-toInteger <- function(x, condition)
+# 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]},
- error = function(e) paste("cannot convert argument",substitute(x),"to integer")
- )
+ 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)))
+ {
+ stop(paste("Argument '",substitute(x),
+ "' does not verify condition ",body(condition), sep=""))
+ }
x
}
-#TODO: merge these 2 next ?!
-serialize = function(coeffs)
+# Check logical arguments
+.toLogical <- function(x)
{
- #.........
- #C function (from data.frame, type of IDs ??! force integers ? [yes])
- #return raw vector
-}
-appendBinary = function(.......)
-{
- #take raw vector, append it (binary mode) to a file
+ 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
}
-#finalizeSerialization = function(...)
-#{
-# #write number of series, and length of each...
-#}
-
-deserialize = function(coeffs, range)
+#' 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
+#' @inheritParams claws
+#'
+#' @return A matrix of size log(L) x n containing contributions in columns
+#'
+#' @export
+curvesToContribs = function(series, wav_filt, contrib_type)
{
- #......
- #C function (from file name)
+ 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 = wavelets::dwt(interpolated_curve, filter=wav_filt, D)@W
+ # Compute the sum of squared discrete wavelet coefficients, for each scale
+ nrj = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) )
+ if (contrib_type!="absolute")
+ nrj = nrj / sum(nrj)
+ if (contrib_type=="logit")
+ nrj = - log(1 - nrj)
+ nrj
+ })
}
-getSeries(data, rank=NULL, id=NULL)
+# 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)
{
- #TODO:
+ 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) ) )
}
-getCoeffs(.....) #FROM BINARY FILE !!!
-{
+#' 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()}.
+#' Note: run it in the folder where the 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)
}
projects / epclust.git / blobdiff