void recommender_base::complete_row(const sfv_t& query, sfv_t& ret) const {
ret.clear();
vector<pair<string, float> > ids;
similar_row(query, ids, complete_row_similar_num_);
if (ids.size() == 0) {
return;
}
size_t exist_row_num = 0;
for (size_t i = 0; i < ids.size(); ++i) {
sfv_t row;
orig_.get_row(ids[i].first, row);
if (row.size() == 0) {
continue;
} else {
++exist_row_num;
}
float ratio = ids[i].second;
for (size_t j = 0; j < row.size(); ++j) {
ret.push_back(make_pair(row[j].first, row[j].second * ratio));
}
}
if (exist_row_num == 0) {
return;
}
sort_and_merge(ret);
for (size_t i = 0; i < ret.size(); ++i) {
ret[i].second /= exist_row_num;
}
}
float inverted_index_storage::calc_l2norm(const sfv_t& sfv){
float ret = 0.f;
for (size_t i = 0; i < sfv.size(); ++i){
ret += sfv[i].second * sfv[i].second;
}
return sqrt(ret);
}
static float calc_norm(const sfv_t& fv) {
float norm = 0;
for (size_t i = 0; i < fv.size(); ++i) {
norm += fv[i].second * fv[i].second;
}
return norm;
}
float recommender_base::calc_l2norm(const sfv_t& query) {
float ret = 0.f;
for (size_t i = 0; i < query.size(); ++i) {
ret += query[i].second * query[i].second;
}
return sqrt(ret);
}
void inverted_index_storage::calc_scores(const sfv_t& query,
vector<pair<string, float> >& scores,
size_t ret_num) const {
float query_norm = calc_l2norm(query);
if (query_norm == 0.f){
return;
}
pfi::data::unordered_map<uint64_t, float> i_scores;
for (size_t i = 0; i < query.size(); ++i){
const string& fid = query[i].first;
float val = query[i].second;
add_inp_scores(fid, val, i_scores);
}
vector<pair<float, uint64_t> > sorted_scores;
for (pfi::data::unordered_map<uint64_t, float>::const_iterator it = i_scores.begin(); it != i_scores.end(); ++it){
float norm = calc_columnl2norm(it->first);
float normed_score = (norm != 0.f) ? it->second / norm / query_norm : 0.f;
sorted_scores.push_back(make_pair(normed_score, it->first));
}
sort(sorted_scores.rbegin(), sorted_scores.rend());
for (size_t i = 0; i < sorted_scores.size() && i < ret_num; ++i){
scores.push_back(make_pair(column2id_.get_key(sorted_scores[i].second), sorted_scores[i].first));
}
}
void AROW::update(const sfv_t& sfv, float alpha, float beta,
const std::string& pos_label, const std::string& neg_label){
for (sfv_t::const_iterator it = sfv.begin(); it != sfv.end(); ++it){
const string& feature = it->first;
float val = it->second;
storage::feature_val2_t ret;
storage_->get2(feature, ret);
storage::val2_t pos_val(0.f, 1.f);
storage::val2_t neg_val(0.f, 1.f);
ClassifierUtil::get_two(ret, pos_label, neg_label, pos_val, neg_val);
storage_->set2(feature, pos_label, storage::val2_t(pos_val.v1 + alpha * pos_val.v2 * val, pos_val.v2 - beta * pos_val.v2 * pos_val.v2 * val * val));
if (neg_label != "")
storage_->set2(feature, neg_label, storage::val2_t(neg_val.v1 - alpha * neg_val.v2 * val, neg_val.v2 - beta * neg_val.v2 * neg_val.v2 * val * val));
}
}
void local_storage_mixture::bulk_update(const sfv_t& sfv, float step_width, const string& inc_class, const string& dec_class){
uint64_t inc_id = class2id_.get_id(inc_class);
if (dec_class != ""){
uint64_t dec_id = class2id_.get_id(dec_class);
for (sfv_t::const_iterator it = sfv.begin(); it != sfv.end(); ++it){
float val = it->second * step_width;
id_feature_val3_t& feature_row = tbl_diff_[it->first];
feature_row[inc_id].v1 += val;
feature_row[dec_id].v1 -= val;
}
} else {
for (sfv_t::const_iterator it = sfv.begin(); it != sfv.end(); ++it){
float val = it->second * step_width;
id_feature_val3_t& feature_row = tbl_diff_[it->first];
feature_row[inc_id].v1 += val;
}
}
}
void sort_and_merge(sfv_t& sfv){
if (sfv.size() == 0) return;
sort(sfv.begin(), sfv.end());
sfv_t ret_sfv;
const string* prev = &sfv[0].first;
float val = sfv[0].second;
for (size_t i = 1; i < sfv.size(); ++i){
if (sfv[i].first == *prev){
val += sfv[i].second;
} else {
ret_sfv.push_back(make_pair(*prev, val));
prev = &sfv[i].first;
val = sfv[i].second;
}
}
ret_sfv.push_back(make_pair(*prev, val));
sfv.swap(ret_sfv);
}
void local_storage_mixture::inp(const sfv_t& sfv, map_feature_val1_t& ret) {
ret.clear();
std::vector<float> ret_id(class2id_.size());
for (sfv_t::const_iterator it = sfv.begin(); it != sfv.end(); ++it){
const string& feature = it->first;
const float val = it->second;
id_feature_val3_t m;
get_internal(feature, m);
for (id_feature_val3_t::const_iterator it3 = m.begin(); it3 != m.end(); ++it3){
ret_id[it3->first] += it3->second.v1 * val;
}
}
for (size_t i = 0; i < ret_id.size(); ++i){
if (ret_id[i] == 0.f) continue;
ret[class2id_.get_key(i)] = ret_id[i];
}
}
void revert_feature(const sfv_t& fv,
fv_converter::datum& data) {
for (size_t i = 0; i < fv.size(); ++i) {
pair<string, float> num_value;
pair<string, string> string_value;
if (revert_num_value(fv[i], num_value)) {
data.num_values_.push_back(num_value);
} else if (revert_string_value(fv[i], string_value)) {
data.string_values_.push_back(string_value);
}
}
}
float recommender_base::calc_similality(sfv_t& q1, sfv_t& q2) {
float q1_norm = calc_l2norm(q1);
float q2_norm = calc_l2norm(q2);
if (q1_norm == 0.f || q2_norm == 0.f) {
return 0.f;
}
sort(q1.begin(), q1.end());
sort(q2.begin(), q2.end());
size_t i1 = 0;
size_t i2 = 0;
float ret = 0.f;
while (i1 < q1.size() && i2 < q2.size()) {
const string& ind1 = q1[i1].first;
const string& ind2 = q2[i2].first;
if (ind1 < ind2) {
++i1;
} else if (ind1 > ind2) {
++i2;
} else {
ret += q1[i1].second * q2[i2].second;
++i1;
++i2;
}
}
return ret / q1_norm / q2_norm;
}
void weight_manager::get_weight(sfv_t& fv) const {
for (sfv_t::iterator it = fv.begin(); it != fv.end(); ++it) {
double global_weight = get_global_weight(it->first);
it->second *= global_weight;
}
fv.erase(remove_if(fv.begin(), fv.end(), is_zero()), fv.end());
}
void NHERD::update(const sfv_t& sfv, float margin, float variance,
const string& pos_label, const string& neg_label){
for (sfv_t::const_iterator it = sfv.begin(); it != sfv.end(); ++it){
const string& feature = it->first;
float val = it->second;
storage::feature_val2_t ret;
storage_->get2(feature, ret);
storage::val2_t pos_val(0.f, 1.f);
storage::val2_t neg_val(0.f, 1.f);
ClassifierUtil::get_two(ret, pos_label, neg_label, pos_val, neg_val);
float val_covariance_pos = val * pos_val.v2;
float val_covariance_neg = val * neg_val.v2;
storage_->set2(feature, pos_label,
storage::val2_t(pos_val.v1 + (1.f - margin) * val_covariance_pos / (val_covariance_pos * val + 1.f / C_),
1.f / ((1.f / pos_val.v2) + (2 * C_ + C_ * C_ * variance) * val * val)));
if (neg_label != "")
storage_->set2(feature, neg_label,
storage::val2_t(neg_val.v1 - (1.f - margin) * val_covariance_neg / (val_covariance_neg * val + 1.f / C_),
1.f / ((1.f / neg_val.v2) + (2 * C_ + C_ * C_ * variance) * val * val)));
}
}
void CW::update(const sfv_t& sfv, float step_width, const string& pos_label, const string& neg_label){
for (sfv_t::const_iterator it = sfv.begin(); it != sfv.end(); ++it){
const string& feature = it->first;
float val = it->second;
storage::feature_val2_t val2;
storage_->get2(feature, val2);
storage::val2_t pos_val(0.f, 1.f);
storage::val2_t neg_val(0.f, 1.f);
ClassifierUtil::get_two(val2, pos_label, neg_label, pos_val, neg_val);
const float C = config.C;
float covar_pos_step = 2.f * step_width * pos_val.v2 * val * val * C;
float covar_neg_step = 2.f * step_width * neg_val.v2 * val * val * C;
storage_->set2(feature, pos_label,
storage::val2_t(pos_val.v1 + step_width * pos_val.v2 * val,
1.f / (1.f / pos_val.v2 + covar_pos_step)));
if (neg_label != "")
storage_->set2(feature, neg_label,
storage::val2_t(neg_val.v1 - step_width * neg_val.v2 * val,
1.f / (1.f / neg_val.v2 + covar_neg_step)));
}
}
void minhash::calc_minhash_values(const sfv_t& sfv, bit_vector& bv) const{
vector<float> min_values_buffer(hash_num_, FLT_MAX);
vector<uint64_t> hash_buffer(hash_num_);
for (size_t i = 0; i < sfv.size(); ++i){
uint64_t key_hash = hash_util::calc_string_hash(sfv[i].first);
float val = sfv[i].second;
for (uint64_t j = 0; j < hash_num_; ++j){
float hashval = calc_hash(key_hash, j, val);
if (hashval < min_values_buffer[j]){
min_values_buffer[j] = hashval;
hash_buffer[j] = key_hash;
}
}
}
bv.resize_and_clear(hash_num_);
for (size_t i = 0; i < hash_buffer.size(); ++i){
if ((hash_buffer[i] & 1LLU) == 1){
bv.set_bit(i);
}
}
}
void sort_and_merge(sfv_t& sfv) {
if (sfv.size() <= 1) {
return;
}
sort(sfv.begin(), sfv.end());
typedef sfv_t::iterator iterator;
iterator cur = sfv.begin();
iterator end = sfv.end();
for (iterator iter = cur+1; iter != end; ++iter) {
if (iter->first == cur->first) {
cur->second += iter->second;
} else {
++cur;
*cur = *iter;
}
}
sfv.erase(cur+1, end);
}
void recommender_base::decode_row(const std::string& id, sfv_t& ret) const {
ret.clear();
orig_.get_row(id, ret);
}
void feature_hasher::hash_feature_keys(sfv_t& fv) const {
for (size_t i = 0, size = fv.size(); i < size; ++i) {
uint64_t id = hash_util::calc_string_hash(fv[i].first) % max_size_;
fv[i].first = pfi::lang::lexical_cast<string>(id);
}
}
void add_feature(const std::string& key, double value, sfv_t& ret_fv) const {
std::stringstream ss;
ss << key << "$" << value;
ret_fv.push_back(make_pair(ss.str(), 1.0));
}
void keyword_weights::update_document_frequency(const sfv_t& fv) {
++document_count_;
for (sfv_t::const_iterator it = fv.begin(); it != fv.end(); ++it) {
++document_frequencies_[it->first];
}
}
void recommender_base::complete_row(const std::string& id, sfv_t& ret) const {
ret.clear();
sfv_t sfv;
orig_.get_row(id, sfv);
complete_row(sfv, ret);
}
void lsh::generate_column_bases(const sfv_t& sfv){
for (size_t i = 0; i < sfv.size(); ++i){
generate_column_base(sfv[i].first);
}
}
void add_feature(const std::string& key, double value, sfv_t& ret_fv) const {
ret_fv.push_back(make_pair(key, std::log(std::max(1.0, value))));
}