X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fstage2.R;h=9c15a741315867c2d4ee13d0c9968445d6bfc1f9;hb=3ccd1e391850c1f055267b05bf89589113884344;hp=f952da2e5dfce72493c757c2d120947c747fe979;hpb=dc1aa85a96bbf815b0d896c22a9b4a539a9e8a9c;p=epclust.git

diff --git a/epclust/R/stage2.R b/epclust/R/stage2.R
index f952da2..9c15a74 100644
--- a/epclust/R/stage2.R
+++ b/epclust/R/stage2.R
@@ -1,139 +1,190 @@
+#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
+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
+}
 
-#(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]
-
-#TODO: une fonction qui fait lignes 59 à 91
-
-#cube:
-# Xcwt4   <- toCWT(conso, noctave = noctave4, dt = 1,
-#                 scalevector = scalevector4,
-#                 lt = delta, smooth = FALSE, 
-#                 nvoice = nvoice)      # observations node with CWT
-# 
-# #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,])
-#  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)
-# 
-# save(Xwer_dist, file = "../res/2009_synchros200WER.Rdata")
-# save(Xwer_dist, file = "../res/2009_synchros200-randomWER.Rdata")
-
-
-
-#lignes 59 à 91 "dépliées" :
-Xcwt4   <- toCWT(conso, noctave = noctave4, dt = 1,
-                 scalevector = scalevector4,
-                 lt = delta, smooth = FALSE, 
-                 nvoice = nvoice)      # observations node with CWT
- 
- #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)
+#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, 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
-       }
-
- #dans sowas.R
-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
-  
+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
+
+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::
+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
 
-#cf. filters en C dans : https://svn.r-project.org/R/trunk/src/library/stats/src/filter.c
+	# 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()
+
+	#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)
+
+		for(j in (i + 1):n)
+		{
+			mat2 <- vect2mat(Xcwt2[j,], delta, lscvect)
+			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)
+	Xwer_dist
+}