Reorder CMakeLists

Reviewed-on: #33

.clang-format

@@ -1,4 +1,10 @@
 # .clang-format
+---
 BasedOnStyle: LLVM
+AccessModifierOffset: -4
+BreakBeforeBraces: Linux
+ColumnLimit: 0
+FixNamespaceComments: false
 IndentWidth: 4
-ColumnLimit: 120
+NamespaceIndentation: All
+TabWidth: 4

.devcontainer/Dockerfile

@@ -35,7 +35,8 @@ RUN rm -fr lcov-2.1
 
 # Install Miniconda
 RUN mkdir -p /opt/conda 
-RUN wget --quiet "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh" -O /opt/conda/miniconda.sh && \
+# RUN wget --quiet "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh" -O /opt/conda/miniconda.sh && \
+RUN wget --quiet "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" -O /opt/conda/miniconda.sh && \
     bash /opt/conda/miniconda.sh -b -p /opt/miniconda
 
 # Add conda to PATH

.gitea/workflows/action.yaml

@@ -0,0 +1,19 @@
+name: Gitea Actions Demo
+run-name: ${{ github.actor }} is testing out Gitea Actions 🚀
+on: [push]
+jobs:
+  Explore-GitHub-Actions:
+    runs-on: ubuntu-latest
+    steps:
+      - run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
+      - run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by GitHub!"
+      - run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
+      - name: Check out repository code
+        uses: actions/checkout@v4
+      - run: echo "💡 The ${{ github.repository }} repository has been cloned to the runner."
+      - run: echo "🖥️ The workflow is now ready to test your code on the runner."
+      - name: List files in the repository
+        run: |
+          ls ${{ github.workspace }}
+      - run: echo "🍏 This job's status is ${{ job.status }}."
+

.gitea/workflows/testing.yaml

@@ -0,0 +1,19 @@
+name: Gitea Actions Demo
+run-name: ${{ github.actor }} is testing out Gitea Actions 🚀
+on: [push]
+jobs:
+  Explore-GitHub-Actions:
+    runs-on: ubuntu-latest
+    steps:
+      - run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
+      - run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by GitHub!"
+      - run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
+      - name: Check out repository code
+        uses: actions/checkout@v4
+      - run: echo "💡 The ${{ github.repository }} repository has been cloned to the runner."
+      - run: echo "🖥️ The workflow is now ready to test your code on the runner."
+      - name: List files in the repository
+        run: |
+          ls ${{ github.workspace }}
+      - run: echo "🍏 This job's status is ${{ job.status }}."
+

CHANGELOG.md

@@ -7,8 +7,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
-## [1.0.7] 2025-03-16
+### Internal
 
+- Add changes to .clang-format to ajust to vscode format style thanks to https://clang-format-configurator.site/
+
+## [1.0.7] 2025-03-16
 
 ### Added
 

CMakeLists.txt

@@ -1,4 +1,4 @@
-cmake_minimum_required(VERSION 3.20)
+cmake_minimum_required(VERSION 3.24)
 
 project(BayesNet
   VERSION 1.0.7
@@ -8,15 +8,11 @@ project(BayesNet
 )
 
 if (CODE_COVERAGE AND NOT ENABLE_TESTING)
-  MESSAGE(FATAL_ERROR "Code coverage requires testing enabled")
+  message(FATAL_ERROR "Code coverage requires testing enabled")
 endif (CODE_COVERAGE AND NOT ENABLE_TESTING)
 
 find_package(Torch REQUIRED)
 
-if (POLICY CMP0135)
-  cmake_policy(SET CMP0135 NEW)
-endif ()
-
 # Global CMake variables
 # ----------------------
 set(CMAKE_CXX_STANDARD 17)
@@ -24,7 +20,7 @@ set(CMAKE_CXX_STANDARD_REQUIRED            ON)
 set(CMAKE_CXX_EXTENSIONS                  OFF)
 set(CMAKE_EXPORT_COMPILE_COMMANDS          ON)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
-SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
 set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -fno-elide-constructors")
 set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -Ofast")
 if (NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
@@ -44,19 +40,28 @@ set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules ${CMAKE_MODULE_P
 include(AddGitSubmodule)
 
 if (CMAKE_BUILD_TYPE STREQUAL "Debug")
-  MESSAGE("Debug mode")
+  message(STATUS "* Debug mode")
+  enable_testing()
   set(ENABLE_TESTING ON)
   set(CODE_COVERAGE ON)
 endif (CMAKE_BUILD_TYPE STREQUAL "Debug")
 
+# Testing
+# -------
+if (ENABLE_TESTING)
+  message(STATUS "Testing enabled")
+  add_subdirectory(tests/lib/catch2)
+  include(CTest)
+  add_subdirectory(tests)
+endif (ENABLE_TESTING)
+if (CODE_COVERAGE)
+  include(CodeCoverage)
+  message(STATUS "Code coverage enabled")
+  set(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
+endif (CODE_COVERAGE)
+
 get_property(LANGUAGES GLOBAL PROPERTY ENABLED_LANGUAGES)
 message(STATUS "Languages=${LANGUAGES}")
-if (CODE_COVERAGE)
-  enable_testing()
-  include(CodeCoverage)
-  MESSAGE(STATUS "Code coverage enabled")
-  SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
-endif (CODE_COVERAGE)
 
 if (ENABLE_CLANG_TIDY)
   include(StaticAnalyzers) # clang-tidy
@@ -74,14 +79,7 @@ add_git_submodule("lib/mdlp")
 add_subdirectory(config)
 add_subdirectory(bayesnet)
 
-# Testing
-# -------
-if (ENABLE_TESTING)
-MESSAGE(STATUS "Testing enabled")
-  add_subdirectory(tests/lib/catch2)
-  include(CTest)
-  add_subdirectory(tests)
-endif (ENABLE_TESTING)
+
 
 # Installation
 # ------------
@@ -100,9 +98,7 @@ if (Doxygen_FOUND)
   set(doxyfile_in ${DOC_DIR}/Doxyfile.in)
   set(doxyfile ${DOC_DIR}/Doxyfile)
   configure_file(${doxyfile_in} ${doxyfile} @ONLY)
-  doxygen_add_docs(doxygen
-      WORKING_DIRECTORY ${DOC_DIR}
-    CONFIG_FILE ${doxyfile})
+  doxygen_add_docs(doxygen WORKING_DIRECTORY ${DOC_DIR} CONFIG_FILE ${doxyfile})
 else (Doxygen_FOUND) 
-  MESSAGE("* Doxygen not found")
+  message(WARNING "* Doxygen not found")
 endif (Doxygen_FOUND)

README.md

@@ -2,12 +2,12 @@
 
 ![C++](https://img.shields.io/badge/c++-%2300599C.svg?style=flat&logo=c%2B%2B&logoColor=white)
 [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](<https://opensource.org/licenses/MIT>)
-![Gitea Release](https://img.shields.io/gitea/v/release/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es:3000)
+![Gitea Release](https://img.shields.io/gitea/v/release/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es)
 [![Codacy Badge](https://app.codacy.com/project/badge/Grade/cf3e0ac71d764650b1bf4d8d00d303b1)](https://app.codacy.com/gh/Doctorado-ML/BayesNet/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
 [![Security Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_BayesNet&metric=security_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
 [![Reliability Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_BayesNet&metric=reliability_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
-![Gitea Last Commit](https://img.shields.io/gitea/last-commit/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es:3000&logo=gitea)
-[![Coverage Badge](https://img.shields.io/badge/Coverage-99,1%25-green)](html/index.html)
+![Gitea Last Commit](https://img.shields.io/gitea/last-commit/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es&logo=gitea)
+[![Coverage Badge](https://img.shields.io/badge/Coverage-99,1%25-green)](https://gitea.rmontanana.es/rmontanana/BayesNet)
 [![DOI](https://zenodo.org/badge/667782806.svg)](https://doi.org/10.5281/zenodo.14210344)
 
 Bayesian Network Classifiers library
@@ -76,8 +76,12 @@ make sample fname=tests/data/glass.arff
 
 #### - [BoostAODE](docs/BoostAODE.md)
 
+#### - XBAODE
+
 #### - BoostA2DE
 
+#### - XBA2DE
+
 ### With Local Discretization
 
 #### - TANLd

bayesnet/ensembles/BoostAODE.cc

@@ -4,25 +4,26 @@
 // SPDX-License-Identifier: MIT
 // ***************************************************************
 
-#include <random> 
-#include <set>
-#include <limits.h>
-#include <tuple>
 #include "BoostAODE.h"
 #include "bayesnet/classifiers/SPODE.h"
-#include <loguru.hpp>
+#include <limits.h>
 #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));
@@ -32,7 +33,7 @@ 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
@@ -45,7 +46,7 @@ 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;
@@ -73,7 +74,7 @@ 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
@@ -81,10 +82,8 @@ namespace bayesnet {
                 std::shuffle(featureSelection.begin(), featureSelection.end(), g);
             }
             // 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)
-            );
+            featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x) { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed); }),
+                                   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());
@@ -176,7 +175,7 @@ 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);
     }

bayesnet/ensembles/XBAODE.cc

@@ -10,12 +10,11 @@
 #include <random>
 #include <tuple>
 
-namespace bayesnet {
-XBAODE::XBAODE() : Boost(false) {
-    validHyperparameters = {"alpha_block", "order",        "convergence",    "convergence_best", "bisection",
-                            "threshold",   "maxTolerance", "predict_voting", "select_features"};
-}
-std::vector<int> XBAODE::initializeModels(const Smoothing_t smoothing) {
+namespace bayesnet
+{
+    XBAODE::XBAODE() : Boost(false) {}
+    std::vector<int> XBAODE::initializeModels(const Smoothing_t smoothing)
+    {
         torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
         std::vector<int> featuresSelected = featureSelection(weights_);
         for (const int &feature : featuresSelected) {
@@ -26,8 +25,9 @@ std::vector<int> XBAODE::initializeModels(const Smoothing_t smoothing) {
         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 XBAODE::trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing) {
+    }
+    void XBAODE::trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing)
+    {
         X_train_ = TensorUtils::to_matrix(X_train);
         y_train_ = TensorUtils::to_vector<int>(y_train);
         if (convergence) {
@@ -180,5 +180,5 @@ void XBAODE::trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_
         }
         notes.push_back("Number of models: " + std::to_string(n_models));
         return;
-}
+    }
 } // namespace bayesnet

diagrams/BayesNet.puml

@@ -96,6 +96,8 @@ abstract C_0002617087915615796317 #aliceblue;line:blue;line.dotted;text:blue {
 {abstract} +topological_order() = 0 : std::vector<std::string>
 {abstract} #trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) = 0 : void
 __
+#notes : std::vector<std::string>
+#status : status_t
 #validHyperparameters : std::vector<std::string>
 }
 class "bayesnet::Metrics" as C_0005895723015084986588
@@ -153,6 +155,7 @@ abstract C_0016351972983202413152 #aliceblue;line:blue;line.dotted;text:blue {
 +topological_order() : std::vector<std::string>
 #trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
 __
+#CLASSIFIER_NOT_FITTED : const std::string
 #className : std::string
 #dataset : torch::Tensor
 #features : std::vector<std::string>
@@ -161,20 +164,44 @@ __
 #metrics : Metrics
 #model : Network
 #n : unsigned int
-#notes : std::vector<std::string>
 #states : std::map<std::string,std::vector<int>>
-#status : status_t
+}
+class "bayesnet::Proposal" as C_0017759964713298103839
+class C_0017759964713298103839 #aliceblue;line:blue;line.dotted;text:blue {
++Proposal(torch::Tensor & pDataset, std::vector<std::string> & features_, std::string & className_) : void
++~Proposal() : void
+..
+#checkInput(const torch::Tensor & X, const torch::Tensor & y) : void
+#fit_local_discretization(const torch::Tensor & y) : std::map<std::string,std::vector<int>>
+#localDiscretizationProposal(const std::map<std::string,std::vector<int>> & states, Network & model) : std::map<std::string,std::vector<int>>
+#prepareX(torch::Tensor & X) : torch::Tensor
+__
+#Xf : torch::Tensor
+#discretizers : map<std::string,mdlp::CPPFImdlp *>
+#y : torch::Tensor
 }
 class "bayesnet::KDB" as C_0008902920152122000044
 class C_0008902920152122000044 #aliceblue;line:blue;line.dotted;text:blue {
 +KDB(int k, float theta = 0.03) : void
 +~KDB() = default : void
 ..
+#add_m_edges(int idx, std::vector<int> & S, torch::Tensor & weights) : void
 #buildModel(const torch::Tensor & weights) : void
 +graph(const std::string & name = "KDB") const : std::vector<std::string>
 +setHyperparameters(const nlohmann::json & hyperparameters_) : void
 __
 }
+class "bayesnet::KDBLd" as C_0002756018222998454702
+class C_0002756018222998454702 #aliceblue;line:blue;line.dotted;text:blue {
++KDBLd(int k) : void
++~KDBLd() = default : void
+..
++fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : KDBLd &
++graph(const std::string & name = "KDB") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
 class "bayesnet::SPODE" as C_0004096182510460307610
 class C_0004096182510460307610 #aliceblue;line:blue;line.dotted;text:blue {
 +SPODE(int root) : void
@@ -182,6 +209,20 @@ class C_0004096182510460307610 #aliceblue;line:blue;line.dotted;text:blue {
 ..
 #buildModel(const torch::Tensor & weights) : void
 +graph(const std::string & name = "SPODE") const : std::vector<std::string>
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
+__
+}
+class "bayesnet::SPODELd" as C_0010957245114062042836
+class C_0010957245114062042836 #aliceblue;line:blue;line.dotted;text:blue {
++SPODELd(int root) : void
++~SPODELd() = default : void
+..
++commonFit(const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
++fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
++fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
++graph(const std::string & name = "SPODELd") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
 __
 }
 class "bayesnet::SPnDE" as C_0016268916386101512883
@@ -200,44 +241,7 @@ class C_0014087955399074584137 #aliceblue;line:blue;line.dotted;text:blue {
 ..
 #buildModel(const torch::Tensor & weights) : void
 +graph(const std::string & name = "TAN") const : std::vector<std::string>
-__
-}
-class "bayesnet::Proposal" as C_0017759964713298103839
-class C_0017759964713298103839 #aliceblue;line:blue;line.dotted;text:blue {
-+Proposal(torch::Tensor & pDataset, std::vector<std::string> & features_, std::string & className_) : void
-+~Proposal() : void
-..
-#checkInput(const torch::Tensor & X, const torch::Tensor & y) : void
-#fit_local_discretization(const torch::Tensor & y) : std::map<std::string,std::vector<int>>
-#localDiscretizationProposal(const std::map<std::string,std::vector<int>> & states, Network & model) : std::map<std::string,std::vector<int>>
-#prepareX(torch::Tensor & X) : torch::Tensor
-__
-#Xf : torch::Tensor
-#discretizers : map<std::string,mdlp::CPPFImdlp *>
-#y : torch::Tensor
-}
-class "bayesnet::KDBLd" as C_0002756018222998454702
-class C_0002756018222998454702 #aliceblue;line:blue;line.dotted;text:blue {
-+KDBLd(int k) : void
-+~KDBLd() = default : void
-..
-+fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : KDBLd &
-+graph(const std::string & name = "KDB") const : std::vector<std::string>
-+predict(torch::Tensor & X) : torch::Tensor
-{static} +version() : std::string
-__
-}
-class "bayesnet::SPODELd" as C_0010957245114062042836
-class C_0010957245114062042836 #aliceblue;line:blue;line.dotted;text:blue {
-+SPODELd(int root) : void
-+~SPODELd() = default : void
-..
-+commonFit(const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
-+fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
-+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
-+graph(const std::string & name = "SPODELd") const : std::vector<std::string>
-+predict(torch::Tensor & X) : torch::Tensor
-{static} +version() : std::string
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
 __
 }
 class "bayesnet::TANLd" as C_0013350632773616302678
@@ -250,6 +254,64 @@ class C_0013350632773616302678 #aliceblue;line:blue;line.dotted;text:blue {
 +predict(torch::Tensor & X) : torch::Tensor
 __
 }
+class "bayesnet::XSp2de" as C_0007640742442325463418
+class C_0007640742442325463418 #aliceblue;line:blue;line.dotted;text:blue {
++XSp2de(int spIndex1, int spIndex2) : void
+..
+#buildModel(const torch::Tensor & weights) : void
++fitx(torch::Tensor & X, torch::Tensor & y, torch::Tensor & weights_, const Smoothing_t smoothing) : void
++getClassNumStates() const : int
++getNFeatures() const : int
++getNumberOfEdges() const : int
++getNumberOfNodes() const : int
++getNumberOfStates() const : int
++graph(const std::string & title) const : std::vector<std::string>
++predict(const std::vector<int> & instance) const : int
++predict(std::vector<std::vector<int>> & test_data) : std::vector<int>
++predict(torch::Tensor & X) : torch::Tensor
++predict_proba(const std::vector<int> & instance) const : std::vector<double>
++predict_proba(std::vector<std::vector<int>> & test_data) : std::vector<std::vector<double>>
++predict_proba(torch::Tensor & X) : torch::Tensor
++score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
++score(torch::Tensor & X, torch::Tensor & y) : float
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
++to_string() const : std::string
+#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::XSpode" as C_0015654113248178830206
+class C_0015654113248178830206 #aliceblue;line:blue;line.dotted;text:blue {
++XSpode(int spIndex) : void
+..
+#buildModel(const torch::Tensor & weights) : void
++fitx(torch::Tensor & X, torch::Tensor & y, torch::Tensor & weights_, const Smoothing_t smoothing) : void
++getClassNumStates() const : int
++getNFeatures() const : int
++getNumberOfEdges() const : int
++getNumberOfNodes() const : int
++getNumberOfStates() const : int
++getStates() : std::vector<int> &
++graph(const std::string & title) const : std::vector<std::string>
++normalize(std::vector<double> & v) const : void
++predict(const std::vector<int> & instance) const : int
++predict(std::vector<std::vector<int>> & X) : std::vector<int>
++predict(torch::Tensor & X) : torch::Tensor
++predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
++predict_proba(torch::Tensor & X) : torch::Tensor
++predict_proba(const std::vector<int> & instance) const : std::vector<double>
++score(torch::Tensor & X, torch::Tensor & y) : float
++score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
++to_string() const : std::string
+#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::TensorUtils" as C_0010304804115474100819
+class C_0010304804115474100819 #aliceblue;line:blue;line.dotted;text:blue {
+{static} +to_matrix(const torch::Tensor & X) : std::vector<std::vector<int>>
+{static} +to_vector<T>(const torch::Tensor & y) : std::vector<T>
+__
+}
 class "bayesnet::Ensemble" as C_0015881931090842884611
 class C_0015881931090842884611 #aliceblue;line:blue;line.dotted;text:blue {
 +Ensemble(bool predict_voting = true) : void
@@ -302,6 +364,17 @@ class C_0006288892608974306258 #aliceblue;line:blue;line.dotted;text:blue {
 +setHyperparameters(const nlohmann::json & hyperparameters) : void
 __
 }
+class "bayesnet::AODELd" as C_0003898187834670349177
+class C_0003898187834670349177 #aliceblue;line:blue;line.dotted;text:blue {
++AODELd(bool predict_voting = true) : void
++~AODELd() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++fit(torch::Tensor & X_, torch::Tensor & y_, const std::vector<std::string> & features_, const std::string & className_, std::map<std::string,std::vector<int>> & states_, const Smoothing_t smoothing) : AODELd &
++graph(const std::string & name = "AODELd") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+__
+}
 abstract "bayesnet::FeatureSelect" as C_0013562609546004646591
 abstract C_0013562609546004646591 #aliceblue;line:blue;line.dotted;text:blue {
 +FeatureSelect(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
@@ -324,15 +397,15 @@ __
 #suLabels : std::vector<double>
 #weights : const torch::Tensor &
 }
-class "bayesnet::(anonymous_60342586)" as C_0005584545181746538542
-class C_0005584545181746538542 #aliceblue;line:blue;line.dotted;text:blue {
+class "bayesnet::(anonymous_60357672)" as C_0006397015156479549697
+class C_0006397015156479549697 #aliceblue;line:blue;line.dotted;text:blue {
 __
 +CFS : std::string
 +FCBF : std::string
 +IWSS : std::string
 }
-class "bayesnet::(anonymous_60343240)" as C_0016227156982041949444
-class C_0016227156982041949444 #aliceblue;line:blue;line.dotted;text:blue {
+class "bayesnet::(anonymous_60358326)" as C_0013066254331852347304
+class C_0013066254331852347304 #aliceblue;line:blue;line.dotted;text:blue {
 __
 +ASC : std::string
 +DESC : std::string
@@ -343,14 +416,17 @@ class C_0009819322948617116148 #aliceblue;line:blue;line.dotted;text:blue {
 +Boost(bool predict_voting = false) : void
 +~Boost() = default : void
 ..
+#add_model(std::unique_ptr<Classifier> model, double significance) : void
 #buildModel(const torch::Tensor & weights) : void
 #featureSelection(torch::Tensor & weights_) : std::vector<int>
+#remove_last_model() : void
 +setHyperparameters(const nlohmann::json & hyperparameters_) : void
 #update_weights(torch::Tensor & ytrain, torch::Tensor & ypred, torch::Tensor & weights) : std::tuple<torch::Tensor &,double,bool>
 #update_weights_block(int k, torch::Tensor & ytrain, torch::Tensor & weights) : std::tuple<torch::Tensor &,double,bool>
 __
 #X_test : torch::Tensor
 #X_train : torch::Tensor
+#alpha_block : bool
 #bisection : bool
 #block_update : bool
 #convergence : bool
@@ -364,31 +440,6 @@ __
 #y_test : torch::Tensor
 #y_train : torch::Tensor
 }
-class "bayesnet::AODELd" as C_0003898187834670349177
-class C_0003898187834670349177 #aliceblue;line:blue;line.dotted;text:blue {
-+AODELd(bool predict_voting = true) : void
-+~AODELd() = default : void
-..
-#buildModel(const torch::Tensor & weights) : void
-+fit(torch::Tensor & X_, torch::Tensor & y_, const std::vector<std::string> & features_, const std::string & className_, std::map<std::string,std::vector<int>> & states_, const Smoothing_t smoothing) : AODELd &
-+graph(const std::string & name = "AODELd") const : std::vector<std::string>
-#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
-__
-}
-class "bayesnet::(anonymous_60275628)" as C_0009086919615463763584
-class C_0009086919615463763584 #aliceblue;line:blue;line.dotted;text:blue {
-__
-+CFS : std::string
-+FCBF : std::string
-+IWSS : std::string
-}
-class "bayesnet::(anonymous_60276282)" as C_0015251985607563196159
-class C_0015251985607563196159 #aliceblue;line:blue;line.dotted;text:blue {
-__
-+ASC : std::string
-+DESC : std::string
-+RAND : std::string
-}
 class "bayesnet::BoostA2DE" as C_0000272055465257861326
 class C_0000272055465257861326 #aliceblue;line:blue;line.dotted;text:blue {
 +BoostA2DE(bool predict_voting = false) : void
@@ -398,15 +449,15 @@ class C_0000272055465257861326 #aliceblue;line:blue;line.dotted;text:blue {
 #trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
 __
 }
-class "bayesnet::(anonymous_60275502)" as C_0016033655851510053155
-class C_0016033655851510053155 #aliceblue;line:blue;line.dotted;text:blue {
+class "bayesnet::(anonymous_60425028)" as C_0000461144706913711531
+class C_0000461144706913711531 #aliceblue;line:blue;line.dotted;text:blue {
 __
 +CFS : std::string
 +FCBF : std::string
 +IWSS : std::string
 }
-class "bayesnet::(anonymous_60276156)" as C_0000379522761622473555
-class C_0000379522761622473555 #aliceblue;line:blue;line.dotted;text:blue {
+class "bayesnet::(anonymous_60425682)" as C_0014849589915262463453
+class C_0014849589915262463453 #aliceblue;line:blue;line.dotted;text:blue {
 __
 +ASC : std::string
 +DESC : std::string
@@ -421,6 +472,38 @@ class C_0002867772739198819061 #aliceblue;line:blue;line.dotted;text:blue {
 #trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
 __
 }
+class "bayesnet::XBA2DE" as C_0008480973840710001141
+class C_0008480973840710001141 #aliceblue;line:blue;line.dotted;text:blue {
++XBA2DE(bool predict_voting = false) : void
++~XBA2DE() = default : void
+..
++getVersion() : std::string
++graph(const std::string & title = "XBA2DE") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::(anonymous_60414016)" as C_0008746994658440620779
+class C_0008746994658440620779 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60414670)" as C_0008030559132212449356
+class C_0008030559132212449356 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::XBAODE" as C_0005198482342493966768
+class C_0005198482342493966768 #aliceblue;line:blue;line.dotted;text:blue {
++XBAODE() : void
+..
++getVersion() : std::string
+#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
+__
+}
 class "bayesnet::CFS" as C_0000093018845530739957
 class C_0000093018845530739957 #aliceblue;line:blue;line.dotted;text:blue {
 +CFS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
@@ -445,43 +528,43 @@ class C_0000066148117395428429 #aliceblue;line:blue;line.dotted;text:blue {
 +fit() : void
 __
 }
-class "bayesnet::(anonymous_60730495)" as C_0004857727320042830573
-class C_0004857727320042830573 #aliceblue;line:blue;line.dotted;text:blue {
+class "bayesnet::(anonymous_60810808)" as C_0012002108046995621535
+class C_0012002108046995621535 #aliceblue;line:blue;line.dotted;text:blue {
 __
 +CFS : std::string
 +FCBF : std::string
 +IWSS : std::string
 }
-class "bayesnet::(anonymous_60731150)" as C_0000076541533312623385
-class C_0000076541533312623385 #aliceblue;line:blue;line.dotted;text:blue {
+class "bayesnet::(anonymous_60811462)" as C_0004735044229422764240
+class C_0004735044229422764240 #aliceblue;line:blue;line.dotted;text:blue {
 __
 +ASC : std::string
 +DESC : std::string
 +RAND : std::string
 }
-class "bayesnet::(anonymous_60653004)" as C_0001444063444142949758
-class C_0001444063444142949758 #aliceblue;line:blue;line.dotted;text:blue {
+class "bayesnet::(anonymous_60804220)" as C_0007082100550474633839
+class C_0007082100550474633839 #aliceblue;line:blue;line.dotted;text:blue {
 __
 +CFS : std::string
 +FCBF : std::string
 +IWSS : std::string
 }
-class "bayesnet::(anonymous_60653658)" as C_0007139277546931322856
-class C_0007139277546931322856 #aliceblue;line:blue;line.dotted;text:blue {
+class "bayesnet::(anonymous_60804874)" as C_0003669430095936529648
+class C_0003669430095936529648 #aliceblue;line:blue;line.dotted;text:blue {
 __
 +ASC : std::string
 +DESC : std::string
 +RAND : std::string
 }
-class "bayesnet::(anonymous_60731375)" as C_0010493853592456211189
-class C_0010493853592456211189 #aliceblue;line:blue;line.dotted;text:blue {
+class "bayesnet::(anonymous_60809706)" as C_0012336951062058157227
+class C_0012336951062058157227 #aliceblue;line:blue;line.dotted;text:blue {
 __
 +CFS : std::string
 +FCBF : std::string
 +IWSS : std::string
 }
-class "bayesnet::(anonymous_60732030)" as C_0007011438637915849564
-class C_0007011438637915849564 #aliceblue;line:blue;line.dotted;text:blue {
+class "bayesnet::(anonymous_60810360)" as C_0002435892998884329673
+class C_0002435892998884329673 #aliceblue;line:blue;line.dotted;text:blue {
 __
 +ASC : std::string
 +DESC : std::string
@@ -513,37 +596,43 @@ C_0010428199432536647474 --> C_0010428199432536647474 : -children
 C_0009493661199123436603 ..> C_0013393078277439680282
 C_0009493661199123436603 o-- C_0010428199432536647474 : -nodes
 C_0002617087915615796317 ..> C_0013393078277439680282
-C_0002617087915615796317 ..> C_0005907365846270811004
+C_0002617087915615796317 o-- C_0005907365846270811004 : #status
 C_0016351972983202413152 ..> C_0013393078277439680282
+C_0016351972983202413152 ..> C_0005907365846270811004
 C_0016351972983202413152 o-- C_0009493661199123436603 : #model
 C_0016351972983202413152 o-- C_0005895723015084986588 : #metrics
-C_0016351972983202413152 o-- C_0005907365846270811004 : #status
 C_0002617087915615796317 <|-- C_0016351972983202413152
 
+C_0017759964713298103839 ..> C_0009493661199123436603
 C_0016351972983202413152 <|-- C_0008902920152122000044
 
-C_0016351972983202413152 <|-- C_0004096182510460307610
-
-C_0016351972983202413152 <|-- C_0016268916386101512883
-
-C_0016351972983202413152 <|-- C_0014087955399074584137
-
-C_0017759964713298103839 ..> C_0009493661199123436603
 C_0002756018222998454702 ..> C_0013393078277439680282
 C_0008902920152122000044 <|-- C_0002756018222998454702
 
 C_0017759964713298103839 <|-- C_0002756018222998454702
 
+C_0016351972983202413152 <|-- C_0004096182510460307610
+
 C_0010957245114062042836 ..> C_0013393078277439680282
 C_0004096182510460307610 <|-- C_0010957245114062042836
 
 C_0017759964713298103839 <|-- C_0010957245114062042836
 
+C_0016351972983202413152 <|-- C_0016268916386101512883
+
+C_0016351972983202413152 <|-- C_0014087955399074584137
+
 C_0013350632773616302678 ..> C_0013393078277439680282
 C_0014087955399074584137 <|-- C_0013350632773616302678
 
 C_0017759964713298103839 <|-- C_0013350632773616302678
 
+C_0007640742442325463418 ..> C_0013393078277439680282
+C_0016351972983202413152 <|-- C_0007640742442325463418
+
+C_0015654113248178830206 ..> C_0013393078277439680282
+C_0016351972983202413152 <|-- C_0015654113248178830206
+
 C_0015881931090842884611 ..> C_0013393078277439680282
 C_0015881931090842884611 o-- C_0016351972983202413152 : #models
 C_0016351972983202413152 <|-- C_0015881931090842884611
@@ -552,22 +641,29 @@ C_0015881931090842884611 <|-- C_0001410789567057647859
 
 C_0015881931090842884611 <|-- C_0006288892608974306258
 
-C_0005895723015084986588 <|-- C_0013562609546004646591
-
-C_0009819322948617116148 --> C_0013562609546004646591 : #featureSelector
-C_0015881931090842884611 <|-- C_0009819322948617116148
-
 C_0003898187834670349177 ..> C_0013393078277439680282
 C_0015881931090842884611 <|-- C_0003898187834670349177
 
 C_0017759964713298103839 <|-- C_0003898187834670349177
 
+C_0005895723015084986588 <|-- C_0013562609546004646591
+
+C_0009819322948617116148 ..> C_0016351972983202413152
+C_0009819322948617116148 --> C_0013562609546004646591 : #featureSelector
+C_0015881931090842884611 <|-- C_0009819322948617116148
+
 C_0000272055465257861326 ..> C_0013393078277439680282
 C_0009819322948617116148 <|-- C_0000272055465257861326
 
 C_0002867772739198819061 ..> C_0013393078277439680282
 C_0009819322948617116148 <|-- C_0002867772739198819061
 
+C_0008480973840710001141 ..> C_0013393078277439680282
+C_0009819322948617116148 <|-- C_0008480973840710001141
+
+C_0005198482342493966768 ..> C_0013393078277439680282
+C_0009819322948617116148 <|-- C_0005198482342493966768
+
 C_0013562609546004646591 <|-- C_0000093018845530739957
 
 C_0013562609546004646591 <|-- C_0001157456122733975432

tests/TestBoostA2DE.cc

@@ -162,7 +162,7 @@ TEST_CASE("Bisection Best", "[BoostA2DE]")
         {"maxTolerance", 3},
         {"convergence", true},
         {"block_update", false},
-        {"convergence_best", false},
+        {"convergence_best", true},
         });
     clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
     REQUIRE(clf.getNumberOfNodes() == 480);