update soft k-means to use stable softmax and add unit tests

Author: psaris

kmeans.q

@@ -91,3 +91,10 @@ show .ut.plt s:(avg raze .ml.silhouette[.ml.edist;X]::) peach I
 ks i:.ml.imax s
 -1"superimpose the centroids on the data";
 show .ut.plot[39;20;.ut.c10;avg] .ml.append[0N;X],'.ml.append[1] C i
+
+-1"a soft version of Lloyds algorithm is available with .ml.lloyds";
+-1".ml.kmeanss and .ml.kmeanssmax should be identical to ml.kmeans";
+X:iris.X
+C:asc flip .ml.kmeans[X] over C0:last 3 .ml.kmeanspp[X]// 2#()
+.ut.assert[C] asc flip .ml.kmeanss[X] over C0
+.ut.assert[C] asc flip .ml.kmeanssmax[500;X] over C0

ml.q

@@ -310,7 +310,7 @@ skmeans:lloyd[cosdist;normalize (avg'')::] / spherical k-means
 kmeanss:lloyds[edist2;wavg\:/:;ismin] / k-means using Lloyd with rf
 / v1 David Mackay using stiffness parameter (b)eta. 1%sqrt b represents the
 / sigma (or radius) of the cluster
-kmeanssmax:{[b;X]lloyds[edist2;wavg\:/:;softmax neg[b]*;X]}
+kmeanssmax:{[b;X]lloyds[edist2;wavg\:/:;ssoftmax neg[b]*;X]}
 
 / using (d)istance (f)unction, find the medoid in matri(X)
 medoid:{[df;X]X@\:imin f2nd[sum df[X]::] X}