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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.
*/
#pragma once
// This is the interface for a learning algorithm
#include<iostream>
#include"memory.h"
using namespace std;
struct vw;
void return_simple_example(vw& all, void*, example& ec);
namespace LEARNER
{
template<class T> struct learner;
typedef learner<char> base_learner;
struct func_data {
void* data;
base_learner* base;
void (*func)(void* data);
};
inline func_data tuple_dbf(void* data, base_learner* base, void (*func)(void* data))
{
func_data foo;
foo.data = data;
foo.base = base;
foo.func = func;
return foo;
}
struct learn_data {
void* data;
base_learner* base;
void (*learn_f)(void* data, base_learner& base, example&);
void (*predict_f)(void* data, base_learner& base, example&);
void (*update_f)(void* data, base_learner& base, example&);
};
struct save_load_data{
void* data;
base_learner* base;
void (*save_load_f)(void*, io_buf&, bool read, bool text);
};
struct finish_example_data{
void* data;
base_learner* base;
void (*finish_example_f)(vw&, void* data, example&);
};
void generic_driver(vw& all);
inline void generic_sl(void*, io_buf&, bool, bool) {}
inline void generic_learner(void* data, base_learner& base, example&) {}
inline void generic_func(void* data) {}
const save_load_data generic_save_load_fd = {NULL, NULL, generic_sl};
const learn_data generic_learn_fd = {NULL, NULL, generic_learner, generic_learner, NULL};
const func_data generic_func_fd = {NULL, NULL, generic_func};
typedef void (*tlearn)(void* d, base_learner& base, example& ec);
typedef void (*tsl)(void* d, io_buf& io, bool read, bool text);
typedef void (*tfunc)(void*d);
typedef void (*tend_example)(vw& all, void* d, example& ec);
template<class T> learner<T>& init_learner();
template<class T> learner<T>& init_learner(T* dat, size_t params_per_weight);
template<class T> learner<T>& init_learner(T* dat, base_learner* base, size_t ws = 1);
template<class T>
struct learner {
private:
func_data init_fd;
learn_data learn_fd;
finish_example_data finish_example_fd;
save_load_data save_load_fd;
func_data end_pass_fd;
func_data end_examples_fd;
func_data finisher_fd;
public:
size_t weights; //this stores the number of "weight vectors" required by the learner.
size_t increment;
//called once for each example. Must work under reduction.
inline void learn(example& ec, size_t i=0)
{
ec.ft_offset += (uint32_t)(increment*i);
learn_fd.learn_f(learn_fd.data, *learn_fd.base, ec);
ec.ft_offset -= (uint32_t)(increment*i);
}
inline void set_learn(void (*u)(T& data, base_learner& base, example&))
{
learn_fd.learn_f = (tlearn)u;
learn_fd.update_f = (tlearn)u;
}
inline void predict(example& ec, size_t i=0)
{
ec.ft_offset += (uint32_t)(increment*i);
learn_fd.predict_f(learn_fd.data, *learn_fd.base, ec);
ec.ft_offset -= (uint32_t)(increment*i);
}
inline void set_predict(void (*u)(T& data, base_learner& base, example&))
{ learn_fd.predict_f = (tlearn)u; }
inline void update(example& ec, size_t i=0)
{
ec.ft_offset += (uint32_t)(increment*i);
learn_fd.update_f(learn_fd.data, *learn_fd.base, ec);
ec.ft_offset -= (uint32_t)(increment*i);
}
inline void set_update(void (*u)(T& data, base_learner& base, example&))
{ learn_fd.update_f = (tlearn)u; }
//called anytime saving or loading needs to happen. Autorecursive.
inline void save_load(io_buf& io, bool read, bool text) { save_load_fd.save_load_f(save_load_fd.data, io, read, text); if (save_load_fd.base) save_load_fd.base->save_load(io, read, text); }
inline void set_save_load(void (*sl)(T&, io_buf&, bool, bool))
{ save_load_fd.save_load_f = (tsl)sl;
save_load_fd.data = learn_fd.data;
save_load_fd.base = learn_fd.base;}
//called to clean up state. Autorecursive.
void set_finish(void (*f)(T&)) { finisher_fd = tuple_dbf(learn_fd.data,learn_fd.base, (tfunc)f); }
inline void finish()
{
if (finisher_fd.data)
{finisher_fd.func(finisher_fd.data); free(finisher_fd.data); }
if (finisher_fd.base) {
finisher_fd.base->finish();
free(finisher_fd.base);
}
}
void end_pass(){
end_pass_fd.func(end_pass_fd.data);
if (end_pass_fd.base) end_pass_fd.base->end_pass(); }//autorecursive
void set_end_pass(void (*f)(T&))
{end_pass_fd = tuple_dbf(learn_fd.data, learn_fd.base, (tfunc)f);}
//called after parsing of examples is complete. Autorecursive.
void end_examples()
{ end_examples_fd.func(end_examples_fd.data);
if (end_examples_fd.base) end_examples_fd.base->end_examples(); }
void set_end_examples(void (*f)(T&))
{end_examples_fd = tuple_dbf(learn_fd.data,learn_fd.base, (tfunc)f);}
//Called at the beginning by the driver. Explicitly not recursive.
void init_driver() { init_fd.func(init_fd.data);}
void set_init_driver(void (*f)(T&))
{ init_fd = tuple_dbf(learn_fd.data,learn_fd.base, (tfunc)f); }
//called after learn example for each example. Explicitly not recursive.
inline void finish_example(vw& all, example& ec) { finish_example_fd.finish_example_f(all, finish_example_fd.data, ec);}
void set_finish_example(void (*f)(vw& all, T&, example&))
{finish_example_fd.data = learn_fd.data;
finish_example_fd.finish_example_f = (tend_example)f;}
friend learner<T>& init_learner<>();
friend learner<T>& init_learner<>(T* dat, size_t params_per_weight);
friend learner<T>& init_learner<>(T* dat, base_learner* base, size_t ws);
};
template<class T> learner<T>& init_learner()
{
learner<T>& ret = calloc_or_die<learner<T> >();
ret.weights = 1;
ret.increment = 1;
ret.learn_fd = LEARNER::generic_learn_fd;
ret.finish_example_fd.data = NULL;
ret.finish_example_fd.finish_example_f = return_simple_example;
ret.end_pass_fd = LEARNER::generic_func_fd;
ret.end_examples_fd = LEARNER::generic_func_fd;
ret.init_fd = LEARNER::generic_func_fd;
ret.finisher_fd = LEARNER::generic_func_fd;
ret.save_load_fd = LEARNER::generic_save_load_fd;
return ret;
}
template<class T> learner<T>& init_learner(T* dat, size_t params_per_weight)
{ // the constructor for all learning algorithms.
learner<T>& ret = init_learner<T>();
ret.learn_fd.data = dat;
ret.finisher_fd.data = dat;
ret.finisher_fd.base = NULL;
ret.finisher_fd.func = LEARNER::generic_func;
ret.increment = params_per_weight;
return ret;
}
template<class T> learner<T>& init_learner(T* dat, base_learner* base, size_t ws = 1)
{ //the reduction constructor, with separate learn and predict functions
learner<T>& ret = calloc_or_die<learner<T> >();
ret = *(learner<T>*)base;
ret.learn_fd.data = dat;
ret.learn_fd.base = base;
ret.finisher_fd.data = dat;
ret.finisher_fd.base = base;
ret.finisher_fd.func = LEARNER::generic_func;
ret.weights = ws;
ret.increment = base->increment * ret.weights;
return ret;
}
template<class T> base_learner* make_base(learner<T>& base)
{ return (base_learner*)&base; }
}
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