learning.h

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/*
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 *  Copyright (c) 2015, Andreas ten Pas
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#ifndef LEARNING_H_
#define LEARNING_H_

#include <Eigen/Dense>
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include <set>
#include <sys/stat.h>
#include <vector>

#include <agile_grasp/rotating_hand.h>

class Learning
{
public:

        Learning() :
                        num_horizontal_cells_(100), num_vertical_cells_(80), num_threads_(1)
        {
        }
        
        Learning(int num_threads) :
                        num_horizontal_cells_(100), num_vertical_cells_(80), num_threads_(num_threads)
        {
        }
  
  void trainBalanced(const std::vector<GraspHypothesis>& hands_list, const std::vector<int>& sizes,
                const std::string& file_name, const Eigen::Matrix3Xd& cam_pos, int max_positive = 1000000000,
                bool is_plotting = false);

        void train(const std::vector<GraspHypothesis>& hands_list, const std::vector<int> & sizes,
                const std::string& file_name, const Eigen::Matrix3Xd& cam_pos, int max_positive = 1000000000,
                bool is_plotting = false);

        void train(const std::vector<GraspHypothesis>& hands_list, const std::string& file_name,
                const Eigen::Matrix3Xd& cam_pos, bool is_plotting = false);
        
        std::vector<GraspHypothesis> classify(const std::vector<GraspHypothesis>& hands_list,
                const std::string& svm_filename, const Eigen::Matrix3Xd& cam_pos, bool is_plotting = false);

private:
  
  struct Instance
  {
    Eigen::Matrix3Xd pts; 
    Eigen::Vector3d binormal; 
    Eigen::Vector3d source_to_center; 
    bool label; 
  };
  
        struct UniqueVectorComparator
        {
                bool operator ()(const Eigen::Vector2i& a, const Eigen::Vector2i& b)
                {
                        for (int i = 0; i < a.size(); i++)
                        {
                                if (a(i) != b(i))
                                {
                                        return a(i) < b(i);
                                }
                        }

                        return false;
                }
        };
  
  Instance createInstance(const GraspHypothesis& h, 
    const Eigen::Matrix3Xd& cam_pos, int cam = -1);

        void convertData(const std::vector<Instance>& instances,
                const std::string& file_name, bool is_plotting = false, 
    bool uses_linear_kernel = false);

        cv::Mat convertToImage(const Instance& ins);
  
        Eigen::VectorXi floorVector(const Eigen::VectorXd& a);

        int num_horizontal_cells_; 
        int num_vertical_cells_; 
        int num_threads_; 
};

#endif /* LEARNING_H_ */