+++ /dev/null
-## File : 05_cluster2stepWER.r
-## Description :
-
-rm(list = ls())
-
-setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
-
-library(Rwave) # CWT
-library(cluster) # pam
-#library(flexclust) # kcca
-source("aux.r") # auxiliary clustering functions
-
-#TODO: [plus tard] alternative à sowa (package disparu) : cwt..
-source("sowas-superseded.r") # auxiliary CWT functions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)
-
-synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200RC.txt")))
-#synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200-random.txt")))
-
-nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing
-synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) #valeurs après 1er janvier
-
-#moyenne pondérée pour compléter deux demi-heures manquantes
-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
-
-
-#(Benjamin)
-#à partir de là, "conso" == courbes synchrones
-n <- nrow(conso)
-delta <- ncol(conso)
-
-
-#17000 colonnes coeff 1, puis 17000 coeff 2... [non : dans chaque tranche du cube]
-
-#TODO: une fonction qui fait lignes 59 à 91
-
-#cube:
-# Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1,
-# scalevector = scalevector4,
-# lt = delta, smooth = FALSE,
-# nvoice = nvoice) # observations node with CWT
-#
-# #matrix:
-# ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect)
-# #Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
-#
-# #NOTE: delta et lscvect pourraient etre gardés à part (communs)
-# 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")
-# save(Xwer_dist, file = "../res/2009_synchros200-randomWER.Rdata")
-
-
-
-#lignes 59 à 91 "dépliées" :
-Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1,
- scalevector = scalevector4,
- lt = delta, smooth = FALSE,
- nvoice = nvoice) # observations node with CWT
-
- #matrix:
- ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect)
- Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
-
- #NOTE: delta et lscvect pourraient etre gardés à part (communs)
- 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,])
-
- #NOTE: vect2mat = as.matrix ?! (dans aux.R)
- vect2mat <- function(vect){
- vect <- as.vector(vect)
- matrix(vect[-(1:2)], delta, lscvect)
- }
-
- 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)
-
-#fonction smCWT (dans aux.R)
- smCWT <- function(CWT, sw= 0, tw= 0, swabs= 0,
- nvoice= 12, noctave= 2, s0= 2, w0= 2*pi,
- lt= 24, dt= 0.5, scalevector )
- {
-# noctave <- adjust.noctave(lt, dt, s0, tw, noctave)
-# scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0
- wsp <- Mod(CWT)
- smwsp <- smooth.matrix(wsp, swabs)
- smsmwsp <- smooth.time(smwsp, tw, dt, scalevector)
- smsmwsp
- }
-
- #dans sowas.R
-smooth.matrix <- function(wt,swabs){
-
- if (swabs != 0)
- smwt <- t(filter(t(wt),rep(1,2*swabs+1)/(2*swabs+1)))
- else
- smwt <- wt
-
- smwt
-
-}
-smooth.time <- function(wt,tw,dt,scalevector){
-
- smwt <- wt
-
- if (tw != 0){
- for (i in 1:length(scalevector)){
-
- twi <- as.integer(scalevector[i]*tw/dt)
- smwt[,i] <- filter(wt[,i],rep(1,2*twi+1)/(2*twi+1))
-
- }
- }
- smwt
-}
-
-#et filter() est dans stats::
-
-#cf. filters en C dans : https://svn.r-project.org/R/trunk/src/library/stats/src/filter.c
-
-
-
-load("../res/2009_synchros200WER.Rdata")
-#load("../res/2009_synchros200-randomWER.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"))
-write.table(clustfactor, file = paste0("~/tmp/clustfactor-randomWER", 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)
-#
-#
-#
projects / epclust.git / blobdiff