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
-}
-
-#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
-}
-
-#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,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)
+ 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
- scalevector4 <- 2^(4:(noctave * 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 = noctave, scalevector = scalevector4,
- smooth = FALSE, nvoice = nvoice)
-
- #matrix:
- Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
-
- for(i in 1:n)
- Xcwt2[i,] <- c(m, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) )
-
- rm(conso, Xcwt4) ; gc()
-
- lscvect = dim(Xcwt4)[2]
+ #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(0.0, 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))
{
- mat1 <- matrix(as.vector(Xcwt2[i,])[-(1:2)], m, lscvect)
-
- for(j in (i + 1):n)
+ 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]
+ #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)
projects / epclust.git / commitdiff
