+#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
+