+## File : 05_cluster2stepWER.r
+## Description :
+
+rm(list = ls())
+
+if(Sys.info()[4] == "mojarrita"){
+ setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+ setwd("~/2014_EDF-Orsay-Lyon2/codes/")
+}
+
+library(Rwave) # CWT
+library(cluster) # pam
+#library(flexclust) # kcca
+source("aux.r") # auxiliary clustering functions
+source("sowas-superseded.r") # auxiliary CWT functions
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0 <- read.table("datesall.txt")[, 1]
+dates <- as.character(dates0[grep("2009", dates0)])
+rm(dates0)
+
+n <- length(identifiants)
+p <- delta <- length(dates)
+
+load("~/tmp/2009_synchros200RND")
+synchros09 <- synchros[[1]]
+#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RANDOM.txt"))
+#synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200RANDOM.txt")))
+nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing
+synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4])
+
+imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518])
+
+conso <- (synchros09)[-201, ]; # series must be on rows
+n <- nrow(conso)
+delta <- ncol(conso)
+
+rm(synchros09, nas)
+
+## 2. Compute WER distance matrix ####
+
+## _.a CWT -- Filtering the lowest freqs (>6m) ####
+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
+Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1,
+ scalevector = scalevector4,
+ lt = delta, smooth = FALSE,
+ nvoice = nvoice) # observations node with CWT
+
+Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect)
+Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
+
+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)){
+ cat(sprintf('\nIter: , %i', i))
+ 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_synchros200RANDOM-WER.Rdata")
+
+#load("../res/2009_synchros200WER.Rdata")
+
+
+## 3. Cluster using WER distance matrix ####
+
+#hc <- hclust(as.dist(Xwer_dist), method = "ward.D")
+#plot(hc)
+#
+# #clust <- cutree(hc, 2)
+#
+ for(K in 2:30){
+ #K <- 3
+ #pamfit <- pam(tdata[-201, ci$selectv], k = K)
+ pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE)
+
+ #table(pamfit$clustering)
+
+ SC <- matrix(0, ncol = p, nrow = K)
+
+ clustfactor <- pamfit$clustering
+ # for(k in 1:K){
+ # clustk <- which(clustfactor == k)
+ # if(length(clustk) > 0) {
+ # if(length(clustk) > 1) {
+ # SCk <- colSums(synchros09[which(clustfactor == k), ])
+ # } else {
+ # SCk <- synchros09[which(clustfactor == k), ]
+ # }
+ # SC[k, ] <- SC[k, ] + SCk
+ # rm(SCk)
+ # }
+ #}
+
+# #write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt"))
+# #write.table(clustfactor, file = "~/tmp/clustfactor3.txt")
+ write.table(clustfactor, file = paste0("~/tmp/clustfactorRANDOM", K, ".txt"))
+ }
+#
+# # Plots
+# layout(1)
+# matplot(t(SC)[48*10 + 1:(48*30), ], type = 'l', ylab = '',col = 1:3, lty = 1)
+# matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1)
+#
+#
+#