X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Futils.R;h=ba643d0b5ad198907e2b24c793244825a5e77446;hb=40f12a2f66d06fd77183ea02b996f5c66f90761c;hp=5c09b568a9830ccac0f7b8a2a92a9836905decfc;hpb=c33af7e44b802c182ea92c79b56a72cae3007301;p=epclust.git

diff --git a/epclust/R/utils.R b/epclust/R/utils.R
index 5c09b56..ba643d0 100644
--- a/epclust/R/utils.R
+++ b/epclust/R/utils.R
@@ -1,36 +1,117 @@
-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
 }
 
-serialize = function(coeffs, file, append)
+# Check logical arguments
+.toLogical <- function(x)
 {
-	#.........
-	#C function (from data.frame, type of IDs ??! force integers ? [yes])
-	#return raw vector
-	#take raw vector, append it (binary mode) to a file
-#TODO: appendCoeffs() en C --> serialize et append to 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
 }
 
-deserialize = function(file, range, serie_length)
+#' 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 series [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, coin=FALSE)
 {
-	#......
-	#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
+# If max == TRUE, sets sizes cannot exceed nb_per_set
+.splitIndices = function(indices, nb_per_set, max=FALSE)
 {
-	#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
+		indices_workers = list(indices)
+	}
+	else
+	{
+		indices_workers = lapply( seq_len(nb_workers), function(i)
+			indices[(nb_per_set*(i-1)+1):(nb_per_set*i)] )
+
+		if (max)
+		{
+			# Sets are not so well balanced, but size is supposed to be critical
+			return ( c( indices_workers, if (rem>0) list((L-rem+1):L) else NULL ) )
+		}
+
+		# Spread the remaining load among the workers
+		rem = L %% nb_per_set
+		while (rem > 0)
+		{
+			index = rem%%nb_workers + 1
+			indices_workers[[index]] = c(indices_workers[[index]], indices[L-rem+1])
+			rem = rem - 1
+		}
+	}
+	indices_workers
 }
 
-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, 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)
 }