+++ /dev/null
-## File : 00_plots-energycon.r
-## Description : Using the full data matrix, we extract handy features to
-## cluster.
-
-rm(list = ls())
-
-library(Rwave) # CWT
-#library(cluster) # pam
-#library(flexclust) # kcca
-source("aux.r") # auxiliary clustering functions
-source("sowas-superseded.r") # auxiliary CWT functions
-source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r')
-setwd("~/recherche/03_projects/2014_EDF-Orsay-Lyon2/codes/")
-
-
-## 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 ####
-
-# if(exists("con")) close(con)
-# con <- file("~/tmp/2009_full.txt") # Establish a connection to the file
-# open(con, "r") # Open the connection
-#
-# nb <- 4
-# actual <- readLines(con = con, n = nb )[-3]
-# 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)
-#
-# datamat <- datamat[, 1:(48 * 14)]
-# p <- ncol(datamat)
-#
-# auxDWT <- t(apply(datamat, 1, toDWT))
-# matcontrib <- t(apply(auxDWT, 1, contrib))
-# rm(auxDWT)
-#
-# close(con); rm(con) # close connection to the file
-
-
-load("~/data/Irlande/Data_CER_clean/SME.RData")
-
-smp <- c(4, 79, 126)
-
-datamat <- t(SME[1:(48 * 7), smp])
-p <- ncol(datamat)
-
-auxDWT <- t(apply(datamat, 1, toDWT))
-matcontrib <- t(apply(auxDWT, 1, contrib))
-rm(auxDWT)
-
-matplot(t(datamat), type = "l", lty = 1,
- col = 1:8, lwd = 2)
-
-pdf("~/courbes.pdf")
-#par(mai = c(1, 1, 0.8, 0.6), mfcol = c(4, 2), cex = 2)
-for(courbe in 1:ncol(SME)) {
- plot(SME[1:(48 * 7), courbe], main = paste(courbe),
- xlab = "", ylab = "", type = "l")
-}
-dev.off()
-
-
-
-
-## Plots for ENERGYCON full article ####
-op <- par()
-
-col <- c("grey", "black", "black")
-## Curves
-pdf('~/curves.pdf', width = 12)
-par(mai = c(1.3, 1.3, 0.1, 0.1), cex = 1.2)
-matplot(t(datamat), type = "l", lty = c(1, 1, 2),
- lwd = 2, col = col, # c(1, 2, 4),
- ylab = "Load", xlab = "Time (1/2 hours)")
-legend("top", c("Cust. A", "Cust. B", "Cust. C"),
- col = col, lty = c(1, 1, 2), ncol = 3, lwd = 2)
-# matplot(scale(t(datamat), scale = F),
-# type = "l", lty = 1, col = c(1, 2, 4), lwd = 2,
-# ylab = "Load", xlab = "Time (1/2 hours)")
-dev.off()
-
-normi <- function(x) x / max(x)
-normi2 <- function(x) (x - min(x)) / (max(x) - min(x))
-
-matplot(apply(datamat, 1, normi2),
- type = "l", lty = 1, col = c(1, 2, 4), lwd = 2,
- ylab = "Load", xlab = "Time (1/2 hours)")
-
-
-cont_dist <- dist(scale(matcontrib))
-cont_dist <- cont_dist / max(cont_dist)
-
-
-delta <- p
-n <- nrow(datamat)
-
-## _.a CWT -- Filtering the lowest freqs (>6m) ####
-nvoice <- 4
-# noctave4 = 2^13 = 8192 half hours ~ 180 days
-noctave4 <- adjust.noctave(N = delta, dt = 1, s0 = 2,
- tw = 0, noctave = 10)
-# 10 here represent 2^10 = 1024 half-hours ~ 2 weeks day
-scalevector4 <- 2^(4:(noctave4 * nvoice) / nvoice) * 2
-lscvect4 <- length(scalevector4)
-lscvect <- lscvect4 # i should clean my code: werFam demands a lscvect
-Xcwt4 <- toCWT(datamat, noctave = noctave4, dt = 1,
- scalevector = scalevector4,
- lt = delta, 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(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) )
-
-rm(conso, Xcwt4); gc()
-
-lscvect <- 41 ## very very nasty: toCWT changes scalevector to 41 (instead of
- ## the original length--37--)
-## _.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(delta * lscvect * (1 - wer2))
- Xwer_dist[j, i] <- Xwer_dist[i, j]
- }
-}
-diag(Xwer_dist) <- numeric(n)
-
-
-Xwer_dist <- Xwer_dist / max(Xwer_dist)
-
-
-pdf("~/cmdscale.pdf", width = 14)
-#layout(matrix(1:2, 1, 2))
-par(mai = c(1, 1, 0.8, 0.6), mfcol = c(1, 2), cex = 2)
-plot(cmdscale(cont_dist), pch = c("A", "B", "C"),#c(15, 17, 19),
- main = "RC based distance",
- #col = c(1, 2, 4),
- xlim = c(-1, 1), ylim = c(-1, 1),
- xlab = "", ylab = "", asp = 1)
-abline(h= 0, lwd = 2); abline(v = 0, lwd = 2); grid(lty = 1)
-
-plot(cmdscale(Xwer_dist), pch = c("A", "B", "C"),#c(15, 17, 19),
- main = "WER distance",
- #col = c(1, 2, 4),
- xlim = c(-1, 1), ylim = c(-1, 1),
- xlab = "", ylab = "", asp = 1)
-abline(h= 0, lwd = 2); abline(v = 0, lwd = 2); grid(lty = 1)
-dev.off()
-
-
-
-## Effectives distribution
-#load('../res/clfit500.Rdata')
-pdf("~/distro500.pdf", width = 12)
-plot(sort(table(clfit$clustering), decreasing = TRUE),
- type ="s", ylab = "Effectives", xlab = "Class", lwd = 2)
-abline(v = 200)
-#dev.off()
-
projects / epclust.git / blobdiff