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Viewing changes to Code/Numerics/NeuralNetworks/itkTrainingFunctionBase.h

  • Committer: Bazaar Package Importer
  • Author(s): Steve M. Robbins
  • Date: 2008-05-31 12:07:29 UTC
  • mfrom: (3.1.3 intrepid)
  • Revision ID: james.westby@ubuntu.com-20080531120729-7g7layn480le43ko
Tags: 3.6.0-3
http://bugs.debian.org/478500
http://bugs.debian.org/478940
http://bugs.debian.org/483312
* debian/patches/gccxml-workaround.patch: New.  Work around gccxml issue
  with #include_next; c.f. http://www.gccxml.org/Bug/view.php?id=7134.  
* debian/patches/gcc43.patch: include <cstring> in itkNeighbourhood.h.
  This only showed up in the tcl wrapping step.

* Above two entries fix FTBFS for GCC 4.3-based systems.
  Closes: #478500.

* debian/patches/sharedforward.patch: New.  Ensure that linux/sparc
  systems are not also configured as a SUN sparc system, which requires
  SUN header sys/isa_defs.h.  Closes: #478940, #483312.

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Code/Numerics/NeuralNetworks/itkTrainingFunctionBase.h
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  Program:   Insight Segmentation & Registration Toolkit
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  Module:    $RCSfile: itkTrainingFunctionBase.h,v $
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  Language:  C++
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  Date:      $Date: 2006/04/18 11:23:29 $
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  Version:   $Revision: 1.6 $
 
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  Date:      $Date: 2007-08-17 13:10:57 $
 
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  Version:   $Revision: 1.7 $
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  Copyright (c) Insight Software Consortium. All rights reserved.
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  See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
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namespace Statistics
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{
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template<class TSample, class TOutput, class ScalarType>
 
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template<class TSample, class TTargetVector, class ScalarType>
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class TrainingFunctionBase : public LightProcessObject
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{
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public:
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  typedef ScalarType ValueType;
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  typedef typename TSample::MeasurementVectorType VectorType;
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  typedef typename TOutput::MeasurementVectorType OutputVectorType;
 
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  typedef typename TTargetVector::MeasurementVectorType OutputVectorType;
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  typedef Array<ValueType> InternalVectorType;
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  typedef std::vector<VectorType> InputSampleVectorType;
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  //typedef MeanSquaredErrorFunction<InternalVectorType, ScalarType> DefaultPerformanceType;
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  void SetTrainingSamples(TSample* samples);
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  void SetTargetValues(TOutput* targets);
 
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  void SetTargetValues(TTargetVector* targets);
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  void SetLearningRate(ValueType);
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  ValueType GetLearningRate();
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  void SetPerformanceFunction(PerformanceFunctionType* f);
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  virtual void
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  Train(NetworkType* itkNotUsed(net), TSample* itkNotUsed(samples), TOutput* itkNotUsed(targets))
 
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  virtual void Train(NetworkType* itkNotUsed(net), TSample* itkNotUsed(samples), TTargetVector* itkNotUsed(targets))
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    {
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    // not implemented
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    };
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    }
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  inline OutputVectorType
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  targetconverter(typename TOutput::MeasurementVectorType v)
 
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  targetconverter(typename TTargetVector::MeasurementVectorType v)
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    {
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    OutputVectorType temp;
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  virtual void PrintSelf( std::ostream& os, Indent indent ) const;
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  TSample*                m_TrainingSamples;// original samples
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  TOutput*                m_SampleTargets;  // original samples
 
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  TTargetVector*                m_SampleTargets;  // original samples
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  InputSampleVectorType   m_InputSamples;   // itk::vectors
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  OutputSampleVectorType  m_Targets;        // itk::vectors
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  long                    m_Iterations;