X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=code%2Fstage2%2Fsrc%2F05_cluster2stepWER-RANDOM.r;fp=code%2Fstage2%2Fsrc%2F05_cluster2stepWER-RANDOM.r;h=0000000000000000000000000000000000000000;hb=27c3a4bf83aa22de0308e7bec023652a16e31f9e;hp=5ba44a1a0569a0097e17a950e016e35eb5983ce9;hpb=572d139adaf3ca05e1c25ad29a71d3b38f0bcef8;p=epclust.git

diff --git a/code/stage2/src/05_cluster2stepWER-RANDOM.r b/code/stage2/src/05_cluster2stepWER-RANDOM.r
deleted file mode 100644
index 5ba44a1..0000000
--- a/code/stage2/src/05_cluster2stepWER-RANDOM.r
+++ /dev/null
@@ -1,135 +0,0 @@
-## 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-source("sowas-superseded.r")  # auxiliary CWT functions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)
-# 
-# 
-#