-#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)
-
-
-#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")
-
-
+library("Rwave")
-#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)
-
-#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
- }
-
- #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
+#Entrée : courbes synchrones, soit après étape 1 itérée, soit après chaqure étape 1
+step2 = function(conso)
+{
+ n <- nrow(conso)
+ delta <- ncol(conso)
+ #TODO: automatic tune of all 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
+ #NOTE: default scalevector == 2^(0:(noctave * nvoice) / nvoice) * s0 (?)
+ scalevector <- 2^(4:(noctave * nvoice) / nvoice) * 2
+ #condition: ( log2(s0*w0/(2*pi)) - 1 ) * nvoice + 1.5 >= 1
+ s0=2
+ w0=2*pi
+ scaled=FALSE
+ s0log = as.integer( (log2( s0*w0/(2*pi) ) - 1) * nvoice + 1.5 )
+ totnoct = noctave + as.integer(s0log/nvoice) + 1
+
+ # (normalized) observations node with CWT
+ Xcwt4 <- lapply(seq_len(n), function(i) {
+ ts <- scale(ts(conso[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,MARGIN=2,sqs,'*')
+ sqres / max(Mod(sqres))
+ })
+
+ Xwer_dist <- matrix(0., n, n)
+ fcoefs = rep(1/3, 3) #moving average on 3 values (TODO: very slow! correct?!)
+ for (i in 1:(n-1))
+ {
+ for (j in (i+1):n)
+ {
+ #TODO: later, compute CWT here (because not enough storage space for 32M series)
+ # '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)) )
+ Xwer_dist[i,j] <- sqrt(delta * ncol(Xcwt4[[1]]) * (1 - wer2))
+ Xwer_dist[j,i] <- Xwer_dist[i,j]
+ }
+ }
+ diag(Xwer_dist) <- numeric(n)
+ Xwer_dist
}
-
-#et filter() est dans stats::
-
-#cf. filters en C dans : https://svn.r-project.org/R/trunk/src/library/stats/src/filter.c
-
projects / epclust.git / blobdiff