rmontanana/BayesNet
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Remove git modules and add vcpkg configuration

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This commit is contained in:
Ricardo Montañana Gómez
2025-04-27 18:33:23 +02:00
parent 306d3a4b55
commit 670b93d0a1
67 changed files with 112 additions and 28157 deletions
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2
bayesnet/classifiers/Proposal.h
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@@ -9,7 +9,7 @@
#include <string>
#include <map>
#include <torch/torch.h>
#include <CPPFImdlp.h>
#include <fimdlp/CPPFImdlp.h>
#include "bayesnet/network/Network.h"
#include "Classifier.h"

21
bayesnet/ensembles/BoostAODE.cc
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@@ -7,23 +7,22 @@
#include "BoostAODE.h"
#include "bayesnet/classifiers/SPODE.h"
#include <limits.h>
#include <loguru.cpp>
#include <loguru.hpp>
// #include <loguru.cpp>
// #include <loguru.hpp>
#include <random>
#include <set>
#include <tuple>
namespace bayesnet
{
namespace bayesnet {
BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting)
{
}
std::vector<int> BoostAODE::initializeModels(const Smoothing_t smoothing)
{
torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
std::vector<int> featuresSelected = featureSelection(weights_);
for (const int &feature : featuresSelected) {
for (const int& feature : featuresSelected) {
std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
model->fit(dataset, features, className, states, weights_, smoothing);
models.push_back(std::move(model));
@@ -33,7 +32,7 @@ namespace bayesnet
notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
return featuresSelected;
}
void BoostAODE::trainModel(const torch::Tensor &weights, const Smoothing_t smoothing)
void BoostAODE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
{
//
// Logging setup
@@ -46,7 +45,7 @@ namespace bayesnet
// as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
fitted = true;
double alpha_t = 0;
torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
bool finished = false;
std::vector<int> featuresUsed;
n_models = 0;
@@ -74,7 +73,7 @@ namespace bayesnet
// validation error is not decreasing
// run out of features
bool ascending = order_algorithm == Orders.ASC;
std::mt19937 g{173};
std::mt19937 g{ 173 };
while (!finished) {
// Step 1: Build ranking with mutual information
auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
@@ -83,7 +82,7 @@ namespace bayesnet
}
// Remove used features
featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x) { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed); }),
end(featureSelection));
end(featureSelection));
int k = bisection ? pow(2, tolerance) : 1;
int counter = 0; // The model counter of the current pack
// VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
@@ -175,7 +174,7 @@ namespace bayesnet
}
notes.push_back("Number of models: " + std::to_string(n_models));
}
std::vector<std::string> BoostAODE::graph(const std::string &title) const
std::vector<std::string> BoostAODE::graph(const std::string& title) const
{
return Ensemble::graph(title);
}