From d03c0621a8f298b19659ebc20a86099ba56d8ff7 Mon Sep 17 00:00:00 2001
From: Benjamin Auder <benjamin.auder@somewhere>
Date: Tue, 31 Jan 2017 11:55:51 +0100
Subject: [PATCH] prepared step2.R for first tests (old code, reindented, all
 in one file)

---
 epclust/R/stage2.R | 419 ++++++++++++++++-----------------------------
 1 file changed, 146 insertions(+), 273 deletions(-)

diff --git a/epclust/R/stage2.R b/epclust/R/stage2.R
index da84035..ebb44d9 100644
--- a/epclust/R/stage2.R
+++ b/epclust/R/stage2.R
@@ -1,297 +1,170 @@
+#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
 }
-- 
2.44.0