ENH: Add Save and Load method for KNN machine learning model.

Code/UtilitiesAdapters/OpenCV/otbKNearestNeighborsMachineLearningModel.txx

@@ -18,16 +18,19 @@
 #ifndef __otbKNearestNeighborsMachineLearningModel_txx
 #define __otbKNearestNeighborsMachineLearningModel_txx
 
+#include <iostream>
+#include <boost/lexical_cast.hpp>
 #include "otbKNearestNeighborsMachineLearningModel.h"
 #include "otbOpenCVUtils.h"
 
 #include <opencv2/opencv.hpp>
+#include "itkMacro.h"
 
 namespace otb
 {
 
-template <class TInputValue, class TOutputValue>
-KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
+template <class TInputValue, class TTargetValue>
+KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
 ::KNearestNeighborsMachineLearningModel() :
  m_K(10), m_IsRegression(false)
 {
@@ -35,17 +38,17 @@ KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
 }
 
 
-template <class TInputValue, class TOutputValue>
-KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
+template <class TInputValue, class TTargetValue>
+KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
 ::~KNearestNeighborsMachineLearningModel()
 {
   delete m_KNearestModel;
 }
 
 /** Train the machine learning model */
-template <class TInputValue, class TOutputValue>
+template <class TInputValue, class TTargetValue>
 void
-KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
+KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
 ::Train()
 {
   //convert listsample to opencv matrix
@@ -59,10 +62,10 @@ KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
   m_KNearestModel->train(samples,labels,cv::Mat(),m_IsRegression, m_K,false);
 }
 
-template <class TInputValue, class TOutputValue>
-typename KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
+template <class TInputValue, class TTargetValue>
+typename KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
 ::TargetSampleType
-KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
+KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
 ::Predict(const InputSampleType & input) const
 {
   //convert listsample to Mat
@@ -73,47 +76,141 @@ KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
 
   TargetSampleType target;
 
-  target[0] = static_cast<TOutputValue>(result);
+  target[0] = static_cast<TTargetValue>(result);
 
   return target;
 }
 
-template <class TInputValue, class TOutputValue>
+template <class TInputValue, class TTargetValue>
 void
-KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
+KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
 ::Save(const std::string & filename, const std::string & name)
 {
-  m_KNearestModel->save(filename.c_str(), name.c_str());
+  //there is no m_KNearestModel->save(filename.c_str(), name.c_str()).
+  //We need to save the K parameter and IsRegression flag used and the samples.
+
+  std::ofstream ofs(filename.c_str());
+  //Save K parameter and IsRegression flag.
+  ofs << "K="<< m_K <<"\n";
+  ofs << "IsRegression="<<m_IsRegression <<"\n";
+
+  //Save the samples. First column is the Label and other columns are the sample data.
+  typename InputListSampleType::ConstIterator sampleIt = this->GetInputListSample()->Begin();
+  typename TargetListSampleType::ConstIterator labelIt = this->GetTargetListSample()->Begin();
+  const unsigned int sampleSize = this->GetInputListSample()->GetMeasurementVectorSize();
+  for(; sampleIt!=this->GetInputListSample()->End(); ++sampleIt,++labelIt)
+  {
+    // Retrieve sample
+    typename InputListSampleType::MeasurementVectorType sample = sampleIt.GetMeasurementVector();
+    ofs <<labelIt.GetMeasurementVector()[0];
+
+    // Loop on sample size
+    for(unsigned int i = 0; i < sampleSize; ++i)
+    {
+      ofs << " " << sample[i];
+    }
+    ofs <<"\n";
+  }
+  ofs.close();
 }
 
-template <class TInputValue, class TOutputValue>
+template <class TInputValue, class TTargetValue>
 void
-KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
+KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
 ::Load(const std::string & filename, const std::string & name)
 {
-  m_KNearestModel->load(filename.c_str(), name.c_str());
+  //there is no m_KNearestModel->load(filename.c_str(), name.c_str());
+  std::ifstream ifs(filename.c_str());
+  if(!ifs)
+  {
+    itkExceptionMacro(<<"Could not read file "<<filename);
+  }
+  //first line is the K parameter of this algorithm.
+  std::string line;
+  std::getline(ifs, line);
+  std::string::size_type pos = line.find_first_of("=", 0);
+  std::string::size_type nextpos = line.find_first_of(" \n\r", pos+1);
+  this->SetK(boost::lexical_cast<int>(line.substr(pos+1, nextpos-pos-1)));
+  //second line is the IsRegression parameter
+  std::getline(ifs, line);
+  pos = line.find_first_of("=", 0);
+  nextpos = line.find_first_of(" \n\r", pos+1);
+  this->SetIsRegression(boost::lexical_cast<bool>(line.substr(pos+1, nextpos-pos-1)));
+
+  //Clear previous listSample (if any)
+  typename InputListSampleType::Pointer samples = InputListSampleType::New();
+  typename TargetListSampleType::Pointer labels = TargetListSampleType::New();
+
+  //Read a txt file. First column is the label, other columns are the sample data.
+  unsigned int nbFeatures = 0;
+  while (!ifs.eof())
+  {
+    std::getline(ifs, line);
+
+    if(nbFeatures == 0)
+      {
+        nbFeatures = std::count(line.begin(),line.end(),' ');
+      }
+
+    if(line.size()>1)
+    {
+      // Parse label
+      pos = line.find_first_of(" ", 0);
+      TargetSampleType label;
+      label[0] = static_cast<TargetValueType>(boost::lexical_cast<unsigned int>(line.substr(0, pos)));
+      // Parse sample features
+      InputSampleType sample(nbFeatures);
+      sample.Fill(0);
+      unsigned int id = 0;
+      nextpos = line.find_first_of(" ", pos+1);
+      while(nextpos != std::string::npos)
+      {
+        nextpos = line.find_first_of(" \n\r", pos+1);
+        std::string subline = line.substr(pos+1, nextpos-pos-1);
+        //sample[id] = static_cast<InputValueType>(boost::lexical_cast<float>(subline));
+        sample[id] = atof(subline.c_str());
+        pos = nextpos;
+        id++;
+      }
+      samples->PushBack(sample);
+      labels->PushBack(label);
+    }
+  }
+  ifs.close();
+
+  this->SetInputListSample(samples);
+  this->SetTargetListSample(labels);
+  Train();
 }
 
-template <class TInputValue, class TOutputValue>
+template <class TInputValue, class TTargetValue>
 bool
-KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
+KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
 ::CanReadFile(const std::string & file)
 {
-  return false;
+  try
+  {
+    this->Load(file);
+  }
+  catch(...)
+  {
+    return false;
+  }
+  return true;
 }
 
-template <class TInputValue, class TOutputValue>
+template <class TInputValue, class TTargetValue>
 bool
-KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
+KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
 ::CanWriteFile(const std::string & file)
 {
   return false;
 }
 
 
-template <class TInputValue, class TOutputValue>
+template <class TInputValue, class TTargetValue>
 void
-KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
+KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
 ::PrintSelf(std::ostream& os, itk::Indent indent) const
 {
   // Call superclass implementation