X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fmain.R;h=2af6f90c00c85afbdc74a7e7237426ed4bd2cfa3;hb=9f05a4a0b703deffd7bdb9cd99b0aaa2246a5c83;hp=9e9b6411088f031a31e72bd3cdcc361430807378;hpb=0486fbadb122cb4d78c5d9f248c29800a59eb24e;p=epclust.git

diff --git a/epclust/R/main.R b/epclust/R/main.R
index 9e9b641..2af6f90 100644
--- a/epclust/R/main.R
+++ b/epclust/R/main.R
@@ -42,13 +42,12 @@
 #' @param nb_series_per_chunk (Maximum) number of series to retrieve in one batch
 #' @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.
-#'   In our method, this function is called on iterated medoids during stage 1
+#'   and outputs K medoids ranks. Default: PAM. In our method, this function is called
+#'   on iterated medoids during stage 1
 #' @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 clusters representatives (curves). Default: PAM.
-#'   In our method, this function is called on a matrix of K1 x K1 (WER) distances computed
-#    between synchrones
+#'   and outputs K medoids ranks. Default: PAM.  In our method, this function is called
+#'   on a matrix of K1 x K1 (WER) distances computed between synchrones
 #' @param nb_items_clust1 (~Maximum) number of items in input of the clustering algorithm
 #'   for stage 1. At worst, a clustering algorithm might be called with ~2*nb_items_clust1
 #'   items; but this could only happen at the last few iterations.
@@ -141,7 +140,7 @@
 claws <- function(getSeries, K1, K2, nb_series_per_chunk,
 	nb_items_clust1=7*K1,
 	algoClust1=function(data,K) cluster::pam(t(data),K,diss=FALSE)$id.med,
-	algoClust2=function(dists,K) t( cluster::pam(dists,K,diss=TRUE)$medoids ),
+	algoClust2=function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med,
 	wav_filt="d8", contrib_type="absolute",
 	WER="end",sync_mean=TRUE,
 	random=TRUE,
@@ -211,7 +210,7 @@ claws <- function(getSeries, K1, K2, nb_series_per_chunk,
 	if (verbose)
 		cat("...Compute contributions and serialize them\n")
 	nb_curves = binarizeTransform(getSeries,
-		function(series) curvesToContribs(series, wf, ctype),
+		function(series) curvesToContribs(series, wav_filt, contrib_type),
 		contribs_file, nb_series_per_chunk, nbytes, endian)
 	getContribs = function(indices) getDataInFile(indices, contribs_file, nbytes, endian)
 
@@ -313,7 +312,7 @@ claws <- function(getSeries, K1, K2, nb_series_per_chunk,
 		if (verbose)
 			cat("...Serialize contributions computed on synchrones\n")
 		ignored = binarizeTransform(getSeries,
-			function(series) curvesToContribs(series, wf, ctype),
+			function(series) curvesToContribs(series, wav_filt, contrib_type),
 			contribs_file, nb_series_per_chunk, nbytes, endian)
 	}
 
@@ -348,14 +347,15 @@ claws <- function(getSeries, K1, K2, nb_series_per_chunk,
 #' @return A [big.]matrix of size log(L) x n containing contributions in columns
 #'
 #' @export
-curvesToContribs = function(series, wav_filt, contrib_type)
+curvesToContribs = function(series, wav_filt, contrib_type, coin=FALSE)
 {
+	series = as.matrix(series) #1D serie could occur
 	L = nrow(series)
 	D = ceiling( log2(L) )
 	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=wf, D)@W
+		W = wavelets::dwt(interpolated_curve, filter=wav_filt, D)@W
 		nrj = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) )
 		if (contrib_type!="absolute")
 			nrj = nrj / sum(nrj)