class %DeepSee.extensions.clusters.DissimilarityModel
extends AbstractModel
• property ConstructHierarchy
as %Boolean [ InitialExpression = 0 ];
• property LinkageType
as %EnumString(VALUELIST=",single,complete,average");
• property Mapper
as %RegisteredObject;
• property MinSize
as %Integer [ InitialExpression = 0 ];
• property NewickTree
as %String;
Hierarchy Tree in Newick Format
• property Outliers
as %Integer;
• property State
[ MultiDimensional ];
• property Tree
as %String [ MultiDimensional ];
• method Execute(K As %Integer)
as %Status
• method GetClusterDissimilarity(k1 As %Integer, k2 As %Integer, Output sc As %Status)
as %Double
• method GetEffNumCL(Output sc As %Status)
as %Integer
• method Init()
as %Status
• method IsPrepared()
as %Boolean
Checks whether the model is ready for an analysis to be executed. This is dependent on a
specific algorithm and therefore this method is overriden by subclasses.
• classmethod New(dsName As %String, type As %EnumString, Output sc As %Status)
as DissimilarityModel
• classmethod Open(dsName As %String, type As %EnumString, Output sc As %Status)
as DissimilarityModel
• method Reduce()
as %Status
• method RelativeClusterCost(k As %Integer, m As %Integer, Output sc As %Status)
as %Double
Returns the realtive cost of a given cluster relative to a medoid point m.
Cluster is identified by its ordinal number k.
Point m is identified by its ordinal number.
• method Save()
as %Status
• method SetCost(costCalculator As %RegisteredObject = "")
as %Status
• method SetSampleData(list As %List)
as %Status
• classmethod Test(N As %Integer, K As %Integer, type As %String, hier As %Boolean = 0, slist As %String = "")
as %Status
• method TotalCost()
