template<class ST> bool CDenseFeatures<ST>::apply_preprocessor(bool force_preprocessing)
{
if (m_subset_stack->has_subsets())
SG_ERROR("A subset is set, cannot call apply_preproc\n")
SG_DEBUG("force: %d\n", force_preprocessing)
if (feature_matrix.matrix && get_num_preprocessors())
{
for (int32_t i = 0; i < get_num_preprocessors(); i++)
{
if ((!is_preprocessed(i) || force_preprocessing))
{
set_preprocessed(i);
CDensePreprocessor<ST>* p =
(CDensePreprocessor<ST>*) get_preprocessor(i);
SG_INFO("preprocessing using preproc %s\n", p->get_name())
if (p->apply_to_feature_matrix(this).matrix == NULL)
{
SG_UNREF(p);
return false;
}
SG_UNREF(p);
}
}
return true;
}
template<class ST> ST* CDenseFeatures<ST>::get_feature_vector(int32_t num, int32_t& len, bool& dofree)
{
/* index conversion for subset, only for array access */
int32_t real_num=m_subset_stack->subset_idx_conversion(num);
len = num_features;
if (feature_matrix.matrix)
{
dofree = false;
return &feature_matrix.matrix[real_num * int64_t(num_features)];
}
ST* feat = NULL;
dofree = false;
if (feature_cache)
{
feat = feature_cache->lock_entry(real_num);
if (feat)
return feat;
else
feat = feature_cache->set_entry(real_num);
}
if (!feat)
dofree = true;
feat = compute_feature_vector(num, len, feat);
if (get_num_preprocessors())
{
int32_t tmp_len = len;
ST* tmp_feat_before = feat;
ST* tmp_feat_after = NULL;
for (int32_t i = 0; i < get_num_preprocessors(); i++)
{
CDensePreprocessor<ST>* p =
(CDensePreprocessor<ST>*) get_preprocessor(i);
// temporary hack
SGVector<ST> applied = p->apply_to_feature_vector(
SGVector<ST>(tmp_feat_before, tmp_len));
tmp_feat_after = applied.vector;
SG_UNREF(p);
if (i != 0) // delete feature vector, except for the the first one, i.e., feat
SG_FREE(tmp_feat_before);
tmp_feat_before = tmp_feat_after;
}
memcpy(feat, tmp_feat_after, sizeof(ST) * tmp_len);
SG_FREE(tmp_feat_after);
len = tmp_len;
}
return feat;
}
template<class ST> SGSparseVector<ST> CSparseFeatures<ST>::get_sparse_feature_vector(int32_t num)
{
REQUIRE(num>=0 && num<get_num_vectors(),
"get_sparse_feature_vector(num=%d): num exceeds [0;%d]\n",
num, get_num_vectors()-1);
index_t real_num=m_subset_stack->subset_idx_conversion(num);
if (sparse_feature_matrix.sparse_matrix)
{
return sparse_feature_matrix[real_num];
}
else
{
SGSparseVector<ST> result;
if (feature_cache)
{
result.features=feature_cache->lock_entry(num);
if (result.features)
return result;
else
{
result.features=feature_cache->set_entry(num);
}
}
//if (!result.features)
// result.do_free=true;
result.features=compute_sparse_feature_vector(num,
result.num_feat_entries, result.features);
if (get_num_preprocessors())
{
int32_t tmp_len=result.num_feat_entries;
SGSparseVectorEntry<ST>* tmp_feat_before=result.features;
SGSparseVectorEntry<ST>* tmp_feat_after = NULL;
for (int32_t i=0; i<get_num_preprocessors(); i++)
{
//tmp_feat_after=((CSparsePreprocessor<ST>*) get_preproc(i))->apply_to_feature_vector(tmp_feat_before, tmp_len);
if (i!=0) // delete feature vector, except for the the first one, i.e., feat
SG_FREE(tmp_feat_before);
tmp_feat_before=tmp_feat_after;
}
if (tmp_feat_after)
{
memcpy(result.features, tmp_feat_after,
sizeof(SGSparseVectorEntry<ST>)*tmp_len);
SG_FREE(tmp_feat_after);
result.num_feat_entries=tmp_len;
}
SG_DEBUG("len: %d len2: %d\n", result.num_feat_entries, get_num_features())
}
return result ;
}
}
