X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fstage2.R;h=fa553560356f8566b50f9ab536911ff5610dea2b;hb=d7d55bc1e74711b0da84578ecdebc43eeb259599;hp=f952da2e5dfce72493c757c2d120947c747fe979;hpb=dc1aa85a96bbf815b0d896c22a9b4a539a9e8a9c;p=epclust.git diff --git a/epclust/R/stage2.R b/epclust/R/stage2.R index f952da2..fa55356 100644 --- a/epclust/R/stage2.R +++ b/epclust/R/stage2.R @@ -1,139 +1,118 @@ -#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) +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 +} -#17000 colonnes coeff 1, puis 17000 coeff 2... [non : dans chaque tranche du cube] +#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 +} -#TODO: une fonction qui fait lignes 59 à 91 +#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 +} -#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") +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) + 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 -#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,]) + # observations node with CWT + Xcwt4 <- toCWT(conso, noctave = noctave, scalevector = scalevector4, + smooth = FALSE, nvoice = nvoice) - #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) + #matrix: + Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1])))) -#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 - } + for(i in 1:n) + Xcwt2[i,] <- c(m, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) - #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 -} + rm(conso, Xcwt4) ; gc() -#et filter() est dans stats:: + lscvect = dim(Xcwt4)[2] -#cf. filters en C dans : https://svn.r-project.org/R/trunk/src/library/stats/src/filter.c + Xwer_dist <- matrix(0.0, n, n) + for(i in 1:(n - 1)) + { + mat1 <- matrix(as.vector(Xcwt2[i,])[-(1:2)], m, lscvect) + 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] + } + } + diag(Xwer_dist) <- numeric(n) + Xwer_dist +}