From KitwarePublic

Class Manifesto of New Statistics Framework

Contents 1 Summary Table 2 List of Classes per Category 2.1 Traits 2.2 Data Objects 2.3 Filters 2.4 Classifiers (Suggested Design) 2.4.1 Elements 2.4.2 Filters 3 Class Diagrams 3.1 Traits 3.2 Data Objects 3.3 Filters 3.4 Classifiers (Suggested Design) 3.4.1 Distance notation 3.4.2 API

Summary Table

The classes that integrate the new statistics framework are categorized in the following table

Conceptual Class	Number
Traits	1
Data Objects	4
Filters	11
Total	16

List of Classes per Category

Traits



• MeasurementVectorTraits

Data Objects



• Sample
• ListSample
• Histogram
• Subsample

Filters

• SampleToHistogramFilter
• MeanFilter
• WeightedMeanFilter
• CovarianceFilter
• WeightedCovarianceFilter
• HistogramToTextureFeaturesFilter
• ImageToListSampleFilter
• ScalarImageToCooccurrenceMatrixFilter
• SampleToSubsampleFilter
• SampleClassifierFilter
• NeighborhoodSubsampler

Classifiers (Suggested Design)

Elements

• MembershipFunctionBase
  • DistanceToCentroidMembershipFunction (plugs in a DistanceMetric)
• DistanceMetrics
  • Euclidean
  • Mahalanobis
  • 1_1

Filters

• Sample, Array of Membership Functions --> MembershipSample(sample,labels) == SampleClassifierFilter
• Sample, Array of Membership Functions --> GoodnessOfFitComponent (sample,weights) == SampleGoodnessOfFitFilter

Class Diagrams

Traits

Data Objects

Filters

Classifiers (Suggested Design)

Distance notation

• Manhattan (L1) = sum of absolute values
• Euclidean = square root of ( sum of squares )
• Euclidean Squared (L2) = sum of squares
• Mahalanobis = square root of ( V . M . VT )

API

• DistanceToCentroidMembershipFunction
  • SetDistanceMetric( const DistanceMetric * ) (new)
  • const GetDistanceMetric() (new)
  • Evaluate( Measurement vector ) (already there)
  • SetCentroid( ) (already there)