void Evaluation::evalLOO (Trainer *trainer, ISamples &samples) {
_trainer = trainer;
destroy_conf_mat ();
init_conf_mat (samples);
ssi_size_t n_samples = samples.getSize ();
_n_total = n_samples;
_result_vec = new ssi_size_t[2*_n_total];
_result_vec_ptr = _result_vec;
ssi_size_t itest = 0;
ssi_size_t *itrain = new ssi_size_t[n_samples - 1];
for (ssi_size_t nsample = 0; nsample < n_samples - 1; ++nsample) {
itrain[nsample] = nsample+1;
}
ISSelectSample strain (&samples);
ISSelectSample stest (&samples);
strain.setSelection (n_samples-1, itrain);
stest.setSelection (1, &itest);
_trainer->release ();
if (_fselmethod) {
_trainer->setSelection (strain, _fselmethod, _pre_fselmethod, _n_pre_feature);
}
if (_preproc_mode) {
_trainer->setPreprocMode (_preproc_mode, _n_streams_refs, _stream_refs);
}
_trainer->train (strain);
eval_h (stest);
for (ssi_size_t nsample = 1; nsample < n_samples; ++nsample) {
itrain[nsample-1] = nsample-1;
itest = nsample;
strain.setSelection (n_samples-1, itrain);
stest.setSelection (1, &itest);
_trainer->release ();
if (_fselmethod) {
_trainer->setSelection (strain, _fselmethod, _pre_fselmethod, _n_pre_feature);
}
if (_preproc_mode) {
_trainer->setPreprocMode (_preproc_mode, _n_streams_refs, _stream_refs);
}
_trainer->train (strain);
eval_h (stest);
}
delete [] itrain;
}
void ex_kmeans () {
SampleList samples;
ssi_size_t n_classes = 4;
ssi_size_t n_sampels = 200;
ssi_size_t n_streams = 1;
ssi_real_t distr[][3] = { 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f };
ModelTools::CreateTestSamples (samples, n_classes, n_sampels, n_streams, distr);
// training
{
KMeans *model = ssi_create (KMeans, "kmeans", true);
model->getOptions()->k = n_classes;
Trainer trainer (model);
trainer.train (samples);
trainer.save ("kmeans");
}
// evaluation
{
Trainer trainer;
Trainer::Load (trainer, "kmeans");
trainer.cluster (samples);
ModelTools::PlotSamples(samples, "kmeans", ssi_rect(650, 0, 400, 400));
}
// split
{
KMeans *model = ssi_create (KMeans, "kmeans", true);
model->load("kmeans.trainer.KMeans.model");
ISSelectSample ss (&samples);
ss.setSelection (model->getIndicesPerClusterSize(1), model->getIndicesPerCluster(1));
ModelTools::PlotSamples (ss, "kmeans", ssi_rect(650,0,400,400));
}
}