rmontanana/BayesNet
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Implement Cestnik & Laplace smoothing

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This commit is contained in:
Ricardo Montañana Gómez
2024-06-09 17:19:38 +02:00
parent 6d9badc33b
commit 684443a788
9 changed files with 28 additions and 22 deletions
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24
bayesnet/network/Network.cc
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@@ -7,17 +7,18 @@
#include <thread>
#include <mutex>
#include <sstream>
#include <numeric>
#include "Network.h"
#include "bayesnet/utils/bayesnetUtils.h"
namespace bayesnet {
Network::Network() : fitted{ false }, maxThreads{ 0.95 }, classNumStates{ 0 }, laplaceSmoothing{ 0 }
Network::Network() : fitted{ false }, maxThreads{ 0.95 }, classNumStates{ 0 }, smoothing{ Smoothing_t::LAPLACE }
{
}
Network::Network(float maxT) : fitted{ false }, maxThreads{ maxT }, classNumStates{ 0 }, laplaceSmoothing{ 0 }
Network::Network(float maxT) : fitted{ false }, maxThreads{ maxT }, classNumStates{ 0 }, smoothing{ Smoothing_t::LAPLACE }
{
}
Network::Network(const Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
Network::Network(const Network& other) : smoothing(other.smoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
maxThreads(other.getMaxThreads()), fitted(other.fitted), samples(other.samples)
{
if (samples.defined())
@@ -164,14 +165,14 @@ namespace bayesnet {
for (int i = 0; i < featureNames.size(); ++i) {
auto row_feature = X.index({ i, "..." });
}
completeFit(states, weights);
completeFit(states, X.size(0), weights);
}
void Network::fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
{
checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states, weights);
this->className = className;
this->samples = samples;
completeFit(states, weights);
completeFit(states, samples.size(1), weights);
}
// input_data comes in nxm, where n is the number of features and m the number of samples
void Network::fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights_, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
@@ -185,16 +186,17 @@ namespace bayesnet {
samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
}
samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
completeFit(states, weights);
completeFit(states, input_data[0].size(), weights);
}
void Network::completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
void Network::completeFit(const std::map<std::string, std::vector<int>>& states, const int n_samples, const torch::Tensor& weights)
{
setStates(states);
laplaceSmoothing = 1.0 / samples.size(1); // To use in CPT computation
std::vector<std::thread> threads;
for (auto& node : nodes) {
threads.emplace_back([this, &node, &weights]() {
node.second->computeCPT(samples, features, laplaceSmoothing, weights);
threads.emplace_back([this, &node, &weights, n_samples]() {
auto numStates = node.second->getNumStates();
double smoothing_factor = smoothing == Smoothing_t::CESTNIK ? static_cast<double>(n_samples) / numStates : 1.0 / static_cast<double>(n_samples);
node.second->computeCPT(samples, features, smoothing_factor, weights);
});
}
for (auto& thread : threads) {
@@ -337,7 +339,7 @@ namespace bayesnet {
thread.join();
}
// Normalize result
double sum = accumulate(result.begin(), result.end(), 0.0);
double sum = std::accumulate(result.begin(), result.end(), 0.0);
transform(result.begin(), result.end(), result.begin(), [sum](const double& value) { return value / sum; });
return result;
}