Refactor to remove root node of network
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0b33c6c04a
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71b88e2c65
@ -107,9 +107,6 @@ void showNodesInfo(bayesnet::Network& network, string className)
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}
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cout << endl;
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}
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cout << "Root: " << network.getRoot()->getName() << endl;
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network.setRoot(className);
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cout << "Now Root should be class: " << network.getRoot()->getName() << endl;
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}
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void showCPDS(bayesnet::Network& network)
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{
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@ -2,10 +2,10 @@
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#include <mutex>
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#include "Network.h"
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namespace bayesnet {
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Network::Network() : laplaceSmoothing(1), root(nullptr), features(vector<string>()), className(""), classNumStates(0), maxThreads(0.8) {}
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Network::Network(float maxT) : laplaceSmoothing(1), root(nullptr), features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT) {}
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Network::Network(float maxT, int smoothing) : laplaceSmoothing(smoothing), root(nullptr), features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT) {}
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Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), root(other.root), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other.getmaxThreads())
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Network::Network() : laplaceSmoothing(1), features(vector<string>()), className(""), classNumStates(0), maxThreads(0.8) {}
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Network::Network(float maxT) : laplaceSmoothing(1), features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT) {}
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Network::Network(float maxT, int smoothing) : laplaceSmoothing(smoothing), features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT) {}
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Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other.getmaxThreads())
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{
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for (auto& pair : other.nodes) {
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nodes[pair.first] = new Node(*pair.second);
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@ -29,9 +29,6 @@ namespace bayesnet {
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return;
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}
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nodes[name] = new Node(name, numStates);
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if (root == nullptr) {
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root = nodes[name];
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}
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}
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vector<string> Network::getFeatures()
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{
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@ -45,17 +42,6 @@ namespace bayesnet {
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{
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return className;
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}
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void Network::setRoot(string name)
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{
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if (nodes.find(name) == nodes.end()) {
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throw invalid_argument("Node " + name + " does not exist");
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}
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root = nodes[name];
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}
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Node* Network::getRoot()
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{
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return root;
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}
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bool Network::isCyclic(const string& nodeId, unordered_set<string>& visited, unordered_set<string>& recStack)
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{
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if (visited.find(nodeId) == visited.end()) // if node hasn't been visited yet
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@ -227,18 +213,15 @@ namespace bayesnet {
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vector<double> result(classNumStates, 0.0);
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vector<thread> threads;
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mutex mtx;
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for (int i = 0; i < classNumStates; ++i) {
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threads.emplace_back([this, &result, &evidence, i, &mtx]() {
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auto completeEvidence = map<string, int>(evidence);
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completeEvidence[getClassName()] = i;
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double factor = computeFactor(completeEvidence);
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lock_guard<mutex> lock(mtx);
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result[i] = factor;
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});
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}
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for (auto& thread : threads) {
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thread.join();
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}
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@ -10,7 +10,6 @@ namespace bayesnet {
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private:
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map<string, Node*> nodes;
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map<string, vector<int>> dataset;
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Node* root;
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float maxThreads;
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int classNumStates;
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vector<string> features;
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@ -34,8 +33,6 @@ namespace bayesnet {
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int getClassNumStates();
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string getClassName();
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void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&);
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void setRoot(string);
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Node* getRoot();
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vector<int> predict(const vector<vector<int>>&);
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vector<pair<int, double>> predict_proba(const vector<vector<int>>&);
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double score(const vector<vector<int>>&, const vector<int>&);
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