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/*=========================================================================
Program: ORFEO Toolbox
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
See OTBCopyright.txt for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef cbLearningApplicationBaseDR_txx
#define cbLearningApplicationBaseDR_txx
#include "cbLearningApplicationBaseDR.h"
namespace otb
{
namespace Wrapper
{
template <class TInputValue, class TOutputValue>
cbLearningApplicationBaseDR<TInputValue,TOutputValue>
::cbLearningApplicationBaseDR()
{
}
template <class TInputValue, class TOutputValue>
cbLearningApplicationBaseDR<TInputValue,TOutputValue>
::~cbLearningApplicationBaseDR()
{
ModelFactoryType::CleanFactories();
}
template <class TInputValue, class TOutputValue>
void
cbLearningApplicationBaseDR<TInputValue,TOutputValue>
::DoInit()
{
AddDocTag(Tags::Learning);
// main choice parameter that will contain all machine learning options
AddParameter(ParameterType_Choice, "model", "moddel to use for the training");
SetParameterDescription("model", "Choice of the dimensionality reduction model to use for the training.");
#ifdef OTB_USE_SHARK
InitAutoencoderParams();
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InitPCAParams();
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#endif
}
template <class TInputValue, class TOutputValue>
void
cbLearningApplicationBaseDR<TInputValue,TOutputValue>
::Reduce(typename ListSampleType::Pointer validationListSample,std::string modelPath)
{/*
// Setup fake reporter
RGBAPixelConverter<int,int>::Pointer dummyFilter =
RGBAPixelConverter<int,int>::New();
dummyFilter->SetProgress(0.0f);
this->AddProcess(dummyFilter,"Classify...");
dummyFilter->InvokeEvent(itk::StartEvent());
// load a machine learning model from file and predict the input sample list
ModelPointerType model = ModelFactoryType::CreateMachineLearningModel(modelPath,
ModelFactoryType::ReadMode);
if (model.IsNull())
{
otbAppLogFATAL(<< "Error when loading model " << modelPath);
}
model->Load(modelPath);
model->SetRegressionMode(this->m_RegressionFlag);
model->SetInputListSample(validationListSample);
model->SetTargetListSample(predictedList);
model->PredictAll();
// update reporter
dummyFilter->UpdateProgress(1.0f);
dummyFilter->InvokeEvent(itk::EndEvent());*/
}
template <class TInputValue, class TOutputValue>
void
cbLearningApplicationBaseDR<TInputValue,TOutputValue>
::Train(typename ListSampleType::Pointer trainingListSample,
std::string modelPath)
{
// get the name of the chosen machine learning model
const std::string modelName = GetParameterString("model");
// call specific train function
if(modelName == "autoencoder")
{
#ifdef OTB_USE_SHARK
TrainAutoencoder<AutoencoderModelType>(trainingListSample,modelPath);
#else
otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration.");
#endif
}
if(modelName == "tiedautoencoder")
{
#ifdef OTB_USE_SHARK
TrainAutoencoder<TiedAutoencoderModelType>(trainingListSample,modelPath);
#else
otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration.");
#endif
}
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if(modelName == "pca")
{
#ifdef OTB_USE_SHARK
TrainPCA(trainingListSample,modelPath);
#else
otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration.");
#endif
}