rmontanana/BayesNet
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Add thread control to vectors predict

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Ricardo Montañana Gómez
2024-06-23 13:02:40 +02:00
parent 59c1cf5b3b
commit 9a14133be5
3 changed files with 32 additions and 13 deletions
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39
bayesnet/network/Network.cc
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@@ -292,17 +292,18 @@ namespace bayesnet {
std::vector<int> predictions(tsamples[0].size(), 0);
std::vector<int> sample;
std::vector<std::thread> threads;
std::mutex mtx;
auto& semaphore = CountingSemaphore::getInstance();
auto worker = [&](const std::vector<int>& sample, const int row, std::vector<int>& predictions) {
semaphore.acquire();
auto worker = [&](const std::vector<int>& sample, const int row, int& prediction) {
std::string threadName = "(V)PWorker-" + std::to_string(row);
#if defined(__linux__)
pthread_setname_np(pthread_self(), threadName.c_str());
#else
pthread_setname_np(threadName.c_str());
#endif
auto classProbabilities = predict_sample(sample);
auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
int predictedClass = distance(classProbabilities.begin(), maxElem);
{
std::lock_guard<std::mutex> lock(mtx);
predictions[row] = predictedClass;
}
prediction = predictedClass;
semaphore.release();
};
for (int row = 0; row < tsamples[0].size(); ++row) {
@@ -310,7 +311,8 @@ namespace bayesnet {
for (int col = 0; col < tsamples.size(); ++col) {
sample.push_back(tsamples[col][row]);
}
threads.emplace_back(worker, sample, row, std::ref(predictions));
semaphore.acquire();
threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
}
for (auto& thread : threads) {
thread.join();
@@ -329,14 +331,31 @@ namespace bayesnet {
throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) +
") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
}
std::vector<std::vector<double>> predictions;
std::vector<std::vector<double>> predictions(tsamples[0].size(), std::vector<double>(classNumStates, 0.0));
std::vector<int> sample;
std::vector<std::thread> threads;
auto& semaphore = CountingSemaphore::getInstance();
auto worker = [&](const std::vector<int>& sample, int row, std::vector<double>& predictions) {
std::string threadName = "(V)PWorker-" + std::to_string(row);
#if defined(__linux__)
pthread_setname_np(pthread_self(), threadName.c_str());
#else
pthread_setname_np(threadName.c_str());
#endif
std::vector<double> classProbabilities = predict_sample(sample);
predictions = classProbabilities;
semaphore.release();
};
for (int row = 0; row < tsamples[0].size(); ++row) {
sample.clear();
for (int col = 0; col < tsamples.size(); ++col) {
sample.push_back(tsamples[col][row]);
}
predictions.push_back(predict_sample(sample));
semaphore.acquire();
threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
}
for (auto& thread : threads) {
thread.join();
}
return predictions;
}