/**   orboost.cpp: implement the ORBoost algorithm (Lin and Li 2006)   for ordinal regression   (c) 2006-2007 Hsuan-Tien Lin**/#include <assert.h>#include <cmath>#include "orboost.h"REGISTER_CREATOR(lemga::ORBoost);namespace lemga {  void ORBoost::train() {    //steepest coordinate decent on (h, w). Then iteratively update (thres, w) for sub_iter times    set_dimensions(*ptd);    std::cerr << "Entering ORBoost Training" << std::endl;    vREAL thres_now(n_rank+1);    vREAL rhopos(n_samples);    vREAL rhoneg(n_samples);    REAL sum_rhodiff, obj;    recompute_dec();    thres_now[0] = - std::numeric_limits<REAL>::infinity();    thres_now[n_rank] = std::numeric_limits<REAL>::infinity();    compute_thres_exploss(thres_now, ordered, form == FORM_FULL);    for(UINT k = 1; k <= n_thres; ++k){      thres[(n_in_agg*n_thres) + k-1] = thres_now[k];    }    update_with_dec(thres_now, rhopos, rhoneg, sum_rhodiff, obj);    std::cerr << "Iteration " << 0 << "/" << max_n_model << " : Obj = " << obj << std::endl;    thres.resize((max_n_model+1) * n_thres);    for (UINT i = 1; i <= max_n_model; ++i) {            pLearnModel plm = learn_weak(rhopos, rhoneg, sum_rhodiff);	      REAL ww(0.0);      int infflag;      for(UINT z=0;z<sub_iter;z++){	REAL w = compute_w(plm, rhopos, rhoneg);	if (isnan(w))	  w = 0;		infflag = isinf(w);	if (infflag)	  w = infflag * std::numeric_limits<REAL>::max();	for(UINT j = 0; j < n_samples; ++j) {	  REAL o = plm->get_output(j)[0];	  dec_value[j] += o * w;	}	ww += w;	compute_thres_exploss(thres_now, ordered, form == FORM_FULL);	update_with_dec(thres_now, rhopos, rhoneg, sum_rhodiff, obj);	if (infflag)	  break;      }      infflag = isinf(ww);      if (infflag)	ww = infflag * std::numeric_limits<REAL>::max();            std::cerr << "Iteration " << i << "/" << max_n_model << " : Obj = " << obj << std::endl;      lm.push_back(plm); lm_wgt.push_back(ww);      n_in_agg++;      for(UINT k = 1; k <= n_thres; ++k)	thres[(n_in_agg*n_thres) + k-1] = thres_now[k];      if (infflag){	thres.resize((n_in_agg+1) * n_thres);	return;      }      plm->set_train_data(ptd);    }  }  REAL ORBoost::compute_w(pLearnModel plm, vREAL& rhopos, vREAL& rhoneg){    REAL wp(reg_param), wn(reg_param);    for(UINT j = 0; j < n_samples; ++j) {      REAL o = plm->get_output(j)[0];      if (o >= 0){	wn += rhopos[j] * o;	wp += rhoneg[j] * o;      }      else{	wp += rhopos[j] * (-o);	wn += rhoneg[j] * (-o);      }    }    /**       for o in [0, 1]          exp(w * o) <= (e^+w - 1)*  o + 1       for o in [-1, 0]          exp(w * o) <= (e^-w - 1)* -o + 1    **/    return 0.5 * log(wn / wp);  }  void ORBoost::update_with_dec(vREAL& thres_now, vREAL& rhopos, vREAL& rhoneg, REAL& sum_rhodiff, REAL& obj){    sum_rhodiff = 0.0;    obj = 0.0;    for(UINT j = 0; j < n_samples; ++j) {      UINT goal = (UINT)ptd->y(j)[0];      if (form == FORM_LR){	rhopos[j] = exp(- dec_value[j] + thres_now[goal-1]);	rhoneg[j] = exp(dec_value[j] - thres_now[goal]);      }      else{ //FORM_FULL	rhopos[j] = 0.0;	rhoneg[j] = 0.0;	for(UINT k = 1; k <= goal-1; ++k)	  rhopos[j] += exp(- dec_value[j] + thres_now[k]);	for(UINT k = goal; k < n_rank; ++k)	  rhoneg[j] += exp(dec_value[j] - thres_now[k]);      }      sum_rhodiff += fabs(rhopos[j] - rhoneg[j]);      obj += (rhopos[j] + rhoneg[j]);    }  }} //namespace lemga