-#Entrée : courbes synchrones, soit après étape 1 itérée, soit après chaqure étape 1
-
-#(Benjamin)
-#à partir de là, "conso" == courbes synchrones
-n <- nrow(conso)
-delta <- ncol(conso)
+library("Rwave")
+#precondition: ( log2(s0*w0/(2*pi)) - 1 ) * nvoice + 1.5 >= 1
+toCWT <- function(X, tw=0, swabs=0, nvoice=12, noctave=5, s0=2, w0=2*pi,
+ spectra=FALSE, smooth=TRUE, scaled=FALSE, scalevector)
+{
+ if(missing(scalevector))
+ scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0
+ s0log=as.integer((log2( s0*w0/(2*pi) )-1)*nvoice+1.5)
+ totnoct=noctave+as.integer(s0log/nvoice)+1
+ res <- lapply(1:nrow(X), function(n) {
+ ts <- scale(ts( X[n,] ), 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)
+ smat <- matrix(rep(sqs,length(t)),nrow=length(t),byrow=TRUE)
+ ts.cwt*smat
+ })
+ if( spectra )
+ res <- lapply(res, function(l) Mod(l)^2 )
+ if( smooth )
+ res <- lapply(res, smCWT, swabs = swabs, tw = tw, scalevector = scalevector)
+ resArray <- array(NA, c(nrow(res[[1]]), ncol(res[[1]]), length(res)))
+ for( l in 1:length(res) )
+ resArray[ , , l] <- res[[l]]
+ resArray
+}
-#17000 colonnes coeff 1, puis 17000 coeff 2... [non : dans chaque tranche du cube]
+#smooth cwt result
+smCWT <- function(CWT, tw= 0, swabs= 0, nvoice= 12, noctave= 2, s0= 2, w0= 2*pi,
+ lt= 24, scalevector )
+{
+ wsp <- Mod(CWT)
+ smwsp <- smooth.matrix(wsp, swabs)
+ smsmwsp <- smooth.time(smwsp, tw, scalevector)
+ smsmwsp
+}
-#TODO: une fonction qui fait lignes 59 à 91
+#dans sowas.R (...donc on ne lisse pas à ce niveau ?)
+smooth.matrix <- function(wt,swabs)
+{
+ if (swabs != 0)
+ {
+ smwt <- t(filter(t(wt),rep(1,2*swabs+1)/(2*swabs+1)))
+ } else
+ {
+ smwt <- wt
+ }
+ smwt
+}
-#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")
+smooth.time <- function(wt,tw,scalevector)
+{
+ smwt <- wt
+ if (tw != 0)
+ {
+ for (i in 1:length(scalevector))
+ {
+ twi <- as.integer(scalevector[i]*tw)
+ smwt[,i] <- filter(wt[,i],rep(1,2*twi+1)/(2*twi+1))
+ }
+ }
+ smwt
+}
+#Entrée : courbes synchrones, soit après étape 1 itérée, soit après chaqure étape 1
+step2 = function(conso)
+{
+ n <- nrow(conso)
+ m <- ncol(conso)
+ #TODO: automatic tune of these parameters ? (for other users)
+ nvoice <- 4
+ # noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(conso))
+ noctave = 13
+ # 4 here represent 2^5 = 32 half-hours ~ 1 day
+ scalevector4 <- 2^(4:(noctave * nvoice) / nvoice) * 2
+ lscvect4 <- length(scalevector4)
+ lscvect <- lscvect4 # i should clean my code: werFam demands a lscvect
-#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,])
+ # observations node with CWT
+ Xcwt4 <- toCWT(conso, noctave = noctave, scalevector = scalevector4,
+ smooth = FALSE, nvoice = nvoice)
- #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)
+ #matrix:
+ Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
-#fonction smCWT (dans aux.R)
- smCWT <- function(CWT, sw= 0, tw= 0, swabs= 0,
- nvoice= 12, noctave= 2, s0= 2, w0= 2*pi,
- lt= 24, dt= 0.5, scalevector )
- {
-# noctave <- adjust.noctave(lt, dt, s0, tw, noctave)
-# scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0
- wsp <- Mod(CWT)
- smwsp <- smooth.matrix(wsp, swabs)
- smsmwsp <- smooth.time(smwsp, tw, dt, scalevector)
- smsmwsp
- }
+ for(i in 1:n)
+ Xcwt2[i,] <- c(m, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) )
- #dans sowas.R
-smooth.matrix <- function(wt,swabs){
-
- if (swabs != 0)
- smwt <- t(filter(t(wt),rep(1,2*swabs+1)/(2*swabs+1)))
- else
- smwt <- wt
-
- smwt
-
-}
-smooth.time <- function(wt,tw,dt,scalevector){
-
- smwt <- wt
-
- if (tw != 0){
- for (i in 1:length(scalevector)){
-
- twi <- as.integer(scalevector[i]*tw/dt)
- smwt[,i] <- filter(wt[,i],rep(1,2*twi+1)/(2*twi+1))
-
- }
- }
- smwt
-}
+ rm(conso, Xcwt4) ; gc()
-#et filter() est dans stats::
+ lscvect = dim(Xcwt4)[2]
-#cf. filters en C dans : https://svn.r-project.org/R/trunk/src/library/stats/src/filter.c
+ Xwer_dist <- matrix(0.0, n, n)
+ for(i in 1:(n - 1))
+ {
+ mat1 <- matrix(as.vector(Xcwt2[i,])[-(1:2)], m, lscvect)
+ for(j in (i + 1):n)
+ {
+ mat2 <- matrix(as.vector(Xcwt2[j,])[-(1:2)], m, lscvect)
+ 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(m * lscvect * (1 - wer2))
+ Xwer_dist[j, i] <- Xwer_dist[i, j]
+ }
+ }
+ diag(Xwer_dist) <- numeric(n)
+ Xwer_dist
+}