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/**
* DESCRIPTION OF THE CLASS
*
* @author David Vojtek
* @date 2018
*/
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#define BOOST_TEST_MODULE Particle_test
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#ifdef _WINDOWS
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#else
#ifndef BOOST_TEST_DYN_LINK
#define BOOST_TEST_DYN_LINK
#endif
#ifndef BOOST_TEST_NO_MAIN
#define BOOST_TEST_NO_MAIN
#endif
#include <turtle/mock.hpp>
#include <boost/test/unit_test.hpp>
#include <boost/test/output_test_stream.hpp>
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#endif
#include "../LearningMethods/ParticleSwarm.h"
MOCK_BASE_CLASS( mock_Error, lib4neuro::ErrorFunction)
{
MOCK_METHOD(eval, 1)
MOCK_METHOD(calculate_error_gradient, 3)
MOCK_METHOD(get_parameters, 0)
MOCK_METHOD(get_dataset, 0)
MOCK_METHOD(get_dimension, 0)
};
/**
* Boost testing suite for testing ParticleSwarm.h
* TODO
*/
BOOST_AUTO_TEST_SUITE(Particle_test)
BOOST_AUTO_TEST_CASE(Particle_construction_test) {
std::vector<double> domain_bound{1, 2, 3, 4, 5};
mock_Error error;
MOCK_EXPECT(error.get_dimension).once().returns(5);
MOCK_EXPECT(error.eval).once().returns(0.8);
BOOST_CHECK_NO_THROW(Particle(&error, &domain_bound));
}
BOOST_AUTO_TEST_CASE(Particle_get_coordinate_test) {
std::vector<double> domain_bound{1, 2, 3, 4, 5};
mock_Error error;
MOCK_EXPECT(error.get_dimension).returns(5);
MOCK_EXPECT(error.eval).returns(0.8);
Particle particle1(&error, &domain_bound);
Particle particle2(&error, &domain_bound);
BOOST_CHECK(*particle1.get_coordinate() != *particle2.get_coordinate());
}
BOOST_AUTO_TEST_CASE(Particle_get_optimal_value_test) {
std::vector<double> domain_bound{1, 2, 3, 4, 5};
mock_Error error;
MOCK_EXPECT(error.get_dimension).returns(5);
MOCK_EXPECT(error.eval).returns(0.8);
Particle particle1(&error, &domain_bound);
BOOST_CHECK_EQUAL(0.8, particle1.get_optimal_value());
}
//Random
//TODO
/*
BOOST_AUTO_TEST_CASE(particle_change_coordiante_test) {
double domain_bound[5] = {1,2,3,4,5};
Neuron *n1 = new NeuronLinear(1, 1);
Neuron *n2 = new NeuronLinear(2, 2);
NeuralNetwork network;
std::vector<std::pair<std::vector<double>, std::vector<double>>> data_vec;
std::vector<double> inp, out;
for (int i = 0; i < 1; i++) {
inp.push_back(i);
out.push_back(i + 4);
data_vec.emplace_back(std::make_pair(inp, out));
network.add_neuron(n1);
network.add_neuron(n2);
network.add_connection_simple(0, 1, 0, 2.5);
network.randomize_weights();
std::vector<size_t> net_input_neurons_indices(1);
std::vector<size_t> net_output_neurons_indices(1);
net_input_neurons_indices[0] = 0;
network.specify_input_neurons(net_input_neurons_indices);
network.specify_output_neurons(net_output_neurons_indices);
DataSet dataSet(&data_vec);
ErrorFunction *error = new MSE(&network, &dataSet);
Particle particle(error, &domain_bound[0]);
particle.change_coordinate(1.0, 2.0, 2.0, &domain_bound[1], 1);
BOOST_CHECK_EQUAL(1.32664, *particle.get_coordinate());
}
BOOST_AUTO_TEST_CASE(particle_optimal_value_test){
double domain_bound[5] = {1,2,3,4,5};
Neuron *n1 = new NeuronLinear(1, 1);
Neuron *n2 = new NeuronLinear(2, 2);
NeuralNetwork network;
std::vector<std::pair<std::vector<double>, std::vector<double>>> data_vec;
std::vector<double> inp, out;
for (int i = 0; i < 1; i++) {
inp.push_back(i);
out.push_back(i + 4);
data_vec.emplace_back(std::make_pair(inp, out));
network.add_neuron(n1);
network.add_neuron(n2);
network.add_connection_simple(0, 1, 0, 2.5);
network.randomize_weights();
std::vector<size_t> net_input_neurons_indices(1);
std::vector<size_t> net_output_neurons_indices(1);
net_input_neurons_indices[0] = 0;
net_output_neurons_indices[0] = 1;
network.specify_input_neurons(net_input_neurons_indices);
network.specify_output_neurons(net_output_neurons_indices);
DataSet dataSet(&data_vec);
ErrorFunction *error = new MSE(&network, &dataSet);
Particle particle(error, &domain_bound[0]);
BOOST_CHECK_CLOSE(1.789708839, particle.get_optimal_value(), 0.00001 );
}
*/
BOOST_AUTO_TEST_SUITE_END()