+#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
-#(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
-
- }
-
+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
}
- #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,])
+#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
- #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)
+ #cwt from package Rwave
+ totts.cwt=cwt(ts,totnoct,nvoice,w0,plot=0)
+ ts.cwt=totts.cwt[,s0log:(s0log+noctave*nvoice)]
-#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
- }
+ #Normalization
+ sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0)
+ smat <- matrix(rep(sqs,length(t)),nrow=length(t),byrow=TRUE)
- #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
+ ts.cwt*smat
}
-#et filter() est dans stats::
-> filter
-function (x, filter, method = c("convolution", "recursive"),
- sides = 2L, circular = FALSE, init = NULL)
+#NOTE: vect2mat = as.matrix ?! (dans aux.R)
+vect2mat <- function(vect)
{
- 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
+ vect <- as.vector(vect)
+ matrix(vect[-(1:2)], delta, lscvect)
}
-<bytecode: 0x1b05db8>
-<environment: namespace:stats>
-
-
-#cf. filters en C dans : https://svn.r-project.org/R/trunk/src/library/stats/src/filter.c
-#ifdef HAVE_CONFIG_H
-# include <config.h>
-#endif
-
-#include <R.h>
-#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)
+#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 )
{
- 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);
+#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
+}
- R_xlen_t i, j, nshift;
- double z, tmp, *x = REAL(sx), *filter = REAL(sfilter), *out = REAL(ans);
+#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
+}
- 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;
+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))
+ }
}
- } else { /* circular */
- for(i = 0; i < nx; i++)
+ 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)
+
+ #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))
{
- 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;
+ 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]
+ }
}
- }
- return ans;
+ diag(Xwer_dist) <- numeric(n)
+ Wwer_dist
}
projects / epclust.git / commitdiff