void SVMLightRunner::librarySVMClassifyReadInputParameters(
int argc, char **argv, char *docfile, char *modelfile,
char *predictionsfile, long int *verbosity, long int *pred_format,
bool use_gmumr, SVMConfiguration &config
) {
LOG(
config.log,
LogLevel::DEBUG_LEVEL,
__debug_prefix__ + ".librarySVMClassifyReadInputParameters() Started."
);
long i;
// GMUM.R changes {
if (!use_gmumr) {
/* set default */
strcpy (modelfile, "svm_model");
strcpy (predictionsfile, "svm_predictions");
(*verbosity)=2;
} else {
// SVMLight verbosity = 1 corresponds to GMUM.R verbosity = 5 (DEBUG_LEVEL)
(*verbosity) = config.log.verbosity - 4;
}
// GMUM.R changes }
(*pred_format)=1;
for(i=1;(i<argc) && ((argv[i])[0] == '-');i++) {
switch ((argv[i])[1])
{
case 'h': libraryPrintHelp(); EXIT(0);
case 'v': i++; (*verbosity)=atol(argv[i]); break;
case 'f': i++; (*pred_format)=atol(argv[i]); break;
default: C_PRINTF("\nUnrecognized option %s!\n\n",argv[i]);
libraryPrintHelp();
EXIT(0);
}
}
// GMUM.R changes {
if(!use_gmumr) {
if((i+1)>=argc) {
C_PRINTF("\nNot enough input parameters!\n\n");
libraryPrintHelp();
EXIT(0);
}
strcpy (docfile, argv[i]);
strcpy (modelfile, argv[i+1]);
if((i+2)<argc) {
strcpy (predictionsfile, argv[i+2]);
}
}
// GMUM.R changes }
if(((*pred_format) != 0) && ((*pred_format) != 1)) {
C_PRINTF("\nOutput format can only take the values 0 or 1!\n\n");
libraryPrintHelp();
EXIT(0);
}
}
void SVMLightRunner::libraryReadDocuments (
char *docfile, DOC ***docs, double **label, long int *totwords,
long int *totdoc, bool use_gmumr, SVMConfiguration &config
) {
LOG(
config.log,
LogLevel::DEBUG_LEVEL,
__debug_prefix__ + ".libraryReadDocuments() Started."
);
char *line,*comment;
WORD *words;
long dnum=0,wpos,dpos=0,dneg=0,dunlab=0,queryid,slackid,max_docs;
long max_words_doc, ll;
double doc_label,costfactor;
FILE *docfl;
if(verbosity>=1) {
C_PRINTF("Scanning examples..."); C_FFLUSH(stdout);
}
// GMUM.R changes {
if (!use_gmumr) {
nol_ll(docfile,&max_docs,&max_words_doc,&ll); /* scan size of input file */
} else {
max_docs = config.target.n_rows;
max_words_doc = config.getDataDim();
// ll used only for file reading
}
// GMUM.R changes }
max_words_doc+=2;
ll+=2;
max_docs+=2;
if(verbosity>=1) {
C_PRINTF("done\n"); C_FFLUSH(stdout);
}
(*docs) = (DOC **)my_malloc(sizeof(DOC *)*max_docs); /* feature vectors */
(*label) = (double *)my_malloc(sizeof(double)*max_docs); /* target values */
// GMUM.R changes {
if (!use_gmumr) {
line = (char *)my_malloc(sizeof(char)*ll);
if ((docfl = fopen (docfile, "r")) == NULL)
{ perror (docfile); EXIT (1); }
}
// GMUM.R changes }
words = (WORD *)my_malloc(sizeof(WORD)*(max_words_doc+10));
if(verbosity>=1) {
C_PRINTF("Reading examples into memory..."); C_FFLUSH(stdout);
}
dnum=0;
(*totwords)=0;
// GMUM.R changes {
bool newline;
if (!use_gmumr) {
newline = (!feof(docfl)) && fgets(line,(int)ll,docfl);
} else {
newline = false;
if (dnum < config.target.n_rows) {
newline = true;
std::string str = SVMConfigurationToSVMLightLearnInputLine(config, dnum);
line = new char[str.size() + 1];
std::copy(str.begin(), str.end(), line);
line[str.size()] = '\0';
}
}
while(newline) {
if (use_gmumr) {
std::string stringline = "";
}
// GMUM.R changes }
if(line[0] == '#') continue; /* line contains comments */
if(!parse_document(line,words,&doc_label,&queryid,&slackid,&costfactor,
&wpos,max_words_doc,&comment)) {
C_PRINTF("\nParsing error in line %ld!\n%s",dnum,line);
EXIT(1);
}
(*label)[dnum]=doc_label;
/* C_PRINTF("docnum=%ld: Class=%f ",dnum,doc_label); */
if(doc_label > 0) dpos++;
if (doc_label < 0) dneg++;
if (doc_label == 0) {
if(config.use_transductive_learning){
dunlab++;
}else{
C_PRINTF("Please for transductive learning pass use_transductive_learning\n");
EXIT(1);
}
}
if((wpos>1) && ((words[wpos-2]).wnum>(*totwords)))
(*totwords)=(words[wpos-2]).wnum;
if((*totwords) > MAXFEATNUM) {
C_PRINTF("\nMaximum feature number exceeds limit defined in MAXFEATNUM!\n");
EXIT(1);
}
(*docs)[dnum] = create_example(dnum,queryid,slackid,costfactor,
create_svector(words,comment,1.0));
/* C_PRINTF("\nNorm=%f\n",((*docs)[dnum]->fvec)->twonorm_sq); */
dnum++;
if(verbosity>=1) {
if((dnum % 100) == 0) {
C_PRINTF("%ld..",dnum); C_FFLUSH(stdout);
}
}
// GMUM.R changes {
if (!use_gmumr) {
newline = (!feof(docfl)) && fgets(line,(int)ll,docfl);
} else {
newline = false;
if (dnum < config.target.n_rows) {
newline = true;
std::string str = SVMConfigurationToSVMLightLearnInputLine(config, dnum);
line = new char[str.size() + 1];
std::copy(str.begin(), str.end(), line);
line[str.size()] = '\0';
}
}
// GMUM.R changes }
}
if (!use_gmumr) {
fclose(docfl);
free(line);
};
free(words);
if(verbosity>=1) {
C_FPRINTF(stdout, "OK. (%ld examples read)\n", dnum);
}
(*totdoc)=dnum;
}
MODEL * SVMLightRunner::libraryReadModel(
char *modelfile, bool use_gmumr, SVMConfiguration &config
) {
LOG(
config.log,
LogLevel::DEBUG_LEVEL,
__debug_prefix__ + ".libraryReadModel() Started."
);
FILE *modelfl;
long i,queryid,slackid;
double costfactor;
long max_sv,max_words,ll,wpos;
char *line,*comment;
WORD *words;
char version_buffer[100];
MODEL *model;
if(verbosity>=1) {
C_PRINTF("Reading model..."); C_FFLUSH(stdout);
}
// GMUM.R changes {
model = (MODEL *)my_malloc(sizeof(MODEL));
if (!use_gmumr) {
nol_ll(modelfile,&max_sv,&max_words,&ll); /* scan size of model file */
max_words+=2;
ll+=2;
words = (WORD *)my_malloc(sizeof(WORD)*(max_words+10));
line = (char *)my_malloc(sizeof(char)*ll);
if ((modelfl = fopen (modelfile, "r")) == NULL)
{ perror (modelfile); EXIT (1); }
fscanf(modelfl,"SVM-light Version %s\n",version_buffer);
if(strcmp(version_buffer,VERSION)) {
perror ("Version of model-file does not match version of svm_classify!");
EXIT (1);
}
fscanf(modelfl,"%ld%*[^\n]\n", &model->kernel_parm.kernel_type);
fscanf(modelfl,"%ld%*[^\n]\n", &model->kernel_parm.poly_degree);
fscanf(modelfl,"%lf%*[^\n]\n", &model->kernel_parm.rbf_gamma);
fscanf(modelfl,"%lf%*[^\n]\n", &model->kernel_parm.coef_lin);
fscanf(modelfl,"%lf%*[^\n]\n", &model->kernel_parm.coef_const);
fscanf(modelfl,"%[^#]%*[^\n]\n", model->kernel_parm.custom);
fscanf(modelfl,"%ld%*[^\n]\n", &model->totwords);
fscanf(modelfl,"%ld%*[^\n]\n", &model->totdoc);
fscanf(modelfl,"%ld%*[^\n]\n", &model->sv_num);
fscanf(modelfl,"%lf%*[^\n]\n", &model->b);
} else { // use_gmumr
max_words = config.getDataDim();
words = (WORD *)my_malloc(sizeof(WORD)*(max_words+10));
LOG(
config.log,
LogLevel::DEBUG_LEVEL,
__debug_prefix__ + ".libraryReadModel() Converting config to model..."
);
/* 0=linear, 1=poly, 2=rbf, 3=sigmoid, 4=custom -- same as GMUM.R! */
model->kernel_parm.kernel_type = static_cast<long int>(config.kernel_type);
// -d int -> parameter d in polynomial kernel
model->kernel_parm.poly_degree = config.degree;
// -g float -> parameter gamma in rbf kernel
model->kernel_parm.rbf_gamma = config.gamma;
// -s float -> parameter s in sigmoid/poly kernel
model->kernel_parm.coef_lin = config.gamma;
// -r float -> parameter c in sigmoid/poly kernel
model->kernel_parm.coef_const = config.coef0;
// -u string -> parameter of user defined kernel
char kernel_parm_custom[50] = "empty";
char * model_kernel_parm_custom = model->kernel_parm.custom;
model_kernel_parm_custom = kernel_parm_custom;
// highest feature index
model->totwords = config.getDataDim();
// number of training documents
model->totdoc = config.target.n_rows;
// number of support vectors plus 1 (!)
model->sv_num = config.l + 1;
/* Threshold b (has opposite sign than SVMClient::predict())
* In svm_common.c:57 in double classify_example_linear():
* return(sum-model->b);
*/
model->b = - config.b;
LOG(
config.log,
LogLevel::DEBUG_LEVEL,
__debug_prefix__ + ".libraryReadModel() Converting config done."
);
}
// GMUM.R changes }
model->supvec = (DOC **)my_malloc(sizeof(DOC *)*model->sv_num);
model->alpha = (double *)my_malloc(sizeof(double)*model->sv_num);
model->index=NULL;
model->lin_weights=NULL;
// GMUM.R changes {
if (!use_gmumr) {
for(i=1;i<model->sv_num;i++) {
fgets(line,(int)ll,modelfl);
if(!parse_document(line,words,&(model->alpha[i]),&queryid,&slackid,
&costfactor,&wpos,max_words,&comment)) {
C_PRINTF("\nParsing error while reading model file in SV %ld!\n%s",
i,line);
EXIT(1);
}
model->supvec[i] = create_example(-1,
0,0,
0.0,
create_svector(words,comment,1.0));
}
fclose(modelfl);
free(line);
} else {
for(i = 1; i < model->sv_num; ++i) {
line = SVMConfigurationToSVMLightModelSVLine(config, i-1);
if(!parse_document(line,words,&(model->alpha[i]),&queryid,&slackid,
&costfactor,&wpos,max_words,&comment)) {
C_PRINTF("\nParsing error while reading model file in SV %ld!\n%s",
i,line);
EXIT(1);
}
model->supvec[i] = create_example(-1,
0,0,
0.0,
create_svector(words,comment,1.0));
free(line);
}
}
// GMUM.R changes }
free(words);
if(verbosity>=1) {
C_FPRINTF(stdout, "OK. (%d support vectors read)\n",(int)(model->sv_num-1));
}
LOG(
config.log,
LogLevel::DEBUG_LEVEL,
__debug_prefix__ + ".libraryReadModel() Done."
);
return(model);
}
int SVMLightRunner::librarySVMClassifyMain(
int argc, char **argv, bool use_gmumr, SVMConfiguration &config
) {
LOG(
config.log,
LogLevel::DEBUG_LEVEL,
__debug_prefix__ + ".librarySVMClassifyMain() Started."
);
DOC *doc; /* test example */
WORD *words;
long max_docs,max_words_doc,lld;
long totdoc=0,queryid,slackid;
long correct=0,incorrect=0,no_accuracy=0;
long res_a=0,res_b=0,res_c=0,res_d=0,wnum,pred_format;
long j;
double t1,runtime=0;
double dist,doc_label,costfactor;
char *line,*comment;
FILE *predfl,*docfl;
MODEL *model;
// GMUM.R changes {
librarySVMClassifyReadInputParameters(
argc, argv, docfile, modelfile, predictionsfile, &verbosity,
&pred_format, use_gmumr, config);
if (!use_gmumr) {
nol_ll(docfile,&max_docs,&max_words_doc,&lld); /* scan size of input file */
lld+=2;
line = (char *)my_malloc(sizeof(char)*lld);
} else {
max_docs = config.target.n_rows;
max_words_doc = config.getDataDim();
config.result = arma::zeros<arma::vec>(max_docs);
// Prevent writing to the file
pred_format = -1;
// lld used only for file reading
}
max_words_doc+=2;
words = (WORD *)my_malloc(sizeof(WORD)*(max_words_doc+10));
// GMUM.R changes }
model=libraryReadModel(modelfile, use_gmumr, config);
// GMUM.R changes }
if(model->kernel_parm.kernel_type == 0) { /* linear kernel */
/* compute weight vector */
add_weight_vector_to_linear_model(model);
}
if(verbosity>=2) {
C_PRINTF("Classifying test examples.."); C_FFLUSH(stdout);
}
// GMUM.R changes {
bool newline;
if (!use_gmumr) {
if ((predfl = fopen (predictionsfile, "w")) == NULL)
{ perror (predictionsfile); EXIT (1); }
if ((docfl = fopen (docfile, "r")) == NULL)
{ perror (docfile); EXIT (1); }
newline = (!feof(docfl)) && fgets(line,(int)lld,docfl);
} else {
newline = false;
if (totdoc < config.getDataExamplesNumber()) {
newline = true;
std::string str = SVMConfigurationToSVMLightLearnInputLine(config, totdoc);
line = new char[str.size() + 1];
std::copy(str.begin(), str.end(), line);
line[str.size()] = '\0';
}
}
while(newline) {
if (use_gmumr) {
std::string stringline = "";
}
// GMUM.R changes }
if(line[0] == '#') continue; /* line contains comments */
parse_document(line,words,&doc_label,&queryid,&slackid,&costfactor,&wnum,
max_words_doc,&comment);
totdoc++;
if(model->kernel_parm.kernel_type == 0) { /* linear kernel */
for(j=0;(words[j]).wnum != 0;j++) { /* Check if feature numbers */
if((words[j]).wnum>model->totwords) /* are not larger than in */
(words[j]).wnum=0; /* model. Remove feature if */
} /* necessary. */
doc = create_example(-1,0,0,0.0,create_svector(words,comment,1.0));
t1=get_runtime();
dist=classify_example_linear(model,doc);
runtime+=(get_runtime()-t1);
free_example(doc,1);
}
else { /* non-linear kernel */
doc = create_example(-1,0,0,0.0,create_svector(words,comment,1.0));
t1=get_runtime();
dist=classify_example(model,doc);
runtime+=(get_runtime()-t1);
free_example(doc,1);
}
if(dist>0) {
if(pred_format==0) { /* old weired output format */
C_FPRINTF(predfl,"%.8g:+1 %.8g:-1\n",dist,-dist);
}
if(doc_label>0) correct++; else incorrect++;
if(doc_label>0) res_a++; else res_b++;
}
else {
if(pred_format==0) { /* old weired output format */
C_FPRINTF(predfl,"%.8g:-1 %.8g:+1\n",-dist,dist);
}
if(doc_label<0) correct++; else incorrect++;
if(doc_label>0) res_c++; else res_d++;
}
if(pred_format==1) { /* output the value of decision function */
C_FPRINTF(predfl,"%.8g\n",dist);
}
if((int)(0.01+(doc_label*doc_label)) != 1)
{ no_accuracy=1; } /* test data is not binary labeled */
if(verbosity>=2) {
if(totdoc % 100 == 0) {
C_PRINTF("%ld..",totdoc); C_FFLUSH(stdout);
}
}
// GMUM.R changes {
if (!use_gmumr) {
newline = (!feof(docfl)) && fgets(line,(int)lld,docfl);
} else {
newline = false;
// Store prediction result in config
config.result[totdoc-1] = dist;
// Read next line
if (totdoc < config.getDataExamplesNumber()) {
newline = true;
std::string str = SVMConfigurationToSVMLightLearnInputLine(config, totdoc);
line = new char[str.size() + 1];
std::copy(str.begin(), str.end(), line);
line[str.size()] = '\0';
}
}
}
if (!use_gmumr) {
fclose(predfl);
fclose(docfl);
free(line);
}
// GMUM.R changes }
free(words);
free_model(model,1);
if(verbosity>=2) {
C_PRINTF("done\n");
/* Note by Gary Boone Date: 29 April 2000 */
/* o Timing is inaccurate. The timer has 0.01 second resolution. */
/* Because classification of a single vector takes less than */
/* 0.01 secs, the timer was underflowing. */
C_PRINTF("Runtime (without IO) in cpu-seconds: %.2f\n",
(float)(runtime/100.0));
}
if((!no_accuracy) && (verbosity>=1)) {
C_PRINTF("Accuracy on test set: %.2f%% (%ld correct, %ld incorrect, %ld total)\n",(float)(correct)*100.0/totdoc,correct,incorrect,totdoc);
C_PRINTF("Precision/recall on test set: %.2f%%/%.2f%%\n",(float)(res_a)*100.0/(res_a+res_b),(float)(res_a)*100.0/(res_a+res_c));
}
return(0);
}
void SVMLightRunner::librarySVMLearnReadInputParameters(
int argc, char *argv[], char *docfile, char *modelfile, char *restartfile,
long *verbosity, LEARN_PARM *learn_parm, KERNEL_PARM *kernel_parm,
bool use_gmumr, SVMConfiguration &config
) {
LOG(
config.log,
LogLevel::DEBUG_LEVEL,
__debug_prefix__ + ".librarySVMClassifyReadInputParameters() Started."
);
long i;
char type[100];
// GMUM.R changes {
/* set default */
strcpy (modelfile, "svm_model");
strcpy (learn_parm->predfile, "trans_predictions");
strcpy (learn_parm->alphafile, "");
strcpy (restartfile, "");
// SVMLight verbosity = 1 corresponds to GMUM.R verbosity = 5 (DEBUG_LEVEL)
(*verbosity)=config.log.verbosity - 4;
learn_parm->biased_hyperplane=1;
learn_parm->sharedslack=0;
learn_parm->remove_inconsistent=0;
learn_parm->skip_final_opt_check=0;
learn_parm->svm_maxqpsize=10;
learn_parm->svm_newvarsinqp=0;
learn_parm->svm_iter_to_shrink=-9999;
learn_parm->kernel_cache_size=40;
/* upper bound C on alphas */
learn_parm->svm_c=config.C;
learn_parm->eps=0.1;
learn_parm->transduction_posratio=-1.0;
/* factor to multiply C for positive examples */
learn_parm->svm_costratio=1.0;
/* FIXME: Find what this one does */
learn_parm->svm_costratio_unlab=1.0;
learn_parm->svm_unlabbound=1E-5;
learn_parm->epsilon_crit=0.001;
learn_parm->epsilon_a=1E-15;
learn_parm->compute_loo=0;
learn_parm->rho=1.0;
learn_parm->xa_depth=0;
kernel_parm->kernel_type=0;
kernel_parm->poly_degree=3;
kernel_parm->rbf_gamma=1.0;
kernel_parm->coef_lin=1;
kernel_parm->coef_const=1;
strcpy(kernel_parm->custom,"empty");
strcpy(type,"c");
if (config.max_iter == -1)
learn_parm->maxiter = 100000;
else
learn_parm->maxiter = config.max_iter;
if (static_cast<long int>(config.kernel_type) == 3) {
// sigmoid tanh(s a*b + c)
// s = 1.0/highest_feature_index
kernel_parm->coef_lin = 1.0/config.getDataDim();
// c
kernel_parm->coef_const = -1.0;
}
/* set userdefined */
if (config.degree){
kernel_parm->poly_degree = config.degree;
}
if (config.gamma){
kernel_parm->rbf_gamma = config.gamma;
}
if (config.gamma){
kernel_parm->coef_lin = config.gamma;
}
if (config.coef0){
kernel_parm->coef_const = config.coef0;
}
learn_parm->transduction_posratio = config.transductive_posratio;
//This is tricky - both LIBSVM and SVMLIGHT have same default eps.
//However in general we should do things like that
learn_parm->epsilon_crit=config.eps;
for(i=1;(i<argc) && ((argv[i])[0] == '-');i++) {
switch ((argv[i])[1])
{
case '?': libraryPrintHelp(); EXIT(0);
case 'z': i++; strcpy(type,argv[i]); break;
case 'v': i++; (*verbosity)=atol(argv[i]); break;
case 'b': i++; learn_parm->biased_hyperplane=atol(argv[i]); break;
case 'i': i++; learn_parm->remove_inconsistent=atol(argv[i]); break;
case 'f': i++; learn_parm->skip_final_opt_check=!atol(argv[i]); break;
case 'q': i++; learn_parm->svm_maxqpsize=atol(argv[i]); break;
case 'n': i++; learn_parm->svm_newvarsinqp=atol(argv[i]); break;
case '#': i++; learn_parm->maxiter=atol(argv[i]); break;
case 'h': i++; learn_parm->svm_iter_to_shrink=atol(argv[i]); break;
case 'm': i++; learn_parm->kernel_cache_size=atol(argv[i]); break;
case 'c': i++; learn_parm->svm_c=atof(argv[i]); break;
case 'w': i++; learn_parm->eps=atof(argv[i]); break;
case 'p': i++; learn_parm->transduction_posratio=atof(argv[i]); break;
case 'j': i++; learn_parm->svm_costratio=atof(argv[i]); break;
case 'e': i++; learn_parm->epsilon_crit=atof(argv[i]); break;
case 'o': i++; learn_parm->rho=atof(argv[i]); break;
case 'k': i++; learn_parm->xa_depth=atol(argv[i]); break;
case 'x': i++; learn_parm->compute_loo=atol(argv[i]); break;
case 't': i++; kernel_parm->kernel_type=atol(argv[i]); break;
case 'd': i++; kernel_parm->poly_degree=atol(argv[i]); break;
case 'g': i++; kernel_parm->rbf_gamma=atof(argv[i]); break;
case 's': i++; kernel_parm->coef_lin=atof(argv[i]); break;
case 'r': i++; kernel_parm->coef_const=atof(argv[i]); break;
case 'u': i++; strcpy(kernel_parm->custom,argv[i]); break;
case 'l': i++; strcpy(learn_parm->predfile,argv[i]); break;
case 'a': i++; strcpy(learn_parm->alphafile,argv[i]); break;
case 'y': i++; strcpy(restartfile,argv[i]); break;
default: C_PRINTF("\n[SVMLight] Unrecognized option %s!\n\n",argv[i]);
libraryPrintHelp();
EXIT(0);
}
}
// GMUM.R changes }
if(!use_gmumr) {
if(i>=argc) {
C_PRINTF("\nNot enough input parameters!\n\n");
libraryWaitAnyKey();
libraryPrintHelp();
EXIT(0);
}
strcpy (docfile, argv[i]);
}
// GMUM.R changes }
if((i+1)<argc) {
strcpy (modelfile, argv[i+1]);
}
if(learn_parm->svm_iter_to_shrink == -9999) {
if(kernel_parm->kernel_type == LINEAR)
learn_parm->svm_iter_to_shrink=2;
else
learn_parm->svm_iter_to_shrink=100;
}
if(strcmp(type,"c")==0) {
learn_parm->type=CLASSIFICATION;
}
else if(strcmp(type,"r")==0) {
learn_parm->type=REGRESSION;
}
else if(strcmp(type,"p")==0) {
learn_parm->type=RANKING;
}
else if(strcmp(type,"o")==0) {
learn_parm->type=OPTIMIZATION;
}
else if(strcmp(type,"s")==0) {
learn_parm->type=OPTIMIZATION;
learn_parm->sharedslack=1;
}
else {
C_PRINTF("\nUnknown type '%s': Valid types are 'c' (classification), 'r' regession, and 'p' preference ranking.\n",type);
libraryWaitAnyKey();
libraryPrintHelp();
EXIT(0);
}
if((learn_parm->skip_final_opt_check)
&& (kernel_parm->kernel_type == LINEAR)) {
C_PRINTF("\nIt does not make sense to skip the final optimality check for linear kernels.\n\n");
learn_parm->skip_final_opt_check=0;
}
if((learn_parm->skip_final_opt_check)
&& (learn_parm->remove_inconsistent)) {
C_PRINTF("\nIt is necessary to do the final optimality check when removing inconsistent \nexamples.\n");
libraryWaitAnyKey();
libraryPrintHelp();
EXIT(0);
}
if((learn_parm->svm_maxqpsize<2)) {
C_PRINTF("\nMaximum size of QP-subproblems not in valid range: %ld [2..]\n",learn_parm->svm_maxqpsize);
libraryWaitAnyKey();
libraryPrintHelp();
EXIT(0);
}
if((learn_parm->svm_maxqpsize<learn_parm->svm_newvarsinqp)) {
C_PRINTF("\nMaximum size of QP-subproblems [%ld] must be larger than the number of\n",learn_parm->svm_maxqpsize);
C_PRINTF("new variables [%ld] entering the working set in each iteration.\n",learn_parm->svm_newvarsinqp);
libraryWaitAnyKey();
libraryPrintHelp();
EXIT(0);
}
if(learn_parm->svm_iter_to_shrink<1) {
C_PRINTF("\nMaximum number of iterations for shrinking not in valid range: %ld [1,..]\n",learn_parm->svm_iter_to_shrink);
libraryWaitAnyKey();
libraryPrintHelp();
EXIT(0);
}
if(learn_parm->svm_c<0) {
C_PRINTF("\nThe C parameter must be greater than zero!\n\n");
libraryWaitAnyKey();
libraryPrintHelp();
EXIT(0);
}
if(learn_parm->transduction_posratio>1) {
C_PRINTF("\nThe fraction of unlabeled examples to classify as positives must\n");
C_PRINTF("be less than 1.0 !!!\n\n");
libraryWaitAnyKey();
libraryPrintHelp();
EXIT(0);
}
if(learn_parm->svm_costratio<=0) {
C_PRINTF("\nThe COSTRATIO parameter must be greater than zero!\n\n");
libraryWaitAnyKey();
libraryPrintHelp();
EXIT(0);
}
if(learn_parm->epsilon_crit<=0) {
C_PRINTF("\nThe epsilon parameter must be greater than zero!\n\n");
libraryWaitAnyKey();
libraryPrintHelp();
EXIT(0);
}
if(learn_parm->rho<0) {
C_PRINTF("\nThe parameter rho for xi/alpha-estimates and leave-one-out pruning must\n");
C_PRINTF("be greater than zero (typically 1.0 or 2.0, see T. Joachims, Estimating the\n");
C_PRINTF("Generalization Performance of an SVM Efficiently, ICML, 2000.)!\n\n");
libraryWaitAnyKey();
libraryPrintHelp();
EXIT(0);
}
if((learn_parm->xa_depth<0) || (learn_parm->xa_depth>100)) {
C_PRINTF("\nThe parameter depth for ext. xi/alpha-estimates must be in [0..100] (zero\n");
C_PRINTF("for switching to the conventional xa/estimates described in T. Joachims,\n");
C_PRINTF("Estimating the Generalization Performance of an SVM Efficiently, ICML, 2000.)\n");
libraryWaitAnyKey();
libraryPrintHelp();
EXIT(0);
}
}
void SVMLightRunner::libraryPrintHelp()
{
C_PRINTF("\nSVM-light %s: Support Vector Machine, learning module %s\n",VERSION,VERSION_DATE);
copyright_notice();
}
void SVMLightRunner::libraryWaitAnyKey()
{
C_PRINTF("\n(more)\n");
(void)getc(stdin);
}
std::string SVMLightRunner::SVMConfigurationToSVMLightLearnInputLine(
SVMConfiguration &config, long int line_num
) {
LOG(
config.log,
LogLevel::DEBUG_LEVEL,
__debug_prefix__ + ".SVMConfigurationToSVMLightLearnInputLine() Started."
);
std::string line_string = "";
std::ostringstream ss;
double target_value = config.target[line_num];
// Handle user-defined labels
if (target_value == config.neg_target) {
ss << -1;
} else if (target_value == config.pos_target) {
ss << 1;
} else if (!target_value) {
ss << 0;
}else{
C_PRINTF("Unrecognized class label %f\n", target_value);
EXIT(1);
}
// Optional feature: cost :)
if (config.use_example_weights || (config.use_class_weights && target_value)) {
double weight = 1.0;
if(config.use_example_weights){
weight *= config.example_weights[line_num];
}
if(config.use_class_weights){
weight *= (target_value == config.pos_target? config.class_weights[1] : config.class_weights[0]);
}
ss << " cost:" << std::setprecision(8) << weight;
}
// Matrix type handling
if (config.isSparse()) {
int current_row = 0;
arma::sp_mat::iterator begin = config.getSparseData().begin_col(line_num);
arma::sp_mat::iterator end = config.getSparseData().end_col(line_num);
for (arma::sp_mat::iterator it = begin; it != end; ++it) {
ss << ' ' << it.row() + 1 << ':' << std::setprecision(8);
current_row = it.row();
ss << *it;
}
//Always output last row
if(current_row != config.getSparseData().n_rows-1){
ss << ' ' << config.getSparseData().n_rows << ':' << std::setprecision(8);
ss << config.getSparseData()(config.getSparseData().n_rows-1, line_num);
}
} else {
for (long int i = 1; i <= config.data.n_cols; ++i) {
ss << ' ' << i << ':' << std::setprecision(8);
ss << config.data(line_num, i-1);
}
}
ss << std::endl;
line_string = ss.str();
return line_string;
}
void C_KOORAUS(SFPOS Pos, PARTEI Partei)
{
C_PRINTF("%c%c", 'a' + 7 - Pos % 8, '1' + Pos / 8);
}
