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

[epclust.git] / old_C_code / stage2 / src / 02_cluster_2009.r

diff --git a/old_C_code/stage2/src/02_cluster_2009.r b/old_C_code/stage2/src/02_cluster_2009.r
new file mode 100644 (file)
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+++ b/old_C_code/stage2/src/02_cluster_2009.r
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+## File: extract-features.r
+
+rm(list = ls())
+
+## a. Load data & libraries ####
+
+library(cluster)
+MOJARRITA <- Sys.info()[4] ==  "mojarrita"
+
+#source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r')
+source('01_StBr.r')
+
+if(MOJARRITA){ 
+  setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+  setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
+}
+
+
+matcontrib0 <- read.table(file = "~/tmp/2009_contrib.txt")
+n     <- nrow(matcontrib0)
+
+sdcontrib <- apply(matcontrib0, 1, sd)
+lims      <- quantile(sdcontrib, probs = c(.005, .995)) # obtain 1%-extreme data
+is_normal <- which((sdcontrib > lims[1]) & (sdcontrib < lims[2]))
+
+matcontri_ext <- matcontrib0[-is_normal, ]#""
+matcontrib    <- matcontrib0[is_normal, ]     # wipe out aberrant data
+
+matcontrib <- t(apply(matcontrib, 1, function(x) x / sum(x)))
+matcontrib <- t(apply(matcontrib, 1, function(p) log(p / (1 - p)) ))
+
+
+## b. Transform data & compute CI ####
+ci     <- CI(matcontrib)
+tdata  <- ci$tdata; rownames(tdata) <- rownames(matcontrib)
+selvar <- ci$selectv
+  
+## c. Clustering  ##########
+K <- 200
+system.time(
+
+#TODO: cette partie en C
+
+  clfit <- clara(x        = tdata[, selvar], 
+                 k        = K, 
+                 sampsize = 4000, 
+                 samples  = 4, 
+                 rngR     = TRUE)
+)
+
+#save(clfit, file = 'clfit500.Rdata')
+# save(clfit, file = 'clfit200RC.Rdata')
+save(clfit, file = 'clfit200.Rdata')
+
+rm(ci, matcontrib0, is_normal, lims, selvar)
+gc()
+
+## d. Analyze results  ##########
+
+#1. Répartition du nombre d'observation par cluster
+#plot(sort(table(clfit$clustering), decreasing = TRUE), 
+#     type = 'l', ylab = 'Fréquence', xlab = 'Classe')
+
+
+#clust <- res$clustering
+#  centres <- aggregate(conso, clust)
+#  table(clust)
+  
+  #sel_veille    <- as.Date(rownames(conso)[sel - 1])
+  #sel_lendemain <- as.Date(rownames(conso)[sel + 1])
+  
+  #res_clust <- data.frame(date     = rownames(conso),
+                          #veille    = weekdays(sel_veille),
+                          #lendemain = weekdays(sel_lendemain),
+  #                        clust     = clust)
+  
+  #for(k in 1:K) {
+  #  assign(paste0("dates_clust", K),
+  #         substr(subset(res_clust, clust == k)$date, 1, 7) )
+  #}
+  
+  #dev.off()
+  
+  #save(file = paste0(dtitle, "_clust.Rdata"), 
+       #res_clust, selvar, K, gap)
+#}
+
+#dates_clust1 <- substr(subset(dates, clust == 1)$date, 1, 7)