+#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
+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
+}
-#(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")
-
-
-
-#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)
+#NOTE: vect2mat = as.matrix ?! (dans aux.R)
+vect2mat <- function(vect)
+{
+ 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
-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
-
+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 <- 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
+
+#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
}
-#et filter() est dans stats::
+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)
+ 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)
-#cf. filters en C dans : https://svn.r-project.org/R/trunk/src/library/stats/src/filter.c
+ #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
+ 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()
+
+ ## _.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)
+ Wwer_dist
+}
projects / epclust.git / blobdiff