-#point avec Jairo:
-#rentrer dans code C cwt continue Rwave
-#passer partie sowas à C
-#fct qui pour deux series (ID, medoides) renvoie distance WER (Rwave ou à moi)
-#transformee croisee , smoothing lissage 3 composantes , + calcul pour WER
-#attention : code fait pour des series temps desynchronisees ! (deltat, dt == 1,2 ...)
-#determiner nvoice noctave (entre octave + petit et + grand)
-
library("Rwave")
#Entrée : courbes synchrones, soit après étape 1 itérée, soit après chaqure étape 1
-#TODO: bout de code qui calcule les courbes synchrones après étapes 1+2 à partir des ID médoïdes
-
-#toCWT: (aux)
-##NOTE: renvoie une matrice 3D
-toCWT <- function(X, sw=0, tw=0, swabs=0, nvoice=12, noctave=5, s0=2, w0=2*pi,
- lt=24, dt=0.5, spectra=FALSE, smooth=TRUE, scaled=FALSE, scalevector)
-{
- noctave <- adjust.noctave(lt, dt, s0, tw, noctave)
- if(missing(scalevector))
- scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0
- res <- lapply(1:nrow(X), function(n) {
- tsX <- ts( X[n,] )
- tsCent <- tsX - mean(tsX)
- if(scaled)
- tsCent <- ts(scale(tsCent))
- tsCent.cwt <- cwt.ts(tsCent, s0, noctave, nvoice, w0)
- tsCent.cwt
- })
- if( spectra )
- res <- lapply(res, function(l) Mod(l)^2 )
- if( smooth )
- res <- lapply(res, smCWT, swabs = swabs, tw = tw, dt = dt, 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
-}
-
-#from sowas
-adjust.noctave <- function(N,dt,s0,tw,noctave)
-{
- if (tw>0)
- {
- dumno <- as.integer((log(N*dt)-log(2*tw*s0))/log(2))
- if (dumno<noctave)
- {
- cat("# noctave adjusted to time smoothing window \n")
- noctave <- dumno
- }
- }
- noctave
-}
-
-#from sowas
-cwt.ts <- function(ts,s0,noctave=5,nvoice=10,w0=2*pi)
-{
- if (class(ts)!="ts")
- stop("# This function needs a time series object as input. You may construct this by using the function ts(data,start,deltat). Try '?ts' for help.\n")
-
- t=time(ts)
- dt=t[2]-t[1]
- s0unit=s0/dt*w0/(2*pi)
- s0log=as.integer((log2(s0unit)-1)*nvoice+1.5)
- if (s0log<1)
- {
- cat(paste("# s0unit = ",s0unit,"\n",sep=""))
- cat(paste("# s0log = ",s0log,"\n",sep=""))
- cat("# s0 too small for w0! \n")
- }
- totnoct=noctave+as.integer(s0log/nvoice)+1
-
- #cwt from package Rwave
- totts.cwt=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
-}
-
-#NOTE: vect2mat = as.matrix ?! (dans aux.R)
-vect2mat <- function(vect, delta, lscvect)
-{
- vect <- as.vector(vect)
- matrix(vect[-(1:2)], delta, lscvect)
-}
-
-#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 (...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
-}
-
-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
-}
-
step2 = function(conso)
{
- #(Benjamin)
- #à partir de là, "conso" == courbes synchrones
- n <- nrow(conso)
+ n <- nrow(conso)
delta <- ncol(conso)
-
- #17000 colonnes coeff 1, puis 17000 coeff 2... [non : dans chaque tranche du cube]
- # #NOTE: delta et lscvect pourraient etre gardés à part (communs)
-
- #TODO: automatic tune of these parameters ? (for other users)
+ #TODO: automatic tune of all these parameters ? (for other users)
nvoice <- 4
- # # noctave4 = 2^13 = 8192 half hours ~ 180 days
- noctave4 <- adjust.noctave(N = delta, dt = 1, s0 = 2, tw = 0, noctave = 13)
- # # 4 here represent 2^5 = 32 half-hours ~ 1 day
- scalevector4 <- 2^(4:(noctave4 * nvoice) / nvoice) * 2
- lscvect4 <- length(scalevector4)
- lscvect <- lscvect4 # i should clean my code: werFam demands a lscvect
-
- # observations node with CWT
- Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1, scalevector = scalevector4, lt = delta,
- smooth = FALSE, nvoice = nvoice)
-
- #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()
-
- #Benjamin: FIX is this OK ?
- lscvect = dim(Xcwt4)[2]
+ # 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))
+ })
- ## _.b WER^2 distances ########
- Xwer_dist <- matrix(0.0, n, n)
- for(i in 1:(n - 1))
+ 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))
{
-#browser()
-##ERROR là sans FIX lscvect :: delta lscvect --> taille ??!
- mat1 <- vect2mat(Xcwt2[i,], delta, lscvect)
-
- for(j in (i + 1):n)
+ for (j in (i+1):n)
{
- mat2 <- vect2mat(Xcwt2[j,], delta, 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(delta * lscvect * (1 - wer2))
- Xwer_dist[j, i] <- Xwer_dist[i, j]
+ #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)