major folder reorganisation, R pkg is now epclust/ at first level. Experimental usage...

[epclust.git] / old_C_code / stage2 / src / unused / analysis-SME.r

diff --git a/old_C_code/stage2/src/unused/analysis-SME.r b/old_C_code/stage2/src/unused/analysis-SME.r
new file mode 100644 (file)
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+## 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+source("sowas-superseded.r")  # auxiliary CWT functions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)
+ }
+ }
+
+