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tolerance <- 3

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This commit is contained in:
Ricardo Montañana Gómez
2025-03-08 18:07:56 +01:00
parent 9c1852c6c3
commit ae7b89b134
4 changed files with 19 additions and 7 deletions
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4
src/experimental_clfs/XA1DE.cpp
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@@ -13,8 +13,8 @@ namespace platform {
auto X = TensorUtils::to_matrix(dataset.slice(0, 0, dataset.size(0) - 1)); auto X = TensorUtils::to_matrix(dataset.slice(0, 0, dataset.size(0) - 1));
auto y = TensorUtils::to_vector<int>(dataset.index({ -1, "..." })); auto y = TensorUtils::to_vector<int>(dataset.index({ -1, "..." }));
int num_instances = X[0].size(); int num_instances = X[0].size();
weights_ = weights; weights_ = torch::full({ num_instances }, 1.0);
normalize_weights(num_instances); normalize_weights(num_instances);
aode_.fit(X, y, features, className, states, weights_, true); aode_.fit(X, y, features, className, states, weights_, true, smoothing);
} }
} }

2
src/experimental_clfs/XBAODE.cpp
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@@ -26,7 +26,7 @@ namespace platform {
y_train_ = TensorUtils::to_vector<int>(y_train); y_train_ = TensorUtils::to_vector<int>(y_train);
X_test_ = TensorUtils::to_matrix(X_test); X_test_ = TensorUtils::to_matrix(X_test);
y_test_ = TensorUtils::to_vector<int>(y_test); y_test_ = TensorUtils::to_vector<int>(y_test);
maxTolerance = 5; maxTolerance = 3;
// //
// Logging setup // Logging setup
// //

5
src/experimental_clfs/XBAODE.h
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@@ -15,7 +15,10 @@
#include "ExpClf.h" #include "ExpClf.h"
namespace platform { namespace platform {
class XBAODE : public ExpClf { class XBAODE {
// Hay que hacer un vector de modelos entrenados y hacer un predict ensemble con todos ellos
// Probar XA1DE con smooth original y laplace y comprobar diferencias si se pasan pesos a 1 o a 1/m
public: public:
XBAODE(); XBAODE();
std::string getVersion() override { return version; }; std::string getVersion() override { return version; };

15
src/experimental_clfs/Xaode.hpp
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@@ -18,6 +18,7 @@
#include <limits> #include <limits>
#include <sstream> #include <sstream>
#include <torch/torch.h> #include <torch/torch.h>
#include <bayesnet/network/Smoothing.h>
namespace platform { namespace platform {
@@ -49,7 +50,7 @@ namespace platform {
// //
// Internally, in COUNTS mode, data_ accumulates raw counts, then // Internally, in COUNTS mode, data_ accumulates raw counts, then
// computeProbabilities(...) normalizes them into conditionals. // computeProbabilities(...) normalizes them into conditionals.
void fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bool all_parents) void fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bool all_parents, const bayesnet::Smoothing_t smoothing)
{ {
int num_instances = X[0].size(); int num_instances = X[0].size();
nFeatures_ = X.size(); nFeatures_ = X.size();
@@ -110,8 +111,16 @@ namespace platform {
instance[nFeatures_] = y[n_instance]; instance[nFeatures_] = y[n_instance];
addSample(instance, weights[n_instance].item<double>()); addSample(instance, weights[n_instance].item<double>());
} }
// alpha_ Laplace smoothing adapted to the number of instances switch (smoothing) {
alpha_ = 1.0 / static_cast<double>(num_instances); case bayesnet::Smoothing_t::ORIGINAL:
alpha_ = 1.0 / num_instances;
break;
case bayesnet::Smoothing_t::LAPLACE:
alpha_ = 1.0;
break;
default:
alpha_ = 0.0; // No smoothing
}
initializer_ = std::numeric_limits<double>::max() / (nFeatures_ * nFeatures_); initializer_ = std::numeric_limits<double>::max() / (nFeatures_ * nFeatures_);
computeProbabilities(); computeProbabilities();
} }