From: Benjamin Auder <benjamin.auder@somewhere>
Date: Sat, 18 Feb 2017 12:12:01 +0000 (+0100)
Subject: simplify stage2.R
X-Git-Url: https://git.auder.net/variants/Chakart/doc/%7B%7B%20path%28%27mixstore_static_about%27%29%20%7D%7D?a=commitdiff_plain;h=d7d55bc1e74711b0da84578ecdebc43eeb259599;p=epclust.git

simplify stage2.R
---

diff --git a/TODO b/TODO
index 3a7c13e..96a8221 100644
--- a/TODO
+++ b/TODO
@@ -72,3 +72,10 @@ Reference values :
 --> 1000(nbTasks) tâches avec itérations possibles, puis phase 2 en fin de chaqune des 1000
 tâches. On obtient 1000xK* médoïdes
 --> Phase 2 sur les 1000xK* médoïdes
+
+#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
+#determiner nvoice noctave (entre octave + petit et + grand)
diff --git a/epclust/R/main.R b/epclust/R/main.R
index 867843b..e18ea7b 100644
--- a/epclust/R/main.R
+++ b/epclust/R/main.R
@@ -181,3 +181,4 @@ processChunk = function(indice, K, WER)
 #TODO: difficulté : retrouver courbe à partir de l'identifiant (DB ok mais le reste ?)
 #aussi : que passe-t-on aux noeuds ? curvesToCoeffs en // ?
 #enfin : WER ?!
+#TODO: bout de code qui calcule les courbes synchrones après étapes 1+2 à partir des ID médoïdes
diff --git a/epclust/R/stage2.R b/epclust/R/stage2.R
index 9c15a74..fa55356 100644
--- a/epclust/R/stage2.R
+++ b/epclust/R/stage2.R
@@ -1,102 +1,39 @@
-#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)
+#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)
 {
-	noctave  <- adjust.noctave(lt, dt, s0, tw, noctave)
 	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) {
-		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
+		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, dt = dt, scalevector = scalevector)
+		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
 }
 
-#from sowas
-adjust.noctave <- function(N,dt,s0,tw,noctave)
-{
-	if (tw>0)
-	{
-		dumno <- as.integer((log(N*dt)-log(2*tw*s0))/log(2))
-		if (dumno<noctave)
-		{
-			cat("# noctave adjusted to time smoothing window \n")
-			noctave <- dumno
-		}
-	}
-	noctave
-}
-
-#from sowas
-cwt.ts <- function(ts,s0,noctave=5,nvoice=10,w0=2*pi)
+#smooth cwt result
+smCWT <- function(CWT, tw=  0, swabs= 0, nvoice= 12, noctave= 2, s0= 2, w0= 2*pi,
+	lt= 24, scalevector )
 {
-	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, delta, lscvect)
-{
-	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 <- smooth.time(smwsp, tw, scalevector)
 	smsmwsp
 }
 
@@ -113,66 +50,57 @@ smooth.matrix <- function(wt,swabs)
 	smwt
 }
 
-smooth.time <- function(wt,tw,dt,scalevector)
+smooth.time <- function(wt,tw,scalevector)
 {
 	smwt <- wt
 	if (tw != 0)
 	{
 		for (i in 1:length(scalevector))
 		{
-			twi <- as.integer(scalevector[i]*tw/dt)
+			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)
 {
-	#(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)
+	m <- ncol(conso)
 
 	#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
+	# 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 = noctave4, dt = 1, scalevector = scalevector4, lt = delta,
+	Xcwt4   <- toCWT(conso, noctave = noctave, scalevector = scalevector4,
 		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()
+		Xcwt2[i,] <- c(m, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) )
+
+	rm(conso, Xcwt4) ; gc()
 
-	#Benjamin: FIX is this OK ?
 	lscvect = dim(Xcwt4)[2]
 
-	## _.b WER^2 distances  ########
 	Xwer_dist    <- matrix(0.0, n, n)
 	for(i in 1:(n - 1))
 	{
-#browser()
-##ERROR là sans FIX lscvect :: delta lscvect --> taille ??!
-		mat1   <- vect2mat(Xcwt2[i,], delta, lscvect)
+		mat1   <- matrix(as.vector(Xcwt2[i,])[-(1:2)], m, lscvect)
 
 		for(j in (i + 1):n)
 		{
-			mat2 <- vect2mat(Xcwt2[j,], delta, lscvect)
+			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))
@@ -180,8 +108,8 @@ step2 = function(conso)
 			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))
+				sum( sum(colSums(smsmWX) * colSums(smsmWY)) )
+			Xwer_dist[i, j] <- sqrt(m * lscvect * (1 - wer2))
 			Xwer_dist[j, i] <- Xwer_dist[i, j]
 		}
 	}