void opf_OPFAgglomerativeLearning(Subgraph **sgtrain, Subgraph **sgeval){
int n, i = 1;
float Acc;
/*while there exists misclassified samples in sgeval*/
do{
fflush(stdout); fprintf(stdout, "\nrunning iteration ... %d ", i++);
n = 0;
opf_OPFTraining(*sgtrain);
opf_OPFClassifying(*sgtrain, *sgeval);
Acc = opf_Accuracy(*sgeval); fprintf(stdout," %f",Acc*100);
opf_MoveMisclassifiedNodes(&(*sgeval), &(*sgtrain), &n);
fprintf(stdout,"\nMisclassified nodes: %d",n);
}while(n);
}
//Learning function: it executes the learning procedure for CompGraph replacing the
//missclassified samples in the evaluation set by non prototypes from
//training set -----
void opf_OPFLearning(Subgraph **sgtrain, Subgraph **sgeval){
int i = 0, iterations = 10;
float Acc = FLT_MIN, AccAnt = FLT_MIN,MaxAcc=FLT_MIN, delta;
Subgraph *sg=NULL;
do{
AccAnt = Acc;
fflush(stdout); fprintf(stdout, "\nrunning iteration ... %d ", i);
opf_OPFTraining(*sgtrain);
opf_OPFClassifying(*sgtrain, *sgeval);
Acc = opf_Accuracy(*sgeval);
if (Acc > MaxAcc){
MaxAcc = Acc;
if (sg!=NULL) DestroySubgraph(&sg);
sg = CopySubgraph(*sgtrain);
}
opf_SwapErrorsbyNonPrototypes(&(*sgtrain), &(*sgeval));
fflush(stdout); fprintf(stdout,"opf_Accuracy in the evaluation set: %.2f %%\n", Acc*100);
i++;
delta = fabs(Acc-AccAnt);
}while ((delta > 0.0001) && (i <= iterations));
DestroySubgraph(&(*sgtrain));
*sgtrain = sg;
}
int main(int argc, char **argv)
{
if (argc != 5)
{
fprintf(stderr, "\nusage FeatureSelection <training set> <evaluating set> <testing set> <search space configuration file>\n");
exit(-1);
}
SearchSpace *s = NULL;
int i;
double time_opt, time_classify, classification_error;
timer tic, toc;
FILE *f = NULL;
TransferFunc optTransfer = NULL;
Subgraph *Train = NULL, *Evaluate = NULL, *Merge = NULL, *Test = NULL, *newTrain = NULL, *newTest = NULL;
Train = ReadSubgraph(argv[1]);
Evaluate = ReadSubgraph(argv[2]);
Test = ReadSubgraph(argv[3]);
s = ReadSearchSpaceFromFile(argv[4], _WCA_);
optTransfer = S2TransferFunction;
for (i = 0; i < Train->nfeats; i++)
{
s->LB[i] = -20;
s->LB[i] = 20;
}
fprintf(stderr, "\nInitializing search space ... ");
InitializeSearchSpace(s, _WCA_);
fprintf(stderr, "\nOk\n");
fflush(stderr);
fprintf(stderr, "\nRunning WCA ... ");
gettimeofday(&tic, NULL);
runWCA(s, FeatureSelectionOPF, Train, Evaluate, optTransfer);
gettimeofday(&toc, NULL);
fflush(stderr);
fprintf(stderr, "\nOK\n");
time_opt = ((toc.tv_sec - tic.tv_sec) * 1000.0 + (toc.tv_usec - tic.tv_usec) * 0.001) / 1000.0;
fprintf(stdout, "\nOptimization time: %f seconds\n", time_opt);
fflush(stderr);
Merge = opf_MergeSubgraph(Train, Evaluate);
fflush(stderr);
fprintf(stderr, "\nWriting new training and testing sets ...\n");
newTrain = CreateSubgraphFromSelectedFeatures(Merge, s->g);
newTest = CreateSubgraphFromSelectedFeatures(Test, s->g);
fprintf(stderr, "\nTraining set\n");
WriteSubgraph(newTrain, "training.wca.dat");
fprintf(stderr, "\n\nTesting set\n");
WriteSubgraph(newTest, "testing.wca.dat");
fflush(stderr);
fprintf(stderr, "\nOK\n");
opf_OPFTraining(newTrain);
gettimeofday(&tic, NULL);
opf_OPFClassifying(newTrain, newTest);
gettimeofday(&toc, NULL);
classification_error = opf_Accuracy(newTest);
time_classify = ((toc.tv_sec - tic.tv_sec) * 1000.0 + (toc.tv_usec - tic.tv_usec) * 0.001) / 1000.0;
fprintf(stdout, "\nClassification time: %f seconds\n", time_classify);
fflush(stderr);
f = fopen("best_feats.txt", "a");
fprintf(f, "%d %d", newTrain->nfeats, (int)s->g[0]);
for (i = 1; i < Train->nfeats; i++)
{
fprintf(f, " %d", (int)s->g[i]);
}
fprintf(f, "\n");
fclose(f);
fprintf(stderr, "\nAccuracy: %.2lf%%\n", 100 * classification_error);
f = fopen("final_accuracy.txt", "a");
fprintf(f, "%lf\n", classification_error);
fclose(f);
fflush(stderr);
fprintf(stderr, "\nDeallocating memory ...");
DestroySubgraph(&Train);
DestroySubgraph(&Evaluate);
DestroySubgraph(&Merge);
DestroySubgraph(&Test);
DestroySubgraph(&newTrain);
DestroySubgraph(&newTest);
fflush(stderr);
fprintf(stderr, "\nOK\n");
f = fopen("optimization.time", "a");
fprintf(f, "%f %f\n", time_opt, time_classify);
fclose(f);
DestroySearchSpace(&s, _WCA_);
return 0;
}
int main(int argc, char **argv)
{
if (argc != 4)
{
fprintf(stderr, "\nusage CombinatorialOPF <training set> <testing set> <input combinatorial matrix>\n");
exit(-1);
}
int i, j, acc_index, m, n;
double time_comb, classification_accuracy, overall_accuracy = 0, **r;
timer tic, toc;
FILE *f = NULL;
Subgraph *Train = NULL, *Test = NULL, *newTrain = NULL, *newTest = NULL;
Train = ReadSubgraph(argv[1]);
Test = ReadSubgraph(argv[2]);
fprintf(stderr, "\nInitializing combinatorial OPF ... ");
f = fopen(argv[3], "r");
fscanf(f, "%d\n", &n);
m = pow(2, n);
r = (double **)calloc(m, sizeof(double *));
for (i = 0; i < m; i++)
r[i] = (double *)calloc(n, sizeof(double));
for (i = 0; i < m; i++)
{
for (j = 0; j < n; j++)
{
fscanf(f, "%lf ", &r[i][j]);
}
fscanf(f, "\n");
}
fclose(f);
fprintf(stderr, "\nOk\n");
fflush(stderr);
fprintf(stderr, "\nRunning OPF ... ");
f = fopen("final_accuracy.txt", "a");
gettimeofday(&tic, NULL);
for (i = 0; i < m; i++)
{
newTrain = CreateSubgraphFromSelectedFeatures(Train, r[i]);
newTest = CreateSubgraphFromSelectedFeatures(Test, r[i]);
opf_OPFTraining(newTrain);
opf_OPFClassifying(newTrain, newTest);
classification_accuracy = opf_Accuracy(newTest);
fprintf(f, "%lf\n", classification_accuracy);
if (classification_accuracy > overall_accuracy)
{
overall_accuracy = classification_accuracy;
acc_index = i;
}
}
gettimeofday(&toc, NULL);
fclose(f);
fflush(stderr);
fprintf(stderr, "\nOK\n");
time_comb = ((toc.tv_sec - tic.tv_sec) * 1000.0 + (toc.tv_usec - tic.tv_usec) * 0.001) / 1000.0;
fprintf(stdout, "\nCombination time: %f seconds\n", time_comb);
fflush(stderr);
f = fopen("best_feats.txt", "a");
fprintf(f, "%d ", n);
for (i = 0; i < n; i++)
{
fprintf(f, "%d ", (int)r[acc_index][i]);
}
fprintf(f, "\n");
fclose(f);
fflush(stderr);
fprintf(stderr, "\nDeallocating memory ...");
DestroySubgraph(&Train);
DestroySubgraph(&Test);
DestroySubgraph(&newTrain);
DestroySubgraph(&newTest);
fflush(stderr);
fprintf(stderr, "\nOK\n");
f = fopen("combination.time", "a");
fprintf(f, "%f\n", time_comb);
fclose(f);
return 0;
}