117 lines
3.0 KiB
C++
117 lines
3.0 KiB
C++
/*
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* Copyright (C) 2019 Emeric Poupon
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*
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* This file is part of LMS.
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*
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* LMS is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* LMS is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with LMS. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include <sstream>
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#include <cassert>
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#include <iostream>
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#include "DataNormalizer.hpp"
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#include "Network.hpp"
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using namespace SOM;
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int main()
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{
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static const InputVector::value_type EPSILON = 0.01;
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{
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Matrix<int> testMatrix {2, 2, 123};
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assert((testMatrix[{0,0}] == 123));
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assert((testMatrix[{0,1}] == 123));
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assert((testMatrix[{1,0}] == 123));
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assert((testMatrix[{1,1}] == 123));
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}
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{
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InputVector test1 {2};
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test1[0] = 0;
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test1[1] = 1;
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InputVector test2 {2};
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test2[0] = 1;
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test2[1] = 0;
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InputVector test3 {test1};
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test3 += test2;
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assert(std::abs(test3[0] - 1) < EPSILON);
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assert(std::abs(test3[1] - 1) < EPSILON);
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}
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{
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Network network {2, 2, 1};
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const InputVector weights {1, 1};
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std::vector<InputVector> trainData {
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{ 1, 50 },
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{ 1, 100 },
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{ 1, 150 },
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{ 1, 200 },
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};
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DataNormalizer normalizer {1};
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normalizer.computeNormalizationFactors(trainData);
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for (auto& data: trainData)
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normalizer.normalizeData(data);
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network.dump(std::cout);
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network.train(trainData, 20);
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network.dump(std::cout);
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std::cout << "MEAN dist = " << network.computeRefVectorsDistanceMean() << std::endl;
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std::cout << "MEDIAN dist = " << network.computeRefVectorsDistanceMedian() << std::endl;
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auto distFunc {network.getDistanceFunc()};
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assert((std::abs(distFunc({1, 0}, {1, 1}, weights) - 1) < EPSILON));
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assert((std::abs(distFunc({1, 0}, {1, 2}, weights) - 4) < EPSILON));
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assert((std::abs(distFunc({1, 0}, {1, 0.33}, weights) - distFunc({1, 0.66}, {1, 1.}, weights)) < EPSILON));
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{
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std::set<Position> positions;
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for (const InputVector& data : trainData)
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positions.insert(network.getClosestRefVectorPosition(data));
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assert(positions.size() == 4);
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}
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{
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Position pos {network.getClosestRefVectorPosition(InputVector{1, 0.66})};
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for (std::size_t i {}; i < 40; ++i)
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{
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InputVector input {1, 130 + static_cast<InputVector::value_type>(i) };
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normalizer.normalizeData(input);
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assert( network.getClosestRefVectorPosition(input) == pos);
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}
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}
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{
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Position pos {network.getClosestRefVectorPosition(InputVector{1, 1})};
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for (std::size_t i {}; i < 40; ++i)
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{
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InputVector input {1, 180 + static_cast<InputVector::value_type>(i) };
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normalizer.normalizeData(input);
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assert( network.getClosestRefVectorPosition(input) == pos);
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}
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}
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}
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return 0;
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}
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