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

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 8662f89..a431ba8 100644 (file)
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -76,7 +76,7 @@ clusteringTask2 = function(medoids, K2, algoClust2, getRefSeries, nb_ref_curves,
        distances = computeWerDists(synchrones, nbytes, endian, ncores_clust, verbose, parll)
        if (verbose)
                cat(paste("   algoClust2() on ",nrow(distances)," items\n", sep=""))
-       medoids[ algoClust2(distances,K2), ]
+       medoids[ ,algoClust2(distances,K2) ]
 }
 
 #' computeSynchrones
@@ -111,7 +111,7 @@ computeSynchrones = function(medoids, getRefSeries, nb_ref_curves,
                }
 
                ref_series = getRefSeries(indices)
-               nb_series = nrow(ref_series)
+               nb_series = ncol(ref_series)
 
                # Get medoids indices for this chunk of series
                mi = computeMedoidsIndices(medoids@address, ref_series)

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)

diff --git a/epclust/tests/testthat/test.clustering.R b/epclust/tests/testthat/test.clustering.R
index 77faeb9..e22835a 100644 (file)
--- a/epclust/tests/testthat/test.clustering.R
+++ b/epclust/tests/testthat/test.clustering.R
@@ -73,8 +73,7 @@ test_that("clusteringTask1 behave as expected",
        ctype = "absolute"
        getContribs = function(indices) curvesToContribs(series[,indices],wf,ctype)
        require("cluster", quietly=TRUE)
-       browser()
-       algoClust1 = function(contribs,K) cluster::pam(contribs,K,diss=FALSE)$id.med
+       algoClust1 = function(contribs,K) cluster::pam(t(contribs),K,diss=FALSE)$id.med
        indices1 = clusteringTask1(1:n, getContribs, K1, algoClust1, 75, verbose=TRUE, parll=FALSE)
        medoids_K1 = getSeries(indices1)
 
@@ -97,14 +96,16 @@ test_that("clusteringTask2 behave as expected",
        s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
        series = matrix(nrow=L, ncol=n)
        for (i in seq_len(n))
-               series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
+               series[,i] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
        getRefSeries = function(indices) {
                indices = indices[indices <= n]
                if (length(indices)>0) series[,indices] else NULL
        }
        # Artificially simulate 60 medoids - perfect situation, all equal to one of the refs
        medoids_K1 = bigmemory::as.big.matrix( sapply( 1:K1, function(i) s[[I(i,K1)]] ) )
-       medoids_K2 = clusteringTask2(medoids_K1, K2, getRefSeries, n, 75, verbose=TRUE, parll=FALSE)
+       algoClust2 = function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med
+       medoids_K2 = clusteringTask2(medoids_K1, K2, algoClust2, getRefSeries,
+               n, 75, sync_mean=TRUE, verbose=TRUE, parll=FALSE)
 
        expect_equal(dim(medoids_K2), c(L,K2))
        # Not easy to evaluate result: at least we expect it to be better than random selection of