bool ex_eval(void *arg) {
ssi_size_t n_classes = 2;
ssi_size_t n_samples = 20;
ssi_size_t n_streams = 1;
ssi_real_t train_distr[][3] = { 0.3f, 0.3f, 0.2f, 0.3f, 0.6f, 0.2f, 0.6f, 0.3f, 0.2f, 0.6f, 0.6f, 0.2f };
ssi_real_t test_distr[][3] = { 0.5f, 0.5f, 0.5f };
SampleList samples;
ModelTools::CreateTestSamples (samples, n_classes, n_samples, n_streams, train_distr);
ssi_char_t string[SSI_MAX_CHAR];
for (ssi_size_t n_class = 1; n_class < n_classes; n_class++) {
ssi_sprint (string, "class%02d", n_class);
samples.addClassName (string);
}
Evaluation eval;
NaiveBayes *model = ssi_create (NaiveBayes, 0, true);
Trainer trainer (model);
trainer.train (samples);
Evaluation2Latex e2latex;
e2latex.open ("eval.tex");
ssi_print_off ("devel set:\n");
eval.eval (&trainer, samples);
eval.print (ssiout);
eval.print_result_vec ();
e2latex.writeHead (eval, "caption", "label");
e2latex.writeText ("results with different evaluation strategies", true);
e2latex.writeEval ("devel", eval);
ssi_print_off("k-fold:\n");
eval.evalKFold (&trainer, samples, 3);
eval.print ();
eval.print_result_vec ();
e2latex.writeEval ("k-fold", eval);
ssi_print_off("split:\n");
eval.evalSplit (&trainer, samples, 0.5f);
eval.print ();
eval.print_result_vec ();
e2latex.writeEval ("split", eval);
ssi_print_off("loo:\n");
eval.evalLOO (&trainer, samples);
eval.print ();
eval.print_result_vec ();
e2latex.writeEval ("loo", eval);
e2latex.writeTail ();
e2latex.close ();
FILE *fp = fopen("eval.csv", "w");
eval.print(fp, Evaluation::PRINT::CSV_EX);
fclose(fp);
return true;
}
