From db6fc17ddd53fb0c64cf957296dc615ba830db56 Mon Sep 17 00:00:00 2001 From: Benjamin Auder Date: Sat, 18 Feb 2017 14:26:42 +0100 Subject: [PATCH] finish simplifications on stage2.R --- epclust/R/stage2.R | 135 ++++++++++++--------------------------------- 1 file changed, 34 insertions(+), 101 deletions(-) diff --git a/epclust/R/stage2.R b/epclust/R/stage2.R index fa55356..3ccbbad 100644 --- a/epclust/R/stage2.R +++ b/epclust/R/stage2.R @@ -1,116 +1,49 @@ 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) -- 2.44.0