diff options
Diffstat (limited to 'vowpalwabbit/lda_core.cc')
| -rw-r--r-- | vowpalwabbit/lda_core.cc | 83 |
1 files changed, 50 insertions, 33 deletions
diff --git a/vowpalwabbit/lda_core.cc b/vowpalwabbit/lda_core.cc index db2281a2..fcf79afc 100644 --- a/vowpalwabbit/lda_core.cc +++ b/vowpalwabbit/lda_core.cc @@ -25,6 +25,7 @@ license as described in the file LICENSE. #include "vw.h"
using namespace LEARNER;
+using namespace std;
namespace LDA {
@@ -44,7 +45,7 @@ public: v_array<int> doc_lengths;
v_array<float> digammas;
v_array<float> v;
- std::vector<index_feature> sorted_features;
+ vector<index_feature> sorted_features;
bool total_lambda_init;
@@ -516,9 +517,9 @@ size_t next_pow2(size_t x) { return ((size_t)1) << i;
}
-void save_load(lda* l, io_buf& model_file, bool read, bool text)
+void save_load(lda& l, io_buf& model_file, bool read, bool text)
{
- vw* all = l->all;
+ vw* all = l.all;
uint32_t length = 1 << all->num_bits;
uint32_t stride = all->reg.stride;
@@ -648,13 +649,13 @@ void save_load(lda* l, io_buf& model_file, bool read, bool text) {
float score = lda_loop(*l.all, l.Elogtheta, &(l.v[d*l.all->lda]), weights, l.examples[d],l.all->power_t);
if (l.all->audit)
- GD::print_audit_features(*l.all, l.examples[d]);
+ GD::print_audit_features(*l.all, *l.examples[d]);
// If the doc is empty, give it loss of 0.
if (l.doc_lengths[d] > 0) {
l.all->sd->sum_loss -= score;
l.all->sd->sum_loss_since_last_dump -= score;
}
- return_simple_example(*l.all, NULL, l.examples[d]);
+ return_simple_example(*l.all, NULL, *l.examples[d]);
}
for (index_feature* s = &l.sorted_features[0]; s <= &l.sorted_features.back();)
@@ -691,54 +692,68 @@ void save_load(lda* l, io_buf& model_file, bool read, bool text) l.doc_lengths.erase();
}
- void learn(lda* l, learner& base, example* ec)
+ void learn(lda& l, learner& base, example& ec)
{
- size_t num_ex = l->examples.size();
- l->examples.push_back(ec);
- l->doc_lengths.push_back(0);
- for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++) {
- feature* f = ec->atomics[*i].begin;
- for (; f != ec->atomics[*i].end; f++) {
+ size_t num_ex = l.examples.size();
+ l.examples.push_back(&ec);
+ l.doc_lengths.push_back(0);
+ for (unsigned char* i = ec.indices.begin; i != ec.indices.end; i++) {
+ feature* f = ec.atomics[*i].begin;
+ for (; f != ec.atomics[*i].end; f++) {
index_feature temp = {(uint32_t)num_ex, *f};
- l->sorted_features.push_back(temp);
- l->doc_lengths[num_ex] += (int)f->x;
+ l.sorted_features.push_back(temp);
+ l.doc_lengths[num_ex] += (int)f->x;
}
}
- if (++num_ex == l->all->minibatch && !ec->test_only)
- learn_batch(*l);
+ if (++num_ex == l.all->minibatch && !ec.test_only)
+ learn_batch(l);
}
// placeholder
- void predict(lda* l, learner& base, example* ec)
+ void predict(lda& l, learner& base, example& ec)
{
- bool test_only = ec->test_only;
- ec->test_only = true;
+ bool test_only = ec.test_only;
+ ec.test_only = true;
learn(l, base, ec);
- ec->test_only = test_only;
+ ec.test_only = test_only;
}
- void end_pass(lda* l)
+ void end_pass(lda& l)
{
- if (l->examples.size())
- learn_batch(*l);
+ if (l.examples.size())
+ learn_batch(l);
}
-void end_examples(lda* l)
+void end_examples(lda& l)
{
- for (size_t i = 0; i < l->all->length(); i++) {
- weight* weights_for_w = & (l->all->reg.weight_vector[i*l->all->reg.stride]);
- float decay = fmin(1.0, exp(l->decay_levels.last() - l->decay_levels.end[(int)(-1- l->example_t +weights_for_w[l->all->lda])]));
- for (size_t k = 0; k < l->all->lda; k++)
+ for (size_t i = 0; i < l.all->length(); i++) {
+ weight* weights_for_w = & (l.all->reg.weight_vector[i*l.all->reg.stride]);
+ float decay = fmin(1.0, exp(l.decay_levels.last() - l.decay_levels.end[(int)(-1- l.example_t +weights_for_w[l.all->lda])]));
+ for (size_t k = 0; k < l.all->lda; k++)
weights_for_w[k] *= decay;
}
}
- void finish_example(vw& all, lda*, example*ec)
+ void finish_example(vw& all, lda&, example& ec)
{}
-learner* setup(vw&all, std::vector<std::string>&opts, po::variables_map& vm)
+ void finish(lda& ld)
+ {
+ ld.sorted_features.~vector<index_feature>();
+ ld.Elogtheta.delete_v();
+ ld.decay_levels.delete_v();
+ ld.total_new.delete_v();
+ ld.examples.delete_v();
+ ld.total_lambda.delete_v();
+ ld.doc_lengths.delete_v();
+ ld.digammas.delete_v();
+ ld.v.delete_v();
+ }
+
+learner* setup(vw&all, vector<string>&opts, po::variables_map& vm)
{
- lda* ld = new lda;
+ lda* ld = (lda*)calloc(1,sizeof(lda));
+ ld->sorted_features = vector<index_feature>();
ld->total_lambda_init = 0;
ld->all = &all;
ld->example_t = all.initial_t;
@@ -773,12 +788,13 @@ learner* setup(vw&all, std::vector<std::string>&opts, po::variables_map& vm) if (vm.count("minibatch")) {
size_t minibatch2 = next_pow2(all.minibatch);
all.p->ring_size = all.p->ring_size > minibatch2 ? all.p->ring_size : minibatch2;
-}
+ }
ld->v.resize(all.lda*all.minibatch);
ld->decay_levels.push_back(0.f);
-
+
+ all.l->finish();
learner* l = new learner(ld, all.reg.stride);
l->set_learn<lda,learn>();
l->set_predict<lda,predict>();
@@ -786,6 +802,7 @@ learner* setup(vw&all, std::vector<std::string>&opts, po::variables_map& vm) l->set_finish_example<lda,finish_example>();
l->set_end_examples<lda,end_examples>();
l->set_end_pass<lda,end_pass>();
+ l->set_finish<lda,finish>();
return l;
}
|