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IRIS for UNIX 2019.2
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  [ENSLIB] >  [%DeepSee] >  [extensions] >  [utils] >  [LDA]
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persistent class %DeepSee.extensions.utils.LDA extends %Library.Persistent, %DeepSee.extensions.BlackBox

This code serves for calculating a linear discriminant analysis (LDA)


Parameters Properties Methods Queries Indices ForeignKeys Triggers
9 13 1


Confusion Dim GroupMean Groups
NGroups Name PooledInverseCovariance Probability

%AddToSaveSet %AddToSyncSet %BMEBuilt %CheckConstraints
%CheckConstraintsForExtent %ClassIsLatestVersion %ClassName %ComposeOid
%ConstructClone %Delete %DeleteExtent %DeleteId
%DispatchClassMethod %DispatchGetModified %DispatchGetProperty %DispatchMethod
%DispatchSetModified %DispatchSetMultidimProperty %DispatchSetProperty %Exists
%ExistsId %Extends %GUID %GUIDSet
%GetLock %GetParameter %GetSwizzleObject %Id
%InsertBatch %IsA %IsModified %IsNull
%KillExtent %KillExtentData %LoadFromMemory %LockExtent
%LockId %New %NormalizeObject %ObjectIsNull
%ObjectModified %Oid %OnBeforeAddToSync %OnDetermineClass
%Open %OpenId %OriginalNamespace %PackageName
%PhysicalAddress %PurgeIndices %Reload %RemoveFromSaveSet
%ResolveConcurrencyConflict %RollBack %Save %SaveDirect
%SaveIndices %SerializeObject %SetModified %SortBegin
%SortEnd %SyncObjectIn %SyncTransport %UnlockExtent
%UnlockId %ValidateIndices %ValidateObject Create
GetClassDef GetDF GetDataLocation GetMahalanobisDistance
GetMajorContributors Test getConfusionMatrixForTestSet getCovector
getSensitivity getSpecificity predict printConfusionMatrix


• property Confusion  [ MultiDimensional ];
• property Dim as %Integer;
• property GroupMean as %Double [ MultiDimensional ];
• property Groups  [ MultiDimensional ];
• property NGroups as %Integer;
• property Name as %String(MAXLEN=256);
• property PooledInverseCovariance as %Double [ MultiDimensional ];
• property Probability as %Double [ MultiDimensional ];
• property UseMahalanobisDistance as %Boolean [ InitialExpression = 0 ];


• method Create(N As %Integer, M As %Integer, ByRef data As %Double, ByRef groups As %Integer, p As %Boolean) as %Status
• method GetDF(ByRef x, Output fv, verobse As %Boolean = 1) as %Status
• method GetMahalanobisDistance(ByRef x, Output fv, verobse As %Boolean = 0) as %Status
• method GetMajorContributors(targetPos As %String, targetNeg As %String, margin As %Double, Output listPos As %List, Output listNeg As %List) as %Status
• classmethod Test()
• method getConfusionMatrixForTestSet(rs As %ResultSet, i1 As %Integer, dim As %Integer, Output C, Output r As %Double) as %Status
• method getCovector(target As %String, Output V) as %Status
• method getSensitivity(g, Output sc As %Status, ByRef confusion="") as %Double
• method getSpecificity(g, Output sc As %Status, ByRef confusion="") as %Double
• method predict(ByRef x, Output sc As %Status, Output maxf As %Double, Output f) as %String
• method printConfusionMatrix(ByRef confusion="") as %Status


•index (NameIdx on Name) [PrimaryKey,Unique];

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