add some TODOs

[epclust.git] / epclust / src / WER.c

diff --git a/epclust/src/WER.c b/epclust/src/WER.c
new file mode 100644 (file)
index 0000000..36bfba7
--- /dev/null
+++ b/epclust/src/WER.c
@@ -0,0 +1,117 @@
+#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
+