X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Falgorithms.R;fp=epclust%2FR%2Falgorithms.R;h=97dce901d54c3dce05f6ebb834c675fd121266d3;hb=7709d507dfab9256a401f2c77ced7bc70d90fec3;hp=0000000000000000000000000000000000000000;hpb=38aef1cbef037257bf03dd1e65fbb682a32e3f2c;p=epclust.git

diff --git a/epclust/R/algorithms.R b/epclust/R/algorithms.R
new file mode 100644
index 0000000..97dce90
--- /dev/null
+++ b/epclust/R/algorithms.R
@@ -0,0 +1,24 @@
+#NOTE: always keep ID in first column
+curvesToCoeffs = function(series, wf)
+{
+	library(wavelets)
+	L = length(series[1,])
+	D = ceiling( log(L-1) )
+	nb_sample_points = 2^D
+	#TODO: parallel::parApply() ?!
+	res = apply(series, 1, function(x) {
+		interpolated_curve = spline(1:(L-1), x[2:L], n=nb_sample_points)$y
+		W = wavelets::dwt(interpolated_curve, filter=wf, D)@W
+		nrj_coeffs = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) )
+		return ( c(x[1], nrj_coeffs) )
+	})
+	return (as.data.frame(res))
+}
+
+getClusters = function(data, K)
+{
+	library(cluster)
+	pam_output = cluster::pam(data, K)
+	return ( list( clusts=pam_output$clustering, medoids=pam_output$medoids,
+		ranks=pam_output$id.med ) )
+}