100 #ifndef __vtkKMeansStatistics_h
101 #define __vtkKMeansStatistics_h
127 vtkSetMacro(DefaultNumberOfClusters,
int);
128 vtkGetMacro(DefaultNumberOfClusters,
int);
133 vtkSetStringMacro(KValuesArrayName);
134 vtkGetStringMacro(KValuesArrayName);
140 vtkSetMacro( MaxNumIterations,
int );
141 vtkGetMacro( MaxNumIterations,
int );
147 vtkSetMacro( Tolerance,
double );
148 vtkGetMacro( Tolerance,
double );
200 AssessFunctor*& dfunc );
206 virtual void UpdateClusterCenters(
vtkTable* newClusterElements,
226 int InitializeDataAndClusterCenters(
vtkTable* inParameters,
240 virtual void CreateInitialClusterCenters(
vtkIdType numToAllocate,
virtual void Test(vtkTable *, vtkMultiBlockDataSet *, vtkTable *)
virtual void Assess(vtkTable *, vtkMultiBlockDataSet *, vtkTable *)=0
static vtkTableAlgorithm * New()
void PrintSelf(ostream &os, vtkIndent indent)
maintain an unordered list of data objects
vtkKMeansDistanceFunctor * DistanceFunctor
int DefaultNumberOfClusters
a vtkAbstractArray subclass for strings
dynamic, self-adjusting array of vtkIdType
virtual void Aggregate(vtkDataObjectCollection *, vtkMultiBlockDataSet *)
A atomic type representing the union of many types.
dynamic, self-adjusting array of double
Base class for statistics algorithms.
dynamic, self-adjusting array of int
a simple class to control print indentation
A class for KMeans clustering.
virtual void Learn(vtkTable *, vtkTable *, vtkMultiBlockDataSet *)=0
virtual void Derive(vtkMultiBlockDataSet *)=0
A table, which contains similar-typed columns of data.
virtual bool SetParameter(const char *parameter, int index, vtkVariant value)
Composite dataset that organizes datasets into blocks.
virtual void SelectAssessFunctor(vtkTable *outData, vtkDataObject *inMeta, vtkStringArray *rowNames, AssessFunctor *&dfunc)=0
general representation of visualization data
measure distance from k-means cluster centers