## 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 source("sowas-superseded.r") # auxiliary CWT functions ## 1.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 #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) # # #