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triton/lib/runtime/predictors/random_forest.cpp
2016-10-01 19:27:42 -04:00

81 lines
2.5 KiB
C++

/*
* Copyright (c) 2015, PHILIPPE TILLET. All rights reserved.
*
* This file is part of ISAAC.
*
* ISAAC is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this library; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
* MA 02110-1301 USA
*/
#include "isaac/runtime/predictors/random_forest.h"
#include "rapidjson/to_array.hpp"
namespace isaac
{
namespace runtime
{
namespace predictors
{
random_forest::tree::tree(rapidjson::Value const & treerep)
{
children_left_ = rapidjson::to_int_array<int>(treerep["children_left"]);
children_right_ = rapidjson::to_int_array<int>(treerep["children_right"]);
threshold_ = rapidjson::to_float_array<float>(treerep["threshold"]);
feature_ = rapidjson::to_float_array<float>(treerep["feature"]);
for(rapidjson::SizeType i = 0 ; i < treerep["value"].Size() ; i++)
value_.push_back(rapidjson::to_float_array<float>(treerep["value"][i]));
D_ = value_[0].size();
}
std::vector<float> const & random_forest::tree::predict(std::vector<int_t> const & x) const
{
int_t idx = 0;
while(children_left_[idx]!=-1)
idx = (x[feature_[idx]] <= threshold_[idx])?children_left_[idx]:children_right_[idx];
return value_[idx];
}
size_t random_forest::tree::D() const { return D_; }
random_forest::random_forest(rapidjson::Value const & estimators)
{
for(rapidjson::SizeType i = 0 ; i < estimators.Size() ; ++i)
estimators_.push_back(tree(estimators[i]));
D_ = estimators_.front().D();
}
std::vector<float> random_forest::predict(std::vector<int_t> const & x) const
{
std::vector<float> res(D_, 0);
for(const auto & elem : estimators_)
{
std::vector<float> const & subres = elem.predict(x);
for(size_t i = 0 ; i < D_ ; ++i)
res[i] += subres[i];
}
for(size_t i = 0 ; i < D_ ; ++i)
res[i] /= estimators_.size();
return res;
}
std::vector<random_forest::tree> const & random_forest::estimators() const
{ return estimators_; }
}
}
}