes, endian=endian)
}
}
- if (is.matrix(data_ascii))
+ if (is_matrix)
index = 1
repeat
{
- if (is.matrix(data_ascii))
+ if ( is_matrix )
{
- range = index:min(nrow(data_ascii),index+nb_per_chunk)
data_chunk =
- if (range[1] <= nrow(data_ascii))
- as.double(t(data_ascii[range,]))
+ if (index <= nrow(data_ascii))
+ as.double(t(data_ascii[index:min(nrow(data_ascii),index+nb_per_chunk-1),]))
else
- integer(0)
+ double(0)
index = index + nb_per_chunk
}
else
if (first_write)
{
- #ecrire file_size-1 / (nbytes*nbWritten) en 0 dans bin_data ! ignored == file_size
+ # Write data_length, = (file_size-1) / (nbytes*nbWritten) at offset 0 in data_bin
ignored = seek(data_bin, 0)
writeBin(data_length, data_bin, size=8, endian=endian)
}
close(data_bin)
- if (methods::is(data_ascii,"connection"))
+ if ( ! is_matrix )
close(data_ascii)
}
+#' @rdname de_serialize
+#' @export
+binarizeTransform = function(getData, transform, data_bin_file, nb_per_chunk,
+ nbytes=4, endian=.Platform$endian)
+{
+ nb_items = 0
+ index = 1
+ repeat
+ {
+ data_chunk = getData((index-1)+seq_len(nb_per_chunk))
+ if (is.null(data_chunk))
+ break
+ transformed_chunk = transform(data_chunk)
+ binarize(transformed_chunk, data_bin_file, nb_per_chunk, ",", nbytes, endian)
+ index = index + nb_per_chunk
+ nb_items = nb_items + nrow(data_chunk)
+ }
+ nb_items
+}
+
#' @rdname de_serialize
#' @export
getDataInFile = function(indices, data_bin_file, nbytes=4, endian=.Platform$endian)
#' @param nbytes Number of bytes to serialize a floating-point number; 4 or 8
#' @param endian Endianness to use for (de)serialization. Use "little" or "big" for portability
#' @param verbose Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage)
+#' @param parll TRUE to fully parallelize; otherwise run sequentially (debug, comparison)
#'
#' @return A matrix of the final medoids curves (K2) in rows
#'
nb_series_per_chunk=50*K1, min_series_per_chunk=5*K1, #chunk size
sep=",", #ASCII input separator
nbytes=4, endian=.Platform$endian, #serialization (write,read)
- verbose=FALSE)
+ verbose=FALSE, parll=TRUE)
{
# Check/transform arguments
- if (!is.matrix(getSeries) && !is.function(getSeries) &&
- !methods::is(getSeries, "connection" && !is.character(getSeries)))
+ if (!is.matrix(getSeries) && !bigmemory::is.big.matrix(getSeries)
+ && !is.function(getSeries)
+ && !methods::is(getSeries,"connection") && !is.character(getSeries))
{
- stop("'getSeries': matrix, function, file or valid connection (no NA)")
+ stop("'getSeries': [big]matrix, function, file or valid connection (no NA)")
}
K1 = .toInteger(K1, function(x) x>=2)
K2 = .toInteger(K2, function(x) x>=2)
nb_curves = 0
if (verbose)
cat("...Compute contributions and serialize them\n")
- repeat
- {
- series = getSeries((index-1)+seq_len(nb_series_per_chunk))
- if (is.null(series))
- break
- contribs_chunk = curvesToContribs(series, wf, ctype)
- binarize(contribs_chunk, contribs_file, nb_series_per_chunk, sep, nbytes, endian)
- index = index + nb_series_per_chunk
- nb_curves = nb_curves + nrow(contribs_chunk)
- }
+ nb_curves = binarizeTransform(getSeries,
+ function(series) curvesToContribs(series, wf, ctype),
+ contribs_file, nb_series_per_chunk, nbytes, endian)
getContribs = function(indices) getDataInFile(indices, contribs_file, nbytes, endian)
if (nb_curves < min_series_per_chunk)
})
if (verbose)
cat(paste("...Run ",ntasks," x stage 1 in parallel\n",sep=""))
-# cl = parallel::makeCluster(ncores_tasks)
-# parallel::clusterExport(cl, varlist=c("getSeries","getContribs","K1","K2",
-# "nb_series_per_chunk","ncores_clust","synchrones_file","sep","nbytes","endian"),
-# envir = environment())
- # 1000*K1 indices [if WER=="end"], or empty vector [if WER=="mix"] --> series on file
-# indices = unlist( parallel::parLapply(cl, indices_tasks, function(inds) {
- indices = unlist( lapply(indices_tasks, function(inds) {
-# require("epclust", quietly=TRUE)
-
- browser() #TODO: CONTINUE DEBUG HERE
+ if (parll)
+ {
+ cl = parallel::makeCluster(ncores_tasks)
+ parallel::clusterExport(cl, varlist=c("getSeries","getContribs","K1","K2","verbose","parll",
+ "nb_series_per_chunk","ncores_clust","synchrones_file","sep","nbytes","endian"),
+ envir = environment())
+ }
- indices_medoids = clusteringTask(inds,getContribs,K1,nb_series_per_chunk,ncores_clust)
+ runTwoStepClustering = function(inds)
+ {
+ if (parll)
+ require("epclust", quietly=TRUE)
+ indices_medoids = clusteringTask1(
+ inds, getContribs, K1, nb_series_per_chunk, ncores_clust, verbose, parll)
if (WER=="mix")
{
- medoids2 = computeClusters2(
- getSeries(indices_medoids), K2, getSeries, nb_series_per_chunk)
+ medoids2 = computeClusters2(getSeries(indices_medoids),
+ K2, getSeries, nb_curves, nb_series_per_chunk, ncores_clust, verbose, parll)
binarize(medoids2, synchrones_file, nb_series_per_chunk, sep, nbytes, endian)
return (vector("integer",0))
}
indices_medoids
- }) )
-# parallel::stopCluster(cl)
+ }
+
+ # 1000*K1 indices [if WER=="end"], or empty vector [if WER=="mix"] --> series on file
+ if (parll)
+ indices = unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) )
+ else
+ indices = unlist( lapply(indices_tasks, runTwoStepClustering) )
+ if (parll)
+ parallel::stopCluster(cl)
getRefSeries = getSeries
synchrones_file = paste(bin_dir,"synchrones",sep="") ; unlink(synchrones_file)
index = 1
if (verbose)
cat("...Serialize contributions computed on synchrones\n")
- repeat
- {
- series = getSeries((index-1)+seq_len(nb_series_per_chunk))
- if (is.null(series))
- break
- contribs_chunk = curvesToContribs(series, wf, ctype)
- binarize(contribs_chunk, contribs_file, nb_series_per_chunk, sep, nbytes, endian)
- index = index + nb_series_per_chunk
- }
+ ignored = binarizeTransform(getSeries,
+ function(series) curvesToContribs(series, wf, ctype),
+ contribs_file, nb_series_per_chunk, nbytes, endian)
}
# Run step2 on resulting indices or series (from file)
if (verbose)
cat("...Run final // stage 1 + stage 2\n")
- indices_medoids = clusteringTask(
- indices, getContribs, K1, nb_series_per_chunk, ncores_tasks*ncores_clust)
- medoids = computeClusters2(getSeries(indices_medoids),K2,getRefSeries,nb_series_per_chunk)
+ indices_medoids = clusteringTask1(
+ indices, getContribs, K1, nb_series_per_chunk, ncores_tasks*ncores_clust, verbose)
+ medoids = computeClusters2(getSeries(indices_medoids),
+ K2, getRefSeries, nb_curves, nb_series_per_chunk, ncores_tasks*ncores_clust, verbose)
# Cleanup
unlink(bin_dir, recursive=TRUE)
}) )
}
-# Helper for main function: check integer arguments with functiional conditions
+# Check integer arguments with functional conditions
.toInteger <- function(x, condition)
{
if (!is.integer(x))
for (i in seq_len(n))
series[i,] = s[[I(i,K)]] + rnorm(L,sd=0.01)
getRefSeries = function(indices) {
- indices = indices[indices < n]
+ indices = indices[indices <= n]
if (length(indices)>0) series[indices,] else NULL
}
- synchrones = computeSynchrones(rbind(s1,s2,s3), getRefSeries, 100)
+ synchrones = computeSynchrones(rbind(s1,s2,s3), getRefSeries, n, 100,
+ verbose=TRUE, parll=FALSE)
expect_equal(dim(synchrones), c(K,L))
for (i in 1:K)
for (i in seq_len(n))
series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
getRefSeries = function(indices) {
- indices = indices[indices < n]
+ indices = indices[indices <= n]
if (length(indices)>0) series[indices,] else NULL
}
# Artificially simulate 60 medoids - perfect situation, all equal to one of the refs
medoids_K1 = do.call(rbind, lapply( 1:K1, function(i) s[[I(i,K1)]] ) )
- medoids_K2 = computeClusters2(medoids_K1, K2, getRefSeries, 75)
+ medoids_K2 = computeClusters2(medoids_K1, K2, getRefSeries, n, 75,
+ verbose=TRUE, parll=FALSE)
expect_equal(dim(medoids_K2), c(K2,L))
# Not easy to evaluate result: at least we expect it to be better than random selection of
# medoids within 1...K1 (among references)
-
distorGood = computeDistortion(series, medoids_K2)
for (i in 1:3)
expect_lte( distorGood, computeDistortion(series,medoids_K1[sample(1:K1, K2),]) )
})
-test_that("clusteringTask + computeClusters2 behave as expected",
+test_that("clusteringTask1 + computeClusters2 behave as expected",
{
n = 900
x = seq(0,9.5,0.1)
wf = "haar"
ctype = "absolute"
getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype)
- medoids_K1 = getSeries( clusteringTask(1:n, getContribs, K1, 75, 4) )
- medoids_K2 = computeClusters2(medoids_K1, K2, getSeries, 120)
+ medoids_K1 = getSeries( clusteringTask1(1:n, getContribs, K1, 75,
+ verbose=TRUE, parll=FALSE) )
+ medoids_K2 = computeClusters2(medoids_K1, K2, getSeries, n, 120,
+ verbose=TRUE, parll=FALSE)
expect_equal(dim(medoids_K1), c(K1,L))
expect_equal(dim(medoids_K2), c(K2,L))
unlink(data_bin_file)
})
+test_that("serialization + transform + getDataInFile retrieve original transforms",
+{
+ data_bin_file = "/tmp/epclust_test_t.bin"
+ unlink(data_bin_file)
+
+ #dataset 200 lignes / 30 columns
+ data_ascii = matrix(runif(200*30,-10,10),ncol=30)
+ nbytes = 8
+ endian = "little"
+
+ binarize(data_ascii, data_bin_file, 500, ",", nbytes, endian)
+ # Serialize transformation (just compute range) into a new binary file
+ trans_bin_file = "/tmp/epclust_test_t_trans.bin"
+ unlink(trans_bin_file)
+ getSeries = function(inds) getDataInFile(inds, data_bin_file, nbytes, endian)
+ binarizeTransform(getSeries, function(series) t(apply(series, 1, range)),
+ trans_bin_file, 250, nbytes, endian)
+ unlink(data_bin_file)
+ expect_equal(file.info(trans_bin_file)$size, 2*nrow(data_ascii)*nbytes+8)
+ for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200))
+ {
+ trans_lines = getDataInFile(indices, trans_bin_file, nbytes, endian)
+ expect_equal(trans_lines, t(apply(data_ascii[indices,],1,range)), tolerance=1e-6)
+ }
+ unlink(trans_bin_file)
+})
+
test_that("serialization + getDataInFile retrieve original data / from connection",
{
data_bin_file = "/tmp/epclust_test_c.bin"
projects / epclust.git / commitdiff