X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fmain.R;h=4fdc5ae899c61aa3347e0aba1a843c12a5ecd012;hb=8546023eccc529272cf3e50f8dad17eed9d7b047;hp=6d3c842d43dac77c81ad4095f16f88036ce60199;hpb=3fb6e823601002c44ffbf913e83c8d24cfa1e819;p=epclust.git diff --git a/epclust/R/main.R b/epclust/R/main.R index 6d3c842..4fdc5ae 100644 --- a/epclust/R/main.R +++ b/epclust/R/main.R @@ -67,7 +67,7 @@ #' @param ncores_tasks Number of parallel tasks ('1' == sequential tasks) #' @param ncores_clust Number of parallel clusterings in one task #' @param sep Separator in CSV input file (if any provided) -#' @param nbytes Number of bytes to serialize a floating-point number: 4 or 8 +#' @param nbytes 4 or 8 bytes to (de)serialize a floating-point number #' @param endian Endianness for (de)serialization: "little" or "big" #' @param verbose FALSE: nothing printed; TRUE: some execution traces #' @param parll TRUE: run in parallel. FALSE: run sequentially @@ -87,6 +87,7 @@ #' @examples #' \dontrun{ #' # WER distances computations are too long for CRAN (for now) +#' parll = FALSE #on this small example, sequential run is faster #' #' # Random series around cos(x,2x,3x)/sin(x,2x,3x) #' x <- seq(0,50,0.05) @@ -95,21 +96,25 @@ #' library(wmtsa) #' series <- do.call( cbind, lapply( 1:6, function(i) #' do.call(cbind, wmtsa::wavBootstrap(ref_series[,i], n.realization=40)) ) ) +#' # Mix series so that all groups are evenly spread +#' permut <- (0:239)%%6 * 40 + (0:239)%/%6 + 1 +#' series = series[,permut] #' #dim(series) #c(240,1001) -#' res_ascii <- claws(series, K1=30, K2=6, 100, verbose=TRUE) +#' res_ascii <- claws(series, K1=30, K2=6, nb_series_per_chunk=500, +#' nb_items_clust=100, random=FALSE, verbose=TRUE, parll=parll) #' #' # Same example, from CSV file #' csv_file <- tempfile(pattern="epclust_series.csv_") #' write.table(t(series), csv_file, sep=",", row.names=FALSE, col.names=FALSE) -#' res_csv <- claws(csv_file, K1=30, K2=6, 100) +#' res_csv <- claws(csv_file, 30, 6, 500, 100, random=FALSE, parll=parll) #' #' # Same example, from binary file #' bin_file <- tempfile(pattern="epclust_series.bin_") #' nbytes <- 8 #' endian <- "little" -#' binarize(csv_file, bin_file, 500, nbytes, endian) +#' binarize(csv_file, bin_file, 500, ",", nbytes, endian) #' getSeries <- function(indices) getDataInFile(indices, bin_file, nbytes, endian) -#' res_bin <- claws(getSeries, K1=30, K2=6, 100) +#' res_bin <- claws(getSeries, 30, 6, 500, 100, random=FALSE, parll=parll) #' unlink(csv_file) #' unlink(bin_file) #' @@ -117,40 +122,41 @@ #' library(DBI) #' series_db <- dbConnect(RSQLite::SQLite(), "file::memory:") #' # Prepare data.frame in DB-format -#' n <- nrow(series) -#' time_values <- data.frame( -#' id <- rep(1:n,each=L), -#' time <- rep( as.POSIXct(1800*(0:n),"GMT",origin="2001-01-01"), L ), -#' value <- as.double(t(series)) ) +#' n <- ncol(series) +#' times_values <- data.frame( +#' id = rep(1:n,each=L), +#' time = rep( as.POSIXct(1800*(1:L),"GMT",origin="2001-01-01"), n ), +#' value = as.double(series) ) #' dbWriteTable(series_db, "times_values", times_values) #' # Fill associative array, map index to identifier #' indexToID_inDB <- as.character( -#' dbGetQuery(series_db, 'SELECT DISTINCT id FROM time_values')[,"id"] ) +#' dbGetQuery(series_db, 'SELECT DISTINCT id FROM times_values')[,"id"] ) #' serie_length <- as.integer( dbGetQuery(series_db, -#' paste("SELECT COUNT * FROM time_values WHERE id == ",indexToID_inDB[1],sep="")) ) +#' paste("SELECT COUNT(*) FROM times_values WHERE id == ",indexToID_inDB[1],sep="")) ) #' getSeries <- function(indices) { +#' indices = indices[ indices <= length(indexToID_inDB) ] +#' if (length(indices) == 0) +#' return (NULL) #' request <- "SELECT id,value FROM times_values WHERE id in (" -#' for (i in indices) -#' request <- paste(request, indexToID_inDB[i], ",", sep="") +#' for (i in seq_along(indices)) { +#' request <- paste(request, indexToID_inDB[ indices[i] ], sep="") +#' if (i < length(indices)) +#' request <- paste(request, ",", sep="") +#' } #' request <- paste(request, ")", sep="") #' df_series <- dbGetQuery(series_db, request) -#' if (length(df_series) >= 1) -#' as.matrix(df_series[,"value"], nrow=serie_length) -#' else -#' NULL +#' matrix(df_series[,"value"], nrow=serie_length) #' } -#' res_db <- claws(getSeries, K1=30, K2=6, 100)) +#' res_db <- claws(getSeries, 30, 6, 500, 100, random=FALSE, parll=parll) #' dbDisconnect(series_db) #' -#' # All results should be the same: -#' library(digest) -#' digest::sha1(res_ascii) -#' digest::sha1(res_csv) -#' digest::sha1(res_bin) -#' digest::sha1(res_db) +#' # All results should be equal: +#' all(res_ascii$ranks == res_csv$ranks +#' & res_ascii$ranks == res_bin$ranks +#' & res_ascii$ranks == res_db$ranks) #' } #' @export -claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1, +claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=5*K1, algoClust1=function(data,K) cluster::pam(t(data),K,diss=FALSE,pamonce=1)$id.med, algoClust2=function(dists,K) cluster::pam(dists,K,diss=TRUE,pamonce=1)$id.med, wav_filt="d8", contrib_type="absolute", WER="end", smooth_lvl=3, nvoice=4, @@ -204,8 +210,6 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1, # Serialize all computed wavelets contributions into a file contribs_file <- ".contribs.epclust.bin" - index <- 1 - nb_curves <- 0 if (verbose) cat("...Compute contributions and serialize them (or retrieve past binary file)\n") if (!file.exists(contribs_file)) @@ -288,7 +292,7 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1, } # As explained above, we obtain after all runs ntasks*[K1 or K2] medoids indices, - # depending wether WER=="end" or "mix", respectively. + # depending whether WER=="end" or "mix", respectively. indices_medoids_all <- if (parll && ntasks>1) unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) ) @@ -305,18 +309,13 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1, # it's better to just re-use ncores_clust ncores_last_stage <- ncores_clust - - -#TODO: here, save all inputs to clusteringTask2 and compare :: must have differences... - - - # Run last clustering tasks to obtain only K2 medoids indices if (verbose) cat("...Run final // stage 1 + stage 2\n") indices_medoids <- clusteringTask1(indices_medoids_all, getContribs, K1, algoClust1, nb_items_clust, ncores_tasks*ncores_clust, verbose, parll) - indices_medoids <- clusteringTask2(indices_medoids, getContribs, K2, algoClust2, + + indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2, nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_last_stage,verbose,parll) # Compute synchrones, that is to say the cumulated power consumptions for each of the K2