-## File : ireland-data.r
-## Description :
-
-rm(list = ls())
-setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
-library(Rwave) # CWT
-library(cluster) # pam
-
-## 1. Read auxiliar data files ####
-source("aux.r") # auxiliary clustering functions
-source("sowas-superseded.r") # auxiliary CWT functions
-load("~/data/Irlande/Data_CER_clean/SME.RData")
-SME <- as.matrix(SME)
-
-nbdays <- nrow(SME) / 48
-nb_clust <- nbdays - 365 # last year to forecast
-
-id_clust <- 1:(48 * nb_clust)
-
-## 2. Compute WER distance matrix ####
-conso <- t(SME[id_clust, ]) # ts are in lines
-N <- delta <- ncol(conso) # length of one ts
-n <- nrow(conso) # number of ts
-
-# # _.a CWT -- Filtering the lowest freqs (>6m) ####
-# nvoice <- 4
-# # noctave4 = 2^12 = 4096 half hours ~ 90 days
-# noctave4 <- adjust.noctave(N = N,
-# dt = 1, s0 = 2,
-# tw = 0, noctave = 12)
-# # 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 = N, smooth = FALSE,
-# nvoice = nvoice) # observations node with CWT
-#
-#
-# Xcwt2 <- matrix(NA_complex_, nrow = n, ncol= 2 + length((c(Xcwt4[,,1]))))
-#
-# for(i in 1:n)
-# Xcwt2[i,] <- c(N, 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(N * lscvect * (1 - wer2))
-# Xwer_dist[j, i] <- Xwer_dist[i, j]
-# }
-# }
-# diag(Xwer_dist) <- numeric(n)
-#
-# save(Xwer_dist, file = "~/werdist-irlanda.Rdata")
-
-load("~/werdist-irlanda.Rdata")
-hc <- hclust(as.dist(Xwer_dist))
-
-plot(hc)
-
-
-Ks <- c(2:10, 15, 20, 25, 30)
-for(k in seq_along(Ks)){
- K <- Ks[k]
- pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE)
-
- fname <- paste0("clustfactor", K)
- write.table(pamfit$clustering,
- file = paste0("~/tmp/clustfactorSME-", K, ".txt"))
-}
-
-
-#dir(path = '~/tmp/', pattern = 'clustfac-
-K <- 2
-fname <- paste0("~/tmp/clustfactorSME-", K, ".txt")
-clustfactor <- read.table(fname)$x # pamfit$clustering
- for(k in 1:K){
- clustk <- which(clustfactor == k)
- if(length(clustk) > 0) {
- if(length(clustk) > 1) {
- SCk <- colSums(SME[, which(clustfactor == k)])
- } else {
- SCk <- synchros09[which(clustfactor == k), ]
- }
- SC[k, ] <- SC[k, ] + SCk
- rm(SCk)
- }
- }
-
-