void confidence_weighted::update(
const common::sfv_t& sfv,
float step_width,
const string& pos_label,
const string& neg_label) {
util::concurrent::scoped_wlock lk(storage_->get_lock());
for (common::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_nolock(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_.regularization_weight;
float covar_pos_step = 2.f * step_width * val * val * C;
float covar_neg_step = 2.f * step_width * val * val * C;
storage_->set2_nolock(
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_nolock(
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)));
}
}
touch(pos_label);
}
void arow::update(
const common::sfv_t& sfv,
float alpha,
float beta,
const std::string& pos_label,
const std::string& neg_label) {
storage::storage_base* sto = get_storage();
for (common::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;
sto->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);
sto->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 != "") {
sto->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 normal_herd::update(
const common::sfv_t& sfv,
float margin,
float variance,
const string& pos_label,
const string& neg_label) {
storage::storage_base* sto = get_storage();
for (common::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;
sto->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;
const float C = config_.C;
sto->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 != "") {
sto->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 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)));
}
}