added updated logistic regression prototype with newer C++ API

pull/3119/head
Rahul Kavi 11 years ago committed by Maksim Shabunin
parent 0e13f33193
commit d5ad4f3255
  1. 87
      modules/ml/include/opencv2/ml.hpp

@ -571,81 +571,66 @@ public:
/****************************************************************************************\
* Logistic Regression *
\****************************************************************************************/
struct CV_EXPORTS_W_MAP CvLR_TrainParams
namespace cv
{
struct CV_EXPORTS LogisticRegressionParams
{
CV_PROP_RW double alpha;
CV_PROP_RW int num_iters;
CV_PROP_RW int norm;
///////////////////////////////////////////////////
// CV_PROP_RW int debug;
///////////////////////////////////////////////////
CV_PROP_RW int regularized;
CV_PROP_RW int train_method;
CV_PROP_RW int minibatchsize;
CV_PROP_RW CvTermCriteria term_crit;
CvLR_TrainParams();
///////////////////////////////////////////////////
// CvLR_TrainParams(double alpha, int num_iters, int norm, int debug, int regularized, int train_method, int minbatchsize);
///////////////////////////////////////////////////
CvLR_TrainParams(double alpha, int num_iters, int norm, int regularized, int train_method, int minbatchsize);
~CvLR_TrainParams();
double alpha;
int num_iters;
int norm;
int regularized;
int train_method;
int mini_batch_size;
CvTermCriteria term_crit;
LogisticRegressionParams();
LogisticRegressionParams(double alpha, int num_iters, int norm, int regularized, int train_method, int minbatchsize);
};
class CV_EXPORTS_W CvLR : public CvStatModel
class CV_EXPORTS LogisticRegression
{
public:
CvLR();
// CvLR(const CvLR_TrainParams& Params);
CvLR(const cv::Mat& data, const cv::Mat& labels, const CvLR_TrainParams& params);
virtual ~CvLR();
enum { REG_L1=0, REG_L2 = 1};
enum { BATCH, MINI_BATCH};
LogisticRegression();
LogisticRegression(cv::InputArray data_ip, cv::InputArray labels_ip, const LogisticRegressionParams& params);
virtual ~LogisticRegression();
enum { REG_L1 = 0, REG_L2 = 1};
enum { BATCH = 0, MINI_BATCH = 1};
virtual bool train(const cv::Mat& data, const cv::Mat& labels);//, const CvLR_TrainParams& params);
virtual bool train(cv::InputArray data_ip, cv::InputArray label_ip);
virtual void predict( cv::InputArray data, cv::OutputArray predicted_labels ) const;
virtual float predict(const cv::Mat& data, cv::Mat& predicted_labels);
virtual float predict(const cv::Mat& data);
virtual void save(std::string filepath) const;
virtual void load(const std::string filepath);
virtual void write( CvFileStorage* storage, const char* name ) const;
virtual void read( CvFileStorage* storage, CvFileNode* node );
virtual void clear();
virtual cv::Mat get_learnt_mat();
cv::Mat get_learnt_thetas() const;
protected:
cv::Mat learnt_thetas;
CvLR_TrainParams params;
LogisticRegressionParams params;
cv::Mat learnt_thetas;
std::string default_model_name;
std::map<int, int> forward_mapper;
std::map<int, int> reverse_mapper;
virtual bool set_default_params();
virtual cv::Mat calc_sigmoid(const cv::Mat& data);
cv::Mat labels_o;
cv::Mat labels_n;
static cv::Mat calc_sigmoid(const cv::Mat& data);
virtual double compute_cost(const cv::Mat& data, const cv::Mat& labels, const cv::Mat& init_theta);
virtual cv::Mat compute_batch_gradient(const cv::Mat& data, const cv::Mat& labels, const cv::Mat& init_theta);
virtual cv::Mat compute_mini_batch_gradient(const cv::Mat& data, const cv::Mat& labels, const cv::Mat& init_theta);
virtual std::map<int, int> get_label_map(const cv::Mat& labels);
virtual bool set_label_map(const cv::Mat& labels);
virtual cv::Mat remap_labels(const cv::Mat& labels, const std::map<int, int> lmap);
static cv::Mat remap_labels(const cv::Mat& labels, const std::map<int, int>& lmap);
//cv::Mat Mapper;
cv::Mat labels_o;
cv::Mat labels_n;
virtual void write(FileStorage& fs) const;
virtual void read(const FileNode& fn);
virtual void clear();
};
}// namespace cv
/****************************************************************************************\
* Auxilary functions declarations *

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