rmontanana/BayesNet
1
0
Fork 0
Code Issues 2 Pull Requests 1 Actions Packages Projects Releases 6 Wiki Activity

Set smoothing as fit parameter

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2024-06-11 11:40:45 +02:00
parent 27a3e5a5e0
commit b34869cc61
30 changed files with 168 additions and 178 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
bayesnet/network/Network.cc
View File

@@ -11,14 +11,14 @@
#include "Network.h"
#include "bayesnet/utils/bayesnetUtils.h"
namespace bayesnet {
Network::Network() : fitted{ false }, maxThreads{ 0.95 }, classNumStates{ 0 }, smoothing{ Smoothing_t::LAPLACE }
Network::Network() : fitted{ false }, maxThreads{ 0.95 }, classNumStates{ 0 }
{
}
Network::Network(float maxT) : fitted{ false }, maxThreads{ maxT }, classNumStates{ 0 }, smoothing{ Smoothing_t::LAPLACE }
Network::Network(float maxT) : fitted{ false }, maxThreads{ maxT }, classNumStates{ 0 }
{
}
Network::Network(const Network& other) : smoothing(other.smoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
Network::Network(const Network& other) : features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
maxThreads(other.getMaxThreads()), fitted(other.fitted), samples(other.samples)
{
if (samples.defined())
@@ -156,7 +156,7 @@ namespace bayesnet {
classNumStates = nodes.at(className)->getNumStates();
}
// X comes in nxm, where n is the number of features and m the number of samples
void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
{
checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states, weights);
this->className = className;
@@ -165,17 +165,17 @@ namespace bayesnet {
for (int i = 0; i < featureNames.size(); ++i) {
auto row_feature = X.index({ i, "..." });
}
completeFit(states, weights);
completeFit(states, weights, smoothing);
}
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)
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, const Smoothing_t smoothing)
{
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, weights, smoothing);
}
// 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)
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, const Smoothing_t smoothing)
{
const torch::Tensor weights = torch::tensor(weights_, torch::kFloat64);
checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states, weights);
@@ -186,15 +186,15 @@ 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, weights, smoothing);
}
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 torch::Tensor& weights, const Smoothing_t smoothing)
{
setStates(states);
std::vector<std::thread> threads;
const double n_samples = static_cast<double>(samples.size(1));
for (auto& node : nodes) {
threads.emplace_back([this, &node, &weights, n_samples]() {
threads.emplace_back([this, &node, &weights, n_samples, smoothing]() {
double numStates = static_cast<double>(node.second->getNumStates());
double smoothing_factor = 0.0;
switch (smoothing) {