Clust

The clust package does standard agglomerative hierarchical clustering: https://en.wikipedia.org/wiki/Hierarchical_clustering

• Docs: clust

Example code

	ix := etable.NewIdxView(table) // works on an index view so it can be filtered etc
	nm, _ := ix.Table.MetaData["name"]
	// first compute the distance matrix using whatever metric you want
	smat := &simat.SimMat{}
	smat.TableCol(ix, colNm, "TrialName", false, metric.Euclidean64)
	// then do the clustering
	cl := clust.Glom(smat, clust.MinDist) // can choose MaxDist or AvgDist or write your own
	// then plot the results
	pt := &etable.Table{}
	clust.Plot(pt, cl, smat)
	plt := &eplot.Plot2D{}
	plt.InitName(plt, colNm)
	plt.Params.Title = "Cluster Plot of: " + nm + " " + colNm
	plt.Params.XAxisCol = "X"
	plt.SetTable(pt)
	// order of params: on, fixMin, min, fixMax, max
	plt.SetColParams("X", false, true, 0, false, 0)
	plt.SetColParams("Y", true, true, 0, false, 0)
	plt.SetColParams("Label", true, false, 0, false, 0)