Mercurial > hg > camir-aes2014
view toolboxes/SVM-light/src/svm_loqo.c @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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/***********************************************************************/ /* */ /* svm_loqo.c */ /* */ /* Interface to the PR_LOQO optimization package for SVM. */ /* */ /* Author: Thorsten Joachims */ /* Date: 19.07.99 */ /* */ /* Copyright (c) 1999 Universitaet Dortmund - All rights reserved */ /* */ /* This software is available for non-commercial use only. It must */ /* not be modified and distributed without prior permission of the */ /* author. The author is not responsible for implications from the */ /* use of this software. */ /* */ /***********************************************************************/ # include <math.h> # include "pr_loqo/pr_loqo.h" # include "svm_common.h" /* Common Block Declarations */ long verbosity; /* /////////////////////////////////////////////////////////////// */ # define DEF_PRECISION_LINEAR 1E-8 # define DEF_PRECISION_NONLINEAR 1E-14 double *optimize_qp(); double *primal=0,*dual=0; double init_margin=0.15; long init_iter=500,precision_violations=0; double model_b; double opt_precision=DEF_PRECISION_LINEAR; /* /////////////////////////////////////////////////////////////// */ void *my_malloc(); double *optimize_qp(qp,epsilon_crit,nx,threshold,learn_parm) QP *qp; double *epsilon_crit; long nx; /* Maximum number of variables in QP */ double *threshold; LEARN_PARM *learn_parm; /* start the optimizer and return the optimal values */ { register long i,j,result; double margin,obj_before,obj_after; double sigdig,dist,epsilon_loqo; int iter; if(!primal) { /* allocate memory at first call */ primal=(double *)my_malloc(sizeof(double)*nx*3); dual=(double *)my_malloc(sizeof(double)*(nx*2+1)); } if(verbosity>=4) { /* really verbose */ printf("\n\n"); for(i=0;i<qp->opt_n;i++) { printf("%f: ",qp->opt_g0[i]); for(j=0;j<qp->opt_n;j++) { printf("%f ",qp->opt_g[i*qp->opt_n+j]); } printf(": a%ld=%.10f < %f",i,qp->opt_xinit[i],qp->opt_up[i]); printf(": y=%f\n",qp->opt_ce[i]); } for(j=0;j<qp->opt_m;j++) { printf("EQ-%ld: %f*a0",j,qp->opt_ce[j]); for(i=1;i<qp->opt_n;i++) { printf(" + %f*a%ld",qp->opt_ce[i],i); } printf(" = %f\n\n",-qp->opt_ce0[0]); } } obj_before=0; /* calculate objective before optimization */ for(i=0;i<qp->opt_n;i++) { obj_before+=(qp->opt_g0[i]*qp->opt_xinit[i]); obj_before+=(0.5*qp->opt_xinit[i]*qp->opt_xinit[i]*qp->opt_g[i*qp->opt_n+i]); for(j=0;j<i;j++) { obj_before+=(qp->opt_xinit[j]*qp->opt_xinit[i]*qp->opt_g[j*qp->opt_n+i]); } } result=STILL_RUNNING; qp->opt_ce0[0]*=(-1.0); /* Run pr_loqo. If a run fails, try again with parameters which lead */ /* to a slower, but more robust setting. */ for(margin=init_margin,iter=init_iter; (margin<=0.9999999) && (result!=OPTIMAL_SOLUTION);) { sigdig=-log10(opt_precision); result=pr_loqo((int)qp->opt_n,(int)qp->opt_m, (double *)qp->opt_g0,(double *)qp->opt_g, (double *)qp->opt_ce,(double *)qp->opt_ce0, (double *)qp->opt_low,(double *)qp->opt_up, (double *)primal,(double *)dual, (int)(verbosity-2), (double)sigdig,(int)iter, (double)margin,(double)(qp->opt_up[0])/4.0,(int)0); if(isnan(dual[0])) { /* check for choldc problem */ if(verbosity>=2) { printf("NOTICE: Restarting PR_LOQO with more conservative parameters.\n"); } if(init_margin<0.80) { /* become more conservative in general */ init_margin=(4.0*margin+1.0)/5.0; } margin=(margin+1.0)/2.0; (opt_precision)*=10.0; /* reduce precision */ if(verbosity>=2) { printf("NOTICE: Reducing precision of PR_LOQO.\n"); } } else if(result!=OPTIMAL_SOLUTION) { iter+=2000; init_iter+=10; (opt_precision)*=10.0; /* reduce precision */ if(verbosity>=2) { printf("NOTICE: Reducing precision of PR_LOQO due to (%ld).\n",result); } } } if(qp->opt_m) /* Thanks to Alex Smola for this hint */ model_b=dual[0]; else model_b=0; /* Check the precision of the alphas. If results of current optimization */ /* violate KT-Conditions, relax the epsilon on the bounds on alphas. */ epsilon_loqo=1E-10; for(i=0;i<qp->opt_n;i++) { dist=-model_b*qp->opt_ce[i]; dist+=(qp->opt_g0[i]+1.0); for(j=0;j<i;j++) { dist+=(primal[j]*qp->opt_g[j*qp->opt_n+i]); } for(j=i;j<qp->opt_n;j++) { dist+=(primal[j]*qp->opt_g[i*qp->opt_n+j]); } /* printf("LOQO: a[%d]=%f, dist=%f, b=%f\n",i,primal[i],dist,dual[0]); */ if((primal[i]<(qp->opt_up[i]-epsilon_loqo)) && (dist < (1.0-(*epsilon_crit)))) { epsilon_loqo=(qp->opt_up[i]-primal[i])*2.0; } else if((primal[i]>(0+epsilon_loqo)) && (dist > (1.0+(*epsilon_crit)))) { epsilon_loqo=primal[i]*2.0; } } for(i=0;i<qp->opt_n;i++) { /* clip alphas to bounds */ if(primal[i]<=(0+epsilon_loqo)) { primal[i]=0; } else if(primal[i]>=(qp->opt_up[i]-epsilon_loqo)) { primal[i]=qp->opt_up[i]; } } obj_after=0; /* calculate objective after optimization */ for(i=0;i<qp->opt_n;i++) { obj_after+=(qp->opt_g0[i]*primal[i]); obj_after+=(0.5*primal[i]*primal[i]*qp->opt_g[i*qp->opt_n+i]); for(j=0;j<i;j++) { obj_after+=(primal[j]*primal[i]*qp->opt_g[j*qp->opt_n+i]); } } /* if optimizer returned NAN values, reset and retry with smaller */ /* working set. */ if(isnan(obj_after) || isnan(model_b)) { for(i=0;i<qp->opt_n;i++) { primal[i]=qp->opt_xinit[i]; } model_b=0; if(learn_parm->svm_maxqpsize>2) { learn_parm->svm_maxqpsize--; /* decrease size of qp-subproblems */ } } if(obj_after >= obj_before) { /* check whether there was progress */ (opt_precision)/=100.0; precision_violations++; if(verbosity>=2) { printf("NOTICE: Increasing Precision of PR_LOQO.\n"); } } if(precision_violations > 500) { (*epsilon_crit)*=10.0; precision_violations=0; if(verbosity>=1) { printf("\nWARNING: Relaxing epsilon on KT-Conditions.\n"); } } (*threshold)=model_b; if(result!=OPTIMAL_SOLUTION) { printf("\nERROR: PR_LOQO did not converge. \n"); return(qp->opt_xinit); } else { return(primal); } }