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 #(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) #lignes 59 à 91 "dépliées" : Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1, scalevector = scalevector4, lt = delta, smooth = FALSE, nvoice = nvoice) # observations node with CWT #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"){ cat("# 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") } else{ 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 } } #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) #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 (...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,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 } #et filter() est dans stats:: > filter function (x, filter, method = c("convolution", "recursive"), sides = 2L, circular = FALSE, init = NULL) { method <- match.arg(method) x <- as.ts(x) storage.mode(x) <- "double" xtsp <- tsp(x) n <- as.integer(NROW(x)) if (is.na(n)) stop("invalid value of nrow(x)", domain = NA) nser <- NCOL(x) filter <- as.double(filter) nfilt <- as.integer(length(filter)) if (is.na(n)) stop("invalid value of length(filter)", domain = NA) if (anyNA(filter)) stop("missing values in 'filter'") if (method == "convolution") { if (nfilt > n) stop("'filter' is longer than time series") sides <- as.integer(sides) if (is.na(sides) || (sides != 1L && sides != 2L)) stop("argument 'sides' must be 1 or 2") circular <- as.logical(circular) if (is.na(circular)) stop("'circular' must be logical and not NA") if (is.matrix(x)) { y <- matrix(NA, n, nser) for (i in seq_len(nser)) y[, i] <- .Call(C_cfilter, x[, i], filter, sides, circular) } else y <- .Call(C_cfilter, x, filter, sides, circular) } else { if (missing(init)) { init <- matrix(0, nfilt, nser) } else { ni <- NROW(init) if (ni != nfilt) stop("length of 'init' must equal length of 'filter'") if (NCOL(init) != 1L && NCOL(init) != nser) { stop(sprintf(ngettext(nser, "'init' must have %d column", "'init' must have 1 or %d columns", domain = "R-stats"), nser), domain = NA) } if (!is.matrix(init)) dim(init) <- c(nfilt, nser) } ind <- seq_len(nfilt) if (is.matrix(x)) { y <- matrix(NA, n, nser) for (i in seq_len(nser)) y[, i] <- .Call(C_rfilter, x[, i], filter, c(rev(init[, i]), double(n)))[-ind] } else y <- .Call(C_rfilter, x, filter, c(rev(init[, 1L]), double(n)))[-ind] } tsp(y) <- xtsp class(y) <- if (nser > 1L) c("mts", "ts") else "ts" y } #cf. filters en C dans : https://svn.r-project.org/R/trunk/src/library/stats/src/filter.c #ifdef HAVE_CONFIG_H # include #endif #include #include "ts.h" #ifndef min #define min(a, b) ((a < b)?(a):(b)) #define max(a, b) ((a < b)?(b):(a)) #endif // currently ISNAN includes NAs #define my_isok(x) (!ISNA(x) & !ISNAN(x)) #Pour method=="convolution" dans filter() (fonction R) SEXP cfilter(SEXP sx, SEXP sfilter, SEXP ssides, SEXP scircular) { if (TYPEOF(sx) != REALSXP || TYPEOF(sfilter) != REALSXP) error("invalid input"); R_xlen_t nx = XLENGTH(sx), nf = XLENGTH(sfilter); int sides = asInteger(ssides), circular = asLogical(scircular); if(sides == NA_INTEGER || circular == NA_LOGICAL) error("invalid input"); SEXP ans = allocVector(REALSXP, nx); R_xlen_t i, j, nshift; double z, tmp, *x = REAL(sx), *filter = REAL(sfilter), *out = REAL(ans); if(sides == 2) nshift = nf /2; else nshift = 0; if(!circular) { for(i = 0; i < nx; i++) { z = 0; if(i + nshift - (nf - 1) < 0 || i + nshift >= nx) { out[i] = NA_REAL; continue; } for(j = max(0, nshift + i - nx); j < min(nf, i + nshift + 1) ; j++) { tmp = x[i + nshift - j]; if(my_isok(tmp)) z += filter[j] * tmp; else { out[i] = NA_REAL; goto bad; } } out[i] = z; bad: continue; } } else { /* circular */ for(i = 0; i < nx; i++) { z = 0; for(j = 0; j < nf; j++) { R_xlen_t ii = i + nshift - j; if(ii < 0) ii += nx; if(ii >= nx) ii -= nx; tmp = x[ii]; if(my_isok(tmp)) z += filter[j] * tmp; else { out[i] = NA_REAL; goto bad2; } } out[i] = z; bad2: continue; } } return ans; }