X-Git-Url: https://git.auder.net/doc/screen_players.png?a=blobdiff_plain;ds=sidebyside;f=old_C_code%2Fstage2_UNFINISHED%2Fsrc%2Funused%2Fanalysis-SME.r;fp=old_C_code%2Fstage2_UNFINISHED%2Fsrc%2Funused%2Fanalysis-SME.r;h=0000000000000000000000000000000000000000;hb=62deb4244895a20a35397dfb062f0b9fe94c5012;hp=89668fab1e7a70e879923b47a398591e590c1dc6;hpb=3eef8d3df59ded9a281cff51f79fe824198a7427;p=epclust.git

diff --git a/old_C_code/stage2_UNFINISHED/src/unused/analysis-SME.r b/old_C_code/stage2_UNFINISHED/src/unused/analysis-SME.r
deleted file mode 100644
index 89668fa..0000000
--- a/old_C_code/stage2_UNFINISHED/src/unused/analysis-SME.r
+++ /dev/null
@@ -1,104 +0,0 @@
-## 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-source("sowas-superseded.r")  # auxiliary CWT functions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)
- }
- }
-
-