diff --git a/Modules/Learning/SVMLearning/include/otbSVMModel.h b/Modules/Learning/SVMLearning/include/otbSVMModel.h
deleted file mode 100644
index 27b918bbb41a05ff09627bdd255d280e83a95df2..0000000000000000000000000000000000000000
--- a/Modules/Learning/SVMLearning/include/otbSVMModel.h
+++ /dev/null
@@ -1,460 +0,0 @@
-/*
- * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
- *
- * This file is part of Orfeo Toolbox
- *
- * https://www.orfeo-toolbox.org/
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef otbSVMModel_h
-#define otbSVMModel_h
-
-#include "itkObjectFactory.h"
-#include "itkDataObject.h"
-#include "itkVariableLengthVector.h"
-#include "itkTimeProbe.h"
-#include "svm.h"
-
-namespace otb
-{
-
-/** \class SVMModel
- * \brief Class for SVM models.
- *
- * \TODO update documentation
- *
- * The basic functionality of the SVMModel framework base class is to
- * generate the models used in SVM classification. It requires input
- * images and a training image to be provided by the user.
- * This object supports data handling of multiband images. The object
- * accepts the input image in vector format only, where each pixel is a
- * vector and each element of the vector corresponds to an entry from
- * 1 particular band of a multiband dataset. A single band image is treated
- * as a vector image with a single element for every vector. The classified
- * image is treated as a single band scalar image.
- *
- * A membership function represents a specific knowledge about
- * a class. In other words, it should tell us how "likely" is that a
- * measurement vector (pattern) belong to the class.
- *
- * As the method name indicates, you can have more than one membership
- * function. One for each classes. The order you put the membership
- * calculator becomes the class label for the class that is represented
- * by the membership calculator.
- *
-
- *
- * \ingroup ClassificationFilters
- *
- * \ingroup OTBSVMLearning
- */
-template <class TValue, class TLabel>
-class ITK_EXPORT SVMModel : public itk::DataObject
-{
-public:
- /** Standard class typedefs. */
- typedef SVMModel Self;
- typedef itk::DataObject Superclass;
- typedef itk::SmartPointer<Self> Pointer;
- typedef itk::SmartPointer<const Self> ConstPointer;
-
- /** Value type */
- typedef TValue ValueType;
- /** Label Type */
- typedef TLabel LabelType;
- typedef std::vector<ValueType> MeasurementType;
- typedef std::pair<MeasurementType, LabelType> SampleType;
- typedef std::vector<SampleType> SamplesVectorType;
- /** Cache vector type */
- typedef std::vector<struct svm_node *> CacheVectorType;
-
- /** Distances vector */
- typedef itk::VariableLengthVector<double> ProbabilitiesVectorType;
- typedef itk::VariableLengthVector<double> DistancesVectorType;
-
- typedef struct svm_node * NodeCacheType;
-
- /** Run-time type information (and related methods). */
- itkNewMacro(Self);
- itkTypeMacro(SVMModel, itk::DataObject);
-
- /** Get the number of classes. */
- unsigned int GetNumberOfClasses(void) const
- {
- if (m_Model) return (unsigned int) (m_Model->nr_class);
- return 0;
- }
-
- /** Get the number of hyperplane. */
- unsigned int GetNumberOfHyperplane(void) const
- {
- if (m_Model) return (unsigned int) (m_Model->nr_class * (m_Model->nr_class - 1) / 2);
- return 0;
- }
-
- /** Gets the model */
- const struct svm_model* GetModel()
- {
- return m_Model;
- }
-
- /** Gets the parameters */
- struct svm_parameter& GetParameters()
- {
- return m_Parameters;
- }
- /** Gets the parameters */
- const struct svm_parameter& GetParameters() const
- {
- return m_Parameters;
- }
-
- /** Saves the model to a file */
- void SaveModel(const char* model_file_name) const;
- void SaveModel(const std::string& model_file_name) const
- {
- //implemented in term of const char * version
- this->SaveModel(model_file_name.c_str());
- }
-
- /** Loads the model from a file */
- void LoadModel(const char* model_file_name);
- void LoadModel(const std::string& model_file_name)
- {
- //implemented in term of const char * version
- this->LoadModel(model_file_name.c_str());
- }
-
- /** Set the SVM type to C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR */
- void SetSVMType(int svmtype)
- {
- m_Parameters.svm_type = svmtype;
- m_ModelUpToDate = false;
- this->Modified();
- }
-
- /** Get the SVM type (C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR) */
- int GetSVMType(void) const
- {
- return m_Parameters.svm_type;
- }
-
- /** Set the kernel type to LINEAR, POLY, RBF, SIGMOID
- linear: u'*v
- polynomial: (gamma*u'*v + coef0)^degree
- radial basis function: exp(-gamma*|u-v|^2)
- sigmoid: tanh(gamma*u'*v + coef0)*/
- void SetKernelType(int kerneltype)
- {
- m_Parameters.kernel_type = kerneltype;
- m_ModelUpToDate = false;
- this->Modified();
- }
-
- /** Get the kernel type */
- int GetKernelType(void) const
- {
- return m_Parameters.kernel_type;
- }
-
- /** Set the degree of the polynomial kernel */
- void SetPolynomialKernelDegree(int degree)
- {
- m_Parameters.degree = degree;
- m_ModelUpToDate = false;
- this->Modified();
- }
-
- /** Get the degree of the polynomial kernel */
- int GetPolynomialKernelDegree(void) const
- {
- return m_Parameters.degree;
- }
-
- /** Set the gamma parameter for poly/rbf/sigmoid kernels */
- virtual void SetKernelGamma(double gamma)
- {
- m_Parameters.gamma = gamma;
- m_ModelUpToDate = false;
- this->Modified();
- }
- /** Get the gamma parameter for poly/rbf/sigmoid kernels */
- double GetKernelGamma(void) const
- {
- return m_Parameters.gamma;
- }
-
- /** Set the coef0 parameter for poly/sigmoid kernels */
- void SetKernelCoef0(double coef0)
- {
- m_Parameters.coef0 = coef0;
- m_ModelUpToDate = false;
- this->Modified();
- }
-
- /** Get the coef0 parameter for poly/sigmoid kernels */
- double GetKernelCoef0(void) const
- {
- //return m_Parameters.coef0;
- return m_Parameters.coef0;
- }
-
- /** Set the Nu parameter for the training */
- void SetNu(double nu)
- {
- m_Parameters.nu = nu;
- m_ModelUpToDate = false;
- this->Modified();
- }
-
- /** Set the Nu parameter for the training */
- double GetNu(void) const
- {
- //return m_Parameters.nu;
- return m_Parameters.nu;
- }
-
- /** Set the cache size in MB for the training */
- void SetCacheSize(int cSize)
- {
- m_Parameters.cache_size = static_cast<double>(cSize);
- m_ModelUpToDate = false;
- this->Modified();
- }
-
- /** Get the cache size in MB for the training */
- int GetCacheSize(void) const
- {
- return static_cast<int>(m_Parameters.cache_size);
- }
-
- /** Set the C parameter for the training for C_SVC, EPSILON_SVR and NU_SVR */
- void SetC(double c)
- {
- m_Parameters.C = c;
- m_ModelUpToDate = false;
- this->Modified();
- }
-
- /** Get the C parameter for the training for C_SVC, EPSILON_SVR and NU_SVR */
- double GetC(void) const
- {
- return m_Parameters.C;
- }
-
- /** Set the tolerance for the stopping criterion for the training*/
- void SetEpsilon(double eps)
- {
- m_Parameters.eps = eps;
- m_ModelUpToDate = false;
- this->Modified();
- }
-
- /** Get the tolerance for the stopping criterion for the training*/
- double GetEpsilon(void) const
- {
- return m_Parameters.eps;
- }
-
- /* Set the value of p for EPSILON_SVR */
- void SetP(double p)
- {
- m_Parameters.p = p;
- m_ModelUpToDate = false;
- this->Modified();
- }
-
- /* Get the value of p for EPSILON_SVR */
- double GetP(void) const
- {
- return m_Parameters.p;
- }
-
- /** Use the shrinking heuristics for the training */
- void DoShrinking(bool s)
- {
- m_Parameters.shrinking = static_cast<int>(s);
- m_ModelUpToDate = false;
- this->Modified();
- }
-
- /** Get Use the shrinking heuristics for the training boolea */
- bool GetDoShrinking(void) const
- {
- return static_cast<bool>(m_Parameters.shrinking);
- }
-
- /** Do probability estimates */
- void DoProbabilityEstimates(bool prob)
- {
- m_Parameters.probability = static_cast<int>(prob);
- m_ModelUpToDate = false;
- this->Modified();
- }
-
- /** Get Do probability estimates boolean */
- bool GetDoProbabilityEstimates(void) const
- {
- return static_cast<bool>(m_Parameters.probability);
- }
-
- /** Test if the model has probabilities */
- bool HasProbabilities(void) const
- {
- return static_cast<bool>(svm_check_probability_model(m_Model));
- }
-
- /** Return number of support vectors */
- int GetNumberOfSupportVectors(void) const
- {
- if (m_Model) return m_Model->l;
- return 0;
- }
-
- /** Return rho values */
- double * GetRho(void) const
- {
- if (m_Model) return m_Model->rho;
- return ITK_NULLPTR;
- }
- /** Return the support vectors */
- svm_node ** GetSupportVectors(void)
- {
- if (m_Model) return m_Model->SV;
- return ITK_NULLPTR;
- }
- /** Set the support vectors and changes the l number of support vectors accordind to sv.*/
- void SetSupportVectors(svm_node ** sv, int nbOfSupportVector);
-
- /** Return the alphas values (SV Coef) */
- double ** GetAlpha(void)
- {
- if (m_Model) return m_Model->sv_coef;
- return ITK_NULLPTR;
- }
- /** Set the alphas values (SV Coef) */
- void SetAlpha(double ** alpha, int nbOfSupportVector);
-
- /** Return the labels lists */
- int * GetLabels()
- {
- if (m_Model) return m_Model->label;
- return ITK_NULLPTR;
- }
-
- /** Get the number of SV per classes */
- int * GetNumberOfSVPerClasse()
- {
- if (m_Model) return m_Model->nSV;
- return ITK_NULLPTR;
- }
-
- struct svm_problem& GetProblem()
- {
- return m_Problem;
- }
-
- /** Allocate the problem */
- void BuildProblem();
-
- /** Check consistency (potentially throws exception) */
- void ConsistencyCheck();
-
- /** Estimate the model */
- void Train();
-
- /** Cross validation (returns the accuracy) */
- double CrossValidation(unsigned int nbFolders);
-
- /** Predict (Please note that due to caching this method is not
-* thread safe. If you want to run multiple concurrent instances of
-* this method, please consider using the GetCopy() method to clone the
-* model.)*/
- LabelType EvaluateLabel(const MeasurementType& measure) const;
-
- /** Evaluate hyperplan distances (Please note that due to caching this method is not
-* thread safe. If you want to run multiple concurrent instances of
-* this method, please consider using the GetCopy() method to clone the
-* model.)**/
- DistancesVectorType EvaluateHyperplanesDistances(const MeasurementType& measure) const;
-
- /** Evaluate probabilities of each class. Returns a probability vector ordered
- * by increasing class label value
- * (Please note that due to caching this method is not thread safe.
- * If you want to run multiple concurrent instances of
- * this method, please consider using the GetCopy() method to clone the
- * model.)**/
- ProbabilitiesVectorType EvaluateProbabilities(const MeasurementType& measure) const;
-
- /** Add a new sample to the list */
- void AddSample(const MeasurementType& measure, const LabelType& label);
-
- /** Clear all samples */
- void ClearSamples();
-
- /** Set the samples vector */
- void SetSamples(const SamplesVectorType& samples);
-
- /** Reset all the model, leaving it in the same state that just
- * before constructor call */
- void Reset();
-
-protected:
- /** Constructor */
- SVMModel();
- /** Destructor */
- ~SVMModel() ITK_OVERRIDE;
- /** Display infos */
- void PrintSelf(std::ostream& os, itk::Indent indent) const ITK_OVERRIDE;
-
-/** Delete any allocated problem */
- void DeleteProblem();
-
- /** Delete any allocated model */
- void DeleteModel();
-
- /** Initializes default parameters */
- void Initialize() ITK_OVERRIDE;
-
-private:
- SVMModel(const Self &); //purposely not implemented
- void operator =(const Self&); //purposely not implemented
-
- /** Container to hold the SVM model itself */
- struct svm_model* m_Model;
-
- /** True if model is up-to-date */
- mutable bool m_ModelUpToDate;
-
- /** Container of the SVM problem */
- struct svm_problem m_Problem;
-
- /** Container of the SVM parameters */
- struct svm_parameter m_Parameters;
-
- /** true if problem is up-to-date */
- bool m_ProblemUpToDate;
-
- /** Contains the samples */
- SamplesVectorType m_Samples;
-}; // class SVMModel
-
-} // namespace otb
-
-#ifndef OTB_MANUAL_INSTANTIATION
-#include "otbSVMModel.txx"
-#endif
-
-#endif
diff --git a/Modules/Learning/SVMLearning/include/otbSVMModel.txx b/Modules/Learning/SVMLearning/include/otbSVMModel.txx
deleted file mode 100644
index 23a231bd332bd91f460aaf60f89a5d5ac5a030a5..0000000000000000000000000000000000000000
--- a/Modules/Learning/SVMLearning/include/otbSVMModel.txx
+++ /dev/null
@@ -1,659 +0,0 @@
-/*
- * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
- *
- * This file is part of Orfeo Toolbox
- *
- * https://www.orfeo-toolbox.org/
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef otbSVMModel_txx
-#define otbSVMModel_txx
-#include "otbSVMModel.h"
-#include "otbMacro.h"
-
-#include <algorithm>
-
-namespace otb
-{
-// TODO: Check memory allocation in this class
-template <class TValue, class TLabel>
-SVMModel<TValue, TLabel>::SVMModel()
-{
- // Default parameters
- this->SetSVMType(C_SVC);
- this->SetKernelType(LINEAR);
- this->SetPolynomialKernelDegree(3);
- this->SetKernelGamma(1.); // 1/k
- this->SetKernelCoef0(1.);
- this->SetNu(0.5);
- this->SetCacheSize(40);
- this->SetC(1);
- this->SetEpsilon(1e-3);
- this->SetP(0.1);
- this->DoShrinking(true);
- this->DoProbabilityEstimates(false);
-
- m_Parameters.nr_weight = 0;
- m_Parameters.weight_label = ITK_NULLPTR;
- m_Parameters.weight = ITK_NULLPTR;
-
- m_Model = ITK_NULLPTR;
-
- this->Initialize();
-}
-
-template <class TValue, class TLabel>
-SVMModel<TValue, TLabel>::~SVMModel()
-{
- this->DeleteModel();
- this->DeleteProblem();
-}
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::Initialize()
-{
- // Initialize model
- /*
- if (!m_Model)
- {
- m_Model = new struct svm_model;
- m_Model->l = 0;
- m_Model->nr_class = 0;
- m_Model->SV = NULL;
- m_Model->sv_coef = NULL;
- m_Model->rho = NULL;
- m_Model->label = NULL;
- m_Model->probA = NULL;
- m_Model->probB = NULL;
- m_Model->nSV = NULL;
-
- m_ModelUpToDate = false;
-
- } */
- m_ModelUpToDate = false;
-
- // Initialize problem
- m_Problem.l = 0;
- m_Problem.y = ITK_NULLPTR;
- m_Problem.x = ITK_NULLPTR;
-
- m_ProblemUpToDate = false;
-}
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::Reset()
-{
- this->DeleteProblem();
- this->DeleteModel();
-
- // Clear samples
- m_Samples.clear();
-
- // Initialize values
- this->Initialize();
-}
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::DeleteModel()
-{
- if(m_Model)
- {
- svm_free_and_destroy_model(&m_Model);
- }
- m_Model = ITK_NULLPTR;
-}
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::DeleteProblem()
-{
-// Deallocate any existing problem
- if (m_Problem.y)
- {
- delete[] m_Problem.y;
- m_Problem.y = ITK_NULLPTR;
- }
-
- if (m_Problem.x)
- {
- for (int i = 0; i < m_Problem.l; ++i)
- {
- if (m_Problem.x[i])
- {
- delete[] m_Problem.x[i];
- }
- }
- delete[] m_Problem.x;
- m_Problem.x = ITK_NULLPTR;
- }
- m_Problem.l = 0;
- m_ProblemUpToDate = false;
-}
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::AddSample(const MeasurementType& measure, const LabelType& label)
-{
- SampleType newSample(measure, label);
- m_Samples.push_back(newSample);
- m_ProblemUpToDate = false;
-}
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::ClearSamples()
-{
- m_Samples.clear();
- m_ProblemUpToDate = false;
-}
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::SetSamples(const SamplesVectorType& samples)
-{
- m_Samples = samples;
- m_ProblemUpToDate = false;
-}
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::BuildProblem()
-{
- // Check if problem is up-to-date
- if (m_ProblemUpToDate)
- {
- return;
- }
-
- // Get number of samples
- int probl = m_Samples.size();
-
- if (probl < 1)
- {
- itkExceptionMacro(<< "No samples, can not build SVM problem.");
- }
- otbMsgDebugMacro(<< "Rebuilding problem ...");
-
- // Get the size of the samples
- long int elements = m_Samples[0].first.size() + 1;
-
- // Deallocate any previous problem
- this->DeleteProblem();
-
- // Allocate the problem
- m_Problem.l = probl;
- m_Problem.y = new double[probl];
- m_Problem.x = new struct svm_node*[probl];
-
- for (int i = 0; i < probl; ++i)
- {
- // Initialize labels to 0
- m_Problem.y[i] = 0;
- m_Problem.x[i] = new struct svm_node[elements];
-
- // Initialize elements (value = 0; index = -1)
- for (unsigned int j = 0; j < static_cast<unsigned int>(elements); ++j)
- {
- m_Problem.x[i][j].index = -1;
- m_Problem.x[i][j].value = 0;
- }
- }
-
- // Iterate on the samples
- typename SamplesVectorType::const_iterator sIt = m_Samples.begin();
- int sampleIndex = 0;
- int maxElementIndex = 0;
-
- while (sIt != m_Samples.end())
- {
-
- // Get the sample measurement and label
- MeasurementType measure = sIt->first;
- LabelType label = sIt->second;
-
- // Set the label
- m_Problem.y[sampleIndex] = label;
-
- int elementIndex = 0;
-
- // Populate the svm nodes
- for (typename MeasurementType::const_iterator eIt = measure.begin();
- eIt != measure.end() && elementIndex < elements; ++eIt, ++elementIndex)
- {
- m_Problem.x[sampleIndex][elementIndex].index = elementIndex + 1;
- m_Problem.x[sampleIndex][elementIndex].value = (*eIt);
- }
-
- // Get the max index
- if (elementIndex > maxElementIndex)
- {
- maxElementIndex = elementIndex;
- }
-
- ++sampleIndex;
- ++sIt;
- }
-
- // Compute the kernel gamma from maxElementIndex if necessary
- if (this->GetKernelGamma() == 0)
- {
- this->SetKernelGamma(1.0 / static_cast<double>(maxElementIndex));
- }
-
- // problem is up-to-date
- m_ProblemUpToDate = true;
-}
-
-template <class TValue, class TLabel>
-double
-SVMModel<TValue, TLabel>::CrossValidation(unsigned int nbFolders)
-{
- // Build problem
- this->BuildProblem();
-
- // Check consistency
- this->ConsistencyCheck();
-
- // Get the length of the problem
- int length = m_Problem.l;
-
- // Temporary memory to store cross validation results
- double *target = new double[length];
-
- // Do cross validation
- svm_cross_validation(&m_Problem, &m_Parameters, nbFolders, target);
-
- // Evaluate accuracy
- int i;
- double total_correct = 0.;
-
- for (i = 0; i < length; ++i)
- {
- if (target[i] == m_Problem.y[i])
- {
- ++total_correct;
- }
- }
- double accuracy = total_correct / length;
-
- // Free temporary memory
- delete[] target;
-
- // return accuracy value
- return accuracy;
-}
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::ConsistencyCheck()
-{
- if (m_Parameters.svm_type == ONE_CLASS && this->GetDoProbabilityEstimates())
- {
- otbMsgDebugMacro(<< "Disabling SVM probability estimates for ONE_CLASS SVM type.");
- this->DoProbabilityEstimates(false);
- }
-
- const char* error_msg = svm_check_parameter(&m_Problem, &m_Parameters);
-
- if (error_msg)
- {
- throw itk::ExceptionObject(__FILE__, __LINE__, error_msg, ITK_LOCATION);
- }
-}
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::Train()
-{
- // If the model is already up-to-date, return
- if (m_ModelUpToDate)
- {
- return;
- }
-
- // Build problem
- this->BuildProblem();
-
- // Check consistency
- this->ConsistencyCheck();
-
- // train the model
- m_Model = svm_train(&m_Problem, &m_Parameters);
-
- // Set the model as up-to-date
- m_ModelUpToDate = true;
-}
-
-template <class TValue, class TLabel>
-typename SVMModel<TValue, TLabel>::LabelType
-SVMModel<TValue, TLabel>::EvaluateLabel(const MeasurementType& measure) const
-{
- // Check if model is up-to-date
- if (!m_ModelUpToDate)
- {
- itkExceptionMacro(<< "Model is not up-to-date, can not predict label");
- }
-
- // Check probability prediction
- bool predict_probability = svm_check_probability_model(m_Model);
-
- if (this->GetSVMType() == ONE_CLASS)
- {
- predict_probability = 0;
- }
-
- // Get type and number of classes
- int svm_type = svm_get_svm_type(m_Model);
- int nr_class = svm_get_nr_class(m_Model);
-
- // Allocate space for labels
- double *prob_estimates = ITK_NULLPTR;
-
- // Eventually allocate space for probabilities
- if (predict_probability)
- {
- if (svm_type == NU_SVR || svm_type == EPSILON_SVR)
- {
- otbMsgDevMacro(
- <<
- "Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma), sigma="
- << svm_get_svr_probability(m_Model));
- }
- else
- {
- prob_estimates = new double[nr_class];
- }
- }
-
- // Allocate nodes (/TODO if performances problems are related to too
- // many allocations, a cache approach can be set)
- struct svm_node * x = new struct svm_node[measure.size() + 1];
-
- int valueIndex = 0;
-
- // Fill the node
- for (typename MeasurementType::const_iterator mIt = measure.begin(); mIt != measure.end(); ++mIt, ++valueIndex)
- {
- x[valueIndex].index = valueIndex + 1;
- x[valueIndex].value = (*mIt);
- }
-
- // terminate node
- x[measure.size()].index = -1;
- x[measure.size()].value = 0;
-
- LabelType label = 0;
-
- if (predict_probability && (svm_type == C_SVC || svm_type == NU_SVC))
- {
- label = static_cast<LabelType>(svm_predict_probability(m_Model, x, prob_estimates));
- }
- else
- {
- label = static_cast<LabelType>(svm_predict(m_Model, x));
- }
-
- // Free allocated memory
- delete[] x;
-
- if (prob_estimates)
- {
- delete[] prob_estimates;
- }
-
- return label;
-}
-
-template <class TValue, class TLabel>
-typename SVMModel<TValue, TLabel>::DistancesVectorType
-SVMModel<TValue, TLabel>::EvaluateHyperplanesDistances(const MeasurementType& measure) const
-{
- // Check if model is up-to-date
- if (!m_ModelUpToDate)
- {
- itkExceptionMacro(<< "Model is not up-to-date, can not predict label");
- }
-
- // Allocate nodes (/TODO if performances problems are related to too
- // many allocations, a cache approach can be set)
- struct svm_node * x = new struct svm_node[measure.size() + 1];
-
- int valueIndex = 0;
-
- // Fill the node
- for (typename MeasurementType::const_iterator mIt = measure.begin(); mIt != measure.end(); ++mIt, ++valueIndex)
- {
- x[valueIndex].index = valueIndex + 1;
- x[valueIndex].value = (*mIt);
- }
-
- // terminate node
- x[measure.size()].index = -1;
- x[measure.size()].value = 0;
-
- // Initialize distances vector
- DistancesVectorType distances(m_Model->nr_class*(m_Model->nr_class - 1) / 2);
-
- // predict distances vector
- svm_predict_values(m_Model, x, (double*) (distances.GetDataPointer()));
-
- // Free allocated memory
- delete[] x;
-
- return (distances);
-}
-
-template <class TValue, class TLabel>
-typename SVMModel<TValue, TLabel>::ProbabilitiesVectorType
-SVMModel<TValue, TLabel>::EvaluateProbabilities(const MeasurementType& measure) const
-{
- // Check if model is up-to-date
- if (!m_ModelUpToDate)
- {
- itkExceptionMacro(<< "Model is not up-to-date, can not predict probabilities");
- }
-
- if (!this->HasProbabilities())
- {
- throw itk::ExceptionObject(__FILE__, __LINE__,
- "Model does not support probability estimates", ITK_LOCATION);
- }
-
- // Get number of classes
- int nr_class = svm_get_nr_class(m_Model);
-
- // Allocate nodes (/TODO if performances problems are related to too
- // many allocations, a cache approach can be set)
- struct svm_node * x = new struct svm_node[measure.size() + 1];
-
- int valueIndex = 0;
-
- // Fill the node
- for (typename MeasurementType::const_iterator mIt = measure.begin(); mIt != measure.end(); ++mIt, ++valueIndex)
- {
- x[valueIndex].index = valueIndex + 1;
- x[valueIndex].value = (*mIt);
- }
-
- // Termination node
- x[measure.size()].index = -1;
- x[measure.size()].value = 0;
-
- double* dec_values = new double[nr_class];
- svm_predict_probability(m_Model, x, dec_values);
-
- // Reorder values in increasing class label
- int* labels = m_Model->label;
- std::vector<int> orderedLabels(nr_class);
- std::copy(labels, labels + nr_class, orderedLabels.begin());
- std::sort(orderedLabels.begin(), orderedLabels.end());
-
- ProbabilitiesVectorType probabilities(nr_class);
- for (int i = 0; i < nr_class; ++i)
- {
- // svm_predict_probability is such that "dec_values[i]" corresponds to label "labels[i]"
- std::vector<int>::iterator it = std::find(orderedLabels.begin(), orderedLabels.end(), labels[i]);
- probabilities[it - orderedLabels.begin()] = dec_values[i];
- }
-
- // Free allocated memory
- delete[] x;
- delete[] dec_values;
-
- return probabilities;
-}
-
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::SaveModel(const char* model_file_name) const
-{
- if (svm_save_model(model_file_name, m_Model) != 0)
- {
- itkExceptionMacro(<< "Problem while saving SVM model "
- << std::string(model_file_name));
- }
-}
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::LoadModel(const char* model_file_name)
-{
- this->DeleteModel();
- m_Model = svm_load_model(model_file_name);
- if (m_Model == ITK_NULLPTR)
- {
- itkExceptionMacro(<< "Problem while loading SVM model "
- << std::string(model_file_name));
- }
- m_Parameters = m_Model->param;
- m_ModelUpToDate = true;
-}
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::PrintSelf(std::ostream& os, itk::Indent indent) const
-{
- Superclass::PrintSelf(os, indent);
-}
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::SetSupportVectors(svm_node ** sv, int nbOfSupportVector)
-{
- if (!m_Model)
- {
- itkExceptionMacro( "Internal SVM model is empty!");
- }
-
- // erase the old SV
- // delete just the first element, it destoyes the whole pointers (cf SV filling with x_space)
- delete[] (m_Model->SV[0]);
-
- for (int n = 0; n < m_Model->l; ++n)
- {
- m_Model->SV[n] = ITK_NULLPTR;
- }
- delete[] (m_Model->SV);
- m_Model->SV = ITK_NULLPTR;
-
- m_Model->SV = new struct svm_node*[m_Model->l];
-
- // copy new SV values
- svm_node **SV = m_Model->SV;
-
- // Compute the total number of SV elements.
- unsigned int elements = 0;
- for (int p = 0; p < nbOfSupportVector; ++p)
- {
- //std::cout << p << " ";
- const svm_node *tempNode = sv[p];
- //std::cout << p << " ";
- while (tempNode->index != -1)
- {
- tempNode++;
- ++elements;
- }
- ++elements; // for -1 values
- }
-
- if (m_Model->l > 0)
- {
- SV[0] = new struct svm_node[elements];
- memcpy(SV[0], sv[0], sizeof(svm_node*) * elements);
- }
- svm_node *x_space = SV[0];
-
- int j = 0;
- for (int i = 0; i < m_Model->l; ++i)
- {
- // SV
- SV[i] = &x_space[j];
- const svm_node *p = sv[i];
- svm_node * pCpy = SV[i];
- while (p->index != -1)
- {
- pCpy->index = p->index;
- pCpy->value = p->value;
- ++p;
- ++pCpy;
- ++j;
- }
- pCpy->index = -1;
- ++j;
- }
-
- if (m_Model->l > 0)
- {
- delete[] SV[0];
- }
-}
-
-template <class TValue, class TLabel>
-void
-SVMModel<TValue, TLabel>::SetAlpha(double ** alpha, int itkNotUsed(nbOfSupportVector))
-{
- if (!m_Model)
- {
- itkExceptionMacro( "Internal SVM model is empty!");
- }
-
- // Erase the old sv_coef
- for (int i = 0; i < m_Model->nr_class - 1; ++i)
- {
- delete[] m_Model->sv_coef[i];
- }
- delete[] m_Model->sv_coef;
-
- // copy new sv_coef values
- m_Model->sv_coef = new double*[m_Model->nr_class - 1];
- for (int i = 0; i < m_Model->nr_class - 1; ++i)
- m_Model->sv_coef[i] = new double[m_Model->l];
-
- for (int i = 0; i < m_Model->l; ++i)
- {
- // sv_coef
- for (int k = 0; k < m_Model->nr_class - 1; ++k)
- {
- m_Model->sv_coef[k][i] = alpha[k][i];
- }
- }
-}
-
-} // end namespace otb
-
-#endif