With sync_mean to average synchrones: bad idea, will be removed

[epclust.git] / epclust / R / main.R

diff --git a/epclust/R/main.R b/epclust/R/main.R
index e003933..2af6f90 100644 (file)
--- 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)
        }
 
@@ -350,13 +349,13 @@ claws <- function(getSeries, K1, K2, nb_series_per_chunk,
 #' @export
 curvesToContribs = function(series, wav_filt, contrib_type, coin=FALSE)
 {
+       series = as.matrix(series) #1D serie could occur
        L = nrow(series)
-       if (coin) browser()
        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)