X-Git-Url: https://git.auder.net/js/app_dev.php?a=blobdiff_plain;f=epclust%2FR%2Fstage2.R;h=3ccbbad10903b256c005b0476b418bf3bbfdd9a3;hb=db6fc17ddd53fb0c64cf957296dc615ba830db56;hp=ebb44d9ae0624a9d518df7d65ee0246f38f520a2;hpb=d03c0621a8f298b19659ebc20a86099ba56d8ff7;p=epclust.git

diff --git a/epclust/R/stage2.R b/epclust/R/stage2.R
index ebb44d9..3ccbbad 100644
--- a/epclust/R/stage2.R
+++ b/epclust/R/stage2.R
@@ -1,170 +1,51 @@
-#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
-
-#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")
-		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
-
-	#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
-}
-
-#NOTE: vect2mat = as.matrix ?! (dans aux.R)
-vect2mat <- function(vect)
-{
-	vect <- as.vector(vect)
-	matrix(vect[-(1:2)], delta, lscvect)
-}
-
-#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
-}
-
 step2 = function(conso)
 {
-	#(Benjamin)
-	#à partir de là, "conso" == courbes synchrones
-	n     <- nrow(conso)
+	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)
+	#TODO: automatic tune of all 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()
+	# noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(conso))
+	noctave = 13
+	# 4 here represent 2^5 = 32 half-hours ~ 1 day
+	#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))
+	})
 
-	## _.b WER^2 distances  ########
-	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   <- vect2mat(Xcwt2[i,])
-
-	for(j in (i + 1):n)
+		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]
+			#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)
-	Wwer_dist
+	Xwer_dist
 }