void AlgLibTools::Samples2MatrixWithClass (ISamples &samples,
ssi_size_t stream_id, ae_matrix* m) {
ae_int_t nfeatures = samples.get (0)->streams[stream_id]->dim;
ae_int_t nsamples = samples.getSize ();
ae_int_t i = 0;
ae_int_t j = 0;
ae_state state;
ae_matrix_clear(m);
ae_matrix_set_length(m, nsamples, nfeatures+1, &state);
ssi_sample_t *sample;
samples.reset ();
while (sample = samples.next ()) {
ssi_real_t *ptr = ssi_pcast (ssi_real_t, sample->streams[stream_id]->ptr);
for (j = 0; j <= nfeatures-1; j++)
{
m->ptr.pp_double[i][j] = ssi_cast (double, *ptr++);
}
m->ptr.pp_double[i][j] = ssi_cast (double, sample->class_id);
i++;
}
//delete sample;
}
void AlgLibTools::Samples2matrix (
ISamples &samples,
ssi_size_t stream_id,
ssi_size_t class_id,
ae_matrix* m,
ae_state *state)
{
ae_int_t nfeatures = samples.get (0)->streams[stream_id]->dim;
ae_int_t nsamples = samples.getSize (class_id);
ae_int_t i = 0;
ae_int_t j = 0;
ae_matrix_clear(m);
ae_matrix_set_length(m, nsamples, nfeatures, state);
ssi_sample_t *sample;
ISSelectClass samples_s (&samples);
samples_s.setSelection (class_id);
samples_s.reset ();
while (sample = samples_s.next ()) {
ssi_real_t *ptr = ssi_pcast (ssi_real_t, sample->streams[stream_id]->ptr);
for(j=0; j<=nfeatures-1; j++)
{
m->ptr.pp_double[i][j] = ssi_cast (double, *ptr++);
}
i++;
}
}
void Evaluation::evalSplit (Trainer *trainer, ISamples &samples, ssi_real_t split) {
if (split <= 0 || split >= 1) {
ssi_err ("split must be a value between 0 and 1");
}
_trainer = trainer;
destroy_conf_mat ();
init_conf_mat (samples);
ssi_size_t *indices = new ssi_size_t[samples.getSize ()];
ssi_size_t *indices_count_lab = new ssi_size_t[samples.getClassSize ()];
ssi_size_t indices_count_all;
indices_count_all = 0;
for (ssi_size_t j = 0; j < samples.getClassSize (); j++) {
indices_count_lab[j] = 0;
}
ssi_size_t label;
ssi_size_t label_size;
for (ssi_size_t j = 0; j < samples.getSize (); j++) {
label = samples.get (j)->class_id;
label_size = samples.getSize (label);
if (++indices_count_lab[label] <= ssi_cast (ssi_size_t, label_size * split + 0.5f)) {
indices[indices_count_all++] = j;
}
}
SampleList strain;
SampleList stest;
// split off samples
ModelTools::SelectSampleList (samples, strain, stest, indices_count_all, indices);
_n_total = stest.getSize ();
_result_vec = new ssi_size_t[2*_n_total];
_result_vec_ptr = _result_vec;
// train with remaining samples
_trainer->release ();
if (_preproc_mode) {
_trainer->setPreprocMode (_preproc_mode, _n_streams_refs, _stream_refs);
} else if (_fselmethod) {
_trainer->setSelection (strain, _fselmethod, _pre_fselmethod, _n_pre_feature);
}
_trainer->train (strain);
// test with remaining samples
eval_h (stest);
delete[] indices;
delete[] indices_count_lab;
}
void Evaluation::evalKFold (Trainer *trainer, ISamples &samples, ssi_size_t k) {
// init confussion matrix
_trainer = trainer;
destroy_conf_mat ();
init_conf_mat (samples);
_n_total = samples.getSize ();
_result_vec = new ssi_size_t[2*_n_total];
_result_vec_ptr = _result_vec;
ssi_size_t *indices = new ssi_size_t[samples.getSize ()];
ssi_size_t *indices_count_lab = new ssi_size_t[samples.getClassSize ()];
ssi_size_t indices_count_all;
for (ssi_size_t i = 0; i < k; ++i) {
indices_count_all = 0;
for (ssi_size_t j = 0; j < samples.getClassSize (); j++) {
indices_count_lab[j] = 0;
}
ssi_size_t label;
for (ssi_size_t j = 0; j < samples.getSize (); j++) {
label = samples.get (j)->class_id;
if (++indices_count_lab[label] % k == i) {
indices[indices_count_all++] = j;
}
}
SampleList strain;
SampleList stest;
// split off i'th fold
ModelTools::SelectSampleList (samples, stest, strain, indices_count_all, indices);
// train with i'th fold
_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);
// test with remaining samples
eval_h (stest);
}
delete[] indices;
delete[] indices_count_lab;
}