Commit | Line | Data |
---|---|---|
8702eb86 | 1 | #' CLAWS: CLustering with wAvelets and Wer distanceS |
7f0781b7 | 2 | #' |
eef6f6c9 BA |
3 | #' Cluster electricity power curves (or any series of similar nature) by applying a |
4 | #' two stage procedure in parallel (see details). | |
5 | #' Input series must be sampled on the same time grid, no missing values. | |
6 | #' | |
7 | #' @details Summary of the function execution flow: | |
8 | #' \enumerate{ | |
9 | #' \item Compute and serialize all contributions, obtained through discrete wavelet | |
10 | #' decomposition (see Antoniadis & al. [2013]) | |
11 | #' \item Divide series into \code{ntasks} groups to process in parallel. In each task: | |
12 | #' \enumerate{ | |
13 | #' \item iterate the first clustering algorithm on its aggregated outputs, | |
0486fbad | 14 | #' on inputs of size \code{nb_items_clust1} |
eef6f6c9 BA |
15 | #' \item optionally, if WER=="mix": |
16 | #' a) compute the K1 synchrones curves, | |
17 | #' b) compute WER distances (K1xK1 matrix) between synchrones and | |
18 | #' c) apply the second clustering algorithm | |
19 | #' } | |
20 | #' \item Launch a final task on the aggregated outputs of all previous tasks: | |
21 | #' in the case WER=="end" this task takes indices in input, otherwise | |
22 | #' (medoid) curves | |
23 | #' } | |
24 | #' The main argument -- \code{getSeries} -- has a quite misleading name, since it can be | |
25 | #' either a [big.]matrix, a CSV file, a connection or a user function to retrieve | |
26 | #' series; the name was chosen because all types of arguments are converted to a function. | |
27 | #' When \code{getSeries} is given as a function, it must take a single argument, | |
28 | #' 'indices', integer vector equal to the indices of the curves to retrieve; | |
29 | #' see SQLite example. The nature and role of other arguments should be clear | |
7f0781b7 | 30 | #' |
8702eb86 BA |
31 | #' @param getSeries Access to the (time-)series, which can be of one of the three |
32 | #' following types: | |
33 | #' \itemize{ | |
eef6f6c9 | 34 | #' \item [big.]matrix: each column contains the (time-ordered) values of one time-serie |
bf5c0844 BA |
35 | #' \item connection: any R connection object providing lines as described above |
36 | #' \item character: name of a CSV file containing series in rows (no header) | |
8702eb86 | 37 | #' \item function: a custom way to retrieve the curves; it has only one argument: |
eef6f6c9 | 38 | #' the indices of the series to be retrieved. See SQLite example |
8702eb86 | 39 | #' } |
eef6f6c9 | 40 | #' @param K1 Number of clusters to be found after stage 1 (K1 << N [number of series]) |
1c6f223e | 41 | #' @param K2 Number of clusters to be found after stage 2 (K2 << K1) |
37c82bba | 42 | #' @param nb_series_per_chunk (Maximum) number of series to retrieve in one batch |
0486fbad | 43 | #' @param algoClust1 Clustering algorithm for stage 1. A function which takes (data, K) |
2b9f5356 | 44 | #' as argument where data is a matrix in columns and K the desired number of clusters, |
0486fbad BA |
45 | #' and outputs K medoids ranks. Default: PAM. |
46 | #' In our method, this function is called on iterated medoids during stage 1 | |
47 | #' @param algoClust2 Clustering algorithm for stage 2. A function which takes (dists, K) | |
2b9f5356 | 48 | #' as argument where dists is a matrix of distances and K the desired number of clusters, |
0486fbad BA |
49 | #' and outputs K clusters representatives (curves). Default: PAM. |
50 | #' In our method, this function is called on a matrix of K1 x K1 (WER) distances computed | |
51 | # between synchrones | |
37c82bba BA |
52 | #' @param nb_items_clust1 (~Maximum) number of items in input of the clustering algorithm |
53 | #' for stage 1. At worst, a clustering algorithm might be called with ~2*nb_items_clust1 | |
54 | #' items; but this could only happen at the last few iterations. | |
eef6f6c9 BA |
55 | #' @param wav_filt Wavelet transform filter; see ?wavelets::wt.filter |
56 | #' @param contrib_type Type of contribution: "relative", "logit" or "absolute" (any prefix) | |
57 | #' @param WER "end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply | |
58 | #' stage 2 at the end of each task | |
0486fbad | 59 | #' @param sync_mean TRUE to compute a synchrone as a mean curve, FALSE for a sum |
4bcfdbee | 60 | #' @param random TRUE (default) for random chunks repartition |
eef6f6c9 BA |
61 | #' @param ntasks Number of tasks (parallel iterations to obtain K1 [if WER=="end"] |
62 | #' or K2 [if WER=="mix"] medoids); default: 1. | |
63 | #' Note: ntasks << N (number of series), so that N is "roughly divisible" by ntasks | |
64 | #' @param ncores_tasks Number of parallel tasks (1 to disable: sequential tasks) | |
65 | #' @param ncores_clust Number of parallel clusterings in one task (4 should be a minimum) | |
4bcfdbee | 66 | #' @param sep Separator in CSV input file (if any provided) |
8702eb86 | 67 | #' @param nbytes Number of bytes to serialize a floating-point number; 4 or 8 |
eef6f6c9 | 68 | #' @param endian Endianness for (de)serialization ("little" or "big") |
4bcfdbee | 69 | #' @param verbose Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage) |
492cd9e7 | 70 | #' @param parll TRUE to fully parallelize; otherwise run sequentially (debug, comparison) |
7f0781b7 | 71 | #' |
eef6f6c9 BA |
72 | #' @return A matrix of the final K2 medoids curves, in columns |
73 | #' | |
74 | #' @references Clustering functional data using Wavelets [2013]; | |
75 | #' A. Antoniadis, X. Brossat, J. Cugliari & J.-M. Poggi. | |
76 | #' Inter. J. of Wavelets, Multiresolution and Information Procesing, | |
77 | #' vol. 11, No 1, pp.1-30. doi:10.1142/S0219691313500033 | |
1c6f223e BA |
78 | #' |
79 | #' @examples | |
4efef8cc | 80 | #' \dontrun{ |
eef6f6c9 | 81 | #' # WER distances computations are too long for CRAN (for now) |
4efef8cc BA |
82 | #' |
83 | #' # Random series around cos(x,2x,3x)/sin(x,2x,3x) | |
84 | #' x = seq(0,500,0.05) | |
85 | #' L = length(x) #10001 | |
eef6f6c9 | 86 | #' ref_series = matrix( c(cos(x),cos(2*x),cos(3*x),sin(x),sin(2*x),sin(3*x)), ncol=6 ) |
4efef8cc | 87 | #' library(wmtsa) |
eef6f6c9 BA |
88 | #' series = do.call( cbind, lapply( 1:6, function(i) |
89 | #' do.call(cbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) ) | |
4efef8cc | 90 | #' #dim(series) #c(2400,10001) |
37c82bba | 91 | #' medoids_ascii = claws(series, K1=60, K2=6, 200, verbose=TRUE) |
4efef8cc BA |
92 | #' |
93 | #' # Same example, from CSV file | |
94 | #' csv_file = "/tmp/epclust_series.csv" | |
95 | #' write.table(series, csv_file, sep=",", row.names=FALSE, col.names=FALSE) | |
37c82bba | 96 | #' medoids_csv = claws(csv_file, K1=60, K2=6, 200) |
4efef8cc BA |
97 | #' |
98 | #' # Same example, from binary file | |
eef6f6c9 BA |
99 | #' bin_file <- "/tmp/epclust_series.bin" |
100 | #' nbytes <- 8 | |
101 | #' endian <- "little" | |
102 | #' binarize(csv_file, bin_file, 500, nbytes, endian) | |
103 | #' getSeries <- function(indices) getDataInFile(indices, bin_file, nbytes, endian) | |
37c82bba | 104 | #' medoids_bin <- claws(getSeries, K1=60, K2=6, 200) |
4efef8cc BA |
105 | #' unlink(csv_file) |
106 | #' unlink(bin_file) | |
107 | #' | |
108 | #' # Same example, from SQLite database | |
109 | #' library(DBI) | |
110 | #' series_db <- dbConnect(RSQLite::SQLite(), "file::memory:") | |
111 | #' # Prepare data.frame in DB-format | |
eef6f6c9 BA |
112 | #' n <- nrow(series) |
113 | #' time_values <- data.frame( | |
4bcfdbee BA |
114 | #' id = rep(1:n,each=L), |
115 | #' time = rep( as.POSIXct(1800*(0:n),"GMT",origin="2001-01-01"), L ), | |
116 | #' value = as.double(t(series)) ) | |
4efef8cc | 117 | #' dbWriteTable(series_db, "times_values", times_values) |
4bcfdbee BA |
118 | #' # Fill associative array, map index to identifier |
119 | #' indexToID_inDB <- as.character( | |
120 | #' dbGetQuery(series_db, 'SELECT DISTINCT id FROM time_values')[,"id"] ) | |
eef6f6c9 BA |
121 | #' serie_length <- as.integer( dbGetQuery(series_db, |
122 | #' paste("SELECT COUNT * FROM time_values WHERE id == ",indexToID_inDB[1],sep="")) ) | |
123 | #' getSeries <- function(indices) { | |
124 | #' request <- "SELECT id,value FROM times_values WHERE id in (" | |
4bcfdbee | 125 | #' for (i in indices) |
eef6f6c9 BA |
126 | #' request <- paste(request, indexToID_inDB[i], ",", sep="") |
127 | #' request <- paste(request, ")", sep="") | |
128 | #' df_series <- dbGetQuery(series_db, request) | |
129 | #' as.matrix(df_series[,"value"], nrow=serie_length) | |
4efef8cc | 130 | #' } |
37c82bba | 131 | #' medoids_db = claws(getSeries, K1=60, K2=6, 200)) |
4bcfdbee BA |
132 | #' dbDisconnect(series_db) |
133 | #' | |
134 | #' # All computed medoids should be the same: | |
135 | #' digest::sha1(medoids_ascii) | |
136 | #' digest::sha1(medoids_csv) | |
137 | #' digest::sha1(medoids_bin) | |
138 | #' digest::sha1(medoids_db) | |
1c6f223e | 139 | #' } |
1c6f223e | 140 | #' @export |
37c82bba | 141 | claws <- function(getSeries, K1, K2, nb_series_per_chunk, |
2b9f5356 | 142 | nb_items_clust1=7*K1, |
0486fbad BA |
143 | algoClust1=function(data,K) cluster::pam(t(data),K,diss=FALSE)$id.med, |
144 | algoClust2=function(dists,K) t( cluster::pam(dists,K,diss=TRUE)$medoids ), | |
37c82bba | 145 | wav_filt="d8", contrib_type="absolute", |
0486fbad | 146 | WER="end",sync_mean=TRUE, |
2b9f5356 BA |
147 | random=TRUE, |
148 | ntasks=1, ncores_tasks=1, ncores_clust=4, | |
149 | sep=",", | |
150 | nbytes=4, endian=.Platform$endian, | |
492cd9e7 | 151 | verbose=FALSE, parll=TRUE) |
ac1d4231 | 152 | { |
0e2dce80 | 153 | # Check/transform arguments |
492cd9e7 BA |
154 | if (!is.matrix(getSeries) && !bigmemory::is.big.matrix(getSeries) |
155 | && !is.function(getSeries) | |
156 | && !methods::is(getSeries,"connection") && !is.character(getSeries)) | |
0e2dce80 | 157 | { |
492cd9e7 | 158 | stop("'getSeries': [big]matrix, function, file or valid connection (no NA)") |
5c652979 | 159 | } |
eef6f6c9 BA |
160 | K1 <- .toInteger(K1, function(x) x>=2) |
161 | K2 <- .toInteger(K2, function(x) x>=2) | |
37c82bba BA |
162 | nb_series_per_chunk <- .toInteger(nb_series_per_chunk, function(x) x>=1) |
163 | # K1 (number of clusters at step 1) cannot exceed nb_series_per_chunk, because we will need | |
164 | # to load K1 series in memory for clustering stage 2. | |
165 | if (K1 > nb_series_per_chunk) | |
166 | stop("'K1' cannot exceed 'nb_series_per_chunk'") | |
eef6f6c9 BA |
167 | nb_items_clust1 <- .toInteger(nb_items_clust1, function(x) x>K1) |
168 | random <- .toLogical(random) | |
0486fbad BA |
169 | tryCatch( {ignored <- wavelets::wt.filter(wav_filt)}, |
170 | error = function(e) stop("Invalid wavelet filter; see ?wavelets::wt.filter") ) | |
eef6f6c9 BA |
171 | ctypes = c("relative","absolute","logit") |
172 | contrib_type = ctypes[ pmatch(contrib_type,ctypes) ] | |
173 | if (is.na(contrib_type)) | |
174 | stop("'contrib_type' in {'relative','absolute','logit'}") | |
7f0781b7 | 175 | if (WER!="end" && WER!="mix") |
eef6f6c9 | 176 | stop("'WER': in {'end','mix'}") |
0486fbad | 177 | sync_mean <- .toLogical(sync_mean) |
eef6f6c9 BA |
178 | random <- .toLogical(random) |
179 | ntasks <- .toInteger(ntasks, function(x) x>=1) | |
180 | ncores_tasks <- .toInteger(ncores_tasks, function(x) x>=1) | |
181 | ncores_clust <- .toInteger(ncores_clust, function(x) x>=1) | |
56857861 BA |
182 | if (!is.character(sep)) |
183 | stop("'sep': character") | |
eef6f6c9 BA |
184 | nbytes <- .toInteger(nbytes, function(x) x==4 || x==8) |
185 | verbose <- .toLogical(verbose) | |
186 | parll <- .toLogical(parll) | |
56857861 | 187 | |
2b9f5356 BA |
188 | # Since we don't make assumptions on initial data, there is a possibility that even |
189 | # when serialized, contributions or synchrones do not fit in RAM. For example, | |
190 | # 30e6 series of length 100,000 would lead to a +4Go contribution matrix. Therefore, | |
191 | # it's safer to place these in (binary) files, located in the following folder. | |
eef6f6c9 | 192 | bin_dir <- ".epclust_bin/" |
56857861 | 193 | dir.create(bin_dir, showWarnings=FALSE, mode="0755") |
2b9f5356 BA |
194 | |
195 | # Binarize series if getSeries is not a function; the aim is to always use a function, | |
196 | # to uniformize treatments. An equally good alternative would be to use a file-backed | |
197 | # bigmemory::big.matrix, but it would break the uniformity. | |
56857861 BA |
198 | if (!is.function(getSeries)) |
199 | { | |
4bcfdbee BA |
200 | if (verbose) |
201 | cat("...Serialize time-series\n") | |
56857861 | 202 | series_file = paste(bin_dir,"data",sep="") ; unlink(series_file) |
4bcfdbee BA |
203 | binarize(getSeries, series_file, nb_series_per_chunk, sep, nbytes, endian) |
204 | getSeries = function(inds) getDataInFile(inds, series_file, nbytes, endian) | |
56857861 | 205 | } |
ac1d4231 | 206 | |
95b5c2e6 | 207 | # Serialize all computed wavelets contributions into a file |
4bcfdbee | 208 | contribs_file = paste(bin_dir,"contribs",sep="") ; unlink(contribs_file) |
7f0781b7 | 209 | index = 1 |
cea14f3a | 210 | nb_curves = 0 |
4bcfdbee BA |
211 | if (verbose) |
212 | cat("...Compute contributions and serialize them\n") | |
492cd9e7 BA |
213 | nb_curves = binarizeTransform(getSeries, |
214 | function(series) curvesToContribs(series, wf, ctype), | |
215 | contribs_file, nb_series_per_chunk, nbytes, endian) | |
4bcfdbee | 216 | getContribs = function(indices) getDataInFile(indices, contribs_file, nbytes, endian) |
8e6accca | 217 | |
2b9f5356 | 218 | # A few sanity checks: do not continue if too few data available. |
eef6f6c9 BA |
219 | if (nb_curves < K2) |
220 | stop("Not enough data: less series than final number of clusters") | |
5c652979 | 221 | nb_series_per_task = round(nb_curves / ntasks) |
eef6f6c9 BA |
222 | if (nb_series_per_task < K2) |
223 | stop("Too many tasks: less series in one task than final number of clusters") | |
ac1d4231 | 224 | |
2b9f5356 BA |
225 | # Generate a random permutation of 1:N (if random==TRUE); otherwise just use arrival |
226 | # (storage) order. | |
227 | indices_all = if (random) sample(nb_curves) else seq_len(nb_curves) | |
228 | # Split (all) indices into ntasks groups of ~same size | |
229 | indices_tasks = lapply(seq_len(ntasks), function(i) { | |
230 | upper_bound = ifelse( i<ntasks, min(nb_series_per_task*i,nb_curves), nb_curves ) | |
231 | indices_all[((i-1)*nb_series_per_task+1):upper_bound] | |
232 | }) | |
233 | ||
234 | if (parll && ntasks>1) | |
235 | { | |
236 | # Initialize parallel runs: outfile="" allow to output verbose traces in the console | |
237 | # under Linux. All necessary variables are passed to the workers. | |
238 | cl = parallel::makeCluster(ncores_tasks, outfile="") | |
0486fbad BA |
239 | varlist = c("getSeries","getContribs","K1","K2","algoClust1","algoClust2", |
240 | "nb_series_per_chunk","nb_items_clust1","ncores_clust","sep", | |
37c82bba | 241 | "nbytes","endian","verbose","parll") |
2b9f5356 BA |
242 | if (WER=="mix") |
243 | varlist = c(varlist, "medoids_file") | |
244 | parallel::clusterExport(cl, varlist, envir = environment()) | |
245 | } | |
246 | ||
247 | # This function achieves one complete clustering task, divided in stage 1 + stage 2. | |
248 | # stage 1: n indices --> clusteringTask1(...) --> K1 medoids | |
249 | # stage 2: K1 medoids --> clusteringTask2(...) --> K2 medoids, | |
250 | # where n = N / ntasks, N being the total number of curves. | |
492cd9e7 BA |
251 | runTwoStepClustering = function(inds) |
252 | { | |
2b9f5356 BA |
253 | # When running in parallel, the environment is blank: we need to load required |
254 | # packages, and pass useful variables. | |
bf5c0844 | 255 | if (parll && ntasks>1) |
492cd9e7 BA |
256 | require("epclust", quietly=TRUE) |
257 | indices_medoids = clusteringTask1( | |
0486fbad | 258 | inds, getContribs, K1, algoClust1, nb_series_per_chunk, ncores_clust, verbose, parll) |
56857861 BA |
259 | if (WER=="mix") |
260 | { | |
eef6f6c9 BA |
261 | if (parll && ntasks>1) |
262 | require("bigmemory", quietly=TRUE) | |
bf5c0844 | 263 | medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) ) |
0486fbad BA |
264 | medoids2 = clusteringTask2(medoids1, K2, algoClust2, getSeries, nb_curves, |
265 | nb_series_per_chunk, sync_mean, nbytes, endian, ncores_clust, verbose, parll) | |
2b9f5356 | 266 | binarize(medoids2, medoids_file, nb_series_per_chunk, sep, nbytes, endian) |
56857861 BA |
267 | return (vector("integer",0)) |
268 | } | |
269 | indices_medoids | |
492cd9e7 BA |
270 | } |
271 | ||
2b9f5356 BA |
272 | # Synchrones (medoids) need to be stored only if WER=="mix"; indeed in this case, every |
273 | # task output is a set of new (medoids) curves. If WER=="end" however, output is just a | |
274 | # set of indices, representing some initial series. | |
275 | if (WER=="mix") | |
276 | {medoids_file = paste(bin_dir,"medoids",sep="") ; unlink(medoids_file)} | |
277 | ||
c45fd663 | 278 | if (verbose) |
e161499b BA |
279 | { |
280 | message = paste("...Run ",ntasks," x stage 1", sep="") | |
281 | if (WER=="mix") | |
282 | message = paste(message," + stage 2", sep="") | |
283 | cat(paste(message,"\n", sep="")) | |
284 | } | |
c45fd663 | 285 | |
2b9f5356 BA |
286 | # As explained above, indices will be assigned to ntasks*K1 medoids indices [if WER=="end"], |
287 | # or nothing (empty vector) if WER=="mix"; in this case, medoids (synchrones) are stored | |
288 | # in a file. | |
eef6f6c9 BA |
289 | indices <- |
290 | if (parll && ntasks>1) | |
291 | unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) ) | |
292 | else | |
293 | unlist( lapply(indices_tasks, runTwoStepClustering) ) | |
bf5c0844 | 294 | if (parll && ntasks>1) |
492cd9e7 | 295 | parallel::stopCluster(cl) |
3465b246 | 296 | |
2b9f5356 BA |
297 | # Right before the final stage, two situations are possible: |
298 | # a. data to be processed now sit in binary format in medoids_file (if WER=="mix") | |
299 | # b. data still is the initial set of curves, referenced by the ntasks*K1 indices | |
300 | # So, the function getSeries() will potentially change. However, computeSynchrones() | |
301 | # requires a function retrieving the initial series. Thus, the next line saves future | |
302 | # conditional instructions. | |
8702eb86 | 303 | getRefSeries = getSeries |
2b9f5356 | 304 | |
e205f218 BA |
305 | if (WER=="mix") |
306 | { | |
307 | indices = seq_len(ntasks*K2) | |
bccecb19 BA |
308 | # Now series (synchrones) must be retrieved from medoids_file |
309 | getSeries = function(inds) getDataInFile(inds, medoids_file, nbytes, endian) | |
2b9f5356 | 310 | # Contributions must be re-computed |
4bcfdbee | 311 | unlink(contribs_file) |
e205f218 | 312 | index = 1 |
4bcfdbee BA |
313 | if (verbose) |
314 | cat("...Serialize contributions computed on synchrones\n") | |
492cd9e7 BA |
315 | ignored = binarizeTransform(getSeries, |
316 | function(series) curvesToContribs(series, wf, ctype), | |
317 | contribs_file, nb_series_per_chunk, nbytes, endian) | |
e205f218 | 318 | } |
0e2dce80 | 319 | |
bccecb19 BA |
320 | #TODO: check THAT |
321 | ||
322 | ||
0e2dce80 | 323 | # Run step2 on resulting indices or series (from file) |
4bcfdbee BA |
324 | if (verbose) |
325 | cat("...Run final // stage 1 + stage 2\n") | |
0486fbad BA |
326 | indices_medoids = clusteringTask1(indices, getContribs, K1, algoClust1, |
327 | nb_series_per_chunk, ncores_tasks*ncores_clust, verbose, parll) | |
bf5c0844 | 328 | medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) ) |
0486fbad BA |
329 | medoids2 = clusteringTask2(medoids1, K2, algoClust2, getRefSeries, nb_curves, |
330 | nb_series_per_chunk, sync_mean, nbytes, endian, ncores_tasks*ncores_clust, verbose, parll) | |
4bcfdbee | 331 | |
2b9f5356 | 332 | # Cleanup: remove temporary binary files and their folder |
4bcfdbee BA |
333 | unlink(bin_dir, recursive=TRUE) |
334 | ||
2b9f5356 BA |
335 | # Return medoids as a standard matrix, since K2 series have to fit in RAM |
336 | # (clustering algorithm 1 takes K1 > K2 of them as input) | |
eef6f6c9 | 337 | medoids2[,] |
56857861 BA |
338 | } |
339 | ||
4bcfdbee BA |
340 | #' curvesToContribs |
341 | #' | |
342 | #' Compute the discrete wavelet coefficients for each series, and aggregate them in | |
343 | #' energy contribution across scales as described in https://arxiv.org/abs/1101.4744v2 | |
344 | #' | |
eef6f6c9 | 345 | #' @param series [big.]matrix of series (in columns), of size L x n |
4bcfdbee BA |
346 | #' @inheritParams claws |
347 | #' | |
eef6f6c9 | 348 | #' @return A [big.]matrix of size log(L) x n containing contributions in columns |
4bcfdbee BA |
349 | #' |
350 | #' @export | |
0fe757f7 | 351 | curvesToContribs = function(series, wav_filt, contrib_type, coin=FALSE) |
56857861 | 352 | { |
eef6f6c9 | 353 | L = nrow(series) |
0fe757f7 | 354 | if (coin) browser() |
56857861 BA |
355 | D = ceiling( log2(L) ) |
356 | nb_sample_points = 2^D | |
eef6f6c9 | 357 | apply(series, 2, function(x) { |
56857861 BA |
358 | interpolated_curve = spline(1:L, x, n=nb_sample_points)$y |
359 | W = wavelets::dwt(interpolated_curve, filter=wf, D)@W | |
4bcfdbee | 360 | nrj = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) ) |
eef6f6c9 BA |
361 | if (contrib_type!="absolute") |
362 | nrj = nrj / sum(nrj) | |
363 | if (contrib_type=="logit") | |
364 | nrj = - log(1 - nrj) | |
365 | nrj | |
366 | }) | |
56857861 BA |
367 | } |
368 | ||
492cd9e7 | 369 | # Check integer arguments with functional conditions |
56857861 BA |
370 | .toInteger <- function(x, condition) |
371 | { | |
eef6f6c9 BA |
372 | errWarn <- function(ignored) |
373 | paste("Cannot convert argument' ",substitute(x),"' to integer", sep="") | |
56857861 | 374 | if (!is.integer(x)) |
eef6f6c9 BA |
375 | tryCatch({x = as.integer(x)[1]; if (is.na(x)) stop()}, |
376 | warning = errWarn, error = errWarn) | |
56857861 | 377 | if (!condition(x)) |
eef6f6c9 BA |
378 | { |
379 | stop(paste("Argument '",substitute(x), | |
380 | "' does not verify condition ",body(condition), sep="")) | |
381 | } | |
382 | x | |
383 | } | |
384 | ||
385 | # Check logical arguments | |
386 | .toLogical <- function(x) | |
387 | { | |
388 | errWarn <- function(ignored) | |
389 | paste("Cannot convert argument' ",substitute(x),"' to logical", sep="") | |
390 | if (!is.logical(x)) | |
391 | tryCatch({x = as.logical(x)[1]; if (is.na(x)) stop()}, | |
392 | warning = errWarn, error = errWarn) | |
56857861 | 393 | x |
cea14f3a | 394 | } |