/**   boostrank-train.cpp: main file for performing ordinal regression training   with various thresholded ensemble algorithms   (c) 2006-2007 Hsuan-Tien Lin**/#include <iostream>#include <fstream>#include "aggrank.h"#include "orboost.h"#include "rankboost.h"#include <stump.h>#include <perceptron.h>#include "softperc.h"void exit_error(char** argv) {  std::cerr << "Usage: " << argv[0] << " trainfile n_train"	    << " #_input bag base n_rank iter modelfile\n"	    << "bag : 10 = RankBoost, cla_thres, reg_param=0.0\n"	    << "      11 = RankBoost, cla_thres, reg_param=1e-32\n"	    << "      20 = RankBoost, abs_thres, reg_param=0.0\n"	    << "      21 = RankBoost, abs_thres, reg_param=1e-32\n"	    << "      30 = ORBoost, FORM_LR, ordered, sub_iter=1, reg_param=0.0\n"	    << "      31 = ORBoost, FORM_LR, ordered, sub_iter=1, reg_param=1e-32\n"	    << "      40 = ORBoost, FORM_FULL, ordered, sub_iter=1, reg_param=0.0\n"	    << "      41 = ORBoost, FORM_FULL, ordered, sub_iter=1, reg_param=1e-32\n"	    << "base: 100 = stump (without constant)\n"	    << "      200 = perc200 with special bias\n"	    << "      201 = perc200 with special bias, scale=1\n"	    << "      204 = perc200 with special bias, scale=4\n";  exit(-1);}int main (unsigned int argc, char* argv[]) {  if (argc < 9)    exit_error(argv);  /* open data file */  std::ifstream fd(argv[1]);  if (!fd.is_open()) {    std::cerr << argv[0] << ": training data file ("	      << argv[1] << ") open error\n";    exit(-2);  }  UINT n_train = atoi(argv[2]);    UINT n_in = atoi(argv[3]);  UINT n_out = 1;  UINT bag = atoi(argv[4]);  UINT base = atoi(argv[5]);  UINT n_rank = atoi(argv[6]);  UINT n_iter = atoi(argv[7]);  std::ofstream fm(argv[8]);  /* load training data */  lemga::DataSet *trd = lemga::load_data(fd, n_train, n_in, n_out);  fd.close();  lemga::LearnModel *pst = 0;  switch(base){  case 100:    {      lemga::Stump* p = new lemga::Stump(n_in);      pst = p;      break;    }  case 200:    {      lemga::Perceptron* p = new lemga::Perceptron(n_in);      p->set_parameter(0, 0, 200);      p->set_train_method(lemga::Perceptron::RAND_COOR_DESCENT_BIAS);      pst = p;      break;    }  case 201:    {      lemga::SoftPerc* p = new lemga::SoftPerc(n_in);      p->set_parameter(0, 0, 200);      p->set_train_method(lemga::Perceptron::RAND_COOR_DESCENT_BIAS);      p->set_scale(1);      pst = p;      break;    }  case 204:    {      lemga::SoftPerc* p = new lemga::SoftPerc(n_in);      p->set_parameter(0, 0, 200);      p->set_train_method(lemga::Perceptron::RAND_COOR_DESCENT_BIAS);      p->set_scale(4);      pst = p;      break;    }  default:    std::cerr << base << " is not a correct base learner\n";    exit(-1);  }  lemga::AggRank *pbag = 0;    switch(bag/10){  case 1:    {      lemga::RankBoost* p = new lemga::RankBoost(n_rank);      p->set_thres_mode(lemga::AggRank::THRES_CLALOSS);      pbag = p;      break;    }  case 2:    {      lemga::RankBoost* p = new lemga::RankBoost(n_rank);      p->set_thres_mode(lemga::AggRank::THRES_ABSLOSS);      pbag = p;      break;    }  case 3:    {      lemga::ORBoost* p = new lemga::ORBoost(n_rank);      p->set_sub_iter(1);      p->set_ordered(true);      p->set_form(lemga::ORBoost::FORM_LR);            pbag = p;      break;    }  case 4:    {      lemga::ORBoost* p = new lemga::ORBoost(n_rank);      p->set_sub_iter(1);      p->set_ordered(true);      p->set_form(lemga::ORBoost::FORM_FULL);            pbag = p;      break;    }  }  if (bag % 10 == 1)    pbag->set_reg_param(1e-32);  else    pbag->set_reg_param(0.0);  pbag->set_base_model(*pst);  pbag->set_max_models(n_iter);  pbag->reset();  pbag->set_train_data(trd);  pbag->train();  fm << (*pbag);  fm.close();  // let C++ free things  return 0;}