major folder reorganisation, R pkg is now epclust/ at first level. Experimental usage...

[epclust.git] / old_C_code / stage2 / src / unused / 05_cluster2stepWER-par.r

diff --git a/old_C_code/stage2/src/unused/05_cluster2stepWER-par.r b/old_C_code/stage2/src/unused/05_cluster2stepWER-par.r
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+## File : 05_cluster2stepWER.r
+## Description : 
+
+rm(list = ls())
+
+if(Sys.info()[4] ==  "mojarrita"){ 
+  setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+  setwd("~/ownCloud/projects/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+source("sowas-superseded.r")  # auxiliary CWT functions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 <- length(dates)
+
+load('~/tmp/2009synchrosdf200WER')
+#load('../res/2009_synchros200WER.Rdata')
+synchros09 <- synchros
+load('~/tmp/2010synchrosdf200WER')
+synchros10 <- synchros; rm(synchros)
+
+conso <- lapply(synchros09, function(ll) { 
+                nas <- which(is.na(ll)[, 1]) # some 1/1/2009 are missing
+                ll[nas, 1] <- rowMeans(ll[nas, 2:4])
+                imput <- ll[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+                cbind(ll[, 1:4180], imput, ll[, 4181:17518]) } )
+
+n     <- nrow(conso[[1]])
+delta <- ncol(conso[[1]])
+rm(synchros09)
+
+
+## 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 <- lapply(conso, function(ll)
+  toCWT(ll, noctave = noctave4, dt = 1,
+        scalevector = scalevector4,
+        lt = delta, smooth = FALSE, 
+        nvoice = nvoice)      # observations node with CWT
+  )
+
+rm(conso)
+
+Xcwt4 <- lapply(conso, function(ll) {
+  aux <- toCWT(ll, noctave = noctave4, dt = 1,
+               scalevector = scalevector4,
+               lt = delta, smooth = FALSE, 
+               nvoice = nvoice)      # observations node with CWT
+  res <- matrix(NA_complex_, nrow = n, ncol= 2 + length((c(aux[,,1]))))
+  for(i in 1:n) 
+    res[i, ] <- c(delta, lscvect, res[,,i] / max(Mod(res[,,i])) ) 
+  res })
+
+rm(conso)
+
+
+#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)){
+#  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")
+
+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/clustfactorWER", 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)
+# 
+# 
+#