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

diff --git a/code/stage2/src/unused/05_cluster2stepWER-RANDOM.r b/code/stage2/src/unused/05_cluster2stepWER-RANDOM.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("~/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+source("sowas-superseded.r")  # auxiliary CWT functions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)
+# 
+# 
+#