From: Benjamin Auder Date: Mon, 7 Nov 2016 15:14:22 +0000 (+0100) Subject: add code Jairo X-Git-Url: https://git.auder.net/images/assets/doc/html/%7B%7B%20targetUrl%20%7D%7D?a=commitdiff_plain;h=ad642dc605bbbd0b3fa890c78fa8d634b1d6f703;p=epclust.git add code Jairo --- diff --git a/codes_Jairo_stage2/00_convertir-donnnes_2009.r b/codes_Jairo_stage2/00_convertir-donnnes_2009.r new file mode 100644 index 0000000..bf2578b --- /dev/null +++ b/codes_Jairo_stage2/00_convertir-donnnes_2009.r @@ -0,0 +1,172 @@ +## File : 00_convertir-donnnes_2009.r +## Description : Converts flat EDF's 32K data into a full data matrix +## layout [individuals, variables]. Rownames are EDF's ids. +## We process the original flat file sequentially by lines +## to avoid exceding the available RAM memory (and so avoiding +## swaping which is a computational burden). + + +rm(list = ls()) + +# setwd("~/ownCloud/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(6000, 3), 7011) # We'll process 1000 x p lines at each + # iteration of the reading loop + +## 2. Process the large flat file #### +## We want to check that every time step recorded for each id. + +con <- file("~/tmp/data/2009.csv") # Establish a connection to the file +open(con, "r") # Open the connection +rien <- readLines(con = con, n = 1); rm(rien) # Discard 1st line + +for(b in seq_along(blocks)){ # Reading loop + nb <- blocks[b] + actual <- readLines(con = con, n = nb * length(dates)) + auxmat <- matrix(unlist(strsplit(actual, ",")), ncol = 4, byrow = TRUE) + rm(actual) + auxdf <- data.frame(id = as.integer(auxmat[, 1]), + date = auxmat[, 2], + val = as.numeric( + ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3]))) + rm(auxmat) # free up some space + + tab <- table(auxdf$id) + idtt <- NULL + for(tt in as.integer(names(which(tab < p)))) { # id with less than p records! + print(tt) + idtt <- c(idtt, which(auxdf$id == tt)) + } + + idmat <- matrix(auxdf$id[-idtt], ncol = p, byrow = TRUE) + alldatesperid <- apply(idmat[-idtt, ], 1, sd) == 0 + valmat <- matrix(auxdf$val[-idtt], ncol = p, byrow = TRUE) + + # store separatelly full records from partial records + write.table(file = paste0("~/tmp/2009_full_", b, ".txt"), valmat, + row.names = idmat[, 1], col.names = FALSE) + write.table(file = paste0("~/tmp/2009_partial_", b, ".txt"), auxdf[idtt, ]) +} + +close(con) # close connection to the file + +rm(auxdf, idmat, valmat, alldatesperid, b, # clean up some memory + idtt, blocks, tab, tt, con) + + +## 3. Complete partial records #### +## After analysis, partial records are only 119 clients from which one only +## time step (01JAN2009:00:00:00) is lacking. + +df_partial <- NULL +for(f in list.files("~/tmp/", "2009_partial_*")) + df_partial <- rbind(df_partial, read.table(paste0('~/tmp/', f))) + +tab <- table(df_partial$id) +id_incomp <- as.integer(names(which(tab < p))) # Incomplete records + +df_partial_full <- rbind(df_partial, + data.frame(id = id_incomp, + date = "01JAN2009:00:00:00", + val = NA)) + +rm(df_partial) + +# tab2 <- table(df_partial_full$id) # Check that df_partial_full is full +# head(sort(tab2)) + + +## 4. Reorder the lines to get the data matrix #### +## As we paste chunks of partial records and impute some time steps, +## the original order of the data is broken. We fix it by reordering +## the ids and then the data. + +idx_ordered <- order(df_partial_full$id) # order ids +df_partial_full2 <- df_partial_full[idx_ordered, ] +rm(df_partial_full) + +# Order data values following the correct dates (as the date is a factor +# we need to seek for each value: this is computationnaly innefficient). + +valmat <- matrix(df_partial_full2$val, ncol = p, byrow = TRUE) +datemat <- matrix(df_partial_full2$date, ncol = p, byrow = TRUE) +idmat <- matrix(df_partial_full2$id, ncol = p, byrow = TRUE) + +# Use this for as a check by running it twice. On the second run no +# printing should be done (because records should be ordered). +for(line in 1:nrow(datemat)) { + if(any(datemat[line, ] != dates)) { # TRUE is line is not ordered + cat(sprintf("\nline %i is not ordered", line)) + + neworder <- match(dates, datemat[line, ]) + valmat[line , ] <- valmat[ line, neworder] + datemat[line , ] <- datemat[line, neworder] + } +} + + +## 5. Write on disk the full data matrix of partial records #### + +write.table(file = "~/tmp/2009_full_Z.txt", valmat, + row.names = idmat[, 1], col.names = FALSE) +rm(list = ls()) +gc() + +## 6. Compile data files in BASH #### + +# cat 2009_full*.txt > 2009_full.txt +# rm 2009_full_*.txt 2009_partial_*.txt + + +## A. data.table & reshape2 #### +## When large RAM memory is available, one could use this code to process +## everything in memory. + +#library(data.table) +#library(reshape2) + +#dt <- fread(input = "~/tmp/data/2009_chunk.csv") + +#dt[, charge := ifelse(is.na(CPP_PUISSANCE_CORRIGEE), +# CPP_PUISSANCE_BRUTE, +# CPP_PUISSANCE_CORRIGEE), ] +#dt[, CPP_PUISSANCE_CORRIGEE := NULL] +#dt[, CPP_PUISSANCE_BRUTE := NULL] + +#dt2 <- dcast.data.table(data = dt, CPP_DATE_PUISSANCE + FK_CCU_ID ~ charge) + + +## Z. Probably stuff to be deleted + +# searchpos <- function(row) { +# str <- strsplit(row, ",") +# +# auxmat <- matrix(unlist(str), ncol = 4, byrow = TRUE); rm(str) +# +# auxdf <- data.frame(id = as.integer(auxmat[, 1]), +# date = auxmat[, 2], +# val = as.numeric( +# ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3])) +# ) +# rm(auxmat) +# +# idmat <- matrix(auxdf$id, ncol = length(dates), byrow = TRUE) +# alldatesperid <- apply(idmat, 1, sd) == 0 +# +# +# # lines <- match(auxdf$id, identifiants) +# # cols <- match(auxdf$date, dates) +# +# return(cbind(lines, cols, auxdf$val)) +# } diff --git a/codes_Jairo_stage2/00_convertir-donnnes_2010.r b/codes_Jairo_stage2/00_convertir-donnnes_2010.r new file mode 100644 index 0000000..1f2326b --- /dev/null +++ b/codes_Jairo_stage2/00_convertir-donnnes_2010.r @@ -0,0 +1,174 @@ +## File : 00_convertir-donnnes_2010.r +## Description : Converts flat EDF's 32K data into a full data matrix +## layout [individuals, variables]. Rownames are EDF's ids. +## We process the original flat file sequentially by lines +## to avoid exceding the available RAM memory (and so avoiding +## swaping which is a computational burden). + + +rm(list = ls()) + +setwd("~/ownCloud/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("2010", dates0)] +rm(dates0) + +n <- length(identifiants) +p <- length(dates) + +blocks <- c(rep(1000, 24), 1011) # We'll process 1000 x p lines at each + # iteration of the reading loop + +## 2. Process the large flat file #### +## We want to check that every time step recorded for each id. + +con <- file("~/tmp/data/2010.csv") # Establish a connection to the file +open(con, "r") # Open the connection +rien <- readLines(con = con, n = 1); rm(rien) # Discard 1st line + +for(b in seq_along(blocks)){ # Reading loop + nb <- blocks[b] + actual <- readLines(con = con, n = nb * length(dates)) + auxmat <- matrix(unlist(strsplit(actual, ",")), ncol = 4, byrow = TRUE) + rm(actual) + auxdf <- data.frame(id = as.integer(auxmat[, 1]), + date = auxmat[, 2], + val = as.numeric( + ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3]))) + rm(auxmat) # free up some space + + tab <- table(auxdf$id) + idtt <- NULL + for(tt in as.integer(names(which(tab < p)))) { # id with less than p records! + print(tt) + idtt <- c(idtt, which(auxdf$id == tt)) + } + + if(is.null(idtt)) { # no incomplete records + idmat <- matrix(auxdf$id, ncol = p, byrow = TRUE) + alldatesperid <- apply(idmat, 1, sd) == 0 + valmat <- matrix(auxdf$val, ncol = p, byrow = TRUE) + } else { + idmat <- matrix(auxdf$id[-idtt], ncol = p, byrow = TRUE) + alldatesperid <- apply(idmat[-idtt, ], 1, sd) == 0 + valmat <- matrix(auxdf$val[-idtt], ncol = p, byrow = TRUE) + + # store separatelly partial records + write.table(file = paste0("~/tmp/2010_partial_", b, ".txt"), auxdf[idtt, ]) + } + + # store full records + write.table(file = paste0("~/tmp/2010_full_", b, ".txt"), valmat, + row.names = idmat[, 1], col.names = FALSE) +} + +close(con) # close connection to the file + +rm(auxdf, idmat, valmat, alldatesperid, b, # clean up some memory + idtt, blocks, tab, tt, con) + + +## 3. Complete partial records #### NOT NECESSARY FOR 2010 +## After analysis, partial records are only 119 clients from which one only +## time step (01JAN2009:00:00:00) is lacking. + +#df_partial <- NULL +#for(f in list.files("~/tmp/", "2009_partial_*")) +# df_partial <- rbind(df_partial, read.table(paste0('~/tmp/', f))) + +#tab <- table(df_partial$id) +#id_incomp <- as.integer(names(which(tab < p))) # Incomplete records + +#df_partial_full <- rbind(df_partial, +# data.frame(id = id_incomp, +# date = "01JAN2009:00:00:00", +# val = NA)) + +#rm(df_partial) + +# tab2 <- table(df_partial_full$id) # Check that df_partial_full is full +# head(sort(tab2)) + + +## 4. Reorder the lines to get the data matrix #### NOT NECESSARY FOR 2010 +## As we paste chunks of partial records and impute some time steps, +## the original order of the data is broken. We fix it by reordering +## the ids and then the data. + +#idx_ordered <- order(df_partial_full$id) # order ids +#df_partial_full2 <- df_partial_full[idx_ordered, ] +#rm(df_partial_full) + +# Order data values following the correct dates (as the date is a factor +# we need to seek for each value: this is computationnaly innefficient). + +#valmat <- matrix(df_partial_full2$val, ncol = p, byrow = TRUE) +#datemat <- matrix(df_partial_full2$date, ncol = p, byrow = TRUE) +#idmat <- matrix(df_partial_full2$id, ncol = p, byrow = TRUE) + +# Use this for as a check by running it twice. On the second run no +# printing should be done (because records should be ordered). +#for(line in 1:nrow(datemat)) { +# if(any(datemat[line, ] != dates)) { # TRUE is line is not ordered +# cat(sprintf("\nline %i is not ordered", line)) +# +# neworder <- match(dates, datemat[line, ]) +# valmat[line , ] <- valmat[ line, neworder] +# datemat[line , ] <- datemat[line, neworder] +# } +#} + + +## 5. Write on disk the full data matrix of partial records #### + +#write.table(file = "~/tmp/2009_full_Z.txt", valmat, +# row.names = idmat[, 1], col.names = FALSE) +rm(list = ls()) +gc() + + +## A. data.table & reshape2 #### +## When large RAM memory is available, one could use this code to process +## everything in memory. + +#library(data.table) +#library(reshape2) + +#dt <- fread(input = "~/tmp/data/2009_chunk.csv") + +#dt[, charge := ifelse(is.na(CPP_PUISSANCE_CORRIGEE), +# CPP_PUISSANCE_BRUTE, +# CPP_PUISSANCE_CORRIGEE), ] +#dt[, CPP_PUISSANCE_CORRIGEE := NULL] +#dt[, CPP_PUISSANCE_BRUTE := NULL] + +#dt2 <- dcast.data.table(data = dt, CPP_DATE_PUISSANCE + FK_CCU_ID ~ charge) + + +## Z. Probably stuff to be deleted + +# searchpos <- function(row) { +# str <- strsplit(row, ",") +# +# auxmat <- matrix(unlist(str), ncol = 4, byrow = TRUE); rm(str) +# +# auxdf <- data.frame(id = as.integer(auxmat[, 1]), +# date = auxmat[, 2], +# val = as.numeric( +# ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3])) +# ) +# rm(auxmat) +# +# idmat <- matrix(auxdf$id, ncol = length(dates), byrow = TRUE) +# alldatesperid <- apply(idmat, 1, sd) == 0 +# +# +# # lines <- match(auxdf$id, identifiants) +# # cols <- match(auxdf$date, dates) +# +# return(cbind(lines, cols, auxdf$val)) +# } diff --git a/codes_Jairo_stage2/00_convertir-donnnes_2011.r b/codes_Jairo_stage2/00_convertir-donnnes_2011.r new file mode 100644 index 0000000..53e8cdf --- /dev/null +++ b/codes_Jairo_stage2/00_convertir-donnnes_2011.r @@ -0,0 +1,176 @@ +## File : 00_convertir-donnnes_2011.r +## Description : Converts flat EDF's 32K data into a full data matrix +## layout [individuals, variables]. Rownames are EDF's ids. +## We process the original flat file sequentially by lines +## to avoid exceding the available RAM memory (and so avoiding +## swaping which is a computational burden). + + +rm(list = ls()) + +setwd("~/Documents/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("2011", dates0)] +rm(dates0) + +n <- length(identifiants) +p <- length(dates) + +blocks <- c(rep(1000, 8), 685) # We'll process 1000 x p lines at each + # iteration of the reading loop + +## 2. Process the large flat file #### +## We want to check that every time step recorded for each id. + +con <- file("~/tmp/data/2011.csv") # Establish a connection to the file +open(con, "r") # Open the connection +rien <- readLines(con = con, n = 1); rm(rien) # Discard 1st line + +for(b in seq_along(blocks)){ # Reading loop + nb <- blocks[b] + actual <- readLines(con = con, n = nb * length(dates)) + auxmat <- matrix(unlist(strsplit(actual, ",")), ncol = 3, byrow = TRUE) + rm(actual) + auxdf <- data.frame(id = as.integer(auxmat[, 3]), + date = auxmat[, 1], + val = as.numeric(auxmat[, 2])) + rm(auxmat) # free up some space + + tab <- table(auxdf$id) + idtt <- NULL + for(tt in as.integer(names(which(tab < p)))) { # id with less than p records! + print(tt) + idtt <- c(idtt, which(auxdf$id == tt)) + } + + if(is.null(idtt)) { # no incomplete records + idmat <- matrix(auxdf$id, ncol = p, byrow = TRUE) + alldatesperid <- apply(idmat, 1, sd) == 0 + valmat <- matrix(auxdf$val, ncol = p, byrow = TRUE) + } else { + idmat <- matrix(auxdf$id[-idtt], ncol = p, byrow = TRUE) + alldatesperid <- apply(idmat[-idtt, ], 1, sd) == 0 + valmat <- matrix(auxdf$val[-idtt], ncol = p, byrow = TRUE) + + # store separatelly partial records + write.table(file = paste0("~/tmp/2011_partial_", b, ".txt"), auxdf[idtt, ]) + } + + # store full records + write.table(file = paste0("~/tmp/2011_full_", b, ".txt"), valmat, + row.names = idmat[, 1], col.names = FALSE) +} + +close(con) # close connection to the file + +rm(auxdf, idmat, valmat, alldatesperid, b, # clean up some memory + idtt, blocks, tab, tt, con) + + +## 3. Complete partial records #### + +# Missing data in 2011 is quite messy. The number of missing records +# vary from one client to another (see tab) + +df_partial <- NULL +for(f in list.files("~/tmp/", "2011_partial_*")) + df_partial <- rbind(df_partial, read.table(paste0('~/tmp/', f))) + +tab <- table(df_partial$id) +id_incomp <- as.integer(names(which(tab < p))) # Incomplete records + +# The equivalent of 2009's df_partial_full is not easy to construct + +#df_partial_full <- rbind(df_partial, +# data.frame(id = id_incomp, +# date = "01JAN2009:00:00:00", +# val = NA)) + +rm(df_partial) + +# tab2 <- table(df_partial_full$id) # Check that df_partial_full is full +# head(sort(tab2)) + + +## 4. Reorder the lines to get the data matrix #### +## As we paste chunks of partial records and impute some time steps, +## the original order of the data is broken. We fix it by reordering +## the ids and then the data. + +idx_ordered <- order(df_partial_full$id) # order ids +df_partial_full2 <- df_partial_full[idx_ordered, ] +rm(df_partial_full) + +# Order data values following the correct dates (as the date is a factor +# we need to seek for each value: this is computationnaly innefficient). + +valmat <- matrix(df_partial_full2$val, ncol = p, byrow = TRUE) +datemat <- matrix(df_partial_full2$date, ncol = p, byrow = TRUE) +idmat <- matrix(df_partial_full2$id, ncol = p, byrow = TRUE) + +# Use this for as a check by running it twice. On the second run no +# printing should be done (because records should be ordered). +for(line in 1:nrow(datemat)) { + if(any(datemat[line, ] != dates)) { # TRUE is line is not ordered + cat(sprintf("\nline %i is not ordered", line)) + + neworder <- match(dates, datemat[line, ]) + valmat[line , ] <- valmat[ line, neworder] + datemat[line , ] <- datemat[line, neworder] + } +} + + +## 5. Write on disk the full data matrix of partial records #### + +write.table(file = "~/tmp/2009_full_Z.txt", valmat, + row.names = idmat[, 1], col.names = FALSE) +rm(list = ls()) +gc() + + +## A. data.table & reshape2 #### +## When large RAM memory is available, one could use this code to process +## everything in memory. + +#library(data.table) +#library(reshape2) + +#dt <- fread(input = "~/tmp/data/2009_chunk.csv") + +#dt[, charge := ifelse(is.na(CPP_PUISSANCE_CORRIGEE), +# CPP_PUISSANCE_BRUTE, +# CPP_PUISSANCE_CORRIGEE), ] +#dt[, CPP_PUISSANCE_CORRIGEE := NULL] +#dt[, CPP_PUISSANCE_BRUTE := NULL] + +#dt2 <- dcast.data.table(data = dt, CPP_DATE_PUISSANCE + FK_CCU_ID ~ charge) + + +## Z. Probably stuff to be deleted + +# searchpos <- function(row) { +# str <- strsplit(row, ",") +# +# auxmat <- matrix(unlist(str), ncol = 4, byrow = TRUE); rm(str) +# +# auxdf <- data.frame(id = as.integer(auxmat[, 1]), +# date = auxmat[, 2], +# val = as.numeric( +# ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3])) +# ) +# rm(auxmat) +# +# idmat <- matrix(auxdf$id, ncol = length(dates), byrow = TRUE) +# alldatesperid <- apply(idmat, 1, sd) == 0 +# +# +# # lines <- match(auxdf$id, identifiants) +# # cols <- match(auxdf$date, dates) +# +# return(cbind(lines, cols, auxdf$val)) +# } diff --git a/codes_Jairo_stage2/00_createCalendar.r b/codes_Jairo_stage2/00_createCalendar.r new file mode 100644 index 0000000..caf48ce --- /dev/null +++ b/codes_Jairo_stage2/00_createCalendar.r @@ -0,0 +1,58 @@ +## File: createCalendar.r +## Description: Create the calendar transitions to be used on KWF predictor +## Author: jc @ September 2015 + +## Function: irishCalendar +## Description: Create the calendar with day of the week and daytype. +## (It is the first step, then we need to obtain the transitions) +irishCalendar <- function(from, to){ + require(timeDate) + y0 <- as.character(substr(from, 1, 4)) + y1 <- as.character(substr(to, 1, 4)) + years <- y0:y1 + IRholidays <- c("NewYearsDay", + #saint patrick + "EasterMonday", + "GBMayDay", # may day + #june holiday + "GBSummerBankHoliday", # august holiday + #october holiday + "ChristmasDay" + #, st stephen day + ) + + + jf <- holiday(year = years, Holiday = IRholidays) + jf <- sort(unique(jf)) + dts <- timeSequence(from = paste(y0, '01', '01', sep = '-'), + to = paste(y1, '12', '31', sep = '-')) + wday <- dayOfWeek(dts) + wdayf <- unname(wday) + for(d in 1:length(wdayf)){ + if(d == 1) fpos <- 1 + if(dts[d] == jf[fpos]){ + wdayf[d] <- "Hol" + if(fpos < length(jf)) fpos <- fpos + 1 + } + } + + res <- data.frame(dow = wday, daytype = wdayf) + i0 <- which(rownames(res) == from) + i1 <- which(rownames(res) == to) + + return(res[i0:i1, ]) +} + + +# Call irishCalendar to create the calendar. +myCal <- irishCalendar(from="2011-01-01", to= "2013-12-31") +n <- nrow(myCal) + +# Obtention of the transitions +gr <- paste(myCal$daytype[1:(n - 1)], myCal$daytype[2:n], sep = '_') +gr[gr == "Hol_Hol"] <- "Sun_Hol" +myCal$gr <- c(gr, "Sat_Sun") + +# Write output to a file +# write.table(file = "calendar_ir.txt", myCal) + diff --git a/codes_Jairo_stage2/00_plots-energycon.r b/codes_Jairo_stage2/00_plots-energycon.r new file mode 100644 index 0000000..9f70085 --- /dev/null +++ b/codes_Jairo_stage2/00_plots-energycon.r @@ -0,0 +1,190 @@ +## 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() + diff --git a/codes_Jairo_stage2/01_StBr.r b/codes_Jairo_stage2/01_StBr.r new file mode 100644 index 0000000..980a855 --- /dev/null +++ b/codes_Jairo_stage2/01_StBr.r @@ -0,0 +1,57 @@ +## File: StBr.r +## Description: screens meaningful variables and performns +## data transformation on clustering + +# rm(list = ls()) + + +## Description: Steinley & Brusco (2006) data transform to cluster +StBrtransform <- function(X){ + apply(X, 2, function(x) 12 * var(x) / (max(x) - min(x))^2 ) +} + + +## Description: Clustering index (Steinley & Brusco (2006)) +CI <- function(X, B = 1000) { # B : number of boostrap replications + + n <- nrow(X) + p <- ncol(X) + + #ci <- apply(X, 2, function(x) 12 * var(x) / (max(x) - min(x))^2 ) + ci <- StBrtransform(X) + + rc <- ci / min(ci) + minV <- which.min(rc) + + Xstar <- scale(X) + newRange <- apply(Xstar, 2, function(x) max(x) - min(x)) + + rmin <- newRange[minV] + + datat <- array(0.0, dim = dim(X)) + + # Reweighting X into datat + for(i in 1:p){ + v <- Xstar[, i] + temp <- rc[i] * (rmin / newRange[i])^2 + datat[, i] <- sqrt(temp) * v + } + + xboot <- matrix(rnorm(n * B), nrow = n) + #cinorm <- apply(xboot, 2, function(x) 12 * var(x) / (max(x) - min(x))^2 ) + cinorm <- StBrtransform(xboot) + ci95 <- median(cinorm) + + #ciStar <- apply(datat, 2, function(x) 12 * var(x) / (max(x) - min(x))^2 ) + ciStar <- StBrtransform(datat) + selectv <- which(ciStar > ci95) + + return(list(selectv = selectv, + tdata = datat) ) +} + + + + +#test <- matrix(rnorm(200), 40, 5) +#CI(test)$selectv diff --git a/codes_Jairo_stage2/01_extract-features2_2009.r b/codes_Jairo_stage2/01_extract-features2_2009.r new file mode 100644 index 0000000..85a2b5b --- /dev/null +++ b/codes_Jairo_stage2/01_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") + diff --git a/codes_Jairo_stage2/01_extract-features_2009.r b/codes_Jairo_stage2/01_extract-features_2009.r new file mode 100644 index 0000000..99ab8c5 --- /dev/null +++ b/codes_Jairo_stage2/01_extract-features_2009.r @@ -0,0 +1,58 @@ +## 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("~/ownCloud/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 #### + +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) + + auxDWT <- t(apply(datamat, 1, toDWT)) + auxcontrib <- t(apply(auxDWT, 1, contrib)) + rm(auxDWT) + + if(b == 1) { + matcontrib <- auxcontrib + } else { + matcontrib <- rbind(matcontrib, auxcontrib) + } + +} + +close(con) # close connection to the file + +write.table(matcontrib, file = "~/tmp/2009_contrib.txt") + diff --git a/codes_Jairo_stage2/01_extract-features_2010.r b/codes_Jairo_stage2/01_extract-features_2010.r new file mode 100644 index 0000000..a1d7d49 --- /dev/null +++ b/codes_Jairo_stage2/01_extract-features_2010.r @@ -0,0 +1,54 @@ +## File : 01_extract-features_2010.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("~/ownCloud/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("2010", dates0)] +rm(dates0) + +n <- length(identifiants) +p <- length(dates) + +blocks <- c(rep(6500, 3), 5511) + + +## 2. Process the large file #### + +close(con) +con <- file("~/tmp/2010_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) + + auxDWT <- t(apply(datamat, 1, toDWT)) + auxcontrib <- t(apply(auxDWT, 1, contrib)) + rm(auxDWT) + + if(b == 1) { + matcontrib <- auxcontrib + } else { + matcontrib <- rbind(matcontrib, auxcontrib) + } + +} + +close(con) # close connection to the file + +write.table(matcontrib, file = "~/tmp/2010_contrib.txt") + diff --git a/codes_Jairo_stage2/02_cluster-par_2009.r b/codes_Jairo_stage2/02_cluster-par_2009.r new file mode 100644 index 0000000..0f138c9 --- /dev/null +++ b/codes_Jairo_stage2/02_cluster-par_2009.r @@ -0,0 +1,106 @@ +## File: extract-features.r + +rm(list = ls()) + +## a. Load data & libraries #### + +#library(cluster) +#library(snow) +library(foreach) +library(doParallel) + +MOJARRITA <- Sys.info()[4] == "mojarrita" + +if(MOJARRITA){ + setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") +} else { + setwd("~/2014_EDF-Orsay-Lyon2/codes/") +} + +#source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r') +source('01_StBr.r') + +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$selectv + +## c. Clustering ########## + +#number of iterations +iters <- 20 + +#setup parallel backend to use 8 processors +cl <- makeCluster(20) +registerDoParallel(cl) + +clfitlist <- foreach(icount(iters)) %dopar% { + library(cluster) + K <- 200 + clara(x = tdata[, selvar], + k = K, + sampsize = 4000, + samples = 4, + rngR = TRUE) +} + +stopCluster(cl) + +#save(clfit, file = 'clfit500.Rdata') +# save(clfit, file = 'clfit200RC.Rdata') +#save(clfitlist, file = 'clfitlist200.Rdata') +#rm(ci, matcontrib0, is_normal, lims, selvar) +#gc() + + +res <- lapply(clfitlist, function(x) x$clustering) +names(res) <- 1:iters + +save(data.frame(res), file = 'res/clfitdf200.Rdata') + + +## 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) diff --git a/codes_Jairo_stage2/02_cluster_2009.r b/codes_Jairo_stage2/02_cluster_2009.r new file mode 100644 index 0000000..f08d82b --- /dev/null +++ b/codes_Jairo_stage2/02_cluster_2009.r @@ -0,0 +1,86 @@ +## 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( + 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) diff --git a/codes_Jairo_stage2/03_compute-sums-of-classes-par_2009.r b/codes_Jairo_stage2/03_compute-sums-of-classes-par_2009.r new file mode 100644 index 0000000..8e5e756 --- /dev/null +++ b/codes_Jairo_stage2/03_compute-sums-of-classes-par_2009.r @@ -0,0 +1,76 @@ +## File : 03_compute-sum-of-classes_2009.r +## Description : Calculer les synchrones pour chaque groupe obtenu par le +## clustering. + +rm(list = ls()) + +MOJARRITA <- Sys.info()[4] == "mojarrita" + +if(MOJARRITA){ + setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") +} else { + setwd("~/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) + +if(MOJARRITA) { + blocks <- c(rep(6500, 3), 5511) + } else { + blocks <- 25011 + } + +# Fit of the clustering : clfit +load('../res/clfitdf200.Rdata') # Loads res that containts + # clusterings memberships +res <- as.data.frame(res) + +lres <- length(res) +K <- 200 #nrow(clfit$clusinfo) + + +## 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)) # the NA introduced by as.numeric + # are NA strings + rownames(datamat) <- substr(auxmat[, 1], 2, 7) + rm(auxmat) + + synchros <- lapply(res, + function(ll) { + aux <- matrix(0, ncol = p, nrow = K) + for(k in 1:K) { + clustk <- which(ll == k) + if(length(clustk) > 1) { + aux[k, ] <- colSums(datamat[ll == k, ]) + } else { + aux[k, ] <- datamat[ll == k, ] + } + } + aux + }) +} + +close(con) # close connection to the file + +# save(synchros, file = "~/tmp/2009synchrosdf200WER") + diff --git a/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM-par_2009.r b/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM-par_2009.r new file mode 100644 index 0000000..cbd373b --- /dev/null +++ b/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM-par_2009.r @@ -0,0 +1,89 @@ +## File : 03_compute-sum-of-classes_2009.r +## Description : Calculer les synchrones pour chaque groupe obtenu par le +## clustering. + +rm(list = ls()) + +MOJARRITA <- Sys.info()[4] == "mojarrita" + +if(MOJARRITA){ + setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") +} else { + setwd("~/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) + +if(MOJARRITA) { + blocks <- c(rep(6500, 3), 5511) + } else { + blocks <- 25011 + } + +# Fit of the clustering : clfit +load('../res/clfitdf200.Rdata') # Loads res that containts + # clusterings memberships +res <- as.data.frame(res) + +resRDN <- as.data.frame(lapply(res, sample)) +#save(file = "../res/clfitdf200RDN.Rdata", resRDN) +load("../res/clfitdf200RDN.Rdata") + +lres <- length(res) +K <- 200 #nrow(clfit$clusinfo) +rm(res) + +## 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 ) + actual_split <- strsplit(actual, " ") + rm(actual) + + #auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) + #datamat <- t(apply(auxmat[, -1], 1, as.numeric)) # the NA introduced by + # as.numeric + auxlist <- lapply(actual_split, function(x) as.numeric(x[-1])) + datamat <- matrix(unlist(auxlist), ncol = p, byrow = TRUE) + rm(auxlist) + + # are NA strings + #rownames(datamat) <- substr(auxmat[, 1], 2, 7) + auxnames <- unlist(lapply(strsplit(actual, " "), "[", 1)) + rownames(datamat) <- substr(auxnames, 2, 7) + + rm(auxmat, actual_split) + + synchros <- lapply(resRDN, + function(ll) { + aux <- matrix(0, ncol = p, nrow = K) + for(k in 1:K) { + clustk <- which(ll == k) + if(length(clustk) > 1) { + aux[k, ] <- colSums(datamat[clustk, ]) + } else { + aux[k, ] <- datamat[clustk, ] + } + } + aux + }) +} + +close(con) # close connection to the file + +# save(synchros, file = "~/tmp/2009synchrosdf200RND") + diff --git a/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM_2009.r b/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM_2009.r new file mode 100644 index 0000000..016b6de --- /dev/null +++ b/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM_2009.r @@ -0,0 +1,82 @@ +## File : 03_compute-sum-of-classes_2009.r +## Description : Calculer les synchrones pour chaque groupe obtenu par le +## clustering. + +rm(list = ls()) + +setwd("~/Documents/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) + +# Fit of the clustering : clfit +#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit500.Rdata') +load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200.Rdata') +#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200RC.Rdata') +# table(clfit$clustering) + +dfclust <- data.frame(cluster = clfit$clustering) # real clustering +K <- nrow(clfit$clusinfo) + +#dfclust2 <- dfclust +dfclust$cluster <- sample(unlist(Map(rep, 1:200, table(dfclust)))) # random clust + + + +#dfclust <- head(dfclust, 50) # just for testing purpouses +synchros <- matrix(0, ncol = p, nrow = K) +rm(clfit) + +## 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) + + # obtain for each line of datamat the cluster membership (if any) + clustfactor <- dfclust$cluster[match(rownames(datamat), rownames(dfclust))] + +# if(length(which(rownames(datamat) == "218095"))>0) print(b); + + for(k in 1:K){ + clustk <- which(clustfactor == k) + if(length(clustk) > 0) { + + if(length(clustk) > 1) { + synchrosk <- colSums(datamat[which(clustfactor == k), ]) + } else { + synchrosk <- datamat[which(clustfactor == k), ] + } + synchros[k, ] <- synchros[k, ] + synchrosk + rm(synchrosk) + } + } +} + +close(con) # close connection to the file + +synchros <- data.frame(t(synchros), total = colSums(synchros)) + +write.table(synchros, file = "~/tmp/2009_synchros200RANDOM.txt") +write.table(dfclust, file = "../res/dfclust200RANDOM.txt") +#write.table(synchros, file = "~/tmp/2009_synchros200RC.txt") + diff --git a/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM_2010.r b/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM_2010.r new file mode 100644 index 0000000..0c75bd0 --- /dev/null +++ b/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM_2010.r @@ -0,0 +1,82 @@ +## File : 03_compute-sum-of-classes_2010.r +## Description : Calculer les synchrones pour chaque groupe obtenu par le +## clustering. + +rm(list = ls()) + +setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") + +## 1. Read auxiliar data files #### + +identifiants <- read.table("identifs.txt")[ ,1] +dates0 <- read.table("datesall.txt")[, 1] +dates <- dates0[grep("2009", dates0)] +rm(dates0) + +n <- length(identifiants) +p <- length(dates) + +blocks <- c(rep(6500, 3), 5511) + +# Fit of the clustering : clfit +#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit500.Rdata') +load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200.Rdata') +#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200RC.Rdata') +# table(clfit$clustering) + +dfclust <- data.frame(cluster = clfit$clustering) # real clustering +K <- nrow(clfit$clusinfo) + +#dfclust2 <- dfclust +dfclust$cluster <- sample(unlist(Map(rep, 1:200, table(dfclust)))) # random clust + + + +#dfclust <- head(dfclust, 50) # just for testing purpouses +synchros <- matrix(0, ncol = p, nrow = K) +rm(clfit) + +## 2. Process the large file #### + +close(con) +con <- file("~/tmp/2010_full.txt") # Establish a connection to the file +open(con, "r") # Open the connection + +for(b in seq_along(blocks)){ # Reading loop + nb <- blocks[b] + actual <- readLines(con = con, n = nb ) + auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) + rm(actual) + + datamat <- t(apply(auxmat[, -1], 1, as.numeric)) + rownames(datamat) <- substr(auxmat[, 1], 2, 7) + rm(auxmat) + + # obtain for each line of datamat the cluster membership (if any) + clustfactor <- dfclust$cluster[match(rownames(datamat), rownames(dfclust))] + +# if(length(which(rownames(datamat) == "218095"))>0) print(b); + + for(k in 1:K){ + clustk <- which(clustfactor == k) + if(length(clustk) > 0) { + + if(length(clustk) > 1) { + synchrosk <- colSums(datamat[which(clustfactor == k), ]) + } else { + synchrosk <- datamat[which(clustfactor == k), ] + } + synchros[k, ] <- synchros[k, ] + synchrosk + rm(synchrosk) + } + } +} + +close(con) # close connection to the file + +synchros <- data.frame(t(synchros), total = colSums(synchros)) + +write.table(synchros, file = "~/tmp/2009_synchros200RANDOM.txt") +write.table(dfclust, file = "../res/dfclust200RANDOM.txt") +#write.table(synchros, file = "~/tmp/2009_synchros200RC.txt") + diff --git a/codes_Jairo_stage2/03_compute-sums-of-classes_2009.r b/codes_Jairo_stage2/03_compute-sums-of-classes_2009.r new file mode 100644 index 0000000..0623ba1 --- /dev/null +++ b/codes_Jairo_stage2/03_compute-sums-of-classes_2009.r @@ -0,0 +1,78 @@ +## File : 03_compute-sum-of-classes_2009.r +## Description : Calculer les synchrones pour chaque groupe obtenu par le +## clustering. + +rm(list = ls()) + +setwd("~/ownCloud/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) + +# Fit of the clustering : clfit +#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit500.Rdata') +#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200.Rdata') +load('../res/clfit200RC.Rdata') +# table(clfit$clustering) + +names(clfit$clustering) <- sample(names(clfit$clustering))# for random classes +dfclust <- data.frame(cluster = clfit$clustering) +# write.table(dfclust, file = "../res/clfit200-random.txt") + +K <- nrow(clfit$clusinfo) +#dfclust <- head(dfclust, 50) # just for testing purpouses +synchros <- matrix(0, ncol = p, nrow = K) +rm(clfit) + +## 2. Process the large file #### + +close(con) +con <- file("~/tmp/2009_full.txt") # Establish a connection to the file +open(con, "r") # Open the connection + +for(b in seq_along(blocks)){ # Reading loop + nb <- blocks[b] + actual <- readLines(con = con, n = nb ) + auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) + rm(actual) + + datamat <- t(apply(auxmat[, -1], 1, as.numeric)) + rownames(datamat) <- substr(auxmat[, 1], 2, 7) + rm(auxmat) + + # obtain for each line of datamat the cluster membership (if any) + clustfactor <- dfclust$cluster[match(rownames(datamat), rownames(dfclust))] + +# if(length(which(rownames(datamat) == "218095"))>0) print(b); + + for(k in 1:K){ + clustk <- which(clustfactor == k) + if(length(clustk) > 0) { + + if(length(clustk) > 1) { + synchrosk <- colSums(datamat[which(clustfactor == k), ]) + } else { + synchrosk <- datamat[which(clustfactor == k), ] + } + synchros[k, ] <- synchros[k, ] + synchrosk + rm(synchrosk) + } + } +} + +close(con) # close connection to the file + +synchros <- data.frame(t(synchros), total = colSums(synchros)) + +# write.table(synchros, file = "~/tmp/2009_synchros200RC.txt") +# write.table(synchros, file = "~/tmp/2009_synchros200-random.txt") + diff --git a/codes_Jairo_stage2/03_compute-sums-of-classes_2010-par.r b/codes_Jairo_stage2/03_compute-sums-of-classes_2010-par.r new file mode 100644 index 0000000..1a4c9a1 --- /dev/null +++ b/codes_Jairo_stage2/03_compute-sums-of-classes_2010-par.r @@ -0,0 +1,79 @@ +## File : 03_compute-sum-of-classes_2010-par.r +## Description : Calculer les synchrones pour chaque groupe obtenu par le +## clustering. + +rm(list = ls()) + +MOJARRITA <- Sys.info()[4] == "mojarrita" + +if(MOJARRITA){ + setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") +} else { + setwd("~/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("2010", dates0)] +rm(dates0) + +n <- length(identifiants) +p <- length(dates) + + +if(MOJARRITA) { + blocks <- c(rep(6500, 3), 5511) +} else { + blocks <- 25011 +} + +# Fit of the clustering : clfit +load('../res/clfitdf200.Rdata') # Loads res that containts +# clusterings memberships +res <- as.data.frame(res) + + +lres <- length(res) +K <- 200 #nrow(clfit$clusinfo) + +## 2. Process the large file #### + +close(con) +con <- file("~/tmp/2010_full.txt") # Establish a connection to the file +open(con, "r") # Open the connection + +for(b in seq_along(blocks)){ # Reading loop + nb <- blocks[b] + actual <- readLines(con = con, n = nb ) + auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) + rm(actual) + + datamat <- t(apply(auxmat[, -1], 1, as.numeric)) # the NA introduced by as.numeric + # are NA strings + rownames(datamat) <- substr(auxmat[, 1], 2, 7) + rm(auxmat) + + synchros <- lapply(res, + function(ll) { + aux <- matrix(0, ncol = p, nrow = K) + for(k in 1:K) { + clustk <- which(ll == k) + if(length(clustk) > 1) { + aux[k, ] <- colSums(datamat[ll == k, ]) + } else { + aux[k, ] <- datamat[ll == k, ] + } + } + aux + }) +} + + close(con) # close connection to the file + +# save(synchros, file = "~/tmp/2010synchrosdf200WER") + + diff --git a/codes_Jairo_stage2/03_compute-sums-of-classes_2010.r b/codes_Jairo_stage2/03_compute-sums-of-classes_2010.r new file mode 100644 index 0000000..18d23b4 --- /dev/null +++ b/codes_Jairo_stage2/03_compute-sums-of-classes_2010.r @@ -0,0 +1,101 @@ +## File : 03_compute-sum-of-classes_2010.r +## Description : Calculer les synchrones pour chaque groupe obtenu par le +## clustering. + +rm(list = ls()) + +setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") + +## 1. Read auxiliar data files #### + +identifiants <- read.table("identifs.txt")[ ,1] +dates0 <- read.table("datesall.txt")[, 1] +dates <- dates0[grep("2010", dates0)] +rm(dates0) + +n <- length(identifiants) +p <- length(dates) + +blocks <- c(rep(6500, 3), 5511) + +# Fit of the clustering : clfit +#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit500.Rdata') +load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200.Rdata') +load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200RC.Rdata') +# table(clfit$clustering) + +dfclust <- data.frame(cluster = clfit$clustering) +# read write.table(dfclust, file = "../res/clfit200RC-random.txt") for random + +K <- nrow(clfit$clusinfo) +#dfclust <- head(dfclust, 50) # just for testing purpouses +synchros <- matrix(0, ncol = p, nrow = K) +rm(clfit) + +## 2. Process the large file #### + +close(con) +con <- file("~/tmp/2010_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) + + # obtain for each line of datamat the cluster membership (if any) + clustfactor <- dfclust$cluster[match(rownames(datamat), rownames(dfclust))] + + for(k in 1:K){ + clustk <- which(clustfactor == k) + if(length(clustk) > 0) { + + if(length(clustk) > 1) { + synchrosk <- colSums(datamat[which(clustfactor == k), ]) + } else { + synchrosk <- datamat[which(clustfactor == k), ] + } + synchros[k, ] <- synchros[k, ] + synchrosk + rm(synchrosk) + } + + } +} + +close(con) # close connection to the file + +synchros <- data.frame(t(synchros), total = colSums(synchros)) + +# write.table(synchros, file = "~/tmp/2010_synchros200RC.txt") +# write.table(synchros, file = "~/tmp/2010_synchros200-random.txt") +# +# + + +dfclust <- read.table("clfit200muchos.txt") + +for(pepe in 1:10) { + synchros <- matrix(0, ncol = p, nrow = K) + clustfactor <- dfclust[match(rownames(datamat), rownames(tdata)), pepe] + for(k in 1:K){ + clustk <- which(clustfactor == k) + if(length(clustk) > 0) { + if(length(clustk) > 1) { + synchrosk <- colSums(datamat[which(clustfactor == k), ]) + } else { + synchrosk <- datamat[which(clustfactor == k), ] + } + synchros[k, ] <- synchros[k, ] + synchrosk + rm(synchrosk) + } + } + + synchros <- data.frame(t(synchros), total = colSums(synchros)) + write.table(synchros, file = paste0(colnames(dfclust)[pepe], "2010.txt")) +} + diff --git a/codes_Jairo_stage2/04_predictions.r b/codes_Jairo_stage2/04_predictions.r new file mode 100644 index 0000000..010880e --- /dev/null +++ b/codes_Jairo_stage2/04_predictions.r @@ -0,0 +1,159 @@ +## File : 04_predictions.r +## Description : Calculer les synchrones pour chaque groupe obtenu par le +## clustering. + +rm(list = ls()) + +setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") + +## 1. Read auxiliar data files #### + +#identifiants <- read.table("identifs.txt")[ ,1] +dates0 <- read.table("datesall.txt")[, 1] +dates09 <- as.character(dates0[grep("2009", dates0)]) +dates10 <- as.character(dates0[grep("2010", dates0)]) +dates <- c(dates09, dates10) +rm(dates0, dates09, dates10) + +n <- length(identifiants) +p <- length(dates) + +# groups for the predictor +gr_calen <- read.table("calendar_transition_groups-1996-2011.txt") +i_start_calen <- which(rownames(gr_calen) == "2009-01-01") +i_end_calen <- which(rownames(gr_calen) == "2010-12-31") +gr <- gr_calen$gr[i_start_calen:i_end_calen] +gr <- gsub("Thu", "TWT", gr) +gr <- gsub("Wed", "TWT", gr) +gr <- gsub("Tue", "TWT", gr) + +days <- seq.Date(from = as.Date("2009/01/01"), + to = as.Date("2010/12/31"), + by = 'day') + +synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt")) +synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200.txt")) + +## 2. Imputation of missing values #### +# (done by linear interpolation) +nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing +synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) + +imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) +imput10 <- synchros10[, 4132:4133] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) + +synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518]) +synchros10 <- cbind(synchros10[, 1:4132], imput10, synchros10[, 4133:17518]) + + +rm(imput09, imput10, nas) +rm(gr_calen, i_start_calen, i_end_calen) + +# Set start and end timepoints for the prediction +i_00 <- which(days == "2009-01-01") +n_00 <- which(days == "2009-12-31") +n_01 <- which(days == "2010-12-31") + + +## 3. Construct predictors #### +library(kerwavfun) +synchros <- (cbind( synchros09, synchros10 )) + +clustfactors <- dir("~/tmp/", pattern = 'clustfactor') +maxK <- length(clustfactors) - 1 + +performK <- data.frame(K = rep(NA_real_, maxK), + mape = rep(NA_real_, maxK), + rmse = rep(NA_real_, maxK)) + +setwd("~/tmp/") +for(cf in seq_along(clustfactors)) { + #clustfactor <- read.table(file = "~/tmp/clustfactor3.txt") + clustfactor <- read.table(file = clustfactors[cf]) + + K <- nrow(unique(clustfactor)) + + performK[cf, 1] <- K + + SC <- matrix(0, ncol = ncol(synchros), nrow = K) + for(k in 1:K){ + clustk <- which(clustfactor == k) + if(length(clustk) > 0) { + if(length(clustk) > 1) { + SCk <- colSums(synchros[which(clustfactor == k), ]) + } else { + SCk <- synchros[which(clustfactor == k), ] + } + SC[k, ] <- SC[k, ] + SCk + rm(SCk) + } + } + + mat_Load <- matrix(colSums(SC), ncol = 48, byrow = TRUE) + mat_ks <- lapply(1:K, function(k) matrix(SC[k, ], ncol = 48, byrow = TRUE)) + + + ## 2. Transform to wkdata (performs DWT) ## + + wk_Load <- wkdata(X = c(t(mat_Load)), + gr = gr[1:nrow(mat_Load)], + p = 48) + + wks <- lapply(mat_ks, function(mat_k) + wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48)) + + ## 3. Begin of big loop over ## + grid <- n_00:(n_01 - 1) # these are the reference days + #h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid)) + + # output matrix of predicted days + # names of the predicted days (reference days + 1) + pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid)) + preds <- lapply(1:K, function(k) pred_Load) + + go <- Sys.time() + for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)] + if((q %/% 10) == (q / 10)) cat(paste(" iteration: ", q, "\n")) + Q <- grid[q] + + select_Load <- select.wkdata(wk_Load, i_00:Q) + aux_Load <- wavkerfun(obj = select_Load, kerneltype = "gaussian", + EPS = .Machine$double.eps) + pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X + + selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q)) + auxs <- lapply(selects, + function(k) wavkerfun(obj = k, + kerneltype = "gaussian", + EPS = .Machine$double.eps)) + for(k in 1:K) + preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X + + } + + top <- Sys.time() + #rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load) + + # Compute the prediction of synchrone by the sum of individuals + pred_Load_sum <- Reduce('+', preds) + + ## 4. Analisis of the predictions #### + resids_Load <- pred_Load - mat_Load[grid + 1, ] # i.e. reference days + 1 + resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1 + + mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ])) + rmse_Load <- sqrt(rowMeans(resids_Load^2)) + + mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ])) + rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2)) + + performK[cf, 2] <- mean(mape_Load, na.rm = TRUE) + performK[cf, 3] <- mean(mape_Load_sum, na.rm = TRUE) +#print(mean(mape_Load, na.rm = TRUE)) +#print(mean(mape_Load_sum, na.rm = TRUE)) + +} + +performK <- performK[order(performK$K), ] +plot(performK$K, performK$rmse, type = 'l', ylim = c(2,3)) +abline(h = mean(performK$mape), col = 2) diff --git a/codes_Jairo_stage2/05_cluster2step.r b/codes_Jairo_stage2/05_cluster2step.r new file mode 100644 index 0000000..70dd0df --- /dev/null +++ b/codes_Jairo_stage2/05_cluster2step.r @@ -0,0 +1,74 @@ +## File : 05_cluster2step.r +## Description : + +rm(list = ls()) + +setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") +source("~/Documents/projects/2014_EDF-Lyon2/R/01_StBr.r") # aux functs +source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r') +library(cluster) + +## 1. Read auxiliar data files #### + +identifiants <- read.table("identifs.txt")[ ,1] +dates0 <- read.table("datesall.txt")[, 1] +dates <- as.character(dates0[grep("2009", dates0)]) +rm(dates0) + +n <- length(identifiants) +p <- length(dates) + +synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt")) +#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt")) +nas <- which(is.na(synchros09)[1, ]) # some 1/1/2009 are missing +synchros09[nas, 1] <- colMeans(synchros09[2:4, nas]) + +#synchros09 <- t(synchros09) # series on rows, use if necessary + +## 2. Compute contributions #### +auxDWT <- t(apply(synchros09, 1, toDWT)) +matcontrib <- t(apply(auxDWT, 1, contrib, rel = TRUE, logit = TRUE)) +rm(auxDWT) + +## 3. Transform data & compute CI #### +ci <- CI(matcontrib[-201, ]) # last row has the total +tdata <- ci$tdata; rownames(tdata) <- rownames(matcontrib)[-201] + +hc <- hclust(dist(tdata[, ci$selectv]), method = "ward.D") +plot(hc) + +#clust <- cutree(hc, 2) + +for(K in 2:30){ + #K <- 3 + pamfit <- pam(tdata[-201, ci$selectv], k = K, stand = FALSE) + + #table(pamfit$clustering) + + SC <- matrix(0, ncol = p, nrow = K) + + clustfactor <- pamfit$clustering +# for(k in 1:K){ +# clustk <- which(clustfactor == k) +# if(length(clustk) > 0) { +# if(length(clustk) > 1) { +# SCk <- colSums(synchros09[which(clustfactor == k), ]) +# } else { +# SCk <- synchros09[which(clustfactor == k), ] +# } +# SC[k, ] <- SC[k, ] + SCk +# rm(SCk) +# } +#} + + write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt")) +#write.table(clustfactor, file = "~/tmp/clustfactor3.txt") +} + +# Plots +layout(1) +matplot(t(SC)[48*10 + 1:(48*30), ], type = 'l', ylab = '',col = 1:3, lty = 1) +matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1) + + + diff --git a/codes_Jairo_stage2/05_cluster2stepWER-RANDOM.r b/codes_Jairo_stage2/05_cluster2stepWER-RANDOM.r new file mode 100644 index 0000000..5ba44a1 --- /dev/null +++ b/codes_Jairo_stage2/05_cluster2stepWER-RANDOM.r @@ -0,0 +1,135 @@ +## File : 05_cluster2stepWER.r +## Description : + +rm(list = ls()) + +if(Sys.info()[4] == "mojarrita"){ + setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") +} else { + setwd("~/2014_EDF-Orsay-Lyon2/codes/") +} + +library(Rwave) # CWT +library(cluster) # pam +#library(flexclust) # kcca +source("aux.r") # auxiliary clustering functions +source("sowas-superseded.r") # auxiliary CWT functions + +## 1. Read auxiliar data files #### + +identifiants <- read.table("identifs.txt")[ ,1] +dates0 <- read.table("datesall.txt")[, 1] +dates <- as.character(dates0[grep("2009", dates0)]) +rm(dates0) + +n <- length(identifiants) +p <- delta <- length(dates) + +load("~/tmp/2009_synchros200RND") +synchros09 <- synchros[[1]] +#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RANDOM.txt")) +#synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200RANDOM.txt"))) +nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing +synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) + +imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) +synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518]) + +conso <- (synchros09)[-201, ]; # series must be on rows +n <- nrow(conso) +delta <- ncol(conso) + +rm(synchros09, nas) + +## 2. Compute WER distance matrix #### + +## _.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 = 13) +# 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 = delta, smooth = FALSE, + nvoice = nvoice) # observations node with CWT + +Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect) +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() + +## _.b WER^2 distances ######## +Xwer_dist <- matrix(0.0, n, n) +for(i in 1:(n - 1)){ + cat(sprintf('\nIter: , %i', i)) + 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) + +save(Xwer_dist, file = "../res/2009_synchros200RANDOM-WER.Rdata") + +#load("../res/2009_synchros200WER.Rdata") + + +## 3. Cluster using WER distance matrix #### + +#hc <- hclust(as.dist(Xwer_dist), method = "ward.D") +#plot(hc) +# +# #clust <- cutree(hc, 2) +# + for(K in 2:30){ + #K <- 3 + #pamfit <- pam(tdata[-201, ci$selectv], k = K) + pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE) + + #table(pamfit$clustering) + + SC <- matrix(0, ncol = p, nrow = K) + + clustfactor <- pamfit$clustering + # for(k in 1:K){ + # clustk <- which(clustfactor == k) + # if(length(clustk) > 0) { + # if(length(clustk) > 1) { + # SCk <- colSums(synchros09[which(clustfactor == k), ]) + # } else { + # SCk <- synchros09[which(clustfactor == k), ] + # } + # SC[k, ] <- SC[k, ] + SCk + # rm(SCk) + # } + #} + +# #write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt")) +# #write.table(clustfactor, file = "~/tmp/clustfactor3.txt") + write.table(clustfactor, file = paste0("~/tmp/clustfactorRANDOM", K, ".txt")) + } +# +# # Plots +# layout(1) +# matplot(t(SC)[48*10 + 1:(48*30), ], type = 'l', ylab = '',col = 1:3, lty = 1) +# matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1) +# +# +# diff --git a/codes_Jairo_stage2/05_cluster2stepWER-par.r b/codes_Jairo_stage2/05_cluster2stepWER-par.r new file mode 100644 index 0000000..90988dc --- /dev/null +++ b/codes_Jairo_stage2/05_cluster2stepWER-par.r @@ -0,0 +1,153 @@ +## File : 05_cluster2stepWER.r +## Description : + +rm(list = ls()) + +if(Sys.info()[4] == "mojarrita"){ + setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") +} else { + setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/") +} + +library(Rwave) # CWT +library(cluster) # pam +#library(flexclust) # kcca +source("aux.r") # auxiliary clustering functions +source("sowas-superseded.r") # auxiliary CWT functions + +## 1. Read auxiliar data files #### + +identifiants <- read.table("identifs.txt")[ ,1] +dates0 <- read.table("datesall.txt")[, 1] +dates <- as.character(dates0[grep("2009", dates0)]) +rm(dates0) + +#n <- length(identifiants) +#p <- length(dates) + +load('~/tmp/2009synchrosdf200WER') +#load('../res/2009_synchros200WER.Rdata') +synchros09 <- synchros +load('~/tmp/2010synchrosdf200WER') +synchros10 <- synchros; rm(synchros) + +conso <- lapply(synchros09, function(ll) { + nas <- which(is.na(ll)[, 1]) # some 1/1/2009 are missing + ll[nas, 1] <- rowMeans(ll[nas, 2:4]) + imput <- ll[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) + cbind(ll[, 1:4180], imput, ll[, 4181:17518]) } ) + +n <- nrow(conso[[1]]) +delta <- ncol(conso[[1]]) +rm(synchros09) + + +## 2. Compute WER distance matrix #### + +## _.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 = 13) +# 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 <- lapply(conso, function(ll) + toCWT(ll, noctave = noctave4, dt = 1, + scalevector = scalevector4, + lt = delta, smooth = FALSE, + nvoice = nvoice) # observations node with CWT + ) + +rm(conso) + +Xcwt4 <- lapply(conso, function(ll) { + aux <- toCWT(ll, noctave = noctave4, dt = 1, + scalevector = scalevector4, + lt = delta, smooth = FALSE, + nvoice = nvoice) # observations node with CWT + res <- matrix(NA_complex_, nrow = n, ncol= 2 + length((c(aux[,,1])))) + for(i in 1:n) + res[i, ] <- c(delta, lscvect, res[,,i] / max(Mod(res[,,i])) ) + res }) + +rm(conso) + + +#Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect) +#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() + +## _.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) +# +# save(Xwer_dist, file = "../res/2009_synchros200WER.Rdata") + +load("../res/2009_synchros200WER.Rdata") + + +## 3. Cluster using WER distance matrix #### + +#hc <- hclust(as.dist(Xwer_dist), method = "ward.D") +#plot(hc) +# +# #clust <- cutree(hc, 2) +# +for(K in 2:30){ + #K <- 3 + #pamfit <- pam(tdata[-201, ci$selectv], k = K) + pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE) + + #table(pamfit$clustering) + + SC <- matrix(0, ncol = p, nrow = K) + + clustfactor <- pamfit$clustering +# for(k in 1:K){ +# clustk <- which(clustfactor == k) +# if(length(clustk) > 0) { +# if(length(clustk) > 1) { +# SCk <- colSums(synchros09[which(clustfactor == k), ]) +# } else { +# SCk <- synchros09[which(clustfactor == k), ] +# } +# SC[k, ] <- SC[k, ] + SCk +# rm(SCk) +# } +#} + +#write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt")) +#write.table(clustfactor, file = "~/tmp/clustfactor3.txt") +write.table(clustfactor, file = paste0("~/tmp/clustfactorWER", K, ".txt")) +} +# +# # Plots +# layout(1) +# matplot(t(SC)[48*10 + 1:(48*30), ], type = 'l', ylab = '',col = 1:3, lty = 1) +# matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1) +# +# +# diff --git a/codes_Jairo_stage2/05_cluster2stepWER.r b/codes_Jairo_stage2/05_cluster2stepWER.r new file mode 100644 index 0000000..dcc7fa3 --- /dev/null +++ b/codes_Jairo_stage2/05_cluster2stepWER.r @@ -0,0 +1,131 @@ +## File : 05_cluster2stepWER.r +## Description : + +rm(list = ls()) + +setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/") + +library(Rwave) # CWT +library(cluster) # pam +#library(flexclust) # kcca +source("aux.r") # auxiliary clustering functions +source("sowas-superseded.r") # auxiliary CWT functions + +## 1. Read auxiliar data files #### + +identifiants <- read.table("identifs.txt")[ ,1] +dates0 <- read.table("datesall.txt")[, 1] +dates <- as.character(dates0[grep("2009", dates0)]) +rm(dates0) + +n <- length(identifiants) +p <- delta <- length(dates) + +synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200RC.txt"))) +#synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200-random.txt"))) + +nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing +synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) + +imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) +synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518]) + +conso <- synchros09[-201, ]; # series must be on rows +n <- nrow(conso) +delta <- ncol(conso) + +rm(synchros09, nas) + +## 2. Compute WER distance matrix #### + +## _.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 = 13) +# # 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 = delta, smooth = FALSE, +# nvoice = nvoice) # observations node with CWT +# +# #Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect) +# #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() +# +# ## _.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) +# +# save(Xwer_dist, file = "../res/2009_synchros200WER.Rdata") +# save(Xwer_dist, file = "../res/2009_synchros200-randomWER.Rdata") + +load("../res/2009_synchros200WER.Rdata") +#load("../res/2009_synchros200-randomWER.Rdata") + +## 3. Cluster using WER distance matrix #### + +#hc <- hclust(as.dist(Xwer_dist), method = "ward.D") +#plot(hc) +# +# #clust <- cutree(hc, 2) +# +for(K in 2:30){ + #K <- 3 + #pamfit <- pam(tdata[-201, ci$selectv], k = K) + pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE) + + #table(pamfit$clustering) + + SC <- matrix(0, ncol = p, nrow = K) + + clustfactor <- pamfit$clustering +# for(k in 1:K){ +# clustk <- which(clustfactor == k) +# if(length(clustk) > 0) { +# if(length(clustk) > 1) { +# SCk <- colSums(synchros09[which(clustfactor == k), ]) +# } else { +# SCk <- synchros09[which(clustfactor == k), ] +# } +# SC[k, ] <- SC[k, ] + SCk +# rm(SCk) +# } +#} + +#write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt")) +#write.table(clustfactor, file = "~/tmp/clustfactor3.txt") +#write.table(clustfactor, file = paste0("~/tmp/clustfactorWER", K, ".txt")) +write.table(clustfactor, file = paste0("~/tmp/clustfactor-randomWER", K, ".txt")) +} +# +# # Plots +# layout(1) +# matplot(t(SC)[48*10 + 1:(48*30), ], type = 'l', ylab = '',col = 1:3, lty = 1) +# matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1) +# +# +# diff --git a/codes_Jairo_stage2/06_predictions-ICAME.r b/codes_Jairo_stage2/06_predictions-ICAME.r new file mode 100644 index 0000000..b7696a2 --- /dev/null +++ b/codes_Jairo_stage2/06_predictions-ICAME.r @@ -0,0 +1,160 @@ +## File : 06_predictions-ICAME.r +## Description : Prévoir les synchrones des classes ICAME. + + +rm(list = ls()) + +if(Sys.info()[4] == "mojarrita"){ + setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") +} else { + setwd("~/2014_EDF-Orsay-Lyon2/codes/") +} + +## 1. Read auxiliar data files #### +#identifiants <- read.table("identifs.txt")[ ,1] +dates0 <- read.table("datesall.txt")[, 1] +dates09 <- as.character(dates0[grep("2009", dates0)]) +dates10 <- as.character(dates0[grep("2010", dates0)]) +dates <- c(dates09, dates10) +rm(dates0, dates09, dates10) + +#n <- length(identifiants) +p <- length(dates) + +# groups for the predictor +gr_calen <- read.table("calendar_transition_groups-1996-2011.txt") +i_start_calen <- which(rownames(gr_calen) == "2009-01-01") +i_end_calen <- which(rownames(gr_calen) == "2010-12-31") +gr <- gr_calen$gr[i_start_calen:i_end_calen] +gr <- gsub("Thu", "TWT", gr) +gr <- gsub("Wed", "TWT", gr) +gr <- gsub("Tue", "TWT", gr) + +days <- seq.Date(from = as.Date("2009/01/01"), + to = as.Date("2010/12/31"), + by = 'day') + + +setwd("../classes ICAME/synchrones_classif_ICAME/") +files_ICAME <- dir(pattern = "*TR-32V*") +K_ICAME <- length(files_ICAME) +SC <- matrix(NA_real_, nrow = K_ICAME, ncol = p + 4) # NA missing in dates + # (summer time saving) + +for(k in 1:K_ICAME){ + df <- read.table(file = files_ICAME[k], header = TRUE) + SC[k, ] <- subset(x = df, # series must be on rows + subset = Year == "2009" | Year == "2010", + select = "Load")[[1]] +} + + +rm(df, files_ICAME, k) +rm(gr_calen, i_start_calen, i_end_calen) + +# Set start and end timepoints for the prediction +i_00 <- which(days == "2009-01-01") +n_00 <- which(days == "2009-12-31") +n_01 <- which(days == "2010-12-31") + + +## 2. Transform data into wkdata #### +library(kerwavfun) + +mat_Load <- matrix(colSums(SC), ncol = 48, byrow = TRUE) +mat_ks <- lapply(1:K_ICAME, function(k) matrix(data = SC[k, ], + ncol = 48, + byrow = TRUE)) + +wk_Load <- wkdata(X = c(t(mat_Load)), + gr = gr[1:nrow(mat_Load)], + p = 48) + +wks <- lapply(mat_ks, function(mat_k) + wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48)) + +## 3. Begin of big loop over grid ## +grid <- n_00:(n_01 - 1) # these are the reference days +#h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid)) + +# output matrix of predicted days +# names of the predicted days (reference days + 1) +pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid)) +preds <- lapply(1:K_ICAME, function(k) pred_Load) + +go <- Sys.time() +for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)] + if((q %/% 10) == (q / 10)) cat(paste(" iteration: ", q, "\n")) + Q <- grid[q] + + select_Load <- select.wkdata(wk_Load, i_00:Q) + aux_Load <- wavkerfun(obj = select_Load, kerneltype = "gaussian", + EPS = .Machine$double.eps) + pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X + + selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q)) + auxs <- lapply(selects, + function(k) wavkerfun(obj = k, + kerneltype = "gaussian", + EPS = .Machine$double.eps)) + for(k in 1:K_ICAME) + preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X + +} +top <- Sys.time() +#rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load) + + +preds <- lapply(preds, function(dd) {dd[dd<0] <- 0; dd} ) +# Compute the prediction of synchrone by the sum of individuals +pred_Load_sum <- Reduce('+', preds) + + +vect_preds <- data.frame(lapply(preds, function(dd) c(t(dd)))) +names(vect_preds) <- paste0("icame", 1:K_ICAME) + +vect_Load <- c(t(pred_Load)) +vect_gtop <- matrix(NA_real_, ncol = K_ICAME, nrow = length(vect_Load)) + + +for(tt in seq_along(vect_gtop)) { + VALID <- !any(is.na(vect_preds[tt, ])) & # compute gtop only when no NAs + tt < 17511 # these rows present a problem ==> why? (matbe too much 0s in preds_indiv) + if(VALID) { + res <- gtop(preds_indiv = as.numeric(vect_preds[tt, ]), + pred_total = vect_Load[tt], + weights_indiv = rep(1, K_ICAME), + weight_total = 1, + bounds_indiv = 0.05 * as.numeric(vect_preds[tt, ])) + vect_gtop[tt, ] <- res$preds_indiv[1:K_ICAME] # last value is the sum + } + } + +vect_gtop_sum <- rowSums(vect_gtop) +pred_gtop_sum <- matrix(vect_gtop_sum, ncol = 48, byrow = TRUE) + +## 4. Analisis of the predictions #### +resids_Load <- pred_Load - mat_Load[grid + 1, ] # i.e. reference days + 1 +resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1 +resids_gtop_sum <- pred_gtop_sum - mat_Load[grid + 1, ] + +mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ])) +rmse_Load <- sqrt(rowMeans(resids_Load^2)) + +mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ])) +rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2)) + +mape_gtop_sum <- 100 * rowMeans(abs(resids_gtop_sum / mat_Load[grid + 1, ])) +rmse_gtop_sum <- sqrt(rowMeans(resids_gtop_sum^2)) + + +mean(mape_Load, na.rm = TRUE) +mean(mape_Load_sum, na.rm = TRUE) +mean(mape_gtop_sum, na.rm = TRUE) +#print(mean(mape_Load, na.rm = TRUE)) +#print(mean(mape_Load_sum, na.rm = TRUE)) + + +mean(rmse_Load, na.rm = TRUE) +mean(rmse_Load_sum, na.rm = TRUE) +mean(rmse_gtop_sum, na.rm = TRUE) diff --git a/codes_Jairo_stage2/06_predictions-RANDOM.r b/codes_Jairo_stage2/06_predictions-RANDOM.r new file mode 100644 index 0000000..efc315c --- /dev/null +++ b/codes_Jairo_stage2/06_predictions-RANDOM.r @@ -0,0 +1,218 @@ +## File : 06_predictions.r +## Description : Calculer les synchrones pour chaque groupe +## obtenu par le clustering. + +rm(list = ls()) + +if(Sys.info()[4] == "mojarrita"){ + setwd("~/recherche/03_projects/2014_EDF-Orsay-Lyon2/codes/") +} else { + setwd("~/2014_EDF-Orsay-Lyon2/codes/") +} + +## 1. Read auxiliar data files #### + +identifiants <- read.table("identifs.txt")[ ,1] +dates0 <- read.table("datesall.txt")[, 1] +dates09 <- as.character(dates0[grep("2009", dates0)]) +dates10 <- as.character(dates0[grep("2010", dates0)]) +dates <- c(dates09, dates10) +rm(dates0, dates09, dates10) + +n <- length(identifiants) +p <- length(dates) + +# groups for the predictor +gr_calen <- read.table("calendar_transition_groups-1996-2011.txt") +i_start_calen <- which(rownames(gr_calen) == "2009-01-01") +i_end_calen <- which(rownames(gr_calen) == "2010-12-31") +gr <- gr_calen$gr[i_start_calen:i_end_calen] +gr <- gsub("Thu", "TWT", gr) +gr <- gsub("Wed", "TWT", gr) +gr <- gsub("Tue", "TWT", gr) + +days <- seq.Date(from = as.Date("2009/01/01"), + to = as.Date("2010/12/31"), + by = 'day') + +#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt")) +#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200.txt")) +#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt")) +#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200RC.txt")) +synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt")) +synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200RC.txt")) + + +synchros09 <- t(synchros09) # series must be on rows +synchros10 <- t(synchros10) # series must be on rows + +## 2. Imputation of missing values #### +# (done by linear interpolation) +nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing +synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) + +imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) +imput10 <- synchros10[, 4132:4133] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) + +synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518]) +synchros10 <- cbind(synchros10[, 1:4132], imput10, synchros10[, 4133:17518]) + + +rm(imput09, imput10, nas) +rm(gr_calen, i_start_calen, i_end_calen) + +# Set start and end timepoints for the prediction +i_00 <- which(days == "2009-01-01") +n_00 <- which(days == "2009-12-31") +n_01 <- which(days == "2010-12-31") + + +## 3. Construct predictors #### +library(kerwavfun) +synchros <- (cbind( synchros09, synchros10 ))[-201, ] + +#clustfactors <- dir("~/tmp/", pattern = 'clustfactorAC') +#clustfactors <- dir("~/tmp/", pattern = 'clustfactorRC') +#clustfactors <- dir("~/tmp/", pattern = 'clustfactorWER') +clustfactors <- dir("~/tmp/", pattern = 'clustfactorRANDOM') + +maxK <- length(clustfactors) - 1 + +performK <- data.frame(K = rep(NA_real_, maxK), + mape = rep(NA_real_, maxK), + rmse = rep(NA_real_, maxK)) + +setwd("~/tmp/") +for(cf in seq_along(clustfactors)) { + #clustfactor <- read.table(file = "~/tmp/clustfactor3.txt") + clustfactor <- read.table(file = clustfactors[cf]) + + K <- nrow(unique(clustfactor)) + + performK[cf, 1] <- K + + SC <- matrix(0, ncol = ncol(synchros), nrow = K) + for(k in 1:K){ + clustk <- which(clustfactor == k) + if(length(clustk) > 0) { + if(length(clustk) > 1) { + SCk <- colSums(synchros[which(clustfactor == k), ]) + } else { + SCk <- synchros[which(clustfactor == k), ] + } + SC[k, ] <- SC[k, ] + SCk + rm(SCk) + } + } + + mat_Load <- matrix(colSums(SC), ncol = 48, byrow = TRUE) + mat_ks <- lapply(1:K, function(k) matrix(SC[k, ], ncol = 48, byrow = TRUE)) + + + ## 2. Transform to wkdata (performs DWT) ## + + wk_Load <- wkdata(X = c(t(mat_Load)), + gr = gr[1:nrow(mat_Load)], + p = 48) + + wks <- lapply(mat_ks, function(mat_k) + wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48)) + + ## 3. Begin of big loop over ## + grid <- n_00:(n_01 - 1) # these are the reference days + #h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid)) + + # output matrix of predicted days + # names of the predicted days (reference days + 1) + pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid)) + preds <- lapply(1:K, function(k) pred_Load) + + go <- Sys.time() + for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)] + if((q %/% 10) == (q / 10)) cat(paste(" iteration: ", q, "\n")) + Q <- grid[q] + + select_Load <- select.wkdata(wk_Load, i_00:Q) + aux_Load <- wavkerfun(obj = select_Load, kerneltype = "gaussian", + EPS = .Machine$double.eps) + pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X + + selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q)) + auxs <- lapply(selects, + function(k) wavkerfun(obj = k, + kerneltype = "gaussian", + EPS = .Machine$double.eps)) + for(k in 1:K) + preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X + + } + + top <- Sys.time() + #rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load) + + # Compute the prediction of synchrone by the sum of individuals + pred_Load_sum <- Reduce('+', preds) + + ## 4. Analisis of the predictions #### + resids_Load <- pred_Load - mat_Load[grid + 1, ] # i.e. reference days + 1 + resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1 + + mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ])) + rmse_Load <- sqrt(rowMeans(resids_Load^2)) + + mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ])) + rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2)) + + performK[cf, 2] <- mean(mape_Load, na.rm = TRUE) + performK[cf, 3] <- mean(mape_Load_sum, na.rm = TRUE) +#print(mean(mape_Load, na.rm = TRUE)) +#print(mean(mape_Load_sum, na.rm = TRUE)) + +} + + +performKwer <- performK + +#performKrc <- read.table("~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt") +#write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt', +# performK) +#write.table(file = '~/ownCloud/projects/2014_EDF-Orsay-Lyon2/res/perform200RANDOM.txt', +# performK) + +#performK <- data.frame(K = performKrc$K, +# rc = performKrc$rmse, +# wer = performKwer$rmse) + + +setwd("~/recherche/03_projects/2014_EDF-Orsay-Lyon2/res/") +performKWER <- read.table("perform200WER.txt") +performKRND <- read.table("perform200RANDOM.txt") +performKrnd <- read.table("perform200-random.txt") + +performKWER <- performKWER[order(performKWER$K), ] +performKRND <- performKRND[order(performKRND$K), ] +performKrnd <- performKrnd[order(performKrnd$K), ] + +perform <- data.frame(k = performKRND$K, + k_check = performKWER$K, + random1 = performKrnd$rmse, + random2 = performKRND$rmse, + wer = performKWER$rmse, + agg = performKWER$mape) + +pdf("~/perf.pdf", width = 10) +plot(perform$k, perform$wer, type = 'l', ylim = c(1.7,2.2), + lwd = 2, col = "darkgrey", xlab = "Nb Cluster", + ylab = "MAPE (in %)") +lines(perform$k, perform$random2, lty = 2, lwd = 2) +lines(perform$k, perform$random1, lty = 3, lwd = 2) +abline(h = mean(perform$agg), lwd = 2) +legend("topright", c('AGG', "Random > WER", "RC > Random", "RC > WER"), + lwd = 2, lty = c(1, 3, 2, 1), + col = c(1, 1, 1, "darkgrey"), bg = "white") +dev.off() + + + + + diff --git a/codes_Jairo_stage2/06_predictions-par.r b/codes_Jairo_stage2/06_predictions-par.r new file mode 100644 index 0000000..4df8d27 --- /dev/null +++ b/codes_Jairo_stage2/06_predictions-par.r @@ -0,0 +1,190 @@ +## File : 06_predictions.r +## Description : Calculer les synchrones pour chaque groupe +## obtenu par le clustering. + +rm(list = ls()) + +if(Sys.info()[4] == "mojarrita"){ + setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") +} else { + setwd("~/2014_EDF-Orsay-Lyon2/codes/") +} + +## 1. Read auxiliar data files #### + +identifiants <- read.table("identifs.txt")[ ,1] +dates0 <- read.table("datesall.txt")[, 1] +dates09 <- as.character(dates0[grep("2009", dates0)]) +dates10 <- as.character(dates0[grep("2010", dates0)]) +dates <- c(dates09, dates10) +rm(dates0, dates09, dates10) + +n <- length(identifiants) +p <- length(dates) + +# groups for the predictor +gr_calen <- read.table("calendar_transition_groups-1996-2011.txt") +i_start_calen <- which(rownames(gr_calen) == "2009-01-01") +i_end_calen <- which(rownames(gr_calen) == "2010-12-31") +gr <- gr_calen$gr[i_start_calen:i_end_calen] +gr <- gsub("Thu", "TWT", gr) +gr <- gsub("Wed", "TWT", gr) +gr <- gsub("Tue", "TWT", gr) + +days <- seq.Date(from = as.Date("2009/01/01"), + to = as.Date("2010/12/31"), + by = 'day') + +#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt")) +#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200.txt")) +synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt")) +synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200RC.txt")) + +synchros09 <- t(synchros09) # series must be on rows +synchros10 <- t(synchros10) # series must be on rows + +## 2. Imputation of missing values #### +# (done by linear interpolation) +nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing +synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) + +imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) +imput10 <- synchros10[, 4132:4133] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) + +synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518]) +synchros10 <- cbind(synchros10[, 1:4132], imput10, synchros10[, 4133:17518]) + + +rm(imput09, imput10, nas) +rm(gr_calen, i_start_calen, i_end_calen) + +# Set start and end timepoints for the prediction +i_00 <- which(days == "2009-01-01") +n_00 <- which(days == "2009-12-31") +n_01 <- which(days == "2010-12-31") + + +## 3. Construct predictors #### +library(kerwavfun) +synchros <- (cbind( synchros09, synchros10 ))[-201, ] + +#clustfactors <- dir("~/tmp/", pattern = 'clustfactorAC') +#clustfactors <- dir("~/tmp/", pattern = 'clustfactorRC') +clustfactors <- dir("~/tmp/", pattern = 'clustfactorWER') + +maxK <- length(clustfactors) - 1 + +performK <- data.frame(K = rep(NA_real_, maxK), + mape = rep(NA_real_, maxK), + rmse = rep(NA_real_, maxK)) + +setwd("~/tmp/") +for(cf in seq_along(clustfactors)) { + #clustfactor <- read.table(file = "~/tmp/clustfactor3.txt") + clustfactor <- read.table(file = clustfactors[cf]) + + K <- nrow(unique(clustfactor)) + + performK[cf, 1] <- K + + SC <- matrix(0, ncol = ncol(synchros), nrow = K) + for(k in 1:K){ + clustk <- which(clustfactor == k) + if(length(clustk) > 0) { + if(length(clustk) > 1) { + SCk <- colSums(synchros[which(clustfactor == k), ]) + } else { + SCk <- synchros[which(clustfactor == k), ] + } + SC[k, ] <- SC[k, ] + SCk + rm(SCk) + } + } + + mat_Load <- matrix(colSums(SC), ncol = 48, byrow = TRUE) + mat_ks <- lapply(1:K, function(k) matrix(SC[k, ], ncol = 48, byrow = TRUE)) + + + ## 2. Transform to wkdata (performs DWT) ## + + wk_Load <- wkdata(X = c(t(mat_Load)), + gr = gr[1:nrow(mat_Load)], + p = 48) + + wks <- lapply(mat_ks, function(mat_k) + wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48)) + + ## 3. Begin of big loop over ## + grid <- n_00:(n_01 - 1) # these are the reference days + #h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid)) + + # output matrix of predicted days + # names of the predicted days (reference days + 1) + pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid)) + preds <- lapply(1:K, function(k) pred_Load) + + go <- Sys.time() + for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)] + if((q %/% 10) == (q / 10)) cat(paste(" iteration: ", q, "\n")) + Q <- grid[q] + + select_Load <- select.wkdata(wk_Load, i_00:Q) + aux_Load <- wavkerfun(obj = select_Load, kerneltype = "gaussian", + EPS = .Machine$double.eps) + pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X + + selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q)) + auxs <- lapply(selects, + function(k) wavkerfun(obj = k, + kerneltype = "gaussian", + EPS = .Machine$double.eps)) + for(k in 1:K) + preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X + + } + + top <- Sys.time() + #rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load) + + # Compute the prediction of synchrone by the sum of individuals + pred_Load_sum <- Reduce('+', preds) + + ## 4. Analisis of the predictions #### + resids_Load <- pred_Load - mat_Load[grid + 1, ] # i.e. reference days + 1 + resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1 + + mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ])) + rmse_Load <- sqrt(rowMeans(resids_Load^2)) + + mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ])) + rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2)) + + performK[cf, 2] <- mean(mape_Load, na.rm = TRUE) + performK[cf, 3] <- mean(mape_Load_sum, na.rm = TRUE) +#print(mean(mape_Load, na.rm = TRUE)) +#print(mean(mape_Load_sum, na.rm = TRUE)) + +} + + +performKwer <- performK + +#performKrc <- read.table("~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt") +#write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt', +# performK) +write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200WER_3.txt', + performK) + +#performK <- data.frame(K = performKrc$K, +# rc = performKrc$rmse, +# wer = performKwer$rmse) + + +performK <- performK[order(performK$K), ] + + +#plot(performK$K, performK$rc, type = 'l', ylim = c(1.7,2.2), +# lwd = 2, col = 2) +#lines(performK$K, performK$wer, col = 3, lwd = 2) +#abline(h = mean(performKrc$mape), lwd = 2) +#legend("topright", c('Sync 32K', 'Cl. RC', "Cl. WER"), lwd = 2, col = 1:3) diff --git a/codes_Jairo_stage2/06_predictions.r b/codes_Jairo_stage2/06_predictions.r new file mode 100644 index 0000000..6697161 --- /dev/null +++ b/codes_Jairo_stage2/06_predictions.r @@ -0,0 +1,204 @@ +## File : 06_predictions.r +## Description : Calculer les synchrones pour chaque groupe +## obtenu par le clustering. + +rm(list = ls()) + +setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/") + + +## 1. Read auxiliar data files #### + +identifiants <- read.table("identifs.txt")[ ,1] +dates0 <- read.table("datesall.txt")[, 1] +dates09 <- as.character(dates0[grep("2009", dates0)]) +dates10 <- as.character(dates0[grep("2010", dates0)]) +dates <- c(dates09, dates10) +rm(dates0, dates09, dates10) + +n <- length(identifiants) +p <- length(dates) + +# groups for the predictor +gr_calen <- read.table("calendar_transition_groups-1996-2011.txt") +i_start_calen <- which(rownames(gr_calen) == "2009-01-01") +i_end_calen <- which(rownames(gr_calen) == "2010-12-31") +gr <- gr_calen$gr[i_start_calen:i_end_calen] +gr <- gsub("Thu", "TWT", gr) +gr <- gsub("Wed", "TWT", gr) +gr <- gsub("Tue", "TWT", gr) + +days <- seq.Date(from = as.Date("2009/01/01"), + to = as.Date("2010/12/31"), + by = 'day') + +#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt")) +#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200.txt")) +#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt")) +#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200RC.txt")) +synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200-random.txt")) +synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200-random.txt")) + + +synchros09 <- t(synchros09) # series must be on rows +synchros10 <- t(synchros10) # series must be on rows + +## 2. Imputation of missing values #### +# (done by linear interpolation) +nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing +synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) + +imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) +imput10 <- synchros10[, 4132:4133] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) + +synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518]) +synchros10 <- cbind(synchros10[, 1:4132], imput10, synchros10[, 4133:17518]) + + +rm(imput09, imput10, nas) +rm(gr_calen, i_start_calen, i_end_calen) + +# Set start and end timepoints for the prediction +i_00 <- which(days == "2009-01-01") +n_00 <- which(days == "2009-12-31") +n_01 <- which(days == "2010-12-31") + + +## 3. Construct predictors #### +library(kerwavfun) +synchros <- (cbind( synchros09, synchros10 ))[-201, ] + +#clustfactors <- dir("~/tmp/", pattern = 'clustfactorAC') +#clustfactors <- dir("~/tmp/", pattern = 'clustfactorRC') +#clustfactors <- dir("~/tmp/", pattern = 'clustfactorWER') +#clustfactors <- dir("~/tmp/", pattern = 'clustfactorRANDOM') +clustfactors <- dir("~/tmp/", pattern = 'clustfactor-randomWER') + +maxK <- length(clustfactors) - 1 + +performK <- data.frame(K = rep(NA_real_, maxK), + mape = rep(NA_real_, maxK), + rmse = rep(NA_real_, maxK)) + +setwd("~/tmp/") +for(cf in seq_along(clustfactors)) { +# clustfactor <- read.table(file = "~/tmp/clustfactor3.txt") + clustfactor <- read.table(file = clustfactors[cf]) + + K <- nrow(unique(clustfactor)) + + performK[cf, 1] <- K + + SC <- matrix(0, ncol = ncol(synchros), nrow = K) + for(k in 1:K){ + clustk <- which(clustfactor == k) + if(length(clustk) > 0) { + if(length(clustk) > 1) { + SCk <- colSums(synchros[which(clustfactor == k), ]) + } else { + SCk <- synchros[which(clustfactor == k), ] + } + SC[k, ] <- SC[k, ] + SCk + rm(SCk) + } + } + + mat_Load <- matrix(colSums(SC), ncol = 48, byrow = TRUE) + mat_ks <- lapply(1:K, function(k) matrix(SC[k, ], ncol = 48, byrow = TRUE)) + + + ## 2. Transform to wkdata (performs DWT) ## + + wk_Load <- wkdata(X = c(t(mat_Load)), + gr = gr[1:nrow(mat_Load)], + p = 48) + + wks <- lapply(mat_ks, function(mat_k) + wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48)) + + ## 3. Begin of big loop over ## + grid <- n_00:(n_01 - 1) # these are the reference days + #h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid)) + + # output matrix of predicted days + # names of the predicted days (reference days + 1) + pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid)) + preds <- lapply(1:K, function(k) pred_Load) + + go <- Sys.time() + for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)] + if((q %/% 50) == (q / 50)) cat(paste(" iteration: ", q, "\n")) + Q <- grid[q] + + select_Load <- select.wkdata(wk_Load, i_00:Q) + aux_Load <- wavkerfun(obj = select_Load, kerneltype = "gaussian", + EPS = .Machine$double.eps) + pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X + + selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q)) + auxs <- lapply(selects, + function(k) wavkerfun(obj = k, + kerneltype = "gaussian", + EPS = .Machine$double.eps)) + for(k in 1:K) + preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X + + } + + top <- Sys.time() + #rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load) + + # Compute the prediction of synchrone by the sum of individuals + pred_Load_sum <- Reduce('+', preds) + + ## 4. Analisis of the predictions #### + resids_Load <- pred_Load - mat_Load[grid + 1, ] # i.e. reference days + 1 + resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1 + + mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ])) + rmse_Load <- sqrt(rowMeans(resids_Load^2)) + + mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ])) + rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2)) + + performK[cf, 2] <- mean(mape_Load, na.rm = TRUE) + performK[cf, 3] <- mean(mape_Load_sum, na.rm = TRUE) +#print(mean(mape_Load, na.rm = TRUE)) +#print(mean(mape_Load_sum, na.rm = TRUE)) + +} + + +#performKrc <- read.table("../res/perform200RC.txt") +#write.table(file = '~/ownCloud/projects/2014_EDF-Orsay-Lyon2/res/perform200-random.txt', +# performK) +#write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt', +# performK) +#write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200WER_3.txt', +# performK) + +#performK <- data.frame(K = performKrc$K, +# rc = performKrc$rmse, +# wer = performKwer$rmse) + +#perform <- rbind(performK, performKwer) +#perform <- perform[order(perform$K), ] + +plot(perform$K, perform$rmse, type = 'l', ylim = c(1.7,2.2), + lwd = 2, col = 2, xlab = "Nb Cluster", ylab = "MAPE (en %)") +lines(performK$K, performKrc$rmse, col = 3, lwd = 2) +abline(h = mean(performK$mape), lwd = 2) +legend("topright", c('AGG', 'DIS euclid', "DIS WER"), lwd = 2, col = 1:3) + + +## Plot article ENERGYCON +performKwer <- read.table("../res/perform200WER.txt") +performKwer <- performKwer[order(performKwer$K), ] + +pdf("~/perf_wer.pdf") +plot(performKwer$K, performKwer$rmse, type = 'l', + ylim = c(1.7,2.2), lwd = 2, col = 2, + xlab = "Nb Cluster", ylab = "MAPE (en %)") +abline(h = mean(performKwer$mape), lwd = 2) +legend("topright", c('AGG', "DIS WER"), lwd = 2, col = 1:2) +dev.off() diff --git a/codes_Jairo_stage2/analysis-SME.r b/codes_Jairo_stage2/analysis-SME.r new file mode 100644 index 0000000..89668fa --- /dev/null +++ b/codes_Jairo_stage2/analysis-SME.r @@ -0,0 +1,104 @@ +## 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) + } + } + + diff --git a/codes_Jairo_stage2/aux.r b/codes_Jairo_stage2/aux.r new file mode 100644 index 0000000..bb9077b --- /dev/null +++ b/codes_Jairo_stage2/aux.r @@ -0,0 +1,126 @@ +#################################################################### +## +## File: aux.r +## +## Description: Miscelaneous functions for clustering with kcca +## +## Modified: june 2010 +## +#################################################################### + + + ####################################################### + + # Transforms a matrix of data (one observation by row) + # into an array where position[ , , i] gives + # the smoothed modulus of the i-th cwt observation + + ######################################################## + + + toCWT <- function(X, sw= 0, tw= 0, swabs= 0, + nvoice= 12, noctave= 5, + s0= 2, w0= 2*pi, lt= 24, dt= 0.5, + spectra = FALSE, smooth = TRUE, + scaled = FALSE, + scalevector) + { noctave <- adjust.noctave(lt, dt, s0, tw, noctave) + if(missing(scalevector)) + scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0 + res <- lapply(1:nrow(X), function(n) + { tsX <- ts( X[n,] ) + tsCent <- tsX - mean(tsX) + if(scaled) tsCent <- ts(scale(tsCent)) + tsCent.cwt <- cwt.ts(tsCent, s0, noctave, nvoice, w0) + tsCent.cwt + } ) + if( spectra ) res <- lapply(res, function(l) Mod(l)^2 ) + if( smooth ) res <- lapply(res, smCWT, swabs = swabs, + tw = tw, dt = dt, + scalevector = scalevector) + resArray <- array(NA, c(nrow(res[[1]]), ncol(res[[1]]), + length(res))) + for( l in 1:length(res) ) resArray[ , , l] <- res[[l]] + resArray + } + + + # =============================================================== + + smCWT <- function(CWT, sw= 0, tw= 0, swabs= 0, + nvoice= 12, noctave= 2, s0= 2, w0= 2*pi, + lt= 24, dt= 0.5, scalevector ) + { +# noctave <- adjust.noctave(lt, dt, s0, tw, noctave) +# scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0 + wsp <- Mod(CWT) + smwsp <- smooth.matrix(wsp, swabs) + smsmwsp <- smooth.time(smwsp, tw, dt, scalevector) + smsmwsp + } + + + # =============================================================== + + toDWT <- function(x, filter.number = 6, family = "DaubLeAsymm") +{ x2 <- spline(x, n = 2^ceiling( log(length(x), 2) ), + method = 'natural')$y + Dx2 <- wd(x2, family = family, filter.number = filter.number)$D + Dx2 +} + + # =============================================================== + + contrib <- function(x) + { J <- log( length(x)+1, 2) + nrj <- numeric(J) + t0 <- 1 + t1 <- 0 + for( j in 1:J ) { + t1 <- t1 + 2^(J-j) + nrj[j] <- sqrt( sum( x[t0:t1]^2 ) ) + t0 <- t1 + 1 + } + return(nrj) + } + + + # ========================================= distance for coh === + + coherence <- function( x, y) + { J <- log(length(x) + 1, 2) + t0 <- 1 + sg2_x <- 0 + sg2_y <- 0 + sg_xy <- 0 + for(j in 0:(J - 1)) + { t1 <- t0 + 2^(J - j)/2 - 1 + tt <- t0:t1 + sg2_x <- sg2_x + mean(x[t0:t1]^2) + sg2_y <- sg2_y + mean(y[t0:t1]^2) + sg_xy <- sg_xy + mean(x[t0:t1] * y[t0:t1]) + t0 <- t1 + 1 + } + res <- sg_xy^2 / sg2_x / sg2_y + res + } + + + vect2mat <- function(vect){ + vect <- as.vector(vect) + matrix(vect[-(1:2)], delta, lscvect) + } + + + # ========================================= # myimg for graphics + jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", + "cyan", "#7FFF7F", "yellow", + "#FF7F00", "red", "#7F0000")) + + myimg <- function(MAT, x = 1:nrow(MAT), y = 1:col(MAT), ... ) + filled.contour( x = x, y = y, z = MAT, + xlab= 'Time', ylab= 'scale', + color.palette = jet.colors, + ... ) + + diff --git a/codes_Jairo_stage2/aux2.r b/codes_Jairo_stage2/aux2.r new file mode 100644 index 0000000..d7bdb79 --- /dev/null +++ b/codes_Jairo_stage2/aux2.r @@ -0,0 +1,25 @@ +# File: aux2.r + +dayimpact <- function(vec) { + num <- tapply(vec, rep(1:365, each = 48), mean, na.rm = TRUE) + den <- tapply(vec, rep(1:365, each = 48), max, na.rm = TRUE) + return(num / den) +} + +nightimpact <- function(vec) { + idnuits <- logical(48) + idnuits[c(1:14, 47:48)] <- TRUE + nuits <- vec[rep(idnuits, 365)] + num <- tapply(nuits, rep(1:365, each = sum(idnuits)), mean, na.rm = TRUE) + den <- tapply(vec, rep(1:365, each = 48), mean, na.rm = TRUE) + return(num / den / 3) +} + +lunchimpact <- function(vec) { + idlunchs <- logical(48) + idlunchs[25:30] <- TRUE + lunchs <- vec[rep(idlunchs, 365)] + num <- tapply(lunchs, rep(1:365, each = sum(idlunchs)), mean, na.rm = TRUE) + den <- tapply(vec, rep(1:365, each = 48), mean, na.rm = TRUE) + return(num / den / 8) +} diff --git a/codes_Jairo_stage2/calendar_ir.txt b/codes_Jairo_stage2/calendar_ir.txt new file mode 100644 index 0000000..ed312aa --- /dev/null +++ b/codes_Jairo_stage2/calendar_ir.txt @@ -0,0 +1,533 @@ + dow daytype gr +2011-01-01 Sat Hol Hol_Sun +2011-01-02 Sun Sun Sun_Mon +2011-01-03 Mon Mon Mon_Tue +2011-01-04 Tue Tue Tue_Wed +2011-01-05 Wed Wed Wed_Thu +2011-01-06 Thu Thu Thu_Fri +2011-01-07 Fri Fri Fri_Sat +2011-01-08 Sat Sat Sat_Sun +2011-01-09 Sun Sun Sun_Mon +2011-01-10 Mon Mon Mon_Tue +2011-01-11 Tue Tue Tue_Wed +2011-01-12 Wed Wed Wed_Thu +2011-01-13 Thu Thu Thu_Fri +2011-01-14 Fri Fri Fri_Sat +2011-01-15 Sat Sat Sat_Sun +2011-01-16 Sun Sun Sun_Mon +2011-01-17 Mon Mon Mon_Tue +2011-01-18 Tue Tue Tue_Wed +2011-01-19 Wed Wed Wed_Thu +2011-01-20 Thu Thu Thu_Fri +2011-01-21 Fri Fri Fri_Sat +2011-01-22 Sat Sat Sat_Sun +2011-01-23 Sun Sun Sun_Mon +2011-01-24 Mon Mon Mon_Tue +2011-01-25 Tue Tue Tue_Wed +2011-01-26 Wed Wed Wed_Thu +2011-01-27 Thu Thu Thu_Fri +2011-01-28 Fri Fri Fri_Sat +2011-01-29 Sat Sat Sat_Sun +2011-01-30 Sun Sun Sun_Mon +2011-01-31 Mon Mon Mon_Tue +2011-02-01 Tue Tue 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+2012-06-15 Fri Fri Fri_Sat diff --git a/codes_Jairo_stage2/calendar_transition_groups-1996-2011.txt b/codes_Jairo_stage2/calendar_transition_groups-1996-2011.txt new file mode 100644 index 0000000..5d118ba --- /dev/null +++ b/codes_Jairo_stage2/calendar_transition_groups-1996-2011.txt @@ -0,0 +1,5845 @@ +"dow" "daytype" "gr" +"1996-01-01" "Mon" "Hol" "Hol_Tue" +"1996-01-02" "Tue" "Tue" "Tue_Wed" +"1996-01-03" "Wed" "Wed" "Wed_Thu" +"1996-01-04" "Thu" "Thu" "Thu_Fri" +"1996-01-05" "Fri" "Fri" "Fri_Sat" +"1996-01-06" "Sat" "Sat" "Sat_Sun" +"1996-01-07" "Sun" "Sun" "Sun_Mon" +"1996-01-08" "Mon" "Mon" "Mon_Tue" +"1996-01-09" "Tue" "Tue" "Tue_Wed" +"1996-01-10" "Wed" "Wed" "Wed_Thu" +"1996-01-11" "Thu" "Thu" "Thu_Fri" +"1996-01-12" "Fri" "Fri" "Fri_Sat" +"1996-01-13" "Sat" "Sat" "Sat_Sun" +"1996-01-14" "Sun" "Sun" "Sun_Mon" +"1996-01-15" "Mon" "Mon" "Mon_Tue" +"1996-01-16" "Tue" "Tue" "Tue_Wed" +"1996-01-17" "Wed" "Wed" "Wed_Thu" +"1996-01-18" "Thu" "Thu" "Thu_Fri" +"1996-01-19" "Fri" 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+30JUN2011:19:30:00 +30JUN2011:20:00:00 +30JUN2011:20:30:00 +30JUN2011:21:00:00 +30JUN2011:21:30:00 +30JUN2011:22:00:00 +30JUN2011:22:30:00 +30JUN2011:23:00:00 +30JUN2011:23:30:00 diff --git a/codes_Jairo_stage2/descriptive-32K.r b/codes_Jairo_stage2/descriptive-32K.r new file mode 100644 index 0000000..5e1b29b --- /dev/null +++ b/codes_Jairo_stage2/descriptive-32K.r @@ -0,0 +1,81 @@ +# File: descriptive-32K.r + +rm(list = ls()) +#library(dplyr) + +## Following lines allow to construct the data file +## totconso-par-indiv.txt. +## They are very time&memory-consumming +#library(data.table) +#d10 <- fread("~/tmp/data/2010.csv") +#tot <- d10[, list(tot= sum(CPP_PUISSANCE_BRUTE), +# min= min(CPP_PUISSANCE_BRUTE), +# max= max(CPP_PUISSANCE_BRUTE), +# sd = sd( CPP_PUISSANCE_BRUTE)), +# by = FK_CCU_ID] +# write.table(file = "~/tmp/data/totconso-par-indiv.txt", tot) + +tot <- read.table("~/tmp/data/totconso-par-indiv.txt") +synchro <- read.table("~/tmp/data/syncrone2010.txt") +mat_synchro <- matrix(c(synchro$V1[1:2], synchro$V1) / 1e6, + ncol = 48, + byrow = TRUE) + +xHour <- seq(1/2, 24, length.out = 48) +xDate <- seq.Date(from = as.Date("2010/1/1"), + to = as.Date("2011/1/1"), + length.out = 365 * 48 - 2) + +# Filter infra daily +plot(xDate, filter(synchro$V1 / 1e6, rep(1 / 48, 48)), + type = 'l', + main = 'Synchrone 2010', + ylab = 'Charge (en Mwh)', + xlab = 'Time') + +# Daily +matplot(xHour, scale(t(mat_synchro)), type = 'l', lty = 1, + col = rgb(0, 0, 0, .25), + main = 'Synchrone jounalière (norm.)', + xlab = 'Heures', ylab = 'Charge (en Mwh)') + + +tot_order <- tot[order(tot$tot), ] +max_order <- tot[order(tot$max), ] + +## Mesures aberrantes ? +tail(cbind(max_order[,1], max_order[,-1] / 1e3)) + + +## I want to recuperate ids: 186733, 191819, 193174 +## which corresponds to places 1, 2481, 3478 +# identiants <- read.table('~/tmp/data/identifs.txt') +# match(c(186733, 191819, 193174), identiants$V1) + +#c186733 <- d10[, FK_CCU_ID == 186733] +#c191819 <- d10[, FK_CCU_ID == 191819] +#c193174 <- d10[, FK_CCU_ID == 193174] + +c186733 <- read.csv("~/tmp/data/c186733.csv")[, 4] / 1e3 + +mat_186733 <- matrix(c(c186733[1:2], c186733), + ncol = 48, + byrow = TRUE) + +# Filter infra daily +plot(xDate, filter(c186733, rep(1 / 48, 48)), + type = 'l', + main = "Consommation individuelle (2010)", + ylab = 'Charge (en Mwh)', + xlab = 'Temps') + +# Daily (raw) +matplot(xHour, t(mat_186733), type = 'l', lty = 1, + col = rgb(0, 0, 0, .25), + #main = "Consommation individuelle journalière " + xlab = 'Heures', ylab = 'Charge (en Mwh)') + +# Daily (standarized) +matplot(xHour, scale(t(mat_186733)), type = 'l', lty = 1, + col = rgb(0, 0, 0, .25), + xlab = 'Heures', ylab = 'Charge (en Mwh)') diff --git a/codes_Jairo_stage2/effectives-per-classe.r b/codes_Jairo_stage2/effectives-per-classe.r new file mode 100644 index 0000000..a63a8fb --- /dev/null +++ b/codes_Jairo_stage2/effectives-per-classe.r @@ -0,0 +1,6 @@ +# compute the effectives per class + +load("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/res/clfit200RC.Rdata") +clustfactor <- read.table("~/tmp/clustfactorWER15.txt") + +aggregate(c(table(res[,1])), by = list(clustfactor$x), FUN = sum) diff --git a/codes_Jairo_stage2/identifs.txt b/codes_Jairo_stage2/identifs.txt new file mode 100644 index 0000000..6526f54 --- /dev/null +++ b/codes_Jairo_stage2/identifs.txt @@ -0,0 +1,25011 @@ +186733 +186734 +186735 +186737 +186738 +186740 +186741 +186742 +186743 +186745 +186747 +186748 +186749 +186750 +186751 +186752 +186754 +186757 +186759 +186760 +186761 +186762 +186764 +186765 +186766 +186767 +186769 +186771 +186772 +186774 +186775 +186776 +186777 +186778 +186779 +186781 +186782 +186784 +186785 +186786 +186787 +186788 +186791 +186792 +186793 +186795 +186796 +186797 +186798 +186799 +186800 +186801 +186802 +186803 +186805 +186806 +186807 +186808 +186809 +186811 +186812 +186813 +186814 +186815 +186816 +186817 +186818 +186819 +186820 +186821 +186823 +186824 +186826 +186827 +186828 +186829 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data set + #---------------------------- + # data: matrix containing data + # kernellist: matrix containing kernel + # s0: lowest scale + # noctave: number of octaves + # nvoice: number of voices per octave + # cv: critical value, all values higher are set to one + + #Time: (rows) n,i the kernel is to be scaled in this direction + #Scale: (cols) m,j + + data[data1){ + linewidth <- 1 + if (sigplot==3) + linewidth <- 5 + + contour(x=t,z=at,levels=0.5,add=TRUE,drawlabels=FALSE,axes=FALSE,lwd=linewidth,col="black") + } + +} + + +plotcv <- function(t,wt,cv){ + + if (length(dim(cv))==0) + + contour(x=t,z=wt,levels=c(cv),drawlabels=FALSE,axes=FALSE,add=TRUE,col="black",lwd=1) + + else{ + + for(i in 1:nrow(cv)){ + + v <- cv[i,] + v[v==0] <- 9999 + m <- matrix(rep(v,length(t)),nrow=length(t),byrow=TRUE) + + contour(x=t,z=wt/m,levels=1,drawlabels=FALSE,axes=FALSE,add=TRUE,col="black",lwd=1) + + } + + } + +} + +plotcoi <- function(t,s0,noctave,w0){ + + tv <- as.vector(t) + tvl <- tv[tv-tv[1]<(tv[length(tv)]-tv[1])/2] + tvr <- tv[tv-tv[1]>=(tv[length(tv)]-tv[1])/2] + + lines(tvl,log2(((tvl-tv[1])*4*pi/((w0+sqrt(2+w0*w0))*sqrt(2)))/s0)/noctave,col="black") + lines(tvr,log2(((tv[length(tv)]-tvr)*4*pi/((w0+sqrt(2+w0*w0))*sqrt(2)))/s0)/noctave,col="black") + +} + +plotmarks <- function(t,s0,noctave,markt,marks){ + + if (markt[1]!=-999){ + + for (i in 1:length(markt)){ + lines(c(markt[i],markt[i]),c(0,1),lty="dotted") + } + + } + + if (marks[1]!=-999){ + + for (i in 1:length(marks)){ + lines(c(t[1],t[length(t)]),c(log2(marks[i]/s0)/noctave,log2(marks[i]/s0)/noctave),lty="dotted") + } + + } + +} + + +##################### +# PLOT.WT +##################### + +plot.wt <- function(wt,markt=-999,marks=-999,t1=NULL,t2=NULL,logscale=FALSE,phase=FALSE,units="",plottitle="",device="screen",file="wt",split=FALSE,color=TRUE,pwidth=10,pheight=5,labsc=1.5,labtext="",sigplot=3,xax=NULL,xlab=NULL,yax=NULL,ylab=NULL){ + + plotwt(wt$modulus,wt$phase,wt$time,wt$s0,wt$noctave,wt$w0,wt$critval,wt$at,markt,marks,t1,t2,logscale,phase,units,plottitle,device,file,split,color,pwidth,pheight,labsc,labtext,sigplot,xax,xlab,yax,ylab) + +} + +plotwt <- + function(wt,phs,t,s0,noctave,w0,cv=NULL,at=NULL,markt=-999,marks=-999,t1=NULL,t2=NULL,logscale=FALSE,phase=FALSE,units="",plottitle="Wavelet Plot",device="screen",file="wavelet",split=FALSE,color=TRUE,pwidth=10,pheight=7,labsc=1,labtext="",sigplot=1,xax=NULL,xlab=NULL,yax=NULL,ylab=NULL){ + + if (is.null(phs)) phase <- FALSE + + mgpv <- c(3,1,0) + if (labsc>1) mgpv[1] <- 3-(labsc-1.5) + + ncolors <- 64 + colors <- wtcolors(ncolors) + if (color==FALSE) colors <- gray((0:ncolors)/ncolors*0.7+0.3) + + rangev <- (0:(ncolors-1))/(ncolors-1) + rangebar <- matrix(rangev,nrow=2,ncol=64,byrow=TRUE) + + s <- 2^(0:(noctave))*s0 + sn <- (0:(noctave))/noctave + + deltat <- (t[length(t)]-t[1])/(length(t)-1) + cut <- FALSE + if ((!is.null(t1)) | (!is.null(t2))){ + if (t1=t[1] & t2<=t[length(t)]){ + + cut <- TRUE + + i1 <- (t1-t[1])/deltat+1 + i2 <- (t2-t[1])/deltat+1 + + t <- t[t>=t1 & t<=t2] + + wt <- wt[i1:i2,] + if (phase) phs <- phs[i1:i2,] + if (length(at)>1) at <- at[i1:i2,] + + } + } + + #---------------- + # Setting layout + #---------------- + + if (device=="ps" && split==FALSE){ + + file <- paste(file,".eps",sep="") + + postscript(file,onefile=TRUE,horizontal=TRUE,paper="special",width=pwidth,height=pheight) + cat(paste("# writing",file," \n")) + + } + + if (device=="ps" && split==TRUE){ + + file1 <- paste(file,".mod.eps",sep="") + + postscript(file1,onefile=TRUE,horizontal=TRUE,paper="special",width=pwidth,height=pheight) + cat(paste("# writing",file1," \n")) + + } + + if (phase==TRUE && split==FALSE) nlo <- layout(matrix(c(1,2,3,4),2,2,byrow=TRUE),widths=c(4,1)) + else nlo <- layout(matrix(c(1,2),ncol=2,byrow=TRUE),widths=c(4,1)) + + + #----------------- + # Plotting modulus + #----------------- + + if (logscale){ + if (units=="") + image(x=t,z=log10(wt),col = colors,axes=FALSE,xlab="Time",ylab="Scale",frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) + else + image(x=t,z=log10(wt),col = colors,axes=FALSE,xlab=paste("Time ","[",units,"]",sep=""),ylab=paste("Scale ","[",units,"]",sep=""),frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) + } + else{ + if (units=="") + image(x=t,z=wt,col = colors,axes=FALSE,xlab="Time",ylab="Scale",frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) + else + image(x=t,z=wt,col = colors,axes=FALSE,xlab=paste("Time ","[",units,"]",sep=""),ylab=paste("Scale ","[",units,"]",sep=""),frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) + } + + text(t[1+as.integer(length(t)*0.1)],0.8,labtext,cex=2) + + if (sigplot==1 | sigplot==3){ + if (is.null(cv)==FALSE){ #Critical values + if (cv[1]!=0 & cv[1]!=-1) plotcv(t,wt,cv) + } + } + + if (sigplot>1) plotat(t,wt,at,sigplot) + + if (!cut) plotcoi(t,s0,noctave,w0) #cone of influence + plotmarks(t,s0,noctave,markt,marks) #additional guiding lines + + if (is.null(xax)) + axis(side=1,at=axTicks(1),cex.axis=labsc) + else + if (is.null(xlab)) + axis(side=1,xax,labels=as.character(xax),cex.axis=labsc) + else + axis(side=1,xax,labels=xlab,cex.axis=labsc) + + if (is.null(yax)) + axis(side=2,sn,labels=as.character(s),cex.axis=labsc) + else + if (is.null(ylab)) + axis(side=2,yax,labels=as.character(yax),cex.axis=labsc) + else + axis(side=2,yax,labels=ylab,cex.axis=labsc) + + title(main=plottitle,cex=labsc) + + image(z=rangebar,axes=FALSE,col=colors,frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) + + if (is.null(cv)==FALSE){ + if (length(dim(cv))==0){ + for (i in 1:length(cv)) + if (cv[i]!=0) lines(c(-1,2),c(cv[i],cv[i])) + } + } + + if (!logscale) + axis(side=2,(0:5)/5,labels=c("0","","","","","1"),cex.axis=labsc) + else{ + labelv <- substr(as.character(c(0:5)*(max(log10(wt),na.rm=TRUE)-min(log10(wt),na.rm=TRUE))/5),1,4) + axis(side=2,(0:5)/5,labels=labelv,cex.axis=labsc) + } + + + #----------------- + # Plotting phase + #----------------- + if (phase==TRUE){ + + if (device=="ps" && split==TRUE){ + + dev.off() + + file2 <- paste(file,".phs.eps",sep="") + + postscript(file2,onefile=TRUE,horizontal=TRUE,paper="special",width=10,height=5) + cat(paste("# writing",file2," \n")) + + } + + colors <- rainbow(ncolors) + if (color==FALSE){ + dummy <- gray((0:ncolors)/ncolors) + colors[1:(ncolors/2)] <- dummy[(ncolors/2+1):ncolors] + colors[(ncolors/2+1):ncolors] <- dummy[1:(ncolors/2)] + } + + if (units=="") + image(x=t,z=phs,col=colors,axes=FALSE,xlab="Time",ylab="Scale",frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) + else + image(x=t,z=phs,col=colors,axes=FALSE,xlab=paste("Time ","[",units,"]",sep=""),ylab=paste("Scale ","[",units,"]",sep=""),frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) + + if (is.null(cv)==FALSE) plotcv(t,wt,cv) + if (sigplot>1) plotat(t,wt,at,sigplot) + if (!cut) plotcoi(t,s0,noctave,w0) + plotmarks(t,s0,noctave,markt,marks) + + if (is.null(xax)) + axis(side=1,at=axTicks(1),cex.axis=labsc) + else + if (is.null(xlab)) + axis(side=1,xax,labels=as.character(xax),cex.axis=labsc) + else + axis(side=1,xax,labels=xlab,cex.axis=labsc) + + if (is.null(yax)) + axis(side=2,sn,labels=as.character(s),cex.axis=labsc) + else + if (is.null(ylab)) + axis(side=2,yax,labels=as.character(yax),cex.axis=labsc) + else + axis(side=2,yax,labels=ylab,cex.axis=labsc) + + + title(main="Phase") + + image(z=rangebar,axes=FALSE,col=colors,frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) + axis(side=2,(0:4)/4,labels=c("-PI","","0","","PI"),cex.axis=labsc) + + } + + if (device=="ps") dev.off() + + } + +############################## +# Surrogates +############################## + +createwavesurrogates <- function(nsurr=1,wt=1,n,dt=1,s0=1,noctave=5,nvoice=10,w0=2*pi){ + + surrmat <- matrix(rep(0,n*nsurr),ncol=nsurr) + + for (i in 1:nsurr){ + + cat(paste("# Creating realization ",i,"\n",sep="")) + + x <- rnorm(n) + xts <- ts(x,start=0,deltat=dt) + + xwt <- cwt.ts(xts,s0,noctave,nvoice,w0) + wtsurr <- wt*xwt + + surri <- as.vector(invmorlet(wtsurr,0,dt,s0,noctave,nvoice,w0)) + + surrmat[,i] <- Re(surri) + + } + + surrmat + +} + +surrwave <- function(x,...) + UseMethod("surrwave") + +surrwave.wt <- function(wt,nsurr=1,spec=TRUE){ + + n <- length(wt$time) + t0 <- wt$time[1] + dt <- (wt$time[13]-t0)/12 + s0 <- wt$s0 + noctave <- wt$noctave + nvoice <- wt$nvoice + w0 <- wt$w0 + + wt <- wt$modulus + if (spec==TRUE) + wt <- sqrt(wt) + + surrmat <- createwavesurrogates(nsurr,wt,n,dt,s0,noctave,nvoice,w0) + + surrts <- ts(surrmat,start=t0,deltat=dt) + + surrts + +} + +surrwave.matrix <- function(mat,nsurr=1,t0=0,dt=1,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0,spec=FALSE){ + + if (sw!=0 & swabs==0) + swabs <- as.integer(sw*nvoice) + + scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 + + if ((noctave*nvoice+1)!=dim(mat)[2]) + cat("# ERROR! nscales unequal noctave*nvoice+1 ! \n") + + n <- dim(mat)[1] + + if (spec==FALSE) + mat <- Mod(mat) + else + mat <- sqrt(Mod(mat)) + + wtsm <- smooth.matrix(mat,swabs) + wt <- smooth.time(wtsm,tw,dt,scalevector) + + surrmat <- createwavesurrogates(nsurr,wt,n,dt,s0,noctave,nvoice,w0) + + surrts <- ts(surrmat,start=t0,deltat=dt) + + surrts + +} + +surrwave.character <- + function(file,nsurr=1,t0=0,dt=1,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0,transpose=TRUE,spec=FALSE){ + + if (sw!=0 & swabs==0) + swabs <- as.integer(sw*nvoice) + + scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 + + if (transpose==FALSE) + mat <- matrix(scan(file,comment.char="#"),ncol=nvoice*noctave+1,byrow=TRUE) + else + mat <- matrix(scan(file,comment.char="#"),ncol=nvoice*noctave+1,byrow=FALSE) + + if ((noctave*nvoice+1)!=dim(mat)[2]) + cat("# ERROR! nscales unequal noctave*nvoice+1 ! \n") + + n <- dim(mat)[1] + + if (spec==FALSE) + mat <- Mod(mat) + else + mat <- sqrt(Mod(mat)) + + wtsm <- smooth.matrix(mat,swabs) + wt <- smooth.time(wtsm,tw,dt,scalevector) + + surrmat <- createwavesurrogates(nsurr,wt,n,dt,s0,noctave,nvoice,w0) + + surrts <- ts(surrmat,start=t0,deltat=dt) + + surrts + + } + +surrwave.ts <- function(ts,nsurr=1,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0){ + + n <- length(ts) + t0 <- time(ts)[1] + dt <- deltat(ts) + if (sw!=0 & swabs==0) + swabs <- as.integer(sw*nvoice) + + scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 + + wt <- Mod(cwt.ts(ts,s0,noctave,nvoice,w0)) + + wtsm <- smooth.matrix(wt,swabs) + wt <- smooth.time(wtsm,tw,dt,scalevector) + + surrmat <- createwavesurrogates(nsurr,wt,n,dt,s0,noctave,nvoice,w0) + + surrts <- ts(surrmat,start=t0,deltat=dt) + + surrts + +} + +invmorlet <- function(wt,t0=0,dt=1,s0=1,noctave=5,nvoice=10,w0=2*pi){ + + if ((noctave*nvoice+1)!=dim(wt)[2]) + cat("# ERROR! nscales unequal noctave*nvoice+1 ! \n") + + n <- dim(wt)[1] + + wt[is.na(wt)] <- 0 + + tsRe <- rep(0,n) + tsIm <- rep(0,n) + + wtRe <- t(Re(wt)) + wtIm <- t(Im(wt)) + + z <- .C("invmorlet", + as.double(as.vector(wtRe)), + as.double(as.vector(wtIm)), + as.integer(n), + as.double(dt), + as.double(s0), + as.integer(noctave), + as.integer(nvoice), + as.double(w0), + tsRetmp = as.double(tsRe), + tsImtmp = as.double(tsIm), + PACKAGE="sowas") + + invvec=complex(real=z$tsRetmp,imaginary=z$tsImtmp) + invts <- ts(data=invvec,start=t0,deltat=dt) + + invts + +} + +################################# +# INPUT / OUTPUT +################################# + +readmatrix <- function(file,M){ + + A <- matrix(scan(file,comment.char="#"),ncol=M,byrow=TRUE) + + A + +} + + +readts <- function(file){ + + A <- matrix(scan(file,comment.char="#"),ncol=2,byrow=TRUE) + + Adum <- A + + Adum[is.na(Adum)] <- 0 + + t <- Adum %*% c(1,0) + x <- A %*% c(0,1) + + N=length(t) + f=1/(t[13]-t[1])*12 + + if ((f>11) && (f<13)) f <- 12 + + timeseries<-ts(data=x,start=t[1],frequency=f) + + timeseries + +} + +writematrix <- function(file,data,header="# R Matrix"){ + + write(header,file) + write(t(data),file,ncol(data),append=TRUE) + +} + +############################ +# HELP FUNCTIONS +############################ + +smooth.matrix <- function(wt,swabs){ + + if (swabs != 0) + smwt <- t(filter(t(wt),rep(1,2*swabs+1)/(2*swabs+1))) + else + smwt <- wt + + smwt + +} + +smooth.time <- function(wt,tw,dt,scalevector){ + + smwt <- wt + + if (tw != 0){ + for (i in 1:length(scalevector)){ + + twi <- as.integer(scalevector[i]*tw/dt) + smwt[,i] <- filter(wt[,i],rep(1,2*twi+1)/(2*twi+1)) + + } + } + + smwt + +} + + +adjust.noctave <- function(N,dt,s0,tw,noctave){ + + if (tw>0){ + dumno <- as.integer((log(N*dt)-log(2*tw*s0))/log(2)) + if (dumno=c] <- 1 + + is1 <- 1 + is2 <- nvoice*(noctave+1) + it1 <- 1 + it2 <- 2*t+1 + + L <- F[1:(2*t+1),is1] + + while (length(L[L!=0])==0) { + is1 <- is1+1 + L <- F[1:(2*t+1),is1] + } + + L <- F[1:(2*t+1),is2] + + while (length(L[L!=0])==0) { + is2 <- is2-1 + L <- F[1:(2*t+1),is2] + } + + + L <- F[it1,1:(nvoice*(noctave+2))] + + while (length(L[L!=0])==0) { + it1 <- it1+1 + L <- F[it1,1:(nvoice*(noctave+2))] + } + + L <- F[it2,1:(nvoice*(noctave+2))] + + while (length(L[L!=0])==0) { + it2 <- it2-1 + L <- F[it2,1:(nvoice*(noctave+2))] + } + + kernel <- list(bitmap=F[(it1-1):(it2+1),(is1-1):(is2+1)],is=is-is1) + + kernel + +} + +kernelRoot <- function(s0=1,w0=6,a=0,noctave=6,nvoice=20,swabs=0,tw=0,dt=1){ + + tol <- 0.005 + cmin <- 0 + cmax <- 1 + cntr <- 0.5 + da <- a + + while (abs(da/a)>tol){ + + da <- kernelArea(cntr,s0,w0,a,noctave,nvoice,swabs,tw,dt) + if (da>0){ + cmin <- cntr + cntr <- mean(c(cntr,cmax)) + } + if (da<0){ + cmax <- cntr + cntr <- mean(c(cntr,cmin)) + } + } + + cntr + +} + +kernelArea <- function(cntr,s0=1,w0=6,a=0,noctave=6,nvoice=20,swabs=0,tw=0,dt=1){ + + # calulates area of reproducing kernel for smoothed data at scale s0*2^noctave + # cntr: height of contour line to define kernel area. This + # parameter is to be estimated! + # s0: lowest scale + # w0: parameter of Morlet Wavelet + # a: area offset (only needed, when finding root. Is set to + # desired area + # noctave: number of octaves + # nvoice: number of voices per octave + # swabs: smoothing window width in scale direction + # dt: sampling time + + s <- s0*2^noctave + + x <- s0*2^(((1:(nvoice*(noctave+2)))-1)/nvoice) + + t <- max(2000,max(x)*tw*2/dt*1.1) + + y <- ((0:(2*t))-t)/2*dt + + X <- matrix(x,ncol=nvoice*(noctave+2),nrow=2*t+1,byrow=T) + Y <- matrix(y,ncol=nvoice*(noctave+2),nrow=2*t+1) + + F <- sqrt(2*s*X/(s*s+X*X))*exp(-0.5*(Y*Y+w0*w0*(X-s)*(X-s))/(s*s+X*X)) + + F <- foldKernel(F,swabs,tw,x,dt) + + F[F>=cntr] <- 1 + F[F