Proposals:Refactoring Statistics Framework 2007 New Statistics Framework: Difference between revisions

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! Conceptual Class !! Number
! Conceptual Class !! Number
|-
|-
| Data Objects || 3
| Traits || 1
|-
|-
| Filters || 4
| Data Objects || 4
|-
|-
| '''Total''' || '''7'''
| Filters || 11
|-
| '''Total''' || '''16'''
|}
|}


= List of Classes per Category =
= List of Classes per Category =
=== Traits ===

* MeasurementVectorTraits


=== Data Objects ===
=== Data Objects ===
Line 24: Line 31:
* ListSample
* ListSample
* Histogram
* Histogram
* Subsample


=== Filters ===
=== Filters ===


* ListSampleToHistogramFilter
* SampleToHistogramFilter
* MeanFilter
* MeanFilter
* WeightedMeanFilter
* WeightedMeanFilter
* CovarianceFilter
* WeightedCovarianceFilter
* HistogramToTextureFeaturesFilter
* 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 =
= Class Diagrams =
== Traits ==
<graphviz>
digraph G {
MeasurementVectorTraits [ shape=box URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1MeasurementVectorTraits.html"];
}
</graphviz>


== Data Objects ==
== Data Objects ==
Line 40: Line 79:
DataObject [URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1DataObject.html"];
DataObject [URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1DataObject.html"];
Sample [shape=box, URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1Sample.html"];
Sample [shape=box, URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1Sample.html"];
Subsample [shape=box, URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1Subsample.html"];
ListSample [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1ListSample.html"];
ListSample [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1ListSample.html"];
Histogram [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1Histogram.html"];
Histogram [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1Histogram.html"];
Line 45: Line 85:
Sample -> Histogram;
Sample -> Histogram;
Sample -> ListSample;
Sample -> ListSample;
Sample -> Subsample;
}
}
</graphviz>
</graphviz>
Line 53: Line 94:
digraph G {
digraph G {
ProcessObject [URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1ProcessObject.html"];
ProcessObject [URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1ProcessObject.html"];
ListSampleToHistogramFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1ListSampleToHistogramFilter.html"];
SampleToHistogramFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1SampleToHistogramFilter.html"];
ImageToListSampleFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1ImageToListSampleFilter.html"];
MeanFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1MeanFilter.html"];
MeanFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1MeanFilter.html"];
WeightedMeanFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1WeightedMeanFilter.html"];
WeightedMeanFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1WeightedMeanFilter.html"];
CovarianceFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1CovarianceFilter.html"];
WeightedCovarianceFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1WeightedCovarianceFilter.html"];
HistogramToTextureFeaturesFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1HistogramToTextureFeaturesFilter.html"];
HistogramToTextureFeaturesFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1HistogramToTextureFeaturesFilter.html"];
ProcessObject -> ListSampleToHistogramFilter
SampleToSubsampleFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1ListSampleToSubsampleFilter.html"];
NeighborhoodSubsampler [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1NeigborhoodSubsampler.html"];
SampleClassifierFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1SampleClassifierFilter.html"];
ScalarImageToCooccurrenceMatrixFilter [shape=box,URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1ScalarImageToCooccurrenceMatrixFilter.html"];
ProcessObject -> SampleToHistogramFilter
ProcessObject -> MeanFilter
ProcessObject -> MeanFilter
ProcessObject -> WeightedMeanFilter
ProcessObject -> HistogramToTextureFeaturesFilter
ProcessObject -> HistogramToTextureFeaturesFilter
ProcessObject -> CovarianceFilter
ProcessObject -> ImageToListSampleFilter
ProcessObject -> SampleClassifierFilter
ProcessObject -> SampleToSubsampleFilter
ProcessObject -> ScalarImageToCooccurrenceMatrixFilter
SampleToSubsampleFilter -> NeighborhoodSubsampler
MeanFilter -> WeightedMeanFilter
CovarianceFilter -> WeightedCovarianceFilter
}
}
</graphviz>
</graphviz>
== Classifiers (Suggested Design) ==
<graphviz>
digraph G {
Object [URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Object.html"];
FunctionBase [URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1FunctionBase.html"];
MembershipFunctionBase [shape=box, URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1MembershipFunctionBase.html"];
DistanceToCentroidMembershipFunction [shape=box, URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1DistanceToCentroidMembershipFunction.html"];
DistanceMetric  [shape=box, URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1DistanceMetric.html"];
EuclideanDistanceMetric  [shape=box, URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1EuclideanDistanceMetric.html"];
MahalanobisDistanceMetric  [shape=box, URL="http://public.kitware.com/Insight/Doxygen/html/classitk_1_1Statistics_1_1MahalanobisDistanceMetric.html"];
Object -> FunctionBase
FunctionBase -> MembershipFunctionBase
FunctionBase -> DistanceMetric
DistanceMetric -> MahalanobisDistanceMetric
DistanceMetric -> EuclideanDistanceMetric
DistanceMetric -> EuclideanSquaredDistanceMetric
DistanceMetric -> ManhattanDistanceMetric
MembershipFunctionBase -> DistanceToCentroidMembershipFunction
}
</graphviz>
=== 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)

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

Error writing graphviz file to disk.

Data Objects

Error writing graphviz file to disk.

Filters

Error writing graphviz file to disk.

Classifiers (Suggested Design)

Error writing graphviz file to disk.


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)
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