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Guillaume Pasero authoredf52ef084
#ifndef cbTrainSOM_txx#define cbTrainSOM_txx#include "otbTrainDimensionalityReductionApplicationBase.h"#include "otbSOMModel.h"namespace otb{namespace Wrapper{template <class TInputValue, class TOutputValue>voidTrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>::InitSOMParams(){ AddChoice("algorithm.som", "OTB SOM"); SetParameterDescription("algorithm.som", "This group of parameters allows setting SOM parameters. " ); AddParameter(ParameterType_Int, "algorithm.som.dim","Dimension of the map"); SetParameterDescription("algorithm.som.dim","Dimension of the SOM map."); AddParameter(ParameterType_StringList , "algorithm.som.s", "Size"); SetParameterDescription("algorithm.som.s", "Size of the SOM map"); MandatoryOff("algorithm.som.s"); AddParameter(ParameterType_StringList , "algorithm.som.n", "Size Neighborhood"); SetParameterDescription("algorithm.som.n", "Size of the initial neighborhood in the SOM map"); MandatoryOff("algorithm.som.n"); AddParameter(ParameterType_Int, "algorithm.som.sx", "SizeX"); SetParameterDescription("algorithm.som.sx", "X size of the SOM map"); MandatoryOff("algorithm.som.sx"); AddParameter(ParameterType_Int, "algorithm.som.sy", "SizeY"); SetParameterDescription("algorithm.som.sy", "Y size of the SOM map"); MandatoryOff("algorithm.som.sy"); AddParameter(ParameterType_Int, "algorithm.som.nx", "NeighborhoodX"); SetParameterDescription("algorithm.som.nx", "X size of the initial neighborhood in the SOM map"); MandatoryOff("algorithm.som.nx"); AddParameter(ParameterType_Int, "algorithm.som.ny", "NeighborhoodY"); SetParameterDescription("algorithm.som.ny", "Y size of the initial neighborhood in the SOM map"); MandatoryOff("algorithm.som.nx"); AddParameter(ParameterType_Int, "algorithm.som.ni", "NumberIteration"); SetParameterDescription("algorithm.som.ni", "Number of iterations for SOM learning"); MandatoryOff("algorithm.som.ni"); AddParameter(ParameterType_Float, "algorithm.som.bi", "BetaInit"); SetParameterDescription("algorithm.som.bi", "Initial learning coefficient"); MandatoryOff("algorithm.som.bi"); AddParameter(ParameterType_Float, "algorithm.som.bf", "BetaFinal"); SetParameterDescription("algorithm.som.bf", "Final learning coefficient"); MandatoryOff("algorithm.som.bf"); AddParameter(ParameterType_Float, "algorithm.som.iv", "InitialValue"); SetParameterDescription("algorithm.som.iv", "Maximum initial neuron weight"); MandatoryOff("algorithm.som.iv"); SetDefaultParameterInt("algorithm.som.sx", 32); SetDefaultParameterInt("algorithm.som.sy", 32); SetDefaultParameterInt("algorithm.som.nx", 10); SetDefaultParameterInt("algorithm.som.ny", 10); SetDefaultParameterInt("algorithm.som.ni", 5);7172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140
SetDefaultParameterFloat("algorithm.som.bi", 1.0);
SetDefaultParameterFloat("algorithm.som.bf", 0.1);
SetDefaultParameterFloat("algorithm.som.iv", 10.0);
}
template <class TInputValue, class TOutputValue>
void
TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
::BeforeTrainSOM(typename ListSampleType::Pointer trainingListSample,
std::string modelPath)
{
//typedef SOMMap<TInputValue,itk::Statistics::EuclideanDistanceMetric<itk::VariableLengthVector<TInputValue>>, 2> Map2DType;
//typedef SOMMap<TInputValue,itk::Statistics::EuclideanDistanceMetric<itk::VariableLengthVector<TInputValue>>, 3> Map3DType;
//typedef SOMMap<TInputValue,itk::Statistics::EuclideanDistanceMetric<itk::VariableLengthVector<TInputValue>>, 4> Map4DType;
//typedef SOMMap<TInputValue,itk::Statistics::EuclideanDistanceMetric<itk::VariableLengthVector<TInputValue>>, 5> Map5DType;
typedef otb::SOMModel<InputValueType, 2> SOM2DModelType;
typedef otb::SOMModel<InputValueType, 3> SOM3DModelType;
typedef otb::SOMModel<InputValueType, 4> SOM4DModelType;
typedef otb::SOMModel<InputValueType, 5> SOM5DModelType;
int SomDim = GetParameterInt("algorithm.som.dim");
std::cout << SomDim << std::endl;
if(SomDim == 2)
{
TrainSOM<SOM2DModelType >(trainingListSample,modelPath);
}
if(SomDim == 3)
{
TrainSOM<SOM3DModelType >(trainingListSample,modelPath);
}
if(SomDim == 4)
{
TrainSOM<SOM4DModelType >(trainingListSample,modelPath);
}
if(SomDim == 5)
{
TrainSOM<SOM5DModelType >(trainingListSample,modelPath);
}
if(SomDim > 5 || SomDim < 2)
{
std::cerr << "k : invalid dimension" << std::endl;
}
}
template <class TInputValue, class TOutputValue>
template <typename somchoice>
void TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
::TrainSOM(typename ListSampleType::Pointer trainingListSample,std::string modelPath)
{
using TemplateEstimatorType = typename somchoice::EstimatorType;
typename somchoice::Pointer dimredTrainer = somchoice::New();
unsigned int dim = dimredTrainer->GetDimension();
std::cout << dim << std::endl;
dimredTrainer->SetNumberOfIterations(GetParameterInt("algorithm.som.ni"));
dimredTrainer->SetBetaInit(GetParameterFloat("algorithm.som.bi"));
dimredTrainer->SetWriteMap(true);
dimredTrainer->SetBetaEnd(GetParameterFloat("algorithm.som.bf"));
dimredTrainer->SetMaxWeight(GetParameterFloat("algorithm.som.iv"));
typename TemplateEstimatorType::SizeType size;
std::vector<std::basic_string<char>> s= GetParameterStringList("algorithm.som.s");
for (unsigned int i=0; i<dim; i++){
size[i]=std::stoi(s[i]);
}
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dimredTrainer->SetMapSize(size);
typename TemplateEstimatorType::SizeType radius;
std::vector<std::basic_string<char>> n= GetParameterStringList("algorithm.som.n");
for (unsigned int i=0; i<dim; i++){
radius[i]=std::stoi(n[i]);
}
dimredTrainer->SetNeighborhoodSizeInit(radius);
dimredTrainer->SetListSample(trainingListSample);
dimredTrainer->Train();
dimredTrainer->Save(modelPath);
}
} //end namespace wrapper
} //end namespace otb
#endif