Proposals:Refactoring Statistics Framework 2007 New Statistics Framework: Difference between revisions
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DistanceMetric -> MahalanobisDistanceMetric | DistanceMetric -> MahalanobisDistanceMetric | ||
DistanceMetric -> EuclideanDistanceMetric | DistanceMetric -> EuclideanDistanceMetric | ||
DistanceMetric -> EuclideanSquaredDistanceMetric | |||
DistanceMetric -> ManhattanDistanceMetric | DistanceMetric -> ManhattanDistanceMetric | ||
MembershipFunctionBase -> DistanceToCentroidMembershipFunction | MembershipFunctionBase -> DistanceToCentroidMembershipFunction | ||
| Line 148: | Line 149: | ||
* Euclidean Squared (L2) = sum of squares | * Euclidean Squared (L2) = sum of squares | ||
* Mahalanobis = square root of ( V . M . VT ) | * Mahalanobis = square root of ( V . M . VT ) | ||
=== API === | |||
* DistanceToCentroidMembershipFunction | |||
** SetDistanceMetric( const DistanceMetric * ) (new) | |||
** const GetDistanceMetric() (new) | |||
** Evaluate( Measurement vector ) (already there) | |||
** SetCentroid( ) (already there) | |||
Latest revision as of 20:57, 17 July 2008
Class Manifesto of New Statistics Framework
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)
