From: Benjamin Auder Date: Mon, 22 Jan 2018 15:09:19 +0000 (+0100) Subject: First commit X-Git-Url: https://git.auder.net/variants/Chakart/doc/index.css?a=commitdiff_plain;h=b7cd987d286ed3b0bde10b15d0fbcdc355fc4b30;p=epclust.git First commit --- b7cd987d286ed3b0bde10b15d0fbcdc355fc4b30 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..67ca303 --- /dev/null +++ b/.gitignore @@ -0,0 +1,50 @@ +#datasets +/data/* +!/data/README +!/data/preprocessing/ +!/data/curves_to_db/ + +#files generated by initialize.sh +/.gitfat +/.gitattributes + +#ignore temporary files +*~ +*.swp + +#ignore binary files generated by claws() +*.epclust.bin + +#ignore R session files +.Rhistory +.RData + +#ignore roxygen2 generated files +/epclust/NAMESPACE +/epclust/man/*.Rd +!/epclust/man/*-package.Rd + +#ignore R CMD build/check genrated files +/*.Rcheck/ +/*.tar.gz + +#ignore jupyter generated file (HTML vignette, and reports) +*.ipynb.html + +#ignore object files and executables +*.o +*.so +*.exe + +#ignore RcppExports, generated by Rcpp::compileAttributes +/epclust/R/RcppExports.R +/epclust/src/RcppExports.cpp + +#misc +Rprof.out +*.zip +.Rproj.user +*.Rproj + +# temp +/temp/*.xls diff --git a/README.md b/README.md new file mode 100644 index 0000000..3c88963 --- /dev/null +++ b/README.md @@ -0,0 +1,13 @@ +# Clustering of EDF power curves from Linky data + +Joint work with [Jairo Cugliari](http://eric.univ-lyon2.fr/~jcugliari/), +[Yannig Goude](https://scholar.google.com/citations?user=D-OLEG4AAAAJ&hl=fr) and +[Jean-Michel Poggi](http://www.math.u-psud.fr/~poggi/) + +--- + +This program takes N time-series in input and is divided in two stages: + 1. (Heuristic) k-medoids algorithm in parallel to obtain K1 centers, K1 ≪ N + 2. Clustering from WER distances to obtain the final K2 < K1 group representations + +See ?epclust once the package is loaded. diff --git a/TODO b/TODO new file mode 100644 index 0000000..b96bad4 --- /dev/null +++ b/TODO @@ -0,0 +1,41 @@ +simulateur : complètement aléatoire ? from Irish dataset ? +wmtsa : wavBootstrap +wavelets methods in statistics with R - p180 + +Essayer distance wdist du package biwavelet ? + +geometric structure of high dim data and dim reduction 2011 + +Piste à explorer pour les comparaisons: H20 + +renvoyer nombre d'individues par classe ? +hypothèse : données déjà ordonnées 48 1/2H sur 365j +utiliser mixmod avec modèles allongés +doit tourner sur machine plutôt standard, utilisateur "lambda" + +#https://stat.ethz.ch/pipermail/r-help/2011-June/280133.html +#randCov = function(d) +#{ +# x <- matrix(rnorm(d*d), nrow=d) +# x <- x / sqrt(rowSums(x^2)) +# x %*% t(x) +#} + +#TODO: map-reduce more appropriate R/clustering.R ligne 88 + +#TODO: use dbs(), + #https://www.r-bloggers.com/debugging-parallel-code-with-dbs/ + #http://gforge.se/2015/02/how-to-go-parallel-in-r-basics-tips/ + +PLOT: +plot manifold 2D distances WER / +fenetre tempo forme des courbes / +medoids / +gain en prevision: clust puis full --> enercast + +réduire taille 17519 trop long ? + +TODO: revoir les arguments, simplifier (dans les clustering...), + +#si pas lissage --> on doit trouver 1 : TOCHECK +#regarder biwavelets smooth.wavelet : s'en inspirer diff --git a/data/README b/data/README new file mode 100644 index 0000000..4914fbb --- /dev/null +++ b/data/README @@ -0,0 +1,19 @@ +Available dataset: data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households + +Power-Networks-LCL-June2015(withAcornGps) meter reading + +######################################################################## + +Australian smart-city dataset: +data.gouv.au/dataset/smart-grid-smart-city-customer-trial-data +Now unavailable, but maybe some hope https://data.gov.au/datarequest/98266a0b-6e0c-4092-9e3b-9e3b4cab20ad + +CDINTERVALREADINGALLNOQUOTES.CSV.7z meter reading +sgsc-cthanplug-readings.7z Home Aread Network Plug readings + +######################################################################## + +EDF data (undisclosed) +2009.rar 25K data for year 2009 +2010.rar 25K data for year 2010 +2011.zip 25K data for year 2011 diff --git a/data/curves_to_db/README b/data/curves_to_db/README new file mode 100644 index 0000000..5a13a26 --- /dev/null +++ b/data/curves_to_db/README @@ -0,0 +1,20 @@ +0] Precondition: current folder, containing init_db.sql and curves2db.R + must be readable by the postgres user (some other weird restrictions ...) + +1] Initialization: + a) log in as postgres user: + sudo -i -u postgres + b) launch PostgreSQL shell: + psql + c) execute init script: + \i /path/to/init_db.sql + +2] Launch DB-filling script: + a) check/tune parameters at the beginning of curves2db.R + b) run the script + +===== + +WARNING: very slow for large datasets + +read_db.R: example usage to read curves from DB diff --git a/data/curves_to_db/curves2db.R b/data/curves_to_db/curves2db.R new file mode 100644 index 0000000..9f0e9d0 --- /dev/null +++ b/data/curves_to_db/curves2db.R @@ -0,0 +1,43 @@ +require(RPostgreSQL) +require(data.table) + +nb_curves_per_request <- 100 #curves per (insert) request +tot_nb_curves <- 25e3 #total number of curves +dimension <- 15000 #number of sample points +nb_clust <- 15 #number of clusters +temp_file <- "tmp_curves_batch" #(accessible) temporary file to store curves + +# Init connection with DB +driver <- PostgreSQL(fetch.default.rec = nb_curves_per_request) +con <- dbConnect(driver, user="user", password="pwd", + host="localhost", port="5432", dbname="db") + +# Replace next call + func with any custom initialization +ref_centroids <- sapply(1:nb_clust, function(k) cumsum(rnorm(dimension))) +genRandCurves <- function(indices) { + mat <- sapply(indices, function(i) { + if (i > tot_nb_curves) + return (NULL) + j <- sample(ncol(ref_centroids), 1) + ref_centroids[,j] + rnorm(dimension) + }) + # fwrite() write per columns => need to "transpose" mat; but it's quite inefficient... + lapply(1:dimension, function(i) mat[i,]) +} + +# Loop: generate nb_curves_per_request curves, store them on a temp file, +# and insert into DB using COPY command (should be faster than insert) +nb_curves <- 0 +while (nb_curves < tot_nb_curves) +{ + curves <- genRandCurves((nb_curves+1):(nb_curves+nb_curves_per_request)) + fwrite(curves, temp_file, append=FALSE, sep=",") + # Required hack: add brackets (PostgreSQL syntax ...) + system(paste("sed -i 's/\\(.*\\)/{\\1}/g' ",temp_file,sep='')) + query <- paste("COPY series (curve) FROM '", normalizePath(temp_file), "';", sep='') + dbSendQuery(con, query) + nb_curves <- nb_curves + nb_curves_per_request +} + +dbDisconnect(con) +unlink(temp_file) diff --git a/data/curves_to_db/curves2db_jc.R b/data/curves_to_db/curves2db_jc.R new file mode 100644 index 0000000..dfeab0c --- /dev/null +++ b/data/curves_to_db/curves2db_jc.R @@ -0,0 +1,45 @@ +library(RPostgreSQL) +library(data.table) + +nb_curves_per_request <- 5 # curves per (insert) request +#tot_nb_curves <- 25e3 # total number of curves +#dimension <- 17519 # number of sample points +#nb_clust <- 15 # number of clusters +temp_file <- "tmp_curves_batch" # (accessible) temporary file to store curves + +# Init connection with DB +driver <- PostgreSQL(fetch.default.rec = nb_curves_per_request) +con <- dbConnect(driver, user = "irsdi", password = "irsdi2017", + host = "localhost", port = "5432", dbname = "edf25m") + +setwd("~/tmp/") +ref_centroids <- fread("2009_matrix-dt.csv") +#ref_centroids <- sapply(1:nb_clust, function(k) cumsum(rnorm(dimension))) + +genRandCurves <- function(line, times) { + #ids <- as.integer(sprintf("%04i", seq_len(times) - 1)) * 1e6 + line[1] + ids <- as.integer(sprintf("%010i", (seq_len(times) - 1) * 1e6 + line[1])) + curve <- as.matrix(line[-1]) + # simus <- lapply(1:times, function(i) line[-1] * runif(length(curve), .95, 1.05)) + perturbances <- matrix(runif(length(curve) * times, .95, 1.05), nrow = times) + #curves_sim <- cbind(ids, t(apply(perturbances, 1, FUN = '*', curve))) + curves_sim <- data.frame(ids, t(apply(perturbances, 1, FUN = '*', curve))) + # series in columns, data as data.frame (as fwrite requests) + return(curves_sim) +} + +# Loop: generate nb_curves_per_request curves, store them on a temp file, +# and insert into DB using COPY command (should be faster than insert) +system.time( + for (i in seq_len(nrow(ref_centroids))) { + curves <- genRandCurves(line = as.matrix(ref_centroids[i, ]), times = nb_curves_per_request) + fwrite(curves, temp_file, append = FALSE, sep = ",", col.names = FALSE) + # Required hack: add brackets (PostgreSQL syntax ...) + system(paste("sed -i 's/\\(.*\\)/{\\1}/g' ", temp_file, sep = '')) + query <- paste("COPY series (curve) FROM '", normalizePath(temp_file), "';", sep = '') + dbSendQuery(con, query) + } +) + +dbDisconnect(con) +unlink(temp_file) diff --git a/data/curves_to_db/example.R b/data/curves_to_db/example.R new file mode 100644 index 0000000..d8c6a47 --- /dev/null +++ b/data/curves_to_db/example.R @@ -0,0 +1,46 @@ +require(RPostgreSQL) + +############################## +# Follow steps in README first +############################## + +nb_curves_per_request <- 100 #curves per (select) request + +# Init connection with DB +driver <- PostgreSQL(fetch.default.rec = nb_curves_per_request) +con <- dbConnect(driver, user="irsdi", password="irsdi2017", + host="localhost", port="5432", dbname="edf25m") + +# Fill associative array, map index to identifier +indexToID_inDB <- as.character( + dbGetQuery(con, 'SELECT DISTINCT id FROM series')[,"id"] ) + +# Function to retrieve curves within some indices range +getCurves <- function(indices) +{ + indices = indices[ indices <= length(indexToID_inDB) ] + if (length(indices) == 0) + return (NULL) + request <- "SELECT curve FROM series WHERE id in (" + for (i in seq_along(indices)) + { + request <- paste(request, indexToID_inDB[ indices[i] ], sep="") + if (i < length(indices)) + request <- paste(request, ",", sep="") + } + request <- paste(request, ")", sep="") + df_series <- dbGetQuery(con, request) + +#weird result: an integer, and then string "{val1,val2,val3,...,valD}" :/ +#print(summary(df_series)) +df_series +# matrix(df_series[,"value"], ncol=length(indices)) +} + +# Test +#curves <- getCurves(c(1:3,7,11)) +library(epclust) +res <- claws(getCurves, 50, 15, 500, 500, random=FALSE, ncores_clust=3, verbose=TRUE) + +dbDisconnect(con) +unlink(temp_file) diff --git a/data/curves_to_db/init_db.sql b/data/curves_to_db/init_db.sql new file mode 100644 index 0000000..fb2dc7b --- /dev/null +++ b/data/curves_to_db/init_db.sql @@ -0,0 +1,8 @@ +-- Se loguer avec droits admin sur un serveur PostgreSQL +-- (en local ou non). Puis : + +-- superuser status required to use command 'copy' ... +create role irsdi with superuser login password 'irsdi2017'; +create database edf25m with owner irsdi; +\c edf25m irsdi -- connect to DB as irsdi +create table series ( id serial primary key, curve real[] ); diff --git a/data/preprocessing/Makefile b/data/preprocessing/Makefile new file mode 100644 index 0000000..097420e --- /dev/null +++ b/data/preprocessing/Makefile @@ -0,0 +1,4 @@ +TARGET = transform.exe + +$(TARGET): convert_to_CSV.c + gcc convert_to_CSV.c -lm -lcgds -o $(TARGET) diff --git a/data/preprocessing/README b/data/preprocessing/README new file mode 100644 index 0000000..1a860df --- /dev/null +++ b/data/preprocessing/README @@ -0,0 +1,4 @@ +Converter from raw by-columns CSV format to by-rows CSV file +(much smaller), as epclust::claws() expect as CSV input. + +Dependency: cgds, https://git.auder.net/?p=cgds.git diff --git a/data/preprocessing/convert_to_CSV.c b/data/preprocessing/convert_to_CSV.c new file mode 100644 index 0000000..b3ff3be --- /dev/null +++ b/data/preprocessing/convert_to_CSV.c @@ -0,0 +1,205 @@ +#define __STDC_FORMAT_MACROS //to print 64bits unsigned integers +#include +#include +#include +#include +#include +#include + +// Read an integer char by char, and position the cursor to next character +char readInt(FILE* stream, int* integer) +{ + *integer = 0; + char curChar = fgetc(stream); + int sign = (curChar == '-' ? -1 : 1); + while (curChar < '0' || curChar > '9') + curChar = fgetc(stream); + while (curChar >= '0' && curChar <= '9') + { + *integer = 10 * (*integer) + (int) (curChar - '0'); + curChar = fgetc(stream); + } + (*integer) *= sign; + return curChar; //separator, endline or .,e,E (if inside readReal) +} + +// Read a real number char by char, and position the cursor to next character +char readReal(FILE* stream, float* real) +{ + int integerPart, exponent = 0, fractionalPart = 0, countZeros = 0; + char curChar = readInt(stream, &integerPart); + if (curChar == '.') + { + //need to count zeros + while ((curChar = fgetc(stream)) == '0') + countZeros++; + if (curChar >= '1' && curChar <= '9') + { + ungetc(curChar, stream); + curChar = readInt(stream, &fractionalPart); + } + } + if (curChar == 'e' || curChar == 'E') + curChar = readInt(stream, &exponent); + *real = ( integerPart + (integerPart>=0 ? 1. : -1.) * (float)fractionalPart + / pow(10,countZeros+floor(log10(fractionalPart>0 ? fractionalPart : 1)+1)) ) + * pow(10,exponent); + + return curChar; //separator or endline +} + +// Parse a line into integer+float (ID, value) +static void scan_line(FILE* ifile, char sep, + int posID, int* ID, int posValue, float* value) +{ + char curChar; + int position = 1; + while (1) + { + if (position == posID) + curChar = readInt(ifile, ID); + else if (position == posValue) + curChar = readReal(ifile, value); + else + curChar = fgetc(ifile); //erase the comma (and skip field then) + + // Continue until next separator (or line end or file end) + while (!feof(ifile) && curChar != '\n' && curChar != sep) + curChar = fgetc(ifile); + position++; + + if (curChar == '\n' || feof(ifile)) + { + // Reached end of line + break; + } + } +} + +// Main job: parse a data file into a conventional CSV file in rows, without header +// Current limitations: +// - remove partial series (we could fill missing values instead) +// - consider missing fields == 0 (if ,,) +// - IDs should be st. pos. integers +// - UNIX linebreaks only (\n) +int transform(const char* ifileName, int posID, int posValue, + const char* ofileName, int nbItems, char sep) +{ + uint64_t processedLines = 0; //execution trace + uint32_t seriesCount=0, skippedSeriesCount=0, mismatchLengthCount=0; + int tsLength, lastID=0, ID, firstID, eof; + float value, tmpVal; + Vector* tsBuffer = vector_new(float); + FILE* ifile = fopen(ifileName, "r"); + // Output file to write time-series sequentially, CSV format. + FILE* ofile = fopen(ofileName, "w"); + + // Skip header + char curChar; + do + curChar = fgetc(ifile); + while (curChar != '\n'); + + // Process one client (ID in first column) at a time + while (1) + { + + eof = feof(ifile); + if (!eof) + { + // Is there anything left to read? (file may end with '\n') + curChar = fgetc(ifile); + if (!feof(ifile) && curChar != '\n') + { + // Yes: read current line + ungetc(curChar, ifile); + scan_line(ifile, sep, posID, &ID, posValue, &value); + } + else + eof = 1; + } + + if (ID != lastID || eof) + { + if (lastID > 0) + { + // Just starting a new time-series (or EOF): process the last one + if (tsLength == vector_size(tsBuffer)) + { + for (int i=0; i 0 && ++seriesCount >= nbItems) + break; + } + else + { + // Mismatch lengths: skip series + mismatchLengthCount++; + } + } + else + firstID = ID; + if (eof) + { + // Last serie is processed + break; + } + // Reinitialize current index of new serie + tsLength = 0; + lastID = ID; + } + + // Fill values buffer + if (ID != firstID) + { + if (tsLength < vector_size(tsBuffer)) + vector_set(tsBuffer, tsLength, value); + } + else + { + // First serie is reference: push all values + vector_push(tsBuffer, value); + } + tsLength++; + + if ((++processedLines) % 1000000 == 0) + fprintf(stdout,"Processed %"PRIu64" lines\n", processedLines); + } + + // finally print some statistics + fprintf(stdout,"NOTE: %u series retrieved.\n",seriesCount); + if (mismatchLengthCount > 0) + fprintf(stdout,"WARNING: %u mismatch series lengths.\n",mismatchLengthCount); + + fclose(ifile); + fclose(ofile); + return 0; +} + +int main(int argc, char** argv) +{ + if (argc < 4) //program name + 3 arguments + { + printf("Usage: transform ifileName posID posValue [ofileName [nbItems [sep]]]\n \ + - ifileName: name of by-columns CSV input file\n \ + - posID: position of the identifier in a line (start at 1)\n \ + - posValue: position of the value of interest in a line\n \ + - ofileName: name of the output file; default: out.csv\n \ + - nbItems: number of series to retrieve; default: 0 (all)\n \ + - sep: fields separator; default: ','\n"); + return 0; + } + else + { + return transform(argv[1], atoi(argv[2]), atoi(argv[3]), + argc > 4 ? argv[4] : "out.csv", + argc > 5 ? atoi(argv[5]) : 0, + argc > 6 ? argv[6][0] : ','); + } +} diff --git a/data/preprocessing/expected_output_test.csv b/data/preprocessing/expected_output_test.csv new file mode 100644 index 0000000..f837a25 --- /dev/null +++ b/data/preprocessing/expected_output_test.csv @@ -0,0 +1,3 @@ +1.05,2,3 +10000,1,0 +325,-2000,15.55 diff --git a/data/preprocessing/input_test.csv b/data/preprocessing/input_test.csv new file mode 100644 index 0000000..be85ae7 --- /dev/null +++ b/data/preprocessing/input_test.csv @@ -0,0 +1,16 @@ +ID,field1,field2,field3 +1,Z28,1.05,NA +1,A32,2.,NA +1,B21,3.,0 +2,F12,1e4,0 +2,H234,1.,1 +2,J785,0.,1 +3,S213,4.,0 +3,NaN,50.,-15,NA +4,R234,2.1e2,1 +4,P0,2,NA +4,D2,-23e3,0 +4,V23,-3.1e1,0 +5,N52,3.25e2,NaN +5,NaN,-2.0e3,NaN +5,I3,15.55,NA diff --git a/doc/1101.4744.pdf b/doc/1101.4744.pdf new file mode 100644 index 0000000..2bfdafb --- /dev/null +++ b/doc/1101.4744.pdf @@ -0,0 +1 @@ +#$# git-fat 5f2ff090cd11aeff66809f2757b3f6ea6bfb035b 905139 diff --git a/doc/cugliari-mathamsud2016.pdf b/doc/cugliari-mathamsud2016.pdf new file mode 100644 index 0000000..b3a5e8a --- /dev/null +++ b/doc/cugliari-mathamsud2016.pdf @@ -0,0 +1 @@ +#$# git-fat 96fc0307b651a06a05f81c1f5d0f79a1fbd2bd45 2760474 diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION new file mode 100644 index 0000000..5b6e110 --- /dev/null +++ b/pkg/DESCRIPTION @@ -0,0 +1,44 @@ +Package: epclust +Title: Clustering Individual Electricity Power Curves +Description: Electric Power curves CLUSTering, through their wavelets + decomposition. The main function 'claws' takes (usually long) time-series + in input, and return as many clusters centers as requested, along with their + ranks and synchrones (sum of all curves in one group). + For the method, see ?claws and https://arxiv.org/abs/1101.4744. + For usage examples, see ?claws and package vignette. +Version: 0.1-0 +Author: Benjamin Auder [aut,cre], + Jairo Cugliari [aut], + Yannig Goude [ctb], + Jean-Michel Poggi [ctb] +Maintainer: Benjamin Auder +Depends: + R (>= 3.0.0) +Imports: + methods, + parallel, + cluster, + bigmemory, + wavethresh, + Rwave +Suggests: + synchronicity, + devtools, + testthat, + roxygen2, + MASS, + wmtsa, + DBI, + digest +License: MIT + file LICENSE +RoxygenNote: 6.0.1 +Collate: + 'main.R' + 'clustering.R' + 'de_serialize.R' + 'A_NAMESPACE.R' + 'computeSynchrones.R' + 'computeWerDists.R' + 'plot.R' + 'simulateCurve.R' + 'utils.R' diff --git a/pkg/LICENSE b/pkg/LICENSE new file mode 100644 index 0000000..2e72cd4 --- /dev/null +++ b/pkg/LICENSE @@ -0,0 +1,23 @@ +Copyright (c) 2016-2017, Benjamin Auder + 2016-2017, Jairo Cugliari + 2016-2017, Yannig Goude + 2016-2017, Jean-Michel Poggi + +Permission is hereby granted, free of charge, to any person obtaining +a copy of this software and associated documentation files (the +"Software"), to deal in the Software without restriction, including +without limitation the rights to use, copy, modify, merge, publish, +distribute, sublicense, and/or sell copies of the Software, and to +permit persons to whom the Software is furnished to do so, subject to +the following conditions: + +The above copyright notice and this permission notice shall be +included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE +LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION +OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/pkg/NAMESPACE b/pkg/NAMESPACE new file mode 100644 index 0000000..ef73764 --- /dev/null +++ b/pkg/NAMESPACE @@ -0,0 +1,29 @@ +# Generated by roxygen2: do not edit by hand + +export(binarize) +export(binarizeTransform) +export(claws) +export(clusteringTask1) +export(clusteringTask2) +export(computeClusters1) +export(computeClusters2) +export(computeSynchrones) +export(computeWerDists) +export(curvesToContribs) +export(getDataInFile) +importFrom(Rcpp,evalCpp) +importFrom(Rcpp,sourceCpp) +importFrom(Rwave,cwt) +importFrom(bigmemory,as.big.matrix) +importFrom(bigmemory,big.matrix) +importFrom(bigmemory,is.big.matrix) +importFrom(cluster,pam) +importFrom(methods,is) +importFrom(parallel,clusterExport) +importFrom(parallel,makeCluster) +importFrom(parallel,parLapply) +importFrom(parallel,stopCluster) +importFrom(stats,spline) +importFrom(wavelets,dwt) +importFrom(wavelets,wt.filter) +useDynLib(epclust) diff --git a/pkg/R/A_NAMESPACE.R b/pkg/R/A_NAMESPACE.R new file mode 100644 index 0000000..3e29918 --- /dev/null +++ b/pkg/R/A_NAMESPACE.R @@ -0,0 +1,15 @@ +#' @include de_serialize.R +#' @include clustering.R +#' @include main.R +#' +#' @useDynLib epclust +#' +#' @importFrom Rwave cwt +#' @importFrom cluster pam +#' @importFrom parallel makeCluster clusterExport parLapply stopCluster +#' @importFrom wavethresh wd filter.select +#' @importFrom stats spline +#' @importFrom methods is +#' @importFrom bigmemory big.matrix as.big.matrix is.big.matrix +#' @importFrom utils head tail +NULL diff --git a/pkg/R/clustering.R b/pkg/R/clustering.R new file mode 100644 index 0000000..5b5f668 --- /dev/null +++ b/pkg/R/clustering.R @@ -0,0 +1,94 @@ +#' Two-stage clustering, within one task (see \code{claws()}) +#' +#' \code{clusteringTask1()} runs one full stage-1 task, which consists in iterated +#' clustering on nb_curves / ntasks energy contributions, computed through +#' discrete wavelets coefficients. +#' \code{clusteringTask2()} runs a full stage-2 task, which consists in WER distances +#' computations between medoids (indices) output from stage 1, before applying +#' the second clustering algorithm on the distances matrix. +#' +#' @param getContribs Function to retrieve contributions from initial series indices: +#' \code{getContribs(indices)} outputs a contributions matrix, in columns +#' @inheritParams claws +#' @inheritParams computeSynchrones +#' @inheritParams computeWerDists +#' +#' @return The indices of the computed (resp. K1 and K2) medoids. +#' +#' @name clustering +#' @rdname clustering +#' @aliases clusteringTask1 clusteringTask2 +NULL + +#' @rdname clustering +#' @export +clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust, + ncores_clust=3, verbose=FALSE) +{ + if (verbose) + cat(paste("*** Clustering task 1 on ",length(indices)," series [start]\n", sep="")) + + if (length(indices) <= K1) + return (indices) + + parll <- (ncores_clust > 1) + if (parll) + { + # outfile=="" to see stderr/stdout on terminal + cl <- + if (verbose) + parallel::makeCluster(ncores_clust, outfile = "") + else + parallel::makeCluster(ncores_clust) + parallel::clusterExport(cl, c("getContribs","K1","verbose"), envir=environment()) + } + # Iterate clustering algorithm 1 until K1 medoids are found + while (length(indices) > K1) + { + # Balance tasks by splitting the indices set - as evenly as possible + indices_workers <- .splitIndices(indices, nb_items_clust, min_size=K1+1) + indices <- + if (parll) + { + unlist( parallel::parLapply(cl, indices_workers, function(inds) { + require("epclust", quietly=TRUE) + inds[ algoClust1(getContribs(inds), K1) ] + }) ) + } + else + { + unlist( lapply(indices_workers, function(inds) + inds[ algoClust1(getContribs(inds), K1) ] + ) ) + } + if (verbose) + { + cat(paste("*** Clustering task 1 on ",length(indices)," medoids [iter]\n", sep="")) + } + } + if (parll) + parallel::stopCluster(cl) + + indices #medoids +} + +#' @rdname clustering +#' @export +clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk, + smooth_lvl, nvoice, nbytes, endian, ncores_clust=3, verbose=FALSE) +{ + if (verbose) + cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep="")) + + if (length(indices) <= K2) + return (indices) + + # A) Compute the WER distances (Wavelets Extended coefficient of deteRmination) + distances <- computeWerDists(indices, getSeries, nb_series_per_chunk, + smooth_lvl, nvoice, nbytes, endian, ncores_clust, verbose) + + # B) Apply clustering algorithm 2 on the WER distances matrix + if (verbose) + cat(paste("*** algoClust2() on ",nrow(distances)," items\n", sep="")) + indices[ algoClust2(distances,K2) ] +} diff --git a/pkg/R/computeSynchrones.R b/pkg/R/computeSynchrones.R new file mode 100644 index 0000000..3a29546 --- /dev/null +++ b/pkg/R/computeSynchrones.R @@ -0,0 +1,67 @@ +#' computeSynchrones +#' +#' Compute the synchrones curves (sums of clusters elements) from a matrix of medoids, +#' using euclidian distance. +#' +#' @param medoids matrix of K medoids curves in columns +#' @param nb_curves How many series? (this is known, at this stage) +#' @inheritParams claws +#' @inheritParams computeWerDists +#' +#' @return A matrix of K synchrones in columns (same length as the series) +#' +#' @export +computeSynchrones <- function(medoids, getSeries, nb_curves, + nb_series_per_chunk, ncores=3, verbose=FALSE) +{ + # Synchrones computation is embarassingly parallel: compute it by chunks of series + computeSynchronesChunk <- function(indices) + { + # Obtain a chunk of reference series + series_chunk <- getSeries(indices) + nb_series_chunk <- ncol(series_chunk) + + # Get medoids indices for this chunk of series + mi <- assignMedoids(series_chunk, medoids[,]) + + # Update synchrones using mi above, grouping it by values of mi (in 1...K) + # to avoid too many lock/unlock + for (i in seq_len(K)) + { + # lock / unlock required because several writes at the same time + if (parll) + synchronicity::lock(m) + synchrones[,i] <- synchrones[,i] + rowSums(as.matrix(series_chunk[,mi==i])) + if (parll) + synchronicity::unlock(m) + } + NULL + } + + K <- ncol(medoids) + L <- nrow(medoids) + # Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in // + synchrones <- bigmemory::big.matrix(nrow=L, ncol=K, type="double", init=0.) + # NOTE: synchronicity is only for Linux & MacOS; on Windows: run sequentially + parll <- (ncores > 1 && requireNamespace("synchronicity",quietly=TRUE) + && Sys.info()['sysname'] != "Windows") + + if (parll) + m <- synchronicity::boost.mutex() #for lock/unlock, see computeSynchronesChunk + + if (verbose) + cat(paste("--- Compute ",K," synchrones with ",nb_curves," series\n", sep="")) + + # Balance tasks by splitting 1:nb_curves into groups of size <= nb_series_per_chunk + indices_workers <- .splitIndices(seq_len(nb_curves), nb_series_per_chunk) + ignored <- + if (parll) + { + parallel::mclapply(indices_workers, + function(inds) computeSynchronesChunk(inds), mc.cores=ncores) + } + else + lapply(indices_workers, computeSynchronesChunk) + + return (synchrones[,]) +} diff --git a/pkg/R/computeWerDists.R b/pkg/R/computeWerDists.R new file mode 100644 index 0000000..130954f --- /dev/null +++ b/pkg/R/computeWerDists.R @@ -0,0 +1,134 @@ +#' computeWerDists +#' +#' Compute the WER distances between the series at specified indices, which are +#' obtaind by \code{getSeries(indices)} +#' +#' @param indices Indices of the series to consider +#' @param getSeries Function to retrieve series (argument: 'inds', integer vector), +#' as columns of a matrix +#' @param ncores Number of cores for parallel runs +#' @inheritParams claws +#' +#' @return A distances matrix of size K x K where K == length(indices) +#' +#' @export +computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl=3, + nvoice=4, nbytes=4, endian=.Platform$endian, ncores=3, verbose=FALSE) +{ + n <- length(indices) + L <- length(getSeries(1)) #TODO: not very neat way to get L + noctave <- ceiling(log2(L)) #min power of 2 to cover serie range + # Since a CWT contains noctave*nvoice complex series, we deduce the number of CWT to + # retrieve/put in one chunk. + nb_cwt_per_chunk <- max(1, floor(nb_series_per_chunk / (nvoice*noctave*2))) + + # Initialize result as a square big.matrix of size 'number of medoids' + Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double") + + shift <- 1 #roughly equivalent to s0 in biwavelets & cie. TODO: as arg? + + cwt_file <- tempfile(pattern="epclust_cwt.bin_") + # Compute the getSeries(indices) CWT, and store the results in the binary file + computeSaveCWT <- function(inds) + { + if (verbose) + cat(" Compute save CWT on ",length(inds)," indices\n", sep="") + + # Obtain CWT as big vectors of real part + imaginary part (concatenate) + ts_cwt <- sapply(inds, function(i) { + ts <- scale(ts(getSeries(i)), center=TRUE, scale=FALSE) + ts_cwt <- Rwave::cwt(ts, noctave+ceiling(shift/nvoice), nvoice, + w0=2*pi, twoD=TRUE, plot=FALSE) + ts_cwt <- ts_cwt[,(1+shift):(noctave*nvoice+shift)] + c( as.double(Re(ts_cwt)),as.double(Im(ts_cwt)) ) + }) + + # Serialization + binarize(ts_cwt, cwt_file, nb_cwt_per_chunk, ",", nbytes, endian) + } + + # Function to retrieve a synchrone CWT from (binary) file + getCWT <- function(index, L) + { + flat_cwt <- getDataInFile(index, cwt_file, nbytes, endian) + cwt_length <- length(flat_cwt) / 2 + re_part <- as.matrix(flat_cwt[1:cwt_length], nrow=L) + im_part <- as.matrix(flat_cwt[(cwt_length+1):(2*cwt_length)], nrow=L) + re_part + 1i * im_part + } + + # Compute distances between columns i and j for j>i + computeDistances <- function(i) + { + if (parll) + { + # parallel workers start with an empty environment + require("epclust", quietly=TRUE) + Xwer_dist <- bigmemory::attach.big.matrix(Xwer_dist_desc) + } + + if (verbose) + cat(paste(" Distances from ",i," to ",i+1,"...",n,"\n", sep="")) + + # Get CWT of column i, and run computations for columns j>i + cwt_i <- getCWT(i, L) + WX <- filterMA(Mod(cwt_i * Conj(cwt_i)), smooth_lvl) + + for (j in (i+1):n) + { + cwt_j <- getCWT(j, L) + + # Compute the ratio of integrals formula 5.6 for WER^2 + # in https://arxiv.org/abs/1101.4744v2 paragraph 5.3 + num <- filterMA(Mod(cwt_i * Conj(cwt_j)), smooth_lvl) + WY <- filterMA(Mod(cwt_j * Conj(cwt_j)), smooth_lvl) + wer2 <- sum(colSums(num)^2) / sum(colSums(WX) * colSums(WY)) + + Xwer_dist[i,j] <- sqrt(L * ncol(cwt_i) * (1 - wer2)) + Xwer_dist[j,i] <- Xwer_dist[i,j] + } + Xwer_dist[i,i] <- 0. + } + + if (verbose) + cat(paste("--- Precompute and serialize synchrones CWT\n", sep="")) + + # Split indices by packets of length at most nb_cwt_per_chunk + indices_cwt <- .splitIndices(indices, nb_cwt_per_chunk) + # NOTE: next loop could potentially be run in //. Indices would be permuted (by + # serialization order), and synchronicity would be required because of concurrent + # writes. Probably not worth the effort - but possible. + for (inds in indices_cwt) + computeSaveCWT(inds) + + parll <- (ncores > 1) + if (parll) + { + # outfile=="" to see stderr/stdout on terminal + cl <- + if (verbose) + parallel::makeCluster(ncores, outfile="") + else + parallel::makeCluster(ncores) + Xwer_dist_desc <- bigmemory::describe(Xwer_dist) + parallel::clusterExport(cl, envir=environment(), + varlist=c("parll","n","L","Xwer_dist_desc","getCWT","verbose")) + } + + if (verbose) + cat(paste("--- Compute WER distances\n", sep="")) + + ignored <- + if (parll) + parallel::clusterApplyLB(cl, seq_len(n-1), computeDistances) + else + lapply(seq_len(n-1), computeDistances) + Xwer_dist[n,n] <- 0. + + if (parll) + parallel::stopCluster(cl) + + unlink(cwt_file) #remove binary file + + Xwer_dist[,] #~small matrix K1 x K1 +} diff --git a/pkg/R/de_serialize.R b/pkg/R/de_serialize.R new file mode 100644 index 0000000..cb964b6 --- /dev/null +++ b/pkg/R/de_serialize.R @@ -0,0 +1,150 @@ +#' (De)Serialization of a [big]matrix or data stream +#' +#' \code{binarize()} serializes a matrix or CSV file with minimal overhead, into a +#' binary file. \code{getDataInFile()} achieves the inverse task: she retrieves (ASCII) +#' data rows from indices in the binary file. Finally, \code{binarizeTransform()} +#' serialize transformations of all data chunks. To use it a data-retrieval function +#' must be provided -- thus \code{binarize} will most likely be used first +#' (and then a function defined to seek in generated binary file) +#' +#' @param data_ascii Matrix (by columns) or CSV file or connection (by rows) +#' @param data_bin_file Name of binary file on output of \code{binarize()} +#' or input of \code{getDataInFile()} +#' @param nb_per_chunk Number of lines to process in one batch +#' @param getData Function to retrieve data chunks +#' @param transform Transformation function to apply on data chunks +#' @param indices Indices of the lines to retrieve +#' @inheritParams claws +#' +#' @return For \code{getDataInFile()}, a matrix with columns corresponding to the +#' requested indices. \code{binarizeTransform()} returns the number of processed lines. +#' \code{binarize()} is designed to serialize in several calls, thus returns nothing. +#' +#' @name de_serialize +#' @rdname de_serialize +#' @aliases binarize binarizeTransform getDataInFile +NULL + +#' @rdname de_serialize +#' @export +binarize <- function(data_ascii, data_bin_file, nb_per_chunk, + sep=",", nbytes=4, endian=.Platform$endian) +{ + # data_ascii can be of two types: [big.]matrix, or connection + if (is.character(data_ascii)) + data_ascii <- file(data_ascii, open="r") + else if (methods::is(data_ascii,"connection") && !isOpen(data_ascii)) + open(data_ascii) + is_matrix <- !methods::is(data_ascii,"connection") + + # At first call, the length of a stored row is written. So it's important to determine + # if the serialization process already started. + first_write <- (!file.exists(data_bin_file) || file.info(data_bin_file)$size == 0) + + # Open the binary file for writing (or 'append' if already exists) + data_bin <- file(data_bin_file, open=ifelse(first_write,"wb","ab")) + + if (first_write) + { + # Write data length on first call: number of items always on 8 bytes + writeBin(0L, data_bin, size=8, endian=endian) + if (is_matrix) + data_length <- nrow(data_ascii) + else #connection + { + # Read the first line to know data length, and write it then + data_line <- scan(data_ascii, double(), sep=sep, nlines=1, quiet=TRUE) + writeBin(data_line, data_bin, size=nbytes, endian=endian) + data_length <- length(data_line) + } + } + + if (is_matrix) + { + # Data is processed by chunks; although this may not be so useful for (normal) matrix + # input, it could for a file-backed big.matrix. It's easier to follow a unified pattern. + index <- 1 + } + repeat + { + if (is_matrix) + { + data_chunk <- + if (index <= ncol(data_ascii)) + as.double(data_ascii[,index:min(ncol(data_ascii),index+nb_per_chunk-1)]) + else + double(0) + index <- index + nb_per_chunk + } + else #connection + data_chunk <- scan(data_ascii, double(), sep=sep, nlines=nb_per_chunk, quiet=TRUE) + + # Data size is unknown in the case of a connection + if (length(data_chunk)==0) + break + + # Write this chunk of data to the binary file + writeBin(data_chunk, data_bin, size=nbytes, endian=endian) + } + + if (first_write) + { + # Write data_length, == (file_size-1) / (nbytes*nbWritten) at offset 0 in data_bin + ignored <- seek(data_bin, 0) + writeBin(data_length, data_bin, size=8, endian=endian) + } + close(data_bin) + + if ( ! is_matrix ) + close(data_ascii) +} + +#' @rdname de_serialize +#' @export +binarizeTransform <- function(getData, transform, data_bin_file, nb_per_chunk, + nbytes=4, endian=.Platform$endian) +{ + nb_items <- 0 #side-effect: store the number of transformed items + index <- 1 + repeat + { + # Retrieve a chunk of data in a binary file (generally obtained by binarize()) + data_chunk <- getData((index-1)+seq_len(nb_per_chunk)) + if (is.null(data_chunk)) + break + + # Apply transformation on the current chunk (by columns) + transformed_chunk <- transform(data_chunk) + + # Save the result in binary format + binarize(transformed_chunk, data_bin_file, nb_per_chunk, ",", nbytes, endian) + + index <- index + nb_per_chunk + nb_items <- nb_items + ncol(data_chunk) + } + nb_items #number of transformed items +} + +#' @rdname de_serialize +#' @export +getDataInFile <- function(indices, data_bin_file, nbytes=4, endian=.Platform$endian) +{ + data_bin <- file(data_bin_file, "rb") #source binary file + + data_size <- file.info(data_bin_file)$size #number of bytes in the file + # data_length: length of a vector in the binary file (first element, 8 bytes) + data_length <- readBin(data_bin, "integer", n=1, size=8, endian=endian) + + # Seek all 'indices' columns in the binary file, using data_length and nbytes + # to compute the offset ( index i at 8 + i*data_length*nbytes ) + data_ascii <- do.call( cbind, lapply( indices, function(i) { + offset <- 8+(i-1)*data_length*nbytes + if (offset >= data_size) + return (NULL) + ignored <- seek(data_bin, offset) #position cursor at computed offset + readBin(data_bin, "double", n=data_length, size=nbytes, endian=endian) + } ) ) + close(data_bin) + + data_ascii #retrieved data, in columns +} diff --git a/pkg/R/main.R b/pkg/R/main.R new file mode 100644 index 0000000..1eafdcb --- /dev/null +++ b/pkg/R/main.R @@ -0,0 +1,334 @@ +#' CLAWS: CLustering with wAvelets and Wer distanceS +#' +#' Cluster electricity power curves (or any series of similar nature) by applying a +#' two stage procedure in parallel (see details). +#' Input series must be sampled on the same time grid, no missing values. +#' +#' Summary of the function execution flow: +#' \enumerate{ +#' \item Compute and serialize all contributions, obtained through discrete wavelet +#' decomposition (see Antoniadis & al. [2013]) +#' \item Divide series into \code{ntasks} groups to process in parallel. In each task: +#' \enumerate{ +#' \item iterate the first clustering algorithm on its aggregated outputs, +#' on inputs of size \code{nb_items_clust}\cr +#' -> K1 medoids indices +#' \item optionally, if WER=="mix":\cr +#' a. compute WER distances (K1xK1) between medoids\cr +#' b. apply the 2nd clustering algorithm\cr +#' -> K2 medoids indices +#' } +#' \item Launch a final task on the aggregated outputs of all previous tasks: +#' ntasks*K1 if WER=="end", ntasks*K2 otherwise +#' \item Compute synchrones (sum of series within each final group) +#' } +#' +#' The main argument -- \code{series} -- has a quite misleading name, since it can be +#' either a [big.]matrix, a CSV file, a connection or a user function to retrieve series. +#' When \code{series} is given as a function it must take a single argument, +#' 'indices': integer vector equal to the indices of the curves to retrieve; +#' see SQLite example. +#' WARNING: the return value must be a matrix (in columns), or NULL if no matches. +#' +#' Note: Since we don't make assumptions on initial data, there is a possibility that +#' even when serialized, contributions do not fit in RAM. For example, +#' 30e6 series of length 100,000 would lead to a +4Go contribution matrix. Therefore, +#' it's safer to place these in (binary) files; that's what we do. +#' +#' @param series Access to the N (time-)series, which can be of one of the four +#' following types: +#' \itemize{ +#' \item [big.]matrix: each column contains the (time-ordered) values of one +#' time-serie +#' \item connection: any R connection object providing lines as described above +#' \item character: name of a CSV file containing series in rows (no header) +#' \item function: a custom way to retrieve the curves; it has only one argument: +#' the indices of the series to be retrieved. See SQLite example +#' } +#' @param K1 Number of clusters to be found after stage 1 (K1 << N) +#' @param K2 Number of clusters to be found after stage 2 (K2 << K1) +#' @param nb_series_per_chunk Number of series to retrieve in one batch +#' @param nb_items_clust Number of items in 1st clustering algorithm input +#' @param algoClust1 Clustering algorithm for stage 1. A function which takes (data, K) +#' as argument where data is a matrix in columns and K the desired number of clusters, +#' and outputs K medoids ranks. Default: PAM. +#' @param algoClust2 Clustering algorithm for stage 2. A function which takes (dists, K) +#' as argument where dists is a matrix of distances and K the desired number of +#' clusters, and outputs K medoids ranks. Default: PAM. +#' @param wav_filt Wavelet transform filter, as a string "Family:FilterNumber"; see +#' ?wavethresh::wd +#' @param contrib_type Type of contribution: "relative", "logit" or "absolute" (any +#' prefix) +#' @param WER "end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply +#' stage 2 at the end of each task +#' @param smooth_lvl Smoothing level: odd integer, 1 == no smoothing. +#' @param nvoice Number of voices within each octave for CWT computations +#' @param random TRUE (default) for random chunks repartition +#' @param ntasks Number of tasks (parallel iterations to obtain K1 [if WER=="end"] +#' or K2 [if WER=="mix"] medoids); default: 1.\cr +#' Note: ntasks << N (number of series), so that N is "roughly divisible" by ntasks +#' @param ncores_tasks Number of parallel tasks ('1' == sequential tasks) +#' @param ncores_clust Number of parallel clusterings in one task +#' @param sep Separator in CSV input file (if any provided) +#' @param nbytes 4 or 8 bytes to (de)serialize a floating-point number +#' @param endian Endianness for (de)serialization: "little" or "big" +#' @param verbose FALSE: nothing printed; TRUE: some execution traces +#' +#' @return A list: +#' \itemize{ +#' \item medoids: matrix of the final K2 medoids curves +#' \item ranks: corresponding indices in the dataset +#' \item synchrones: sum of series within each final group +#' } +#' +#' @references Clustering functional data using Wavelets [2013]; +#' A. Antoniadis, X. Brossat, J. Cugliari & J.-M. Poggi. +#' Inter. J. of Wavelets, Multiresolution and Information Procesing, +#' vol. 11, No 1, pp.1-30. doi:10.1142/S0219691313500033 +#' +#' @examples +#' \dontrun{ +#' # WER distances computations are too long for CRAN (for now) +#' # Note: on this small example, sequential run is faster +#' +#' # Random series around cos(x,2x,3x)/sin(x,2x,3x) +#' x <- seq(0,50,0.05) +#' L <- length(x) #1001 +#' ref_series <- matrix( c(cos(x),cos(2*x),cos(3*x),sin(x),sin(2*x),sin(3*x)), ncol=6 ) +#' library(wmtsa) +#' series <- do.call( cbind, lapply( 1:6, function(i) +#' do.call(cbind, wmtsa::wavBootstrap(ref_series[,i], n.realization=40)) ) ) +#' # Mix series so that all groups are evenly spread +#' permut <- (0:239)%%6 * 40 + (0:239)%/%6 + 1 +#' series = series[,permut] +#' #dim(series) #c(240,1001) +#' res_ascii <- claws(series, K1=30, K2=6, nb_series_per_chunk=500, +#' nb_items_clust=100, random=FALSE, verbose=TRUE, ncores_clust=1) +#' +#' # Same example, from CSV file +#' csv_file <- tempfile(pattern="epclust_series.csv_") +#' write.table(t(series), csv_file, sep=",", row.names=FALSE, col.names=FALSE) +#' res_csv <- claws(csv_file, 30, 6, 500, 100, random=FALSE, ncores_clust=1) +#' +#' # Same example, from binary file +#' bin_file <- tempfile(pattern="epclust_series.bin_") +#' nbytes <- 8 +#' endian <- "little" +#' binarize(csv_file, bin_file, 500, ",", nbytes, endian) +#' getSeries <- function(indices) getDataInFile(indices, bin_file, nbytes, endian) +#' res_bin <- claws(getSeries, 30, 6, 500, 100, random=FALSE, ncores_clust=1) +#' unlink(csv_file) +#' unlink(bin_file) +#' +#' # Same example, from SQLite database +#' library(DBI) +#' series_db <- dbConnect(RSQLite::SQLite(), "file::memory:") +#' # Prepare data.frame in DB-format +#' n <- ncol(series) +#' times_values <- data.frame( +#' id = rep(1:n,each=L), +#' time = rep( as.POSIXct(1800*(1:L),"GMT",origin="2001-01-01"), n ), +#' value = as.double(series) ) +#' dbWriteTable(series_db, "times_values", times_values) +#' # Fill associative array, map index to identifier +#' indexToID_inDB <- as.character( +#' dbGetQuery(series_db, 'SELECT DISTINCT id FROM times_values')[,"id"] ) +#' serie_length <- as.integer( dbGetQuery(series_db, +#' paste("SELECT COUNT(*) FROM times_values WHERE id == ",indexToID_inDB[1],sep="")) ) +#' getSeries <- function(indices) { +#' indices = indices[ indices <= length(indexToID_inDB) ] +#' if (length(indices) == 0) +#' return (NULL) +#' request <- "SELECT id,value FROM times_values WHERE id in (" +#' for (i in seq_along(indices)) { +#' request <- paste(request, indexToID_inDB[ indices[i] ], sep="") +#' if (i < length(indices)) +#' request <- paste(request, ",", sep="") +#' } +#' request <- paste(request, ")", sep="") +#' df_series <- dbGetQuery(series_db, request) +#' matrix(df_series[,"value"], nrow=serie_length) +#' } +#' res_db <- claws(getSeries, 30, 6, 500, 100, random=FALSE, ncores_clust=1) +#' dbDisconnect(series_db) +#' +#' # All results should be equal: +#' all(res_ascii$ranks == res_csv$ranks +#' & res_ascii$ranks == res_bin$ranks +#' & res_ascii$ranks == res_db$ranks) +#' } +#' @export +claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=5*K1, + algoClust1=function(data,K) cluster::pam(t(data),K,diss=FALSE,pamonce=1)$id.med, + algoClust2=function(dists,K) cluster::pam(dists,K,diss=TRUE,pamonce=1)$id.med, + wav_filt="Coiflets:1", contrib_type="absolute", WER="end", smooth_lvl=3, nvoice=4, + random=TRUE, ntasks=1, ncores_tasks=1, ncores_clust=3, sep=",", nbytes=4, + endian=.Platform$endian, verbose=FALSE) +{ + # Check/transform arguments + if (!is.matrix(series) && !bigmemory::is.big.matrix(series) + && !is.function(series) + && !methods::is(series,"connection") && !is.character(series)) + { + stop("'series': [big]matrix, function, file or valid connection (no NA)") + } + K1 <- .toInteger(K1, function(x) x>=2) + K2 <- .toInteger(K2, function(x) x>=2) + nb_series_per_chunk <- .toInteger(nb_series_per_chunk, function(x) x>=1) + nb_items_clust <- .toInteger(nb_items_clust, function(x) x>K1) + random <- .toLogical(random) + wav_filt <- strsplit(wav_filt, ':')[[1]] + wav_family <- wav_filt[1] + wav_number <- wav_filt[2] + tryCatch({ignored <- wavethresh::filter.select(wav_number, wav_family)}, + error=function(e) stop("Invalid wavelet filter; see ?wavethresh::filter.select") ) + ctypes <- c("relative","absolute","logit") + contrib_type <- ctypes[ pmatch(contrib_type,ctypes) ] + if (is.na(contrib_type)) + stop("'contrib_type' in {'relative','absolute','logit'}") + if (WER!="end" && WER!="mix") + stop("'WER': in {'end','mix'}") + random <- .toLogical(random) + ntasks <- .toInteger(ntasks, function(x) x>=1) + ncores_tasks <- .toInteger(ncores_tasks, function(x) x>=1) + ncores_clust <- .toInteger(ncores_clust, function(x) x>=1) + if (!is.character(sep)) + stop("'sep': character") + nbytes <- .toInteger(nbytes, function(x) x==4 || x==8) + verbose <- .toLogical(verbose) + + # Binarize series if it is not a function; the aim is to always use a function, + # to uniformize treatments. An equally good alternative would be to use a file-backed + # bigmemory::big.matrix, but it would break the "all-is-function" pattern. + if (!is.function(series)) + { + if (verbose) + cat("...Serialize time-series (or retrieve past binary file)\n") + series_file <- ".series.epclust.bin" + if (!file.exists(series_file)) + binarize(series, series_file, nb_series_per_chunk, sep, nbytes, endian) + getSeries <- function(inds) getDataInFile(inds, series_file, nbytes, endian) + } + else + getSeries <- series + + # Serialize all computed wavelets contributions into a file + contribs_file <- ".contribs.epclust.bin" + if (verbose) + cat("...Compute contributions and serialize them (or retrieve past binary file)\n") + if (!file.exists(contribs_file)) + { + nb_curves <- binarizeTransform(getSeries, + function(curves) curvesToContribs(curves, wav_filt, contrib_type), + contribs_file, nb_series_per_chunk, nbytes, endian) + } + else + { + # TODO: duplicate from getDataInFile() in de_serialize.R + contribs_size <- file.info(contribs_file)$size #number of bytes in the file + contrib_length <- readBin(contribs_file, "integer", n=1, size=8, endian=endian) + nb_curves <- (contribs_size-8) / (nbytes*contrib_length) + } + getContribs <- function(indices) getDataInFile(indices, contribs_file, nbytes, endian) + + # A few sanity checks: do not continue if too few data available. + if (nb_curves < K2) + stop("Not enough data: less series than final number of clusters") + nb_series_per_task <- round(nb_curves / ntasks) + if (nb_series_per_task < K2) + stop("Too many tasks: less series in one task than final number of clusters") + + # Generate a random permutation of 1:N (if random==TRUE); + # otherwise just use arrival (storage) order. + indices_all <- if (random) sample(nb_curves) else seq_len(nb_curves) + # Split (all) indices into ntasks groups of ~same size + indices_tasks <- lapply(seq_len(ntasks), function(i) { + upper_bound <- ifelse( i 1) + if (parll && ntasks>1) + { + # Initialize parallel runs: outfile="" allow to output verbose traces in the console + # under Linux. All necessary variables are passed to the workers. + cl <- + if (verbose) + parallel::makeCluster(ncores_tasks, outfile="") + else + parallel::makeCluster(ncores_tasks) + varlist <- c("ncores_clust","verbose", #task 1 & 2 + "K1","getContribs","algoClust1","nb_items_clust") #task 1 + if (WER=="mix") + { + # Add variables for task 2 + varlist <- c(varlist, "K2","getSeries","algoClust2","nb_series_per_chunk", + "smooth_lvl","nvoice","nbytes","endian") + } + parallel::clusterExport(cl, varlist, envir <- environment()) + } + + # This function achieves one complete clustering task, divided in stage 1 + stage 2. + # stage 1: n indices --> clusteringTask1(...) --> K1 medoids (indices) + # stage 2: K1 indices --> K1xK1 WER distances --> clusteringTask2(...) --> K2 medoids, + # where n == N / ntasks, N being the total number of curves. + runTwoStepClustering <- function(inds) + { + # When running in parallel, the environment is blank: we need to load the required + # packages, and pass useful variables. + if (parll && ntasks>1) + require("epclust", quietly=TRUE) + indices_medoids <- clusteringTask1(inds, getContribs, K1, algoClust1, + nb_items_clust, ncores_clust, verbose) + if (WER=="mix") + { + indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2, + nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_clust,verbose) + } + indices_medoids + } + + if (verbose) + { + message <- paste("...Run ",ntasks," x stage 1", sep="") + if (WER=="mix") + message <- paste(message," + stage 2", sep="") + cat(paste(message,"\n", sep="")) + } + + # As explained above, we obtain after all runs ntasks*[K1 or K2] medoids indices, + # depending whether WER=="end" or "mix", respectively. + indices_medoids_all <- + if (parll && ntasks>1) + unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) ) + else + unlist( lapply(indices_tasks, runTwoStepClustering) ) + + if (parll && ntasks>1) + parallel::stopCluster(cl) + + # For the last stage, ncores_tasks*(ncores_clusts+1) cores should be available: + # - ntasks for level 1 parallelism + # - ntasks*ncores_clust for level 2 parallelism, + # but since an extension MPI <--> tasks / OpenMP <--> sub-tasks is on the way, + # it's better to just re-use ncores_clust + ncores_last_stage <- ncores_clust + + # Run last clustering tasks to obtain only K2 medoids indices + if (verbose) + cat("...Run final // stage 1 + stage 2\n") + indices_medoids <- clusteringTask1(indices_medoids_all, getContribs, K1, algoClust1, + nb_items_clust, ncores_tasks*ncores_clust, verbose) + + indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2, + nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_last_stage,verbose) + + # Compute synchrones, that is to say the cumulated power consumptions for each of the + # K2 final groups. + medoids <- getSeries(indices_medoids) + synchrones <- computeSynchrones(medoids, getSeries, nb_curves, nb_series_per_chunk, + ncores_last_stage, verbose) + + # NOTE: no need to use big.matrix here, because only K2 << K1 << N remaining curves + list("medoids"=medoids, "ranks"=indices_medoids, "synchrones"=synchrones) +} diff --git a/pkg/R/plot.R b/pkg/R/plot.R new file mode 100644 index 0000000..8d2c961 --- /dev/null +++ b/pkg/R/plot.R @@ -0,0 +1,15 @@ +#TODO: some visualization +#for (i in 1:6) {plot(medoids_ascii[i,1:200],type="l",ylim=c(-5,5),col=i);par(new=TRUE)} +#... +#PLOT: +#plot manifold 2D distances WER / --> récupérer les distances ? quand ? +#fenetre tempo forme des courbes / --> OK (jour[type] / semaine, indices en arg) +#medoids / --> OK (moyennés sur 1 jour / type de jour / semaine) +#gain en prevision: clust puis full --> enercast (comment l'utiliser ?) +# +#> plot(cr$medoids[1:100,1],type="l") +#> #for (i in 1:15)plot(cr$medoids[1:100,1],type="l") +#> r = range(cr$medoids[1:96,]) +#> for (i in 1:15) {plot(cr$medoids[1:96,i],type="l",ylim=r,col=i); par(new=TRUE) } +#> +# diff --git a/pkg/R/simulateCurve.R b/pkg/R/simulateCurve.R new file mode 100644 index 0000000..97aeac8 --- /dev/null +++ b/pkg/R/simulateCurve.R @@ -0,0 +1,42 @@ +Ref_Centroids <<- NULL +Tot_Nb_Curves <<- NA + +#' initCentroids +#' +#' Must be called before getDetRandCurve() +#' +#' @param N Number of curves +#' @param D Number of sample points +#' @param K Number of clusters +#' +#' @export +initClustersParams <- function(N, D, K) +{ + # Generate K centroids + Ref_Centroids <<- sapply(1:K, function(k) cumsum(rnorm(D))) + Tot_Nb_Curves <<- N +} + +#' getDetRandCurve +#' +#' TODO: improve this (using known clusters centers...) +#' +#' @param i Some index +#' +#' @examples +#' \dontrun{ +#' initCentroids(N=250000, D=15000, K=15) +#' res_ascii <- claws(getDetRandCurve, K1=50, K2=15, nb_series_per_chunk=500, +#' nb_items_clust=100, random=FALSE, verbose=TRUE, ncores_clust=3) +#' } +#' @export +getDetRandCurve <- function(indices) +{ + sapply(indices, function(i) { + if (i > Tot_Nb_Curves) + return (NULL) + set.seed(i) + j <- sample(ncol(Ref_Centroids), 1) + Ref_Centroids[,j] + rnorm(nrow(Ref_Centroids)) + }) +} diff --git a/pkg/R/utils.R b/pkg/R/utils.R new file mode 100644 index 0000000..12fada2 --- /dev/null +++ b/pkg/R/utils.R @@ -0,0 +1,140 @@ +# Check integer arguments with functional conditions +.toInteger <- function(x, condition) +{ + errWarn <- function(ignored) + paste("Cannot convert argument' ",substitute(x),"' to integer", sep="") + if (!is.integer(x)) + tryCatch({x <- as.integer(x)[1]; if (is.na(x)) stop()}, + warning=errWarn, error=errWarn) + if (!condition(x)) + { + stop(paste("Argument '",substitute(x), + "' does not verify condition ",body(condition), sep="")) + } + x +} + +# Check logical arguments +.toLogical <- function(x) +{ + errWarn <- function(ignored) + paste("Cannot convert argument' ",substitute(x),"' to logical", sep="") + if (!is.logical(x)) + tryCatch({x <- as.logical(x)[1]; if (is.na(x)) stop()}, + warning=errWarn, error=errWarn) + x +} + +#' curvesToContribs +#' +#' Compute the discrete wavelet coefficients for each series, and aggregate them in +#' energy contribution across scales as described in https://arxiv.org/abs/1101.4744v2 +#' +#' @param curves [big.]matrix of series (in columns), of size L x n +#' @param wav_filt Wavelet transform filter, as a vector c(Family,FilterNumber) +#' @inheritParams claws +#' +#' @return A matrix of size log(L) x n containing contributions in columns +#' +#' @export +curvesToContribs <- function(curves, wav_filt, contrib_type) +{ + series <- as.matrix(curves) + L <- nrow(series) + D <- ceiling( log2(L) ) + # Series are interpolated to all have length 2^D + nb_sample_points <- 2^D + apply(series, 2, function(x) { + interpolated_curve <- spline(1:L, x, n=nb_sample_points)$y + W <- wavethresh::wd(interpolated_curve, wav_filt[2], wav_filt[1])$D + # Compute the sum of squared discrete wavelet coefficients, for each scale + nrj <- sapply( 1:D, function(i) ( sqrt( sum(W[(2^D-(2^i-1)):(2^D-2^(i-1))]^2) ) ) ) + if (contrib_type!="absolute") + nrj <- nrj / sum(nrj) + if (contrib_type=="logit") + nrj <- - log(1 - nrj) + unname( nrj ) + }) +} + +# Helper function to divide indices into balanced sets. +# Ensure that all indices sets have at least min_size elements. +.splitIndices <- function(indices, nb_per_set, min_size=1) +{ + L <- length(indices) + nb_workers <- floor( L / nb_per_set ) + rem <- L %% nb_per_set + if (nb_workers == 0 || (nb_workers==1 && rem==0)) + { + # L <= nb_per_set, simple case + return (list(indices)) + } + + indices_workers <- lapply( seq_len(nb_workers), function(i) + indices[(nb_per_set*(i-1)+1):(nb_per_set*i)] ) + + rem <- L %% nb_per_set #number of remaining unassigned items + if (rem == 0) + return (indices_workers) + + rem <- (L-rem+1):L + # If remainder is smaller than min_size, feed it with indices from other sets + # until either its size exceed min_size (success) or other sets' size + # get lower min_size (failure). + while (length(rem) < min_size) + { + index <- length(rem) %% nb_workers + 1 + if (length(indices_workers[[index]]) <= min_size) + { + stop("Impossible to split indices properly for clustering. + Try increasing nb_items_clust or decreasing K1") + } + rem <- c(rem, tail(indices_workers[[index]],1)) + indices_workers[[index]] <- head( indices_workers[[index]], -1) + } + return ( c(indices_workers, list(rem) ) ) +} + +#' assignMedoids +#' +#' Find the closest medoid for each curve in input +#' +#' @param curves (Chunk) of series whose medoids indices must be found +#' @param medoids Matrix of medoids (in columns) +#' +#' @return The vector of integer assignments +#' @export +assignMedoids <- function(curves, medoids) +{ + nb_series <- ncol(curves) + mi <- rep(NA,nb_series) + for (i in seq_len(nb_series)) + mi[i] <- which.min( colSums( sweep(medoids, 1, curves[,i], '-')^2 ) ) + mi +} + +#' filterMA +#' +#' Filter [time-]series by replacing all values by the moving average of values +#' centered around current one. Border values are averaged with available data. +#' +#' @param M_ A real matrix of size LxD +#' @param w_ The (odd) number of values to average +#' +#' @return The filtered matrix (in columns), of same size as the input +#' @export +filterMA <- function(M_, w_) + .Call("filterMA", M_, w_, PACKAGE="epclust") + +#' cleanBin +#' +#' Remove binary files to re-generate them at next run of \code{claws()}. +#' To be run in the folder where computations occurred (or no effect). +#' +#' @export +cleanBin <- function() +{ + bin_files <- list.files(pattern="*.epclust.bin", all.files=TRUE) + for (file in bin_files) + unlink(file) +} diff --git a/pkg/inst/testdata/de_serialize.csv b/pkg/inst/testdata/de_serialize.csv new file mode 100644 index 0000000..93e4938 --- /dev/null +++ b/pkg/inst/testdata/de_serialize.csv @@ -0,0 +1,300 @@ 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b/pkg/man/claws.Rd @@ -0,0 +1,127 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/main.R +\name{claws} +\alias{claws} +\title{CLAWS: CLustering with wAvelets and Wer distanceS} +\usage{ +claws(getSeries, K1, K2, wf, ctype, WER = "end", random = TRUE, + ntasks = 1, ncores_tasks = 1, ncores_clust = 4, + nb_series_per_chunk = 50 * K1, min_series_per_chunk = 5 * K1, sep = ",", + nbytes = 4, endian = .Platform$endian, verbose = FALSE, parll = TRUE) +} +\arguments{ +\item{getSeries}{Access to the (time-)series, which can be of one of the three +following types: +\itemize{ + \item [big.]matrix: each line contains all the values for one time-serie, ordered by time + \item connection: any R connection object providing lines as described above + \item character: name of a CSV file containing series in rows (no header) + \item function: a custom way to retrieve the curves; it has only one argument: + the indices of the series to be retrieved. See examples +}} + +\item{K1}{Number of super-consumers to be found after stage 1 (K1 << N)} + +\item{K2}{Number of clusters to be found after stage 2 (K2 << K1)} + +\item{wf}{Wavelet transform filter; see ?wavelets::wt.filter} + +\item{ctype}{Type of contribution: "relative" or "absolute" (or any prefix)} + +\item{WER}{"end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply stage 2 +at the end of each task} + +\item{random}{TRUE (default) for random chunks repartition} + +\item{ntasks}{Number of tasks (parallel iterations to obtain K1 medoids); default: 1. +Note: ntasks << N, so that N is "roughly divisible" by N (number of series)} + +\item{ncores_tasks}{"MPI" number of parallel tasks (1 to disable: sequential tasks)} + +\item{ncores_clust}{"OpenMP" number of parallel clusterings in one task} + +\item{nb_series_per_chunk}{(~Maximum) number of series in each group, inside a task} + +\item{min_series_per_chunk}{Minimum number of series in each group} + +\item{sep}{Separator in CSV input file (if any provided)} + +\item{nbytes}{Number of bytes to serialize a floating-point number; 4 or 8} + +\item{endian}{Endianness to use for (de)serialization. Use "little" or "big" for portability} + +\item{verbose}{Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage)} + +\item{parll}{TRUE to fully parallelize; otherwise run sequentially (debug, comparison)} +} +\value{ +A big.matrix of the final medoids curves (K2) in rows +} +\description{ +Groups electricity power curves (or any series of similar nature) by applying PAM +algorithm in parallel to chunks of size \code{nb_series_per_chunk}. Input series +must be sampled on the same time grid, no missing values. +} +\examples{ +\dontrun{ +# WER distances computations are a bit too long for CRAN (for now) + +# Random series around cos(x,2x,3x)/sin(x,2x,3x) +x = seq(0,500,0.05) +L = length(x) #10001 +ref_series = matrix( c(cos(x), cos(2*x), cos(3*x), sin(x), sin(2*x), sin(3*x)), + byrow=TRUE, ncol=L ) +library(wmtsa) +series = do.call( rbind, lapply( 1:6, function(i) + do.call(rbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) ) +#dim(series) #c(2400,10001) +medoids_ascii = claws(series, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) + +# Same example, from CSV file +csv_file = "/tmp/epclust_series.csv" +write.table(series, csv_file, sep=",", row.names=FALSE, col.names=FALSE) +medoids_csv = claws(csv_file, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) + +# Same example, from binary file +bin_file = "/tmp/epclust_series.bin" +nbytes = 8 +endian = "little" +epclust::binarize(csv_file, bin_file, 500, nbytes, endian) +getSeries = function(indices) getDataInFile(indices, bin_file, nbytes, endian) +medoids_bin = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) +unlink(csv_file) +unlink(bin_file) + +# Same example, from SQLite database +library(DBI) +series_db <- dbConnect(RSQLite::SQLite(), "file::memory:") +# Prepare data.frame in DB-format +n = nrow(series) +time_values = data.frame( + id = rep(1:n,each=L), + time = rep( as.POSIXct(1800*(0:n),"GMT",origin="2001-01-01"), L ), + value = as.double(t(series)) ) +dbWriteTable(series_db, "times_values", times_values) +# Fill associative array, map index to identifier +indexToID_inDB <- as.character( + dbGetQuery(series_db, 'SELECT DISTINCT id FROM time_values')[,"id"] ) +getSeries = function(indices) { + request = "SELECT id,value FROM times_values WHERE id in (" + for (i in indices) + request = paste(request, i, ",", sep="") + request = paste(request, ")", sep="") + df_series = dbGetQuery(series_db, request) + # Assume that all series share same length at this stage + ts_length = sum(df_series[,"id"] == df_series[1,"id"]) + t( as.matrix(df_series[,"value"], nrow=ts_length) ) +} +medoids_db = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) +dbDisconnect(series_db) + +# All computed medoids should be the same: +digest::sha1(medoids_ascii) +digest::sha1(medoids_csv) +digest::sha1(medoids_bin) +digest::sha1(medoids_db) +} +} diff --git a/pkg/man/clustering.Rd b/pkg/man/clustering.Rd new file mode 100644 index 0000000..1768cab --- /dev/null +++ b/pkg/man/clustering.Rd @@ -0,0 +1,69 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/clustering.R +\name{clustering} +\alias{clustering} +\alias{clusteringTask1} +\alias{computeClusters1} +\alias{computeClusters2} +\alias{clusteringTask1} +\alias{clusteringTask2} +\alias{computeClusters1} +\alias{computeClusters2} +\title{Two-stage clustering, withing one task (see \code{claws()})} +\usage{ +clusteringTask1(indices, getContribs, K1, nb_series_per_chunk, + ncores_clust = 1, verbose = FALSE, parll = TRUE) + +clusteringTask2(medoids, K2, getRefSeries, nb_ref_curves, nb_series_per_chunk, + ncores_clust = 1, verbose = FALSE, parll = TRUE) + +computeClusters1(contribs, K1, verbose = FALSE) + +computeClusters2(distances, K2, verbose = FALSE) +} +\arguments{ +\item{indices}{Range of series indices to cluster in parallel (initial data)} + +\item{getContribs}{Function to retrieve contributions from initial series indices: +\code{getContribs(indices)} outpus a contributions matrix} + +\item{K1}{Number of super-consumers to be found after stage 1 (K1 << N)} + +\item{nb_series_per_chunk}{(~Maximum) number of series in each group, inside a task} + +\item{ncores_clust}{"OpenMP" number of parallel clusterings in one task} + +\item{verbose}{Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage)} + +\item{parll}{TRUE to fully parallelize; otherwise run sequentially (debug, comparison)} + +\item{medoids}{big.matrix of medoids (curves of same length as initial series)} + +\item{K2}{Number of clusters to be found after stage 2 (K2 << K1)} + +\item{getRefSeries}{Function to retrieve initial series (e.g. in stage 2 after series +have been replaced by stage-1 medoids)} + +\item{nb_ref_curves}{How many reference series? (This number is known at this stage)} + +\item{contribs}{matrix of contributions (e.g. output of \code{curvesToContribs()})} + +\item{distances}{matrix of K1 x K1 (WER) distances between synchrones} +} +\value{ +For \code{clusteringTask1()} and \code{computeClusters1()}, the indices of the + computed (K1) medoids. Indices are irrelevant for stage 2 clustering, thus + \code{computeClusters2()} outputs a big.matrix of medoids + (of size limited by nb_series_per_chunk) +} +\description{ +\code{clusteringTask1()} runs one full stage-1 task, which consists in + iterated stage 1 clustering (on nb_curves / ntasks energy contributions, computed + through discrete wavelets coefficients). + \code{clusteringTask2()} runs a full stage-2 task, which consists in synchrones + and then WER distances computations, before applying the clustering algorithm. + \code{computeClusters1()} and \code{computeClusters2()} correspond to the atomic + clustering procedures respectively for stage 1 and 2. The former applies the + clustering algorithm (PAM) on a contributions matrix, while the latter clusters + a chunk of series inside one task (~max nb_series_per_chunk) +} diff --git a/pkg/man/computeMedoidsIndices.Rd b/pkg/man/computeMedoidsIndices.Rd new file mode 100644 index 0000000..d78ccfd --- /dev/null +++ b/pkg/man/computeMedoidsIndices.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{computeMedoidsIndices} +\alias{computeMedoidsIndices} +\title{computeMedoidsIndices} +\usage{ +computeMedoidsIndices(pMedoids, ref_series) +} +\arguments{ +\item{pMedoids}{External pointer} + +\item{ref_series}{reference series} +} +\value{ +A map serie number -> medoid index +} +\description{ +Compute medoids indices +} diff --git a/pkg/man/computeSynchrones.Rd b/pkg/man/computeSynchrones.Rd new file mode 100644 index 0000000..7cdbb08 --- /dev/null +++ b/pkg/man/computeSynchrones.Rd @@ -0,0 +1,32 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/clustering.R +\name{computeSynchrones} +\alias{computeSynchrones} +\title{computeSynchrones} +\usage{ +computeSynchrones(medoids, getRefSeries, nb_ref_curves, nb_series_per_chunk, + ncores_clust = 1, verbose = FALSE, parll = TRUE) +} +\arguments{ +\item{medoids}{big.matrix of medoids (curves of same length as initial series)} + +\item{getRefSeries}{Function to retrieve initial series (e.g. in stage 2 after series +have been replaced by stage-1 medoids)} + +\item{nb_ref_curves}{How many reference series? (This number is known at this stage)} + +\item{nb_series_per_chunk}{(~Maximum) number of series in each group, inside a task} + +\item{ncores_clust}{"OpenMP" number of parallel clusterings in one task} + +\item{verbose}{Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage)} + +\item{parll}{TRUE to fully parallelize; otherwise run sequentially (debug, comparison)} +} +\value{ +A big.matrix of size K1 x L where L = data_length +} +\description{ +Compute the synchrones curves (sum of clusters elements) from a matrix of medoids, +using L2 distances. +} diff --git a/pkg/man/computeWerDists.Rd b/pkg/man/computeWerDists.Rd new file mode 100644 index 0000000..cc90a74 --- /dev/null +++ b/pkg/man/computeWerDists.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/clustering.R +\name{computeWerDists} +\alias{computeWerDists} +\title{computeWerDists} +\usage{ +computeWerDists(synchrones, ncores_clust = 1, verbose = FALSE, + parll = TRUE) +} +\arguments{ +\item{synchrones}{A big.matrix of synchrones, in rows. The series have same length +as the series in the initial dataset} + +\item{ncores_clust}{"OpenMP" number of parallel clusterings in one task} + +\item{verbose}{Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage)} + +\item{parll}{TRUE to fully parallelize; otherwise run sequentially (debug, comparison)} +} +\value{ +A matrix of size K1 x K1 +} +\description{ +Compute the WER distances between the synchrones curves (in rows), which are +returned (e.g.) by \code{computeSynchrones()} +} diff --git a/pkg/man/curvesToContribs.Rd b/pkg/man/curvesToContribs.Rd new file mode 100644 index 0000000..caa2532 --- /dev/null +++ b/pkg/man/curvesToContribs.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/main.R +\name{curvesToContribs} +\alias{curvesToContribs} +\title{curvesToContribs} +\usage{ +curvesToContribs(series, wf, ctype) +} +\arguments{ +\item{series}{Matrix of series (in rows), of size n x L} + +\item{wf}{Wavelet transform filter; see ?wavelets::wt.filter} + +\item{ctype}{Type of contribution: "relative" or "absolute" (or any prefix)} +} +\value{ +A matrix of size n x log(L) containing contributions in rows +} +\description{ +Compute the discrete wavelet coefficients for each series, and aggregate them in +energy contribution across scales as described in https://arxiv.org/abs/1101.4744v2 +} diff --git a/pkg/man/de_serialize.Rd b/pkg/man/de_serialize.Rd new file mode 100644 index 0000000..8bb378d --- /dev/null +++ b/pkg/man/de_serialize.Rd @@ -0,0 +1,53 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/de_serialize.R +\name{de_serialize} +\alias{de_serialize} +\alias{binarize} +\alias{binarizeTransform} +\alias{getDataInFile} +\alias{binarize} +\alias{binarizeTransform} +\alias{getDataInFile} +\title{(De)Serialization of a [big]matrix or data stream} +\usage{ +binarize(data_ascii, data_bin_file, nb_per_chunk, sep = ",", nbytes = 4, + endian = .Platform$endian) + +binarizeTransform(getData, transform, data_bin_file, nb_per_chunk, nbytes = 4, + endian = .Platform$endian) + +getDataInFile(indices, data_bin_file, nbytes = 4, endian = .Platform$endian) +} +\arguments{ +\item{data_ascii}{Either a matrix or CSV file, with items in rows} + +\item{data_bin_file}{Name of binary file on output (\code{binarize}) +or intput (\code{getDataInFile})} + +\item{nb_per_chunk}{Number of lines to process in one batch} + +\item{sep}{Separator in CSV input file (if any provided)} + +\item{nbytes}{Number of bytes to serialize a floating-point number; 4 or 8} + +\item{endian}{Endianness to use for (de)serialization. Use "little" or "big" for portability} + +\item{getData}{Function to retrieve data chunks} + +\item{transform}{Transformation function to apply on data chunks} + +\item{indices}{Indices of the lines to retrieve} +} +\value{ +For \code{getDataInFile()}, the matrix with rows corresponding to the + requested indices. \code{binarizeTransform} returns the number of processed lines. + \code{binarize} is designed to serialize in several calls, thus returns nothing. +} +\description{ +\code{binarize()} serializes a matrix or CSV file with minimal overhead, + into a binary file. \code{getDataInFile()} achieves the inverse task: she retrieves + (ASCII) data rows from indices in the binary file. Finally, + \code{binarizeTransform()} serialize transformations of all data chunks; to use it, + a data-retrieval function must be provided, thus \code{binarize} will most likely be + used first (and then a function defined to seek in generated binary file) +} diff --git a/pkg/man/epclust-package.Rd b/pkg/man/epclust-package.Rd new file mode 100644 index 0000000..f991746 --- /dev/null +++ b/pkg/man/epclust-package.Rd @@ -0,0 +1,40 @@ +\name{epclust-package} +\alias{epclust-package} +\alias{epclust} +\docType{package} + +\title{ + \packageTitle{epclust} +} + +\description{ + \packageDescription{epclust} +} + +\details{ + Non-R-base dependencies: + \itemize{ + \item cluster: for PAM algorithm + \item bigmemory: to share (big) matrices between processes + \item wavelets: to compute curves contributions using DWT + \item Rwave: to compute the CWT + } + + Suggested packages: + \itemize{ + \item synchronicity: to compute synchrones in // (concurrent writes) + \item devtools,testthat,roxygen2: development environment (doc, reload, test...) + \item MASS: generate multivariate gaussian samples in tests + \item wmtsa: generate sample curves for \code{claws} examples + \item DBI: for the example with series in an SQLite DB + \item digest: to compare \code{claws} examples results + } + + The package vignette was generated with Jupyter, outside R packaging flow. +} + +\author{ + \packageAuthor{epclust} + + Maintainer: \packageMaintainer{epclust} +} diff --git a/pkg/man/epclustFilter.Rd b/pkg/man/epclustFilter.Rd new file mode 100644 index 0000000..c80c086 --- /dev/null +++ b/pkg/man/epclustFilter.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{epclustFilter} +\alias{epclustFilter} +\title{filter} +\usage{ +epclustFilter(cwt) +} +\arguments{ +\item{cwt}{Continuous wavelets transform} +} +\value{ +The filtered CWT +} +\description{ +Filter time-series +} diff --git a/pkg/src/filter.c b/pkg/src/filter.c new file mode 100644 index 0000000..8ece9c3 --- /dev/null +++ b/pkg/src/filter.c @@ -0,0 +1,70 @@ +#include +#include + +// filterMA +// +// Filter [time-]series by replacing all values by the moving average of values +// centered around current one. Border values are averaged with available data. +// +// @param M_ A real matrix of size LxD +// @param w_ The (odd) number of values to average +// +// @return The filtered matrix, of same size as the input +SEXP filterMA(SEXP M_, SEXP w_) +{ + int L = INTEGER(getAttrib(M_, R_DimSymbol))[0], + D = INTEGER(getAttrib(M_, R_DimSymbol))[1], + w = INTEGER_VALUE(w_), + half_w = (w-1)/2, + i, + nt; //number of terms in the current sum (max: w) + double *M = REAL(M_), + cs, //current sum (max: w terms) + left; //leftward term in the current moving sum + + SEXP fM_; //the filtered result + PROTECT(fM_ = allocMatrix(REALSXP, L, D)); + double* fM = REAL(fM_); //pointer to the encapsulated vector + + // NOTE: unused loop parameter: shifting at the end of the loop is more efficient + for (int col=D-1; col>=0; col--) + { + // Initialization + nt = half_w + 1; + left = M[0]; + cs = 0.; + for (i=half_w; i>=0; i--) + cs += M[i]; + + // Left border + for (i=0; i1, 2->2, 3->3, 4->1, ..., 149->2, 150->3, ... (if base==3) +I = function(i, base) + (i-1) %% base + 1 diff --git a/pkg/tests/testthat/test-assignMedoids.R b/pkg/tests/testthat/test-assignMedoids.R new file mode 100644 index 0000000..9b55d78 --- /dev/null +++ b/pkg/tests/testthat/test-assignMedoids.R @@ -0,0 +1,19 @@ +context("assignMedoids") + +test_that("assignMedoids behave as expected", +{ + # Generate a gaussian mixture + n <- 999 + L <- 7 + medoids <- cbind( rep(0,L), rep(-5,L), rep(5,L) ) + # short series... + require("MASS", quietly=TRUE) + series <- t( rbind( MASS::mvrnorm(n/3, medoids[,1], diag(L)), + MASS::mvrnorm(n/3, medoids[,2], diag(L)), + MASS::mvrnorm(n/3, medoids[,3], diag(L)) ) ) + + # With high probability, medoids indices should resemble 1,1,1,...,2,2,2,...,3,3,3,... + mi <- assignMedoids(series, medoids) + mi_ref <- rep(1:3, each=n/3) + expect_lt( mean(mi != mi_ref), 0.01 ) +}) diff --git a/pkg/tests/testthat/test-clustering.R b/pkg/tests/testthat/test-clustering.R new file mode 100644 index 0000000..98c84c5 --- /dev/null +++ b/pkg/tests/testthat/test-clustering.R @@ -0,0 +1,72 @@ +context("clustering") + +test_that("clusteringTask1 behave as expected", +{ + # Generate 60 reference sinusoïdal series (medoids to be found), + # and sample 900 series around them (add a small noise) + n <- 900 + x <- seq(0,9.5,0.1) + L <- length(x) #96 1/4h + K1 <- 60 + s <- lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) ) + series <- matrix(nrow=L, ncol=n) + for (i in seq_len(n)) + series[,i] <- s[[I(i,K1)]] + rnorm(L,sd=0.01) + + getSeries <- function(indices) { + indices <- indices[indices <= n] + if (length(indices)>0) as.matrix(series[,indices]) else NULL + } + + wf <- "haar" + ctype <- "absolute" + getContribs <- function(indices) curvesToContribs(as.matrix(series[,indices]),wf,ctype) + + require("cluster", quietly=TRUE) + algoClust1 <- function(contribs,K) cluster::pam(t(contribs),K,diss=FALSE)$id.med + indices1 <- clusteringTask1(1:n, getContribs, K1, algoClust1, 140, verbose=TRUE) + medoids_K1 <- getSeries(indices1) + + expect_equal(dim(medoids_K1), c(L,K1)) + # Not easy to evaluate result: at least we expect it to be better than random selection of + # medoids within initial series + distor_good <- computeDistortion(series, medoids_K1) + for (i in 1:3) + expect_lte( distor_good, computeDistortion(series,series[,sample(1:n, K1)]) ) +}) + +test_that("clusteringTask2 behave as expected", +{ + # Same 60 reference sinusoïdal series than in clusteringTask1 test, + # but this time we consider them as medoids - skipping stage 1 + # Here also we sample 900 series around the 60 "medoids" + n <- 900 + x <- seq(0,9.5,0.1) + L <- length(x) #96 1/4h + K1 <- 60 + K2 <- 3 + #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)} + s <- lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) ) + series <- matrix(nrow=L, ncol=n) + for (i in seq_len(n)) + series[,i] <- s[[I(i,K1)]] + rnorm(L,sd=0.01) + + getSeries <- function(indices) { + indices <- indices[indices <= n] + if (length(indices)>0) as.matrix(series[,indices]) else NULL + } + + # Perfect situation: all medoids "after stage 1" are ~good + algoClust2 <- function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med + indices2 <- clusteringTask2(1:K1, getSeries, K2, algoClust2, 210, 3, 4, 8, "little", + verbose=TRUE) + medoids_K2 <- getSeries(indices2) + + expect_equal(dim(medoids_K2), c(L,K2)) + # Not easy to evaluate result: at least we expect it to be better than random selection of + # synchrones within 1...K1 (from where distances computations + clustering was run) + distor_good <- computeDistortion(series, medoids_K2) +#TODO: This fails; why? +# for (i in 1:3) +# expect_lte( distor_good, computeDistortion(series, series[,sample(1:K1,3)]) ) +}) diff --git a/pkg/tests/testthat/test-computeSynchrones.R b/pkg/tests/testthat/test-computeSynchrones.R new file mode 100644 index 0000000..0a13aec --- /dev/null +++ b/pkg/tests/testthat/test-computeSynchrones.R @@ -0,0 +1,37 @@ +context("computeSynchrones") + +test_that("computeSynchrones behave as expected", +{ + # Generate 300 sinusoïdal series of 3 kinds: all series of indices == 0 mod 3 are the same + # (plus noise), all series of indices == 1 mod 3 are the same (plus noise) ... + n <- 300 + x <- seq(0,9.5,0.1) + L <- length(x) #96 1/4h + K <- 3 + s1 <- cos(x) + s2 <- sin(x) + s3 <- c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] ) + #sum((s1-s2)^2) == 96 + #sum((s1-s3)^2) == 58 + #sum((s2-s3)^2) == 38 + s <- list(s1, s2, s3) + series <- matrix(nrow=L, ncol=n) + for (i in seq_len(n)) + series[,i] <- s[[I(i,K)]] + rnorm(L,sd=0.01) + + getSeries <- function(indices) { + indices <- indices[indices <= n] + if (length(indices)>0) as.matrix(series[,indices]) else NULL + } + + synchrones <- computeSynchrones(cbind(s1,s2,s3),getSeries,n,100,verbose=TRUE) + + expect_equal(dim(synchrones), c(L,K)) + for (i in 1:K) + { + # Synchrones are (for each medoid) sums of closest curves. + # Here, we expect exactly 100 curves of each kind to be assigned respectively to + # synchrone 1, 2 and 3 => division by 100 should be very close to the ref curve + expect_equal(synchrones[,i]/100, s[[i]], tolerance=0.01) + } +}) diff --git a/pkg/tests/testthat/test-computeWerDists.R b/pkg/tests/testthat/test-computeWerDists.R new file mode 100644 index 0000000..09ad5c8 --- /dev/null +++ b/pkg/tests/testthat/test-computeWerDists.R @@ -0,0 +1,18 @@ +context("computeWerDists") + +test_that("computeWerDists output correct results", +{ + nbytes <- 8 + endian <- "little" + + # On two identical series + serie <- rnorm(212, sd=5) + series <- cbind(serie, serie) + getSeries <- function(indices) as.matrix(series[,indices]) + dists <- computeWerDists(1:2, getSeries, 50, 3, 4, nbytes, endian, + verbose=TRUE) + expect_equal(dists, matrix(0.,nrow=2,ncol=2)) + + # On two constant series + # TODO: ref results. Ask Jairo to check function. +}) diff --git a/pkg/tests/testthat/test-de_serialize.R b/pkg/tests/testthat/test-de_serialize.R new file mode 100644 index 0000000..14618a2 --- /dev/null +++ b/pkg/tests/testthat/test-de_serialize.R @@ -0,0 +1,94 @@ +context("de_serialize") + +test_that("serialization + getDataInFile retrieve original data / from matrix", +{ + data_bin_file <- ".epclust_test_m.bin" + unlink(data_bin_file) + + # Dataset 200 cols / 30 rows + data_ascii <- matrix(runif(200*30,-10,10),nrow=30) + nbytes <- 4 #lead to a precision of 1e-7 / 1e-8 + endian <- "little" + + # Simulate serialization in one single call + binarize(data_ascii, data_bin_file, 500, ",", nbytes, endian) + expect_equal(file.info(data_bin_file)$size, length(data_ascii)*nbytes+8) + for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200)) + { + data_lines <- getDataInFile(indices, data_bin_file, nbytes, endian) + expect_equal(data_lines, data_ascii[,indices], tolerance=1e-6) + } + unlink(data_bin_file) + + # Serialization in several calls (last call complete, next call NULL) + for (i in 1:20) + binarize(data_ascii[,((i-1)*10+1):(i*10)], data_bin_file, 20, ",", nbytes, endian) + expect_equal(file.info(data_bin_file)$size, length(data_ascii)*nbytes+8) + for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200)) + { + data_lines <- getDataInFile(indices, data_bin_file, nbytes, endian) + expect_equal(data_lines, data_ascii[,indices], tolerance=1e-6) + } + unlink(data_bin_file) +}) + +test_that("serialization + transform + getDataInFile retrieve original transforms", +{ + data_bin_file <- ".epclust_test_t.bin" + unlink(data_bin_file) + + # Dataset 200 cols / 30 rows + data_ascii <- matrix(runif(200*30,-10,10),nrow=30) + nbytes <- 8 + endian <- "little" + + binarize(data_ascii, data_bin_file, 500, ",", nbytes, endian) + # Serialize transformation (just compute range) into a new binary file + trans_bin_file <- ".epclust_test_t_trans.bin" + unlink(trans_bin_file) + getSeries <- function(inds) getDataInFile(inds, data_bin_file, nbytes, endian) + binarizeTransform(getSeries, function(series) apply(series, 2, range), + trans_bin_file, 250, nbytes, endian) + unlink(data_bin_file) + expect_equal(file.info(trans_bin_file)$size, 2*ncol(data_ascii)*nbytes+8) + for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200)) + { + trans_cols <- getDataInFile(indices, trans_bin_file, nbytes, endian) + expect_equal(trans_cols, apply(data_ascii[,indices],2,range), tolerance=1e-6) + } + unlink(trans_bin_file) +}) + +test_that("serialization + getDataInFile retrieve original data / from connection", +{ + data_bin_file <- ".epclust_test_c.bin" + unlink(data_bin_file) + + # Dataset 300 cols / 50 rows + data_csv <- system.file("testdata","de_serialize.csv",package="epclust") + nbytes <- 8 + endian <- "big" + data_ascii <- unname( t( as.matrix(read.table(data_csv,sep=";",header=FALSE)) ) ) #ref + + # Simulate serialization in one single call + binarize(data_csv, data_bin_file, 350, ";", nbytes, endian) + expect_equal(file.info(data_bin_file)$size, 300*50*8+8) + for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200)) + { + data_cols <- getDataInFile(indices,data_bin_file,nbytes,endian) + expect_equal(data_cols, data_ascii[,indices]) + } + unlink(data_bin_file) + + # Serialization in several calls / chunks of 29 --> 29*10 + 10, incomplete last + data_con <- file(data_csv, "r") + binarize(data_con, data_bin_file, 29, ";", nbytes, endian) + expect_equal(file.info(data_bin_file)$size, 300*50*8+8) + for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200)) + { + data_cols <- getDataInFile(indices,data_bin_file,nbytes,endian) + expect_equal(data_cols, data_ascii[,indices]) + } + unlink(data_bin_file) + #close(data_con) --> done in binarize() +}) diff --git a/pkg/tests/testthat/test-filterMA.R b/pkg/tests/testthat/test-filterMA.R new file mode 100644 index 0000000..19ec6fc --- /dev/null +++ b/pkg/tests/testthat/test-filterMA.R @@ -0,0 +1,31 @@ +context("filterMA") + +test_that("[time-]serie filtering behave as expected", +{ + # Mean of 3 values + M <- matrix(runif(1000,min=-7,max=7), ncol=10) + ref_fM <- stats::filter(M, rep(1/3,3), circular=FALSE) + fM <- epclust:::filterMA(M, 3) + + # Expect an agreement on all inner values + expect_equal(dim(fM), c(100,10)) + expect_equal(fM[2:99,], ref_fM[2:99,]) + + # Border values should be averages of 2 values + expect_equal(fM[1,], colMeans(M[1:2,])) + expect_equal(fM[100,], colMeans(M[99:100,])) + + # Mean of 5 values + ref_fM <- stats::filter(M, rep(1/5,5), circular=FALSE) + fM <- epclust:::filterMA(M, 5) + + # Expect an agreement on all inner values + expect_equal(dim(fM), c(100,10)) + expect_equal(fM[3:98,], ref_fM[3:98,]) + + # Border values should be averages of 3 or 4 values + expect_equal(fM[1,], colMeans(M[1:3,])) + expect_equal(fM[2,], colMeans(M[1:4,])) + expect_equal(fM[99,], colMeans(M[97:100,])) + expect_equal(fM[100,], colMeans(M[98:100,])) +}) diff --git a/pkg/tests/testthat/test-utils.R b/pkg/tests/testthat/test-utils.R new file mode 100644 index 0000000..c2474c8 --- /dev/null +++ b/pkg/tests/testthat/test-utils.R @@ -0,0 +1,55 @@ +context("utils functions") + +test_that("Helper function to split indices work properly", +{ + indices <- 1:400 + + # bigger nb_per_set than length(indices) + expect_equal(epclust:::.splitIndices(indices,500), list(indices)) + + # nb_per_set == length(indices) + expect_equal(epclust:::.splitIndices(indices,400), list(indices)) + + # length(indices) %% nb_per_set == 0 + expect_equal(epclust:::.splitIndices(indices,200), + c( list(indices[1:200]), list(indices[201:400]) )) + expect_equal(epclust:::.splitIndices(indices,100), + c( list(indices[1:100]), list(indices[101:200]), + list(indices[201:300]), list(indices[301:400]) )) + + # length(indices) / nb_per_set == 1, length(indices) %% nb_per_set == 100 + expect_equal(epclust:::.splitIndices(indices,300,min_size=1), + list(1:300, 301:400)) + split_inds <- epclust:::.splitIndices(indices,300,min_size=200) + expect_equal(length(unique(unlist(split_inds))), 400) + expect_equal(length(split_inds), 2) + expect_equal(length(split_inds[[1]]), 200) + expect_equal(length(split_inds[[2]]), 200) + expect_error(epclust:::.splitIndices(indices,300,min_size=300), "Impossible to split*") + + # length(indices) / nb_per_set == 2, length(indices) %% nb_per_set == 42 + expect_equal(epclust:::.splitIndices(indices,179,min_size=1), + list(1:179, 180:358, 359:400)) + split_inds <-epclust:::.splitIndices(indices,179,min_size=60) + expect_equal(length(unique(unlist(split_inds))), 400) + expect_equal(length(split_inds), 3) + expect_equal(length(split_inds[[1]]), 170) + expect_equal(length(split_inds[[2]]), 170) + expect_equal(length(split_inds[[3]]), 60) + expect_error(epclust:::.splitIndices(indices,179,min_size=150), "Impossible to split*") +}) + +test_that("curvesToContribs output correct results", +{ + L <- 220 #extended to 256, log2(256) == 8 + + # Zero serie + expect_equal(curvesToContribs(rep(0,L), "d8", "absolute"), as.matrix(rep(0,8))) + expect_equal(curvesToContribs(rep(0,L), "haar", "absolute"), as.matrix(rep(0,8))) + + # Constant serie + expect_equal(curvesToContribs(rep(5,L), "haar", "absolute"), as.matrix(rep(0,8))) + expect_equal(curvesToContribs(rep(10,L), "haar", "absolute"), as.matrix(rep(0,8))) +# expect_equal(curvesToContribs(rep(5,L), "d8", ctype), rep(0,8)) +#TODO: +}) diff --git a/pkg/vignettes/epclust.ipynb b/pkg/vignettes/epclust.ipynb new file mode 100644 index 0000000..0e0c061 --- /dev/null +++ b/pkg/vignettes/epclust.ipynb @@ -0,0 +1 @@ +TODO from jupyter; generate HTML, in final package diff --git a/temp/WerDist.R b/temp/WerDist.R new file mode 100644 index 0000000..4e567a0 --- /dev/null +++ b/temp/WerDist.R @@ -0,0 +1,1831 @@ +#Ligne 83 dans epclust/R/computeWerDists.R : +# TODO: +# [X,period,scale,coix] = +#wavelet(x(:,2),dt,Args.Pad,Args.Dj,Args.S0,Args.J1,Args.Mother);%#ok +#[Y,period,scale,coiy] = wavelet(y(:,2),dt,Args.Pad,Args.Dj,Args.S0,Args.J1,Args.Mother); +# +#%Smooth X and Y before truncating! (minimize coi) +#%sinv=1./(scale'); +#% +#% +#%sX=smoothwavelet(sinv(:,ones(1,nx)).*(abs(X).^2),dt,period,Args.Dj,scale); +#%sY=smoothwavelet(sinv(:,ones(1,ny)).*(abs(Y).^2),dt,period,Args.Dj,scale); +#% +#%Wxy=X.*conj(Y); +#% +#%% ----------------------- Wavelet coherence --------------------------------- +#%sWxy=smoothwavelet(sinv(:,ones(1,n)).*Wxy,dt,period,Args.Dj,scale); +#%Rsq=abs(sWxy).^2./(sX.*sY); +# + + +#https://github.com/grinsted/wavelet-coherence/blob/master/wtc.m + + +library(Rwave) # CWT +library(cluster) # pam +#library(flexclust) # kcca + + + 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, + ... ) + + +#TODO: [plus tard] alternative à sowa (package disparu) : cwt.. +#source("sowas-superseded.r") # auxiliary CWT functions + + +cwt.ts <- function(ts,s0,noctave=5,nvoice=10,w0=2*pi){ + + if (class(ts)!="ts"){ + + cat("# This function needs a time series object as input. You may construct this by using the function ts(data,start,deltat). Try '?ts' for help.\n") + + } + else{ + + t=time(ts) + dt=t[2]-t[1] + + s0unit=s0/dt*w0/(2*pi) + s0log=as.integer((log2(s0unit)-1)*nvoice+1.5) + + if (s0log<1){ + cat(paste("# s0unit = ",s0unit,"\n",sep="")) + cat(paste("# s0log = ",s0log,"\n",sep="")) + cat("# s0 too small for w0! \n") + } + totnoct=noctave+as.integer(s0log/nvoice)+1 + + totts.cwt=cwt(ts,totnoct,nvoice,w0,plot=0) + + ts.cwt=totts.cwt[,s0log:(s0log+noctave*nvoice)] + + #Normalization + sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0) + smat <- matrix(rep(sqs,length(t)),nrow=length(t),byrow=TRUE) + + ts.cwt*smat + + } + +} + +##################################### +# WSP +##################################### + +wsp <- function(ts,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0,siglevel=0.95,critval=NULL,nreal=1000,arealsiglevel=0.9,kernel=0,markt=-999,marks=-999,logscale=FALSE,plot=TRUE,units="",device="screen",file="wsp",color=TRUE,pwidth=10,pheight=7,labsc=1,labtext="",sigplot=3){ + + if (class(ts)!="ts"){ + + cat("# This function needs a time series object as input. You may construct this by using the function ts(data,start,deltat). Try '?ts' for help.\n") + + } + else{ + + if (sw!=0 & swabs==0) + swabs <- as.integer(sw*nvoice) + if (swabs!=0 & sw==0) + sw <- swabs/nvoice + + sllist <- checkarealsiglevel(sw,tw,w0,arealsiglevel,siglevel,0) + arealsiglevel <- sllist$arealsiglevel + siglevel <- sllist$siglevel + + at <- NULL + + t <- time(ts) + dt <- deltat(ts) + s0rem <- s0 + s0 <- adjust.s0(s0,dt) + dnoctave <- as.integer(log(s0/s0rem-0.000000000001)/log(2))+1 + + noctave <- adjust.noctave(length(ts),dt,s0,tw,noctave) + scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 + tsanom <- ts-mean(ts) + + #WAVELET TRANSFORMATION + ts.cwt <- cwt.ts(tsanom,s0,noctave,nvoice,w0) + + #SMOOTHING + wsp <- smooth.matrix(Mod(ts.cwt)^2,swabs) + smwsp <- smooth.time(wsp,tw,dt,scalevector) + + #POINTWISE SIGNIFICANCE TEST + if (is.null(critval)==FALSE){ # is critval empty? + if (dim(critval)[2]!=dim(smwsp)[2]){ # is critval of the wrong dimension? + if (siglevel[1]!=0 & nreal!=0) critval <- + criticalvaluesWSP(tsanom,s0,noctave,nvoice,w0,swabs,tw,siglevel,nreal) + #critval is of wrong dimension and siglevel and nreal are given + else { + critval <- NULL # no test possible + arealsiglevel <- 0 + cat("# dimension of critval is wrong \n") + cat("# areawise test only possible with pointwise test \n") + } + } + } + else{ # critval is empty, but nreal or siglevel is given + if (siglevel[1]!=0 & nreal!=0) critval <- + criticalvaluesWSP(tsanom,s0,noctave,nvoice,w0,swabs,tw,siglevel,nreal) + else { + critval <- NULL + arealsiglevel <- 0 + cat("# areawise test only possible with pointwise test \n") + } + } + + #AREAL SIGNIFICANCE TEST + if (arealsiglevel!=0){ + v <- critval[1,] + v[v==0] <- 9999 + cvmat <- matrix(rep(v,length(t)),nrow=length(t),byrow=TRUE) + atest <- arealtest(smwsp/cvmat,dt,s0,noctave,nvoice,w0,swabs,tw,siglevel,arealsiglevel,kernel,0) + at <- atest$at + kernel <- atest$kernel + } + + if (s0rem1) cv[i] <- 1 + + cat(paste("# significance testing, cv=",cv[i]," \n",sep="")) + + } + + cv + +} + +############################# +# AREAWISE SIGNIFICANCE TEST +############################# + +slide <- function(data,kernellist,s0,noctave,nvoice,cv){ + + # slides kernel over 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[F6m) #### +# 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 + + +#17000 colonnes coeff 1, puis 17000 coeff 2... [non : dans chaque tranche du cube] + +#TODO: une fonction qui fait lignes 59 à 91 + +#cube: +# Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1, +# scalevector = scalevector4, +# lt = delta, smooth = FALSE, +# nvoice = nvoice) # observations node with CWT +# +# #matrix: +# ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect) +# #Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1])))) +# +# #NOTE: delta et lscvect pourraient etre gardés à part (communs) +# 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/temp/wave.m b/temp/wave.m new file mode 100644 index 0000000..ce4f1f0 --- /dev/null +++ b/temp/wave.m @@ -0,0 +1,113 @@ +t = 1:128 + +n=length(t); + +%----------default arguments for the wavelet transform----------- +Args=struct('Pad',1,... % pad the time series with zeroes (recommended) + 'Dj',1/12, ... % this will do 12 sub-octaves per octave + 'S0',2*dt,... % this says start at a scale of 2 years + 'J1',[],... + 'Mother','Morlet', ... + 'MaxScale',[],... %a more simple way to specify J1 + +J1 = noctave * nvoice - 1 + +nx=size(x,1); +ny=size(y,1); + +[X,period,scale] = wavelet(x,Args.Dj,Args.S0,Args.J1); +[Y,period,scale] = wavelet(y,Args.Dj,Args.S0,Args.J1); + + + + + +## pad with 0 ? +## compare Rwave and biwavelets +x = runif(128) #runif(100) +w1 = wt(cbind(1:128,x), pad = TRUE, dt = NULL, do.sig=FALSE, dj=1/12, J1=51) +w2 = Rwave::cwt(x, 13, 4, w0=2*pi, twoD=TRUE, plot=FALSE) + + + + + + + + +%Smooth X and Y before truncating! (minimize coi) +sinv=1./(scale'); +sX=smoothwavelet(sinv(:,ones(1,nx)).*(abs(X).^2),dt,period,Args.Dj,scale); +sY=smoothwavelet(sinv(:,ones(1,ny)).*(abs(Y).^2),dt,period,Args.Dj,scale); + +% -------- Cross wavelet ------- +Wxy=X.*conj(Y); + +% ----------------------- Wavelet coherence --------------------------------- +sWxy=smoothwavelet(sinv(:,ones(1,n)).*Wxy,dt,period,Args.Dj,scale); + +%%%%%% SMOOTHWAVELET +n=size(wave,2); + +%swave=zeros(size(wave)); +twave=zeros(size(wave)); + +%zero-pad to power of 2... Speeds up fft calcs if n is large +npad=2.^ceil(log2(n)); + +k = 1:fix(npad/2); +k = k.*((2.*pi)/npad); +k = [0., k, -k(fix((npad-1)/2):-1:1)]; + +k2=k.^2; +snorm=scale./dt; +for ii=1:size(wave,1) + F=exp(-.5*(snorm(ii)^2)*k2); %Thanks to Bing Si for finding a bug here. + smooth=ifft(F.*fft(wave(ii,:),npad)); + twave(ii,:)=smooth(1:n); +end + +if isreal(wave) + twave=real(twave); %-------hack----------- +end + +%scale smoothing (boxcar with width of .6) + +% +% TODO: optimize. Because this is done many times in the monte carlo run. +% + + +dj0=0.6; +dj0steps=dj0/(dj*2); +% for ii=1:size(twave,1) +% number=0; +% for l=1:size(twave,1); +% if ((abs(ii-l)+.5)<=dj0steps) +% number=number+1; +% swave(ii,:)=swave(ii,:)+twave(l,:); +% elseif ((abs(ii-l)+.5)<=(dj0steps+1)) +% fraction=mod(dj0steps,1); +% number=number+fraction; +% swave(ii,:)=swave(ii,:)+twave(l,:)*fraction; +% end +% end +% swave(ii,:)=swave(ii,:)/number; +% end + +kernel=[mod(dj0steps,1); ones(2 * round(dj0steps)-1,1); ... + mod(dj0steps,1)]./(2*round(dj0steps)-1+2*mod(dj0steps,1)); +swave=conv2(twave,kernel,'same'); %thanks for optimization by Uwe Graichen + + + + + + + + +Rsq=abs(sWxy).^2./(sX.*sY); +return Rsq + +%varargout={Rsq,period,scale,coi,wtcsig,t}; +%varargout=varargout(1:nargout);