X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=old_C_code%2Fstage2_UNFINISHED%2Fsrc%2Funused%2F01_extract-features2_2009.r;fp=old_C_code%2Fstage2_UNFINISHED%2Fsrc%2Funused%2F01_extract-features2_2009.r;h=85a2b5b499aa9ca13d55bd74b3210b6c7fbb704a;hb=14cb6cf8266c0e1299f16a4b2352f54dbae26f44;hp=0000000000000000000000000000000000000000;hpb=5edda192f1a634ba284acdea7b6302f60df7b000;p=epclust.git

diff --git a/old_C_code/stage2_UNFINISHED/src/unused/01_extract-features2_2009.r b/old_C_code/stage2_UNFINISHED/src/unused/01_extract-features2_2009.r
new file mode 100644
index 0000000..85a2b5b
--- /dev/null
+++ b/old_C_code/stage2_UNFINISHED/src/unused/01_extract-features2_2009.r
@@ -0,0 +1,67 @@
+## File : 01_extract-features_2009.r
+## Description : Using the full data matrix, we extract handy features to
+##               cluster.
+
+rm(list = ls())
+
+#source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r')
+setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+source('aux2.r')
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2009", dates0)]
+rm(dates0)
+
+n <- length(identifiants)
+p <- length(dates)
+
+blocks <- c(rep(6500, 3), 5511)  
+
+# table( substr(dates, 11, 15) ) # Sunlight time saving produces an 
+                                 # unbalanced number of time points
+                                 # per time stepa across the year
+
+
+## 2. Process the large file ####
+
+close(con)
+con <- file("~/tmp/2009_full.txt")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+
+for(b in seq_along(blocks)){        # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb )
+  auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE)
+  rm(actual)
+
+  datamat <- t(apply(auxmat[, -1], 1, as.numeric))
+  rownames(datamat) <- substr(auxmat[, 1], 2, 7)
+  rm(auxmat)
+
+  nas <- which(is.na(datamat)[, 1]) # some 1/1/2009 are missing
+  if(length(nas)>0) datamat[nas, 1] <- rowMeans(datamat[nas, 2:4])
+  
+  imput   <- datamat[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+  datamat <- cbind(datamat[, 1:4180], imput, datamat[, 4181:17518])
+  
+  dayimp   <- t(apply(datamat, 1, dayimpact))
+  nightimp <- t(apply(datamat, 1, nightimpact))
+  lunchimp <- t(apply(datamat, 1, lunchimpact))
+  
+  auxfeat  <- cbind(dayimp, nightimp, lunchimp)
+  
+  if(b == 1) {
+    matfeat <- auxfeat 
+  } else {
+    matfeat <- rbind(matfeat, auxfeat)
+  }
+  
+}
+
+close(con)                      # close connection to the file
+
+write.table(matfeat, file = "~/tmp/2009_impacts.txt")
+