codes_Jairo_stage2/05_cluster2stepWER.r

   1 ## File : 05_cluster2stepWER.r
   2 ## Description :
   3
   4 rm(list = ls())
   5
   6 setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
   7
   8 library(Rwave)       # CWT
   9 library(cluster)     # pam
  10 #library(flexclust)   # kcca
  11 source("aux.r")               # auxiliary clustering functions
  12 source("sowas-superseded.r")  # auxiliary CWT functions
  13
  14 ## 1. Read auxiliar data files ####
  15
  16 identifiants <- read.table("identifs.txt")[ ,1]
  17 dates0       <- read.table("datesall.txt")[, 1]
  18 dates        <- as.character(dates0[grep("2009", dates0)])
  19 rm(dates0)
  20
  21 n     <- length(identifiants)
  22 p <- delta <- length(dates)
  23
  24 synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200RC.txt")))
  25 #synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200-random.txt")))
  26
  27 nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing
  28 synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4])
  29
  30 imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
  31 synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518])
  32
  33 conso <- synchros09[-201, ];  # series must be on rows
  34 n     <- nrow(conso)
  35 delta <- ncol(conso)
  36
  37 rm(synchros09, nas)
  38
  39 ## 2. Compute WER distance matrix ####
  40
  41 ## _.a CWT -- Filtering the lowest freqs (>6m) ####
  42 # nvoice   <- 4
  43 # # noctave4 = 2^13 = 8192 half hours ~ 180 days
  44 # noctave4 <- adjust.noctave(N = delta, dt = 1, s0 = 2,
  45 #                           tw = 0, noctave = 13)
  46 # # 4 here represent 2^5 = 32 half-hours ~ 1 day
  47 # scalevector4  <- 2^(4:(noctave4 * nvoice) / nvoice) * 2
  48 # lscvect4      <- length(scalevector4)
  49 # lscvect <- lscvect4  # i should clean my code: werFam demands a lscvect
  50 # Xcwt4   <- toCWT(conso, noctave = noctave4, dt = 1,
  51 #                 scalevector = scalevector4,
  52 #                 lt = delta, smooth = FALSE,
  53 #                 nvoice = nvoice)      # observations node with CWT
  54 #
  55 # #Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect)
  56 # #Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
  57 #
  58 # for(i in 1:n)
  59 #  Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) )
  60 #
  61 # #rm(conso, Xcwt4); gc()
  62 #
  63 # ## _.b WER^2 distances  ########
  64 # Xwer_dist    <- matrix(0.0, n, n)
  65 # for(i in 1:(n - 1)){
  66 #  mat1   <- vect2mat(Xcwt2[i,])
  67 #  for(j in (i + 1):n){
  68 #     mat2 <- vect2mat(Xcwt2[j,])
  69 #     num     <- Mod(mat1 * Conj(mat2))
  70 #     WX      <- Mod(mat1 * Conj(mat1))
  71 #     WY      <- Mod(mat2 * Conj(mat2))
  72 #     smsmnum <- smCWT(num, scalevector = scalevector4)
  73 #     smsmWX  <- smCWT(WX,  scalevector = scalevector4)
  74 #     smsmWY  <- smCWT(WY,  scalevector = scalevector4)
  75 #     wer2    <- sum(colSums(smsmnum)^2)  /
  76 #       sum( sum(colSums(smsmWX) * colSums(smsmWY)) )
  77 #     Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2))
  78 #     Xwer_dist[j, i] <- Xwer_dist[i, j]
  79 #   }
  80 # }
  81 # diag(Xwer_dist) <- numeric(n)
  82 #
  83 # save(Xwer_dist, file = "../res/2009_synchros200WER.Rdata")
  84 # save(Xwer_dist, file = "../res/2009_synchros200-randomWER.Rdata")
  85
  86 load("../res/2009_synchros200WER.Rdata")
  87 #load("../res/2009_synchros200-randomWER.Rdata")
  88
  89 ## 3. Cluster using WER distance matrix ####
  90
  91 #hc    <- hclust(as.dist(Xwer_dist), method = "ward.D")
  92 #plot(hc)
  93 #
  94 # #clust <- cutree(hc, 2)
  95 #
  96 for(K in 2:30){
  97   #K <- 3
  98   #pamfit <- pam(tdata[-201, ci$selectv], k = K)
  99   pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE)
 100
 101   #table(pamfit$clustering)
 102
 103   SC <- matrix(0, ncol = p, nrow = K)
 104
 105   clustfactor <- pamfit$clustering
 106 #  for(k in 1:K){
 107 #    clustk <- which(clustfactor == k)
 108 #  if(length(clustk) > 0) {
 109 #    if(length(clustk) > 1) {
 110 #      SCk <- colSums(synchros09[which(clustfactor == k), ])
 111 #      } else {
 112 #        SCk <- synchros09[which(clustfactor == k), ]
 113 #      }
 114 #      SC[k, ] <- SC[k, ] + SCk
 115 #      rm(SCk)
 116 #  }
 117 #}
 118
 119 #write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt"))
 120 #write.table(clustfactor, file = "~/tmp/clustfactor3.txt")
 121 #write.table(clustfactor, file = paste0("~/tmp/clustfactorWER", K, ".txt"))
 122 write.table(clustfactor, file = paste0("~/tmp/clustfactor-randomWER", K, ".txt"))
 123 }
 124 #
 125 # # Plots
 126 # layout(1)
 127 # matplot(t(SC)[48*10 + 1:(48*30), ],  type = 'l', ylab = '',col = 1:3, lty = 1)
 128 # matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1)
 129 #
 130 #
 131 #