From: Benjamin Auder Date: Wed, 8 Mar 2017 20:50:53 +0000 (+0100) Subject: improvements X-Git-Url: https://git.auder.net/variants/current/css/doc/img/%7B%7B%20targetUrl%20%7D%7D?a=commitdiff_plain;h=e161499b97c782aadfc287c22b55f85724f86fae;p=epclust.git improvements --- diff --git a/.gitignore b/.gitignore index af4c22c..3a326ea 100644 --- a/.gitignore +++ b/.gitignore @@ -32,3 +32,7 @@ #ignore jupyter generated file (HTML vignette, and reports) *.ipynb.html + +#ignore object files +*.o +*.so diff --git a/epclust/DESCRIPTION b/epclust/DESCRIPTION index 304fdff..1f2b5ea 100644 --- a/epclust/DESCRIPTION +++ b/epclust/DESCRIPTION @@ -30,8 +30,9 @@ Suggests: DBI License: MIT + file LICENSE RoxygenNote: 6.0.1 -Collate: +Collate: 'main.R' 'clustering.R' 'de_serialize.R' 'a_NAMESPACE.R' + 'plot.R' diff --git a/epclust/R/a_NAMESPACE.R b/epclust/R/a_NAMESPACE.R index 89aa453..e9aa830 100644 --- a/epclust/R/a_NAMESPACE.R +++ b/epclust/R/a_NAMESPACE.R @@ -2,12 +2,13 @@ #' @include clustering.R #' @include main.R #' +#' @useDynLib epclust +#' #' @importFrom Rwave cwt #' @importFrom cluster pam #' @importFrom parallel makeCluster clusterExport parLapply stopCluster #' @importFrom wavelets dwt wt.filter -#' @importFrom stats filter spline -#' @importFrom utils tail +#' @importFrom stats spline #' @importFrom methods is -#' @importFrom bigmemory as.big.matrix is.big.matrix +#' @importFrom bigmemory big.matrix as.big.matrix is.big.matrix NULL diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R index cda7fbe..92adda2 100644 --- a/epclust/R/clustering.R +++ b/epclust/R/clustering.R @@ -33,15 +33,7 @@ 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="")) - - 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) ] - } + cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep="")) if (parll) { @@ -51,10 +43,20 @@ clusteringTask1 = function( 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) ) + indices <- + if (parll) + { + unlist( parallel::parLapply(cl, indices_workers, function(inds) { + require("epclust", quietly=TRUE) + inds[ computeClusters1(getContribs(inds), K1, verbose) ] + }) ) + } + else + { + unlist( lapply(indices_workers, function(inds) + inds[ computeClusters1(getContribs(inds), K1, verbose) ] + ) ) + } } if (parll) parallel::stopCluster(cl) @@ -67,27 +69,35 @@ clusteringTask1 = function( clusteringTask2 = function(medoids, K2, getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE) { + if (verbose) + cat(paste("*** Clustering task 2 on ",nrow(medoids)," lines\n", sep="")) + if (nrow(medoids) <= K2) return (medoids) synchrones = computeSynchrones(medoids, getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust, verbose, parll) distances = computeWerDists(synchrones, ncores_clust, verbose, parll) # PAM in package 'cluster' cannot take big.matrix in input: need to cast it - mat_dists = matrix(nrow=K1, ncol=K1) - for (i in seq_len(K1)) - mat_dists[i,] = distances[i,] - medoids[ computeClusters2(mat_dists,K2), ] + medoids[ computeClusters2(distances[,],K2,verbose), ] } #' @rdname clustering #' @export -computeClusters1 = function(contribs, K1) +computeClusters1 = function(contribs, K1, verbose=FALSE) +{ + if (verbose) + cat(paste(" computeClusters1() on ",nrow(contribs)," lines\n", sep="")) cluster::pam(contribs, K1, diss=FALSE)$id.med +} #' @rdname clustering #' @export -computeClusters2 = function(distances, K2) +computeClusters2 = function(distances, K2, verbose=FALSE) +{ + if (verbose) + cat(paste(" computeClusters2() on ",nrow(distances)," lines\n", sep="")) cluster::pam(distances, K2, diss=TRUE)$id.med +} #' computeSynchrones #' @@ -106,29 +116,41 @@ computeClusters2 = function(distances, K2) computeSynchrones = function(medoids, getRefSeries, nb_ref_curves, nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE) { + if (verbose) + cat(paste("--- Compute synchrones\n", sep="")) + computeSynchronesChunk = function(indices) { - if (verbose) - cat(paste("--- Compute synchrones for ",length(indices)," lines\n", sep="")) ref_series = getRefSeries(indices) + nb_series = nrow(ref_series) #get medoids indices for this chunk of series - for (i in seq_len(nrow(ref_series))) + + #TODO: debug this (address is OK but values are garbage: why?) +# mi = .Call("computeMedoidsIndices", medoids@address, ref_series, PACKAGE="epclust") + + #R-equivalent, requiring a matrix (thus potentially breaking "fit-in-memory" hope) + mat_meds = medoids[,] + mi = rep(NA,nb_series) + for (i in 1:nb_series) + mi[i] <- which.min( rowSums( sweep(mat_meds, 2, ref_series[i,], '-')^2 ) ) + rm(mat_meds); gc() + + for (i in seq_len(nb_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,1] = counts[j,1] + 1 + synchrones[mi[i],] = synchrones[mi[i],] + ref_series[i,] + counts[mi[i],1] = counts[mi[i],1] + 1 if (parll) synchronicity::unlock(m) } } - K = nrow(medoids) + 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=ncol(medoids),type="double",init=0.) - counts = bigmemory::big.matrix(nrow=K,ncol=1,type="double",init=0) + synchrones = bigmemory::big.matrix(nrow=K, ncol=L, 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) && parll && Sys.info()['sysname'] != "Windows") @@ -144,9 +166,10 @@ computeSynchrones = function(medoids, getRefSeries, } indices_workers = .spreadIndices(seq_len(nb_ref_curves), nb_series_per_chunk) + browser() ignored <- if (parll) - parallel::parLapply(indices_workers, computeSynchronesChunk) + parallel::parLapply(cl, indices_workers, computeSynchronesChunk) else lapply(indices_workers, computeSynchronesChunk) @@ -179,11 +202,8 @@ computeSynchrones = function(medoids, getRefSeries, #' @export computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE) { - - - -#TODO: re-organize to call computeWerDist(x,y) [C] (in //?) from two indices + big.matrix - + if (verbose) + cat(paste("--- Compute WER dists\n", sep="")) n <- nrow(synchrones) delta <- ncol(synchrones) @@ -201,10 +221,20 @@ computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE) s0log = as.integer( (log2( s0*w0/(2*pi) ) - 1) * nvoice + 1.5 ) totnoct = noctave + as.integer(s0log/nvoice) + 1 + Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double") + fcoefs = rep(1/3, 3) #moving average on 3 values + + # Generate n(n-1)/2 pairs for WER distances computations + pairs = list() + V = seq_len(n) + for (i in 1:n) + { + V = V[-1] + pairs = c(pairs, lapply(V, function(v) c(i,v))) + } + 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=FALSE) ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)] @@ -214,6 +244,22 @@ computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE) sqres / max(Mod(sqres)) } + computeDistancesIJ = function(pair) + { + i = pair[1] ; j = pair[2] + if (verbose && j==i+1) + cat(paste(" Distances (",i,",",j,"), (",i,",",j+1,") ...\n", sep="")) + cwt_i = computeCWT(i) + cwt_j = computeCWT(j) + num <- .Call("filter", Mod(cwt_i * Conj(cwt_j)), PACKAGE="epclust") + WX <- .Call("filter", Mod(cwt_i * Conj(cwt_i)), PACKAGE="epclust") + WY <- .Call("filter", Mod(cwt_j * Conj(cwt_j)), PACKAGE="epclust") + wer2 <- sum(colSums(num)^2) / sum(colSums(WX) * colSums(WY)) + Xwer_dist[i,j] <- sqrt(delta * ncol(cwt_i) * (1 - wer2)) + Xwer_dist[j,i] <- Xwer_dist[i,j] + Xwer_dist[i,i] = 0. + } + if (parll) { cl = parallel::makeCluster(ncores_clust) @@ -222,59 +268,15 @@ computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE) envir=environment()) } - # list of CWT from synchrones - # TODO: fit in RAM, OK? If not, 2 options: serialize, compute individual distances - Xcwt4 <- + ignored <- if (parll) - parallel::parLapply(cl, seq_len(n), computeCWT) + parallel::parLapply(cl, pairs, computeDistancesIJ) else - lapply(seq_len(n), computeCWT) + lapply(pairs, computeDistancesIJ) 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?!) - if (verbose) - cat("*** Compute WER distances from CWT\n") - - #TODO: computeDistances(i,j), et répartir les n(n-1)/2 couples d'indices - #là c'est trop déséquilibré - - computeDistancesLineI = function(i) - { - if (verbose) - cat(paste(" Line ",i,"\n", sep="")) - for (j in (i+1):n) - { - #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() - - ignored <- - if (parll) - { - parallel::mclapply(seq_len(n-1), computeDistancesLineI, - mc.cores=ncores_clust, mc.allow.recursive=FALSE) - } - else - lapply(seq_len(n-1), computeDistancesLineI) Xwer_dist[n,n] = 0. Xwer_dist } diff --git a/epclust/R/main.R b/epclust/R/main.R index 2037dbe..977e61b 100644 --- a/epclust/R/main.R +++ b/epclust/R/main.R @@ -186,7 +186,12 @@ claws = function(getSeries, K1, K2, indices_all[((i-1)*nb_series_per_task+1):upper_bound] }) if (verbose) - cat(paste("...Run ",ntasks," x stage 1 in parallel\n",sep="")) + { + message = paste("...Run ",ntasks," x stage 1", sep="") + if (WER=="mix") + message = paste(message," + stage 2", sep="") + cat(paste(message,"\n", sep="")) + } if (WER=="mix") {synchrones_file = paste(bin_dir,"synchrones",sep="") ; unlink(synchrones_file)} if (parll && ntasks>1) @@ -229,7 +234,7 @@ claws = function(getSeries, K1, K2, indices_medoids = clusteringTask1( indices, getContribs, K1, nb_series_per_chunk, ncores_tasks*ncores_clust, verbose, parll) medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) ) - medoids2 = computeClusters2(medoids1, K2, + medoids2 = clusteringTask2(medoids1, K2, getRefSeries, nb_curves, nb_series_per_chunk, ncores_tasks*ncores_clust, verbose, parll) # Cleanup diff --git a/epclust/src/WER.c b/epclust/src/WER.c deleted file mode 100644 index 36bfba7..0000000 --- a/epclust/src/WER.c +++ /dev/null @@ -1,117 +0,0 @@ -#include -#include -#include - -#ifndef M_PI -#define M_PI 3.14159265358979323846 -#endif - -// n: number of synchrones, m: length of a synchrone -float computeWerDist(float* s1, float* s2, int n, int m) -{ - //TODO: automatic tune of all these parameters ? (for other users) - int nvoice = 4; - //noctave 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(synchrones)) - int noctave = 13 - // 4 here represent 2^5 = 32 half-hours ~ 1 day - //NOTE: default scalevector == 2^(0:(noctave * nvoice) / nvoice) * s0 (?) - //R: scalevector <- 2^(4:(noctave * nvoice) / nvoice + 1) - int* scalevector = (int*)malloc( (noctave*nvoice-4 + 1) * sizeof(int)) - for (int i=4; i<=noctave*nvoice; i++) - scalevector[i-4] = pow(2., (float)i/nvoice + 1.); - //condition: ( log2(s0*w0/(2*pi)) - 1 ) * nvoice + 1.5 >= 1 - int s0 = 2; - double w0 = 2*M_PI; - bool scaled = false; - int s0log = as.integer( (log2( s0*w0/(2*pi) ) - 1) * nvoice + 1.5 ) - int totnoct = noctave + as.integer(s0log/nvoice) + 1 - - - - - -///TODO: continue - - - - 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)] - #Normalization - sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0) - sqres <- sweep(ts.cwt,2,sqs,'*') - sqres / max(Mod(sqres)) - } - - if (parll) - { - cl = parallel::makeCluster(ncores_clust) - parallel::clusterExport(cl, - varlist=c("synchrones","totnoct","nvoice","w0","s0log","noctave","s0","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?!) - if (verbose) - cat("*** Compute WER distances from CWT\n") - - #TODO: computeDistances(i,j), et répartir les n(n-1)/2 couples d'indices - #là c'est trop déséquilibré - - computeDistancesLineI = function(i) - { - if (verbose) - cat(paste(" Line ",i,"\n", sep="")) - for (j in (i+1):n) - { - #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() - - ignored <- - if (parll) - { - parallel::mclapply(seq_len(n-1), computeDistancesLineI, - mc.cores=ncores_clust, mc.allow.recursive=FALSE) - } - else - lapply(seq_len(n-1), computeDistancesLineI) - Xwer_dist[n,n] = 0. - - mat_dists = matrix(nrow=n, ncol=n) - #TODO: avoid this loop? - for (i in 1:n) - mat_dists[i,] = Xwer_dist[i,] - mat_dists - diff --git a/epclust/src/computeMedoidsIndices.c b/epclust/src/computeMedoidsIndices.c new file mode 100644 index 0000000..98a0111 --- /dev/null +++ b/epclust/src/computeMedoidsIndices.c @@ -0,0 +1,51 @@ +#include +#include +#include +#include +#include + +#include + +// (K,L): dim(medoids) +// mi: medoids indices +SEXP computeMedoidsIndices(SEXP medoids_, SEXP ref_series_) +{ + double *medoids = (double*) R_ExternalPtrAddr(medoids_), + *ref_series = REAL(ref_series_); + int nb_series = INTEGER(getAttrib(ref_series_, R_DimSymbol))[0], + K = INTEGER(getAttrib(medoids_, R_DimSymbol))[0], + L = INTEGER(getAttrib(ref_series_, R_DimSymbol))[1], + *mi = (int*)malloc(nb_series*sizeof(int)); + + //TODO: debug this: medoids have same addresses on both sides, but this side fails + printf("MED: %x\n",medoids); + + for (int i=0; i +#include +#include +#include + +#include + +SEXP filter(SEXP cwt_) +{ + int L = INTEGER(getAttrib(cwt_, R_DimSymbol))[0], + D = INTEGER(getAttrib(cwt_, R_DimSymbol))[1]; + double *cwt = REAL(cwt_); + SEXP R_fcwt; + PROTECT(R_fcwt = allocMatrix(REALSXP, L, D)); + double* fcwt = REAL(R_fcwt); + + //TODO: coding style is terrible... no time for now. + for (int col=0; col0) series[indices,] else NULL } - synchrones = computeSynchrones(rbind(s1,s2,s3), getRefSeries, n, 100, - verbose=TRUE, parll=FALSE) + synchrones = computeSynchrones(bigmemory::as.big.matrix(rbind(s1,s2,s3)), getRefSeries, + n, 100, verbose=TRUE, parll=FALSE) expect_equal(dim(synchrones), c(K,L)) for (i in 1:K) expect_equal(synchrones[i,], s[[i]], tolerance=0.01) }) +# NOTE: medoids can be a big.matrix computeDistortion = function(series, medoids) { n = nrow(series) ; L = ncol(series) distortion = 0. + if (bigmemory::is.big.matrix(medoids)) + medoids = medoids[,] for (i in seq_len(n)) distortion = distortion + min( rowSums( sweep(medoids,2,series[i,],'-')^2 ) / L ) distortion / n } -test_that("computeClusters2 behave as expected", +test_that("clusteringTask1 behave as expected", { n = 900 x = seq(0,9.5,0.1) L = length(x) #96 1/4h K1 = 60 - K2 = 3 - #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)} s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) ) series = matrix(nrow=n, ncol=L) for (i in seq_len(n)) series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01) - getRefSeries = function(indices) { + getSeries = function(indices) { 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, n, 75, - verbose=TRUE, parll=FALSE) + wf = "haar" + ctype = "absolute" + getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype) + indices1 = clusteringTask1(1:n, getContribs, K1, 75, verbose=TRUE, parll=FALSE) + medoids_K1 = getSeries(indices1) - expect_equal(dim(medoids_K2), c(K2,L)) + expect_equal(dim(medoids_K1), c(K1,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) + # medoids within initial series + distorGood = computeDistortion(series, medoids_K1) for (i in 1:3) - expect_lte( distorGood, computeDistortion(series,medoids_K1[sample(1:K1, K2),]) ) + expect_lte( distorGood, computeDistortion(series,series[sample(1:n, K1),]) ) }) -test_that("clusteringTask1 + computeClusters2 behave as expected", +test_that("clusteringTask2 behave as expected", { n = 900 x = seq(0,9.5,0.1) L = length(x) #96 1/4h K1 = 60 K2 = 3 + #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)} s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) ) series = matrix(nrow=n, ncol=L) for (i in seq_len(n)) series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01) - getSeries = function(indices) { + getRefSeries = function(indices) { indices = indices[indices <= n] if (length(indices)>0) series[indices,] else NULL } - wf = "haar" - ctype = "absolute" - getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype) - 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) + # Artificially simulate 60 medoids - perfect situation, all equal to one of the refs + medoids_K1 = bigmemory::as.big.matrix( + do.call(rbind, lapply( 1:K1, function(i) s[[I(i,K1)]] ) ) ) + medoids_K2 = clusteringTask2(medoids_K1, K2, getRefSeries, n, 75, verbose=TRUE, parll=FALSE) - expect_equal(dim(medoids_K1), c(K1,L)) 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) @@ -143,3 +153,36 @@ test_that("clusteringTask1 + computeClusters2 behave as expected", for (i in 1:3) expect_lte( distorGood, computeDistortion(series,medoids_K1[sample(1:K1, K2),]) ) }) + +#NOTE: rather redundant test +#test_that("clusteringTask1 + clusteringTask2 behave as expected", +#{ +# n = 900 +# x = seq(0,9.5,0.1) +# L = length(x) #96 1/4h +# K1 = 60 +# K2 = 3 +# s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) ) +# series = matrix(nrow=n, ncol=L) +# for (i in seq_len(n)) +# series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01) +# getSeries = function(indices) { +# indices = indices[indices <= n] +# if (length(indices)>0) series[indices,] else NULL +# } +# wf = "haar" +# ctype = "absolute" +# getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype) +# require("bigmemory", quietly=TRUE) +# indices1 = clusteringTask1(1:n, getContribs, K1, 75, verbose=TRUE, parll=FALSE) +# medoids_K1 = bigmemory::as.big.matrix( getSeries(indices1) ) +# medoids_K2 = clusteringTask2(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)) +# # 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),]) ) +#})