#' }
#' @param K1 Number of clusters to be found after stage 1 (K1 << N [number of series])
#' @param K2 Number of clusters to be found after stage 2 (K2 << K1)
-#' @param nb_per_chunk (Maximum) number of items to retrieve in one batch, for both types of
-#' retrieval: resp. series and contribution; in a vector of size 2
+#' @param nb_series_per_chunk (Maximum) number of series to retrieve in one batch
#' @param algo_clust1 Clustering algorithm for stage 1. A function which takes (data, K)
#' as argument where data is a matrix in columns and K the desired number of clusters,
#' and outputs K medoids ranks. Default: PAM
#' @param algo_clust2 Clustering algorithm for stage 2. A function which takes (dists, K)
#' as argument where dists is a matrix of distances and K the desired number of clusters,
#' and outputs K clusters representatives (curves). Default: k-means
-#' @param nb_items_clust1 (Maximum) number of items in input of the clustering algorithm
-#' for stage 1
+#' @param nb_items_clust1 (~Maximum) number of items in input of the clustering algorithm
+#' for stage 1. At worst, a clustering algorithm might be called with ~2*nb_items_clust1
+#' items; but this could only happen at the last few iterations.
#' @param wav_filt Wavelet transform filter; see ?wavelets::wt.filter
#' @param contrib_type Type of contribution: "relative", "logit" or "absolute" (any prefix)
#' @param WER "end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply
#' series = do.call( cbind, lapply( 1:6, function(i)
#' do.call(cbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) )
#' #dim(series) #c(2400,10001)
-#' medoids_ascii = claws(series, K1=60, K2=6, nb_per_chunk=c(200,500), verbose=TRUE)
+#' medoids_ascii = claws(series, K1=60, K2=6, 200, verbose=TRUE)
#'
#' # Same example, from CSV file
#' csv_file = "/tmp/epclust_series.csv"
#' write.table(series, csv_file, sep=",", row.names=FALSE, col.names=FALSE)
-#' medoids_csv = claws(csv_file, K1=60, K2=6, nb_per_chunk=c(200,500))
+#' medoids_csv = claws(csv_file, K1=60, K2=6, 200)
#'
#' # Same example, from binary file
#' bin_file <- "/tmp/epclust_series.bin"
#' endian <- "little"
#' binarize(csv_file, bin_file, 500, nbytes, endian)
#' getSeries <- function(indices) getDataInFile(indices, bin_file, nbytes, endian)
-#' medoids_bin <- claws(getSeries, K1=60, K2=6, nb_per_chunk=c(200,500))
+#' medoids_bin <- claws(getSeries, K1=60, K2=6, 200)
#' unlink(csv_file)
#' unlink(bin_file)
#'
#' df_series <- dbGetQuery(series_db, request)
#' as.matrix(df_series[,"value"], nrow=serie_length)
#' }
-#' medoids_db = claws(getSeries, K1=60, K2=6, nb_per_chunk=c(200,500))
+#' medoids_db = claws(getSeries, K1=60, K2=6, 200))
#' dbDisconnect(series_db)
#'
#' # All computed medoids should be the same:
#' digest::sha1(medoids_db)
#' }
#' @export
-claws <- function(getSeries, K1, K2, nb_per_chunk,
+claws <- function(getSeries, K1, K2, nb_series_per_chunk,
nb_items_clust1=7*K1,
algo_clust1=function(data,K) cluster::pam(data,K,diss=FALSE),
algo_clust2=function(dists,K) stats::kmeans(dists,K,iter.max=50,nstart=3),
- wav_filt="d8",contrib_type="absolute",
+ wav_filt="d8", contrib_type="absolute",
WER="end",
random=TRUE,
ntasks=1, ncores_tasks=1, ncores_clust=4,
}
K1 <- .toInteger(K1, function(x) x>=2)
K2 <- .toInteger(K2, function(x) x>=2)
- if (!is.numeric(nb_per_chunk) || length(nb_per_chunk)!=2)
- stop("'nb_per_chunk': numeric, size 2")
- nb_per_chunk[1] <- .toInteger(nb_per_chunk[1], function(x) x>=1)
- # A batch of contributions should have at least as many elements as a batch of series,
- # because it always contains much less values
- nb_per_chunk[2] <- max(.toInteger(nb_per_chunk[2],function(x) x>=1), nb_per_chunk[1])
+ nb_series_per_chunk <- .toInteger(nb_series_per_chunk, function(x) x>=1)
+ # K1 (number of clusters at step 1) cannot exceed nb_series_per_chunk, because we will need
+ # to load K1 series in memory for clustering stage 2.
+ if (K1 > nb_series_per_chunk)
+ stop("'K1' cannot exceed 'nb_series_per_chunk'")
nb_items_clust1 <- .toInteger(nb_items_clust1, function(x) x>K1)
random <- .toLogical(random)
tryCatch
# under Linux. All necessary variables are passed to the workers.
cl = parallel::makeCluster(ncores_tasks, outfile="")
varlist = c("getSeries","getContribs","K1","K2","algo_clust1","algo_clust2",
- "nb_per_chunk","nb_items_clust","ncores_clust","sep","nbytes","endian",
- "verbose","parll")
+ "nb_series_per_chunk","nb_items_clust","ncores_clust","sep",
+ "nbytes","endian","verbose","parll")
if (WER=="mix")
varlist = c(varlist, "medoids_file")
parallel::clusterExport(cl, varlist, envir = environment())