diff --git a/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainSOM.txx b/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainSOM.txx
index 17067cc4bddbc02411d13a20a5cfd2173894faec..51cdd9e1acf1ededba9b54f93260e10c16962572 100644
--- a/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainSOM.txx
+++ b/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainSOM.txx
@@ -34,62 +34,47 @@ TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
{
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);
- SetDefaultParameterFloat("algorithm.som.bi", 1.0);
- SetDefaultParameterFloat("algorithm.som.bf", 0.1);
- SetDefaultParameterFloat("algorithm.som.iv", 10.0);
-
-
+ "This group of parameters allows setting SOM parameters. ");
+
+ AddParameter(ParameterType_StringList , "algorithm.som.s", "Map size");
+ SetParameterDescription("algorithm.som.s", "Sizes of the SOM map (one per "
+ "dimension). For instance, [12;15] means a 2D map of size 12x15. Support"
+ "2D to 5D maps.");
+ MandatoryOff("algorithm.som.s");
+
+ AddParameter(ParameterType_StringList , "algorithm.som.n", "Neighborhood sizes");
+ SetParameterDescription("algorithm.som.n", "Sizes of the initial neighborhood "
+ "in the SOM map (one per dimension). The number of sizes should be the same"
+ " as the map sizes");
+ MandatoryOff("algorithm.som.n");
+
+ 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");
+
+ std::vector<std::string> size(2, std::string("10"));
+ std::vector<std::string> radius(2, std::string("3"));
+ SetParameterStringList("algorithm.som.s", size, false);
+ SetParameterStringList("algorithm.som.n", radius, false);
+ DisableParameter("algorithm.som.s");
+ DisableParameter("algorithm.som.n");
+
+ SetDefaultParameterInt("algorithm.som.ni", 5);
+ SetDefaultParameterFloat("algorithm.som.bi", 1.0);
+ SetDefaultParameterFloat("algorithm.som.bf", 0.1);
+ SetDefaultParameterFloat("algorithm.som.iv", 10.0);
}
template <class TInputValue, class TOutputValue>
@@ -98,8 +83,8 @@ TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
::BeforeTrainSOM(typename ListSampleType::Pointer trainingListSample,
std::string modelPath)
{
- int SomDim = GetParameterInt("algorithm.som.dim");
- std::cout << SomDim << std::endl;
+ std::vector<std::string> s = GetParameterStringList("algorithm.som.s");
+ int SomDim = s.size();
if(SomDim == 2)
{
@@ -123,10 +108,11 @@ TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
{
typedef otb::SOMModel<InputValueType, 5> SOM5DModelType;
TrainSOM<SOM5DModelType >(trainingListSample,modelPath);
- }
+ }
if(SomDim > 5 || SomDim < 2)
{
- std::cerr << "k : invalid dimension" << std::endl;
+ otbAppLogFATAL(<< "Invalid number of dimensions : " << SomDim <<
+ ". Only support 2, 3, 4 or 5 dimensions");
}
}
@@ -136,26 +122,28 @@ void TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
::TrainSOM(typename ListSampleType::Pointer trainingListSample,std::string modelPath)
{
typename TSOM::Pointer dimredTrainer = TSOM::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 TSOM::SizeType size;
- std::vector<std::basic_string<char>> s= GetParameterStringList("algorithm.som.s");
- for (unsigned int i=0; i<dim; i++)
+ std::vector<std::string> s = GetParameterStringList("algorithm.som.s");
+ for (unsigned int i=0; i<s.size(); i++)
{
- size[i]=std::stoi(s[i]);
+ size[i]=boost::lexical_cast<unsigned int>(s[i]);
}
dimredTrainer->SetMapSize(size);
typename TSOM::SizeType radius;
- std::vector<std::basic_string<char>> n= GetParameterStringList("algorithm.som.n");
- for (unsigned int i=0; i<dim; i++)
+ std::vector<std::string> n = GetParameterStringList("algorithm.som.n");
+ if (n.size() != s.size())
+ {
+ otbAppLogFATAL(<< "Wrong number of neighborhood radii : expected "<< s.size() << " ; got "<< n.size());
+ }
+ for (unsigned int i=0; i < n.size(); i++)
{
- radius[i]=std::stoi(n[i]);
+ radius[i]=boost::lexical_cast<unsigned int>(n[i]);
}
dimredTrainer->SetNeighborhoodSizeInit(radius);
dimredTrainer->SetInputListSample(trainingListSample);