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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 "allreduce.h"
#include "sparse_dense.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);
float single_quad_weight(weight* weights, feature& page_feature, feature* offer_feature, size_t mask);
void quadratic(v_array<feature> &f, const v_array<feature> &first_part,
const v_array<feature> &second_part, size_t thread_mask);
void print_audit_features(vw&, example* ec);
void train(weight* weights, const v_array<feature> &features, float update);
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);
void drive_gd(void*);
void finish_gd(void*);
void learn_gd(void*, example* ec);
void save_load(void* in, io_buf& model_file, bool read, bool text);
void save_load_regressor(vw& all, io_buf& model_file, bool read, bool text);
void output_and_account_example(example* ec);
bool command_example(vw&, example* ec);
template <float (*T)(vw&,float,uint32_t)>
float inline_predict(vw& all, example* &ec)
{
float prediction = all.p->lp->get_initial(ec->ld);
for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++)
prediction += sd_add<T>(all, ec->atomics[*i].begin, ec->atomics[*i].end, ec->ft_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++)
prediction += one_pf_quad_predict<T>(all,*temp.begin,ec->atomics[(int)(*i)[1]], ec->ft_offset);
}
}
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++) {
prediction += one_pf_cubic_predict<T>(all,*temp1.begin,*temp2.begin,ec->atomics[(int)(*i)[2]], ec->ft_offset);
}
}
}
return prediction;
}
}
#endif
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