[68172a] | 1 | /*
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| 2 | * Project: MoleCuilder
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| 3 | * Description: creates and alters molecular systems
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| 4 | * Copyright (C) 2012 University of Bonn. All rights reserved.
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[5aaa43] | 5 | * Copyright (C) 2013 Frederik Heber. All rights reserved.
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[68172a] | 6 | * Please see the COPYING file or "Copyright notice" in builder.cpp for details.
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| 7 | *
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| 8 | *
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| 9 | * This file is part of MoleCuilder.
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| 10 | *
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| 11 | * MoleCuilder is free software: you can redistribute it and/or modify
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| 12 | * it under the terms of the GNU General Public License as published by
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| 13 | * the Free Software Foundation, either version 2 of the License, or
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| 14 | * (at your option) any later version.
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| 15 | *
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| 16 | * MoleCuilder is distributed in the hope that it will be useful,
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| 17 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 18 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 19 | * GNU General Public License for more details.
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| 20 | *
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| 21 | * You should have received a copy of the GNU General Public License
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| 22 | * along with MoleCuilder. If not, see <http://www.gnu.org/licenses/>.
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| 23 | */
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| 24 |
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| 25 | /*
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| 26 | * TrainingData.cpp
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| 27 | *
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| 28 | * Created on: 15.10.2012
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| 29 | * Author: heber
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| 30 | */
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| 31 |
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| 32 | // include config.h
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| 33 | #ifdef HAVE_CONFIG_H
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| 34 | #include <config.h>
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| 35 | #endif
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| 36 |
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| 37 | #include "CodePatterns/MemDebug.hpp"
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| 38 |
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| 39 | #include "TrainingData.hpp"
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| 40 |
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[dd8094] | 41 | #include <algorithm>
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[04cc7e] | 42 | #include <boost/bind.hpp>
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[dd8094] | 43 | #include <boost/lambda/lambda.hpp>
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[68172a] | 44 | #include <iostream>
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| 45 |
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[04cc7e] | 46 | #include "CodePatterns/Assert.hpp"
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[dd8094] | 47 | #include "CodePatterns/Log.hpp"
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[68172a] | 48 | #include "CodePatterns/toString.hpp"
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| 49 |
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[fbf143] | 50 | #include "Fragmentation/Summation/SetValues/Fragment.hpp"
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[68172a] | 51 | #include "FunctionApproximation/FunctionModel.hpp"
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[af2c7ec] | 52 | #include "FunctionApproximation/Extractors.hpp"
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[68172a] | 53 |
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| 54 | void TrainingData::operator()(const range_t &range) {
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| 55 | for (HomologyContainer::const_iterator iter = range.first; iter != range.second; ++iter) {
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[bf1d1b] | 56 | const Fragment &fragment = iter->second.fragment;
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[af2c7ec] | 57 | // create internal list of arguments
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| 58 | FunctionModel::arguments_t all_args = Extractors::gatherAllSymmetricDistances(
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| 59 | fragment.getPositions(),
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| 60 | fragment.getCharges(),
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| 61 | DistanceVector.size()
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[68172a] | 62 | );
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[af2c7ec] | 63 | DistanceVector.push_back( all_args );
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[bf1d1b] | 64 | const double &energy = iter->second.energy;
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[68172a] | 65 | EnergyVector.push_back( FunctionModel::results_t(1, energy) );
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[af2c7ec] | 66 | // filter distances out of list of all arguments
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| 67 | FunctionModel::arguments_t args = filter(all_args);
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| 68 | LOG(3, "DEBUG: Filtered arguments are " << args << ".");
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| 69 | ArgumentVector.push_back( args );
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[68172a] | 70 | }
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| 71 | }
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| 72 |
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| 73 | const double TrainingData::getL2Error(const FunctionModel &model) const
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| 74 | {
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| 75 | double L2sum = 0.;
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| 76 |
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[af2c7ec] | 77 | FunctionApproximation::inputs_t::const_iterator initer = ArgumentVector.begin();
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[68172a] | 78 | FunctionApproximation::outputs_t::const_iterator outiter = EnergyVector.begin();
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[af2c7ec] | 79 | for (; initer != ArgumentVector.end(); ++initer, ++outiter) {
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[68172a] | 80 | const FunctionModel::results_t result = model((*initer));
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| 81 | const double temp = fabs((*outiter)[0] - result[0]);
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| 82 | L2sum += temp*temp;
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| 83 | }
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| 84 | return L2sum;
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| 85 | }
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| 86 |
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| 87 | const double TrainingData::getLMaxError(const FunctionModel &model) const
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| 88 | {
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| 89 | double Lmax = 0.;
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[af2c7ec] | 90 | // size_t maxindex = -1;
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| 91 | FunctionApproximation::inputs_t::const_iterator initer = ArgumentVector.begin();
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[68172a] | 92 | FunctionApproximation::outputs_t::const_iterator outiter = EnergyVector.begin();
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[af2c7ec] | 93 | for (; initer != ArgumentVector.end(); ++initer, ++outiter) {
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[68172a] | 94 | const FunctionModel::results_t result = model((*initer));
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| 95 | const double temp = fabs((*outiter)[0] - result[0]);
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| 96 | if (temp > Lmax) {
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| 97 | Lmax = temp;
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[af2c7ec] | 98 | // maxindex = std::distance(
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| 99 | // const_cast<const FunctionApproximation::inputs_t &>(ArgumentVector).begin(),
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| 100 | // initer
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| 101 | // );
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[68172a] | 102 | }
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| 103 | }
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| 104 | return Lmax;
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| 105 | }
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| 106 |
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[04cc7e] | 107 | const TrainingData::DistanceEnergyTable_t TrainingData::getDistanceEnergyTable() const
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| 108 | {
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| 109 | TrainingData::DistanceEnergyTable_t table;
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| 110 |
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| 111 | /// extract distance member variable from argument_t and first value from results_t
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| 112 | OutputVector_t::const_iterator ergiter = EnergyVector.begin();
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[af2c7ec] | 113 | for (InputVector_t::const_iterator iter = ArgumentVector.begin();
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| 114 | iter != ArgumentVector.end(); ++iter, ++ergiter) {
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[04cc7e] | 115 | ASSERT( ergiter != EnergyVector.end(),
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| 116 | "TrainingData::getDistanceEnergyTable() - less output than input values.");
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| 117 | std::vector< double > values(iter->size(), 0.);
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| 118 | // transform all distances
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| 119 | const FunctionModel::arguments_t &args = *iter;
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| 120 | std::transform(
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| 121 | args.begin(), args.end(),
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| 122 | values.begin(),
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| 123 | boost::bind(&argument_t::distance, _1));
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| 124 |
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| 125 | // get first energy value
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| 126 | values.push_back((*ergiter)[0]);
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| 127 |
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| 128 | // push as table row
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| 129 | table.push_back(values);
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| 130 | }
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| 131 |
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| 132 | return table;
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| 133 | }
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| 134 |
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[dd8094] | 135 | const FunctionModel::results_t TrainingData::getTrainingOutputAverage() const
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| 136 | {
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| 137 | if (EnergyVector.size() != 0) {
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| 138 | FunctionApproximation::outputs_t::const_iterator outiter = EnergyVector.begin();
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| 139 | FunctionModel::results_t result(*outiter);
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| 140 | for (++outiter; outiter != EnergyVector.end(); ++outiter)
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| 141 | for (size_t index = 0; index < (*outiter).size(); ++index)
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| 142 | result[index] += (*outiter)[index];
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| 143 | LOG(2, "DEBUG: Sum of EnergyVector is " << result << ".");
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| 144 | const double factor = 1./EnergyVector.size();
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| 145 | std::transform(result.begin(), result.end(), result.begin(),
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| 146 | boost::lambda::_1 * factor);
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| 147 | LOG(2, "DEBUG: Average EnergyVector is " << result << ".");
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| 148 | return result;
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| 149 | }
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| 150 | return FunctionModel::results_t();
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| 151 | }
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| 152 |
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[68172a] | 153 | std::ostream &operator<<(std::ostream &out, const TrainingData &data)
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| 154 | {
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[af2c7ec] | 155 | const TrainingData::InputVector_t &DistanceVector = data.getAllArguments();
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[68172a] | 156 | const TrainingData::OutputVector_t &EnergyVector = data.getTrainingOutputs();
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| 157 | out << "(" << DistanceVector.size()
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| 158 | << "," << EnergyVector.size() << ") data pairs: " << std::endl;
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| 159 | FunctionApproximation::inputs_t::const_iterator initer = DistanceVector.begin();
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| 160 | FunctionApproximation::outputs_t::const_iterator outiter = EnergyVector.begin();
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| 161 | for (; initer != DistanceVector.end(); ++initer, ++outiter) {
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| 162 | for (size_t index = 0; index < (*initer).size(); ++index)
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| 163 | out << "(" << (*initer)[index].indices.first << "," << (*initer)[index].indices.second
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| 164 | << ") " << (*initer)[index].distance;
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| 165 | out << " with energy ";
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| 166 | out << (*outiter);
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| 167 | out << std::endl;
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| 168 | }
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| 169 | return out;
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| 170 | }
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