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sandbox.cpp
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sandbox.cpp
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#include <cassert>
#include <string>
#include <pgm/pgm.h>
#include <cmath>
#include <iostream>
void test_simulated_annealing()
{
std::cout << "\nsimulated annealing\n";
// bayesnet
pgm::Bayesnet bn;
#ifndef NDEBUG
assert(bn.add_node("A", {"F", "T"}));
assert(bn.add_node("B", {"F", "T"}));
assert(bn.add_node("C", {"F", "T"}));
assert(bn.add_node("D", {"F", "T"}));
assert(bn.add_node("E", {"F", "T"}));
#else
bn.add_node("A", {"F", "T"});
bn.add_node("B", {"F", "T"});
bn.add_node("C", {"F", "T"});
bn.add_node("D", {"F", "T"});
bn.add_node("E", {"F", "T"});
#endif
// dataset
pgm::Dataset dataset;
for (std::size_t i = 0; i < 20; ++i)
dataset.push({{"A", "T"}, {"B", "F"}, {"C", "T"}, {"D", "T"}, {"E", "T"}});
for (std::size_t i = 0; i < 15; ++i)
dataset.push({{"A", "T"}, {"B", "F"}, {"C", "F"}, {"D", "F"}, {"E", "F"}});
for (std::size_t i = 0; i < 10; ++i)
dataset.push({{"A", "F"}, {"B", "T"}, {"C", "F"}, {"D", "T"}, {"E", "T"}});
for (std::size_t i = 0; i < 15; ++i)
dataset.push({{"A", "F"}, {"B", "F"}, {"C", "T"}, {"D", "T"}, {"E", "T"}});
for (std::size_t i = 0; i < 5; ++i)
dataset.push({{"A", "F"}, {"B", "F"}, {"C", "F"}, {"D", "F"}, {"E", "F"}});
for (std::size_t i = 0; i < 2; ++i)
dataset.push({{"A", "T"}, {"B", "T"}, {"C", "F"}, {"D", "T"}, {"E", "F"}});
pgm::SimulatedAnnealing annealing;
annealing.verbose(true);
annealing.init_as_naive_bayes("A");
pgm::Fcll score(dataset, "A");
pgm::SampleEstimate estimate;
annealing(bn, score);
estimate(bn, dataset);
std::cout << bn;
std::size_t correct = 0;
for (std::size_t i = 0; i < dataset.size(); ++i)
{
auto row = dataset[i];
if (row["A"] == bn.infer("A", row))
++correct;
}
std::cout << "Correct : " << correct << "/" << dataset.size() << "\n";
pgm::write_dot(bn, "test_bn.dot");
}
void test_greedy_hill_climbing()
{
std::cout << "\ngreedy hill climbing\n";
// bayesnet
pgm::Bayesnet bn;
#ifndef NDEBUG
assert(bn.add_node("A", {"F", "T"}));
assert(bn.add_node("B", {"F", "T"}));
assert(bn.add_node("C", {"F", "T"}));
assert(bn.add_node("D", {"F", "T"}));
assert(bn.add_node("E", {"F", "T"}));
#else
bn.add_node("A", {"F", "T"});
bn.add_node("B", {"F", "T"});
bn.add_node("C", {"F", "T"});
bn.add_node("D", {"F", "T"});
bn.add_node("E", {"F", "T"});
#endif
// dataset
pgm::Dataset dataset;
for (std::size_t i = 0; i < 20; ++i)
dataset.push({{"A", "T"}, {"B", "F"}, {"C", "T"}, {"D", "T"}, {"E", "T"}});
for (std::size_t i = 0; i < 15; ++i)
dataset.push({{"A", "T"}, {"B", "F"}, {"C", "F"}, {"D", "F"}, {"E", "F"}});
for (std::size_t i = 0; i < 10; ++i)
dataset.push({{"A", "F"}, {"B", "T"}, {"C", "F"}, {"D", "T"}, {"E", "T"}});
for (std::size_t i = 0; i < 15; ++i)
dataset.push({{"A", "F"}, {"B", "F"}, {"C", "T"}, {"D", "T"}, {"E", "T"}});
for (std::size_t i = 0; i < 5; ++i)
dataset.push({{"A", "F"}, {"B", "F"}, {"C", "F"}, {"D", "F"}, {"E", "F"}});
for (std::size_t i = 0; i < 2; ++i)
dataset.push({{"A", "T"}, {"B", "T"}, {"C", "F"}, {"D", "T"}, {"E", "F"}});
pgm::GreedyHillClimbing hillclimb;
hillclimb.verbose(true);
hillclimb.init_as_naive_bayes("A");
pgm::Fcll score(dataset, "A");
pgm::SampleEstimate estimate;
hillclimb(bn, score);
estimate(bn, dataset);
std::cout << bn;
std::size_t correct = 0;
for (std::size_t i = 0; i < dataset.size(); ++i)
{
auto row = dataset[i];
if (row["A"] == bn.infer("A", row))
++correct;
}
std::cout << "Correct : " << correct << "/" << dataset.size() << "\n";
}
int main()
{
test_simulated_annealing();
test_greedy_hill_climbing();
return 0;
}