diff --git a/Modules/Applications/AppDimensionalityReduction/app/otbTrainDimensionalityReduction.cxx b/Modules/Applications/AppDimensionalityReduction/app/otbTrainDimensionalityReduction.cxx
index dea0331ceacd22cb5966e6d86b080e5bb45b56cc..91c600722aa6e79b7d6a30aa6123f201cab8d544 100644
--- a/Modules/Applications/AppDimensionalityReduction/app/otbTrainDimensionalityReduction.cxx
+++ b/Modules/Applications/AppDimensionalityReduction/app/otbTrainDimensionalityReduction.cxx
@@ -59,14 +59,14 @@ private:
SetParameterDescription("io.vd", "Input geometries used for training (note : all geometries from the layer will be used)");
AddParameter(ParameterType_OutputFilename, "io.out", "Output model");
- SetParameterDescription("io.out", "Output file containing the model estimated (.txt format).");
+ SetParameterDescription("io.out", "Output file containing the estimated model (.txt format).");
AddParameter(ParameterType_InputFilename, "io.stats", "Input XML image statistics file");
MandatoryOff("io.stats");
SetParameterDescription("io.stats", "XML file containing mean and variance of each feature.");
- AddParameter(ParameterType_StringList, "feat", "Field names to be calculated."); //
+ AddParameter(ParameterType_StringList, "feat", "Field names to be used for training."); //
SetParameterDescription("feat","List of field names in the input vector data used as features for training."); //
Superclass::DoInit();
diff --git a/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainAutoencoder.txx b/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainAutoencoder.txx
index dc787d5d15fe012e50026adb8a3ae1e20b086dde..b12a2517e59594544d44589dce03e609abfe384b 100644
--- a/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainAutoencoder.txx
+++ b/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainAutoencoder.txx
@@ -16,87 +16,87 @@ TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
{
- AddChoice("model.tiedautoencoder", "Shark Tied Autoencoder");
- AddChoice("model.autoencoder", "Shark Autoencoder");
- SetParameterDescription("model.autoencoder",
+ AddChoice("algorithm.tiedautoencoder", "Shark Tied Autoencoder");
+ AddChoice("algorithm.autoencoder", "Shark Autoencoder");
+ SetParameterDescription("algorithm.autoencoder",
"This group of parameters allows setting Shark autoencoder parameters. "
);
//Tied Autoencoder
- AddParameter(ParameterType_Choice, "model.autoencoder.istied",
+ AddParameter(ParameterType_Choice, "algorithm.autoencoder.istied",
"tied weighth <tied/untied>");
SetParameterDescription(
- "model.autoencoder.istied",
+ "algorithm.autoencoder.istied",
"Parameter that determine if the weights are tied or not <tied/untied>");
- AddChoice("model.autoencoder.istied.yes","Tied weigths");
- AddChoice("model.autoencoder.istied.no","Untied weights");
+ AddChoice("algorithm.autoencoder.istied.yes","Tied weigths");
+ AddChoice("algorithm.autoencoder.istied.no","Untied weights");
//Number Of Iterations
- AddParameter(ParameterType_Int, "model.autoencoder.nbiter",
+ AddParameter(ParameterType_Int, "algorithm.autoencoder.nbiter",
"Maximum number of iterations during training");
- SetParameterInt("model.autoencoder.nbiter",100, false);
+ SetParameterInt("algorithm.autoencoder.nbiter",100, false);
SetParameterDescription(
- "model.autoencoder.nbiter",
+ "algorithm.autoencoder.nbiter",
"The maximum number of iterations used during training.");
- AddParameter(ParameterType_Int, "model.autoencoder.nbiterfinetuning",
+ AddParameter(ParameterType_Int, "algorithm.autoencoder.nbiterfinetuning",
"Maximum number of iterations during training");
- SetParameterInt("model.autoencoder.nbiterfinetuning",0, false);
+ SetParameterInt("algorithm.autoencoder.nbiterfinetuning",0, false);
SetParameterDescription(
- "model.autoencoder.nbiterfinetuning",
+ "algorithm.autoencoder.nbiterfinetuning",
"The maximum number of iterations used during fine tuning of the whole network.");
- AddParameter(ParameterType_Float, "model.autoencoder.epsilon",
+ AddParameter(ParameterType_Float, "algorithm.autoencoder.epsilon",
" ");
- SetParameterFloat("model.autoencoder.epsilon",0, false);
+ SetParameterFloat("algorithm.autoencoder.epsilon",0, false);
SetParameterDescription(
- "model.autoencoder.epsilon",
+ "algorithm.autoencoder.epsilon",
" ");
- AddParameter(ParameterType_Float, "model.autoencoder.initfactor",
+ AddParameter(ParameterType_Float, "algorithm.autoencoder.initfactor",
" ");
- SetParameterFloat("model.autoencoder.initfactor",1, false);
+ SetParameterFloat("algorithm.autoencoder.initfactor",1, false);
SetParameterDescription(
- "model.autoencoder.initfactor", "parameter that control the weight initialization of the autoencoder");
+ "algorithm.autoencoder.initfactor", "parameter that control the weight initialization of the autoencoder");
//Number Of Hidden Neurons
- AddParameter(ParameterType_StringList , "model.autoencoder.nbneuron", "Size");
- /*AddParameter(ParameterType_Int, "model.autoencoder.nbneuron",
+ AddParameter(ParameterType_StringList , "algorithm.autoencoder.nbneuron", "Size");
+ /*AddParameter(ParameterType_Int, "algorithm.autoencoder.nbneuron",
"Number of neurons in the hidden layer");
- SetParameterInt("model.autoencoder.nbneuron",10, false);*/
+ SetParameterInt("algorithm.autoencoder.nbneuron",10, false);*/
SetParameterDescription(
- "model.autoencoder.nbneuron",
+ "algorithm.autoencoder.nbneuron",
"The number of neurons in each hidden layer.");
//Regularization
- AddParameter(ParameterType_StringList, "model.autoencoder.regularization", "Strength of the regularization");
- SetParameterDescription("model.autoencoder.regularization",
- "Strength of the L2 regularization used during training");
+ AddParameter(ParameterType_StringList, "algorithm.autoencoder.regularization", "Strength of the regularization");
+ SetParameterDescription("algorithm.autoencoder.regularization",
+ "Strength of the L2 regularization used during training");
//Noise strength
- AddParameter(ParameterType_StringList, "model.autoencoder.noise", "Strength of the noise");
- SetParameterDescription("model.autoencoder.noise",
- "Strength of the noise");
+ AddParameter(ParameterType_StringList, "algorithm.autoencoder.noise", "Strength of the noise");
+ SetParameterDescription("algorithm.autoencoder.noise",
+ "Strength of the noise");
// Sparsity parameter
- AddParameter(ParameterType_StringList, "model.autoencoder.rho", "Sparsity parameter");
- SetParameterDescription("model.autoencoder.rho",
- "Sparsity parameter");
+ AddParameter(ParameterType_StringList, "algorithm.autoencoder.rho", "Sparsity parameter");
+ SetParameterDescription("algorithm.autoencoder.rho",
+ "Sparsity parameter");
// Sparsity regularization strength
- AddParameter(ParameterType_StringList, "model.autoencoder.beta", "Sparsity regularization strength");
- SetParameterDescription("model.autoencoder.beta",
- "Sparsity regularization strength");
+ AddParameter(ParameterType_StringList, "algorithm.autoencoder.beta", "Sparsity regularization strength");
+ SetParameterDescription("algorithm.autoencoder.beta",
+ "Sparsity regularization strength");
- AddParameter(ParameterType_OutputFilename, "model.autoencoder.learningcurve", "Learning curve");
- SetParameterDescription("model.autoencoder.learningcurve", "Learning error values");
- MandatoryOff("model.autoencoder.learningcurve");
+ AddParameter(ParameterType_OutputFilename, "algorithm.autoencoder.learningcurve", "Learning curve");
+ SetParameterDescription("algorithm.autoencoder.learningcurve", "Learning error values");
+ MandatoryOff("algorithm.autoencoder.learningcurve");
}
@@ -107,10 +107,10 @@ TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
::BeforeTrainAutoencoder(typename ListSampleType::Pointer trainingListSample,
std::string modelPath)
{
- std::string TiedWeigth = GetParameterString("model.autoencoder.istied");
- std::cout << TiedWeigth << std::endl;
+ std::string TiedWeigth = GetParameterString("algorithm.autoencoder.istied");
+ std::cout << TiedWeigth << std::endl;
- if(TiedWeigth == "no")
+ if(TiedWeigth == "no")
{
TrainAutoencoder<AutoencoderModelType>(trainingListSample,modelPath);
}
@@ -138,11 +138,11 @@ void TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>::Trai
itk::Array<float> regularization;
itk::Array<float> rho;
itk::Array<float> beta;
- std::vector<std::basic_string<char>> s_nbneuron= GetParameterStringList("model.autoencoder.nbneuron");
- std::vector<std::basic_string<char>> s_noise= GetParameterStringList("model.autoencoder.noise");
- std::vector<std::basic_string<char>> s_regularization= GetParameterStringList("model.autoencoder.regularization");
- std::vector<std::basic_string<char>> s_rho= GetParameterStringList("model.autoencoder.rho");
- std::vector<std::basic_string<char>> s_beta= GetParameterStringList("model.autoencoder.beta");
+ std::vector<std::basic_string<char>> s_nbneuron= GetParameterStringList("algorithm.autoencoder.nbneuron");
+ std::vector<std::basic_string<char>> s_noise= GetParameterStringList("algorithm.autoencoder.noise");
+ std::vector<std::basic_string<char>> s_regularization= GetParameterStringList("algorithm.autoencoder.regularization");
+ std::vector<std::basic_string<char>> s_rho= GetParameterStringList("algorithm.autoencoder.rho");
+ std::vector<std::basic_string<char>> s_beta= GetParameterStringList("algorithm.autoencoder.beta");
nb_neuron.SetSize(s_nbneuron.size());
noise.SetSize(s_nbneuron.size());
regularization.SetSize(s_nbneuron.size());
@@ -156,22 +156,22 @@ void TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>::Trai
beta[i]=std::stof(s_beta[i]);
}
dimredTrainer->SetNumberOfHiddenNeurons(nb_neuron);
- dimredTrainer->SetNumberOfIterations(GetParameterInt("model.autoencoder.nbiter"));
- dimredTrainer->SetNumberOfIterationsFineTuning(GetParameterInt("model.autoencoder.nbiterfinetuning"));
- dimredTrainer->SetEpsilon(GetParameterFloat("model.autoencoder.epsilon"));
- dimredTrainer->SetInitFactor(GetParameterFloat("model.autoencoder.initfactor"));
+ dimredTrainer->SetNumberOfIterations(GetParameterInt("algorithm.autoencoder.nbiter"));
+ dimredTrainer->SetNumberOfIterationsFineTuning(GetParameterInt("algorithm.autoencoder.nbiterfinetuning"));
+ dimredTrainer->SetEpsilon(GetParameterFloat("algorithm.autoencoder.epsilon"));
+ dimredTrainer->SetInitFactor(GetParameterFloat("algorithm.autoencoder.initfactor"));
dimredTrainer->SetRegularization(regularization);
dimredTrainer->SetNoise(noise);
dimredTrainer->SetRho(rho);
dimredTrainer->SetBeta(beta);
dimredTrainer->SetWriteWeights(true);
- if (HasValue("model.autoencoder.learningcurve") && IsParameterEnabled("model.autoencoder.learningcurve"))
- {
- std::cout << "yo" << std::endl;
- dimredTrainer->SetWriteLearningCurve(true);
- dimredTrainer->SetLearningCurveFileName(GetParameterString("model.autoencoder.learningcurve"));
- }
+ if (HasValue("algorithm.autoencoder.learningcurve") && IsParameterEnabled("algorithm.autoencoder.learningcurve"))
+ {
+ std::cout << "yo" << std::endl;
+ dimredTrainer->SetWriteLearningCurve(true);
+ dimredTrainer->SetLearningCurveFileName(GetParameterString("algorithm.autoencoder.learningcurve"));
+ }
dimredTrainer->SetInputListSample(trainingListSample);
std::cout << "before train" << std::endl;
diff --git a/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainPCA.txx b/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainPCA.txx
index 003ebb1ccf510e81c9e848452ae4a9e4a16d793b..66a87aaade278bb00615545fdd5cd010035214d4 100644
--- a/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainPCA.txx
+++ b/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainPCA.txx
@@ -16,18 +16,18 @@ TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
{
- AddChoice("model.pca", "Shark PCA");
- SetParameterDescription("model.pca",
+ AddChoice("algorithm.pca", "Shark PCA");
+ SetParameterDescription("algorithm.pca",
"This group of parameters allows setting Shark PCA parameters. "
);
//Output Dimension
- AddParameter(ParameterType_Int, "model.pca.dim",
+ AddParameter(ParameterType_Int, "algorithm.pca.dim",
"Dimension of the output of the pca transformation");
- SetParameterInt("model.pca.dim",10, false);
+ SetParameterInt("algorithm.pca.dim",10, false);
SetParameterDescription(
- "model.pca.dim",
+ "algorithm.pca.dim",
"Dimension of the output of the pca transformation.");
@@ -38,7 +38,7 @@ void TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
::TrainPCA(typename ListSampleType::Pointer trainingListSample,std::string modelPath)
{
typename PCAModelType::Pointer dimredTrainer = PCAModelType::New();
- dimredTrainer->SetDimension(GetParameterInt("model.pca.dim"));
+ dimredTrainer->SetDimension(GetParameterInt("algorithm.pca.dim"));
dimredTrainer->SetInputListSample(trainingListSample);
dimredTrainer->SetWriteEigenvectors(true);
dimredTrainer->Train();
diff --git a/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainSOM.txx b/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainSOM.txx
index 61975c9708c6ef8b112e10f20882fb60b6d7da23..8a05ecccd7ada09798e90b245a62c093d1995706 100644
--- a/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainSOM.txx
+++ b/Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainSOM.txx
@@ -14,62 +14,62 @@ TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
::InitSOMParams()
{
- AddChoice("model.som", "OTB SOM");
- SetParameterDescription("model.som",
+ AddChoice("algorithm.som", "OTB SOM");
+ SetParameterDescription("algorithm.som",
"This group of parameters allows setting SOM parameters. "
);
- AddParameter(ParameterType_Int, "model.som.dim","Dimension of the map");
- SetParameterDescription("model.som.dim","Dimension of the SOM map.");
+ AddParameter(ParameterType_Int, "algorithm.som.dim","Dimension of the map");
+ SetParameterDescription("algorithm.som.dim","Dimension of the SOM map.");
- AddParameter(ParameterType_StringList , "model.som.s", "Size");
- SetParameterDescription("model.som.s", "Size of the SOM map");
- MandatoryOff("model.som.s");
+ AddParameter(ParameterType_StringList , "algorithm.som.s", "Size");
+ SetParameterDescription("algorithm.som.s", "Size of the SOM map");
+ MandatoryOff("algorithm.som.s");
- AddParameter(ParameterType_StringList , "model.som.n", "Size Neighborhood");
- SetParameterDescription("model.som.n", "Size of the initial neighborhood in the SOM map");
- MandatoryOff("model.som.n");
+ 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, "model.som.sx", "SizeX");
- SetParameterDescription("model.som.sx", "X size of the SOM map");
- MandatoryOff("model.som.sx");
+ AddParameter(ParameterType_Int, "algorithm.som.sx", "SizeX");
+ SetParameterDescription("algorithm.som.sx", "X size of the SOM map");
+ MandatoryOff("algorithm.som.sx");
- AddParameter(ParameterType_Int, "model.som.sy", "SizeY");
- SetParameterDescription("model.som.sy", "Y size of the SOM map");
- MandatoryOff("model.som.sy");
+ AddParameter(ParameterType_Int, "algorithm.som.sy", "SizeY");
+ SetParameterDescription("algorithm.som.sy", "Y size of the SOM map");
+ MandatoryOff("algorithm.som.sy");
- AddParameter(ParameterType_Int, "model.som.nx", "NeighborhoodX");
- SetParameterDescription("model.som.nx", "X size of the initial neighborhood in the SOM map");
- MandatoryOff("model.som.nx");
+ 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, "model.som.ny", "NeighborhoodY");
- SetParameterDescription("model.som.ny", "Y size of the initial neighborhood in the SOM map");
- MandatoryOff("model.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, "model.som.ni", "NumberIteration");
- SetParameterDescription("model.som.ni", "Number of iterations for SOM learning");
- MandatoryOff("model.som.ni");
+ AddParameter(ParameterType_Int, "algorithm.som.ni", "NumberIteration");
+ SetParameterDescription("algorithm.som.ni", "Number of iterations for SOM learning");
+ MandatoryOff("algorithm.som.ni");
- AddParameter(ParameterType_Float, "model.som.bi", "BetaInit");
- SetParameterDescription("model.som.bi", "Initial learning coefficient");
- MandatoryOff("model.som.bi");
+ AddParameter(ParameterType_Float, "algorithm.som.bi", "BetaInit");
+ SetParameterDescription("algorithm.som.bi", "Initial learning coefficient");
+ MandatoryOff("algorithm.som.bi");
- AddParameter(ParameterType_Float, "model.som.bf", "BetaFinal");
- SetParameterDescription("model.som.bf", "Final learning coefficient");
- MandatoryOff("model.som.bf");
+ AddParameter(ParameterType_Float, "algorithm.som.bf", "BetaFinal");
+ SetParameterDescription("algorithm.som.bf", "Final learning coefficient");
+ MandatoryOff("algorithm.som.bf");
- AddParameter(ParameterType_Float, "model.som.iv", "InitialValue");
- SetParameterDescription("model.som.iv", "Maximum initial neuron weight");
- MandatoryOff("model.som.iv");
+ AddParameter(ParameterType_Float, "algorithm.som.iv", "InitialValue");
+ SetParameterDescription("algorithm.som.iv", "Maximum initial neuron weight");
+ MandatoryOff("algorithm.som.iv");
- SetDefaultParameterInt("model.som.sx", 32);
- SetDefaultParameterInt("model.som.sy", 32);
- SetDefaultParameterInt("model.som.nx", 10);
- SetDefaultParameterInt("model.som.ny", 10);
- SetDefaultParameterInt("model.som.ni", 5);
- SetDefaultParameterFloat("model.som.bi", 1.0);
- SetDefaultParameterFloat("model.som.bf", 0.1);
- SetDefaultParameterFloat("model.som.iv", 10.0);
+ 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);
}
@@ -80,10 +80,10 @@ TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
::BeforeTrainSOM(typename ListSampleType::Pointer trainingListSample,
std::string modelPath)
{
- int SomDim = GetParameterInt("model.som.dim");
- std::cout << SomDim << std::endl;
+ int SomDim = GetParameterInt("algorithm.som.dim");
+ std::cout << SomDim << std::endl;
- if(SomDim == 2)
+ if(SomDim == 2)
{
TrainSOM<SOM2DModelType >(trainingListSample,modelPath);
}
@@ -118,23 +118,23 @@ void TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
typename somchoice::Pointer dimredTrainer = somchoice::New();
unsigned int dim = dimredTrainer->GetDimension();
std::cout << dim << std::endl;
- dimredTrainer->SetNumberOfIterations(GetParameterInt("model.som.ni"));
- dimredTrainer->SetBetaInit(GetParameterFloat("model.som.bi"));
+ dimredTrainer->SetNumberOfIterations(GetParameterInt("algorithm.som.ni"));
+ dimredTrainer->SetBetaInit(GetParameterFloat("algorithm.som.bi"));
dimredTrainer->SetWriteMap(true);
- dimredTrainer->SetBetaEnd(GetParameterFloat("model.som.bf"));
- dimredTrainer->SetMaxWeight(GetParameterFloat("model.som.iv"));
+ dimredTrainer->SetBetaEnd(GetParameterFloat("algorithm.som.bf"));
+ dimredTrainer->SetMaxWeight(GetParameterFloat("algorithm.som.iv"));
typename TemplateEstimatorType::SizeType size;
- std::vector<std::basic_string<char>> s= GetParameterStringList("model.som.s");
+ std::vector<std::basic_string<char>> s= GetParameterStringList("algorithm.som.s");
for (int i=0; i<dim; i++){
size[i]=std::stoi(s[i]);
}
dimredTrainer->SetMapSize(size);
typename TemplateEstimatorType::SizeType radius;
- std::vector<std::basic_string<char>> n= GetParameterStringList("model.som.n");
- for (int i=0; i<dim; i++){
- radius[i]=std::stoi(n[i]);
- }
+ std::vector<std::basic_string<char>> n= GetParameterStringList("algorithm.som.n");
+ for (int i=0; i<dim; i++){
+ radius[i]=std::stoi(n[i]);
+ }
dimredTrainer->SetNeighborhoodSizeInit(radius);
dimredTrainer->SetListSample(trainingListSample);
dimredTrainer->Train();
diff --git a/Modules/Applications/AppDimensionalityReduction/include/otbTrainDimensionalityReductionApplicationBase.txx b/Modules/Applications/AppDimensionalityReduction/include/otbTrainDimensionalityReductionApplicationBase.txx
index 04065aa97c97c045f798a6bd28fb6ff496d1fbb4..77a3f477cc4ab1c331e26592bed5e883f99e3bf4 100644
--- a/Modules/Applications/AppDimensionalityReduction/include/otbTrainDimensionalityReductionApplicationBase.txx
+++ b/Modules/Applications/AppDimensionalityReduction/include/otbTrainDimensionalityReductionApplicationBase.txx
@@ -45,8 +45,8 @@ TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
AddDocTag(Tags::Learning);
// main choice parameter that will contain all dimensionality reduction options
- AddParameter(ParameterType_Choice, "model", "model to use for the training");
- SetParameterDescription("model", "Choice of the dimensionality reduction model to use for the training.");
+ AddParameter(ParameterType_Choice, "algorithm", "algorithm to use for the training");
+ SetParameterDescription("algorithm", "Choice of the dimensionality reduction algorithm to use for the training.");
InitSOMParams();
@@ -73,10 +73,10 @@ TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
{
// get the name of the chosen machine learning model
- const std::string modelName = GetParameterString("model");
- // call specific train function
+ const std::string modelName = GetParameterString("algorithm");
+ // call specific train function
- if(modelName == "som")
+ if(modelName == "som")
{
BeforeTrainSOM(trainingListSample,modelPath);
}