-#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)
+#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)
{
- noctave <- adjust.noctave(lt, dt, s0, tw, noctave)
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) {
- 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
+ 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, dt = dt, scalevector = scalevector)
+ 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
}
-#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)
+#smooth cwt result
+smCWT <- function(CWT, tw= 0, swabs= 0, nvoice= 12, noctave= 2, s0= 2, w0= 2*pi,
+ lt= 24, scalevector )
{
- 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 <- smooth.time(smwsp, tw, scalevector)
smsmwsp
}
smwt
}
-smooth.time <- function(wt,tw,dt,scalevector)
+smooth.time <- function(wt,tw,scalevector)
{
smwt <- wt
if (tw != 0)
{
for (i in 1:length(scalevector))
{
- twi <- as.integer(scalevector[i]*tw/dt)
+ 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)
{
- #(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]
- # #NOTE: delta et lscvect pourraient etre gardés à part (communs)
+ m <- ncol(conso)
#TODO: automatic tune of 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
+ # 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 = noctave4, dt = 1, scalevector = scalevector4, lt = delta,
+ Xcwt4 <- toCWT(conso, noctave = noctave, scalevector = scalevector4,
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()
+ Xcwt2[i,] <- c(m, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) )
+
+ rm(conso, Xcwt4) ; gc()
- #Benjamin: FIX is this OK ?
lscvect = dim(Xcwt4)[2]
- ## _.b WER^2 distances ########
Xwer_dist <- matrix(0.0, n, n)
for(i in 1:(n - 1))
{
-#browser()
-##ERROR là sans FIX lscvect :: delta lscvect --> taille ??!
- mat1 <- vect2mat(Xcwt2[i,], delta, lscvect)
+ mat1 <- matrix(as.vector(Xcwt2[i,])[-(1:2)], m, lscvect)
for(j in (i + 1):n)
{
- mat2 <- vect2mat(Xcwt2[j,], delta, lscvect)
+ 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))
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))
+ sum( sum(colSums(smsmWX) * colSums(smsmWY)) )
+ Xwer_dist[i, j] <- sqrt(m * lscvect * (1 - wer2))
Xwer_dist[j, i] <- Xwer_dist[i, j]
}
}
projects / epclust.git / commitdiff