-#include <stdlib.h>
-#include <math.h>
-#include <stdbool.h>
-
-#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
-
projects / epclust.git / commitdiff