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otbSharkKMeansMachineLearningModel.hxx 7.86 KiB
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/*
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 * Copyright (C) 2005-2019 Centre National d'Etudes Spatiales (CNES)
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 *
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 * This file is part of Orfeo Toolbox
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 *
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 *     https://www.orfeo-toolbox.org/
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 *
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 * Licensed under the Apache License, Version 2.0 (the "License");
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 * you may not use this file except in compliance with the License.
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 * You may obtain a copy of the License at
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 *
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 *     http://www.apache.org/licenses/LICENSE-2.0
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 *
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 * Unless required by applicable law or agreed to in writing, software
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 * distributed under the License is distributed on an "AS IS" BASIS,
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 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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 * See the License for the specific language governing permissions and
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 * limitations under the License.
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 */
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#ifndef otbSharkKMeansMachineLearningModel_hxx
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#define otbSharkKMeansMachineLearningModel_hxx
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#include <fstream>
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#include "boost/make_shared.hpp"
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#include "itkMacro.h"
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#include "otbSharkKMeansMachineLearningModel.h"
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#if defined(__GNUC__) || defined(__clang__)
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#pragma GCC diagnostic push
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#pragma GCC diagnostic ignored "-Wshadow"
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#pragma GCC diagnostic ignored "-Wunused-parameter"
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#pragma GCC diagnostic ignored "-Woverloaded-virtual"
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#pragma GCC diagnostic ignored "-Wignored-qualifiers"
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#endif
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#include "otb_shark.h"
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#include "otbSharkUtils.h"
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#include "shark/Algorithms/KMeans.h" //k-means algorithm
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#include "shark/Models/Clustering/HardClusteringModel.h"
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#include "shark/Models/Clustering/SoftClusteringModel.h"
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#include <shark/Data/Csv.h> //load the csv file
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#if defined(__GNUC__) || defined(__clang__)
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#pragma GCC diagnostic pop
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#endif
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namespace otb
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{
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template<class TInputValue, class TOutputValue>
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SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
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::SharkKMeansMachineLearningModel() :
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        m_K(2), m_MaximumNumberOfIterations( 10 )
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{
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  // Default set HardClusteringModel
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  this->m_ConfidenceIndex = true;
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  m_ClusteringModel = boost::make_shared<ClusteringModelType>( &m_Centroids );
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}
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template<class TInputValue, class TOutputValue>
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SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
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::~SharkKMeansMachineLearningModel()
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{
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}
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/** Train the machine learning model */
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template<class TInputValue, class TOutputValue>
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void
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SharkKMeansMachineLearningModel<TInputValue, TOutputValue> ::Train() { // Parse input data and convert to Shark Data std::vector<shark::RealVector> vector_data; otb::Shark::ListSampleToSharkVector( this->GetInputListSample(), vector_data ); shark::Data<shark::RealVector> data = shark::createDataFromRange( vector_data ); // Use a Hard Clustering Model for classification shark::kMeans( data, m_K, m_Centroids, m_MaximumNumberOfIterations ); m_ClusteringModel = boost::make_shared<ClusteringModelType>( &m_Centroids ); } template<class TInputValue, class TOutputValue> typename SharkKMeansMachineLearningModel<TInputValue, TOutputValue> ::TargetSampleType SharkKMeansMachineLearningModel<TInputValue, TOutputValue> ::DoPredict(const InputSampleType &value, ConfidenceValueType *quality, ProbaSampleType *proba) const { shark::RealVector data( value.Size()); for( size_t i = 0; i < value.Size(); i++ ) { data.push_back( value[i] ); } // Change quality measurement only if SoftClustering or other clustering method is used. if( quality != nullptr ) { //unsigned int probas = (*m_ClusteringModel)( data ); ( *quality ) = ConfidenceValueType( 1.); } if (proba != nullptr) { if (!this->m_ProbaIndex) { itkExceptionMacro("Probability per class not available for this classifier !"); } } TargetSampleType target; ClusteringOutputType predictedValue = (*m_ClusteringModel)( data ); target[0] = static_cast<TOutputValue>(predictedValue); return target; } template<class TInputValue, class TOutputValue> void SharkKMeansMachineLearningModel<TInputValue, TOutputValue> ::DoPredictBatch(const InputListSampleType *input, const unsigned int &startIndex, const unsigned int &size, TargetListSampleType *targets, ConfidenceListSampleType *quality, ProbaListSampleType * proba) const { // Perform check on input values assert( input != nullptr ); assert( targets != nullptr ); // input list sample and target list sample should be initialized and without assert( input->Size() == targets->Size() && "Input sample list and target label list do not have the same size." ); assert( ( ( quality == nullptr ) || ( quality->Size() == input->Size() ) ) && "Quality samples list is not null and does not have the same size as input samples list" ); if( startIndex + size > input->Size() ) { itkExceptionMacro( <<"requested range ["<<startIndex<<", "<<startIndex+size<<"[ partially outside input sample list range.[0,"<<input->Size()<<"[" ); }