| 1 | library("Rwave") |
| 2 | |
| 3 | #precondition: ( log2(s0*w0/(2*pi)) - 1 ) * nvoice + 1.5 >= 1 |
| 4 | toCWT <- function(X, tw=0, swabs=0, nvoice=12, noctave=5, s0=2, w0=2*pi, |
| 5 | spectra=FALSE, smooth=TRUE, scaled=FALSE, scalevector) |
| 6 | { |
| 7 | if(missing(scalevector)) |
| 8 | scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0 |
| 9 | s0log=as.integer((log2( s0*w0/(2*pi) )-1)*nvoice+1.5) |
| 10 | totnoct=noctave+as.integer(s0log/nvoice)+1 |
| 11 | res <- lapply(1:nrow(X), function(n) { |
| 12 | ts <- scale(ts( X[n,] ), center=TRUE, scale=scaled) |
| 13 | totts.cwt = Rwave::cwt(ts,totnoct,nvoice,w0,plot=0) |
| 14 | ts.cwt=totts.cwt[,s0log:(s0log+noctave*nvoice)] |
| 15 | #Normalization |
| 16 | sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0) |
| 17 | smat <- matrix(rep(sqs,length(t)),nrow=length(t),byrow=TRUE) |
| 18 | ts.cwt*smat |
| 19 | }) |
| 20 | if( spectra ) |
| 21 | res <- lapply(res, function(l) Mod(l)^2 ) |
| 22 | if( smooth ) |
| 23 | res <- lapply(res, smCWT, swabs = swabs, tw = tw, scalevector = scalevector) |
| 24 | resArray <- array(NA, c(nrow(res[[1]]), ncol(res[[1]]), length(res))) |
| 25 | for( l in 1:length(res) ) |
| 26 | resArray[ , , l] <- res[[l]] |
| 27 | resArray |
| 28 | } |
| 29 | |
| 30 | #smooth cwt result |
| 31 | smCWT <- function(CWT, tw= 0, swabs= 0, nvoice= 12, noctave= 2, s0= 2, w0= 2*pi, |
| 32 | lt= 24, scalevector ) |
| 33 | { |
| 34 | wsp <- Mod(CWT) |
| 35 | smwsp <- smooth.matrix(wsp, swabs) |
| 36 | smsmwsp <- smooth.time(smwsp, tw, scalevector) |
| 37 | smsmwsp |
| 38 | } |
| 39 | |
| 40 | #dans sowas.R (...donc on ne lisse pas à ce niveau ?) |
| 41 | smooth.matrix <- function(wt,swabs) |
| 42 | { |
| 43 | if (swabs != 0) |
| 44 | { |
| 45 | smwt <- t(filter(t(wt),rep(1,2*swabs+1)/(2*swabs+1))) |
| 46 | } else |
| 47 | { |
| 48 | smwt <- wt |
| 49 | } |
| 50 | smwt |
| 51 | } |
| 52 | |
| 53 | smooth.time <- function(wt,tw,scalevector) |
| 54 | { |
| 55 | smwt <- wt |
| 56 | if (tw != 0) |
| 57 | { |
| 58 | for (i in 1:length(scalevector)) |
| 59 | { |
| 60 | twi <- as.integer(scalevector[i]*tw) |
| 61 | smwt[,i] <- filter(wt[,i],rep(1,2*twi+1)/(2*twi+1)) |
| 62 | } |
| 63 | } |
| 64 | smwt |
| 65 | } |
| 66 | |
| 67 | #Entrée : courbes synchrones, soit après étape 1 itérée, soit après chaqure étape 1 |
| 68 | step2 = function(conso) |
| 69 | { |
| 70 | n <- nrow(conso) |
| 71 | m <- ncol(conso) |
| 72 | |
| 73 | #TODO: automatic tune of these parameters ? (for other users) |
| 74 | nvoice <- 4 |
| 75 | # noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(conso)) |
| 76 | noctave = 13 |
| 77 | # 4 here represent 2^5 = 32 half-hours ~ 1 day |
| 78 | scalevector4 <- 2^(4:(noctave * nvoice) / nvoice) * 2 |
| 79 | lscvect4 <- length(scalevector4) |
| 80 | lscvect <- lscvect4 # i should clean my code: werFam demands a lscvect |
| 81 | |
| 82 | # observations node with CWT |
| 83 | Xcwt4 <- toCWT(conso, noctave = noctave, scalevector = scalevector4, |
| 84 | smooth = FALSE, nvoice = nvoice) |
| 85 | |
| 86 | #matrix: |
| 87 | Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1])))) |
| 88 | |
| 89 | for(i in 1:n) |
| 90 | Xcwt2[i,] <- c(m, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) |
| 91 | |
| 92 | rm(conso, Xcwt4) ; gc() |
| 93 | |
| 94 | lscvect = dim(Xcwt4)[2] |
| 95 | |
| 96 | Xwer_dist <- matrix(0.0, n, n) |
| 97 | for(i in 1:(n - 1)) |
| 98 | { |
| 99 | mat1 <- matrix(as.vector(Xcwt2[i,])[-(1:2)], m, lscvect) |
| 100 | |
| 101 | for(j in (i + 1):n) |
| 102 | { |
| 103 | mat2 <- matrix(as.vector(Xcwt2[j,])[-(1:2)], m, lscvect) |
| 104 | num <- Mod(mat1 * Conj(mat2)) |
| 105 | WX <- Mod(mat1 * Conj(mat1)) |
| 106 | WY <- Mod(mat2 * Conj(mat2)) |
| 107 | smsmnum <- smCWT(num, scalevector = scalevector4) |
| 108 | smsmWX <- smCWT(WX, scalevector = scalevector4) |
| 109 | smsmWY <- smCWT(WY, scalevector = scalevector4) |
| 110 | wer2 <- sum(colSums(smsmnum)^2) / |
| 111 | sum( sum(colSums(smsmWX) * colSums(smsmWY)) ) |
| 112 | Xwer_dist[i, j] <- sqrt(m * lscvect * (1 - wer2)) |
| 113 | Xwer_dist[j, i] <- Xwer_dist[i, j] |
| 114 | } |
| 115 | } |
| 116 | diag(Xwer_dist) <- numeric(n) |
| 117 | Xwer_dist |
| 118 | } |