Add Makefile & tests
This commit is contained in:
221
sample/main.cc
221
sample/main.cc
@@ -30,23 +30,23 @@ void usage(const char* path)
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<< " -f, --file[=FILENAME]\t {diabetes, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors}."
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<< endl;
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cout << " -p, --path[=FILENAME]\t folder where the data files are located, default " << PATH << endl;
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cout << " -n, --net=[FILENAME]\t default=file parameter value" << endl;
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cout << " -m, --model={AODE, KDB, SPODE, TAN}\t " << endl;
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}
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tuple<string, string, string> parse_arguments(int argc, char** argv)
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{
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string file_name;
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string network_name;
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string model_name;
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string path = PATH;
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const vector<struct option> long_options = {
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{"help", no_argument, nullptr, 'h'},
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{"file", required_argument, nullptr, 'f'},
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{"path", required_argument, nullptr, 'p'},
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{"net", required_argument, nullptr, 'n'},
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{"model", required_argument, nullptr, 'm'},
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{nullptr, no_argument, nullptr, 0}
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};
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while (true) {
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const auto c = getopt_long(argc, argv, "hf:p:n:", long_options.data(), nullptr);
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const auto c = getopt_long(argc, argv, "hf:p:m:", long_options.data(), nullptr);
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if (c == -1)
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break;
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switch (c) {
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@@ -56,8 +56,8 @@ tuple<string, string, string> parse_arguments(int argc, char** argv)
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case 'f':
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file_name = string(optarg);
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break;
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case 'n':
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network_name = string(optarg);
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case 'm':
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model_name = string(optarg);
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break;
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case 'p':
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path = optarg;
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@@ -75,12 +75,22 @@ tuple<string, string, string> parse_arguments(int argc, char** argv)
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usage(argv[0]);
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exit(1);
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}
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if (network_name.empty()) {
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network_name = file_name;
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}
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return make_tuple(file_name, path, network_name);
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return make_tuple(file_name, path, model_name);
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}
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inline constexpr auto hash_conv(const std::string_view sv)
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{
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unsigned long hash{ 5381 };
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for (unsigned char c : sv) {
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hash = ((hash << 5) + hash) ^ c;
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}
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return hash;
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}
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inline constexpr auto operator"" _sh(const char* str, size_t len)
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{
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return hash_conv(std::string_view{ str, len });
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}
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pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t>& X, mdlp::labels_t& y, vector<string> features)
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{
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@@ -96,39 +106,6 @@ pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t
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}
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return { Xd, maxes };
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}
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void showNodesInfo(bayesnet::Network& network, string className)
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{
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cout << "Nodes:" << endl;
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for (auto& node : network.getNodes()) {
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auto name = node.first;
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cout << "*" << node.second->getName() << " States -> " << node.second->getNumStates() << endl;
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cout << "-Parents:";
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for (auto parent : node.second->getParents()) {
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cout << " " << parent->getName();
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}
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cout << endl;
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cout << "-Children:";
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for (auto child : node.second->getChildren()) {
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cout << " " << child->getName();
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}
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cout << endl;
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}
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}
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void showCPDS(bayesnet::Network& network)
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{
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cout << "CPDs:" << endl;
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auto& nodes = network.getNodes();
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for (auto it = nodes.begin(); it != nodes.end(); it++) {
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cout << "* Name: " << it->first << " " << it->second->getName() << " -> " << it->second->getNumStates() << endl;
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cout << "Parents: ";
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for (auto parent : it->second->getParents()) {
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cout << parent->getName() << " -> " << parent->getNumStates() << ", ";
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}
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cout << endl;
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auto cpd = it->second->getCPT();
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cout << cpd << endl;
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}
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}
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bool file_exists(const std::string& name)
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{
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@@ -140,7 +117,7 @@ bool file_exists(const std::string& name)
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}
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}
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pair<string, string> get_options(int argc, char** argv)
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tuple<string, string, string> get_options(int argc, char** argv)
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{
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map<string, bool> datasets = {
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{"diabetes", true},
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@@ -152,58 +129,35 @@ pair<string, string> get_options(int argc, char** argv)
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{"liver-disorders", true},
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{"mfeat-factors", true},
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};
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vector <string> models = { "AODE", "KDB", "SPODE", "TAN" };
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string file_name;
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string path;
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string network_name;
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tie(file_name, path, network_name) = parse_arguments(argc, argv);
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string model_name;
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tie(file_name, path, model_name) = parse_arguments(argc, argv);
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if (datasets.find(file_name) == datasets.end()) {
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cout << "Invalid file name: " << file_name << endl;
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usage(argv[0]);
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exit(1);
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}
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file_name = path + file_name + ".arff";
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if (!file_exists(file_name)) {
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cout << "Data File " << file_name << " does not exist" << endl;
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if (!file_exists(path + file_name + ".arff")) {
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cout << "Data File " << path + file_name + ".arff" << " does not exist" << endl;
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usage(argv[0]);
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exit(1);
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}
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network_name = path + network_name + ".net";
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if (!file_exists(network_name)) {
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cout << "Network File " << network_name << " does not exist" << endl;
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if (find(models.begin(), models.end(), model_name) == models.end()) {
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cout << "Invalid model name: " << model_name << endl;
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usage(argv[0]);
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exit(1);
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}
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return { file_name, network_name };
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return { file_name, path, model_name };
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}
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void build_network(bayesnet::Network& network, string network_name, map<string, int> maxes)
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{
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ifstream file(network_name);
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string line;
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while (getline(file, line)) {
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if (line[0] == '#') {
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continue;
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}
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istringstream iss(line);
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string parent, child;
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if (!(iss >> parent >> child)) {
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break;
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}
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network.addNode(parent, maxes[parent]);
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network.addNode(child, maxes[child]);
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network.addEdge(parent, child);
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}
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file.close();
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}
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int main(int argc, char** argv)
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{
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string file_name, network_name;
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tie(file_name, network_name) = get_options(argc, argv);
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string file_name, path, model_name;
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tie(file_name, path, model_name) = get_options(argc, argv);
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auto handler = ArffFiles();
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handler.load(file_name);
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handler.load(path + file_name + ".arff");
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// Get Dataset X, y
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vector<mdlp::samples_t>& X = handler.getX();
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mdlp::labels_t& y = handler.getY();
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@@ -218,91 +172,54 @@ int main(int argc, char** argv)
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map<string, int> maxes;
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tie(Xd, maxes) = discretize(X, y, features);
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maxes[className] = *max_element(y.begin(), y.end()) + 1;
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cout << "Features: ";
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for (auto feature : features) {
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cout << "[" << feature << "] ";
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}
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cout << endl;
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cout << "Class name: " << className << endl;
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// Build Network
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// auto network = bayesnet::Network(1.0);
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// build_network(network, network_name, maxes);
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// network.fit(Xd, y, features, className);
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// cout << "Hello, Bayesian Networks!" << endl;
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// showNodesInfo(network, className);
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// //showCPDS(network);
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// cout << "Score: " << network.score(Xd, y) << endl;
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// cout << "PyTorch version: " << TORCH_VERSION << endl;
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// cout << "BayesNet version: " << network.version() << endl;
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// unsigned int nthreads = std::thread::hardware_concurrency();
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// cout << "Computer has " << nthreads << " cores." << endl;
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// cout << "****************** First ******************" << endl;
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// auto metrics = bayesnet::Metrics(network.getSamples(), features, className, network.getClassNumStates());
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// cout << "conditionalEdgeWeight " << endl;
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// auto conditional = metrics.conditionalEdgeWeights();
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// cout << conditional << endl;
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// long m = features.size() + 1;
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// auto matrix = torch::from_blob(conditional.data(), { m, m });
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// cout << matrix << endl;
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// cout << "****************** Second ******************" << endl;
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// auto metrics2 = bayesnet::Metrics(Xd, y, features, className, network.getClassNumStates());
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// cout << "conditionalEdgeWeight " << endl;
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// auto conditional2 = metrics2.conditionalEdgeWeights();
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// cout << conditional2 << endl;
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// long m2 = features.size() + 1;
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// auto matrix2 = torch::from_blob(conditional2.data(), { m, m });
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// cout << matrix2 << endl;
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cout << "****************** Preparing ******************" << endl;
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map<string, vector<int>> states;
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for (auto feature : features) {
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states[feature] = vector<int>(maxes[feature]);
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}
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states[className] = vector<int>(
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maxes[className]);
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cout << "****************** KDB ******************" << endl;
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double score;
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vector<string> lines;
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vector<string> graph;
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auto kdb = bayesnet::KDB(2);
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kdb.fit(Xd, y, features, className, states);
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for (auto line : kdb.show()) {
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cout << line << endl;
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}
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cout << "Score: " << kdb.score(Xd, y) << endl;
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ofstream file("kdb.dot");
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file << kdb.graph();
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file.close();
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cout << "****************** KDB ******************" << endl;
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cout << "****************** SPODE ******************" << endl;
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auto spode = bayesnet::SPODE(2);
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spode.fit(Xd, y, features, className, states);
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for (auto line : spode.show()) {
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cout << line << endl;
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}
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cout << "Score: " << spode.score(Xd, y) << endl;
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file.open("spode.dot");
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file << spode.graph();
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file.close();
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cout << "****************** SPODE ******************" << endl;
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cout << "****************** AODE ******************" << endl;
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auto aode = bayesnet::AODE();
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aode.fit(Xd, y, features, className, states);
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for (auto line : aode.show()) {
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cout << line << endl;
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}
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cout << "Score: " << aode.score(Xd, y) << endl;
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file.open("aode.dot");
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for (auto line : aode.graph())
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file << line;
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file.close();
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cout << "****************** AODE ******************" << endl;
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cout << "****************** TAN ******************" << endl;
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auto spode = bayesnet::SPODE(2);
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auto tan = bayesnet::TAN();
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tan.fit(Xd, y, features, className, states);
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for (auto line : tan.show()) {
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switch (hash_conv(model_name)) {
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case "AODE"_sh:
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aode.fit(Xd, y, features, className, states);
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lines = aode.show();
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score = aode.score(Xd, y);
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graph = aode.graph();
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break;
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case "KDB"_sh:
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kdb.fit(Xd, y, features, className, states);
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lines = kdb.show();
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score = kdb.score(Xd, y);
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graph = kdb.graph();
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break;
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case "SPODE"_sh:
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spode.fit(Xd, y, features, className, states);
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lines = spode.show();
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score = spode.score(Xd, y);
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graph = spode.graph();
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break;
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case "TAN"_sh:
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tan.fit(Xd, y, features, className, states);
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lines = tan.show();
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score = tan.score(Xd, y);
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graph = tan.graph();
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break;
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}
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for (auto line : lines) {
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cout << line << endl;
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}
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cout << "Score: " << tan.score(Xd, y) << endl;
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file.open("tan.dot");
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file << tan.graph();
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cout << "Score: " << score << endl;
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auto dot_file = model_name + "_" + file_name;
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ofstream file(dot_file + ".dot");
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file << graph;
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file.close();
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cout << "****************** TAN ******************" << endl;
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cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
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cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
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return 0;
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}
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