void run(unsigned int size, unsigned int select) {
BayesNet<> bn;
map<vector<int>, map<int, double>> cpt;
map<int, double> dist;
map<vector<int>, map<int, double>> sinkCPT;
for (int i = 0; i < size; i++) {
if (i == select) {
dist.insert(make_pair(0, 0.0));
dist.insert(make_pair(1, 1.0));
cpt.insert(CondProb<>::CondCase(vector<int>(), dist));
bn.add_node(i, CondProb<>(cpt));
cpt.clear();
dist.insert(make_pair(0, 0.0));
dist.insert(make_pair(1, 1.0));
vector<int> parents(size, 0);
parents[i] = 1;
sinkCPT.insert(CondProb<>::CondCase(parents, dist));
} else {
dist.insert(make_pair(0, 1.0));
dist.insert(make_pair(1, 0.0));
cpt.insert(CondProb<>::CondCase(vector<int>(), dist));
bn.add_node(i, CondProb<>(cpt));
cpt.clear();
dist.insert(make_pair(0, 1.0));
dist.insert(make_pair(1, 0.0));
vector<int> parents(size, 0);
parents[i] = 1;
sinkCPT.insert(CondProb<>::CondCase(parents, dist));
}
cpt.clear();
dist.clear();
}
set<int> parents;
for (int i = 0; i < size; i++)
parents.insert(i);
bn.add_node(size, parents, CondProb<>(sinkCPT));
map<int, int> values;
for (int i = 0; i < size; i++)
values.insert(make_pair(0, 0));
values.insert(make_pair(select, 1));
map<std::map<int, int>, double> marginal_dist = bn.marginal_dist(values, 512, SampleStrategy::GIBBS);
assertEquals(marginal_dist[values], (double)1.0);
}
void canMarkovBlanket() {
BayesNet<> bn;
map<vector<int>, map<int, double>> cpt;
map<int, double> dist;
dist.insert(make_pair(0, 1.0));
dist.insert(make_pair(1, 0.0));
cpt.insert(CondProb<>::CondCase(vector<int>(), dist));
bn.add_node(0, CondProb<>(cpt));
cpt.clear();
dist.clear();
dist.insert(make_pair(0, 1.0));
dist.insert(make_pair(1, 0.0));
cpt.insert(CondProb<>::CondCase(vector<int>(), dist));
bn.add_node(1, CondProb<>(cpt));
cpt.clear();
dist.clear();
dist.insert(make_pair(1, 0.7));
dist.insert(make_pair(0, 0.3));
cpt.insert(CondProb<>::CondCase(vector<int> {0, 0}, dist));
dist.insert(make_pair(1, 0.5));
dist.insert(make_pair(0, 0.5));
cpt.insert(CondProb<>::CondCase(vector<int> {0, 1}, dist));
dist.insert(make_pair(1, 0.5));
dist.insert(make_pair(0, 0.5));
cpt.insert(CondProb<>::CondCase(vector<int> {1, 0}, dist));
dist.insert(make_pair(1, 0.3));
dist.insert(make_pair(0, 0.7));
cpt.insert(CondProb<>::CondCase(vector<int> {1, 1}, dist));
bn.add_node(2, {0, 1}, CondProb<int>({1, 2}, cpt));
cpt.clear();
dist.clear();
dist.insert(make_pair(1, 0.7));
dist.insert(make_pair(0, 0.3));
cpt.insert(CondProb<>::CondCase(vector<int> {0}, dist));
dist.insert(make_pair(1, 0.5));
dist.insert(make_pair(0, 0.5));
cpt.insert(CondProb<>::CondCase(vector<int> {1}, dist));
bn.add_node(3, {2}, CondProb<int>({2}, cpt));
set<int> blanket = bn.markov_blanket(2);
assertEquals(blanket, set<int> {2, 0, 1, 3});
}
void canSample() {
BayesNet<> bn;
map<vector<int>, map<int, double>> cpt;
map<int, double> dist;
dist.insert(make_pair(0, 1.0));
dist.insert(make_pair(1, 0.0));
cpt.insert(CondProb<>::CondCase(vector<int>(), dist));
bn.add_node(0, CondProb<>(cpt));
assertEquals(bn.sample_node(0), 0);
cpt.clear();
dist.clear();
dist.insert(make_pair(0, 0.0));
dist.insert(make_pair(1, 1.0));
cpt.insert(CondProb<>::CondCase(vector<int>(), dist));
bn.add_node(1, CondProb<>(cpt));
assertEquals(bn.sample_node(1), 1);
}
void canMarginalizeNode() {
BayesNet<> bn;
map<vector<int>, map<int, double>> cpt;
map<int, double> dist;
dist.insert(make_pair(0, 0.7));
dist.insert(make_pair(1, 0.3));
cpt.insert(CondProb<>::CondCase(vector<int>(), dist));
bn.add_node(0, CondProb<>(cpt));
map<int, double> marginal_dist = bn.marginal_dist(0, 512);
assertTrue(marginal_dist[0] > 0.65 && marginal_dist[0] < 0.75);
assertTrue(marginal_dist[1] > 0.25 && marginal_dist[1] < 0.35);
}
void canAverage() {
BayesNet<> bn;
map<vector<int>, map<int, double>> cpt;
map<int, double> dist;
dist.insert(make_pair(0, 1.0));
dist.insert(make_pair(1, 0.0));
cpt.insert(CondProb<>::CondCase(vector<int>(), dist));
bn.add_node(0, CondProb<>(cpt));
cpt.clear();
dist.clear();
dist.insert(make_pair(0, 1.0));
dist.insert(make_pair(1, 0.0));
cpt.insert(CondProb<>::CondCase(vector<int>(), dist));
bn.add_node(1, CondProb<>(cpt));
cpt.clear();
dist.clear();
dist.insert(make_pair(1, 0.7));
dist.insert(make_pair(0, 0.3));
cpt.insert(CondProb<>::CondCase(vector<int> {0, 0}, dist));
dist.insert(make_pair(1, 0.5));
dist.insert(make_pair(0, 0.5));
cpt.insert(CondProb<>::CondCase(vector<int> {0, 1}, dist));
dist.insert(make_pair(1, 0.5));
dist.insert(make_pair(0, 0.5));
cpt.insert(CondProb<>::CondCase(vector<int> {1, 0}, dist));
dist.insert(make_pair(1, 0.3));
dist.insert(make_pair(0, 0.7));
cpt.insert(CondProb<>::CondCase(vector<int> {1, 1}, dist));
bn.add_node(2, {0, 1}, CondProb<int>({1, 2}, cpt));
double ev = bn.average_value(2, 1024);
assertTrue(ev > 0.6 && ev < 0.8);
}
void canMarginalizeNetwork() {
BayesNet<> bn;
map<vector<int>, map<int, double>> cpt;
map<int, double> dist;
dist.insert(make_pair(0, 0.7));
dist.insert(make_pair(1, 0.3));
cpt.insert(CondProb<>::CondCase(vector<int>(), dist));
bn.add_node(0, CondProb<>(cpt));
map<int, int> values;
values.insert(make_pair(0, 0));
map<std::map<int, int>, double> marginal_dist = bn.marginal_dist(values, 512, SampleStrategy::GIBBS);
assertTrue(marginal_dist[values] > 0.65 && marginal_dist[values] < 0.75);
}
