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Complete and fix KDB

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This commit is contained in:
Ricardo Montañana Gómez
2023-07-13 16:59:06 +02:00
parent 3fcf1e40c9
commit e52fdc718f
7 changed files with 75 additions and 37 deletions
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59
sample/main.cc
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@@ -221,41 +221,44 @@ int main(int argc, char** argv)
cout << endl;
cout << "Class name: " << className << endl;
// Build Network
auto network = bayesnet::Network(1.0);
build_network(network, network_name, maxes);
network.fit(Xd, y, features, className);
cout << "Hello, Bayesian Networks!" << endl;
showNodesInfo(network, className);
//showCPDS(network);
cout << "Score: " << network.score(Xd, y) << endl;
cout << "PyTorch version: " << TORCH_VERSION << endl;
cout << "BayesNet version: " << network.version() << endl;
unsigned int nthreads = std::thread::hardware_concurrency();
cout << "Computer has " << nthreads << " cores." << endl;
cout << "****************** First ******************" << endl;
auto metrics = bayesnet::Metrics(network.getSamples(), features, className, network.getClassNumStates());
cout << "conditionalEdgeWeight " << endl;
auto conditional = metrics.conditionalEdgeWeights();
cout << conditional << endl;
long m = features.size() + 1;
auto matrix = torch::from_blob(conditional.data(), { m, m });
cout << matrix << endl;
cout << "****************** Second ******************" << endl;
auto metrics2 = bayesnet::Metrics(Xd, y, features, className, network.getClassNumStates());
cout << "conditionalEdgeWeight " << endl;
auto conditional2 = metrics2.conditionalEdgeWeights();
cout << conditional2 << endl;
long m2 = features.size() + 1;
auto matrix2 = torch::from_blob(conditional2.data(), { m, m });
cout << matrix2 << endl;
// auto network = bayesnet::Network(1.0);
// build_network(network, network_name, maxes);
// network.fit(Xd, y, features, className);
// cout << "Hello, Bayesian Networks!" << endl;
// showNodesInfo(network, className);
// //showCPDS(network);
// cout << "Score: " << network.score(Xd, y) << endl;
// cout << "PyTorch version: " << TORCH_VERSION << endl;
// cout << "BayesNet version: " << network.version() << endl;
// unsigned int nthreads = std::thread::hardware_concurrency();
// cout << "Computer has " << nthreads << " cores." << endl;
// cout << "****************** First ******************" << endl;
// auto metrics = bayesnet::Metrics(network.getSamples(), features, className, network.getClassNumStates());
// cout << "conditionalEdgeWeight " << endl;
// auto conditional = metrics.conditionalEdgeWeights();
// cout << conditional << endl;
// long m = features.size() + 1;
// auto matrix = torch::from_blob(conditional.data(), { m, m });
// cout << matrix << endl;
// cout << "****************** Second ******************" << endl;
// auto metrics2 = bayesnet::Metrics(Xd, y, features, className, network.getClassNumStates());
// cout << "conditionalEdgeWeight " << endl;
// auto conditional2 = metrics2.conditionalEdgeWeights();
// cout << conditional2 << endl;
// long m2 = features.size() + 1;
// auto matrix2 = torch::from_blob(conditional2.data(), { m, m });
// cout << matrix2 << endl;
cout << "****************** KDB ******************" << endl;
map<string, vector<int>> states;
for (auto feature : features) {
states[feature] = vector<int>(maxes[feature]);
}
states[className] = vector<int>(maxes[className]);
auto kdb = bayesnet::KDB(1);
auto kdb = bayesnet::KDB(2);
kdb.fit(Xd, y, features, className, states);
for (auto line : kdb.show()) {
cout << line << endl;
}
cout << "****************** KDB ******************" << endl;
return 0;
}