Complete fixing the linter warnings
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parent
b882569169
commit
4ebc9c2013
@ -125,7 +125,6 @@ namespace bayesnet {
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}
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}
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void Classifier::addNodes()
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void Classifier::addNodes()
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{
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{
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auto test = model.getEdges();
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// Add all nodes to the network
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// Add all nodes to the network
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for (auto feature : features) {
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for (auto feature : features) {
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model.addNode(feature, states[feature].size());
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model.addNode(feature, states[feature].size());
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@ -148,10 +148,10 @@ namespace bayesnet {
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}
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}
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int Ensemble::getNumberOfStates()
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int Ensemble::getNumberOfStates()
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{
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{
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int states = 0;
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int nstates = 0;
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for (auto i = 0; i < n_models; ++i) {
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for (auto i = 0; i < n_models; ++i) {
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states += models[i]->getNumberOfStates();
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nstates += models[i]->getNumberOfStates();
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}
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}
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return states;
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return nstates;
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}
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}
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}
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}
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@ -7,9 +7,8 @@
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namespace bayesnet {
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namespace bayesnet {
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using namespace std;
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using namespace std;
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Graph::Graph(int V) : V(V)
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Graph::Graph(int V) : V(V), parent(vector<int>(V))
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{
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{
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parent = vector<int>(V);
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for (int i = 0; i < V; i++)
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for (int i = 0; i < V; i++)
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parent[i] = i;
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parent[i] = i;
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G.clear();
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G.clear();
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@ -8,7 +8,7 @@ namespace bayesnet {
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Network::Network(float maxT, int smoothing) : laplaceSmoothing(smoothing), features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false) {}
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Network::Network(float maxT, int smoothing) : laplaceSmoothing(smoothing), features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false) {}
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Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other.getmaxThreads()), fitted(other.fitted)
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Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other.getmaxThreads()), fitted(other.fitted)
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{
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{
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for (auto& pair : other.nodes) {
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for (const auto& pair : other.nodes) {
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nodes[pair.first] = std::make_unique<Node>(*pair.second);
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nodes[pair.first] = std::make_unique<Node>(*pair.second);
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}
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}
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}
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}
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@ -145,9 +145,6 @@ namespace bayesnet {
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while (nextNodeIndex < nodes.size()) {
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while (nextNodeIndex < nodes.size()) {
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unique_lock<mutex> lock(mtx);
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unique_lock<mutex> lock(mtx);
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cv.wait(lock, [&activeThreads, &maxThreadsRunning]() { return activeThreads < maxThreadsRunning; });
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cv.wait(lock, [&activeThreads, &maxThreadsRunning]() { return activeThreads < maxThreadsRunning; });
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if (nextNodeIndex >= nodes.size()) {
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break; // No more work remaining
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}
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threads.emplace_back([this, &nextNodeIndex, &mtx, &cv, &activeThreads]() {
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threads.emplace_back([this, &nextNodeIndex, &mtx, &cv, &activeThreads]() {
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while (true) {
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while (true) {
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unique_lock<mutex> lock(mtx);
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unique_lock<mutex> lock(mtx);
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@ -262,9 +259,7 @@ namespace bayesnet {
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// Normalize result
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// Normalize result
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double sum = accumulate(result.begin(), result.end(), 0.0);
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double sum = accumulate(result.begin(), result.end(), 0.0);
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for (double& value : result) {
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transform(result.begin(), result.end(), result.begin(), [sum](double& value) { return value / sum; });
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value /= sum;
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}
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return result;
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return result;
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}
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}
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vector<string> Network::show()
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vector<string> Network::show()
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