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Update models and remove normalize weights in XA1DE

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This commit is contained in:
Ricardo Montañana Gómez
2025-03-17 13:28:35 +01:00
parent c2a4e3e64e
commit c9ab88e475
3 changed files with 8 additions and 5 deletions
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2
src/experimental_clfs/XA1DE.cpp
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@@ -14,7 +14,7 @@ namespace platform {
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_ = torch::full({ num_instances }, 1.0); weights_ = torch::full({ num_instances }, 1.0);
normalize_weights(num_instances); //normalize_weights(num_instances);
aode_.fit(X, y, features, className, states, weights_, true, smoothing); aode_.fit(X, y, features, className, states, weights_, true, smoothing);
} }
} }

3
src/main/Models.h
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@@ -6,13 +6,14 @@
#include <bayesnet/ensembles/A2DE.h> #include <bayesnet/ensembles/A2DE.h>
#include <bayesnet/ensembles/AODELd.h> #include <bayesnet/ensembles/AODELd.h>
#include <bayesnet/ensembles/XBAODE.h> #include <bayesnet/ensembles/XBAODE.h>
#include <bayesnet/ensembles/XBA2DE.h>
#include <bayesnet/ensembles/BoostAODE.h> #include <bayesnet/ensembles/BoostAODE.h>
#include <bayesnet/ensembles/BoostA2DE.h> #include <bayesnet/ensembles/BoostA2DE.h>
#include <bayesnet/classifiers/TAN.h> #include <bayesnet/classifiers/TAN.h>
#include <bayesnet/classifiers/KDB.h> #include <bayesnet/classifiers/KDB.h>
#include <bayesnet/classifiers/SPODE.h> #include <bayesnet/classifiers/SPODE.h>
#include <bayesnet/classifiers/XSPODE.h> #include <bayesnet/classifiers/XSPODE.h>
#include <bayesnet/classifiers/XSPnDE.h> #include <bayesnet/classifiers/XSP2DE.h>
#include <bayesnet/classifiers/SPnDE.h> #include <bayesnet/classifiers/SPnDE.h>
#include <bayesnet/classifiers/TANLd.h> #include <bayesnet/classifiers/TANLd.h>
#include <bayesnet/classifiers/KDBLd.h> #include <bayesnet/classifiers/KDBLd.h>

6
src/main/modelRegister.h
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@@ -37,10 +37,12 @@ namespace platform {
[](void) -> bayesnet::BaseClassifier* { return new pywrap::XGBoost();}); [](void) -> bayesnet::BaseClassifier* { return new pywrap::XGBoost();});
static Registrar registrarXSPODE("XSPODE", static Registrar registrarXSPODE("XSPODE",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::XSpode(0);}); [](void) -> bayesnet::BaseClassifier* { return new bayesnet::XSpode(0);});
static Registrar registrarXSPnDE("XSPnDE", static Registrar registrarXSP2DE("XSP2DE",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::XSpnde(0, 1);}); [](void) -> bayesnet::BaseClassifier* { return new bayesnet::XSp2de(0, 1);});
static Registrar registrarXBAODE("XBAODE", static Registrar registrarXBAODE("XBAODE",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::XBAODE();}); [](void) -> bayesnet::BaseClassifier* { return new bayesnet::XBAODE();});
static Registrar registrarXBA2DE("XBA2DE",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::XBA2DE();});
static Registrar registrarXA1DE("XA1DE", static Registrar registrarXA1DE("XA1DE",
[](void) -> bayesnet::BaseClassifier* { return new XA1DE();}); [](void) -> bayesnet::BaseClassifier* { return new XA1DE();});
} }