REFAC: work on CrossValidation in LibSVMMachineLearningModel

Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h

@@ -212,6 +212,19 @@ public:
     return static_cast<int>(m_Parameters.cache_size);
   }
 
+  itkSetMacro(CVFolders, unsigned int);
+  itkGetMacro(CVFolders, unsigned int);
+
+  itkGetMacro(InitialCrossValidationAccuracy, double);
+
+  itkGetMacro(FinalCrossValidationAccuracy, double);
+
+  itkSetMacro(CoarseOptimizationNumberOfSteps, unsigned int);
+  itkGetMacro(CoarseOptimizationNumberOfSteps, unsigned int);
+
+  itkSetMacro(FineOptimizationNumberOfSteps, unsigned int);
+  itkGetMacro(FineOptimizationNumberOfSteps, unsigned int);
+
 protected:
   /** Constructor */
   LibSVMMachineLearningModel();
@@ -237,7 +250,7 @@ private:
 
   void DeleteModel(void);
 
-  double CrossValidation(unsigned int nbFolders);
+  double CrossValidation(void);
 
   void OptimizeParameters(void);
 
@@ -253,6 +266,21 @@ private:
   /** Do parameters optimization, default : false */
   bool m_ParameterOptimization;
 
+  /** Number of Cross Validation folders*/
+  unsigned int m_CVFolders;
+
+  /** Initial cross validation accuracy */
+  double m_InitialCrossValidationAccuracy;
+
+  /** Final cross validationa accuracy */
+  double m_FinalCrossValidationAccuracy;
+
+  /** Number of steps for the coarse search */
+  unsigned int m_CoarseOptimizationNumberOfSteps;
+
+  /** Number of steps for the fine search */
+  unsigned int m_FineOptimizationNumberOfSteps;
+
   /** Temporary array to store cross-validation results */
   std::vector<double> m_TmpTarget;
 

Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx

@@ -23,6 +23,8 @@
 
 #include <fstream>
 #include "otbLibSVMMachineLearningModel.h"
+#include "otbSVMCrossValidationCostFunction.h"
+#include "otbExhaustiveExponentialOptimizer.h"
 #include "otbMacro.h"
 
 namespace otb
@@ -46,6 +48,11 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
   this->SetCacheSize(40); // MB
   this->m_ParameterOptimization = false;
   this->m_IsRegressionSupported = true;
+  this->SetCVFolders(5);
+  this->m_InitialCrossValidationAccuracy = 0.;
+  this->m_FinalCrossValidationAccuracy = 0.;
+  this->m_CoarseOptimizationNumberOfSteps = 5;
+  this->m_FineOptimizationNumberOfSteps = 5;
 
   this->m_Parameters.nr_weight = 0;
   this->m_Parameters.weight_label = ITK_NULLPTR;
@@ -375,7 +382,7 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
 template <class TInputValue, class TOutputValue>
 double
 LibSVMMachineLearningModel<TInputValue,TOutputValue>
-::CrossValidation(unsigned int nbFolders)
+::CrossValidation(void)
 {
   double accuracy = 0.0;
   // Get the length of the problem
@@ -384,7 +391,7 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
     return accuracy;
 
   // Do cross validation
-  svm_cross_validation(&m_Problem, &m_Parameters, nbFolders, &m_TmpTarget[0]);
+  svm_cross_validation(&m_Problem, &m_Parameters, m_CVFolders, &m_TmpTarget[0]);
 
   // Evaluate accuracy
   double total_correct = 0.;
@@ -406,12 +413,9 @@ void
 LibSVMMachineLearningModel<TInputValue,TOutputValue>
 ::OptimizeParameters()
 {
-  typedef SVMCrossValidationCostFunction<SVMModelType> CrossValidationFunctionType;
-
+  typedef SVMCrossValidationCostFunction<this> CrossValidationFunctionType;
   typename CrossValidationFunctionType::Pointer crossValidationFunction = CrossValidationFunctionType::New();
-
-  crossValidationFunction->SetModel(this->GetModel());
-  crossValidationFunction->SetNumberOfCrossValidationFolders(m_NumberOfCrossValidationFolders);
+  crossValidationFunction->SetModel(this);
 
   typename CrossValidationFunctionType::ParametersType initialParameters, coarseBestParameters, fineBestParameters;