-#(Benjamin)
-#à partir de là, "conso" == courbes synchrones
-n <- nrow(conso)
-delta <- ncol(conso)
-
-
-#17000 colonnes coeff 1, puis 17000 coeff 2... [non : dans chaque tranche du cube]
-
-#TODO: une fonction qui fait lignes 59 à 91
-
-#cube:
-# Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1,
-# scalevector = scalevector4,
-# lt = delta, smooth = FALSE,
-# nvoice = nvoice) # observations node with CWT
-#
-# #matrix:
-# ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect)
-# #Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
-#
-# #NOTE: delta et lscvect pourraient etre gardés à part (communs)
-# for(i in 1:n)
-# Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) )
-#
-# #rm(conso, Xcwt4); gc()
-#
-# ## _.b WER^2 distances ########
-# Xwer_dist <- matrix(0.0, n, n)
-# for(i in 1:(n - 1)){
-# mat1 <- vect2mat(Xcwt2[i,])
-# for(j in (i + 1):n){
-# mat2 <- vect2mat(Xcwt2[j,])
-# num <- Mod(mat1 * Conj(mat2))
-# WX <- Mod(mat1 * Conj(mat1))
-# WY <- Mod(mat2 * Conj(mat2))
-# smsmnum <- smCWT(num, scalevector = scalevector4)
-# smsmWX <- smCWT(WX, scalevector = scalevector4)
-# smsmWY <- smCWT(WY, scalevector = scalevector4)
-# wer2 <- sum(colSums(smsmnum)^2) /
-# sum( sum(colSums(smsmWX) * colSums(smsmWY)) )
-# Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2))
-# Xwer_dist[j, i] <- Xwer_dist[i, j]
-# }
-# }
-# diag(Xwer_dist) <- numeric(n)
-#
-# save(Xwer_dist, file = "../res/2009_synchros200WER.Rdata")
-# save(Xwer_dist, file = "../res/2009_synchros200-randomWER.Rdata")
-
-
-
-#lignes 59 à 91 "dépliées" :
-Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1,
- scalevector = scalevector4,
- lt = delta, smooth = FALSE,
- nvoice = nvoice) # observations node with CWT
-
- #matrix:
- ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect)
- Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
-
- #NOTE: delta et lscvect pourraient etre gardés à part (communs)
- for(i in 1:n)
- Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) )
-
- #rm(conso, Xcwt4); gc()
-
- ## _.b WER^2 distances ########
- Xwer_dist <- matrix(0.0, n, n)
- for(i in 1:(n - 1)){
- mat1 <- vect2mat(Xcwt2[i,])
-
- #NOTE: vect2mat = as.matrix ?! (dans aux.R)
- vect2mat <- function(vect){
- vect <- as.vector(vect)
- matrix(vect[-(1:2)], delta, lscvect)
- }
-
- for(j in (i + 1):n){
- mat2 <- vect2mat(Xcwt2[j,])
- num <- Mod(mat1 * Conj(mat2))
- WX <- Mod(mat1 * Conj(mat1))
- WY <- Mod(mat2 * Conj(mat2))
- smsmnum <- smCWT(num, scalevector = scalevector4)
- smsmWX <- smCWT(WX, scalevector = scalevector4)
- smsmWY <- smCWT(WY, scalevector = scalevector4)
- wer2 <- sum(colSums(smsmnum)^2) /
- sum( sum(colSums(smsmWX) * colSums(smsmWY)) )
- Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2))
- Xwer_dist[j, i] <- Xwer_dist[i, j]
- }
- }
- diag(Xwer_dist) <- numeric(n)