Welcome to mirror list, hosted at ThFree Co, Russian Federation.

github.com/moses-smt/vowpal_wabbit.git - Unnamed repository; edit this file 'description' to name the repository.
summaryrefslogtreecommitdiff
path: root/vowpalwabbit/gd.cc
diff options
context:
space:
mode:
Diffstat (limited to 'vowpalwabbit/gd.cc')
-rw-r--r--vowpalwabbit/gd.cc170
1 files changed, 85 insertions, 85 deletions
diff --git a/vowpalwabbit/gd.cc b/vowpalwabbit/gd.cc
index 816ac171..021c8876 100644
--- a/vowpalwabbit/gd.cc
+++ b/vowpalwabbit/gd.cc
@@ -43,7 +43,7 @@ namespace GD
size_t no_win_counter;
size_t early_stop_thres;
float initial_constant;
- void (*predict)(gd*, learner&, example*);
+ void (*predict)(gd&, learner&, example&);
vw* all;
};
@@ -57,12 +57,12 @@ namespace GD
};
template <void (*T)(train_data&, float, float&)>
- void generic_train(vw& all, example* &ec, float update, bool sqrt_norm)
+ void generic_train(vw& all, example& ec, float update, bool sqrt_norm)
{
if (fabs(update) == 0.)
return;
- float total_weight = ec->example_t;
+ float total_weight = ec.example_t;
if(!all.holdout_set_off)
total_weight -= (float)all.sd->weighted_holdout_examples; //exclude weights from test_only examples
@@ -124,9 +124,9 @@ float InvSqrt(float x){
}
}
- void end_pass(gd* g)
+ void end_pass(gd& g)
{
- vw* all = g->all;
+ vw* all = g.all;
sync_weights(*all);
if(all->span_server != "") {
@@ -144,9 +144,9 @@ float InvSqrt(float x){
if(!all->holdout_set_off)
{
- if(summarize_holdout_set(*all, g->no_win_counter))
+ if(summarize_holdout_set(*all, g.no_win_counter))
finalize_regressor(*all, all->final_regressor_name);
- if((g->early_stop_thres == g->no_win_counter) &&
+ if((g.early_stop_thres == g.no_win_counter) &&
((all->check_holdout_every_n_passes <= 1) ||
((all->current_pass % all->check_holdout_every_n_passes) == 0)))
all-> early_terminate = true;
@@ -258,17 +258,17 @@ void audit_triple(vw& all, feature& f0, audit_data* f0_audit, feature& f1, audit
audit_features(all, right_features, audit_right, results, prepend, ns_pre, halfhash + offset);
}
-void print_features(vw& all, example* &ec)
+void print_features(vw& all, example& ec)
{
weight* weights = all.reg.weight_vector;
if (all.lda > 0)
{
size_t count = 0;
- for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++)
- count += ec->audit_features[*i].size() + ec->atomics[*i].size();
- for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++)
- for (audit_data *f = ec->audit_features[*i].begin; f != ec->audit_features[*i].end; f++)
+ for (unsigned char* i = ec.indices.begin; i != ec.indices.end; i++)
+ count += ec.audit_features[*i].size() + ec.atomics[*i].size();
+ for (unsigned char* i = ec.indices.begin; i != ec.indices.end; i++)
+ for (audit_data *f = ec.audit_features[*i].begin; f != ec.audit_features[*i].end; f++)
{
cout << '\t' << f->space << '^' << f->feature << ':' << (f->weight_index/all.reg.stride & all.parse_mask) << ':' << f->x;
for (size_t k = 0; k < all.lda; k++)
@@ -282,21 +282,21 @@ void print_features(vw& all, example* &ec)
string empty;
string ns_pre;
- for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++){
+ for (unsigned char* i = ec.indices.begin; i != ec.indices.end; i++){
ns_pre = "";
- audit_features(all, ec->atomics[*i], ec->audit_features[*i], features, empty, ns_pre, ec->ft_offset);
+ audit_features(all, ec.atomics[*i], ec.audit_features[*i], features, empty, ns_pre, ec.ft_offset);
ns_pre = "";
}
for (vector<string>::iterator i = all.pairs.begin(); i != all.pairs.end();i++)
{
int fst = (*i)[0];
int snd = (*i)[1];
- for (size_t j = 0; j < ec->atomics[fst].size(); j++)
+ for (size_t j = 0; j < ec.atomics[fst].size(); j++)
{
audit_data* a = NULL;
- if (ec->audit_features[fst].size() > 0)
- a = & ec->audit_features[fst][j];
- audit_quad(all, ec->atomics[fst][j], a, ec->atomics[snd], ec->audit_features[snd], features, ns_pre);
+ if (ec.audit_features[fst].size() > 0)
+ a = & ec.audit_features[fst][j];
+ audit_quad(all, ec.atomics[fst][j], a, ec.atomics[snd], ec.audit_features[snd], features, ns_pre);
}
}
@@ -305,17 +305,17 @@ void print_features(vw& all, example* &ec)
int fst = (*i)[0];
int snd = (*i)[1];
int trd = (*i)[2];
- for (size_t j = 0; j < ec->atomics[fst].size(); j++)
+ for (size_t j = 0; j < ec.atomics[fst].size(); j++)
{
audit_data* a1 = NULL;
- if (ec->audit_features[fst].size() > 0)
- a1 = & ec->audit_features[fst][j];
- for (size_t k = 0; k < ec->atomics[snd].size(); k++)
+ if (ec.audit_features[fst].size() > 0)
+ a1 = & ec.audit_features[fst][j];
+ for (size_t k = 0; k < ec.atomics[snd].size(); k++)
{
audit_data* a2 = NULL;
- if (ec->audit_features[snd].size() > 0)
- a2 = & ec->audit_features[snd][k];
- audit_triple(all, ec->atomics[fst][j], a1, ec->atomics[snd][k], a2, ec->atomics[trd], ec->audit_features[trd], features, ns_pre);
+ if (ec.audit_features[snd].size() > 0)
+ a2 = & ec.audit_features[snd][k];
+ audit_triple(all, ec.atomics[fst][j], a1, ec.atomics[snd][k], a2, ec.atomics[trd], ec.audit_features[trd], features, ns_pre);
}
}
}
@@ -329,10 +329,10 @@ void print_features(vw& all, example* &ec)
}
}
-void print_audit_features(vw& all, example* ec)
+void print_audit_features(vw& all, example& ec)
{
if(all.audit)
- print_result(all.stdout_fileno,ec->final_prediction,-1,ec->tag);
+ print_result(all.stdout_fileno,ec.final_prediction,-1,ec.tag);
fflush(stdout);
print_features(all, ec);
}
@@ -352,9 +352,9 @@ float finalize_prediction(vw& all, float ret)
}
template<bool normalized_training, bool reg_mode_odd, bool power_t_half>
-void predict(gd* g, learner& base, example* ec)
+void predict(gd& g, learner& base, example& ec)
{
- vw* all = g->all;
+ vw* all = g.all;
if (normalized_training) {
if(power_t_half) {
@@ -363,27 +363,27 @@ void predict(gd* g, learner& base, example* ec)
float gravity = (float)all->sd->gravity;
if (all->adaptive)
if (all->normalized_idx == 1)
- ec->partial_prediction = inline_predict<float, vec_add_trunc_rescale<true, 1> >(*all, ec, gravity);
+ ec.partial_prediction = inline_predict<float, vec_add_trunc_rescale<true, 1> >(*all, ec, gravity);
else
- ec->partial_prediction = inline_predict<float, vec_add_trunc_rescale<true, 2> >(*all, ec, gravity);
+ ec.partial_prediction = inline_predict<float, vec_add_trunc_rescale<true, 2> >(*all, ec, gravity);
else
if (all->normalized_idx == 1)
- ec->partial_prediction = inline_predict<float, vec_add_trunc_rescale<false, 1> >(*all, ec, gravity);
+ ec.partial_prediction = inline_predict<float, vec_add_trunc_rescale<false, 1> >(*all, ec, gravity);
else
- ec->partial_prediction = inline_predict<float, vec_add_trunc_rescale<false, 2> >(*all, ec, gravity);
+ ec.partial_prediction = inline_predict<float, vec_add_trunc_rescale<false, 2> >(*all, ec, gravity);
}
else
{
if (all->adaptive)
if (all->normalized_idx == 1)
- ec->partial_prediction = inline_predict<vec_add_rescale<true, 1> >(*all, ec);
+ ec.partial_prediction = inline_predict<vec_add_rescale<true, 1> >(*all, ec);
else
- ec->partial_prediction = inline_predict<vec_add_rescale<true, 2> >(*all, ec);
+ ec.partial_prediction = inline_predict<vec_add_rescale<true, 2> >(*all, ec);
else
if (all->normalized_idx == 1)
- ec->partial_prediction = inline_predict<vec_add_rescale<false, 1> >(*all, ec);
+ ec.partial_prediction = inline_predict<vec_add_rescale<false, 1> >(*all, ec);
else
- ec->partial_prediction = inline_predict<vec_add_rescale<false, 2> >(*all, ec);
+ ec.partial_prediction = inline_predict<vec_add_rescale<false, 2> >(*all, ec);
}
}
else {
@@ -392,28 +392,28 @@ void predict(gd* g, learner& base, example* ec)
gnp temp = {(float)all->sd->gravity, all->power_t};
if (all->adaptive)
if (all->normalized_idx == 1)
- ec->partial_prediction = inline_predict<gnp, vec_add_trunc_rescale_general<true, 1> >(*all, ec, temp);
+ ec.partial_prediction = inline_predict<gnp, vec_add_trunc_rescale_general<true, 1> >(*all, ec, temp);
else
- ec->partial_prediction = inline_predict<gnp, vec_add_trunc_rescale_general<true, 2> >(*all, ec, temp);
+ ec.partial_prediction = inline_predict<gnp, vec_add_trunc_rescale_general<true, 2> >(*all, ec, temp);
else
if (all->normalized_idx == 1)
- ec->partial_prediction = inline_predict<gnp, vec_add_trunc_rescale_general<false, 1> >(*all, ec, temp);
+ ec.partial_prediction = inline_predict<gnp, vec_add_trunc_rescale_general<false, 1> >(*all, ec, temp);
else
- ec->partial_prediction = inline_predict<gnp, vec_add_trunc_rescale_general<false, 2> >(*all, ec, temp);
+ ec.partial_prediction = inline_predict<gnp, vec_add_trunc_rescale_general<false, 2> >(*all, ec, temp);
}
else
{
float power_t = all->power_t;
if (all->adaptive)
if (all->normalized_idx == 1)
- ec->partial_prediction = inline_predict<float, vec_add_rescale_general<true, 1> >(*all, ec, power_t);
+ ec.partial_prediction = inline_predict<float, vec_add_rescale_general<true, 1> >(*all, ec, power_t);
else
- ec->partial_prediction = inline_predict<float, vec_add_rescale_general<true, 2> >(*all, ec, power_t);
+ ec.partial_prediction = inline_predict<float, vec_add_rescale_general<true, 2> >(*all, ec, power_t);
else
if (all->normalized_idx == 1)
- ec->partial_prediction = inline_predict<float, vec_add_rescale_general<false, 1> >(*all, ec, power_t);
+ ec.partial_prediction = inline_predict<float, vec_add_rescale_general<false, 1> >(*all, ec, power_t);
else
- ec->partial_prediction = inline_predict<float, vec_add_rescale_general<false, 2> >(*all, ec, power_t);
+ ec.partial_prediction = inline_predict<float, vec_add_rescale_general<false, 2> >(*all, ec, power_t);
}
}
}
@@ -422,13 +422,13 @@ void predict(gd* g, learner& base, example* ec)
if (reg_mode_odd)
{
float gravity = (float)all->sd->gravity;
- ec->partial_prediction = inline_predict<float, vec_add_trunc>(*all, ec, gravity);
+ ec.partial_prediction = inline_predict<float, vec_add_trunc>(*all, ec, gravity);
}
else
- ec->partial_prediction = inline_predict<vec_add>(*all, ec);
+ ec.partial_prediction = inline_predict<vec_add>(*all, ec);
}
- ec->final_prediction = finalize_prediction(*all, ec->partial_prediction * (float)all->sd->contraction);
+ ec.final_prediction = finalize_prediction(*all, ec.partial_prediction * (float)all->sd->contraction);
if (all->audit || all->hash_inv)
print_audit_features(*all, ec);
@@ -495,10 +495,10 @@ inline void powert_norm_compute(norm_data& nd, float x, float& fw) {
}
template <void (*T)(norm_data&,float,float&)>
-float compute_norm(vw& all, example* &ec)
+float compute_norm(vw& all, example& ec)
{//We must traverse the features in _precisely_ the same order as during training.
- label_data* ld = (label_data*)ec->ld;
- float g = all.loss->getSquareGrad(ec->final_prediction, ld->label) * ld->weight;
+ label_data* ld = (label_data*)ec.ld;
+ float g = all.loss->getSquareGrad(ec.final_prediction, ld->label) * ld->weight;
if (g==0) return 1.;
norm_data nd = {g, 0., 0., all.power_t};
@@ -506,7 +506,7 @@ float compute_norm(vw& all, example* &ec)
foreach_feature<norm_data,T>(all, ec, nd);
if(all.normalized_updates) {
- float total_weight = ec->example_t;
+ float total_weight = ec.example_t;
if(!all.holdout_set_off)
total_weight -= (float)all.sd->weighted_holdout_examples; //exclude weights from test_only examples
@@ -527,13 +527,13 @@ float compute_norm(vw& all, example* &ec)
}
template<bool adaptive, bool normalized, bool feature_mask_off, size_t normalized_idx, size_t feature_mask_idx>
-void local_predict(vw& all, gd& g, example* ec)
+void local_predict(vw& all, gd& g, example& ec)
{
- label_data* ld = (label_data*)ec->ld;
+ label_data* ld = (label_data*)ec.ld;
if(g.active_simulation){
- float k = ec->example_t - ld->weight;
- ec->revert_weight = all.loss->getRevertingWeight(all.sd, ec->final_prediction, all.eta/powf(k,all.power_t));
+ float k = ec.example_t - ld->weight;
+ ec.revert_weight = all.loss->getRevertingWeight(all.sd, ec.final_prediction, all.eta/powf(k,all.power_t));
float importance = query_decision(all, ec, k);
if(importance > 0){
all.sd->queries += 1;
@@ -547,16 +547,16 @@ void local_predict(vw& all, gd& g, example* ec)
if(all.active && ld->label != FLT_MAX)
t = (float)all.sd->weighted_unlabeled_examples;
else
- t = (float)(ec->example_t - all.sd->weighted_holdout_examples);
+ t = (float)(ec.example_t - all.sd->weighted_holdout_examples);
- ec->eta_round = 0;
+ ec.eta_round = 0;
if (ld->label != FLT_MAX)
- ec->loss = all.loss->getLoss(all.sd, ec->final_prediction, ld->label) * ld->weight;
+ ec.loss = all.loss->getLoss(all.sd, ec.final_prediction, ld->label) * ld->weight;
- if (ld->label != FLT_MAX && !ec->test_only)
+ if (ld->label != FLT_MAX && !ec.test_only)
{
- if (all.training && ec->loss > 0.)
+ if (all.training && ec.loss > 0.)
{
float eta_t;
float norm;
@@ -566,22 +566,22 @@ void local_predict(vw& all, gd& g, example* ec)
else
norm = compute_norm<powert_norm_compute<adaptive, normalized, feature_mask_off, normalized_idx, feature_mask_idx> >(all,ec);
else
- norm = ec->total_sum_feat_sq;
+ norm = ec.total_sum_feat_sq;
eta_t = all.eta * norm * ld->weight;
if(!adaptive && all.power_t != 0) eta_t *= powf(t,-all.power_t);
float update = 0.f;
if( all.invariant_updates )
- update = all.loss->getUpdate(ec->final_prediction, ld->label, eta_t, norm);
+ update = all.loss->getUpdate(ec.final_prediction, ld->label, eta_t, norm);
else
- update = all.loss->getUnsafeUpdate(ec->final_prediction, ld->label, eta_t, norm);
+ update = all.loss->getUnsafeUpdate(ec.final_prediction, ld->label, eta_t, norm);
- ec->eta_round = (float) (update / all.sd->contraction);
+ ec.eta_round = (float) (update / all.sd->contraction);
- if (all.reg_mode && fabs(ec->eta_round) > 1e-8) {
- double dev1 = all.loss->first_derivative(all.sd, ec->final_prediction, ld->label);
- double eta_bar = (fabs(dev1) > 1e-8) ? (-ec->eta_round / dev1) : 0.0;
+ if (all.reg_mode && fabs(ec.eta_round) > 1e-8) {
+ double dev1 = all.loss->first_derivative(all.sd, ec.final_prediction, ld->label);
+ double eta_bar = (fabs(dev1) > 1e-8) ? (-ec.eta_round / dev1) : 0.0;
if (fabs(dev1) > 1e-8)
all.sd->contraction *= (1. - all.l2_lambda * eta_bar * norm);
//all.sd->contraction /= (1. + all.l2_lambda * eta_bar * norm);
@@ -590,23 +590,23 @@ void local_predict(vw& all, gd& g, example* ec)
}
}
else if(all.active)
- ec->revert_weight = all.loss->getRevertingWeight(all.sd, ec->final_prediction, all.eta/powf(t,all.power_t));
+ ec.revert_weight = all.loss->getRevertingWeight(all.sd, ec.final_prediction, all.eta/powf(t,all.power_t));
}
template<bool adaptive, bool normalized, bool feature_mask_off, size_t normalized_idx, size_t feature_mask_idx>
-void update(gd* g, learner& base, example* ec)
+void update(gd& g, learner& base, example& ec)
{
- vw* all = g->all;
+ vw* all = g.all;
- local_predict<adaptive, normalized, feature_mask_off, normalized_idx, feature_mask_idx > (*all, *g, ec);
+ local_predict<adaptive, normalized, feature_mask_off, normalized_idx, feature_mask_idx > (*all, g, ec);
- if (ec->eta_round != 0.)
+ if (ec.eta_round != 0.)
{
if(all->power_t == 0.5)
- generic_train<specialized_update<adaptive, normalized, feature_mask_off, normalized_idx, feature_mask_idx> > (*all,ec,(float)ec->eta_round,true);
+ generic_train<specialized_update<adaptive, normalized, feature_mask_off, normalized_idx, feature_mask_idx> > (*all,ec,(float)ec.eta_round,true);
else
- generic_train<general_update<adaptive, normalized, feature_mask_off, normalized_idx, feature_mask_idx> >(*all,ec,(float)ec->eta_round,false);
+ generic_train<general_update<adaptive, normalized, feature_mask_off, normalized_idx, feature_mask_idx> >(*all,ec,(float)ec.eta_round,false);
if (all->sd->contraction < 1e-10) // updating weights now to avoid numerical instability
sync_weights(*all);
@@ -614,16 +614,16 @@ void update(gd* g, learner& base, example* ec)
}
template<bool adaptive, bool normalized, bool feature_mask_off, size_t normalized_idx, size_t feature_mask_idx>
-void learn(gd* g, learner& base, example* ec)
+void learn(gd& g, learner& base, example& ec)
{
- vw* all = g->all;
- label_data* ld = (label_data*)ec->ld;
+ vw* all = g.all;
+ label_data* ld = (label_data*)ec.ld;
- assert(ec->in_use);
+ assert(ec.in_use);
- g->predict(g,base,ec);
+ g.predict(g,base,ec);
- if ((all->holdout_set_off || !ec->test_only) && ld->weight > 0)
+ if ((all->holdout_set_off || !ec.test_only) && ld->weight > 0)
update<adaptive, normalized, feature_mask_off, normalized_idx, feature_mask_idx>(g,base,ec);
}
@@ -830,9 +830,9 @@ void save_load_online_state(vw& all, io_buf& model_file, bool read, bool text)
while ((!read && i < length) || (read && brw >0));
}
-void save_load(gd* g, io_buf& model_file, bool read, bool text)
+void save_load(gd& g, io_buf& model_file, bool read, bool text)
{
- vw* all = g->all;
+ vw* all = g.all;
if(read)
{
initialize_regressor(*all);
@@ -850,8 +850,8 @@ void save_load(gd* g, io_buf& model_file, bool read, bool text)
}
}
- if (g->initial_constant != 0.0)
- VW::set_weight(*all, constant, 0, g->initial_constant);
+ if (g.initial_constant != 0.0)
+ VW::set_weight(*all, constant, 0, g.initial_constant);
}
generated by cgit v1.2.3 (git 2.25.1) at 2024-08-31 20:39:28 +0300