void MiraFeatureVector::InitSparse(const SparseVector& sparse, size_t ignoreLimit)
{
vector<size_t> sparseFeats = sparse.feats();
bool bFirst = true;
size_t lastFeat = 0;
m_sparseFeats.reserve(sparseFeats.size());
m_sparseVals.reserve(sparseFeats.size());
for(size_t i=0; i<sparseFeats.size(); i++) {
if (sparseFeats[i] < ignoreLimit) continue;
size_t feat = m_dense.size() + sparseFeats[i];
m_sparseFeats.push_back(feat);
m_sparseVals.push_back(sparse.get(sparseFeats[i]));
// Check ordered property
if(bFirst) {
bFirst = false;
} else {
if(lastFeat>=feat) {
cerr << "Error: Feature indeces must be strictly ascending coming out of SparseVector" << endl;
exit(1);
}
}
lastFeat = feat;
}
}
MiraFeatureVector::MiraFeatureVector(const SparseVector& sparse, size_t num_dense)
{
m_dense.resize(num_dense);
//Assume that features with id [0,num_dense) are the dense features
for (size_t id = 0; id < num_dense; ++id) {
m_dense[id] = sparse.get(id);
}
InitSparse(sparse,num_dense);
}