old_C_code/stage2_UNFINISHED/src/unused/05_cluster2stepWER-RANDOM.r

   1 ## File : 05_cluster2stepWER.r
   2 ## Description :
   3
   4 rm(list = ls())
   5
   6 if(Sys.info()[4] ==  "mojarrita"){
   7   setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
   8 } else {
   9   setwd("~/2014_EDF-Orsay-Lyon2/codes/")
  10 }
  11
  12 library(Rwave)       # CWT
  13 library(cluster)     # pam
  14 #library(flexclust)   # kcca
  15 source("aux.r")               # auxiliary clustering functions
  16 source("sowas-superseded.r")  # auxiliary CWT functions
  17
  18 ## 1. Read auxiliar data files ####
  19
  20 identifiants <- read.table("identifs.txt")[ ,1]
  21 dates0       <- read.table("datesall.txt")[, 1]
  22 dates        <- as.character(dates0[grep("2009", dates0)])
  23 rm(dates0)
  24
  25 n     <- length(identifiants)
  26 p <- delta <- length(dates)
  27
  28 load("~/tmp/2009_synchros200RND")
  29 synchros09 <- synchros[[1]]
  30 #synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RANDOM.txt"))
  31 #synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200RANDOM.txt")))
  32 nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing
  33 synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4])
  34
  35 imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
  36 synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518])
  37
  38 conso <- (synchros09)[-201, ];  # series must be on rows
  39 n     <- nrow(conso)
  40 delta <- ncol(conso)
  41
  42 rm(synchros09, nas)
  43
  44 ## 2. Compute WER distance matrix ####
  45
  46 ## _.a CWT -- Filtering the lowest freqs (>6m) ####
  47 nvoice   <- 4
  48 # noctave4 = 2^13 = 8192 half hours ~ 180 days
  49 noctave4 <- adjust.noctave(N = delta, dt = 1, s0 = 2,
  50                            tw = 0, noctave = 13)
  51 # 4 here represent 2^5 = 32 half-hours ~ 1 day
  52 scalevector4  <- 2^(4:(noctave4 * nvoice) / nvoice) * 2
  53 lscvect4      <- length(scalevector4)
  54 lscvect <- lscvect4  # i should clean my code: werFam demands a lscvect
  55 Xcwt4   <- toCWT(conso, noctave = noctave4, dt = 1,
  56                  scalevector = scalevector4,
  57                  lt = delta, smooth = FALSE,
  58                  nvoice = nvoice)      # observations node with CWT
  59
  60 Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect)
  61 Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
  62
  63 for(i in 1:n)
  64   Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) )
  65
  66 rm(conso, Xcwt4); gc()
  67
  68 ## _.b WER^2 distances  ########
  69 Xwer_dist    <- matrix(0.0, n, n)
  70 for(i in 1:(n - 1)){
  71   cat(sprintf('\nIter: , %i', i))
  72   mat1   <- vect2mat(Xcwt2[i,])
  73   for(j in (i + 1):n){
  74     mat2 <- vect2mat(Xcwt2[j,])
  75     num     <- Mod(mat1 * Conj(mat2))
  76     WX      <- Mod(mat1 * Conj(mat1))
  77     WY      <- Mod(mat2 * Conj(mat2))
  78     smsmnum <- smCWT(num, scalevector = scalevector4)
  79     smsmWX  <- smCWT(WX,  scalevector = scalevector4)
  80     smsmWY  <- smCWT(WY,  scalevector = scalevector4)
  81     wer2    <- sum(colSums(smsmnum)^2)  /
  82       sum( sum(colSums(smsmWX) * colSums(smsmWY)) )
  83     Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2))
  84     Xwer_dist[j, i] <- Xwer_dist[i, j]
  85   }
  86 }
  87 diag(Xwer_dist) <- numeric(n)
  88
  89 save(Xwer_dist, file = "../res/2009_synchros200RANDOM-WER.Rdata")
  90
  91 #load("../res/2009_synchros200WER.Rdata")
  92
  93
  94 ## 3. Cluster using WER distance matrix ####
  95
  96 #hc    <- hclust(as.dist(Xwer_dist), method = "ward.D")
  97 #plot(hc)
  98 #
  99 # #clust <- cutree(hc, 2)
 100 #
 101  for(K in 2:30){
 102    #K <- 3
 103    #pamfit <- pam(tdata[-201, ci$selectv], k = K)
 104    pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE)
 105
 106    #table(pamfit$clustering)
 107
 108    SC <- matrix(0, ncol = p, nrow = K)
 109
 110    clustfactor <- pamfit$clustering
 111  #  for(k in 1:K){
 112  #    clustk <- which(clustfactor == k)
 113  #  if(length(clustk) > 0) {
 114  #    if(length(clustk) > 1) {
 115  #      SCk <- colSums(synchros09[which(clustfactor == k), ])
 116  #      } else {
 117  #        SCk <- synchros09[which(clustfactor == k), ]
 118  #      }
 119  #      SC[k, ] <- SC[k, ] + SCk
 120  #      rm(SCk)
 121  #  }
 122  #}
 123
 124 # #write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt"))
 125 # #write.table(clustfactor, file = "~/tmp/clustfactor3.txt")
 126  write.table(clustfactor, file = paste0("~/tmp/clustfactorRANDOM", K, ".txt"))
 127  }
 128 #
 129 # # Plots
 130 # layout(1)
 131 # matplot(t(SC)[48*10 + 1:(48*30), ],  type = 'l', ylab = '',col = 1:3, lty = 1)
 132 # matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1)
 133 #
 134 #
 135 #