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/*
Copyright (c) by respective owners including Yahoo!, Microsoft, and
individual contributors. All rights reserved. Released under a BSD
license as described in the file LICENSE.
*/
#ifndef GD_H
#define GD_H
#ifdef __FreeBSD__
#include <sys/socket.h>
#endif
#include <math.h>
#include "example.h"
#include "parse_regressor.h"
#include "parser.h"
#include "sparse_dense.h"
#include "v_array.h"
namespace GD{
void print_result(int f, float res, v_array<char> tag);
void print_audit_features(regressor ®, example& ec, size_t offset);
float finalize_prediction(vw&, float ret);
void print_audit_features(vw&, example& ec);
void train_one_example(regressor& r, example* ex);
void train_offset_example(regressor& r, example* ex, size_t offset);
void compute_update(example* ec);
void offset_train(regressor ®, example* &ec, float update, size_t offset);
void train_one_example_single_thread(regressor& r, example* ex);
LEARNER::learner* setup(vw& all, po::variables_map& vm);
void save_load_regressor(vw& all, io_buf& model_file, bool read, bool text);
void output_and_account_example(example* ec);
template <class R, void (*T)(R&, float, float&)>
void foreach_feature(weight* weight_vector, size_t weight_mask, feature* begin, feature* end, R& dat, uint32_t offset=0, float mult=1.)
{
for (feature* f = begin; f!= end; f++)
T(dat, mult*f->x, weight_vector[(f->weight_index + offset) & weight_mask]);
}
template <class R, void (*T)(R&, float, float&)>
void foreach_feature(vw& all, example& ec, R& dat)
{
uint32_t offset = ec.ft_offset;
for (unsigned char* i = ec.indices.begin; i != ec.indices.end; i++)
foreach_feature<R,T>(all.reg.weight_vector, all.reg.weight_mask, ec.atomics[*i].begin, ec.atomics[*i].end, dat, offset);
for (vector<string>::iterator i = all.pairs.begin(); i != all.pairs.end();i++) {
if (ec.atomics[(int)(*i)[0]].size() > 0) {
v_array<feature> temp = ec.atomics[(int)(*i)[0]];
for (; temp.begin != temp.end; temp.begin++)
{
uint32_t halfhash = quadratic_constant * (temp.begin->weight_index + offset);
foreach_feature<R,T>(all.reg.weight_vector, all.reg.weight_mask, ec.atomics[(int)(*i)[1]].begin, ec.atomics[(int)(*i)[1]].end, dat,
halfhash, temp.begin->x);
}
}
}
for (vector<string>::iterator i = all.triples.begin(); i != all.triples.end();i++) {
if ((ec.atomics[(int)(*i)[0]].size() == 0) || (ec.atomics[(int)(*i)[1]].size() == 0) || (ec.atomics[(int)(*i)[2]].size() == 0)) { continue; }
v_array<feature> temp1 = ec.atomics[(int)(*i)[0]];
for (; temp1.begin != temp1.end; temp1.begin++) {
v_array<feature> temp2 = ec.atomics[(int)(*i)[1]];
for (; temp2.begin != temp2.end; temp2.begin++) {
uint32_t halfhash = cubic_constant2 * (cubic_constant * (temp1.begin->weight_index + offset) + temp2.begin->weight_index + offset);
float mult = temp1.begin->x * temp2.begin->x;
foreach_feature<R,T>(all.reg.weight_vector, all.reg.weight_mask, ec.atomics[(int)(*i)[2]].begin, ec.atomics[(int)(*i)[2]].end, dat, halfhash, mult);
}
}
}
}
template <class R, void (*T)(predict_data<R>&, float, float&)>
float inline_predict(vw& all, example& ec, R extra)
{
predict_data<R> temp = {all.p->lp.get_initial(ec.ld), extra};
foreach_feature<predict_data<R>, T>(all, ec, temp);
return temp.prediction;
}
template <void (*T)(float&, float, float&)>
float inline_predict(vw& all, example& ec)
{
float temp = all.p->lp.get_initial(ec.ld);
foreach_feature<float, T>(all, ec, temp);
return temp;
}
}
#endif
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