static void bssm_param_plain_write(const GthBSSMParam *bssm_param, FILE *outfp)
{
GtStr *str;
gt_assert(bssm_param && outfp);
str = gt_str_new();
gt_str_append_cstr(str, "BSSM = {\n");
if (bssm_param->gt_donor_model_set) {
write_model(str, "gt_donor_model", &bssm_param->gt_donor_model);
gt_str_append_cstr(str, ",\n");
}
if (bssm_param->gc_donor_model_set) {
write_model(str, "gc_donor_model", &bssm_param->gc_donor_model);
gt_str_append_cstr(str, ",\n");
}
if (bssm_param->ag_acceptor_model_set) {
write_model(str, "ag_acceptor_model", &bssm_param->ag_acceptor_model);
gt_str_append_char(str, '\n');
}
gt_str_append_cstr(str, "}\n");
gt_xfwrite(gt_str_get(str), sizeof (char), gt_str_length(str), outfp);
gt_str_delete(str);
}
int _svm_learn (int argc, char* argv[])
{
char docfile[200]; /* file with training examples */
char modelfile[200]; /* file for resulting classifier */
char restartfile[200]; /* file with initial alphas */
DOC **docs; /* training examples */
long totwords,totdoc,i;
double *target;
double *alpha_in=NULL;
KERNEL_CACHE *kernel_cache;
LEARN_PARM learn_parm;
KERNEL_PARM kernel_parm;
MODEL *model=(MODEL *)my_malloc(sizeof(MODEL));
HIDEO_ENV *hideo_env=create_env();
model->td_pred=NULL;
model->n_td_pred=0;
_read_input_parameters(argc,argv,docfile,modelfile,restartfile,&verbosity,
&learn_parm,&kernel_parm);
read_documents(docfile,&docs,&target,&totwords,&totdoc);
if(restartfile[0]) alpha_in=read_alphas(restartfile,totdoc);
if(kernel_parm.kernel_type == LINEAR) { /* don't need the cache */
kernel_cache=NULL;
}
else {
/* Always get a new kernel cache. It is not possible to use the
same cache for two different training runs */
kernel_cache=kernel_cache_init(totdoc,learn_parm.kernel_cache_size);
}
if(learn_parm.type == CLASSIFICATION) {
svm_learn_classification(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,kernel_cache,model,alpha_in,hideo_env);
}
else if(learn_parm.type == REGRESSION) {
svm_learn_regression(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,&kernel_cache,model,hideo_env);
}
else if(learn_parm.type == RANKING) {
svm_learn_ranking(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,&kernel_cache,model,hideo_env);
}
else if(learn_parm.type == OPTIMIZATION) {
svm_learn_optimization(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,kernel_cache,model,alpha_in,hideo_env);
}
if(kernel_cache) {
/* Free the memory used for the cache. */
kernel_cache_cleanup(kernel_cache);
}
/* Warning: The model contains references to the original data 'docs'.
If you want to free the original data, and only keep the model, you
have to make a deep copy of 'model'. */
/* deep_copy_of_model=copy_model(model); */
write_model(modelfile,model);
free(alpha_in);
free_model(model,0);
for(i=0;i<totdoc;i++)
free_example(docs[i],1);
free(docs);
free(target);
free_env(hideo_env);
return(0);
}
int main (int argc, char* argv[])
{
DOC *docs; /* training examples */
long max_docs,max_words_doc;
long totwords,totdoc,ll,i;
long kernel_cache_size;
double *target;
KERNEL_CACHE kernel_cache;
LEARN_PARM learn_parm;
KERNEL_PARM kernel_parm;
MODEL model;
read_input_parameters(argc,argv,docfile,modelfile,&verbosity,
&kernel_cache_size,&learn_parm,&kernel_parm);
if(verbosity>=1) {
printf("Scanning examples..."); fflush(stdout);
}
nol_ll(docfile,&max_docs,&max_words_doc,&ll); /* scan size of input file */
max_words_doc+=10;
ll+=10;
max_docs+=2;
if(verbosity>=1) {
printf("done\n"); fflush(stdout);
}
docs = (DOC *)my_malloc(sizeof(DOC)*max_docs); /* feature vectors */
target = (double *)my_malloc(sizeof(double)*max_docs); /* target values */
//printf("\nMax docs: %ld, approximated number of feature occurences %ld, maximal length of a line %ld\n\n",max_docs,max_words_doc,ll);
read_documents(docfile,docs,target,max_words_doc,ll,&totwords,&totdoc,&kernel_parm);
printf("\nNumber of examples: %ld, linear space size: %ld\n\n",totdoc,totwords);
//if(kernel_parm.kernel_type==5) totwords=totdoc; // The number of features is proportional to the number of parse-trees, i.e. totdoc
// or should we still use totwords to approximate svm_maxqpsize for the Tree Kernel (see hideo.c) ???????
if(kernel_parm.kernel_type == LINEAR) { /* don't need the cache */
if(learn_parm.type == CLASSIFICATION) {
svm_learn_classification(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,NULL,&model);
}
else if(learn_parm.type == REGRESSION) {
svm_learn_regression(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,NULL,&model);
}
else if(learn_parm.type == RANKING) {
svm_learn_ranking(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,NULL,&model);
}
}
else {
if(learn_parm.type == CLASSIFICATION) {
/* Always get a new kernel cache. It is not possible to use the
same cache for two different training runs */
kernel_cache_init(&kernel_cache,totdoc,kernel_cache_size);
svm_learn_classification(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,&kernel_cache,&model);
/* Free the memory used for the cache. */
kernel_cache_cleanup(&kernel_cache);
}
else if(learn_parm.type == REGRESSION) {
/* Always get a new kernel cache. It is not possible to use the
same cache for two different training runs */
kernel_cache_init(&kernel_cache,2*totdoc,kernel_cache_size);
svm_learn_regression(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,&kernel_cache,&model);
/* Free the memory used for the cache. */
kernel_cache_cleanup(&kernel_cache);
}
else if(learn_parm.type == RANKING) {
printf("Learning rankings is not implemented for non-linear kernels in this version!\n");
exit(1);
}
else if(learn_parm.type == PERCEPTRON) {
perceptron_learn_classification(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,&kernel_cache,&model,modelfile);
}
else if(learn_parm.type == PERCEPTRON_BATCH) {
batch_perceptron_learn_classification(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,kernel_cache_size,&model);
}
}
/* Warning: The model contains references to the original data 'docs'.
If you want to free the original data, and only keep the model, you
have to make a deep copy of 'model'. */
write_model(modelfile,&model);
free(model.supvec);
free(model.alpha);
free(model.index);
for(i=0;i<totdoc;i++){
freeExample(&docs[i]);
}
free(docs);
free(target);
return(0);
}
/*************************************************************************
This function performs the entire training process of the model
Allows for training in stages, gives better output and checks that
previous stages are intialized
**************************************************************************/
void AllScoreModels::trainModelsInStages(
const char *newModelName,
const FileManager& fm,
const SpectraAggregator& sa,
mass_t initialToleranceEstimate,
int startTrainingStage,
int endTrainingStage,
int specificCharge,
int specificSize,
int specificRegion,
const char *pathNegativeSpectra)
{
setTrainingStageNames();
if (endTrainingStage >= trainingStages_.size())
endTrainingStage = trainingStages_.size() -1;
// check what stages were initialized already
checkModelsInitializationStatus(true);
model_name = newModelName;
config_.set_model_name(string(newModelName));
// check if starting stage dependencies
if (startTrainingStage>0)
{
int i;
for (i=0; i<startTrainingStage && i<7; i++)
if (! trainingStages_[i].indWasInitialized)
break;
if (i<startTrainingStage && i<7)
{
cout << "Stage " << i << " (" << trainingStages_[i].name << ") was not initialized!" << endl;
cout << "Starting training at this stage!" << endl;
startTrainingStage=i;
}
}
// start training according to stages
// partition according to sizes
if (startTrainingStage<=0 && endTrainingStage>=0)
{
trainingStages_[0].writeNameHeader();
config_.computeSizeThresholds(sa);
}
// selection of fragments
if (startTrainingStage<=1 && endTrainingStage>=1)
{
trainingStages_[1].writeNameHeader();
config_.setTolerances(initialToleranceEstimate);
config_.selectFragmentIonTypes(sa, 16, 0.05);
}
// fragment tolerance and precursor tolerance
if (startTrainingStage<=2 && endTrainingStage>=2)
{
trainingStages_[2].writeNameHeader();
config_.learnTolerancesFromData(sa, initialToleranceEstimate);
write_model();
}
// SQS - spectra quality score
if (startTrainingStage<=3 && endTrainingStage>=3)
{
trainingStages_[3].writeNameHeader();
if (! pathNegativeSpectra)
{
cout << "Error: to train SQS models you must supply a file with negative spectra samples (with the -neg_spec_list flag)." << endl;
write_model();
exit(1);
}
pmcsqs.trainSqsModels(&config_, sa, pathNegativeSpectra, specificCharge);
write_model();
}
// PMCR - precursor mass correction
if (startTrainingStage<=4 && endTrainingStage>=4)
{
trainingStages_[4].writeNameHeader();
pmcsqs.trainPmcRankModels(&config_, sa, specificCharge);
write_model();
}
// Node scores
if (startTrainingStage<=5 && endTrainingStage>=5)
{
trainingStages_[5].writeNameHeader();
prmNodeScoreModel_.trainNodeScoreModels(static_cast<void*>(this), newModelName, sa,
specificCharge, specificSize, specificRegion);
write_model();
}
// Node score normalizer
if (startTrainingStage<=6 && endTrainingStage>=6)
{
trainingStages_[6].writeNameHeader();
prmNodeScoreModel_.learnPrmNormalizerValue(static_cast<void*>(this), sa);
prmNodeScoreModel_.write_prm_normalizer_values();
}
// Edge scores
if (startTrainingStage<=7 && endTrainingStage>=7)
{
trainingStages_[7].writeNameHeader();
edgeModel_.train_all_edge_models(sa,static_cast<void*>(this),specificCharge);
write_model();
}
// rerank model (database scores)
if (startTrainingStage<=8 && endTrainingStage>=7)
{
trainingStages_[8].writeNameHeader();
}
exit(0);
cout << endl << "STAGE 7: Train Amino Acid models" << endl;
cout << "********************************" << endl << endl;
if (startTrainingStage>7)
{
cout << endl << "Already done." << endl;
}
else
{
if (specificCharge>0)
cout << "+++ Only specified charge " << specificCharge << endl << endl;
amino_acid_probs.train_amino_acid_prob_models(fm,this,specificCharge, specificSize);
}
/* cout << endl << "STAGE 8: Train Cumulative de novo probability models" << endl;
cout << "****************************************************" << endl << endl;
if (startTrainingStage>8)
{
cout << endl << "Already done." << endl;
}
else
{
if (specific_charge>0)
cout << "+++ Only specified charge " << specific_charge << endl << endl;
.train_seq_prob_models(fm,this,specific_charge,specific_size);
} */
if (endTrainingStage<=8)
{
write_model();
exit(0);
}
exit(0);
}
int SVMLightRunner::librarySVMLearnMain(
int argc, char **argv, bool use_gmumr, SVMConfiguration &config
) {
LOG(
config.log,
LogLevel::DEBUG_LEVEL,
__debug_prefix__ + ".librarySVMLearnMain() Started."
);
DOC **docs; /* training examples */
long totwords,totdoc,i;
double *target;
double *alpha_in=NULL;
KERNEL_CACHE *kernel_cache;
LEARN_PARM learn_parm;
KERNEL_PARM kernel_parm;
MODEL *model=(MODEL *)my_malloc(sizeof(MODEL));
// GMUM.R changes {
librarySVMLearnReadInputParameters(
argc, argv, docfile, modelfile, restartfile, &verbosity, &learn_parm,
&kernel_parm, use_gmumr, config
);
kernel_parm.kernel_type = static_cast<long int>(config.kernel_type);
libraryReadDocuments(
docfile, &docs, &target, &totwords, &totdoc, use_gmumr, config
);
// GMUM.R changes }
if(restartfile[0]) alpha_in=read_alphas(restartfile,totdoc);
if(kernel_parm.kernel_type == LINEAR) { /* don't need the cache */
kernel_cache=NULL;
}
else {
/* Always get a new kernel cache. It is not possible to use the
* same cache for two different training runs */
kernel_cache=kernel_cache_init(totdoc,learn_parm.kernel_cache_size);
}
//gmum.r
init_global_params_QP();
if(learn_parm.type == CLASSIFICATION) {
svm_learn_classification(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,kernel_cache,model,alpha_in);
}
else if(learn_parm.type == REGRESSION) {
svm_learn_regression(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,&kernel_cache,model);
}
else if(learn_parm.type == RANKING) {
svm_learn_ranking(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,&kernel_cache,model);
}
else if(learn_parm.type == OPTIMIZATION) {
svm_learn_optimization(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,kernel_cache,model,alpha_in);
}
//gmum.r
config.iter = learn_parm.iterations;
if(kernel_cache) {
/* Free the memory used for the cache. */
kernel_cache_cleanup(kernel_cache);
}
/* Warning: The model contains references to the original data 'docs'.
If you want to free the original data, and only keep the model, you
have to make a deep copy of 'model'. */
/* deep_copy_of_model=copy_model(model); */
// GMUM.R changes {
if (!use_gmumr) {
write_model(modelfile,model);
} else {
SVMLightModelToSVMConfiguration(model, config);
}
// GMUM.R changes }
free(alpha_in);
free_model(model,0);
for(i=0;i<totdoc;i++)
free_example(docs[i],1);
free(docs);
free(target);
LOG(
config.log,
LogLevel::DEBUG_LEVEL,
__debug_prefix__ + ".librarySVMLearnMain() Done."
);
return(0);
}
void write_struct_model(char *file, STRUCTMODEL *sm,
STRUCT_LEARN_PARM *sparm)
{
/* Writes structural model sm to file file. */
write_model(file,sm->svm_model);
}
int main(int argc, char *argv[])
{
lprec *lp = NULL;
char *filen, *wlp = NULL, *wmps = NULL, *wfmps = NULL, plp = FALSE;
int i;
int verbose = IMPORTANT /* CRITICAL */;
int debug = -1;
MYBOOL report = FALSE;
MYBOOL nonames = FALSE, norownames = FALSE, nocolnames = FALSE;
MYBOOL write_model_after = FALSE;
MYBOOL noint = FALSE;
int print_sol = -1;
MYBOOL print_stats = FALSE;
int floor_first = -1;
MYBOOL do_set_bb_depthlimit = FALSE;
int bb_depthlimit = 0;
MYBOOL do_set_solutionlimit = FALSE;
int solutionlimit = 0;
MYBOOL break_at_first = FALSE;
int scaling = 0;
double scaleloop = 0;
MYBOOL tracing = FALSE;
short filetype = filetypeLP;
int anti_degen1 = -1;
int anti_degen2 = -1;
short print_timing = FALSE;
short parse_only = FALSE;
int do_presolve = -1;
short objective = 0;
short PRINT_SOLUTION = 2;
int improve = -1;
int pivoting1 = -1;
int pivoting2 = -1;
int bb_rule1 = -1;
int bb_rule2 = -1;
int max_num_inv = -1;
int scalemode1 = -1;
int scalemode2 = -1;
int crashmode = -1;
char *guessbasis = NULL;
/* short timeoutok = FALSE; */
long sectimeout = -1;
int result;
MYBOOL preferdual = AUTOMATIC;
int simplextype = -1;
MYBOOL do_set_obj_bound = FALSE;
REAL obj_bound = 0;
REAL mip_absgap = -1;
REAL mip_relgap = -1;
REAL epsperturb = -1;
REAL epsint = -1;
REAL epspivot = -1;
REAL epsd = -1;
REAL epsb = -1;
REAL epsel = -1;
MYBOOL do_set_break_at_value = FALSE;
REAL break_at_value = 0;
REAL accuracy_error0, accuracy_error = -1;
FILE *fpin = stdin;
char *bfp = NULL;
char *rxliname = NULL, *rxli = NULL, *rxlidata = NULL, *rxlioptions = NULL, *wxliname = NULL, *wxlisol = NULL, *wxli = NULL, *wxlioptions = NULL, *wxlisoloptions = NULL;
char *rbasname = NULL, *wbasname = NULL;
char *debugdump_before = NULL;
char *debugdump_after = NULL;
char *rparname = NULL;
char *rparoptions = NULL;
char *wparname = NULL;
char *wparoptions = NULL;
char obj_in_basis = -1;
char mps_ibm = FALSE;
char mps_negobjconst = FALSE;
char mps_free = FALSE;
MYBOOL ok;
# define SCALINGTHRESHOLD 0.03
/* read command line arguments */
# if defined FORTIFY
Fortify_EnterScope();
# endif
for(i = 1; i < argc; i++) {
ok = FALSE;
if(strncmp(argv[i], "-v", 2) == 0) {
if (argv[i][2])
verbose = atoi(argv[i] + 2);
else
verbose = NORMAL;
}
else if(strcmp(argv[i], "-d") == 0)
debug = TRUE;
else if(strcmp(argv[i], "-R") == 0)
report = TRUE;
else if(strcmp(argv[i], "-i") == 0)
print_sol = TRUE;
else if(strcmp(argv[i], "-ia") == 0)
print_sol = AUTOMATIC;
else if(strcmp(argv[i], "-stat") == 0)
print_stats = TRUE;
else if(strcmp(argv[i], "-nonames") == 0)
nonames = TRUE;
else if(strcmp(argv[i], "-norownames") == 0)
norownames = TRUE;
else if(strcmp(argv[i], "-nocolnames") == 0)
nocolnames = TRUE;
else if((strcmp(argv[i], "-c") == 0) || (strcmp(argv[i], "-cc") == 0))
floor_first = BRANCH_CEILING;
else if(strcmp(argv[i], "-cf") == 0)
floor_first = BRANCH_FLOOR;
else if(strcmp(argv[i], "-ca") == 0)
floor_first = BRANCH_AUTOMATIC;
else if((strcmp(argv[i], "-depth") == 0) && (i + 1 < argc)) {
do_set_bb_depthlimit = TRUE;
bb_depthlimit = atoi(argv[++i]);
}
else if(strcmp(argv[i], "-Bw") == 0)
or_value(&bb_rule2, NODE_WEIGHTREVERSEMODE);
else if(strcmp(argv[i], "-Bb") == 0)
or_value(&bb_rule2, NODE_BRANCHREVERSEMODE);
else if(strcmp(argv[i], "-Bg") == 0)
or_value(&bb_rule2, NODE_GREEDYMODE);
else if(strcmp(argv[i], "-Bp") == 0)
or_value(&bb_rule2, NODE_PSEUDOCOSTMODE);
else if(strcmp(argv[i], "-BR") == 0)
or_value(&bb_rule2, NODE_PSEUDORATIOSELECT);
else if(strcmp(argv[i], "-Bf") == 0)
or_value(&bb_rule2, NODE_DEPTHFIRSTMODE);
else if(strcmp(argv[i], "-Br") == 0)
or_value(&bb_rule2, NODE_RANDOMIZEMODE);
else if(strcmp(argv[i], "-BG") == 0)
or_value(&bb_rule2, 0 /* NODE_GUBMODE */); /* doesn't work yet */
else if(strcmp(argv[i], "-Bd") == 0)
or_value(&bb_rule2, NODE_DYNAMICMODE);
else if(strcmp(argv[i], "-Bs") == 0)
or_value(&bb_rule2, NODE_RESTARTMODE);
else if(strcmp(argv[i], "-BB") == 0)
or_value(&bb_rule2, NODE_BREADTHFIRSTMODE);
else if(strcmp(argv[i], "-Bo") == 0)
or_value(&bb_rule2, NODE_AUTOORDER);
else if(strcmp(argv[i], "-Bc") == 0)
or_value(&bb_rule2, NODE_RCOSTFIXING);
else if(strcmp(argv[i], "-Bi") == 0)
or_value(&bb_rule2, NODE_STRONGINIT);
else if(strncmp(argv[i], "-B", 2) == 0) {
if (argv[i][2])
set_value(&bb_rule1, atoi(argv[i] + 2));
else
set_value(&bb_rule1, NODE_FIRSTSELECT);
}
else if((strcmp(argv[i], "-n") == 0) && (i + 1 < argc)) {
do_set_solutionlimit = TRUE;
solutionlimit = atoi(argv[++i]);
}
else if((strcmp(argv[i], "-b") == 0) && (i + 1 < argc)) {
obj_bound = atof(argv[++i]);
do_set_obj_bound = TRUE;
}
else if(((strcmp(argv[i], "-g") == 0) || (strcmp(argv[i], "-ga") == 0)) && (i + 1 < argc))
mip_absgap = atof(argv[++i]);
else if((strcmp(argv[i], "-gr") == 0) && (i + 1 < argc))
mip_relgap = atof(argv[++i]);
else if((strcmp(argv[i], "-e") == 0) && (i + 1 < argc)) {
epsint = atof(argv[++i]);
if((epsint <= 0.0) || (epsint >= 0.5)) {
fprintf(stderr, "Invalid tolerance %g; 0 < epsilon < 0.5\n",
(double)epsint);
EndOfPgr(FORCED_EXIT);
}
}
else if((strcmp(argv[i], "-r") == 0) && (i + 1 < argc))
max_num_inv = atoi(argv[++i]);
else if((strcmp(argv[i], "-o") == 0) && (i + 1 < argc)) {
break_at_value = atof(argv[++i]);
do_set_break_at_value = TRUE;
}
else if(strcmp(argv[i], "-f") == 0)
break_at_first = TRUE;
else if(strcmp(argv[i], "-timeoutok") == 0)
/* timeoutok = TRUE */; /* option no longer needed, but still accepted */
else if(strcmp(argv[i], "-h") == 0) {
print_help(argv);
EndOfPgr(EXIT_SUCCESS);
}
else if(strcmp(argv[i], "-prim") == 0)
preferdual = FALSE;
else if(strcmp(argv[i], "-dual") == 0)
preferdual = TRUE;
else if(strcmp(argv[i], "-simplexpp") == 0)
simplextype = SIMPLEX_PRIMAL_PRIMAL;
else if(strcmp(argv[i], "-simplexdp") == 0)
simplextype = SIMPLEX_DUAL_PRIMAL;
else if(strcmp(argv[i], "-simplexpd") == 0)
simplextype = SIMPLEX_PRIMAL_DUAL;
else if(strcmp(argv[i], "-simplexdd") == 0)
simplextype = SIMPLEX_DUAL_DUAL;
else if(strcmp(argv[i], "-sp") == 0)
or_value(&scalemode2, SCALE_POWER2);
else if(strcmp(argv[i], "-si") == 0)
or_value(&scalemode2, SCALE_INTEGERS);
else if(strcmp(argv[i], "-se") == 0)
or_value(&scalemode2, SCALE_EQUILIBRATE);
else if(strcmp(argv[i], "-sq") == 0)
or_value(&scalemode2, SCALE_QUADRATIC);
else if(strcmp(argv[i], "-sl") == 0)
or_value(&scalemode2, SCALE_LOGARITHMIC);
else if(strcmp(argv[i], "-sd") == 0)
or_value(&scalemode2, SCALE_DYNUPDATE);
else if(strcmp(argv[i], "-sr") == 0)
or_value(&scalemode2, SCALE_ROWSONLY);
else if(strcmp(argv[i], "-sc") == 0)
or_value(&scalemode2, SCALE_COLSONLY);
else if(strncmp(argv[i], "-s", 2) == 0) {
set_value(&scalemode1, SCALE_NONE);
scaling = SCALE_MEAN;
if (argv[i][2]) {
switch (atoi(argv[i] + 2)) {
case 0:
scaling = SCALE_NONE;
break;
case 1:
set_value(&scalemode1, SCALE_GEOMETRIC);
break;
case 2:
set_value(&scalemode1, SCALE_CURTISREID);
break;
case 3:
set_value(&scalemode1, SCALE_EXTREME);
break;
case 4:
set_value(&scalemode1, SCALE_MEAN);
break;
case 5:
set_value(&scalemode1, SCALE_MEAN | SCALE_LOGARITHMIC);
break;
case 6:
set_value(&scalemode1, SCALE_RANGE);
break;
case 7:
set_value(&scalemode1, SCALE_MEAN | SCALE_QUADRATIC);
break;
}
}
else
set_value(&scalemode1, SCALE_MEAN);
if((i + 1 < argc) && (isNum(argv[i + 1])))
scaleloop = atoi(argv[++i]);
}
else if(strncmp(argv[i], "-C", 2) == 0)
crashmode = atoi(argv[i] + 2);
else if((strcmp(argv[i],"-gbas") == 0) && (i + 1 < argc))
guessbasis = argv[++i];
else if(strcmp(argv[i], "-t") == 0)
tracing = TRUE;
else if(strncmp(argv[i], "-S", 2) == 0) {
if (argv[i][2])
PRINT_SOLUTION = (short) atoi(argv[i] + 2);
else
PRINT_SOLUTION = 2;
}
else if(strncmp(argv[i], "-improve", 8) == 0) {
if (argv[i][8])
or_value(&improve, atoi(argv[i] + 8));
}
else if(strcmp(argv[i], "-pivll") == 0)
or_value(&pivoting2, PRICE_LOOPLEFT);
else if(strcmp(argv[i], "-pivla") == 0)
or_value(&pivoting2, PRICE_LOOPALTERNATE);
#if defined EnablePartialOptimization
else if(strcmp(argv[i], "-pivpc") == 0)
or_value(&pivoting2, PRICE_AUTOPARTIALCOLS);
else if(strcmp(argv[i], "-pivpr") == 0)
or_value(&pivoting2, PRICE_AUTOPARTIALROWS);
else if(strcmp(argv[i], "-pivp") == 0)
or_value(&pivoting2, PRICE_AUTOPARTIAL);
#endif
else if(strcmp(argv[i], "-pivf") == 0)
or_value(&pivoting2, PRICE_PRIMALFALLBACK);
else if(strcmp(argv[i], "-pivm") == 0)
or_value(&pivoting2, PRICE_MULTIPLE);
else if(strcmp(argv[i], "-piva") == 0)
or_value(&pivoting2, PRICE_ADAPTIVE);
else if(strcmp(argv[i], "-pivr") == 0)
or_value(&pivoting2, PRICE_RANDOMIZE);
else if(strcmp(argv[i], "-pivh") == 0)
or_value(&pivoting2, PRICE_HARRISTWOPASS);
else if(strcmp(argv[i], "-pivt") == 0)
or_value(&pivoting2, PRICE_TRUENORMINIT);
else if(strncmp(argv[i], "-piv", 4) == 0) {
if (argv[i][4])
set_value(&pivoting1, atoi(argv[i] + 4));
else
set_value(&pivoting1, PRICER_DEVEX | PRICE_ADAPTIVE);
}
#if defined PARSER_LP
else if(strcmp(argv[i],"-lp") == 0)
filetype = filetypeLP;
#endif
else if((strcmp(argv[i],"-wlp") == 0) && (i + 1 < argc))
wlp = argv[++i];
else if(strcmp(argv[i],"-plp") == 0)
plp = TRUE;
else if(strcmp(argv[i],"-mps") == 0)
filetype = filetypeMPS;
else if(strcmp(argv[i],"-mps_ibm") == 0)
mps_ibm = TRUE;
else if(strcmp(argv[i],"-mps_negobjconst") == 0)
mps_negobjconst = TRUE;
else if(strcmp(argv[i],"-mps_free") == 0)
mps_free = TRUE;
else if(strcmp(argv[i],"-fmps") == 0)
filetype = filetypeFREEMPS;
else if((strcmp(argv[i],"-wmps") == 0) && (i + 1 < argc))
wmps = argv[++i];
else if((strcmp(argv[i],"-wfmps") == 0) && (i + 1 < argc))
wfmps = argv[++i];
else if(strcmp(argv[i],"-wafter") == 0)
write_model_after = TRUE;
else if(strcmp(argv[i],"-degen") == 0)
set_value(&anti_degen1, ANTIDEGEN_DEFAULT);
else if(strcmp(argv[i],"-degenf") == 0)
or_value(&anti_degen2, ANTIDEGEN_FIXEDVARS);
else if(strcmp(argv[i],"-degenc") == 0)
or_value(&anti_degen2, ANTIDEGEN_COLUMNCHECK);
else if(strcmp(argv[i],"-degens") == 0)
or_value(&anti_degen2, ANTIDEGEN_STALLING);
else if(strcmp(argv[i],"-degenn") == 0)
or_value(&anti_degen2, ANTIDEGEN_NUMFAILURE);
else if(strcmp(argv[i],"-degenl") == 0)
or_value(&anti_degen2, ANTIDEGEN_LOSTFEAS);
else if(strcmp(argv[i],"-degeni") == 0)
or_value(&anti_degen2, ANTIDEGEN_INFEASIBLE);
else if(strcmp(argv[i],"-degend") == 0)
or_value(&anti_degen2, ANTIDEGEN_DYNAMIC);
else if(strcmp(argv[i],"-degenb") == 0)
or_value(&anti_degen2, ANTIDEGEN_DURINGBB);
else if(strcmp(argv[i],"-degenr") == 0)
or_value(&anti_degen2, ANTIDEGEN_RHSPERTURB);
else if(strcmp(argv[i],"-degenp") == 0)
or_value(&anti_degen2, ANTIDEGEN_BOUNDFLIP);
else if(strcmp(argv[i],"-time") == 0) {
if(clock() == -1)
fprintf(stderr, "CPU times not available on this machine\n");
else
print_timing = TRUE;
}
else if((strcmp(argv[i],"-bfp") == 0) && (i + 1 < argc))
bfp = argv[++i];
else if((strcmp(argv[i],"-rxli") == 0) && (i + 2 < argc)) {
rxliname = argv[++i];
rxli = argv[++i];
fpin = NULL;
filetype = filetypeXLI;
}
else if((strcmp(argv[i],"-rxlidata") == 0) && (i + 1 < argc))
rxlidata = argv[++i];
else if((strcmp(argv[i],"-rxliopt") == 0) && (i + 1 < argc))
rxlioptions = argv[++i];
else if((strcmp(argv[i],"-wxli") == 0) && (i + 2 < argc)) {
wxliname = argv[++i];
wxli = argv[++i];
}
else if((strcmp(argv[i],"-wxliopt") == 0) && (i + 1 < argc))
wxlioptions = argv[++i];
else if((strcmp(argv[i],"-wxlisol") == 0) && (i + 2 < argc)) {
wxliname = argv[++i];
wxlisol = argv[++i];
}
else if((strcmp(argv[i],"-wxlisolopt") == 0) && (i + 1 < argc))
wxlisoloptions = argv[++i];
else if((strcmp(argv[i],"-rbas") == 0) && (i + 1 < argc))
rbasname = argv[++i];
else if((strcmp(argv[i],"-wbas") == 0) && (i + 1 < argc))
wbasname = argv[++i];
else if((strcmp(argv[i],"-Db") == 0) && (i + 1 < argc))
debugdump_before = argv[++i];
else if((strcmp(argv[i],"-Da") == 0) && (i + 1 < argc))
debugdump_after = argv[++i];
else if((strcmp(argv[i],"-timeout") == 0) && (i + 1 < argc))
sectimeout = atol(argv[++i]);
else if((strcmp(argv[i],"-trej") == 0) && (i + 1 < argc))
epspivot = atof(argv[++i]);
else if((strcmp(argv[i],"-epsp") == 0) && (i + 1 < argc))
epsperturb = atof(argv[++i]);
else if((strcmp(argv[i],"-epsd") == 0) && (i + 1 < argc))
epsd = atof(argv[++i]);
else if((strcmp(argv[i],"-epsb") == 0) && (i + 1 < argc))
epsb = atof(argv[++i]);
else if((strcmp(argv[i],"-epsel") == 0) && (i + 1 < argc))
epsel = atof(argv[++i]);
else if(strcmp(argv[i],"-parse_only") == 0)
parse_only = TRUE;
else
ok = TRUE;
if(!ok)
;
else if(strcmp(argv[i],"-presolverow") == 0)
or_value(&do_presolve, PRESOLVE_ROWS);
else if(strcmp(argv[i],"-presolvecol") == 0)
or_value(&do_presolve, PRESOLVE_COLS);
else if(strcmp(argv[i],"-presolve") == 0)
or_value(&do_presolve, PRESOLVE_ROWS | PRESOLVE_COLS);
else if(strcmp(argv[i],"-presolvel") == 0)
or_value(&do_presolve, PRESOLVE_LINDEP);
else if(strcmp(argv[i],"-presolves") == 0)
or_value(&do_presolve, PRESOLVE_SOS);
else if(strcmp(argv[i],"-presolver") == 0)
or_value(&do_presolve, PRESOLVE_REDUCEMIP);
else if(strcmp(argv[i],"-presolvek") == 0)
or_value(&do_presolve, PRESOLVE_KNAPSACK);
else if(strcmp(argv[i],"-presolveq") == 0)
or_value(&do_presolve, PRESOLVE_ELIMEQ2);
else if(strcmp(argv[i],"-presolvem") == 0)
or_value(&do_presolve, PRESOLVE_MERGEROWS);
else if(strcmp(argv[i],"-presolvefd") == 0)
or_value(&do_presolve, PRESOLVE_COLFIXDUAL);
else if(strcmp(argv[i],"-presolvebnd") == 0)
or_value(&do_presolve, PRESOLVE_BOUNDS);
else if(strcmp(argv[i],"-presolved") == 0)
or_value(&do_presolve, PRESOLVE_DUALS);
else if(strcmp(argv[i],"-presolvef") == 0)
or_value(&do_presolve, PRESOLVE_IMPLIEDFREE);
else if(strcmp(argv[i],"-presolveslk") == 0)
or_value(&do_presolve, PRESOLVE_IMPLIEDSLK);
else if(strcmp(argv[i],"-presolveg") == 0)
or_value(&do_presolve, PRESOLVE_REDUCEGCD);
else if(strcmp(argv[i],"-presolveb") == 0)
or_value(&do_presolve, PRESOLVE_PROBEFIX);
else if(strcmp(argv[i],"-presolvec") == 0)
or_value(&do_presolve, PRESOLVE_PROBEREDUCE);
else if(strcmp(argv[i],"-presolverowd") == 0)
or_value(&do_presolve, PRESOLVE_ROWDOMINATE);
else if(strcmp(argv[i],"-presolvecold") == 0)
or_value(&do_presolve, PRESOLVE_COLDOMINATE);
else if(strcmp(argv[i],"-min") == 0)
objective = -1;
else if(strcmp(argv[i],"-max") == 0)
objective = 1;
else if(strcmp(argv[i],"-noint") == 0)
noint = TRUE;
else if((strcmp(argv[i],"-rpar") == 0) && (i + 1 < argc))
i++;
else if((strcmp(argv[i],"-rparopt") == 0) && (i + 1 < argc))
i++;
else if((strcmp(argv[i],"-wpar") == 0) && (i + 1 < argc))
i++;
else if((strcmp(argv[i],"-wparopt") == 0) && (i + 1 < argc))
i++;
else if(strcmp(argv[i],"-o0") == 0)
obj_in_basis = FALSE;
else if(strcmp(argv[i],"-o1") == 0)
obj_in_basis = TRUE;
else if((strcmp(argv[i], "-ac") == 0) && (i + 1 < argc))
accuracy_error = atof(argv[++i]);
else if(fpin == stdin) {
filen = argv[i];
if(*filen == '<')
filen++;
if((fpin = fopen(filen, "r")) == NULL) {
print_help(argv);
fprintf(stderr,"\nError, Unable to open input file '%s'\n",
argv[i]);
EndOfPgr(FORCED_EXIT);
}
}
else {
filen = argv[i];
if(*filen != '>') {
print_help(argv);
fprintf(stderr, "\nError, Unrecognized command line argument '%s'\n",
argv[i]);
EndOfPgr(FORCED_EXIT);
}
}
}
signal(SIGABRT,/* (void (*) OF((int))) */ SIGABRT_func);
if ((filetype != filetypeXLI) && (fpin == NULL)) {
lp = NULL;
fprintf(stderr, "Cannot combine -rxli option with -lp, -mps, -fmps.\n");
}
else {
switch(filetype) {
#if defined PARSER_LP
case filetypeLP:
lp = read_lp(fpin, verbose, NULL);
break;
#endif
case filetypeMPS:
lp = read_mps(fpin, verbose | (mps_free ? MPS_FREE : 0) | (mps_ibm ? MPS_IBM : 0) | (mps_negobjconst ? MPS_NEGOBJCONST : 0));
break;
case filetypeFREEMPS:
lp = read_freemps(fpin, verbose | (mps_ibm ? MPS_IBM : 0) | (mps_negobjconst ? MPS_NEGOBJCONST : 0));
break;
case filetypeXLI:
lp = read_XLI(rxliname, rxli, rxlidata, rxlioptions, verbose);
break;
}
}
if((fpin != NULL) && (fpin != stdin))
fclose(fpin);
if(print_timing)
print_cpu_times("Parsing input");
if(lp == NULL) {
fprintf(stderr, "Unable to read model.\n");
EndOfPgr(FORCED_EXIT);
}
for(i = 1; i < argc; i++) {
if((strcmp(argv[i],"-rpar") == 0) && (i + 1 < argc)) {
if(rparname != NULL) {
if(!read_params(lp, rparname, rparoptions)) {
fprintf(stderr, "Unable to read parameter file (%s)\n", rparname);
delete_lp(lp);
EndOfPgr(FORCED_EXIT);
}
}
rparname = argv[++i];
}
else if((strcmp(argv[i],"-rparopt") == 0) && (i + 1 < argc))
rparoptions = argv[++i];
else if((strcmp(argv[i],"-wpar") == 0) && (i + 1 < argc))
wparname = argv[++i];
else if((strcmp(argv[i],"-wparopt") == 0) && (i + 1 < argc))
wparoptions = argv[++i];
}
if(rparname != NULL)
if(!read_params(lp, rparname, rparoptions)) {
fprintf(stderr, "Unable to read parameter file (%s)\n", rparname);
delete_lp(lp);
EndOfPgr(FORCED_EXIT);
}
if((nonames) || (nocolnames))
set_use_names(lp, FALSE, FALSE);
if((nonames) || (norownames))
set_use_names(lp, TRUE, FALSE);
if(objective != 0) {
if(objective == 1)
set_maxim(lp);
else
set_minim(lp);
}
if (obj_in_basis != -1)
set_obj_in_basis(lp, obj_in_basis);
if(noint) { /* remove integer conditions */
for(i = get_Ncolumns(lp); i >= 1; i--) {
if(is_SOS_var(lp, i)) {
fprintf(stderr, "Unable to remove integer conditions because there is at least one SOS constraint\n");
delete_lp(lp);
EndOfPgr(FORCED_EXIT);
}
set_semicont(lp, i, FALSE);
set_int(lp, i, FALSE);
}
}
if(!write_model_after)
write_model(lp, plp, wlp, wmps, wfmps, wxli, NULL, wxliname, wxlioptions);
if(print_stats)
print_statistics(lp);
if(parse_only) {
if(!write_model_after) {
delete_lp(lp);
EndOfPgr(0);
}
/* else if(!sectimeout) */
sectimeout = 1;
}
if(PRINT_SOLUTION >= 5)
print_lp(lp);
#if 0
put_abortfunc(lp,(abortfunc *) myabortfunc, NULL);
#endif
if(sectimeout > 0)
set_timeout(lp, sectimeout);
if(print_sol >= 0)
set_print_sol(lp, print_sol);
if(epsint >= 0)
set_epsint(lp, epsint);
if(epspivot >= 0)
set_epspivot(lp, epspivot);
if(epsperturb >= 0)
set_epsperturb(lp, epsperturb);
if(epsd >= 0)
set_epsd(lp, epsd);
if(epsb >= 0)
set_epsb(lp, epsb);
if(epsel >= 0)
set_epsel(lp, epsel);
if(debug >= 0)
set_debug(lp, (MYBOOL) debug);
if(floor_first != -1)
set_bb_floorfirst(lp, floor_first);
if(do_set_bb_depthlimit)
set_bb_depthlimit(lp, bb_depthlimit);
if(do_set_solutionlimit)
set_solutionlimit(lp, solutionlimit);
if(tracing)
set_trace(lp, tracing);
if(do_set_obj_bound)
set_obj_bound(lp, obj_bound);
if(do_set_break_at_value)
set_break_at_value(lp, break_at_value);
if(break_at_first)
set_break_at_first(lp, break_at_first);
if(mip_absgap >= 0)
set_mip_gap(lp, TRUE, mip_absgap);
if(mip_relgap >= 0)
set_mip_gap(lp, FALSE, mip_relgap);
if((anti_degen1 != -1) || (anti_degen2 != -1)) {
if((anti_degen1 == -1) || (anti_degen2 != -1))
anti_degen1 = 0;
if(anti_degen2 == -1)
anti_degen2 = 0;
set_anti_degen(lp, anti_degen1 | anti_degen2);
}
set_presolve(lp, ((do_presolve == -1) ? get_presolve(lp): do_presolve) | ((PRINT_SOLUTION >= 4) ? PRESOLVE_SENSDUALS : 0), get_presolveloops(lp));
if(improve != -1)
set_improve(lp, improve);
if(max_num_inv >= 0)
set_maxpivot(lp, max_num_inv);
if(preferdual != AUTOMATIC)
set_preferdual(lp, preferdual);
if((pivoting1 != -1) || (pivoting2 != -1)) {
if(pivoting1 == -1)
pivoting1 = get_pivoting(lp) & PRICER_LASTOPTION;
if(pivoting2 == -1)
pivoting2 = 0;
set_pivoting(lp, pivoting1 | pivoting2);
}
if((scalemode1 != -1) || (scalemode2 != -1)) {
if(scalemode1 == -1)
scalemode1 = get_scaling(lp) & SCALE_CURTISREID;
if(scalemode2 == -1)
scalemode2 = 0;
set_scaling(lp, scalemode1 | scalemode2);
}
if(crashmode != -1)
set_basiscrash(lp, crashmode);
if((bb_rule1 != -1) || (bb_rule2 != -1)) {
if(bb_rule1 == -1)
bb_rule1 = get_bb_rule(lp) & NODE_USERSELECT;
if(bb_rule2 == -1)
bb_rule2 = 0;
set_bb_rule(lp, bb_rule1 | bb_rule2);
}
if(simplextype != -1)
set_simplextype(lp, simplextype);
if(bfp != NULL)
if(!set_BFP(lp, bfp)) {
fprintf(stderr, "Unable to set BFP package.\n");
delete_lp(lp);
EndOfPgr(FORCED_EXIT);
}
if(debugdump_before != NULL)
print_debugdump(lp, debugdump_before);
if(report)
put_msgfunc(lp, LPMessageCB, NULL, MSG_LPFEASIBLE | MSG_LPOPTIMAL | MSG_MILPFEASIBLE | MSG_MILPBETTER | MSG_PERFORMANCE);
if(scaling) {
if(scaleloop <= 0)
scaleloop = 5;
if(scaleloop - (int) scaleloop < SCALINGTHRESHOLD)
scaleloop = (int) scaleloop + SCALINGTHRESHOLD;
set_scalelimit(lp, scaleloop);
}
if (accuracy_error != -1)
set_break_numeric_accuracy(lp, accuracy_error);
if(guessbasis != NULL) {
REAL *guessvector, a;
int *basisvector;
int Nrows = get_Nrows(lp);
int Ncolumns = get_Ncolumns(lp);
int col;
char buf[50], *ptr;
FILE *fp;
if ((fp = fopen(guessbasis, "r")) != NULL) {
guessvector = (REAL *) calloc(1+Ncolumns, sizeof(*guessvector));
basisvector = (int *) malloc((1+Nrows+Ncolumns)*sizeof(*basisvector));
if ((guessvector != NULL) && (basisvector != NULL)) {
while ((!feof(fp)) && (fgets(buf, sizeof(buf), fp) != NULL)) {
ptr = strrchr(buf, ':');
if (ptr == NULL) {
printf("Mallformed line: %s\n", buf);
}
else {
a = atof(ptr + 1);
while ((ptr > buf) && (isspace(ptr[-1])))
ptr--;
*ptr = 0;
col = get_nameindex(lp, buf, FALSE);
if (col < 1)
printf("guess_basis: Unknown variable name %s\n", buf);
else
guessvector[col] = a;
}
}
if (guess_basis(lp, guessvector, basisvector)) {
if (!set_basis(lp, basisvector, TRUE))
printf("Unable to set guessed basis.\n");
}
else
printf("Unable to guess basis from provided variables.\n");
}
else
printf("guess_basis: Out of memory.\n");
if (basisvector != NULL)
free(basisvector);
if (guessvector != NULL)
free(guessvector);
fclose(fp);
}
else
printf("Unable to open file %s\n", guessbasis);
}
if(rbasname != NULL)
if(!read_basis(lp, rbasname, NULL)) {
fprintf(stderr, "Unable to read basis file.\n");
delete_lp(lp);
EndOfPgr(FORCED_EXIT);
}
result = solve(lp);
if(wbasname != NULL)
if(!write_basis(lp, wbasname))
fprintf(stderr, "Unable to write basis file.\n");
if(write_model_after)
write_model(lp, plp, wlp, wmps, wfmps, wxli, NULL, wxliname, wxlioptions);
write_model(lp, FALSE, NULL, NULL, NULL, NULL, wxlisol, wxliname, wxlisoloptions);
if(PRINT_SOLUTION >= 6)
print_scales(lp);
if((print_timing) && (!parse_only))
print_cpu_times("solving");
if(debugdump_after != NULL)
print_debugdump(lp, debugdump_after);
if(wparname != NULL)
if(!write_params(lp, wparname, wparoptions)) {
fprintf(stderr, "Unable to write parameter file (%s)\n", wparname);
delete_lp(lp);
EndOfPgr(FORCED_EXIT);
}
if(parse_only) {
delete_lp(lp);
EndOfPgr(0);
}
/*
if((timeoutok) && (result == TIMEOUT) && (get_solutioncount(lp) > 0))
result = OPTIMAL;
*/
switch(result) {
case SUBOPTIMAL:
case PRESOLVED:
case OPTIMAL:
case PROCBREAK:
case FEASFOUND:
if ((result == SUBOPTIMAL) && (PRINT_SOLUTION >= 1))
printf("Suboptimal solution\n");
if (result == PRESOLVED)
printf("Presolved solution\n");
if (PRINT_SOLUTION >= 1)
print_objective(lp);
if (PRINT_SOLUTION >= 2)
print_solution(lp, 1);
if (PRINT_SOLUTION >= 3)
print_constraints(lp, 1);
if (PRINT_SOLUTION >= 4)
print_duals(lp);
if(tracing)
fprintf(stderr,
"Branch & Bound depth: %d\nNodes processed: %.0f\nSimplex pivots: %.0f\nNumber of equal solutions: %d\n",
get_max_level(lp), (REAL) get_total_nodes(lp), (REAL) get_total_iter(lp), get_solutioncount(lp));
break;
case NOMEMORY:
if (PRINT_SOLUTION >= 1)
printf("Out of memory\n");
break;
case INFEASIBLE:
if (PRINT_SOLUTION >= 1)
printf("This problem is infeasible\n");
break;
case UNBOUNDED:
if (PRINT_SOLUTION >= 1)
printf("This problem is unbounded\n");
break;
case PROCFAIL:
if (PRINT_SOLUTION >= 1)
printf("The B&B routine failed\n");
break;
case TIMEOUT:
if (PRINT_SOLUTION >= 1)
printf("Timeout\n");
break;
case USERABORT:
if (PRINT_SOLUTION >= 1)
printf("User aborted\n");
break;
case ACCURACYERROR:
if (PRINT_SOLUTION >= 1)
printf("Accuracy error\n");
break;
default:
if (PRINT_SOLUTION >= 1)
printf("lp_solve failed\n");
break;
}
if (PRINT_SOLUTION >= 7)
print_tableau(lp);
delete_lp(lp);
EndOfPgr(result);
return(result);
}