Update version number

Reviewed-on: #40

.clang-format

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

.clang-tidy

@@ -5,11 +5,12 @@ Checks: '-*,
          cppcoreguidelines-*,
          modernize-*,
          performance-*,
+         -modernize-use-nodiscard,
          -cppcoreguidelines-pro-type-vararg,
          -modernize-use-trailing-return-type,
          -bugprone-exception-escape'
 
-HeaderFilterRegex: 'src/*'
+HeaderFilterRegex: 'bayesnet/*'
 AnalyzeTemporaryDtors: false
 WarningsAsErrors: ''
 FormatStyle: file

.clang-uml

@@ -0,0 +1,39 @@
+compilation_database_dir: build_Debug
+output_directory: diagrams
+diagrams:
+  BayesNet:
+    type: class
+    glob:
+      - bayesnet/*.h
+      - bayesnet/classifiers/*.h
+      - bayesnet/classifiers/*.cc
+      - bayesnet/ensembles/*.h
+      - bayesnet/ensembles/*.cc
+      - bayesnet/feature_selection/*.h
+      - bayesnet/feature_selection/*.cc
+      - bayesnet/network/*.h
+      - bayesnet/network/*.cc
+      - bayesnet/utils/*.h
+      - bayesnet/utils/*.cc
+    include:
+      # Only include entities from the following namespaces
+      namespaces:
+        - bayesnet
+    exclude:
+      access:
+        - private
+    plantuml:
+      style:
+        # Apply this style to all classes in the diagram
+        class: "#aliceblue;line:blue;line.dotted;text:blue"
+        # Apply this style to all packages in the diagram
+        package: "#back:grey"
+        # Make all template instantiation relations point upwards and draw them
+        # as green and dotted lines
+        instantiation: "up[#green,dotted]"
+      cmd: "/usr/bin/plantuml -tsvg \"diagrams/{}.puml\""
+      before:
+        - 'title clang-uml class diagram model'
+    mermaid:
+      before:
+        - 'classDiagram'

.devcontainer/Dockerfile

@@ -0,0 +1,57 @@
+FROM mcr.microsoft.com/devcontainers/cpp:ubuntu22.04
+
+ARG REINSTALL_CMAKE_VERSION_FROM_SOURCE="3.29.3"
+
+# Optionally install the cmake for vcpkg
+COPY ./reinstall-cmake.sh /tmp/
+
+RUN if [ "${REINSTALL_CMAKE_VERSION_FROM_SOURCE}" != "none" ]; then \
+    chmod +x /tmp/reinstall-cmake.sh && /tmp/reinstall-cmake.sh ${REINSTALL_CMAKE_VERSION_FROM_SOURCE}; \
+    fi \
+    && rm -f /tmp/reinstall-cmake.sh
+
+
+# [Optional] Uncomment this section to install additional vcpkg ports.
+# RUN su vscode -c "${VCPKG_ROOT}/vcpkg install <your-port-name-here>"
+
+# [Optional] Uncomment this section to install additional packages.
+RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
+    && apt-get -y install --no-install-recommends wget software-properties-common libdatetime-perl libcapture-tiny-perl libdatetime-format-dateparse-perl libgd-perl
+
+# Add PPA for GCC 13
+RUN add-apt-repository ppa:ubuntu-toolchain-r/test
+RUN apt-get update
+
+# Install GCC 13.1
+RUN apt-get install -y gcc-13 g++-13 doxygen
+
+# Install lcov 2.1
+RUN wget --quiet https://github.com/linux-test-project/lcov/releases/download/v2.1/lcov-2.1.tar.gz && \
+    tar -xvf lcov-2.1.tar.gz && \
+    cd lcov-2.1 && \
+    make install
+RUN rm lcov-2.1.tar.gz
+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 && \
+    bash /opt/conda/miniconda.sh -b -p /opt/miniconda
+
+# Add conda to PATH
+ENV PATH=/opt/miniconda/bin:$PATH
+
+# add CXX and CC to the environment with gcc 13
+ENV CXX=/usr/bin/g++-13
+ENV CC=/usr/bin/gcc-13
+
+# link the last gcov version
+RUN rm /usr/bin/gcov
+RUN ln -s /usr/bin/gcov-13 /usr/bin/gcov
+
+# change ownership of /opt/miniconda to vscode user
+RUN chown -R vscode:vscode /opt/miniconda
+
+USER vscode
+RUN conda init
+RUN conda install -y -c conda-forge yaml pytorch

.devcontainer/devcontainer.json

@@ -0,0 +1,37 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the
+// README at: https://github.com/devcontainers/templates/tree/main/src/cpp
+{
+	"name": "C++",
+	"build": {
+		"dockerfile": "Dockerfile"
+	},
+	// "features": {
+	// 	"ghcr.io/devcontainers/features/conda:1": {}
+	// }
+	// Features to add to the dev container. More info: https://containers.dev/features.
+	// "features": {},
+	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+	// "forwardPorts": [],
+	// Use 'postCreateCommand' to run commands after the container is created.
+	"postCreateCommand": "make release && make debug && echo 'Done!'",
+	// Configure tool-specific properties.
+	// "customizations": {},
+	"customizations": {
+		// Configure properties specific to VS Code.
+		"vscode": {
+			"settings": {},
+			"extensions": [
+				"ms-vscode.cpptools",
+				"ms-vscode.cpptools-extension-pack",
+				"ms-vscode.cpptools-themes",
+				"ms-vscode.cmake-tools",
+				"ms-azuretools.vscode-docker",
+				"jbenden.c-cpp-flylint",
+				"matepek.vscode-catch2-test-adapter",
+				"GitHub.copilot"
+			]
+		}
+	}
+	// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
+	// "remoteUser": "root"
+}

.devcontainer/reinstall-cmake.sh

@@ -0,0 +1,59 @@
+#!/usr/bin/env bash
+#-------------------------------------------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See https://go.microsoft.com/fwlink/?linkid=2090316 for license information.
+#-------------------------------------------------------------------------------------------------------------
+#
+set -e
+
+CMAKE_VERSION=${1:-"none"}
+
+if [ "${CMAKE_VERSION}" = "none" ]; then
+    echo "No CMake version specified, skipping CMake reinstallation"
+    exit 0
+fi
+
+# Cleanup temporary directory and associated files when exiting the script.
+cleanup() {
+    EXIT_CODE=$?
+    set +e
+    if [[ -n "${TMP_DIR}" ]]; then
+        echo "Executing cleanup of tmp files"
+        rm -Rf "${TMP_DIR}"
+    fi
+    exit $EXIT_CODE
+}
+trap cleanup EXIT
+
+
+echo "Installing CMake..."
+apt-get -y purge --auto-remove cmake
+mkdir -p /opt/cmake
+
+architecture=$(dpkg --print-architecture)
+case "${architecture}" in
+    arm64)
+        ARCH=aarch64 ;;
+    amd64)
+        ARCH=x86_64 ;;
+    *)
+        echo "Unsupported architecture ${architecture}."
+        exit 1
+        ;;
+esac
+
+CMAKE_BINARY_NAME="cmake-${CMAKE_VERSION}-linux-${ARCH}.sh"
+CMAKE_CHECKSUM_NAME="cmake-${CMAKE_VERSION}-SHA-256.txt"
+TMP_DIR=$(mktemp -d -t cmake-XXXXXXXXXX)
+
+echo "${TMP_DIR}"
+cd "${TMP_DIR}"
+
+curl -sSL "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/${CMAKE_BINARY_NAME}" -O
+curl -sSL "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/${CMAKE_CHECKSUM_NAME}" -O
+
+sha256sum -c --ignore-missing "${CMAKE_CHECKSUM_NAME}"
+sh "${TMP_DIR}/${CMAKE_BINARY_NAME}" --prefix=/opt/cmake --skip-license
+
+ln -s /opt/cmake/bin/cmake /usr/local/bin/cmake
+ln -s /opt/cmake/bin/ctest /usr/local/bin/ctest

.github/dependabot.yml

@@ -0,0 +1,12 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for more information:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+# https://containers.dev/guide/dependabot
+
+version: 2
+updates:
+ - package-ecosystem: "devcontainers"
+   directory: "/"
+   schedule:
+     interval: weekly

.gitignore

@@ -31,7 +31,19 @@
 *.exe
 *.out
 *.app
-build/
+build/**
+build_*/**
 *.dSYM/**
 cmake-build*/**
 .idea
+puml/**
+.vscode/settings.json
+sample/build
+**/.DS_Store
+docs/manual
+docs/man3
+docs/man
+docs/Doxyfile
+.cache
+vcpkg_installed
+CMakeUserPresets.json

.gitmodules

@@ -1,12 +0,0 @@
-[submodule "lib/mdlp"]
-	path = lib/mdlp
-	url = https://github.com/rmontanana/mdlp
-[submodule "lib/catch2"]
-	path = lib/catch2
-	url = https://github.com/catchorg/Catch2.git
-[submodule "lib/argparse"]
-	path = lib/argparse
-	url = https://github.com/p-ranav/argparse
-[submodule "lib/json"]
-	path = lib/json
-	url = https://github.com/nlohmann/json.git

.sonarlint/connectedMode.json

@@ -0,0 +1,4 @@
+{
+    "sonarCloudOrganization": "rmontanana",
+    "projectKey": "rmontanana_BayesNet"
+}

.vscode/c_cpp_properties.json

@@ -0,0 +1,50 @@
+{
+    "configurations": [
+        {
+            "name": "Mac",
+            "includePath": [
+                "/Users/rmontanana/Code/BayesNet/**"
+            ],
+            "defines": [],
+            "macFrameworkPath": [
+                "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include"
+            ],
+            "cStandard": "c17",
+            "cppStandard": "c++17",
+            "compileCommands": "",
+            "intelliSenseMode": "macos-clang-arm64",
+            "mergeConfigurations": false,
+            "browse": {
+                "path": [
+                    "/Users/rmontanana/Code/BayesNet/**",
+                    "${workspaceFolder}"
+                ],
+                "limitSymbolsToIncludedHeaders": true
+            },
+            "configurationProvider": "ms-vscode.cmake-tools"
+        },
+        {
+            "name": "Linux",
+            "includePath": [
+                "/home/rmontanana/Code/BayesNet/**",
+                "/home/rmontanana/Code/libtorch/include/torch/csrc/api/include/",
+                "/home/rmontanana/Code/BayesNet/lib/"
+            ],
+            "defines": [],
+            "cStandard": "c17",
+            "cppStandard": "c++17",
+            "intelliSenseMode": "linux-gcc-x64",
+            "mergeConfigurations": false,
+            "compilerPath": "/usr/bin/g++",
+            "browse": {
+                "path": [
+                    "/home/rmontanana/Code/BayesNet/**",
+                    "${workspaceFolder}"
+                ],
+                "limitSymbolsToIncludedHeaders": true
+            },
+            "configurationProvider": "ms-vscode.cmake-tools"
+        }
+    ],
+    "version": 4
+}

.vscode/launch.json

@@ -5,47 +5,44 @@
             "type": "lldb",
             "request": "launch",
             "name": "sample",
-            "program": "${workspaceFolder}/build/sample/BayesNetSample",
+            "program": "${workspaceFolder}/sample/build/bayesnet_sample",
             "args": [
-                "-d",
-                "iris",
-                "-m",
-                "KDB",
-                "-s",
-                "271",
-                "-p",
-                "/Users/rmontanana/Code/discretizbench/datasets/",
-            ],
-            //"cwd": "${workspaceFolder}/build/sample/",
+                "${workspaceFolder}/tests/data/glass.arff"
+            ]
         },
         {
             "type": "lldb",
             "request": "launch",
-            "name": "experiment",
-            "program": "${workspaceFolder}/build/src/Platform/main",
+            "name": "test",
+            "program": "${workspaceFolder}/build_Debug/tests/TestBayesNet",
             "args": [
-                "-m",
-                "SPODELd",
-                "-p",
-                "/Users/rmontanana/Code/discretizbench/datasets",
-                "--stratified",
-                "-d",
-                "iris"
+                "[XBAODE]"
             ],
-            "cwd": "/Users/rmontanana/Code/discretizbench",
+            "cwd": "${workspaceFolder}/build_Debug/tests"
         },
         {
-            "name": "Build & debug active file",
+            "name": "(gdb) Launch",
             "type": "cppdbg",
             "request": "launch",
-            "program": "${workspaceFolder}/build/bayesnet",
+            "program": "enter program name, for example ${workspaceFolder}/a.out",
             "args": [],
             "stopAtEntry": false,
-            "cwd": "${workspaceFolder}",
+            "cwd": "${fileDirname}",
             "environment": [],
             "externalConsole": false,
-            "MIMode": "lldb",
-            "preLaunchTask": "CMake: build"
+            "MIMode": "gdb",
+            "setupCommands": [
+                {
+                    "description": "Enable pretty-printing for gdb",
+                    "text": "-enable-pretty-printing",
+                    "ignoreFailures": true
+                },
+                {
+                    "description": "Set Disassembly Flavor to Intel",
+                    "text": "-gdb-set disassembly-flavor intel",
+                    "ignoreFailures": true
+                }
+            ]
         }
     ]
 }

.vscode/settings.json

@@ -1,109 +0,0 @@
-{
-    "files.associations": {
-        "*.rmd": "markdown",
-        "*.py": "python",
-        "vector": "cpp",
-        "__bit_reference": "cpp",
-        "__bits": "cpp",
-        "__config": "cpp",
-        "__debug": "cpp",
-        "__errc": "cpp",
-        "__hash_table": "cpp",
-        "__locale": "cpp",
-        "__mutex_base": "cpp",
-        "__node_handle": "cpp",
-        "__nullptr": "cpp",
-        "__split_buffer": "cpp",
-        "__string": "cpp",
-        "__threading_support": "cpp",
-        "__tuple": "cpp",
-        "array": "cpp",
-        "atomic": "cpp",
-        "bitset": "cpp",
-        "cctype": "cpp",
-        "chrono": "cpp",
-        "clocale": "cpp",
-        "cmath": "cpp",
-        "compare": "cpp",
-        "complex": "cpp",
-        "concepts": "cpp",
-        "cstdarg": "cpp",
-        "cstddef": "cpp",
-        "cstdint": "cpp",
-        "cstdio": "cpp",
-        "cstdlib": "cpp",
-        "cstring": "cpp",
-        "ctime": "cpp",
-        "cwchar": "cpp",
-        "cwctype": "cpp",
-        "exception": "cpp",
-        "initializer_list": "cpp",
-        "ios": "cpp",
-        "iosfwd": "cpp",
-        "istream": "cpp",
-        "limits": "cpp",
-        "locale": "cpp",
-        "memory": "cpp",
-        "mutex": "cpp",
-        "new": "cpp",
-        "optional": "cpp",
-        "ostream": "cpp",
-        "ratio": "cpp",
-        "sstream": "cpp",
-        "stdexcept": "cpp",
-        "streambuf": "cpp",
-        "string": "cpp",
-        "string_view": "cpp",
-        "system_error": "cpp",
-        "tuple": "cpp",
-        "type_traits": "cpp",
-        "typeinfo": "cpp",
-        "unordered_map": "cpp",
-        "variant": "cpp",
-        "algorithm": "cpp",
-        "iostream": "cpp",
-        "iomanip": "cpp",
-        "numeric": "cpp",
-        "set": "cpp",
-        "__tree": "cpp",
-        "deque": "cpp",
-        "list": "cpp",
-        "map": "cpp",
-        "unordered_set": "cpp",
-        "any": "cpp",
-        "condition_variable": "cpp",
-        "forward_list": "cpp",
-        "fstream": "cpp",
-        "stack": "cpp",
-        "thread": "cpp",
-        "__memory": "cpp",
-        "filesystem": "cpp",
-        "*.toml": "toml",
-        "utility": "cpp",
-        "__verbose_abort": "cpp",
-        "bit": "cpp",
-        "random": "cpp",
-        "*.tcc": "cpp",
-        "functional": "cpp",
-        "iterator": "cpp",
-        "memory_resource": "cpp",
-        "format": "cpp",
-        "valarray": "cpp",
-        "regex": "cpp",
-        "span": "cpp",
-        "cfenv": "cpp",
-        "cinttypes": "cpp",
-        "csetjmp": "cpp",
-        "future": "cpp",
-        "queue": "cpp",
-        "typeindex": "cpp",
-        "shared_mutex": "cpp",
-        "*.ipp": "cpp",
-        "cassert": "cpp",
-        "charconv": "cpp",
-        "source_location": "cpp",
-        "ranges": "cpp"
-    },
-    "cmake.configureOnOpen": false,
-    "C_Cpp.default.configurationProvider": "ms-vscode.cmake-tools"
-}

.vscode/tasks.json

@@ -32,6 +32,29 @@
             ],
             "group": "build",
             "detail": "Task generated by Debugger."
+        },
+        {
+            "type": "cppbuild",
+            "label": "C/C++: g++ build active file",
+            "command": "/usr/bin/g++",
+            "args": [
+                "-fdiagnostics-color=always",
+                "-g",
+                "${file}",
+                "-o",
+                "${fileDirname}/${fileBasenameNoExtension}"
+            ],
+            "options": {
+                "cwd": "${fileDirname}"
+            },
+            "problemMatcher": [
+                "$gcc"
+            ],
+            "group": {
+                "kind": "build",
+                "isDefault": true
+            },
+            "detail": "Task generated by Debugger."
         }
     ]
 }

CHANGELOG.md

@@ -0,0 +1,196 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [1.2.1] - 2025-07-19
+
+### Internal
+
+- Update Libtorch to version 2.7.1
+- Update libraries versions:
+  - mdlp: 2.1.1
+  - Folding: 1.1.2
+  - ArffFiles: 1.2.1
+
+## [1.2.0] - 2025-07-08
+
+### Internal
+
+- Add docs generation to CMakeLists.txt.
+- Add new hyperparameters to the Ld classifiers:
+  - *ld_algorithm*: algorithm to use for local discretization, with the following options: "MDLP", "BINQ", "BINU".
+  - *ld_proposed_cuts*: number of cut points to return.
+  - *mdlp_min_length*: minimum length of a partition in MDLP algorithm to be evaluated for partition.
+  - *mdlp_max_depth*: maximum level of recursion in MDLP algorithm.
+  - *max_iterations*: maximum number of iterations of discretization-build model loop.
+  - *verbose_convergence*: display status messages during the convergence process.
+- Remove vcpkg as a dependency manager, now the library is built with Conan package manager and CMake.
+- Add `build_type` option to the sample target in the Makefile to allow building in *Debug* or *Release* mode. Default is *Debug*.
+
+## [1.1.1] - 2025-05-20
+
+### Internal
+
+- Fix CFS metric expression in the FeatureSelection class.
+- Fix the vcpkg configuration in building the library.
+- Fix the sample app to use the vcpkg configuration.
+- Refactor the computeCPT method in the Node class with libtorch vectorized operations.
+- Refactor the sample to use local discretization models.
+
+### Added
+
+- Add predict_proba method to all Ld classifiers.
+- Add L1FS feature selection methods to the FeatureSelection class.
+
+## [1.1.0] - 2025-04-27
+
+### Internal
+
+- Add changes to .clang-format to adjust to vscode format style thanks to <https://clang-format-configurator.site/>
+- Remove all the dependencies as git submodules and add them as vcpkg dependencies.
+- Fix the dependencies versions for this specific BayesNet version.
+
+## [1.0.7] 2025-03-16
+
+### Added
+
+- A new hyperparameter to the BoostAODE class, *alphablock*, to control the way &alpha; is computed, with the last model or with the ensmble built so far. Default value is *false*.
+- A new hyperparameter to the SPODE class, *parent*, to set the root node of the model. If no value is set the root parameter of the constructor is used.
+- A new hyperparameter to the TAN class, *parent*, to set the root node of the model. If not set the first feature is used as root.
+- A new model named XSPODE, an optimized for speed averaged one dependence estimator.
+- A new model named XSP2DE, an optimized for speed averaged two dependence estimator.
+- A new model named XBAODE, an optimized for speed BoostAODE model.
+- A new model named XBA2DE, an optimized for speed BoostA2DE model.
+
+### Internal
+
+- Optimize ComputeCPT method in the Node class.
+- Add methods getCount and getMaxCount to the CountingSemaphore class, returning the current count and the maximum count of threads respectively.
+
+### Changed
+
+- Hyperparameter *maxTolerance* in the BoostAODE class is now in [1, 6] range (it was in [1, 4] range before).
+
+## [1.0.6] 2024-11-23
+
+### Fixed
+
+- Prevent existing edges to be added to the network in the `add_edge` method.
+- Don't allow to add nodes or edges on already fiited networks.
+- Number of threads spawned
+- Network class tests
+
+### Added
+
+- Library logo generated with <https://openart.ai> to README.md
+- Link to the coverage report in the README.md coverage label.
+- *convergence_best* hyperparameter to the BoostAODE class, to control the way the prior accuracy is computed if convergence is set. Default value is *false*.
+- SPnDE model.
+- A2DE model.
+- BoostA2DE model.
+- A2DE & SPnDE tests.
+- Add tests to reach 99% of coverage.
+- Add tests to check the correct version of the mdlp, folding and json libraries.
+- Library documentation generated with Doxygen.
+- Link to documentation in the README.md.
+- Three types of smoothing the Bayesian Network ORIGINAL, LAPLACE and CESTNIK.
+
+### Internal
+
+- Fixed doxygen optional dependency
+- Add env parallel variable to Makefile
+- Add CountingSemaphore class to manage the number of threads spawned.
+- Ignore CUDA language in CMake CodeCoverage module.
+- Update mdlp library as a git submodule.
+- Create library ShuffleArffFile to limit the number of samples with a parameter and shuffle them.
+- Refactor catch2 library location to test/lib
+- Refactor loadDataset function in tests.
+- Remove conditionalEdgeWeights method in BayesMetrics.
+- Refactor Coverage Report generation.
+- Add devcontainer to work on apple silicon.
+- Change build cmake folder names to Debug & Release.
+- Add a Makefile target (doc) to generate the documentation.
+- Add a Makefile target (doc-install) to install the documentation.
+
+### Libraries versions
+
+- mdlp: 2.0.1
+- Folding: 1.1.0
+- json: 3.11
+- ArffFiles: 1.1.0
+
+## [1.0.5] 2024-04-20
+
+### Added
+
+- Install command and instructions in README.md
+- Prefix to install command to install the package in the any location.
+- The 'block_update' hyperparameter to the BoostAODE class, to control the way weights/significances are updated. Default value is false.
+- Html report of coverage in the coverage folder. It is created with *make viewcoverage*
+- Badges of coverage and code quality (codacy) in README.md. Coverage badge is updated with *make viewcoverage*
+- Tests to reach 97% of coverage.
+- Copyright header to source files.
+- Diagrams to README.md: UML class diagram & dependency diagram
+- Action to create diagrams to Makefile: *make diagrams*
+
+### Changed
+
+- Sample app now is a separate target in the Makefile and shows how to use the library with a sample dataset
+- The worse model count in BoostAODE is reset to 0 every time a new model produces better accuracy, so the tolerance of the model is meant to be the number of **consecutive** models that produce worse accuracy.
+- Default hyperparameter values in BoostAODE: bisection is true, maxTolerance is 3, convergence is true
+
+### Removed
+
+- The 'predict_single' hyperparameter from the BoostAODE class.
+- The 'repeatSparent' hyperparameter from the BoostAODE class.
+
+## [1.0.4] 2024-03-06
+
+### Added
+
+- Change *ascending* hyperparameter to *order* with these possible values *{"asc", "desc", "rand"}*, Default is *"desc"*.
+- Add the *predict_single* hyperparameter to control if only the last model created is used to predict in boost training or the whole ensemble (all the models built so far). Default is true.
+- sample app to show how to use the library (make sample)
+
+### Changed
+
+- Change the library structure adding folders for each group of classes (classifiers, ensembles, etc).
+- The significances of the models generated under the feature selection algorithm are now computed after all the models have been generated and an &alpha;<sub>t</sub> value is computed and assigned to each model.
+
+## [1.0.3] 2024-02-25
+
+### Added
+
+- Voting / probability aggregation in Ensemble classes
+- predict_proba method in Classifier
+- predict_proba method in BoostAODE
+- predict_voting parameter in BoostAODE constructor to use voting or probability to predict (default is voting)
+- hyperparameter predict_voting to AODE, AODELd and BoostAODE (Ensemble child classes)
+- tests to check predict & predict_proba coherence
+
+## [1.0.2] - 2024-02-20
+
+### Fixed
+
+- Fix bug in BoostAODE: do not include the model if epsilon sub t is greater than 0.5
+- Fix bug in BoostAODE: compare accuracy with previous accuracy instead of the first of the ensemble if convergence true
+
+## [1.0.1] - 2024-02-12
+
+### Added
+
+- Notes in Classifier class
+- BoostAODE: Add note with used features in initialization with feature selection
+- BoostAODE: Add note with the number of models
+- BoostAODE: Add note with the number of features used to create models if not all features are used
+- Test version number in TestBayesModels
+- Add tests with feature_select and notes on BoostAODE
+
+### Fixed
+
+- Network predict test
+- Network predict_proba test
+- Network score test

CLAUDE.md

@@ -0,0 +1,191 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+BayesNet is a C++ library implementing Bayesian Network Classifiers. It provides various algorithms for machine learning classification including TAN, KDB, SPODE, SPnDE, AODE, A2DE, and their ensemble variants (Boost, XB). The library also includes local discretization variants (Ld) and feature selection algorithms.
+
+## Build System & Dependencies
+
+### Dependency Management
+
+The project supports **two package managers**:
+
+#### vcpkg (Default)
+
+- Uses vcpkg with private registry at <https://github.com/rmontanana/vcpkg-stash>
+- Core dependencies: libtorch, nlohmann-json, folding, fimdlp, arff-files, catch2
+- All dependencies defined in `vcpkg.json` with version overrides
+
+#### Conan (Alternative)
+
+- Modern C++ package manager with better dependency resolution
+- Configured via `conanfile.py` for packaging and distribution
+- Supports subset of dependencies (libtorch, nlohmann-json, catch2)
+- Custom dependencies (folding, fimdlp, arff-files) need custom Conan recipes
+
+### Build Commands
+
+#### Using vcpkg (Default)
+
+```bash
+# Initialize dependencies
+make init
+
+# Build debug version (with tests and coverage)
+make debug
+make buildd
+
+# Build release version  
+make release
+make buildr
+
+# Run tests
+make test
+
+# Generate coverage report
+make coverage
+make viewcoverage
+
+# Clean project
+make clean
+```
+
+#### Using Conan
+
+```bash
+# Install Conan first: pip install conan
+
+# Initialize dependencies
+make conan-init
+
+# Build debug version (with tests and coverage)
+make conan-debug
+make buildd
+
+# Build release version
+make conan-release
+make buildr
+
+# Create and test Conan package
+make conan-create
+
+# Upload to Conan remote
+make conan-upload remote=myremote
+
+# Clean Conan cache and builds
+make conan-clean
+```
+
+### CMake Configuration
+
+- Uses CMake 3.27+ with C++17 standard
+- Debug builds automatically enable testing and coverage
+- Release builds optimize with `-Ofast`
+- **Automatic package manager detection**: CMake detects whether Conan or vcpkg is being used
+- Supports both static library and package manager installation
+- Conditional dependency linking based on availability
+
+## Testing Framework
+
+- **Catch2** testing framework (version 3.8.1)
+- Test executable: `TestBayesNet` in `build_Debug/tests/`
+- Individual test categories can be run: `./TestBayesNet "[CategoryName]"`
+- Coverage reporting with lcov/genhtml
+
+### Test Categories
+
+- A2DE, BoostA2DE, BoostAODE, XSPODE, XSPnDE, XBAODE, XBA2DE
+- Classifier, Ensemble, FeatureSelection, Metrics, Models
+- Network, Node, MST, Modules
+
+## Code Architecture
+
+### Core Structure
+
+```
+bayesnet/
+├── BaseClassifier.h          # Abstract base for all classifiers
+├── classifiers/             # Basic Bayesian classifiers (TAN, KDB, SPODE, etc.)
+├── ensembles/              # Ensemble methods (AODE, A2DE, Boost variants)
+├── feature_selection/      # Feature selection algorithms (CFS, FCBF, IWSS, L1FS)
+├── network/               # Bayesian network structure (Network, Node)
+└── utils/                 # Utilities (metrics, MST, tensor operations)
+```
+
+### Key Design Patterns
+
+- **BaseClassifier** abstract interface for all algorithms
+- Template-based design with both std::vector and torch::Tensor support
+- Network/Node abstraction for Bayesian network representation
+- Feature selection as separate, composable modules
+
+### Data Handling
+
+- Supports both discrete integer data and continuous data with discretization
+- ARFF file format support through arff-files library
+- Tensor operations via PyTorch C++ (libtorch)
+- Local discretization variants use fimdlp library
+
+## Documentation & Tools
+
+- **Doxygen** for API documentation: `make doc`
+- **lcov** for coverage reports: `make coverage`
+- **plantuml + clang-uml** for UML diagrams: `make diagrams`
+- Man pages available in `docs/man3/`
+
+## Sample Applications
+
+Sample code in `sample/` directory demonstrates library usage:
+
+```bash
+make sample fname=tests/data/iris.arff model=TANLd
+```
+
+## Package Distribution
+
+### Creating Conan Packages
+
+```bash
+# Create package locally
+make conan-create
+
+# Test package installation
+cd test_package
+conan create ..
+
+# Upload to remote repository
+make conan-upload remote=myremote profile=myprofile
+```
+
+### Using the Library
+
+With Conan:
+
+```python
+# conanfile.txt or conanfile.py
+[requires]
+bayesnet/1.1.2@user/channel
+
+[generators]
+cmake
+```
+
+With vcpkg:
+
+```json
+{
+  "dependencies": ["bayesnet"]
+}
+```
+
+## Common Development Tasks
+
+- **Add new classifier**: Extend BaseClassifier, implement in appropriate subdirectory
+- **Add new test**: Update `tests/CMakeLists.txt` and create test in `tests/`
+- **Modify build**: Edit main `CMakeLists.txt` or use Makefile targets
+- **Update dependencies**:
+  - vcpkg: Modify `vcpkg.json` and run `make init`
+  - Conan: Modify `conanfile.py` and run `make conan-init`
+- **Package for distribution**: Use `make conan-create` for Conan packaging

CMakeGraphVizOptions.cmake

@@ -0,0 +1,5 @@
+# Set the default graph title
+set(GRAPHVIZ_GRAPH_NAME "BayesNet dependency graph")
+
+set(GRAPHVIZ_SHARED_LIBS OFF)
+set(GRAPHVIZ_STATIC_LIBS ON)

CMakeLists.txt

@@ -1,21 +1,14 @@
-cmake_minimum_required(VERSION 3.20)
+cmake_minimum_required(VERSION 3.27)
 
-project(BayesNet
-  VERSION 0.1.0
+project(bayesnet
+  VERSION 1.2.1
   DESCRIPTION "Bayesian Network and basic classifiers Library."
   HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
   LANGUAGES CXX
 )
 
-if (CODE_COVERAGE AND NOT ENABLE_TESTING)
-  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 ()
+set(CMAKE_CXX_STANDARD 17)
+cmake_policy(SET CMP0135 NEW)
 
 # Global CMake variables
 # ----------------------
@@ -24,57 +17,118 @@ 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_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
+
+
+if (CMAKE_BUILD_TYPE STREQUAL "Debug")
+    MESSAGE("Debug mode")
+else(CMAKE_BUILD_TYPE STREQUAL "Debug")
+    MESSAGE("Release mode")
+endif (CMAKE_BUILD_TYPE STREQUAL "Debug")
 
 # Options
 # -------
-option(ENABLE_CLANG_TIDY "Enable to add clang tidy."              OFF)
-option(ENABLE_TESTING "Unit testing build"                        OFF)
-option(CODE_COVERAGE "Collect coverage from test library"         OFF)
+option(ENABLE_TESTING "Unit testing build"                       OFF)
+    
+find_package(Torch CONFIG REQUIRED)
+if(NOT TARGET torch::torch)
+    add_library(torch::torch INTERFACE IMPORTED GLOBAL)
+    # expose include paths and libraries that the find-module discovered
+    set_target_properties(torch::torch PROPERTIES
+        INTERFACE_INCLUDE_DIRECTORIES "${TORCH_INCLUDE_DIRS}"
+        INTERFACE_LINK_LIBRARIES      "${TORCH_LIBRARIES}")
+endif()
 
-# CMakes modules
-# --------------
-set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules ${CMAKE_MODULE_PATH})
+find_package(fimdlp CONFIG REQUIRED)
+find_package(folding CONFIG REQUIRED)
+find_package(nlohmann_json REQUIRED)
 
-include(AddGitSubmodule)
-if (CODE_COVERAGE)
-    enable_testing()
-    include(CodeCoverage)
-    MESSAGE("Code coverage enabled")
-    set(CMAKE_C_FLAGS " ${CMAKE_C_FLAGS} -fprofile-arcs -ftest-coverage")
-    set(CMAKE_CXX_FLAGS " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage")
-    SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
-endif (CODE_COVERAGE)
-
-if (ENABLE_CLANG_TIDY)
-  include(StaticAnalyzers) # clang-tidy
-endif (ENABLE_CLANG_TIDY)
-
-# External libraries - dependencies of BayesNet
-# ---------------------------------------------
-# include(FetchContent)
-add_git_submodule("lib/mdlp")
-add_git_submodule("lib/argparse")
-add_git_submodule("lib/json")
-
-# Subdirectories
-# --------------
 add_subdirectory(config)
-add_subdirectory(lib/Files)
-add_subdirectory(src/BayesNet)
-add_subdirectory(src/Platform)
-add_subdirectory(sample)
 
-file(GLOB BayesNet_HEADERS CONFIGURE_DEPENDS ${BayesNet_SOURCE_DIR}/src/BayesNet/*.h ${BayesNet_SOURCE_DIR}/BayesNet/*.hpp)
-file(GLOB BayesNet_SOURCES CONFIGURE_DEPENDS ${BayesNet_SOURCE_DIR}/src/BayesNet/*.cc ${BayesNet_SOURCE_DIR}/src/BayesNet/*.cpp)
-file(GLOB Platform_SOURCES CONFIGURE_DEPENDS ${BayesNet_SOURCE_DIR}/src/Platform/*.cc ${BayesNet_SOURCE_DIR}/src/Platform/*.cpp)
+# Add the library
+# ---------------
+include_directories(
+    ${bayesnet_SOURCE_DIR}
+    ${CMAKE_BINARY_DIR}/configured_files/include
+)
+
+file(GLOB_RECURSE Sources "bayesnet/*.cc")
+
+add_library(bayesnet ${Sources})
+
+target_link_libraries(bayesnet
+    nlohmann_json::nlohmann_json
+    folding::folding
+    fimdlp::fimdlp
+    torch::torch
+    arff-files::arff-files
+)
+
+
 
 # Testing
 # -------
-
 if (ENABLE_TESTING)
-  MESSAGE("Testing enabled")
-  add_git_submodule("lib/catch2")
-  
-  include(CTest)
-  add_subdirectory(tests)
+    MESSAGE(STATUS "Testing enabled")
+    set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -fno-elide-constructors")
+    if (NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+        set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fno-default-inline")
+    endif()
+    find_package(Catch2 CONFIG REQUIRED)
+    find_package(arff-files CONFIG REQUIRED)
+    enable_testing()
+    include(CTest)
+    add_subdirectory(tests)
 endif (ENABLE_TESTING)
+
+# Installation
+# ------------
+include(CMakePackageConfigHelpers)
+write_basic_package_version_file(
+    "${CMAKE_CURRENT_BINARY_DIR}/bayesnetConfigVersion.cmake"
+    VERSION ${PROJECT_VERSION}
+    COMPATIBILITY AnyNewerVersion
+)
+
+configure_package_config_file(
+    ${CMAKE_CURRENT_SOURCE_DIR}/bayesnetConfig.cmake.in
+    "${CMAKE_CURRENT_BINARY_DIR}/bayesnetConfig.cmake"
+    INSTALL_DESTINATION share/bayesnet)
+
+install(TARGETS bayesnet
+        EXPORT bayesnetTargets
+        ARCHIVE DESTINATION lib
+        LIBRARY DESTINATION lib)
+
+install(DIRECTORY bayesnet/ 
+    DESTINATION include/bayesnet 
+    FILES_MATCHING 
+    PATTERN "*.h")
+install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/bayesnet/config.h 
+    DESTINATION include/bayesnet)
+
+install(EXPORT bayesnetTargets
+        FILE bayesnetTargets.cmake
+        NAMESPACE bayesnet::
+        DESTINATION share/bayesnet)
+
+install(FILES
+    "${CMAKE_CURRENT_BINARY_DIR}/bayesnetConfig.cmake"
+    "${CMAKE_CURRENT_BINARY_DIR}/bayesnetConfigVersion.cmake"
+    DESTINATION share/bayesnet
+)
+# Documentation
+# -------------
+find_package(Doxygen)
+if (Doxygen_FOUND)
+  set(DOC_DIR ${CMAKE_CURRENT_SOURCE_DIR}/docs)
+  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})
+else (Doxygen_FOUND) 
+  MESSAGE("* Doxygen not found")
+endif (Doxygen_FOUND)

CONAN_README.md

@@ -0,0 +1,86 @@
+# Using BayesNet with Conan
+
+This document explains how to use Conan as an alternative package manager for BayesNet.
+
+## Prerequisites
+
+```bash
+pip install conan
+conan remote add Cimmeria https://conan.rmontanana.es/artifactory/api/conan/Cimmeria
+conan profile new default --detect
+```
+
+## Quick Start
+
+### As a Consumer
+
+1. Create a `conanfile.txt` in your project:
+
+    ```ini
+    [requires]
+    libtorch/2.7.0
+    bayesnet/1.2.0
+
+    [generators]
+    CMakeDeps
+    CMakeToolchain
+    ```
+
+1. Install dependencies:
+
+    ```bash
+    conan install . --build=missing
+    ```
+
+1. In your CMakeLists.txt:
+
+    ```cmake
+    find_package(bayesnet REQUIRED)
+    target_link_libraries(your_target bayesnet::bayesnet)
+    ```
+
+### Building BayesNet with Conan
+
+```bash
+# Install dependencies
+make conan-init
+
+# Build debug version
+make debug
+make buildd
+
+# Build release version
+make release
+make buildr
+
+# Create package
+make conan-create
+```
+
+## Current Limitations
+
+- Custom dependencies (folding, fimdlp, arff-files) are not available in ConanCenter
+- These need to be built as custom Conan packages or replaced with alternatives
+- The conanfile.py currently comments out these dependencies
+
+## Creating Custom Dependency Packages
+
+For the custom dependencies, you'll need to create Conan recipes:
+
+1. **folding**: Cross-validation library
+1. **fimdlp**: Discretization library
+1. **arff-files**: ARFF file format parser
+
+Contact the maintainer or create custom recipes for these packages.
+
+## Package Distribution
+
+Once custom dependencies are resolved:
+
+```bash
+# Create and test package
+make conan-create
+
+# Upload to your remote
+conan upload bayesnet/1.2.0 -r myremote
+```

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) <year> <copyright holders>
+Copyright (c) 2023 Ricardo Montañana Gómez
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 

Makefile

@@ -1,6 +1,52 @@
 SHELL := /bin/bash
 .DEFAULT_GOAL := help
-.PHONY: coverage setup help build test
+.PHONY: viewcoverage coverage setup help install uninstall diagrams buildr buildd test clean updatebadge doc doc-install init clean-test debug release conan-create conan-upload conan-clean sample
+
+f_release = build_Release
+f_debug = build_Debug
+f_diagrams = diagrams
+app_targets = bayesnet
+test_targets = TestBayesNet
+clang-uml = clang-uml
+plantuml = plantuml
+lcov = lcov
+genhtml = genhtml
+dot = dot
+docsrcdir = docs/manual
+mansrcdir = docs/man3
+mandestdir = /usr/local/share/man
+sed_command_link = 's/e">LCOV -/e"><a href="https:\/\/rmontanana.github.io\/bayesnet">Back to manual<\/a> LCOV -/g'
+sed_command_diagram = 's/Diagram"/Diagram" width="100%" height="100%" /g'
+# Set the number of parallel jobs to the number of available processors minus 7
+CPUS := $(shell getconf _NPROCESSORS_ONLN 2>/dev/null \
+                 || nproc --all 2>/dev/null \
+                 || sysctl -n hw.ncpu)
+
+# --- Your desired job count: CPUs – 7, but never less than 1 --------------
+JOBS := $(shell n=$(CPUS); [ $${n} -gt 7 ] && echo $$((n-7)) || echo 1)
+
+
+define ClearTests
+	@for t in $(test_targets); do \
+		if [ -f $(f_debug)/tests/$$t ]; then \
+			echo ">>> Cleaning $$t..." ; \
+			rm -f $(f_debug)/tests/$$t ; \
+		fi ; \
+	done
+	@nfiles="$(find . -name "*.gcda" -print0)" ; \
+	if test "${nfiles}" != "" ; then \
+		find . -name "*.gcda" -print0 | xargs -0 rm 2>/dev/null ;\
+	fi ; 
+endef
+
+define setup_target
+	@echo ">>> Setup the project for $(1)..."
+	@if [ -d $(2) ]; then rm -fr $(2); fi
+	@conan install . --build=missing -of $(2) -s build_type=$(1)
+	@cmake -S . -B $(2) -DCMAKE_TOOLCHAIN_FILE=$(2)/build/$(1)/generators/conan_toolchain.cmake -DCMAKE_BUILD_TYPE=$(1) -D$(3)
+	@echo ">>> Will build using $(JOBS) parallel jobs"
+	@echo ">>> Done"
+endef
 
 setup: ## Install dependencies for tests and coverage
 	@if [ "$(shell uname)" = "Darwin" ]; then \
@@ -9,51 +55,194 @@ setup: ## Install dependencies for tests and coverage
 	fi
 	@if [ "$(shell uname)" = "Linux" ]; then \
 		pip install gcovr; \
+		sudo dnf install lcov;\
 	fi
+	@echo "* You should install plantuml & graphviz for the diagrams"
 
-dependency: ## Create a dependency graph diagram of the project (build/dependency.png)
-	cd build && cmake .. --graphviz=dependency.dot && dot -Tpng dependency.dot -o dependency.png
-
-build: ## Build the main and BayesNetSample
-	cmake --build build -t main -t BayesNetSample -j 32
-
-clean: ## Clean the debug info
-	@echo ">>> Cleaning Debug BayesNet ...";
-	find . -name "*.gcda" -print0 | xargs -0 rm
+clean: ## Clean the project
+	@echo ">>> Cleaning the project..."
+	@if test -f CMakeCache.txt ; then echo "- Deleting CMakeCache.txt"; rm -f CMakeCache.txt; fimake 
+	@for folder in $(f_release) $(f_debug) vpcpkg_installed install_test ; do \
+	if test -d "$$folder" ; then \
+		echo "- Deleting $$folder folder" ; \
+		rm -rf "$$folder"; \
+	fi; \
+	done
+	@$(MAKE) clean-test
 	@echo ">>> Done";
 
-debug: ## Build a debug version of the project
-	@echo ">>> Building Debug BayesNet ...";
-	@if [ -d ./build ]; then rm -rf ./build; fi
-	@mkdir build; 
-	cmake -S . -B build -D CMAKE_BUILD_TYPE=Debug -D ENABLE_TESTING=ON -D CODE_COVERAGE=ON; \
-	cmake --build build -j 32;
+# Build targets
+# =============
+
+debug: ## Setup debug version using Conan
+	@$(call setup_target,"Debug","$(f_debug)","ENABLE_TESTING=ON")
+
+release: ## Setup release version using Conan
+	@$(call setup_target,"Release","$(f_release)","ENABLE_TESTING=OFF")
+
+buildd: ## Build the debug targets
+	cmake --build $(f_debug) --config Debug -t $(app_targets) --parallel $(JOBS)
+
+buildr: ## Build the release targets
+	cmake --build $(f_release) --config Release -t $(app_targets) --parallel $(JOBS)
+
+
+# Install targets
+# ===============
+
+uninstall: ## Uninstall library
+	@echo ">>> Uninstalling BayesNet...";
+	xargs rm < $(f_release)/install_manifest.txt
 	@echo ">>> Done";
 
-release: ## Build a Release version of the project
-	@echo ">>> Building Release BayesNet ...";
-	@if [ -d ./build ]; then rm -rf ./build; fi
-	@mkdir build; 
-	cmake -S . -B build -D CMAKE_BUILD_TYPE=Release; \
-	cmake --build build -t main -t BayesNetSample -j 32;
-	@echo ">>> Done";	
+prefix = "/usr/local"
+install: ## Install library
+	@echo ">>> Installing BayesNet...";
+	@cmake --install $(f_release) --prefix $(prefix)
+	@echo ">>> Done";
 
-test: ## Run tests
-	@echo "* Running tests...";
-	find . -name "*.gcda" -print0 | xargs -0 rm
-	@cd build; \
-	cmake --build . --target unit_tests ;
-	@cd build/tests; \
-	./unit_tests;
+
+# Test targets
+# ============
+
+clean-test: ## Clean the tests info
+	@echo ">>> Cleaning Debug BayesNet tests...";
+	$(call ClearTests)
+	@echo ">>> Done";
+
+opt = ""
+test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section
+	@echo ">>> Running BayesNet tests...";
+	@$(MAKE) clean-test
+	@cmake --build $(f_debug) -t $(test_targets) --parallel $(JOBS)
+	@for t in $(test_targets); do \
+		echo ">>> Running $$t...";\
+		if [ -f $(f_debug)/tests/$$t ]; then \
+			cd $(f_debug)/tests ; \
+			./$$t $(opt) ; \
+			cd ../.. ; \
+		fi ; \
+	done
+	@echo ">>> Done";
 
 coverage: ## Run tests and generate coverage report (build/index.html)
-	@echo "*Building tests...";
-	find . -name "*.gcda" -print0 | xargs -0 rm
-	@cd build; \
-	cmake --build . --target unit_tests ;
-	@cd build/tests; \
-	./unit_tests;
-	gcovr ;
+	@echo ">>> Building tests with coverage..."
+	@which $(lcov) || (echo ">>ease install lcov"; exit 1)
+	@if [ ! -f $(f_debug)/tests/coverage.info ] ; then $(MAKE) test ; fi
+	@echo ">>> Building report..."
+	@cd $(f_debug)/tests; \
+	$(lcov) --directory CMakeFiles --capture --demangle-cpp --ignore-errors source,source --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info '/usr/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'lib/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'include/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'libtorch/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'tests/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'bayesnet/utils/loguru.*' --ignore-errors unused --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info '/opt/miniconda/*' --ignore-errors unused --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info '*/.conan2/*' --ignore-errors unused --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --summary coverage.info
+	@$(MAKE) updatebadge
+	@echo ">>> Done";	
+
+viewcoverage: ## View the html coverage report
+	@which $(genhtml) >/dev/null || (echo ">>> Please install lcov (genhtml not found)"; exit 1)
+	@if [ ! -d $(docsrcdir)/coverage ]; then mkdir -p $(docsrcdir)/coverage; fi
+	@if [ ! -f $(f_debug)/tests/coverage.info ]; then \
+		echo ">>> No coverage.info file found. Run make coverage first!"; \
+		exit 1; \
+	fi
+	@$(genhtml) $(f_debug)/tests/coverage.info --demangle-cpp --output-directory $(docsrcdir)/coverage --title "BayesNet Coverage Report" -s -k -f --legend >/dev/null 2>&1;
+	@xdg-open $(docsrcdir)/coverage/index.html || open $(docsrcdir)/coverage/index.html 2>/dev/null
+	@echo ">>> Done";
+
+updatebadge: ## Update the coverage badge in README.md
+	@which python || (echo ">>> Please install python"; exit 1)
+	@if [ ! -f $(f_debug)/tests/coverage.info ]; then \
+		echo ">>> No coverage.info file found. Run make coverage first!"; \
+		exit 1; \
+	fi
+	@echo ">>> Updating coverage badge..."
+	@env python update_coverage.py $(f_debug)/tests
+	@echo ">>> Done";
+
+# Documentation targets
+# =====================
+
+doc: ## Generate documentation
+	@echo ">>> Generating documentation..."
+	@cmake --build $(f_release) -t doxygen
+	@cp -rp diagrams $(docsrcdir)
+	@
+	@if [ "$(shell uname)" = "Darwin" ]; then \
+		sed -i "" $(sed_command_link) $(docsrcdir)/coverage/index.html ; \
+		sed -i "" $(sed_command_diagram) $(docsrcdir)/index.html ; \
+	else \
+		sed -i $(sed_command_link) $(docsrcdir)/coverage/index.html ; \
+		sed -i $(sed_command_diagram) $(docsrcdir)/index.html ; \
+	fi
+	@echo ">>> Done";
+
+diagrams: ## Create an UML class diagram & dependency of the project (diagrams/BayesNet.png)
+	@echo ">>> Creating diagrams..."
+	@which $(plantuml) || (echo ">>> Please install plantuml"; exit 1)
+	@which $(dot) || (echo ">>> Please install graphviz"; exit 1)
+	@which $(clang-uml) || (echo ">>> Please install clang-uml"; exit 1)
+	@export PLANTUML_LIMIT_SIZE=16384
+	@echo ">>> Creating UML class diagram of the project...";
+	@$(clang-uml) -p 
+	@cd $(f_diagrams); \
+	$(plantuml) -tsvg BayesNet.puml
+	@echo ">>> Creating dependency graph diagram of the project...";
+	$(MAKE) debug
+	cd $(f_debug) && cmake .. --graphviz=dependency.dot 
+	@$(dot) -Tsvg $(f_debug)/dependency.dot.BayesNet -o $(f_diagrams)/dependency.svg
+	@echo ">>> Done";
+
+docdir = ""
+doc-install: ## Install documentation
+	@echo ">>> Installing documentation..."
+	@if [ "$(docdir)" = "" ]; then \
+		echo "docdir parameter has to be set when calling doc-install, i.e. docdir=../bayesnet_help"; \
+		exit 1; \
+	fi
+	@if [ ! -d $(docdir) ]; then \
+		@$(MAKE) doc; \
+	fi
+	@cp -rp $(docsrcdir)/* $(docdir)
+	@sudo cp -rp $(mansrcdir) $(mandestdir)
+	@echo ">>> Done";
+
+# Conan package manager targets
+# =============================
+
+conan-create: ## Create Conan package
+	@echo ">>> Creating Conan package..."
+	@conan create . --build=missing -tf "" -s:a build_type=Release
+	@conan create . --build=missing -tf "" -s:a build_type=Debug -o "&:enable_coverage=False" -o "&:enable_testing=False"
+	@echo ">>> Done"
+
+conan-clean: ## Clean Conan cache and build folders
+	@echo ">>> Cleaning Conan cache and build folders..."
+	@conan remove "*" --confirm
+	@conan cache clean
+	@if test -d "$(f_release)" ; then rm -rf "$(f_release)"; fi
+	@if test -d "$(f_debug)" ; then rm -rf "$(f_debug)"; fi
+	@echo ">>> Done"
+
+fname = "tests/data/iris.arff"
+model = "TANLd"
+build_type = "Debug"
+sample: ## Build sample with Conan
+	@echo ">>> Building Sample with Conan...";
+	@if [ -d ./sample/build ]; then rm -rf ./sample/build; fi
+	@cd sample && conan install . --output-folder=build --build=missing -s build_type=$(build_type) -o "&:enable_coverage=False" -o "&:enable_testing=False"
+	@cd sample && cmake -B build -S . -DCMAKE_BUILD_TYPE=$(build_type) -DCMAKE_TOOLCHAIN_FILE=build/conan_toolchain.cmake && \
+	cmake --build build -t bayesnet_sample --parallel $(JOBS)
+	sample/build/bayesnet_sample $(fname) $(model)
+	@echo ">>> Done";
+
+# Help target
+# ===========
 
 help: ## Show help message
 	@IFS=$$'\n' ; \

README.md

@@ -1,5 +1,170 @@
-# BayesNet
+# <img src="logo.png" alt="logo" width="50"/>  BayesNet
 
-Bayesian Network Classifier with libtorch from scratch
+![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)
+[![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)
+[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Doctorado-ML/BayesNet)
+![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)
 
-## 1. Introduction
+Bayesian Network Classifiers library
+
+## Using the Library
+
+### Using Conan Package Manager
+
+You can use the library with the [Conan](https://conan.io/) package manager. In your project you need to add the following files:
+
+#### conanfile.txt
+
+```txt
+[requires]
+bayesnet/1.1.2
+
+[generators]
+CMakeDeps
+CMakeToolchain
+```
+
+#### CMakeLists.txt
+
+Include the following lines in your `CMakeLists.txt` file:
+
+```cmake
+find_package(bayesnet REQUIRED)
+
+add_executable(myapp main.cpp)
+
+target_link_libraries(myapp PRIVATE bayesnet::bayesnet)
+```
+
+Then install the dependencies and build your project:
+
+```bash
+conan install . --output-folder=build --build=missing
+cmake -B build -S . -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=build/conan_toolchain.cmake
+cmake --build build
+```
+
+**Note: In the `sample` folder you can find a sample application that uses the library. You can use it as a reference to create your own application.**
+
+## Building and Testing
+
+The project uses [Conan](https://conan.io/) for dependency management and provides convenient Makefile targets for common tasks.
+
+### Prerequisites
+
+- [Conan](https://conan.io/) package manager (`pip install conan`)
+- CMake 3.27+
+- C++17 compatible compiler
+
+### Getting the code
+
+```bash
+git clone https://github.com/doctorado-ml/bayesnet
+cd bayesnet
+```
+
+### Build Commands
+
+#### Release Build
+
+```bash
+make release        # Configure release build with Conan
+make buildr         # Build the release version
+```
+
+#### Debug Build & Tests
+
+```bash
+make debug          # Configure debug build with Conan
+make buildd         # Build the debug version
+make test           # Run the tests
+```
+
+#### Coverage Analysis
+
+```bash
+make coverage       # Run tests with coverage analysis
+make viewcoverage   # View coverage report in browser
+```
+
+#### Sample Application
+
+Run the sample application with different datasets and models:
+
+```bash
+make sample                                    # Run with default settings
+make sample fname=tests/data/glass.arff       # Use glass dataset
+make sample fname=tests/data/iris.arff model=AODE  # Use specific model
+```
+
+### Available Makefile Targets
+
+- `debug` - Configure debug build using Conan
+- `release` - Configure release build using Conan  
+- `buildd` - Build debug targets
+- `buildr` - Build release targets
+- `test` - Run all tests (use `opt="-s"` for verbose output)
+- `coverage` - Generate test coverage report
+- `viewcoverage` - Open coverage report in browser
+- `sample` - Build and run sample application
+- `conan-create` - Create Conan package
+- `conan-upload` - Upload package to Conan remote
+- `conan-clean` - Clean Conan cache and build folders
+- `clean` - Clean all build artifacts
+- `doc` - Generate documentation
+- `diagrams` - Generate UML diagrams
+- `help` - Show all available targets
+
+## Models
+
+#### - TAN
+
+#### - KDB
+
+#### - SPODE
+
+#### - SPnDE
+
+#### - AODE
+
+#### - A2DE
+
+#### - [BoostAODE](docs/BoostAODE.md)
+
+#### - XBAODE
+
+#### - BoostA2DE
+
+#### - XBA2DE
+
+### With Local Discretization
+
+#### - TANLd
+
+#### - KDBLd
+
+#### - SPODELd
+
+#### - AODELd
+
+## Documentation
+
+### [Manual](https://rmontanana.github.io/bayesnet/)
+
+### [Coverage report](https://rmontanana.github.io/bayesnet/coverage/index.html)
+
+## Diagrams
+
+### UML Class Diagram
+
+![BayesNet UML Class Diagram](diagrams/BayesNet.svg)
+
+### Dependency Diagram
+
+![BayesNet Dependency Diagram](diagrams/dependency.svg)

REVISION_TECNICA_BAYESNET.md

@@ -0,0 +1,518 @@
+# Revisión Técnica de BayesNet - Informe Completo
+
+## Resumen Ejecutivo
+
+Como desarrollador experto en C++, he realizado una revisión técnica exhaustiva de la biblioteca BayesNet, evaluando su arquitectura, calidad de código, rendimiento y mantenibilidad. A continuación presento un análisis detallado con recomendaciones priorizadas para mejorar la biblioteca.
+
+## 1. Fortalezas Identificadas
+
+### 1.1 Arquitectura y Diseño
+- **✅ Diseño orientado a objetos bien estructurado** con jerarquía clara de clases
+- **✅ Uso adecuado de smart pointers** (std::unique_ptr) en la mayoría del código
+- **✅ Abstracción coherente** a través de BaseClassifier
+- **✅ Separación clara de responsabilidades** entre módulos
+- **✅ Documentación API con Doxygen** completa y actualizada
+
+### 1.2 Gestión de Dependencias y Build
+- **✅ Sistema vcpkg** bien configurado para gestión de dependencias
+- **✅ CMake moderno** (3.27+) con configuración robusta
+- **✅ Separación Debug/Release** con optimizaciones apropiadas
+- **✅ Sistema de testing integrado** con Catch2
+
+### 1.3 Testing y Cobertura
+- **✅ 17 archivos de test** cubriendo los componentes principales
+- **✅ Tests parametrizados** con múltiples datasets
+- **✅ Integración con lcov** para reportes de cobertura
+- **✅ Tests automáticos** en el proceso de build
+
+## 2. Debilidades y Problemas Críticos
+
+### 2.1 Problemas de Gestión de Memoria
+
+#### **🔴 CRÍTICO: Memory Leak Potencial**
+**Archivo:** `/bayesnet/ensembles/Boost.cc` (líneas 124-141)
+```cpp
+// PROBLEMA: Raw pointer sin RAII
+FeatureSelect* featureSelector = nullptr;
+if (select_features_algorithm == SelectFeatures.CFS) {
+    featureSelector = new CFS(...);  // ❌ Riesgo de leak
+}
+// ...
+delete featureSelector; // ❌ Puede fallar por excepción
+```
+
+**Impacto:** Memory leak si se lanza excepción entre `new` y `delete`
+**Prioridad:** ALTA
+
+### 2.2 Problemas de Performance
+
+#### **🔴 CRÍTICO: Complejidad O(n³)**
+**Archivo:** `/bayesnet/utils/BayesMetrics.cc` (líneas 41-53)
+```cpp
+for (int i = 0; i < n - 1; ++i) {
+    if (std::find(featuresExcluded.begin(), featuresExcluded.end(), i) != featuresExcluded.end()) {
+        continue; // ❌ O(n) en bucle anidado
+    }
+    for (int j = i + 1; j < n; ++j) {
+        if (std::find(featuresExcluded.begin(), featuresExcluded.end(), j) != featuresExcluded.end()) {
+            continue; // ❌ O(n) en bucle anidado  
+        }
+        // Más operaciones costosas...
+    }
+}
+```
+
+**Impacto:** Con 100 features = 1,250,000 operaciones de búsqueda
+**Prioridad:** ALTA
+
+#### **🔴 CRÍTICO: Threading Ineficiente**
+**Archivo:** `/bayesnet/network/Network.cc` (líneas 269-273)
+```cpp
+for (int i = 0; i < samples.size(1); ++i) {
+    threads.emplace_back(worker, sample, i); // ❌ Thread per sample
+}
+```
+
+**Impacto:** Con 10,000 muestras = 10,000 threads (context switching excesivo)
+**Prioridad:** ALTA
+
+### 2.3 Problemas de Calidad de Código
+
+#### **🟡 MODERADO: Funciones Excesivamente Largas**
+- `XSP2DE.cc`: 575 líneas (violación de SRP)
+- `Boost::setHyperparameters()`: 150+ líneas 
+- `L1FS::fitLasso()`: 200+ líneas de complejidad algoritmica alta
+
+#### **🟡 MODERADO: Validación Insuficiente**
+```cpp
+// En múltiples archivos: falta validación de entrada
+if (features.empty()) {
+    // Sin manejo de caso edge
+}
+```
+
+### 2.4 Problemas de Algoritmos
+
+#### **🟡 MODERADO: Union-Find Subóptimo**
+**Archivo:** `/bayesnet/utils/Mst.cc`
+```cpp
+// ❌ Sin compresión de caminos ni unión por rango
+int find_set(int i) {
+    if (i != parent[i])
+        i = find_set(parent[i]); // Ineficiente O(n)
+    return i;
+}
+```
+
+**Impacto:** Algoritmo MST subóptimo O(V²) en lugar de O(E log V)
+
+## 3. Plan de Mejoras Priorizadas
+
+### 3.1 Fase 1: Problemas Críticos (Semanas 1-2)
+
+#### **Tarea 1.1: Eliminar Memory Leak en Boost.cc**
+```cpp
+// ANTES (línea 51 en Boost.h):
+FeatureSelect* featureSelector = nullptr;
+
+// DESPUÉS:
+std::unique_ptr<FeatureSelect> featureSelector;
+
+// ANTES (líneas 124-141 en Boost.cc):
+if (select_features_algorithm == SelectFeatures.CFS) {
+    featureSelector = new CFS(...);
+}
+// ...
+delete featureSelector;
+
+// DESPUÉS:
+if (select_features_algorithm == SelectFeatures.CFS) {
+    featureSelector = std::make_unique<CFS>(...);
+}
+// ... automática limpieza del smart pointer
+```
+
+**Estimación:** 2 horas
+**Prioridad:** CRÍTICA
+
+#### **Tarea 1.2: Optimizar BayesMetrics::SelectKPairs()**
+```cpp
+// SOLUCIÓN PROPUESTA:
+std::vector<std::pair<int, int>> Metrics::SelectKPairs(
+    const torch::Tensor& weights, 
+    std::vector<int>& featuresExcluded, 
+    bool ascending, unsigned k) {
+    
+    // ✅ O(1) lookups en lugar de O(n)
+    std::unordered_set<int> excludedSet(featuresExcluded.begin(), featuresExcluded.end());
+    
+    auto n = features.size();
+    scoresKPairs.clear();
+    scoresKPairs.reserve((n * (n-1)) / 2); // ✅ Reserve memoria
+    
+    for (int i = 0; i < n - 1; ++i) {
+        if (excludedSet.count(i)) continue; // ✅ O(1)
+        for (int j = i + 1; j < n; ++j) {
+            if (excludedSet.count(j)) continue; // ✅ O(1)
+            // resto del procesamiento...
+        }
+    }
+    
+    // ✅ nth_element en lugar de sort completo
+    if (k > 0 && k < scoresKPairs.size()) {
+        std::nth_element(scoresKPairs.begin(), 
+                        scoresKPairs.begin() + k, 
+                        scoresKPairs.end());
+        scoresKPairs.resize(k);
+    }
+    return pairsKBest;
+}
+```
+
+**Beneficio:** 50x mejora de performance (de O(n³) a O(n² log k))
+**Estimación:** 4 horas
+**Prioridad:** CRÍTICA
+
+#### **Tarea 1.3: Implementar Thread Pool**
+```cpp
+// SOLUCIÓN PROPUESTA para Network.cc:
+void Network::predict_tensor_optimized(const torch::Tensor& samples, const bool proba) {
+    const int num_threads = std::min(
+        static_cast<int>(std::thread::hardware_concurrency()), 
+        static_cast<int>(samples.size(1))
+    );
+    const int batch_size = (samples.size(1) + num_threads - 1) / num_threads;
+    
+    std::vector<std::thread> threads;
+    threads.reserve(num_threads);
+    
+    for (int t = 0; t < num_threads; ++t) {
+        int start = t * batch_size;
+        int end = std::min(start + batch_size, static_cast<int>(samples.size(1)));
+        
+        threads.emplace_back([this, &samples, &result, start, end]() {
+            for (int i = start; i < end; ++i) {
+                const auto sample = samples.index({ "...", i });
+                auto prediction = predict_sample(sample);
+                // Thread-safe escritura
+                std::lock_guard<std::mutex> lock(result_mutex);
+                result.index_put_({ i, "..." }, torch::tensor(prediction));
+            }
+        });
+    }
+    
+    for (auto& thread : threads) {
+        thread.join();
+    }
+}
+```
+
+**Beneficio:** 4-8x mejora en predicción con múltiples cores
+**Estimación:** 6 horas
+**Prioridad:** CRÍTICA
+
+### 3.2 Fase 2: Optimizaciones Importantes (Semanas 3-4)
+
+#### **Tarea 2.1: Refactoring de Funciones Largas**
+
+**XSP2DE.cc** - Dividir en funciones más pequeñas:
+```cpp
+// ANTES: Una función de 575 líneas
+void XSP2DE::buildModel(const torch::Tensor& weights) {
+    // ... 575 líneas de código
+}
+
+// DESPUÉS: Funciones especializadas
+class XSP2DE {
+private:
+    void initializeHyperparameters();
+    void selectFeatures(const torch::Tensor& weights);
+    void buildSubModels();
+    void trainIndividualModels(const torch::Tensor& weights);
+    
+public:
+    void buildModel(const torch::Tensor& weights) override {
+        initializeHyperparameters();
+        selectFeatures(weights);
+        buildSubModels();
+        trainIndividualModels(weights);
+    }
+};
+```
+
+**Estimación:** 8 horas
+**Beneficio:** Mejora mantenibilidad y testing
+
+#### **Tarea 2.2: Optimizar Union-Find en MST**
+```cpp
+// SOLUCIÓN PROPUESTA para Mst.cc:
+class UnionFind {
+private:
+    std::vector<int> parent, rank;
+    
+public:
+    UnionFind(int n) : parent(n), rank(n, 0) {
+        std::iota(parent.begin(), parent.end(), 0);
+    }
+    
+    int find_set(int i) {
+        if (i != parent[i])
+            parent[i] = find_set(parent[i]); // ✅ Path compression
+        return parent[i];
+    }
+    
+    bool union_set(int u, int v) {
+        u = find_set(u); 
+        v = find_set(v);
+        if (u == v) return false;
+        
+        // ✅ Union by rank
+        if (rank[u] < rank[v]) std::swap(u, v);
+        parent[v] = u;
+        if (rank[u] == rank[v]) rank[u]++;
+        return true;
+    }
+};
+```
+
+**Beneficio:** Mejora de O(V²) a O(E log V)
+**Estimación:** 4 horas
+
+#### **Tarea 2.3: Eliminar Copias Innecesarias de Tensores**
+```cpp
+// ANTES (múltiples archivos):
+X = X.to(torch::kFloat32);  // ❌ Copia completa
+y = y.to(torch::kFloat32);  // ❌ Copia completa
+
+// DESPUÉS:
+torch::Tensor X = samples.index({Slice(0, n_features), Slice()})
+                        .t()
+                        .to(torch::kFloat32); // ✅ Una sola conversión
+
+torch::Tensor y = samples.index({-1, Slice()})
+                        .to(torch::kFloat32); // ✅ Una sola conversión
+```
+
+**Beneficio:** ~30% menos uso de memoria
+**Estimación:** 6 horas
+
+### 3.3 Fase 3: Mejoras de Robustez (Semanas 5-6)
+
+#### **Tarea 3.1: Implementar Validación Comprehensiva**
+```cpp
+// TEMPLATE PARA VALIDACIÓN:
+template<typename T>
+void validateInput(const std::vector<T>& data, const std::string& name) {
+    if (data.empty()) {
+        throw std::invalid_argument(name + " cannot be empty");
+    }
+}
+
+void validateTensorDimensions(const torch::Tensor& tensor, 
+                             const std::vector<int64_t>& expected_dims) {
+    if (tensor.sizes() != expected_dims) {
+        throw std::invalid_argument("Tensor dimensions mismatch");
+    }
+}
+```
+
+#### **Tarea 3.2: Implementar Jerarquía de Excepciones**
+```cpp
+// PROPUESTA DE JERARQUÍA:
+namespace bayesnet {
+    class BayesNetException : public std::exception {
+    public:
+        explicit BayesNetException(const std::string& msg) : message(msg) {}
+        const char* what() const noexcept override { return message.c_str(); }
+    private:
+        std::string message;
+    };
+    
+    class InvalidInputException : public BayesNetException {
+    public:
+        explicit InvalidInputException(const std::string& msg) 
+            : BayesNetException("Invalid input: " + msg) {}
+    };
+    
+    class ModelNotFittedException : public BayesNetException {
+    public:
+        ModelNotFittedException() 
+            : BayesNetException("Model has not been fitted") {}
+    };
+    
+    class DimensionMismatchException : public BayesNetException {
+    public:
+        explicit DimensionMismatchException(const std::string& msg) 
+            : BayesNetException("Dimension mismatch: " + msg) {}
+    };
+}
+```
+
+#### **Tarea 3.3: Mejorar Cobertura de Tests**
+```cpp
+// TESTS ADICIONALES NECESARIOS:
+TEST_CASE("Edge Cases", "[FeatureSelection]") {
+    SECTION("Empty dataset") {
+        torch::Tensor empty_dataset = torch::empty({0, 0});
+        std::vector<std::string> empty_features;
+        
+        REQUIRE_THROWS_AS(
+            CFS(empty_dataset, empty_features, "class", 0, 2, torch::ones({1})),
+            InvalidInputException
+        );
+    }
+    
+    SECTION("Single feature") {
+        // Test comportamiento con un solo feature
+    }
+    
+    SECTION("All features excluded") {
+        // Test cuando todas las features están excluidas
+    }
+}
+```
+
+### 3.4 Fase 4: Mejoras de Performance Avanzadas (Semanas 7-8)
+
+#### **Tarea 4.1: Paralelización con OpenMP**
+```cpp
+// EXAMPLE PARA BUCLES CRÍTICOS:
+#include <omp.h>
+
+void computeIntensiveOperation(const torch::Tensor& data) {
+    const int n = data.size(0);
+    std::vector<double> results(n);
+    
+    #pragma omp parallel for
+    for (int i = 0; i < n; ++i) {
+        results[i] = expensiveComputation(data[i]);
+    }
+}
+```
+
+#### **Tarea 4.2: Memory Pool para Operaciones Frecuentes**
+```cpp
+// PROPUESTA DE MEMORY POOL:
+class TensorPool {
+private:
+    std::stack<torch::Tensor> available_tensors;
+    std::mutex pool_mutex;
+    
+public:
+    torch::Tensor acquire(const std::vector<int64_t>& shape) {
+        std::lock_guard<std::mutex> lock(pool_mutex);
+        if (!available_tensors.empty()) {
+            auto tensor = available_tensors.top();
+            available_tensors.pop();
+            return tensor.resize_(shape);
+        }
+        return torch::zeros(shape);
+    }
+    
+    void release(torch::Tensor tensor) {
+        std::lock_guard<std::mutex> lock(pool_mutex);
+        available_tensors.push(tensor);
+    }
+};
+```
+
+## 4. Estimaciones y Timeline
+
+### 4.1 Resumen de Esfuerzo
+| Fase | Tareas | Estimación | Beneficio |
+|------|--------|------------|-----------|
+| Fase 1 | Problemas Críticos | 12 horas | 10-50x mejora performance |
+| Fase 2 | Optimizaciones | 18 horas | Mantenibilidad + 30% menos memoria |
+| Fase 3 | Robustez | 16 horas | Estabilidad y debugging |
+| Fase 4 | Performance Avanzada | 12 horas | Escalabilidad |
+| **Total** | | **58 horas** | **Transformación significativa** |
+
+### 4.2 Timeline Sugerido
+```
+Semana 1: [CRÍTICO] Memory leak + BayesMetrics
+Semana 2: [CRÍTICO] Thread pool + validación básica  
+Semana 3: [IMPORTANTE] Refactoring XSP2DE + MST
+Semana 4: [IMPORTANTE] Optimización tensores + duplicación
+Semana 5: [ROBUSTEZ] Validación + excepciones
+Semana 6: [ROBUSTEZ] Tests adicionales + edge cases
+Semana 7: [AVANZADO] Paralelización OpenMP
+Semana 8: [AVANZADO] Memory pool + optimizaciones finales
+```
+
+## 5. Impacto Esperado
+
+### 5.1 Performance
+- **50x más rápido** en operaciones de feature selection
+- **4-8x más rápido** en predicción con datasets grandes
+- **30% menos uso de memoria** eliminando copias innecesarias
+- **Escalabilidad mejorada** con paralelización
+
+### 5.2 Mantenibilidad
+- **Funciones más pequeñas** y especializadas
+- **Mejor separación de responsabilidades**
+- **Testing más comprehensivo**
+- **Debugging más fácil** con excepciones específicas
+
+### 5.3 Robustez
+- **Eliminación de memory leaks**
+- **Validación comprehensiva de entrada**
+- **Manejo robusto de casos edge**
+- **Mejor reportes de error**
+
+## 6. Recomendaciones Adicionales
+
+### 6.1 Herramientas de Desarrollo
+- **Análisis estático:** Implementar clang-static-analyzer y cppcheck
+- **Sanitizers:** Usar AddressSanitizer y ThreadSanitizer en CI
+- **Profiling:** Integrar valgrind y perf para análisis de performance
+- **Benchmarking:** Implementar Google Benchmark para tests de regression
+
+### 6.2 Proceso de Desarrollo
+- **Code reviews obligatorios** para cambios críticos
+- **CI/CD con tests automáticos** en múltiples plataformas
+- **Métricas de calidad** integradas (cobertura, complejidad ciclomática)
+- **Documentación de algoritmos** con complejidad y referencias
+
+### 6.3 Monitoreo de Performance
+```cpp
+// PROPUESTA DE PROFILING INTEGRADO:
+class PerformanceProfiler {
+private:
+    std::unordered_map<std::string, std::chrono::duration<double>> timings;
+    
+public:
+    class ScopedTimer {
+        // RAII timer para medir automáticamente
+    };
+    
+    void startProfiling(const std::string& operation);
+    void endProfiling(const std::string& operation);
+    void generateReport();
+};
+```
+
+## 7. Conclusiones
+
+BayesNet es una biblioteca sólida con una arquitectura bien diseñada y uso apropiado de técnicas modernas de C++. Sin embargo, existen oportunidades significativas de mejora que pueden transformar dramáticamente su performance y mantenibilidad.
+
+### Prioridades Inmediatas:
+1. **Eliminar memory leak crítico** en Boost.cc
+2. **Optimizar algoritmo O(n³)** en BayesMetrics.cc  
+3. **Implementar thread pool eficiente** en Network.cc
+
+### Beneficios del Plan de Mejoras:
+- **Performance:** 10-50x mejora en operaciones críticas
+- **Memoria:** 30% reducción en uso de memoria
+- **Mantenibilidad:** Código más modular y testing comprehensivo
+- **Robustez:** Eliminación de crashes y mejor handling de errores
+
+La implementación de estas mejoras convertirá BayesNet en una biblioteca de clase industrial, ready para production en entornos de alto rendimiento y misión crítica.
+
+---
+
+**Próximos Pasos Recomendados:**
+1. Revisar y aprobar este plan de mejoras
+2. Establecer prioridades basadas en necesidades del proyecto
+3. Implementar mejoras en el orden sugerido
+4. Establecer métricas de success para cada fase
+5. Configurar CI/CD para validar mejoras automáticamente

TAN_iris.dot

@@ -1,12 +0,0 @@
-digraph BayesNet {
-label=<BayesNet >
-fontsize=30
-fontcolor=blue
-labelloc=t
-layout=circo
- class [shape=circle, fontcolor=red, fillcolor=lightblue, style=filled ] 
- class -> sepallength class -> sepalwidth class -> petallength class -> petalwidth petallength [shape=circle] 
- petallength -> sepallength petalwidth [shape=circle] 
- sepallength [shape=circle] 
- sepallength -> sepalwidth sepalwidth [shape=circle] 
- sepalwidth -> petalwidth }

bayesnet/BaseClassifier.h

@@ -0,0 +1,49 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#pragma once
+#include <vector>
+#include <torch/torch.h>
+#include <nlohmann/json.hpp>
+#include "bayesnet/network/Network.h"
+
+namespace bayesnet {
+    enum status_t { NORMAL, WARNING, ERROR };
+    class BaseClassifier {
+    public:
+        virtual ~BaseClassifier() = default;
+        // X is nxm std::vector, y is nx1 std::vector
+        virtual BaseClassifier& 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 Smoothing_t smoothing) = 0;
+        // X is nxm tensor, y is nx1 tensor
+        virtual BaseClassifier& 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) = 0;
+        virtual BaseClassifier& 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) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
+        torch::Tensor virtual predict(torch::Tensor& X) = 0;
+        std::vector<int> virtual predict(std::vector<std::vector<int >>& X) = 0;
+        torch::Tensor virtual predict_proba(torch::Tensor& X) = 0;
+        std::vector<std::vector<double>> virtual predict_proba(std::vector<std::vector<int >>& X) = 0;
+        status_t virtual getStatus() const = 0;
+        float virtual score(std::vector<std::vector<int>>& X, std::vector<int>& y) = 0;
+        float virtual score(torch::Tensor& X, torch::Tensor& y) = 0;
+        int virtual getNumberOfNodes() const = 0;
+        int virtual getNumberOfEdges() const = 0;
+        int virtual getNumberOfStates() const = 0;
+        int virtual getClassNumStates() const = 0;
+        std::vector<std::string> virtual show() const = 0;
+        std::vector<std::string> virtual graph(const std::string& title = "") const = 0;
+        virtual std::string getVersion() = 0;
+        std::vector<std::string> virtual topological_order() = 0;
+        std::vector<std::string> virtual getNotes() const = 0;
+        std::string virtual dump_cpt() const = 0;
+        virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
+        std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
+    protected:
+        virtual void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
+        std::vector<std::string> validHyperparameters;
+        std::vector<std::string> notes; // Used to store messages occurred during the fit process
+        status_t status = NORMAL;
+    };
+}

bayesnet/classifiers/Classifier.cc

@@ -0,0 +1,193 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <sstream>
+#include "bayesnet/utils/bayesnetUtils.h"
+#include "Classifier.h"
+
+namespace bayesnet {
+    Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
+    Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
+    {
+        this->features = features;
+        this->className = className;
+        this->states = states;
+        m = dataset.size(1);
+        n = features.size();
+        checkFitParameters();
+        auto n_classes = states.at(className).size();
+        metrics = Metrics(dataset, features, className, n_classes);
+        model.initialize();
+        buildModel(weights);
+        trainModel(weights, smoothing);
+        fitted = true;
+        return *this;
+    }
+    void Classifier::buildDataset(torch::Tensor& ytmp)
+    {
+        try {
+            auto yresized = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);
+            dataset = torch::cat({ dataset, yresized }, 0);
+        }
+        catch (const std::exception& e) {
+            std::stringstream oss;
+            oss << "* Error in X and y dimensions *\n";
+            oss << "X dimensions: " << dataset.sizes() << "\n";
+            oss << "y dimensions: " << ytmp.sizes();
+            throw std::runtime_error(oss.str());
+        }
+    }
+    void Classifier::trainModel(const torch::Tensor& weights, Smoothing_t smoothing)
+    {
+        model.fit(dataset, weights, features, className, states, smoothing);
+    }
+    // X is nxm where n is the number of features and m the number of samples
+    Classifier& Classifier::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)
+    {
+        dataset = X;
+        buildDataset(y);
+        const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
+        return build(features, className, states, weights, smoothing);
+    }
+    // X is nxm where n is the number of features and m the number of samples
+    Classifier& Classifier::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 Smoothing_t smoothing)
+    {
+        dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kInt32);
+        for (int i = 0; i < X.size(); ++i) {
+            dataset.index_put_({ i, "..." }, torch::tensor(X[i], torch::kInt32));
+        }
+        auto ytmp = torch::tensor(y, torch::kInt32);
+        buildDataset(ytmp);
+        const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
+        return build(features, className, states, weights, smoothing);
+    }
+    Classifier& Classifier::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)
+    {
+        this->dataset = dataset;
+        const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
+        return build(features, className, states, weights, smoothing);
+    }
+    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
+    {
+        this->dataset = dataset;
+        return build(features, className, states, weights, smoothing);
+    }
+    void Classifier::checkFitParameters()
+    {
+        if (torch::is_floating_point(dataset)) {
+            throw std::invalid_argument("dataset (X, y) must be of type Integer");
+        }
+        if (dataset.size(0) - 1 != features.size()) {
+            throw std::invalid_argument("Classifier: X " + std::to_string(dataset.size(0) - 1) + " and features " + std::to_string(features.size()) + " must have the same number of features");
+        }
+        if (states.find(className) == states.end()) {
+            throw std::invalid_argument("class name not found in states");
+        }
+        for (auto feature : features) {
+            if (states.find(feature) == states.end()) {
+                throw std::invalid_argument("feature [" + feature + "] not found in states");
+            }
+        }
+    }
+    torch::Tensor Classifier::predict(torch::Tensor& X)
+    {
+        if (!fitted) {
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
+        }
+        return model.predict(X);
+    }
+    std::vector<int> Classifier::predict(std::vector<std::vector<int>>& X)
+    {
+        if (!fitted) {
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
+        }
+        auto m_ = X[0].size();
+        auto n_ = X.size();
+        std::vector<std::vector<int>> Xd(n_, std::vector<int>(m_, 0));
+        for (auto i = 0; i < n_; i++) {
+            Xd[i] = std::vector<int>(X[i].begin(), X[i].end());
+        }
+        auto yp = model.predict(Xd);
+        return yp;
+    }
+    torch::Tensor Classifier::predict_proba(torch::Tensor& X)
+    {
+        if (!fitted) {
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
+        }
+        return model.predict_proba(X);
+    }
+    std::vector<std::vector<double>> Classifier::predict_proba(std::vector<std::vector<int>>& X)
+    {
+        if (!fitted) {
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
+        }
+        auto m_ = X[0].size();
+        auto n_ = X.size();
+        std::vector<std::vector<int>> Xd(n_, std::vector<int>(m_, 0));
+        // Convert to nxm vector
+        for (auto i = 0; i < n_; i++) {
+            Xd[i] = std::vector<int>(X[i].begin(), X[i].end());
+        }
+        auto yp = model.predict_proba(Xd);
+        return yp;
+    }
+    float Classifier::score(torch::Tensor& X, torch::Tensor& y)
+    {
+        torch::Tensor y_pred = predict(X);
+        return (y_pred == y).sum().item<float>() / y.size(0);
+    }
+    float Classifier::score(std::vector<std::vector<int>>& X, std::vector<int>& y)
+    {
+        if (!fitted) {
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
+        }
+        return model.score(X, y);
+    }
+    std::vector<std::string> Classifier::show() const
+    {
+        return model.show();
+    }
+    void Classifier::addNodes()
+    {
+        // Add all nodes to the network
+        for (const auto& feature : features) {
+            model.addNode(feature);
+        }
+        model.addNode(className);
+    }
+    int Classifier::getNumberOfNodes() const
+    {
+        // Features does not include class
+        return fitted ? model.getFeatures().size() : 0;
+    }
+    int Classifier::getNumberOfEdges() const
+    {
+        return fitted ? model.getNumEdges() : 0;
+    }
+    int Classifier::getNumberOfStates() const
+    {
+        return fitted ? model.getStates() : 0;
+    }
+    int Classifier::getClassNumStates() const
+    {
+        return fitted ? model.getClassNumStates() : 0;
+    }
+    std::vector<std::string> Classifier::topological_order()
+    {
+        return model.topological_sort();
+    }
+    std::string Classifier::dump_cpt() const
+    {
+        return model.dump_cpt();
+    }
+    void Classifier::setHyperparameters(const nlohmann::json& hyperparameters)
+    {
+        if (!hyperparameters.empty()) {
+            throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
+        }
+    }
+}

bayesnet/classifiers/Classifier.h

@@ -0,0 +1,64 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef CLASSIFIER_H
+#define CLASSIFIER_H
+#include <torch/torch.h>
+#include "bayesnet/utils/BayesMetrics.h"
+#include "bayesnet/BaseClassifier.h"
+
+namespace bayesnet {
+    class Classifier : public BaseClassifier {
+    public:
+        Classifier(Network model);
+        virtual ~Classifier() = default;
+        Classifier& 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 Smoothing_t smoothing) override;
+        Classifier& 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) override;
+        Classifier& 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) override;
+        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing) override;
+        void addNodes();
+        int getNumberOfNodes() const override;
+        int getNumberOfEdges() const override;
+        int getNumberOfStates() const override;
+        int getClassNumStates() const override;
+        torch::Tensor predict(torch::Tensor& X) override;
+        std::vector<int> predict(std::vector<std::vector<int>>& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override;
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
+        status_t getStatus() const override { return status; }
+        std::string getVersion() override { return { project_version.begin(), project_version.end() }; };
+        float score(torch::Tensor& X, torch::Tensor& y) override;
+        float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
+        std::vector<std::string> show() const override;
+        std::vector<std::string> topological_order()  override;
+        std::vector<std::string> getNotes() const override { return notes; }
+        std::string dump_cpt() const override;
+        void setHyperparameters(const nlohmann::json& hyperparameters) override; //For classifiers that don't have hyperparameters
+        Network& getModel() { return model; }
+    protected:
+        bool fitted;
+        unsigned int m, n; // m: number of samples, n: number of features
+        Network model;
+        Metrics metrics;
+        std::vector<std::string> features;
+        std::string className;
+        std::map<std::string, std::vector<int>> states;
+        torch::Tensor dataset; // (n+1)xm tensor
+        void checkFitParameters();
+        virtual void buildModel(const torch::Tensor& weights) = 0;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
+        void buildDataset(torch::Tensor& y);
+        const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
+    private:
+        Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
+    };
+}
+#endif
+
+
+
+
+

bayesnet/classifiers/KDB.cc

@@ -1,10 +1,31 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#include "bayesnet/utils/bayesnetUtils.h"
 #include "KDB.h"
 
 namespace bayesnet {
-    using namespace torch;
+    KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta)
+    {
+        validHyperparameters = { "k", "theta" };
 
-    KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta) {}
-    void KDB::buildModel()
+    }
+    void KDB::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("k")) {
+            k = hyperparameters["k"];
+            hyperparameters.erase("k");
+        }
+        if (hyperparameters.contains("theta")) {
+            theta = hyperparameters["theta"];
+            hyperparameters.erase("theta");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
+    void KDB::buildModel(const torch::Tensor& weights)
     {
         /*
         1. For each feature Xi, compute mutual information, I(X;C),
@@ -28,16 +49,16 @@ namespace bayesnet {
         // 1. For each feature Xi, compute mutual information, I(X;C),
         // where C is the class.
         addNodes();
-        const Tensor& y = dataset.index({ -1, "..." });
-        vector <float> mi;
+        const torch::Tensor& y = dataset.index({ -1, "..." });
+        std::vector<double> mi;
         for (auto i = 0; i < features.size(); i++) {
-            Tensor firstFeature = dataset.index({ i, "..." });
-            mi.push_back(metrics.mutualInformation(firstFeature, y));
+            torch::Tensor firstFeature = dataset.index({ i, "..." });
+            mi.push_back(metrics.mutualInformation(firstFeature, y, weights));
         }
         // 2. Compute class conditional mutual information I(Xi;XjIC), f or each
-        auto conditionalEdgeWeights = metrics.conditionalEdge();
+        auto conditionalEdgeWeights = metrics.conditionalEdge(weights);
         // 3. Let the used variable list, S, be empty.
-        vector<int> S;
+        std::vector<int> S;
         // 4. Let the DAG network being constructed, BN, begin with a single
         // class node, C.
         // 5. Repeat until S includes all domain features
@@ -55,9 +76,9 @@ namespace bayesnet {
             S.push_back(idx);
         }
     }
-    void KDB::add_m_edges(int idx, vector<int>& S, Tensor& weights)
+    void KDB::add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights)
     {
-        auto n_edges = min(k, static_cast<int>(S.size()));
+        auto n_edges = std::min(k, static_cast<int>(S.size()));
         auto cond_w = clone(weights);
         bool exit_cond = k == 0;
         int num = 0;
@@ -69,7 +90,7 @@ namespace bayesnet {
                     model.addEdge(features[max_minfo], features[idx]);
                     num++;
                 }
-                catch (const invalid_argument& e) {
+                catch (const std::invalid_argument& e) {
                     // Loops are not allowed
                 }
             }
@@ -79,11 +100,11 @@ namespace bayesnet {
             exit_cond = num == n_edges || candidates.size(0) == 0;
         }
     }
-    vector<string> KDB::graph(const string& title) const
+    std::vector<std::string> KDB::graph(const std::string& title) const
     {
-        string header{ title };
+        std::string header{ title };
         if (title == "KDB") {
-            header += " (k=" + to_string(k) + ", theta=" + to_string(theta) + ")";
+            header += " (k=" + std::to_string(k) + ", theta=" + std::to_string(theta) + ")";
         }
         return model.graph(header);
     }

bayesnet/classifiers/KDB.h

@@ -0,0 +1,25 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef KDB_H
+#define KDB_H
+#include <torch/torch.h>
+#include "Classifier.h"
+namespace bayesnet {
+    class KDB : public Classifier {
+    public:
+        explicit KDB(int k, float theta = 0.03);
+        virtual ~KDB() = default;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
+        std::vector<std::string> graph(const std::string& name = "KDB") const override;
+    protected:
+        int k;
+        float theta;
+        void add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights);
+        void buildModel(const torch::Tensor& weights) override;
+    };
+}
+#endif

bayesnet/classifiers/KDBLd.cc

@@ -0,0 +1,56 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "KDBLd.h"
+#include <memory>
+
+namespace bayesnet {
+    KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className, KDB::notes)
+    {
+        validHyperparameters = validHyperparameters_ld;
+        validHyperparameters.push_back("k");
+        validHyperparameters.push_back("theta");
+    }
+    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
+    {
+        checkInput(X_, y_);
+        Xf = X_;
+        y = y_;
+        return commonFit(features_, className_, states_, smoothing);
+    }
+    KDBLd& KDBLd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
+    {
+        if (!torch::is_floating_point(dataset)) {
+            throw std::runtime_error("Dataset must be a floating point tensor");
+        }
+        Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
+        y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);
+        return commonFit(features_, className_, states_, smoothing);
+    }
+
+    KDBLd& KDBLd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
+    {
+        features = features_;
+        className = className_;
+        states = iterativeLocalDiscretization(y, static_cast<KDB*>(this), dataset, features, className, states_, smoothing);
+        KDB::fit(dataset, features, className, states, smoothing);
+        return *this;
+    }
+    torch::Tensor KDBLd::predict(torch::Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return KDB::predict(Xt);
+    }
+    torch::Tensor KDBLd::predict_proba(torch::Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return KDB::predict_proba(Xt);
+    }
+    std::vector<std::string> KDBLd::graph(const std::string& name) const
+    {
+        return KDB::graph(name);
+    }
+}

bayesnet/classifiers/KDBLd.h

@@ -0,0 +1,32 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef KDBLD_H
+#define KDBLD_H
+#include "Proposal.h"
+#include "KDB.h"
+
+namespace bayesnet {
+    class KDBLd : public KDB, public Proposal {
+    public:
+        explicit KDBLd(int k);
+        virtual ~KDBLd() = default;
+        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
+        KDBLd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
+        KDBLd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
+        std::vector<std::string> graph(const std::string& name = "KDB") const override;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override
+        {
+            auto hyperparameters = hyperparameters_;
+            Proposal::setHyperparameters(hyperparameters);
+            KDB::setHyperparameters(hyperparameters);
+        }
+        torch::Tensor predict(torch::Tensor& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override;
+        static inline std::string version() { return "0.0.1"; };
+    };
+}
+#endif // !KDBLD_H

bayesnet/classifiers/Proposal.cc

@@ -0,0 +1,241 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "Proposal.h"
+#include <iostream>
+#include <cmath>
+#include <limits>
+#include "Classifier.h"
+#include "KDB.h"
+#include "TAN.h"
+#include "SPODE.h"
+#include "KDBLd.h"
+#include "TANLd.h"
+
+namespace bayesnet {
+    Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_, std::vector<std::string>& notes_) : pDataset(dataset_), pFeatures(features_), pClassName(className_), notes(notes_)
+    {
+    }
+    void Proposal::setHyperparameters(nlohmann::json& hyperparameters)
+    {
+        if (hyperparameters.contains("ld_proposed_cuts")) {
+            ld_params.proposed_cuts = hyperparameters["ld_proposed_cuts"];
+            hyperparameters.erase("ld_proposed_cuts");
+        }
+        if (hyperparameters.contains("mdlp_max_depth")) {
+            ld_params.max_depth = hyperparameters["mdlp_max_depth"];
+            hyperparameters.erase("mdlp_max_depth");
+        }
+        if (hyperparameters.contains("mdlp_min_length")) {
+            ld_params.min_length = hyperparameters["mdlp_min_length"];
+            hyperparameters.erase("mdlp_min_length");
+        }
+        if (hyperparameters.contains("ld_algorithm")) {
+            auto algorithm = hyperparameters["ld_algorithm"];
+            hyperparameters.erase("ld_algorithm");
+            if (algorithm == "MDLP") {
+                discretizationType = discretization_t::MDLP;
+            } else if (algorithm == "BINQ") {
+                discretizationType = discretization_t::BINQ;
+            } else if (algorithm == "BINU") {
+                discretizationType = discretization_t::BINU;
+            } else {
+                throw std::invalid_argument("Invalid discretization algorithm: " + algorithm.get<std::string>());
+            }
+        }
+        // Convergence parameters
+        if (hyperparameters.contains("max_iterations")) {
+            convergence_params.maxIterations = hyperparameters["max_iterations"];
+            hyperparameters.erase("max_iterations");
+        }
+        if (hyperparameters.contains("verbose_convergence")) {
+            convergence_params.verbose = hyperparameters["verbose_convergence"];
+            hyperparameters.erase("verbose_convergence");
+        }
+    }
+
+    void Proposal::checkInput(const torch::Tensor& X, const torch::Tensor& y)
+    {
+        if (!torch::is_floating_point(X)) {
+            throw std::invalid_argument("X must be a floating point tensor");
+        }
+        if (torch::is_floating_point(y)) {
+            throw std::invalid_argument("y must be an integer tensor");
+        }
+    }
+    // Fit method for single classifier
+    map<std::string, std::vector<int>> Proposal::localDiscretizationProposal(const map<std::string, std::vector<int>>& oldStates, Network& model)
+    {
+        // order of local discretization is important. no good 0, 1, 2...
+        // although we rediscretize features after the local discretization of every feature
+        auto order = model.topological_sort();
+        auto& nodes = model.getNodes();
+        map<std::string, std::vector<int>> states = oldStates;
+        std::vector<int> indicesToReDiscretize;
+        bool upgrade = false; // Flag to check if we need to upgrade the model
+        for (auto feature : order) {
+            auto nodeParents = nodes[feature]->getParents();
+            if (nodeParents.size() < 2) continue; // Only has class as parent
+            upgrade = true;
+            int index = find(pFeatures.begin(), pFeatures.end(), feature) - pFeatures.begin();
+            indicesToReDiscretize.push_back(index); // We need to re-discretize this feature
+            std::vector<std::string> parents;
+            transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](const auto& p) { return p->getName(); });
+            // Remove class as parent as it will be added later
+            parents.erase(remove(parents.begin(), parents.end(), pClassName), parents.end());
+            // Get the indices of the parents
+            std::vector<int> indices;
+            indices.push_back(-1); // Add class index
+            transform(parents.begin(), parents.end(), back_inserter(indices), [&](const auto& p) {return find(pFeatures.begin(), pFeatures.end(), p) - pFeatures.begin(); });
+            // Now we fit the discretizer of the feature, conditioned on its parents and the class i.e. discretizer.fit(X[index], X[indices] + y)
+            std::vector<std::string> yJoinParents(Xf.size(1));
+            for (auto idx : indices) {
+                for (int i = 0; i < Xf.size(1); ++i) {
+                    yJoinParents[i] += to_string(pDataset.index({ idx, i }).item<int>());
+                }
+            }
+            auto yxv = factorize(yJoinParents);
+            auto xvf_ptr = Xf.index({ index }).data_ptr<float>();
+            auto xvf = std::vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));
+            discretizers[feature]->fit(xvf, yxv);
+        }
+        if (upgrade) {
+            // Discretize again X (only the affected indices) with the new fitted discretizers
+            for (auto index : indicesToReDiscretize) {
+                auto Xt_ptr = Xf.index({ index }).data_ptr<float>();
+                auto Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
+                pDataset.index_put_({ index, "..." }, torch::tensor(discretizers[pFeatures[index]]->transform(Xt)));
+                auto xStates = std::vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
+                iota(xStates.begin(), xStates.end(), 0);
+                //Update new states of the feature/node
+                states[pFeatures[index]] = xStates;
+            }
+            const torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);
+            model.fit(pDataset, weights, pFeatures, pClassName, states, Smoothing_t::ORIGINAL);
+        }
+        return states;
+    }
+    map<std::string, std::vector<int>> Proposal::fit_local_discretization(const torch::Tensor& y)
+    {
+        // Discretize the continuous input data and build pDataset (Classifier::dataset)
+        int m = Xf.size(1);
+        int n = Xf.size(0);
+        map<std::string, std::vector<int>> states;
+        pDataset = torch::zeros({ n + 1, m }, torch::kInt32);
+        auto yv = std::vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
+        // discretize input data by feature(row)
+        std::unique_ptr<mdlp::Discretizer> discretizer;
+        for (auto i = 0; i < pFeatures.size(); ++i) {
+            if (discretizationType == discretization_t::BINQ) {
+                discretizer = std::make_unique<mdlp::BinDisc>(ld_params.proposed_cuts, mdlp::strategy_t::QUANTILE);
+            } else if (discretizationType == discretization_t::BINU) {
+                discretizer = std::make_unique<mdlp::BinDisc>(ld_params.proposed_cuts, mdlp::strategy_t::UNIFORM);
+            } else { // Default is MDLP
+                discretizer = std::make_unique<mdlp::CPPFImdlp>(ld_params.min_length, ld_params.max_depth, ld_params.proposed_cuts);
+            }
+            auto Xt_ptr = Xf.index({ i }).data_ptr<float>();
+            auto Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
+            discretizer->fit(Xt, yv);
+            pDataset.index_put_({ i, "..." }, torch::tensor(discretizer->transform(Xt)));
+            auto xStates = std::vector<int>(discretizer->getCutPoints().size() + 1);
+            iota(xStates.begin(), xStates.end(), 0);
+            states[pFeatures[i]] = xStates;
+            discretizers[pFeatures[i]] = std::move(discretizer);
+        }
+        int n_classes = torch::max(y).item<int>() + 1;
+        auto yStates = std::vector<int>(n_classes);
+        iota(yStates.begin(), yStates.end(), 0);
+        states[pClassName] = yStates;
+        pDataset.index_put_({ n, "..." }, y);
+        return states;
+    }
+    torch::Tensor Proposal::prepareX(torch::Tensor& X)
+    {
+        auto Xtd = torch::zeros_like(X, torch::kInt32);
+        for (int i = 0; i < X.size(0); ++i) {
+            auto Xt = std::vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));
+            auto Xd = discretizers[pFeatures[i]]->transform(Xt);
+            Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));
+        }
+        return Xtd;
+    }
+    std::vector<int> Proposal::factorize(const std::vector<std::string>& labels_t)
+    {
+        std::vector<int> yy;
+        yy.reserve(labels_t.size());
+        std::map<std::string, int> labelMap;
+        int i = 0;
+        for (const std::string& label : labels_t) {
+            if (labelMap.find(label) == labelMap.end()) {
+                labelMap[label] = i++;
+                bool allDigits = std::all_of(label.begin(), label.end(), ::isdigit);
+            }
+            yy.push_back(labelMap[label]);
+        }
+        return yy;
+    }
+
+    template<typename Classifier>
+    map<std::string, std::vector<int>> Proposal::iterativeLocalDiscretization(
+        const torch::Tensor& y,
+        Classifier* classifier,
+        torch::Tensor& dataset,
+        const std::vector<std::string>& features,
+        const std::string& className,
+        const map<std::string, std::vector<int>>& initialStates,
+        Smoothing_t smoothing
+    )
+    {
+        // Phase 1: Initial discretization (same as original)
+        auto currentStates = fit_local_discretization(y);
+        auto previousModel = Network();
+
+        if (convergence_params.verbose) {
+            std::cout << "Starting iterative local discretization with "
+                << convergence_params.maxIterations << " max iterations" << std::endl;
+        }
+
+        const torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);
+        for (int iteration = 0; iteration < convergence_params.maxIterations; ++iteration) {
+            if (convergence_params.verbose) {
+                std::cout << "Iteration " << (iteration + 1) << "/" << convergence_params.maxIterations << std::endl;
+            }
+
+            // Phase 2: Build model with current discretization
+            classifier->fit(dataset, features, className, currentStates, weights, smoothing);
+
+            // Phase 3: Network-aware discretization refinement
+            currentStates = localDiscretizationProposal(currentStates, classifier->getModel());
+
+            // Check convergence
+            if (iteration > 0 && previousModel == classifier->getModel()) {
+                if (convergence_params.verbose) {
+                    std::cout << "Converged after " << (iteration + 1) << " iterations" << std::endl;
+                }
+                notes.push_back("Converged after " + std::to_string(iteration + 1) + " of "
+                    + std::to_string(convergence_params.maxIterations) + " iterations");
+                break;
+            }
+
+            // Update for next iteration
+            previousModel = classifier->getModel();
+        }
+
+        return currentStates;
+    }
+
+    // Explicit template instantiation for common classifier types
+    template map<std::string, std::vector<int>> Proposal::iterativeLocalDiscretization<KDB>(
+        const torch::Tensor&, KDB*, torch::Tensor&, const std::vector<std::string>&,
+        const std::string&, const map<std::string, std::vector<int>>&, Smoothing_t);
+
+    template map<std::string, std::vector<int>> Proposal::iterativeLocalDiscretization<TAN>(
+        const torch::Tensor&, TAN*, torch::Tensor&, const std::vector<std::string>&,
+        const std::string&, const map<std::string, std::vector<int>>&, Smoothing_t);
+    template map<std::string, std::vector<int>> Proposal::iterativeLocalDiscretization<SPODE>(
+        const torch::Tensor&, SPODE*, torch::Tensor&, const std::vector<std::string>&,
+        const std::string&, const map<std::string, std::vector<int>>&, Smoothing_t);
+}

bayesnet/classifiers/Proposal.h

@@ -0,0 +1,76 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef PROPOSAL_H
+#define PROPOSAL_H
+#include <string>
+#include <map>
+#include <torch/torch.h>
+#include <fimdlp/CPPFImdlp.h>
+#include <fimdlp/BinDisc.h>
+#include "bayesnet/network/Network.h"
+#include <nlohmann/json.hpp>
+#include "Classifier.h"
+
+namespace bayesnet {
+    class Proposal {
+    public:
+        Proposal(torch::Tensor& pDataset, std::vector<std::string>& features_, std::string& className_, std::vector<std::string>& notes);
+        void setHyperparameters(nlohmann::json& hyperparameters_);
+    protected:
+        void checkInput(const torch::Tensor& X, const torch::Tensor& y);
+        torch::Tensor prepareX(torch::Tensor& X);
+        map<std::string, std::vector<int>> localDiscretizationProposal(const map<std::string, std::vector<int>>& states, Network& model);
+        map<std::string, std::vector<int>> fit_local_discretization(const torch::Tensor& y);
+
+        // Iterative discretization method
+        template<typename Classifier>
+        map<std::string, std::vector<int>> iterativeLocalDiscretization(
+            const torch::Tensor& y,
+            Classifier* classifier,
+            torch::Tensor& dataset,
+            const std::vector<std::string>& features,
+            const std::string& className,
+            const map<std::string, std::vector<int>>& initialStates,
+            const Smoothing_t smoothing
+        );
+
+        torch::Tensor Xf; // X continuous nxm tensor
+        torch::Tensor y; // y discrete nx1 tensor
+        map<std::string, std::unique_ptr<mdlp::Discretizer>> discretizers;
+
+        // MDLP parameters
+        struct {
+            size_t min_length = 3; // Minimum length of the interval to consider it in mdlp
+            float proposed_cuts = 0.0; // Proposed cuts for the Discretization algorithm
+            int max_depth = std::numeric_limits<int>::max(); // Maximum depth of the MDLP tree
+        } ld_params;
+
+        // Convergence parameters
+        struct {
+            int maxIterations = 10;
+            bool verbose = false;
+        } convergence_params;
+
+        nlohmann::json validHyperparameters_ld = {
+            "ld_algorithm", "ld_proposed_cuts", "mdlp_min_length", "mdlp_max_depth",
+            "max_iterations", "verbose_convergence"
+        };
+    private:
+        std::vector<int> factorize(const std::vector<std::string>& labels_t);
+        std::vector<std::string>& notes; // Notes during fit from BaseClassifier
+        torch::Tensor& pDataset; // (n+1)xm tensor
+        std::vector<std::string>& pFeatures;
+        std::string& pClassName;
+        enum class discretization_t {
+            MDLP,
+            BINQ,
+            BINU
+        } discretizationType = discretization_t::MDLP; // Default discretization type
+    };
+}
+
+#endif  

bayesnet/classifiers/SPODE.cc

@@ -0,0 +1,46 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "SPODE.h"
+
+namespace bayesnet {
+
+    SPODE::SPODE(int root) : Classifier(Network()), root(root)
+    {
+        validHyperparameters = { "parent" };
+    }
+
+    void SPODE::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("parent")) {
+            root = hyperparameters["parent"];
+            hyperparameters.erase("parent");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
+    void SPODE::buildModel(const torch::Tensor& weights)
+    {
+        // 0. Add all nodes to the model
+        addNodes();
+        // 1. Add edges from the class node to all other nodes
+        // 2. Add edges from the root node to all other nodes
+        if (root >= static_cast<int>(features.size())) {
+            throw std::invalid_argument("The parent node is not in the dataset");
+        }
+        for (int i = 0; i < static_cast<int>(features.size()); ++i) {
+            model.addEdge(className, features[i]);
+            if (i != root) {
+                model.addEdge(features[root], features[i]);
+            }
+        }
+    }
+    std::vector<std::string> SPODE::graph(const std::string& name) const
+    {
+        return model.graph(name);
+    }
+
+}

bayesnet/classifiers/SPODE.h

@@ -0,0 +1,24 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef SPODE_H
+#define SPODE_H
+#include "Classifier.h"
+
+namespace bayesnet {
+    class SPODE : public Classifier {
+    public:
+        explicit SPODE(int root);
+        virtual ~SPODE() = default;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
+        std::vector<std::string> graph(const std::string& name = "SPODE") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    private:
+        int root;
+    };
+}
+#endif

bayesnet/classifiers/SPODELd.cc

@@ -0,0 +1,55 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "SPODELd.h"
+
+namespace bayesnet {
+    SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className, SPODE::notes)
+    {
+        validHyperparameters = validHyperparameters_ld; // Inherits the valid hyperparameters from Proposal
+    }
+
+    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
+    {
+        checkInput(X_, y_);
+        Xf = X_;
+        y = y_;
+        return commonFit(features_, className_, states_, smoothing);
+    }
+
+    SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
+    {
+        if (!torch::is_floating_point(dataset)) {
+            throw std::runtime_error("Dataset must be a floating point tensor");
+        }
+        Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
+        y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);
+        return commonFit(features_, className_, states_, smoothing);
+    }
+
+    SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
+    {
+        features = features_;
+        className = className_;
+        states = iterativeLocalDiscretization(y, static_cast<SPODE*>(this), dataset, features, className, states_, smoothing);
+        SPODE::fit(dataset, features, className, states, smoothing);
+        return *this;
+    }
+    torch::Tensor SPODELd::predict(torch::Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return SPODE::predict(Xt);
+    }
+    torch::Tensor SPODELd::predict_proba(torch::Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return SPODE::predict_proba(Xt);
+    }
+    std::vector<std::string> SPODELd::graph(const std::string& name) const
+    {
+        return SPODE::graph(name);
+    }
+}

bayesnet/classifiers/SPODELd.h

@@ -0,0 +1,32 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef SPODELD_H
+#define SPODELD_H
+#include "SPODE.h"
+#include "Proposal.h"
+
+namespace bayesnet {
+    class SPODELd : public SPODE, public Proposal {
+    public:
+        explicit SPODELd(int root);
+        virtual ~SPODELd() = default;
+        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
+        SPODELd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
+        SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
+        std::vector<std::string> graph(const std::string& name = "SPODELd") const override;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override
+        {
+            auto hyperparameters = hyperparameters_;
+            Proposal::setHyperparameters(hyperparameters);
+            SPODE::setHyperparameters(hyperparameters);
+        }
+        torch::Tensor predict(torch::Tensor& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override;
+        static inline std::string version() { return "0.0.1"; };
+    };
+}
+#endif // !SPODELD_H

bayesnet/classifiers/SPnDE.cc

@@ -0,0 +1,38 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "SPnDE.h"
+
+namespace bayesnet {
+
+    SPnDE::SPnDE(std::vector<int> parents) : Classifier(Network()), parents(parents) {}
+
+    void SPnDE::buildModel(const torch::Tensor& weights)
+    {
+        // 0. Add all nodes to the model
+        addNodes();
+        std::vector<int> attributes;
+        for (int i = 0; i < static_cast<int>(features.size()); ++i) {
+            if (std::find(parents.begin(), parents.end(), i) == parents.end()) {
+                attributes.push_back(i);
+            }
+        }
+        // 1. Add edges from the class node to all other nodes
+        // 2. Add edges from the parents nodes to all other nodes
+        for (const auto& attribute : attributes) {
+            model.addEdge(className, features[attribute]);
+            for (const auto& root : parents) {
+
+                model.addEdge(features[root], features[attribute]);
+            }
+        }
+    }
+    std::vector<std::string> SPnDE::graph(const std::string& name) const
+    {
+        return model.graph(name);
+    }
+
+}

bayesnet/classifiers/SPnDE.h

@@ -0,0 +1,26 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef SPnDE_H
+#define SPnDE_H
+#include <vector>
+#include "Classifier.h"
+
+namespace bayesnet {
+    class SPnDE : public Classifier {
+    public:
+        explicit SPnDE(std::vector<int> parents);
+        virtual ~SPnDE() = default;
+        std::vector<std::string> graph(const std::string& name = "SPnDE") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    private:
+        std::vector<int> parents;
+
+
+    };
+}
+#endif

bayesnet/classifiers/TAN.cc

@@ -0,0 +1,60 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "TAN.h"
+
+namespace bayesnet {
+    TAN::TAN() : Classifier(Network())
+    {
+        validHyperparameters = { "parent" };
+    }
+
+    void TAN::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("parent")) {
+            parent = hyperparameters["parent"];
+            hyperparameters.erase("parent");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
+    void TAN::buildModel(const torch::Tensor& weights)
+    {
+        // 0. Add all nodes to the model
+        addNodes();
+        // 1. Compute mutual information between each feature and the class and set the root node
+        // as the highest mutual information with the class
+        auto mi = std::vector <std::pair<int, float >>();
+        torch::Tensor class_dataset = dataset.index({ -1, "..." });
+        for (int i = 0; i < static_cast<int>(features.size()); ++i) {
+            torch::Tensor feature_dataset = dataset.index({ i, "..." });
+            auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset, weights);
+            mi.push_back({ i, mi_value });
+        }
+        sort(mi.begin(), mi.end(), [](const auto& left, const auto& right) {return left.second < right.second;});
+        auto root = parent == -1 ? mi[mi.size() - 1].first : parent;
+        if (root >= static_cast<int>(features.size())) {
+            throw std::invalid_argument("The parent node is not in the dataset");
+        }
+        // 2. Compute mutual information between each feature and the class
+        auto weights_matrix = metrics.conditionalEdge(weights);
+        // 3. Compute the maximum spanning tree
+        auto mst = metrics.maximumSpanningTree(features, weights_matrix, root);
+        // 4. Add edges from the maximum spanning tree to the model
+        for (auto i = 0; i < mst.size(); ++i) {
+            auto [from, to] = mst[i];
+            model.addEdge(features[from], features[to]);
+        }
+        // 5. Add edges from the class to all features
+        for (auto feature : features) {
+            model.addEdge(className, feature);
+        }
+    }
+    std::vector<std::string> TAN::graph(const std::string& title) const
+    {
+        return model.graph(title);
+    }
+}

bayesnet/classifiers/TAN.h

@@ -0,0 +1,23 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef TAN_H
+#define TAN_H
+#include "Classifier.h"
+namespace bayesnet {
+    class TAN : public Classifier {
+    public:
+        TAN();
+        virtual ~TAN() = default;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
+        std::vector<std::string> graph(const std::string& name = "TAN") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    private:
+        int parent = -1;
+    };
+}
+#endif

bayesnet/classifiers/TANLd.cc

@@ -0,0 +1,54 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "TANLd.h"
+#include <memory>
+
+namespace bayesnet {
+    TANLd::TANLd() : TAN(), Proposal(dataset, features, className, TAN::notes)
+    {
+        validHyperparameters = validHyperparameters_ld; // Inherits the valid hyperparameters from Proposal
+    }
+    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
+    {
+        checkInput(X_, y_);
+        Xf = X_;
+        y = y_;
+        return commonFit(features_, className_, states_, smoothing);
+    }
+    TANLd& TANLd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
+    {
+        if (!torch::is_floating_point(dataset)) {
+            throw std::runtime_error("Dataset must be a floating point tensor");
+        }
+        Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
+        y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);
+        return commonFit(features_, className_, states_, smoothing);
+    }
+
+    TANLd& TANLd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
+    {
+        features = features_;
+        className = className_;
+        states = iterativeLocalDiscretization(y, static_cast<TAN*>(this), dataset, features, className, states_, smoothing);
+        TAN::fit(dataset, features, className, states, smoothing);
+        return *this;
+    }
+    torch::Tensor TANLd::predict(torch::Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return TAN::predict(Xt);
+    }
+    torch::Tensor TANLd::predict_proba(torch::Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return TAN::predict_proba(Xt);
+    }
+    std::vector<std::string> TANLd::graph(const std::string& name) const
+    {
+        return TAN::graph(name);
+    }
+}

bayesnet/classifiers/TANLd.h

@@ -0,0 +1,32 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef TANLD_H
+#define TANLD_H
+#include "TAN.h"
+#include "Proposal.h"
+
+namespace bayesnet {
+    class TANLd : public TAN, public Proposal {
+    private:
+    public:
+        TANLd();
+        virtual ~TANLd() = default;
+        TANLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
+        TANLd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
+        TANLd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
+        std::vector<std::string> graph(const std::string& name = "TANLd") const override;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override
+        {
+            auto hyperparameters = hyperparameters_;
+            Proposal::setHyperparameters(hyperparameters);
+            TAN::setHyperparameters(hyperparameters);
+        }
+        torch::Tensor predict(torch::Tensor& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override;
+    };
+}
+#endif // !TANLD_H

bayesnet/classifiers/XSP2DE.cc

@@ -0,0 +1,575 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "XSP2DE.h"
+#include <pthread.h>   // for pthread_setname_np on linux
+#include <cassert>
+#include <cmath>
+#include <limits>
+#include <stdexcept>
+#include <iostream>
+#include "bayesnet/utils/TensorUtils.h"
+
+namespace bayesnet {
+
+// --------------------------------------
+// Constructor
+// --------------------------------------
+XSp2de::XSp2de(int spIndex1, int spIndex2)
+  : superParent1_{ spIndex1 }
+  , superParent2_{ spIndex2 }
+  , nFeatures_{0}
+  , statesClass_{0}
+  , alpha_{1.0}
+  , initializer_{1.0}
+  , semaphore_{ CountingSemaphore::getInstance() }
+  , Classifier(Network())
+{
+  validHyperparameters = { "parent1", "parent2" };
+}
+
+// --------------------------------------
+// setHyperparameters
+// --------------------------------------
+void XSp2de::setHyperparameters(const nlohmann::json &hyperparameters_)
+{
+  auto hyperparameters = hyperparameters_;
+  if (hyperparameters.contains("parent1")) {
+    superParent1_ = hyperparameters["parent1"];
+    hyperparameters.erase("parent1");
+  }
+  if (hyperparameters.contains("parent2")) {
+    superParent2_ = hyperparameters["parent2"];
+    hyperparameters.erase("parent2");
+  }
+  // Hand off anything else to base Classifier
+  Classifier::setHyperparameters(hyperparameters);
+}
+
+// --------------------------------------
+// fitx
+// --------------------------------------
+void XSp2de::fitx(torch::Tensor & X, torch::Tensor & y, 
+                  torch::Tensor & weights_, const Smoothing_t smoothing)
+{
+  m = X.size(1);  // number of samples
+  n = X.size(0);  // number of features
+  dataset = X;
+
+  // Build the dataset in your environment if needed:
+  buildDataset(y);
+
+  // Construct the data structures needed for counting
+  buildModel(weights_);
+
+  // Accumulate counts & convert to probabilities
+  trainModel(weights_, smoothing);
+  fitted = true;
+}
+
+// --------------------------------------
+// buildModel
+// --------------------------------------
+void XSp2de::buildModel(const torch::Tensor &weights)
+{
+  nFeatures_ = n;
+
+  // Derive the number of states for each feature from the dataset
+  // states_[f] = max value in dataset[f] + 1.
+  states_.resize(nFeatures_);
+  for (int f = 0; f < nFeatures_; f++) {
+    // This is naive: we take max in feature f. You might adapt for real data.
+    states_[f] = dataset[f].max().item<int>() + 1;
+  }
+  // Class states:
+  statesClass_ = dataset[-1].max().item<int>() + 1;
+
+  // Initialize the class counts
+  classCounts_.resize(statesClass_, 0.0);
+
+  // For sp1 -> p(sp1Val| c)
+  sp1FeatureCounts_.resize(states_[superParent1_] * statesClass_, 0.0);
+
+  // For sp2 -> p(sp2Val| c)
+  sp2FeatureCounts_.resize(states_[superParent2_] * statesClass_, 0.0);
+
+  // For child features, we store p(childVal | c, sp1Val, sp2Val).
+  // childCounts_ will hold raw counts. We’ll gather them in one big vector.
+  // We need an offset for each feature.
+  childOffsets_.resize(nFeatures_, -1);
+
+  int totalSize = 0;
+  for (int f = 0; f < nFeatures_; f++) {
+    if (f == superParent1_ || f == superParent2_) {
+      // skip the superparents
+      childOffsets_[f] = -1;
+      continue;
+    }
+    childOffsets_[f] = totalSize;
+    // block size for a single child f: states_[f] * statesClass_ 
+    //                               * states_[superParent1_] 
+    //                               * states_[superParent2_].
+    totalSize += (states_[f] * statesClass_ 
+                  * states_[superParent1_] 
+                  * states_[superParent2_]);
+  }
+  childCounts_.resize(totalSize, 0.0);
+}
+
+// --------------------------------------
+// trainModel
+// --------------------------------------
+void XSp2de::trainModel(const torch::Tensor &weights, 
+                        const bayesnet::Smoothing_t smoothing)
+{
+  // Accumulate raw counts
+  for (int i = 0; i < m; i++) {
+    std::vector<int> instance(nFeatures_ + 1);
+    for (int f = 0; f < nFeatures_; f++) {
+      instance[f] = dataset[f][i].item<int>();
+    }
+    instance[nFeatures_] = dataset[-1][i].item<int>();  // class
+    double w = weights[i].item<double>();
+    addSample(instance, w);
+  }
+
+  // Choose alpha based on smoothing:
+  switch (smoothing) {
+    case bayesnet::Smoothing_t::ORIGINAL:
+      alpha_ = 1.0 / m;
+      break;
+    case bayesnet::Smoothing_t::LAPLACE:
+      alpha_ = 1.0;
+      break;
+    default:
+      alpha_ = 0.0; // no smoothing
+  }
+
+  // Large initializer factor for numerical stability
+  initializer_ = std::numeric_limits<double>::max() / (nFeatures_ * nFeatures_);
+
+  // Convert raw counts to probabilities
+  computeProbabilities();
+}
+
+// --------------------------------------
+// addSample
+// --------------------------------------
+void XSp2de::addSample(const std::vector<int> &instance, double weight)
+{
+  if (weight <= 0.0)
+    return;
+
+  int c = instance.back();
+  // increment classCounts
+  classCounts_[c] += weight;
+
+  int sp1Val = instance[superParent1_];
+  int sp2Val = instance[superParent2_];
+
+  // p(sp1|c)
+  sp1FeatureCounts_[sp1Val * statesClass_ + c] += weight;
+
+  // p(sp2|c)
+  sp2FeatureCounts_[sp2Val * statesClass_ + c] += weight;
+
+  // p(childVal| c, sp1Val, sp2Val)
+  for (int f = 0; f < nFeatures_; f++) {
+    if (f == superParent1_ || f == superParent2_)
+      continue;
+
+    int childVal = instance[f];
+    int offset = childOffsets_[f];
+    // block layout: 
+    //    offset + (sp1Val*(states_[sp2_]* states_[f]* statesClass_)) 
+    //            + (sp2Val*(states_[f]* statesClass_)) 
+    //            + childVal*(statesClass_) 
+    //            + c
+    int blockSizeSp2 = states_[superParent2_] 
+                       * states_[f] 
+                       * statesClass_;
+    int blockSizeChild = states_[f] * statesClass_;
+
+    int idx = offset 
+            + sp1Val*blockSizeSp2 
+            + sp2Val*blockSizeChild 
+            + childVal*statesClass_ 
+            + c;
+    childCounts_[idx] += weight;
+  }
+}
+
+// --------------------------------------
+// computeProbabilities
+// --------------------------------------
+void XSp2de::computeProbabilities()
+{
+  double totalCount = std::accumulate(classCounts_.begin(), 
+                                      classCounts_.end(), 0.0);
+
+  // classPriors_
+  classPriors_.resize(statesClass_, 0.0);
+  if (totalCount <= 0.0) {
+    // fallback => uniform
+    double unif = 1.0 / static_cast<double>(statesClass_);
+    for (int c = 0; c < statesClass_; c++) {
+      classPriors_[c] = unif;
+    }
+  } else {
+    for (int c = 0; c < statesClass_; c++) {
+      classPriors_[c] = 
+        (classCounts_[c] + alpha_) 
+        / (totalCount + alpha_ * statesClass_);
+    }
+  }
+
+  // p(sp1Val| c)
+  sp1FeatureProbs_.resize(sp1FeatureCounts_.size());
+  int sp1Card = states_[superParent1_];
+  for (int spVal = 0; spVal < sp1Card; spVal++) {
+    for (int c = 0; c < statesClass_; c++) {
+      double denom = classCounts_[c] + alpha_ * sp1Card;
+      double num = sp1FeatureCounts_[spVal * statesClass_ + c] + alpha_;
+      sp1FeatureProbs_[spVal * statesClass_ + c] = 
+         (denom <= 0.0 ? 0.0 : num / denom);
+    }
+  }
+
+  // p(sp2Val| c)
+  sp2FeatureProbs_.resize(sp2FeatureCounts_.size());
+  int sp2Card = states_[superParent2_];
+  for (int spVal = 0; spVal < sp2Card; spVal++) {
+    for (int c = 0; c < statesClass_; c++) {
+      double denom = classCounts_[c] + alpha_ * sp2Card;
+      double num = sp2FeatureCounts_[spVal * statesClass_ + c] + alpha_;
+      sp2FeatureProbs_[spVal * statesClass_ + c] = 
+         (denom <= 0.0 ? 0.0 : num / denom);
+    }
+  }
+
+  // p(childVal| c, sp1Val, sp2Val)
+  childProbs_.resize(childCounts_.size());
+  int offset = 0;
+  for (int f = 0; f < nFeatures_; f++) {
+    if (f == superParent1_ || f == superParent2_) 
+      continue;
+
+    int fCard = states_[f];
+    int sp1Card_ = states_[superParent1_];
+    int sp2Card_ = states_[superParent2_];
+    int childBlockSizeSp2 = sp2Card_ * fCard * statesClass_;
+    int childBlockSizeF   = fCard * statesClass_;
+
+    int blockSize = fCard * sp1Card_ * sp2Card_ * statesClass_;
+    for (int sp1Val = 0; sp1Val < sp1Card_; sp1Val++) {
+      for (int sp2Val = 0; sp2Val < sp2Card_; sp2Val++) {
+        for (int childVal = 0; childVal < fCard; childVal++) {
+          for (int c = 0; c < statesClass_; c++) {
+            // index in childCounts_ 
+            int idx = offset 
+                    + sp1Val*childBlockSizeSp2 
+                    + sp2Val*childBlockSizeF 
+                    + childVal*statesClass_ 
+                    + c;
+            double num = childCounts_[idx] + alpha_;
+            // denominator is the count of (sp1Val,sp2Val,c) plus alpha * fCard
+            // We can find that by summing childVal dimension, but we already
+            // have it in childCounts_[...] or we can re-check the superparent 
+            // counts if your approach is purely hierarchical. 
+            // Here we'll do it like the XSpode approach: sp1&sp2 are 
+            // conditionally independent given c, so denominators come from 
+            // summing the relevant block or we treat sp1,sp2 as "parents."
+            // A simpler approach: 
+            double sumSp1Sp2C = 0.0;
+            // sum over all childVal:
+            for (int cv = 0; cv < fCard; cv++) {
+              int idx2 = offset
+                       + sp1Val*childBlockSizeSp2
+                       + sp2Val*childBlockSizeF
+                       + cv*statesClass_ + c;
+              sumSp1Sp2C += childCounts_[idx2];
+            }
+            double denom = sumSp1Sp2C + alpha_ * fCard;
+            childProbs_[idx] = (denom <= 0.0 ? 0.0 : num / denom);
+          }
+        }
+      }
+    }
+    offset += blockSize;
+  }
+}
+
+// --------------------------------------
+// predict_proba (single instance)
+// --------------------------------------
+std::vector<double> XSp2de::predict_proba(const std::vector<int> &instance) const
+{
+  if (!fitted) {
+    throw std::logic_error(CLASSIFIER_NOT_FITTED);
+  }
+  std::vector<double> probs(statesClass_, 0.0);
+
+  int sp1Val = instance[superParent1_];
+  int sp2Val = instance[superParent2_];
+
+  // Start with p(c) * p(sp1Val| c) * p(sp2Val| c)
+  for (int c = 0; c < statesClass_; c++) {
+    double pC = classPriors_[c];
+    double pSp1C = sp1FeatureProbs_[sp1Val * statesClass_ + c];
+    double pSp2C = sp2FeatureProbs_[sp2Val * statesClass_ + c];
+    probs[c] = pC * pSp1C * pSp2C * initializer_;
+  }
+
+  // Multiply by each child feature f
+  int offset = 0;
+  for (int f = 0; f < nFeatures_; f++) {
+    if (f == superParent1_ || f == superParent2_) 
+      continue;
+
+    int valF = instance[f];
+    int fCard = states_[f];
+    int sp1Card = states_[superParent1_];
+    int sp2Card = states_[superParent2_];
+    int blockSizeSp2 = sp2Card * fCard * statesClass_;
+    int blockSizeF   = fCard * statesClass_;
+
+    // base index for childProbs_ for this child and sp1Val, sp2Val
+    int base = offset 
+             + sp1Val*blockSizeSp2 
+             + sp2Val*blockSizeF 
+             + valF*statesClass_;
+    for (int c = 0; c < statesClass_; c++) {
+      probs[c] *= childProbs_[base + c];
+    }
+    offset += (fCard * sp1Card * sp2Card * statesClass_);
+  }
+
+  // Normalize
+  normalize(probs);
+  return probs;
+}
+
+// --------------------------------------
+// predict_proba (batch)
+// --------------------------------------
+std::vector<std::vector<double>> XSp2de::predict_proba(std::vector<std::vector<int>> &test_data)
+{
+  int test_size = test_data[0].size();  // each feature is test_data[f], size = #samples
+  int sample_size = test_data.size();   // = nFeatures_
+  std::vector<std::vector<double>> probabilities(
+      test_size, std::vector<double>(statesClass_, 0.0));
+
+  // same concurrency approach
+  int chunk_size = std::min(150, int(test_size / semaphore_.getMaxCount()) + 1);
+  std::vector<std::thread> threads;
+
+  auto worker = [&](const std::vector<std::vector<int>> &samples, 
+                    int begin, 
+                    int chunk, 
+                    int sample_size, 
+                    std::vector<std::vector<double>> &predictions) {
+    std::string threadName =
+      "XSp2de-" + std::to_string(begin) + "-" + std::to_string(chunk);
+#if defined(__linux__)
+    pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+    pthread_setname_np(threadName.c_str());
+#endif
+
+    std::vector<int> instance(sample_size);
+    for (int sample = begin; sample < begin + chunk; ++sample) {
+      for (int feature = 0; feature < sample_size; ++feature) {
+        instance[feature] = samples[feature][sample];
+      }
+      predictions[sample] = predict_proba(instance);
+    }
+    semaphore_.release();
+  };
+
+  for (int begin = 0; begin < test_size; begin += chunk_size) {
+    int chunk = std::min(chunk_size, test_size - begin);
+    semaphore_.acquire();
+    threads.emplace_back(worker, test_data, begin, chunk, sample_size, 
+                         std::ref(probabilities));
+  }
+  for (auto &th : threads) {
+    th.join();
+  }
+  return probabilities;
+}
+
+// --------------------------------------
+// predict (single instance)
+// --------------------------------------
+int XSp2de::predict(const std::vector<int> &instance) const
+{
+  auto p = predict_proba(instance);
+  return static_cast<int>(
+    std::distance(p.begin(), std::max_element(p.begin(), p.end()))
+  );
+}
+
+// --------------------------------------
+// predict (batch of data)
+// --------------------------------------
+std::vector<int> XSp2de::predict(std::vector<std::vector<int>> &test_data)
+{
+  auto probabilities = predict_proba(test_data);
+  std::vector<int> predictions(probabilities.size(), 0);
+
+  for (size_t i = 0; i < probabilities.size(); i++) {
+    predictions[i] = static_cast<int>(
+      std::distance(probabilities[i].begin(), 
+                    std::max_element(probabilities[i].begin(), 
+                                     probabilities[i].end()))
+    );
+  }
+  return predictions;
+}
+
+// --------------------------------------
+// predict (torch::Tensor version)
+// --------------------------------------
+torch::Tensor XSp2de::predict(torch::Tensor &X)
+{
+  auto X_ = TensorUtils::to_matrix(X);
+  auto result_v = predict(X_);
+  return torch::tensor(result_v, torch::kInt32);
+}
+
+// --------------------------------------
+// predict_proba (torch::Tensor version)
+// --------------------------------------
+torch::Tensor XSp2de::predict_proba(torch::Tensor &X)
+{
+  auto X_ = TensorUtils::to_matrix(X);
+  auto result_v = predict_proba(X_);
+  int n_samples = X.size(1);
+  torch::Tensor result =
+    torch::zeros({ n_samples, statesClass_ }, torch::kDouble);
+  for (int i = 0; i < (int)result_v.size(); ++i) {
+    result.index_put_({ i, "..." }, torch::tensor(result_v[i]));
+  }
+  return result;
+}
+
+// --------------------------------------
+// score (torch::Tensor version)
+// --------------------------------------
+float XSp2de::score(torch::Tensor &X, torch::Tensor &y)
+{
+  torch::Tensor y_pred = predict(X);
+  return (y_pred == y).sum().item<float>() / y.size(0);
+}
+
+// --------------------------------------
+// score (vector version)
+// --------------------------------------
+float XSp2de::score(std::vector<std::vector<int>> &X, std::vector<int> &y)
+{
+  auto y_pred = predict(X);
+  int correct = 0;
+  for (size_t i = 0; i < y_pred.size(); ++i) {
+    if (y_pred[i] == y[i]) {
+      correct++;
+    }
+  }
+  return static_cast<float>(correct) / static_cast<float>(y_pred.size());
+}
+
+// --------------------------------------
+// Utility: normalize
+// --------------------------------------
+void XSp2de::normalize(std::vector<double> &v) const
+{
+  double sum = 0.0;
+  for (auto &val : v) {
+    sum += val;
+  }
+  if (sum > 0.0) {
+    for (auto &val : v) {
+      val /= sum;
+    }
+  }
+}
+
+// --------------------------------------
+// to_string
+// --------------------------------------
+std::string XSp2de::to_string() const
+{
+  std::ostringstream oss;
+  oss << "----- XSp2de Model -----\n"
+      << "nFeatures_    = " << nFeatures_    << "\n"
+      << "superParent1_ = " << superParent1_ << "\n"
+      << "superParent2_ = " << superParent2_ << "\n"
+      << "statesClass_  = " << statesClass_  << "\n\n";
+
+  oss << "States: [";
+  for (auto s : states_) oss << s << " ";
+  oss << "]\n";
+
+  oss << "classCounts_:\n";
+  for (auto v : classCounts_) oss << v << " ";
+  oss << "\nclassPriors_:\n";
+  for (auto v : classPriors_) oss << v << " ";
+  oss << "\nsp1FeatureCounts_ (size=" << sp1FeatureCounts_.size() << ")\n";
+  for (auto v : sp1FeatureCounts_) oss << v << " ";
+  oss << "\nsp2FeatureCounts_ (size=" << sp2FeatureCounts_.size() << ")\n";
+  for (auto v : sp2FeatureCounts_) oss << v << " ";
+  oss << "\nchildCounts_ (size=" << childCounts_.size() << ")\n";
+  for (auto v : childCounts_) oss << v << " ";
+
+  oss << "\nchildOffsets_:\n";
+  for (auto c : childOffsets_) oss << c << " ";
+
+  oss << "\n----------------------------------------\n";
+  return oss.str();
+}
+
+// --------------------------------------
+// Some introspection about the graph
+// --------------------------------------
+int XSp2de::getNumberOfNodes() const 
+{
+  // nFeatures + 1 class node
+  return nFeatures_ + 1;
+}
+
+int XSp2de::getClassNumStates() const 
+{ 
+  return statesClass_; 
+}
+
+int XSp2de::getNFeatures() const 
+{ 
+  return nFeatures_; 
+}
+
+int XSp2de::getNumberOfStates() const
+{
+  // purely an example. Possibly you want to sum up actual 
+  // cardinalities or something else. 
+  return std::accumulate(states_.begin(), states_.end(), 0) * nFeatures_;
+}
+
+int XSp2de::getNumberOfEdges() const
+{
+  // In an SPNDE with n=2, for each feature we have edges from class, sp1, sp2. 
+  // So that’s 3*(nFeatures_) edges, minus the ones for the superparents themselves, 
+  // plus the edges from class->superparent1, class->superparent2. 
+  // For a quick approximation:
+  //   - class->sp1, class->sp2 => 2 edges
+  //   - class->child => (nFeatures -2) edges
+  //   - sp1->child, sp2->child => 2*(nFeatures -2) edges
+  // total = 2 + (nFeatures-2) + 2*(nFeatures-2) = 2 + 3*(nFeatures-2) 
+  //         = 3nFeatures - 4 (just an example).
+  // You can adapt to your liking:
+  return 3 * nFeatures_ - 4; 
+}
+
+} // namespace bayesnet
+

bayesnet/classifiers/XSP2DE.h

@@ -0,0 +1,75 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef XSP2DE_H
+#define XSP2DE_H
+
+#include "Classifier.h"
+#include "bayesnet/utils/CountingSemaphore.h"
+#include <torch/torch.h>
+#include <vector>
+
+namespace bayesnet {
+
+class XSp2de : public Classifier {
+  public:
+    XSp2de(int spIndex1, int spIndex2);
+    void setHyperparameters(const nlohmann::json &hyperparameters_) override;
+    void fitx(torch::Tensor &X, torch::Tensor &y, torch::Tensor &weights_, const Smoothing_t smoothing);
+    std::vector<double> predict_proba(const std::vector<int> &instance) const;
+    std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>> &test_data) override;
+    int predict(const std::vector<int> &instance) const;
+    std::vector<int> predict(std::vector<std::vector<int>> &test_data) override;
+    torch::Tensor predict(torch::Tensor &X) override;
+    torch::Tensor predict_proba(torch::Tensor &X) override;
+
+    float score(torch::Tensor &X, torch::Tensor &y) override;
+    float score(std::vector<std::vector<int>> &X, std::vector<int> &y) override;
+    std::string to_string() const;
+    std::vector<std::string> graph(const std::string &title) const override {
+        return std::vector<std::string>({title});
+    }
+
+    int getNumberOfNodes() const override;
+    int getNumberOfEdges() const override;
+    int getNFeatures() const;
+    int getClassNumStates() const override;
+    int getNumberOfStates() const override;
+
+  protected:
+    void buildModel(const torch::Tensor &weights) override;
+    void trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing) override;
+
+  private:
+    void addSample(const std::vector<int> &instance, double weight);
+    void normalize(std::vector<double> &v) const;
+    void computeProbabilities();
+
+    int superParent1_;
+    int superParent2_;
+    int nFeatures_;
+    int statesClass_;
+    double alpha_;
+    double initializer_;
+
+    std::vector<int> states_;
+    std::vector<double> classCounts_;
+    std::vector<double> classPriors_;
+    std::vector<double> sp1FeatureCounts_, sp1FeatureProbs_;
+    std::vector<double> sp2FeatureCounts_, sp2FeatureProbs_;
+    // childOffsets_[f] will be the offset into childCounts_ for feature f.
+    // If f is either superParent1 or superParent2, childOffsets_[f] = -1
+    std::vector<int> childOffsets_;
+    // For each child f, we store p(x_f | c, sp1Val, sp2Val).  We'll store the raw
+    // counts in childCounts_, and the probabilities in childProbs_, with a
+    // dimension block of size: states_[f]* statesClass_* states_[sp1]* states_[sp2].
+    std::vector<double> childCounts_;
+    std::vector<double> childProbs_;
+    CountingSemaphore &semaphore_;
+};
+
+} // namespace bayesnet
+#endif // XSP2DE_H

bayesnet/classifiers/XSPODE.cc

@@ -0,0 +1,450 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#include <algorithm>
+#include <cmath>
+#include <limits>
+#include <numeric>
+#include <sstream>
+#include <stdexcept>
+#include "XSPODE.h"
+#include "bayesnet/utils/TensorUtils.h"
+
+namespace bayesnet {
+
+  // --------------------------------------
+  // Constructor
+  // --------------------------------------
+  XSpode::XSpode(int spIndex)
+    : superParent_{ spIndex }, nFeatures_{ 0 }, statesClass_{ 0 }, alpha_{ 1.0 },
+    initializer_{ 1.0 }, semaphore_{ CountingSemaphore::getInstance() },
+    Classifier(Network())
+  {
+    validHyperparameters = { "parent" };
+  }
+
+  void XSpode::setHyperparameters(const nlohmann::json& hyperparameters_)
+  {
+    auto hyperparameters = hyperparameters_;
+    if (hyperparameters.contains("parent")) {
+      superParent_ = hyperparameters["parent"];
+      hyperparameters.erase("parent");
+    }
+    Classifier::setHyperparameters(hyperparameters);
+  }
+
+  void XSpode::fitx(torch::Tensor & X, torch::Tensor& y, torch::Tensor& weights_, const Smoothing_t smoothing)
+  {
+    m = X.size(1);
+    n = X.size(0);
+    dataset = X;
+    buildDataset(y);
+    buildModel(weights_);
+    trainModel(weights_, smoothing);
+    fitted = true;
+  }
+
+  // --------------------------------------
+  // trainModel
+  // --------------------------------------
+  // Initialize storage needed for the super-parent and child features counts and
+  // probs.
+  // --------------------------------------
+  void XSpode::buildModel(const torch::Tensor& weights)
+  {
+    int numInstances = m;
+    nFeatures_ = n;
+
+    // Derive the number of states for each feature and for the class.
+    // (This is just one approach; adapt to match your environment.)
+    // Here, we assume the user also gave us the total #states per feature in e.g.
+    // statesMap. We'll simply reconstruct the integer states_ array. The last
+    // entry is statesClass_.
+    states_.resize(nFeatures_);
+    for (int f = 0; f < nFeatures_; f++) {
+      // Suppose you look up in “statesMap” by the feature name, or read directly
+      // from X. We'll assume states_[f] = max value in X[f] + 1.
+      states_[f] = dataset[f].max().item<int>() + 1;
+    }
+    // For the class: states_.back() = max(y)+1
+    statesClass_ = dataset[-1].max().item<int>() + 1;
+
+    // Initialize counts
+    classCounts_.resize(statesClass_, 0.0);
+    // p(x_sp = spVal | c)
+    // We'll store these counts in spFeatureCounts_[spVal * statesClass_ + c].
+    spFeatureCounts_.resize(states_[superParent_] * statesClass_, 0.0);
+
+    // For each child ≠ sp, we store p(childVal| c, spVal) in a separate block of
+    // childCounts_. childCounts_ will be sized as sum_{child≠sp} (states_[child]
+    // * statesClass_ * states_[sp]). We also need an offset for each child to
+    // index into childCounts_.
+    childOffsets_.resize(nFeatures_, -1);
+    int totalSize = 0;
+    for (int f = 0; f < nFeatures_; f++) {
+      if (f == superParent_)
+        continue; // skip sp
+      childOffsets_[f] = totalSize;
+      // block size for this child's counts: states_[f] * statesClass_ *
+      // states_[superParent_]
+      totalSize += (states_[f] * statesClass_ * states_[superParent_]);
+    }
+    childCounts_.resize(totalSize, 0.0);
+  }
+  // --------------------------------------
+  // buildModel
+  // --------------------------------------
+  //
+  // We only store conditional probabilities for:
+  //   p(x_sp| c)   (the super-parent feature)
+  //   p(x_child| c, x_sp)  for all child ≠ sp
+  //
+  // --------------------------------------
+  void XSpode::trainModel(const torch::Tensor& weights,
+    const bayesnet::Smoothing_t smoothing)
+  {
+    // Accumulate raw counts
+    for (int i = 0; i < m; i++) {
+      std::vector<int> instance(nFeatures_ + 1);
+      for (int f = 0; f < nFeatures_; f++) {
+        instance[f] = dataset[f][i].item<int>();
+      }
+      instance[nFeatures_] = dataset[-1][i].item<int>();
+      addSample(instance, weights[i].item<double>());
+    }
+    switch (smoothing) {
+      case bayesnet::Smoothing_t::ORIGINAL:
+        alpha_ = 1.0 / m;
+        break;
+      case bayesnet::Smoothing_t::LAPLACE:
+        alpha_ = 1.0;
+        break;
+      default:
+        alpha_ = 0.0; // No smoothing
+    }
+    initializer_ = std::numeric_limits<double>::max() /
+      (nFeatures_ * nFeatures_); // for numerical stability
+    // Convert raw counts to probabilities
+    computeProbabilities();
+  }
+
+  // --------------------------------------
+  // addSample
+  // --------------------------------------
+  //
+  // instance has size nFeatures_ + 1, with the class at the end.
+  // We add 1 to the appropriate counters for each (c, superParentVal, childVal).
+  //
+  void XSpode::addSample(const std::vector<int>& instance, double weight)
+  {
+    if (weight <= 0.0)
+      return;
+
+    int c = instance.back();
+    // (A) increment classCounts
+    classCounts_[c] += weight;
+
+    // (B) increment super-parent counts => p(x_sp | c)
+    int spVal = instance[superParent_];
+    spFeatureCounts_[spVal * statesClass_ + c] += weight;
+
+    // (C) increment child counts => p(childVal | c, x_sp)
+    for (int f = 0; f < nFeatures_; f++) {
+      if (f == superParent_)
+        continue;
+      int childVal = instance[f];
+      int offset = childOffsets_[f];
+      // Compute index in childCounts_.
+      // Layout: [ offset + (spVal * states_[f] + childVal) * statesClass_ + c ]
+      int blockSize = states_[f] * statesClass_;
+      int idx = offset + spVal * blockSize + childVal * statesClass_ + c;
+      childCounts_[idx] += weight;
+    }
+  }
+
+  // --------------------------------------
+  // computeProbabilities
+  // --------------------------------------
+  //
+  // Once all samples are added in COUNTS mode, call this to:
+  //    p(c)
+  //    p(x_sp = spVal | c)
+  //    p(x_child = v | c, x_sp = s_sp)
+  //
+  // --------------------------------------
+  void XSpode::computeProbabilities()
+  {
+    double totalCount =
+      std::accumulate(classCounts_.begin(), classCounts_.end(), 0.0);
+
+    // p(c) => classPriors_
+    classPriors_.resize(statesClass_, 0.0);
+    if (totalCount <= 0.0) {
+      // fallback => uniform
+      double unif = 1.0 / static_cast<double>(statesClass_);
+      for (int c = 0; c < statesClass_; c++) {
+        classPriors_[c] = unif;
+      }
+    } else {
+      for (int c = 0; c < statesClass_; c++) {
+        classPriors_[c] =
+          (classCounts_[c] + alpha_) / (totalCount + alpha_ * statesClass_);
+      }
+    }
+
+    // p(x_sp | c)
+    spFeatureProbs_.resize(spFeatureCounts_.size());
+    // denominator for spVal * statesClass_ + c is just classCounts_[c] + alpha_ *
+    // (#states of sp)
+    int spCard = states_[superParent_];
+    for (int spVal = 0; spVal < spCard; spVal++) {
+      for (int c = 0; c < statesClass_; c++) {
+        double denom = classCounts_[c] + alpha_ * spCard;
+        double num = spFeatureCounts_[spVal * statesClass_ + c] + alpha_;
+        spFeatureProbs_[spVal * statesClass_ + c] = (denom <= 0.0 ? 0.0 : num / denom);
+      }
+    }
+
+    // p(x_child | c, x_sp)
+    childProbs_.resize(childCounts_.size());
+    for (int f = 0; f < nFeatures_; f++) {
+      if (f == superParent_)
+        continue;
+      int offset = childOffsets_[f];
+      int childCard = states_[f];
+
+      // For each spVal, c, childVal in childCounts_:
+      for (int spVal = 0; spVal < spCard; spVal++) {
+        for (int childVal = 0; childVal < childCard; childVal++) {
+          for (int c = 0; c < statesClass_; c++) {
+            int idx = offset + spVal * (childCard * statesClass_) +
+              childVal * statesClass_ + c;
+
+            double num = childCounts_[idx] + alpha_;
+            // denominator = spFeatureCounts_[spVal * statesClass_ + c] + alpha_ *
+            // (#states of child)
+            double denom =
+              spFeatureCounts_[spVal * statesClass_ + c] + alpha_ * childCard;
+            childProbs_[idx] = (denom <= 0.0 ? 0.0 : num / denom);
+          }
+        }
+      }
+    }
+  }
+
+  // --------------------------------------
+  // predict_proba
+  // --------------------------------------
+  //
+  // For a single instance x of dimension nFeatures_:
+  //  P(c | x) ∝ p(c) × p(x_sp | c) × ∏(child ≠ sp) p(x_child | c, x_sp).
+  //
+  // --------------------------------------
+  std::vector<double> XSpode::predict_proba(const std::vector<int>& instance) const
+  {
+    if (!fitted) {
+      throw std::logic_error(CLASSIFIER_NOT_FITTED);
+    }
+    std::vector<double> probs(statesClass_, 0.0);
+    // Multiply p(c) × p(x_sp | c)
+    int spVal = instance[superParent_];
+    for (int c = 0; c < statesClass_; c++) {
+      double pc = classPriors_[c];
+      double pSpC = spFeatureProbs_[spVal * statesClass_ + c];
+      probs[c] = pc * pSpC * initializer_;
+    }
+
+    // Multiply by each child’s probability p(x_child | c, x_sp)
+    for (int feature = 0; feature < nFeatures_; feature++) {
+      if (feature == superParent_)
+        continue; // skip sp
+      int sf = instance[feature];
+      int offset = childOffsets_[feature];
+      int childCard = states_[feature]; // not used directly, but for clarity
+      // Index into childProbs_ = offset + spVal*(childCard*statesClass_) +
+      // childVal*statesClass_ + c
+      int base = offset + spVal * (childCard * statesClass_) + sf * statesClass_;
+      for (int c = 0; c < statesClass_; c++) {
+        probs[c] *= childProbs_[base + c];
+      }
+    }
+
+    // Normalize
+    normalize(probs);
+    return probs;
+  }
+  std::vector<std::vector<double>> XSpode::predict_proba(std::vector<std::vector<int>>& test_data)
+  {
+    int test_size = test_data[0].size();
+    int sample_size = test_data.size();
+    auto probabilities = std::vector<std::vector<double>>(
+      test_size, std::vector<double>(statesClass_));
+
+    int chunk_size = std::min(150, int(test_size / semaphore_.getMaxCount()) + 1);
+    std::vector<std::thread> threads;
+    auto worker = [&](const std::vector<std::vector<int>>& samples, int begin,
+      int chunk, int sample_size,
+      std::vector<std::vector<double>>& predictions) {
+        std::string threadName =
+          "(V)PWorker-" + std::to_string(begin) + "-" + std::to_string(chunk);
+#if defined(__linux__)
+        pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+        pthread_setname_np(threadName.c_str());
+#endif
+
+        std::vector<int> instance(sample_size);
+        for (int sample = begin; sample < begin + chunk; ++sample) {
+          for (int feature = 0; feature < sample_size; ++feature) {
+            instance[feature] = samples[feature][sample];
+          }
+          predictions[sample] = predict_proba(instance);
+        }
+        semaphore_.release();
+      };
+    for (int begin = 0; begin < test_size; begin += chunk_size) {
+      int chunk = std::min(chunk_size, test_size - begin);
+      semaphore_.acquire();
+      threads.emplace_back(worker, test_data, begin, chunk, sample_size, std::ref(probabilities));
+    }
+    for (auto& thread : threads) {
+      thread.join();
+    }
+    return probabilities;
+  }
+
+  // --------------------------------------
+  // Utility: normalize
+  // --------------------------------------
+  void XSpode::normalize(std::vector<double>& v) const
+  {
+    double sum = 0.0;
+    for (auto val : v) {
+      sum += val;
+    }
+    if (sum <= 0.0) {
+      return;
+    }
+    for (auto& val : v) {
+      val /= sum;
+    }
+  }
+
+  // --------------------------------------
+  // representation of the model
+  // --------------------------------------
+  std::string XSpode::to_string() const
+  {
+    std::ostringstream oss;
+    oss << "----- XSpode Model -----" << std::endl
+      << "nFeatures_  = " << nFeatures_ << std::endl
+      << "superParent_ = " << superParent_ << std::endl
+      << "statesClass_ = " << statesClass_ << std::endl
+      << std::endl;
+
+    oss << "States: [";
+    for (int s : states_)
+      oss << s << " ";
+    oss << "]" << std::endl;
+    oss << "classCounts_: [";
+    for (double c : classCounts_)
+      oss << c << " ";
+    oss << "]" << std::endl;
+    oss << "classPriors_: [";
+    for (double c : classPriors_)
+      oss << c << " ";
+    oss << "]" << std::endl;
+    oss << "spFeatureCounts_: size = " << spFeatureCounts_.size() << std::endl
+      << "[";
+    for (double c : spFeatureCounts_)
+      oss << c << " ";
+    oss << "]" << std::endl;
+    oss << "spFeatureProbs_: size = " << spFeatureProbs_.size() << std::endl
+      << "[";
+    for (double c : spFeatureProbs_)
+      oss << c << " ";
+    oss << "]" << std::endl;
+    oss << "childCounts_: size = " << childCounts_.size() << std::endl << "[";
+    for (double cc : childCounts_)
+      oss << cc << " ";
+    oss << "]" << std::endl;
+
+    for (double cp : childProbs_)
+      oss << cp << " ";
+    oss << "]" << std::endl;
+    oss << "childOffsets_: [";
+    for (int co : childOffsets_)
+      oss << co << " ";
+    oss << "]" << std::endl;
+    oss << std::string(40,'-') << std::endl;
+    return oss.str();
+  }
+  int XSpode::getNumberOfNodes() const { return nFeatures_ + 1; }
+  int XSpode::getClassNumStates() const { return statesClass_; }
+  int XSpode::getNFeatures() const { return nFeatures_; }
+  int XSpode::getNumberOfStates() const
+  {
+    return std::accumulate(states_.begin(), states_.end(), 0) * nFeatures_;
+  }
+  int XSpode::getNumberOfEdges() const
+  {
+    return 2 * nFeatures_ + 1;
+  }
+
+  // ------------------------------------------------------
+  // Predict overrides (classifier interface)
+  // ------------------------------------------------------
+  int XSpode::predict(const std::vector<int>& instance) const
+  {
+    auto p = predict_proba(instance);
+    return static_cast<int>(std::distance(p.begin(), std::max_element(p.begin(), p.end())));
+  }
+  std::vector<int> XSpode::predict(std::vector<std::vector<int>>& test_data)
+  {
+    auto probabilities = predict_proba(test_data);
+    std::vector<int> predictions(probabilities.size(), 0);
+
+    for (size_t i = 0; i < probabilities.size(); i++) {
+      predictions[i] = std::distance(
+        probabilities[i].begin(),
+        std::max_element(probabilities[i].begin(), probabilities[i].end()));
+    }
+    return predictions;
+  }
+  torch::Tensor XSpode::predict(torch::Tensor& X)
+  {
+    auto X_ = TensorUtils::to_matrix(X);
+    auto result_v = predict(X_);
+    return torch::tensor(result_v, torch::kInt32);
+  }
+  torch::Tensor XSpode::predict_proba(torch::Tensor& X)
+  {
+    auto X_ = TensorUtils::to_matrix(X);
+    auto result_v = predict_proba(X_);
+    int n_samples = X.size(1);
+    torch::Tensor result =
+      torch::zeros({ n_samples, statesClass_ }, torch::kDouble);
+    for (int i = 0; i < result_v.size(); ++i) {
+      result.index_put_({ i, "..." }, torch::tensor(result_v[i]));
+    }
+    return result;
+  }
+  float XSpode::score(torch::Tensor& X, torch::Tensor& y)
+  {
+    torch::Tensor y_pred = predict(X);
+    return (y_pred == y).sum().item<float>() / y.size(0);
+  }
+  float XSpode::score(std::vector<std::vector<int>>& X, std::vector<int>& y)
+  {
+    auto y_pred = this->predict(X);
+    int correct = 0;
+    for (int i = 0; i < y_pred.size(); ++i) {
+      if (y_pred[i] == y[i]) {
+        correct++;
+      }
+    }
+    return (double)correct / y_pred.size();
+  }
+} // namespace bayesnet

bayesnet/classifiers/XSPODE.h

@@ -0,0 +1,76 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef XSPODE_H
+#define XSPODE_H
+
+#include <vector>
+#include <torch/torch.h>
+#include "Classifier.h"
+#include "bayesnet/utils/CountingSemaphore.h"
+
+namespace bayesnet {
+
+    class XSpode : public Classifier {
+    public:
+        explicit XSpode(int spIndex);
+        std::vector<double> predict_proba(const std::vector<int>& instance) const;
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
+        int predict(const std::vector<int>& instance) const;
+        void normalize(std::vector<double>& v) const;
+        std::string to_string() const;
+        int getNFeatures() const;
+        int getNumberOfNodes() const override;
+        int getNumberOfEdges() const override;
+        int getNumberOfStates() const override;
+        int getClassNumStates() const override;
+        std::vector<int>& getStates();
+        std::vector<std::string> graph(const std::string& title) const override { return std::vector<std::string>({ title }); }
+        void fitx(torch::Tensor& X, torch::Tensor& y, torch::Tensor& weights_, const Smoothing_t smoothing);
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
+
+        //
+        // Classifier interface
+        //
+        torch::Tensor predict(torch::Tensor& X) override;
+        std::vector<int> predict(std::vector<std::vector<int>>& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override;
+        float score(torch::Tensor& X, torch::Tensor& y) override;
+        float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
+    private:
+        void addSample(const std::vector<int>& instance, double weight);
+        void computeProbabilities();
+        int superParent_;
+        int nFeatures_;
+        int statesClass_;
+        std::vector<int> states_;          // [states_feat0, ..., states_feat(N-1)] (class not included in this array)
+
+        // Class counts
+        std::vector<double> classCounts_;  // [c], accumulative
+        std::vector<double> classPriors_;  // [c], after normalization
+
+        // For p(x_sp = spVal | c)
+        std::vector<double> spFeatureCounts_; // [spVal * statesClass_ + c]
+        std::vector<double> spFeatureProbs_;  // same shape, after normalization
+
+        // For p(x_child = childVal | x_sp = spVal, c)
+        // childCounts_ is big enough to hold all child features except sp:
+        //   For each child f, we store childOffsets_[f] as the start index, then
+        //   childVal, spVal, c => the data.
+        std::vector<double> childCounts_;
+        std::vector<double> childProbs_;
+        std::vector<int>    childOffsets_;
+
+        double alpha_ = 1.0;
+        double initializer_; // for numerical stability
+        CountingSemaphore& semaphore_;
+    };
+}
+
+#endif // XSPODE_H

bayesnet/ensembles/A2DE.cc

@@ -0,0 +1,40 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "A2DE.h"
+
+namespace bayesnet {
+    A2DE::A2DE(bool predict_voting) : Ensemble(predict_voting)
+    {
+        validHyperparameters = { "predict_voting" };
+    }
+    void A2DE::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("predict_voting")) {
+            predict_voting = hyperparameters["predict_voting"];
+            hyperparameters.erase("predict_voting");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
+    void A2DE::buildModel(const torch::Tensor& weights)
+    {
+        models.clear();
+        significanceModels.clear();
+        for (int i = 0; i < features.size() - 1; ++i) {
+            for (int j = i + 1; j < features.size(); ++j) {
+                auto model = std::make_unique<SPnDE>(std::vector<int>({ i, j }));
+                models.push_back(std::move(model));
+            }
+        }
+        n_models = static_cast<unsigned>(models.size());
+        significanceModels = std::vector<double>(n_models, 1.0);
+    }
+    std::vector<std::string> A2DE::graph(const std::string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

bayesnet/ensembles/A2DE.h

@@ -0,0 +1,22 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef A2DE_H
+#define A2DE_H
+#include "bayesnet/classifiers/SPnDE.h"
+#include "Ensemble.h"
+namespace bayesnet {
+    class A2DE : public Ensemble {
+    public:
+        A2DE(bool predict_voting = false);
+        virtual ~A2DE() {};
+        void setHyperparameters(const nlohmann::json& hyperparameters) override;
+        std::vector<std::string> graph(const std::string& title = "A2DE") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    };
+}
+#endif

bayesnet/ensembles/AODE.cc

@@ -0,0 +1,38 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "AODE.h"
+
+namespace bayesnet {
+    AODE::AODE(bool predict_voting) : Ensemble(predict_voting)
+    {
+        validHyperparameters = { "predict_voting" };
+
+    }
+    void AODE::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("predict_voting")) {
+            predict_voting = hyperparameters["predict_voting"];
+            hyperparameters.erase("predict_voting");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
+    void AODE::buildModel(const torch::Tensor& weights)
+    {
+        models.clear();
+        significanceModels.clear();
+        for (int i = 0; i < features.size(); ++i) {
+            models.push_back(std::make_unique<SPODE>(i));
+        }
+        n_models = models.size();
+        significanceModels = std::vector<double>(n_models, 1.0);
+    }
+    std::vector<std::string> AODE::graph(const std::string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

bayesnet/ensembles/AODE.h

@@ -0,0 +1,22 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef AODE_H
+#define AODE_H
+#include "bayesnet/classifiers/SPODE.h"
+#include "Ensemble.h"
+namespace bayesnet {
+    class AODE : public Ensemble {
+    public:
+        AODE(bool predict_voting = false);
+        virtual ~AODE() {};
+        void setHyperparameters(const nlohmann::json& hyperparameters) override;
+        std::vector<std::string> graph(const std::string& title = "AODE") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    };
+}
+#endif

bayesnet/ensembles/AODELd.cc

@@ -0,0 +1,50 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "AODELd.h"
+
+namespace bayesnet {
+    AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className, Ensemble::notes)
+    {
+        validHyperparameters = validHyperparameters_ld; // Inherits the valid hyperparameters from Proposal
+    }
+    AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
+    {
+        checkInput(X_, y_);
+        features = features_;
+        className = className_;
+        Xf = X_;
+        y = y_;
+        // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
+        states = fit_local_discretization(y);
+        // We have discretized the input data
+        // 1st we need to fit the model to build the normal AODE structure, Ensemble::fit  
+        // calls buildModel to initialize the base models
+        Ensemble::fit(dataset, features, className, states, smoothing);
+        return *this;
+
+    }
+    void AODELd::buildModel(const torch::Tensor& weights)
+    {
+        models.clear();
+        for (int i = 0; i < features.size(); ++i) {
+            models.push_back(std::make_unique<SPODELd>(i));
+            models.back()->setHyperparameters(hyperparameters);
+        }
+        n_models = models.size();
+        significanceModels = std::vector<double>(n_models, 1.0);
+    }
+    void AODELd::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
+    {
+        for (const auto& model : models) {
+            model->fit(Xf, y, features, className, states, smoothing);
+        }
+    }
+    std::vector<std::string> AODELd::graph(const std::string& name) const
+    {
+        return Ensemble::graph(name);
+    }
+}

bayesnet/ensembles/AODELd.h

@@ -0,0 +1,31 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef AODELD_H
+#define AODELD_H
+#include "bayesnet/classifiers/Proposal.h"
+#include "bayesnet/classifiers/SPODELd.h"
+#include "Ensemble.h"
+
+namespace bayesnet {
+    class AODELd : public Ensemble, public Proposal {
+    public:
+        AODELd(bool predict_voting = true);
+        virtual ~AODELd() = default;
+        AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing) override;
+        std::vector<std::string> graph(const std::string& name = "AODELd") const override;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override
+        {
+            hyperparameters = hyperparameters_;
+        }
+    protected:
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
+        void buildModel(const torch::Tensor& weights) override;
+    private:
+        nlohmann::json hyperparameters = {}; // Hyperparameters for the model
+    };
+}
+#endif // !AODELD_H

bayesnet/ensembles/Boost.cc

@@ -0,0 +1,268 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#include "Boost.h"
+#include "bayesnet/feature_selection/CFS.h"
+#include "bayesnet/feature_selection/FCBF.h"
+#include "bayesnet/feature_selection/IWSS.h"
+#include <folding.hpp>
+
+namespace bayesnet {
+Boost::Boost(bool predict_voting) : Ensemble(predict_voting) {
+    validHyperparameters = {"alpha_block", "order",        "convergence",    "convergence_best", "bisection",
+                            "threshold",   "maxTolerance", "predict_voting", "select_features",  "block_update"};
+}
+void Boost::setHyperparameters(const nlohmann::json &hyperparameters_) {
+    auto hyperparameters = hyperparameters_;
+    if (hyperparameters.contains("order")) {
+        std::vector<std::string> algos = {Orders.ASC, Orders.DESC, Orders.RAND};
+        order_algorithm = hyperparameters["order"];
+        if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {
+            throw std::invalid_argument("Invalid order algorithm, valid values [" + Orders.ASC + ", " + Orders.DESC +
+                                        ", " + Orders.RAND + "]");
+        }
+        hyperparameters.erase("order");
+    }
+    if (hyperparameters.contains("alpha_block")) {
+        alpha_block = hyperparameters["alpha_block"];
+        hyperparameters.erase("alpha_block");
+    }
+    if (hyperparameters.contains("convergence")) {
+        convergence = hyperparameters["convergence"];
+        hyperparameters.erase("convergence");
+    }
+    if (hyperparameters.contains("convergence_best")) {
+        convergence_best = hyperparameters["convergence_best"];
+        hyperparameters.erase("convergence_best");
+    }
+    if (hyperparameters.contains("bisection")) {
+        bisection = hyperparameters["bisection"];
+        hyperparameters.erase("bisection");
+    }
+    if (hyperparameters.contains("threshold")) {
+        threshold = hyperparameters["threshold"];
+        hyperparameters.erase("threshold");
+    }
+    if (hyperparameters.contains("maxTolerance")) {
+        maxTolerance = hyperparameters["maxTolerance"];
+        if (maxTolerance < 1 || maxTolerance > 6)
+            throw std::invalid_argument("Invalid maxTolerance value, must be greater in [1, 6]");
+        hyperparameters.erase("maxTolerance");
+    }
+    if (hyperparameters.contains("predict_voting")) {
+        predict_voting = hyperparameters["predict_voting"];
+        hyperparameters.erase("predict_voting");
+    }
+    if (hyperparameters.contains("select_features")) {
+        auto selectedAlgorithm = hyperparameters["select_features"];
+        std::vector<std::string> algos = {SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF};
+        selectFeatures = true;
+        select_features_algorithm = selectedAlgorithm;
+        if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
+            throw std::invalid_argument("Invalid selectFeatures value, valid values [" + SelectFeatures.IWSS + ", " +
+                                        SelectFeatures.CFS + ", " + SelectFeatures.FCBF + "]");
+        }
+        hyperparameters.erase("select_features");
+    }
+    if (hyperparameters.contains("block_update")) {
+        block_update = hyperparameters["block_update"];
+        hyperparameters.erase("block_update");
+    }
+    if (block_update && alpha_block) {
+        throw std::invalid_argument("alpha_block and block_update cannot be true at the same time");
+    }
+    if (block_update && !bisection) {
+        throw std::invalid_argument("block_update needs bisection to be true");
+    }
+    Classifier::setHyperparameters(hyperparameters);
+}
+void Boost::add_model(std::unique_ptr<Classifier> model, double significance) {
+    models.push_back(std::move(model));
+    n_models++;
+    significanceModels.push_back(significance);
+}
+void Boost::remove_last_model() {
+    models.pop_back();
+    significanceModels.pop_back();
+    n_models--;
+}
+void Boost::buildModel(const torch::Tensor &weights) {
+    // Models shall be built in trainModel
+    models.clear();
+    significanceModels.clear();
+    n_models = 0;
+    // Prepare the validation dataset
+    auto y_ = dataset.index({-1, "..."});
+    if (convergence) {
+        // Prepare train & validation sets from train data
+        auto fold = folding::StratifiedKFold(5, y_, 271);
+        auto [train, test] = fold.getFold(0);
+        auto train_t = torch::tensor(train);
+        auto test_t = torch::tensor(test);
+        // Get train and validation sets
+        X_train = dataset.index({torch::indexing::Slice(0, dataset.size(0) - 1), train_t});
+        y_train = dataset.index({-1, train_t});
+        X_test = dataset.index({torch::indexing::Slice(0, dataset.size(0) - 1), test_t});
+        y_test = dataset.index({-1, test_t});
+        dataset = X_train;
+        m = X_train.size(1);
+        auto n_classes = states.at(className).size();
+        // Build dataset with train data
+        buildDataset(y_train);
+        metrics = Metrics(dataset, features, className, n_classes);
+    } else {
+        // Use all data to train
+        X_train = dataset.index({torch::indexing::Slice(0, dataset.size(0) - 1), "..."});
+        y_train = y_;
+    }
+}
+std::vector<int> Boost::featureSelection(torch::Tensor &weights_) {
+    int maxFeatures = 0;
+    if (select_features_algorithm == SelectFeatures.CFS) {
+        featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);
+    } else if (select_features_algorithm == SelectFeatures.IWSS) {
+        if (threshold < 0 || threshold > 0.5) {
+            throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.IWSS + " [0, 0.5]");
+        }
+        featureSelector =
+            new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
+    } else if (select_features_algorithm == SelectFeatures.FCBF) {
+        if (threshold < 1e-7 || threshold > 1) {
+            throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.FCBF + " [1e-7, 1]");
+        }
+        featureSelector =
+            new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
+    }
+    featureSelector->fit();
+    auto featuresUsed = featureSelector->getFeatures();
+    delete featureSelector;
+    return featuresUsed;
+}
+std::tuple<torch::Tensor &, double, bool> Boost::update_weights(torch::Tensor &ytrain, torch::Tensor &ypred,
+                                                                torch::Tensor &weights) {
+    bool terminate = false;
+    double alpha_t = 0;
+    auto mask_wrong = ypred != ytrain;
+    auto mask_right = ypred == ytrain;
+    auto masked_weights = weights * mask_wrong.to(weights.dtype());
+    double epsilon_t = masked_weights.sum().item<double>();
+    // std::cout << "epsilon_t: " << epsilon_t << " count wrong: " << mask_wrong.sum().item<int>() << " count right: "
+    // << mask_right.sum().item<int>() << std::endl;
+    if (epsilon_t > 0.5) {
+        // Inverse the weights policy (plot ln(wt))
+        // "In each round of AdaBoost, there is a sanity check to ensure that the current base
+        // learner is better than random guess" (Zhi-Hua Zhou, 2012)
+        terminate = true;
+    } else {
+        double wt = (1 - epsilon_t) / epsilon_t;
+        alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
+        // Step 3.2: Update weights for next classifier
+        // Step 3.2.1: Update weights of wrong samples
+        weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;
+        // Step 3.2.2: Update weights of right samples
+        weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;
+        // Step 3.3: Normalise the weights
+        double totalWeights = torch::sum(weights).item<double>();
+        weights = weights / totalWeights;
+    }
+    return {weights, alpha_t, terminate};
+}
+std::tuple<torch::Tensor &, double, bool> Boost::update_weights_block(int k, torch::Tensor &ytrain,
+                                                                      torch::Tensor &weights) {
+    /* Update Block algorithm
+        k = # of models in block
+        n_models = # of models in ensemble to make predictions
+        n_models_bak = # models saved
+        models = vector of models to make predictions
+        models_bak = models not used to make predictions
+        significances_bak = backup of significances vector
+
+        Case list
+        A) k = 1, n_models = 1		=> n = 0 , n_models = n + k
+        B) k = 1, n_models = n + 1	=> n_models = n + k
+        C) k > 1, n_models = k + 1 	=> n= 1, n_models = n + k
+        D) k > 1, n_models = k		=> n = 0, n_models = n + k
+        E) k > 1, n_models = k + n	=> n_models = n + k
+
+        A, D) n=0, k > 0, n_models == k
+        1. n_models_bak <- n_models
+        2. significances_bak <- significances
+        3. significances = vector(k, 1)
+        4. Don’t move any classifiers out of models
+        5. n_models <- k
+        6. Make prediction, compute alpha, update weights
+        7. Don’t restore any classifiers to models
+        8. significances <- significances_bak
+        9. Update last k significances
+        10. n_models <- n_models_bak
+
+        B, C, E) n > 0, k > 0, n_models == n + k
+        1. n_models_bak <- n_models
+        2. significances_bak <- significances
+        3. significances = vector(k, 1)
+        4. Move first n classifiers to models_bak
+        5. n_models <- k
+        6. Make prediction, compute alpha, update weights
+        7. Insert classifiers in models_bak to be the first n models
+        8. significances <- significances_bak
+        9. Update last k significances
+        10. n_models <- n_models_bak
+    */
+    //
+    // Make predict with only the last k models
+    //
+    std::unique_ptr<Classifier> model;
+    std::vector<std::unique_ptr<Classifier>> models_bak;
+    // 1. n_models_bak <- n_models 2. significances_bak <- significances
+    auto significance_bak = significanceModels;
+    auto n_models_bak = n_models;
+    // 3. significances = vector(k, 1)
+    significanceModels = std::vector<double>(k, 1.0);
+    // 4. Move first n classifiers to models_bak
+    // backup the first n_models - k models (if n_models == k, don't backup any)
+    for (int i = 0; i < n_models - k; ++i) {
+        model = std::move(models[0]);
+        models.erase(models.begin());
+        models_bak.push_back(std::move(model));
+    }
+    assert(models.size() == k);
+    // 5. n_models <- k
+    n_models = k;
+    // 6. Make prediction, compute alpha, update weights
+    auto ypred = predict(X_train);
+    //
+    // Update weights
+    //
+    double alpha_t;
+    bool terminate;
+    std::tie(weights, alpha_t, terminate) = update_weights(y_train, ypred, weights);
+    //
+    // Restore the models if needed
+    //
+    // 7. Insert classifiers in models_bak to be the first n models
+    // if n_models_bak == k, don't restore any, because none of them were moved
+    if (k != n_models_bak) {
+        // Insert in the same order as they were extracted
+        int bak_size = models_bak.size();
+        for (int i = 0; i < bak_size; ++i) {
+            model = std::move(models_bak[bak_size - 1 - i]);
+            models_bak.erase(models_bak.end() - 1);
+            models.insert(models.begin(), std::move(model));
+        }
+    }
+    // 8. significances <- significances_bak
+    significanceModels = significance_bak;
+    //
+    // Update the significance of the last k models
+    //
+    // 9. Update last k significances
+    for (int i = 0; i < k; ++i) {
+        significanceModels[n_models_bak - k + i] = alpha_t;
+    }
+    // 10. n_models <- n_models_bak
+    n_models = n_models_bak;
+    return {weights, alpha_t, terminate};
+}
+} // namespace bayesnet

bayesnet/ensembles/Boost.h

@@ -0,0 +1,57 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef BOOST_H
+#define BOOST_H
+#include <string>
+#include <tuple>
+#include <vector>
+#include <nlohmann/json.hpp>
+#include <torch/torch.h>
+#include "Ensemble.h"
+#include "bayesnet/feature_selection/FeatureSelect.h"
+namespace bayesnet {
+    const struct {
+        std::string CFS = "CFS";
+        std::string FCBF = "FCBF";
+        std::string IWSS = "IWSS";
+    }SelectFeatures;
+    const struct {
+        std::string ASC = "asc";
+        std::string DESC = "desc";
+        std::string RAND = "rand";
+    }Orders;
+    class Boost : public Ensemble {
+    public:
+        explicit Boost(bool predict_voting = false);
+        virtual ~Boost() override = default;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
+    protected:
+        std::vector<int> featureSelection(torch::Tensor& weights_);
+        void buildModel(const torch::Tensor& weights) override;
+        std::tuple<torch::Tensor&, double, bool> 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);
+        void add_model(std::unique_ptr<Classifier> model, double significance);
+        void remove_last_model();
+        //
+        // Attributes
+        //
+        torch::Tensor X_train, y_train, X_test, y_test;
+        // Hyperparameters
+        bool bisection = true; // if true, use bisection stratety to add k models at once to the ensemble
+        int maxTolerance = 3;
+        std::string order_algorithm = Orders.DESC; // order to process the KBest features asc, desc, rand
+        bool convergence = true; //if true, stop when the model does not improve
+        bool convergence_best = false; // wether to keep the best accuracy to the moment or the last accuracy as prior accuracy
+        bool selectFeatures = false; // if true, use feature selection
+        std::string select_features_algorithm; // Selected feature selection algorithm
+        FeatureSelect* featureSelector = nullptr;
+        double threshold = -1;
+        bool block_update = false; // if true, use block update algorithm, only meaningful if bisection is true
+        bool alpha_block = false; // if true, the alpha is computed with the ensemble built so far and the new model
+    };
+}
+#endif

bayesnet/ensembles/BoostA2DE.cc

@@ -0,0 +1,165 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <limits.h>
+#include <tuple>
+#include <folding.hpp>
+#include "BoostA2DE.h"
+
+namespace bayesnet {
+
+    BoostA2DE::BoostA2DE(bool predict_voting) : Boost(predict_voting)
+    {
+    }
+    std::vector<int> BoostA2DE::initializeModels(const Smoothing_t smoothing)
+    {
+        torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
+        std::vector<int> featuresSelected = featureSelection(weights_);
+        if (featuresSelected.size() < 2) {
+            notes.push_back("No features selected in initialization");
+            status = ERROR;
+            return std::vector<int>();
+        }
+        for (int i = 0; i < featuresSelected.size() - 1; i++) {
+            for (int j = i + 1; j < featuresSelected.size(); j++) {
+                auto parents = { featuresSelected[i], featuresSelected[j] };
+                std::unique_ptr<Classifier> model = std::make_unique<SPnDE>(parents);
+                model->fit(dataset, features, className, states, weights_, smoothing);
+                models.push_back(std::move(model));
+                significanceModels.push_back(1.0); // They will be updated later in trainModel
+                n_models++;
+            }
+        }
+        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 BoostA2DE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
+    {
+        //
+        // Logging setup
+        //
+        // loguru::set_thread_name("BoostA2DE");
+        // loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
+        // loguru::add_file("boostA2DE.log", loguru::Truncate, loguru::Verbosity_MAX);
+
+        // Algorithm based on the adaboost algorithm for classification
+        // 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);
+        bool finished = false;
+        std::vector<int> featuresUsed;
+        if (selectFeatures) {
+            featuresUsed = initializeModels(smoothing);
+            if (featuresUsed.size() == 0) {
+                return;
+            }
+            auto ypred = predict(X_train);
+            std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
+            // Update significance of the models
+            for (int i = 0; i < n_models; ++i) {
+                significanceModels[i] = alpha_t;
+            }
+            if (finished) {
+                return;
+            }
+        }
+        int numItemsPack = 0; // The counter of the models inserted in the current pack
+        // Variables to control the accuracy finish condition
+        double priorAccuracy = 0.0;
+        double improvement = 1.0;
+        double convergence_threshold = 1e-4;
+        int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
+        // Step 0: Set the finish condition
+        // epsilon sub t > 0.5 => inverse the weights policy
+        // validation error is not decreasing
+        // run out of features
+        bool ascending = order_algorithm == Orders.ASC;
+        std::mt19937 g{ 173 };
+        std::vector<std::pair<int, int>> pairSelection;
+        while (!finished) {
+            // Step 1: Build ranking with mutual information
+            pairSelection = metrics.SelectKPairs(weights_, featuresUsed, ascending, 0); // Get all the pairs sorted
+            if (order_algorithm == Orders.RAND) {
+                std::shuffle(pairSelection.begin(), pairSelection.end(), g);
+            }
+            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());
+            while (counter++ < k && pairSelection.size() > 0) {
+                auto feature_pair = pairSelection[0];
+                pairSelection.erase(pairSelection.begin());
+                std::unique_ptr<Classifier> model;
+                model = std::make_unique<SPnDE>(std::vector<int>({ feature_pair.first, feature_pair.second }));
+                model->fit(dataset, features, className, states, weights_, smoothing);
+                alpha_t = 0.0;
+                if (!block_update) {
+                    auto ypred = model->predict(X_train);
+                    // Step 3.1: Compute the classifier amout of say
+                    std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
+                }
+                // Step 3.4: Store classifier and its accuracy to weigh its future vote
+                numItemsPack++;
+                models.push_back(std::move(model));
+                significanceModels.push_back(alpha_t);
+                n_models++;
+                // VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());
+            }
+            if (block_update) {
+                std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
+            }
+            if (convergence && !finished) {
+                auto y_val_predict = predict(X_test);
+                double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
+                if (priorAccuracy == 0) {
+                    priorAccuracy = accuracy;
+                } else {
+                    improvement = accuracy - priorAccuracy;
+                }
+                if (improvement < convergence_threshold) {
+                    // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance++;
+                } else {
+                    // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance = 0; // Reset the counter if the model performs better
+                    numItemsPack = 0;
+                }
+                if (convergence_best) {
+                    // Keep the best accuracy until now as the prior accuracy
+                    priorAccuracy = std::max(accuracy, priorAccuracy);
+                } else {
+                    // Keep the last accuray obtained as the prior accuracy
+                    priorAccuracy = accuracy;
+                }
+            }
+            // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());
+            finished = finished || tolerance > maxTolerance || pairSelection.size() == 0;
+        }
+        if (tolerance > maxTolerance) {
+            if (numItemsPack < n_models) {
+                notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
+                for (int i = 0; i < numItemsPack; ++i) {
+                    significanceModels.pop_back();
+                    models.pop_back();
+                    n_models--;
+                }
+            } else {
+                notes.push_back("Convergence threshold reached & 0 models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
+            }
+        }
+        if (pairSelection.size() > 0) {
+            notes.push_back("Pairs not used in train: " + std::to_string(pairSelection.size()));
+            status = WARNING;
+        }
+        notes.push_back("Number of models: " + std::to_string(n_models));
+    }
+    std::vector<std::string> BoostA2DE::graph(const std::string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

bayesnet/ensembles/BoostA2DE.h

@@ -0,0 +1,25 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef BOOSTA2DE_H
+#define BOOSTA2DE_H
+#include <string>
+#include <vector>
+#include "bayesnet/classifiers/SPnDE.h"
+#include "Boost.h"
+namespace bayesnet {
+    class BoostA2DE : public Boost {
+    public:
+        explicit BoostA2DE(bool predict_voting = false);
+        virtual ~BoostA2DE() = default;
+        std::vector<std::string> graph(const std::string& title = "BoostA2DE") const override;
+    protected:
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
+    private:
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
+    };
+}
+#endif

bayesnet/ensembles/BoostAODE.cc

@@ -0,0 +1,181 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "BoostAODE.h"
+#include "bayesnet/classifiers/SPODE.h"
+#include <limits.h>
+// #include <loguru.cpp>
+// #include <loguru.hpp>
+#include <random>
+#include <set>
+#include <tuple>
+
+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);
+        std::vector<int> featuresSelected = featureSelection(weights_);
+        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));
+            significanceModels.push_back(1.0); // They will be updated later in trainModel
+            n_models++;
+        }
+        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)
+    {
+        //
+        // Logging setup
+        //
+        // loguru::set_thread_name("BoostAODE");
+        // loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
+        // loguru::add_file("boostAODE.log", loguru::Truncate, loguru::Verbosity_MAX);
+
+        // Algorithm based on the adaboost algorithm for classification
+        // 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);
+        bool finished = false;
+        std::vector<int> featuresUsed;
+        n_models = 0;
+        if (selectFeatures) {
+            featuresUsed = initializeModels(smoothing);
+            auto ypred = predict(X_train);
+            std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
+            // Update significance of the models
+            for (int i = 0; i < n_models; ++i) {
+                significanceModels.push_back(alpha_t);
+            }
+            // VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t, n_models);
+            if (finished) {
+                return;
+            }
+        }
+        int numItemsPack = 0; // The counter of the models inserted in the current pack
+        // Variables to control the accuracy finish condition
+        double priorAccuracy = 0.0;
+        double improvement = 1.0;
+        double convergence_threshold = 1e-4;
+        int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
+        // Step 0: Set the finish condition
+        // epsilon sub t > 0.5 => inverse the weights policy
+        // validation error is not decreasing
+        // run out of features
+        bool ascending = order_algorithm == Orders.ASC;
+        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
+            if (order_algorithm == Orders.RAND) {
+                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));
+            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());
+            while (counter++ < k && featureSelection.size() > 0) {
+                auto feature = featureSelection[0];
+                featureSelection.erase(featureSelection.begin());
+                std::unique_ptr<Classifier> model;
+                model = std::make_unique<SPODE>(feature);
+                model->fit(dataset, features, className, states, weights_, smoothing);
+                alpha_t = 0.0;
+                if (!block_update) {
+                    torch::Tensor ypred;
+                    if (alpha_block) {
+                        //
+                        // Compute the prediction with the current ensemble + model
+                        //
+                        // Add the model to the ensemble
+                        n_models++;
+                        models.push_back(std::move(model));
+                        significanceModels.push_back(1);
+                        // Compute the prediction
+                        ypred = predict(X_train);
+                        // Remove the model from the ensemble
+                        model = std::move(models.back());
+                        models.pop_back();
+                        significanceModels.pop_back();
+                        n_models--;
+                    } else {
+                        ypred = model->predict(X_train);
+                    }
+                    // Step 3.1: Compute the classifier amout of say
+                    std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
+                }
+                // Step 3.4: Store classifier and its accuracy to weigh its future vote
+                numItemsPack++;
+                featuresUsed.push_back(feature);
+                models.push_back(std::move(model));
+                significanceModels.push_back(alpha_t);
+                n_models++;
+                // VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d featuresUsed: %zu", finished, numItemsPack, n_models, featuresUsed.size());
+            }
+            if (block_update) {
+                std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
+            }
+            if (convergence && !finished) {
+                auto y_val_predict = predict(X_test);
+                double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
+                if (priorAccuracy == 0) {
+                    priorAccuracy = accuracy;
+                } else {
+                    improvement = accuracy - priorAccuracy;
+                }
+                if (improvement < convergence_threshold) {
+                    // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance++;
+                } else {
+                    // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance = 0; // Reset the counter if the model performs better
+                    numItemsPack = 0;
+                }
+                if (convergence_best) {
+                    // Keep the best accuracy until now as the prior accuracy
+                    priorAccuracy = std::max(accuracy, priorAccuracy);
+                } else {
+                    // Keep the last accuray obtained as the prior accuracy
+                    priorAccuracy = accuracy;
+                }
+            }
+            // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());
+            finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
+        }
+        if (tolerance > maxTolerance) {
+            if (numItemsPack < n_models) {
+                notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
+                for (int i = 0; i < numItemsPack; ++i) {
+                    significanceModels.pop_back();
+                    models.pop_back();
+                    n_models--;
+                }
+            } else {
+                notes.push_back("Convergence threshold reached & 0 models eliminated");
+                // VLG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
+            }
+        }
+        if (featuresUsed.size() != features.size()) {
+            notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()));
+            status = WARNING;
+        }
+        notes.push_back("Number of models: " + std::to_string(n_models));
+    }
+    std::vector<std::string> BoostAODE::graph(const std::string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

bayesnet/ensembles/BoostAODE.h

@@ -0,0 +1,25 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef BOOSTAODE_H
+#define BOOSTAODE_H
+#include <string>
+#include <vector>
+#include "Boost.h"
+
+namespace bayesnet {
+    class BoostAODE : public Boost {
+    public:
+        explicit BoostAODE(bool predict_voting = false);
+        virtual ~BoostAODE() = default;
+        std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
+    protected:
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
+    private:
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
+    };
+}
+#endif

bayesnet/ensembles/Ensemble.cc

@@ -0,0 +1,197 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#include "Ensemble.h"
+
+namespace bayesnet {
+
+    Ensemble::Ensemble(bool predict_voting) : Classifier(Network()), n_models(0), predict_voting(predict_voting)
+    {
+    };
+    const std::string ENSEMBLE_NOT_FITTED = "Ensemble has not been fitted";
+    void Ensemble::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
+    {
+        n_models = models.size();
+        for (auto i = 0; i < n_models; ++i) {
+            // fit with std::vectors
+            models[i]->fit(dataset, features, className, states, smoothing);
+        }
+    }
+    std::vector<int> Ensemble::compute_arg_max(std::vector<std::vector<double>>& X)
+    {
+        std::vector<int> y_pred;
+        for (auto i = 0; i < X.size(); ++i) {
+            auto max = std::max_element(X[i].begin(), X[i].end());
+            y_pred.push_back(std::distance(X[i].begin(), max));
+        }
+        return y_pred;
+    }
+    torch::Tensor Ensemble::compute_arg_max(torch::Tensor& X)
+    {
+        auto y_pred = torch::argmax(X, 1);
+        return y_pred;
+    }
+    torch::Tensor Ensemble::voting(torch::Tensor& votes)
+    {
+        // Convert m x n_models tensor to a m x n_class_states with voting probabilities
+        auto y_pred_ = votes.accessor<int, 2>();
+        std::vector<int> y_pred_final;
+        int numClasses = states.at(className).size();
+        // votes is m x n_models with the prediction of every model for each sample
+        auto result = torch::zeros({ votes.size(0), numClasses }, torch::kFloat32);
+        auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
+        for (int i = 0; i < votes.size(0); ++i) {
+            // n_votes store in each index (value of class) the significance added by each model
+            // i.e. n_votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions
+            std::vector<double> n_votes(numClasses, 0.0);
+            for (int j = 0; j < n_models; ++j) {
+                n_votes[y_pred_[i][j]] += significanceModels.at(j);
+            }
+            result[i] = torch::tensor(n_votes);
+        }
+        // To only do one division and gain precision
+        result /= sum;
+        return result;
+    }
+    std::vector<std::vector<double>> Ensemble::predict_proba(std::vector<std::vector<int>>& X)
+    {
+        if (!fitted) {
+            throw std::logic_error(ENSEMBLE_NOT_FITTED);
+        }
+        return predict_voting ? predict_average_voting(X) : predict_average_proba(X);
+    }
+    torch::Tensor Ensemble::predict_proba(torch::Tensor& X)
+    {
+        if (!fitted) {
+            throw std::logic_error(ENSEMBLE_NOT_FITTED);
+        }
+        return predict_voting ? predict_average_voting(X) : predict_average_proba(X);
+    }
+    std::vector<int> Ensemble::predict(std::vector<std::vector<int>>& X)
+    {
+        auto res = predict_proba(X);
+        return compute_arg_max(res);
+    }
+    torch::Tensor Ensemble::predict(torch::Tensor& X)
+    {
+        auto res = predict_proba(X);
+        return compute_arg_max(res);
+    }
+    torch::Tensor Ensemble::predict_average_proba(torch::Tensor& X)
+    {
+        auto n_states = models[0]->getClassNumStates();
+        torch::Tensor y_pred = torch::zeros({ X.size(1), n_states }, torch::kFloat32);
+        for (auto i = 0; i < n_models; ++i) {
+            auto ypredict = models[i]->predict_proba(X);
+            /*std::cout << "model " << i << " prediction: " << ypredict << " significance " << significanceModels[i] << std::endl;*/
+            y_pred += ypredict * significanceModels[i];
+        }
+        auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
+        y_pred /= sum;
+        return y_pred;
+    }
+    std::vector<std::vector<double>> Ensemble::predict_average_proba(std::vector<std::vector<int>>& X)
+    {
+        auto n_states = models[0]->getClassNumStates();
+        std::vector<std::vector<double>> y_pred(X[0].size(), std::vector<double>(n_states, 0.0));
+        for (auto i = 0; i < n_models; ++i) {
+            auto ypredict = models[i]->predict_proba(X);
+            assert(ypredict.size() == y_pred.size());
+            assert(ypredict[0].size() == y_pred[0].size());
+            // Multiply each prediction by the significance of the model and then add it to the final prediction
+            for (auto j = 0; j < ypredict.size(); ++j) {
+                std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),
+                    [significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });
+            }
+        }
+        auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
+        //Divide each element of the prediction by the sum of the significances
+        for (auto j = 0; j < y_pred.size(); ++j) {
+            std::transform(y_pred[j].begin(), y_pred[j].end(), y_pred[j].begin(), [sum](double x) { return x / sum; });
+        }
+        return y_pred;
+    }
+    std::vector<std::vector<double>> Ensemble::predict_average_voting(std::vector<std::vector<int>>& X)
+    {
+        torch::Tensor Xt = bayesnet::vectorToTensor(X, false);
+        auto y_pred = predict_average_voting(Xt);
+        std::vector<std::vector<double>> result = tensorToVectorDouble(y_pred);
+        return result;
+    }
+    torch::Tensor Ensemble::predict_average_voting(torch::Tensor& X)
+    {
+        // Build a m x n_models tensor with the predictions of each model
+        torch::Tensor y_pred = torch::zeros({ X.size(1), n_models }, torch::kInt32);
+        for (auto i = 0; i < n_models; ++i) {
+            auto ypredict = models[i]->predict(X);
+            y_pred.index_put_({ "...", i }, ypredict);
+        }
+        return voting(y_pred);
+    }
+    float Ensemble::score(torch::Tensor& X, torch::Tensor& y)
+    {
+        auto y_pred = predict(X);
+        int correct = 0;
+        for (int i = 0; i < y_pred.size(0); ++i) {
+            if (y_pred[i].item<int>() == y[i].item<int>()) {
+                correct++;
+            }
+        }
+        return (double)correct / y_pred.size(0);
+    }
+    float Ensemble::score(std::vector<std::vector<int>>& X, std::vector<int>& y)
+    {
+        auto y_pred = predict(X);
+        int correct = 0;
+        for (int i = 0; i < y_pred.size(); ++i) {
+            if (y_pred[i] == y[i]) {
+                correct++;
+            }
+        }
+        return (double)correct / y_pred.size();
+    }
+    std::vector<std::string> Ensemble::show() const
+    {
+        auto result = std::vector<std::string>();
+        for (auto i = 0; i < n_models; ++i) {
+            auto res = models[i]->show();
+            result.insert(result.end(), res.begin(), res.end());
+        }
+        return result;
+    }
+    std::vector<std::string> Ensemble::graph(const std::string& title) const
+    {
+        auto result = std::vector<std::string>();
+        for (auto i = 0; i < n_models; ++i) {
+            auto res = models[i]->graph(title + "_" + std::to_string(i));
+            result.insert(result.end(), res.begin(), res.end());
+        }
+        return result;
+    }
+    int Ensemble::getNumberOfNodes() const
+    {
+        int nodes = 0;
+        for (auto i = 0; i < n_models; ++i) {
+            nodes += models[i]->getNumberOfNodes();
+        }
+        return nodes;
+    }
+    int Ensemble::getNumberOfEdges() const
+    {
+        int edges = 0;
+        for (auto i = 0; i < n_models; ++i) {
+            edges += models[i]->getNumberOfEdges();
+        }
+        return edges;
+    }
+    int Ensemble::getNumberOfStates() const
+    {
+        int nstates = 0;
+        for (auto i = 0; i < n_models; ++i) {
+            nstates += models[i]->getNumberOfStates();
+        }
+        return nstates;
+    }
+}

bayesnet/ensembles/Ensemble.h

@@ -0,0 +1,59 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef ENSEMBLE_H
+#define ENSEMBLE_H
+#include <torch/torch.h>
+#include "bayesnet/utils/BayesMetrics.h"
+#include "bayesnet/utils/bayesnetUtils.h"
+#include "bayesnet/classifiers/Classifier.h"
+
+namespace bayesnet {
+    class Ensemble : public Classifier {
+    public:
+        Ensemble(bool predict_voting = true);
+        virtual ~Ensemble() = default;
+        torch::Tensor predict(torch::Tensor& X) override;
+        std::vector<int> predict(std::vector<std::vector<int>>& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override;
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
+        float score(torch::Tensor& X, torch::Tensor& y) override;
+        float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
+        int getNumberOfNodes() const override;
+        int getNumberOfEdges() const override;
+        int getNumberOfStates() const override;
+        std::vector<std::string> show() const override;
+        std::vector<std::string> graph(const std::string& title) const override;
+        std::vector<std::string> topological_order()  override
+        {
+            return std::vector<std::string>();
+        }
+        std::string dump_cpt() const override
+        {
+            std::string output;
+            for (auto& model : models) {
+                output += model->dump_cpt();
+                output += std::string(80, '-') + "\n";
+            }
+            return output;
+        }
+    protected:
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
+        torch::Tensor predict_average_voting(torch::Tensor& X);
+        std::vector<std::vector<double>> predict_average_voting(std::vector<std::vector<int>>& X);
+        torch::Tensor predict_average_proba(torch::Tensor& X);
+        std::vector<std::vector<double>> predict_average_proba(std::vector<std::vector<int>>& X);
+        torch::Tensor compute_arg_max(torch::Tensor& X);
+        std::vector<int> compute_arg_max(std::vector<std::vector<double>>& X);
+        torch::Tensor voting(torch::Tensor& votes);
+        // Attributes
+        unsigned n_models;
+        std::vector<std::unique_ptr<Classifier>> models;
+        std::vector<double> significanceModels;
+        bool predict_voting;
+    };
+}
+#endif

bayesnet/ensembles/XBA2DE.cc

@@ -0,0 +1,168 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <folding.hpp>
+#include <limits.h>
+#include "XBA2DE.h"
+#include "bayesnet/classifiers/XSP2DE.h"
+#include "bayesnet/utils/TensorUtils.h"
+
+namespace bayesnet {
+
+XBA2DE::XBA2DE(bool predict_voting) : Boost(predict_voting) {}
+std::vector<int> XBA2DE::initializeModels(const Smoothing_t smoothing) {
+    torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
+    std::vector<int> featuresSelected = featureSelection(weights_);
+    if (featuresSelected.size() < 2) {
+        notes.push_back("No features selected in initialization");
+        status = ERROR;
+        return std::vector<int>();
+    }
+    for (int i = 0; i < featuresSelected.size() - 1; i++) {
+        for (int j = i + 1; j < featuresSelected.size(); j++) {
+            std::unique_ptr<Classifier> model = std::make_unique<XSp2de>(featuresSelected[i], featuresSelected[j]);
+            model->fit(dataset, features, className, states, weights_, smoothing);
+            add_model(std::move(model), 1.0);
+        }
+    }
+    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 XBA2DE::trainModel(const torch::Tensor &weights, const Smoothing_t smoothing) {
+    //
+    // Logging setup
+    //
+    // loguru::set_thread_name("XBA2DE");
+    // loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
+    // loguru::add_file("boostA2DE.log", loguru::Truncate, loguru::Verbosity_MAX);
+
+    // Algorithm based on the adaboost algorithm for classification
+    // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
+    X_train_ = TensorUtils::to_matrix(X_train);
+    y_train_ = TensorUtils::to_vector<int>(y_train);
+    if (convergence) {
+        X_test_ = TensorUtils::to_matrix(X_test);
+        y_test_ = TensorUtils::to_vector<int>(y_test);
+    }
+    fitted = true;
+    double alpha_t = 0;
+    torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
+    bool finished = false;
+    std::vector<int> featuresUsed;
+    if (selectFeatures) {
+        featuresUsed = initializeModels(smoothing);
+        if (featuresUsed.size() == 0) {
+            return;
+        }
+        auto ypred = predict(X_train);
+        std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
+        // Update significance of the models
+        for (int i = 0; i < n_models; ++i) {
+            significanceModels[i] = alpha_t;
+        }
+        if (finished) {
+            return;
+        }
+    }
+    int numItemsPack = 0; // The counter of the models inserted in the current pack
+    // Variables to control the accuracy finish condition
+    double priorAccuracy = 0.0;
+    double improvement = 1.0;
+    double convergence_threshold = 1e-4;
+    int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
+    // Step 0: Set the finish condition
+    // epsilon sub t > 0.5 => inverse the weights policy
+    // validation error is not decreasing
+    // run out of features
+    bool ascending = order_algorithm == Orders.ASC;
+    std::mt19937 g{173};
+    std::vector<std::pair<int, int>> pairSelection;
+    while (!finished) {
+        // Step 1: Build ranking with mutual information
+        pairSelection = metrics.SelectKPairs(weights_, featuresUsed, ascending, 0); // Get all the pairs sorted
+        if (order_algorithm == Orders.RAND) {
+            std::shuffle(pairSelection.begin(), pairSelection.end(), g);
+        }
+        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());
+        while (counter++ < k && pairSelection.size() > 0) {
+            auto feature_pair = pairSelection[0];
+            pairSelection.erase(pairSelection.begin());
+            std::unique_ptr<Classifier> model;
+            model = std::make_unique<XSp2de>(feature_pair.first, feature_pair.second);
+            model->fit(dataset, features, className, states, weights_, smoothing);
+            alpha_t = 0.0;
+            if (!block_update) {
+                auto ypred = model->predict(X_train);
+                // Step 3.1: Compute the classifier amout of say
+                std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
+            }
+            // Step 3.4: Store classifier and its accuracy to weigh its future vote
+            numItemsPack++;
+            models.push_back(std::move(model));
+            significanceModels.push_back(alpha_t);
+            n_models++;
+            // VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models,
+            // featuresUsed.size());
+        }
+        if (block_update) {
+            std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
+        }
+        if (convergence && !finished) {
+            auto y_val_predict = predict(X_test);
+            double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
+            if (priorAccuracy == 0) {
+                priorAccuracy = accuracy;
+            } else {
+                improvement = accuracy - priorAccuracy;
+            }
+            if (improvement < convergence_threshold) {
+                // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f
+                // current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                tolerance++;
+            } else {
+                // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f
+                // prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                tolerance = 0; // Reset the counter if the model performs better
+                numItemsPack = 0;
+            }
+            if (convergence_best) {
+                // Keep the best accuracy until now as the prior accuracy
+                priorAccuracy = std::max(accuracy, priorAccuracy);
+            } else {
+                // Keep the last accuray obtained as the prior accuracy
+                priorAccuracy = accuracy;
+            }
+        }
+        // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(),
+        // features.size());
+        finished = finished || tolerance > maxTolerance || pairSelection.size() == 0;
+    }
+    if (tolerance > maxTolerance) {
+        if (numItemsPack < n_models) {
+            notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
+            // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
+            for (int i = 0; i < numItemsPack; ++i) {
+                significanceModels.pop_back();
+                models.pop_back();
+                n_models--;
+            }
+        } else {
+            notes.push_back("Convergence threshold reached & 0 models eliminated");
+            // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d",
+            // n_models, numItemsPack);
+        }
+    }
+    if (pairSelection.size() > 0) {
+        notes.push_back("Pairs not used in train: " + std::to_string(pairSelection.size()));
+        status = WARNING;
+    }
+    notes.push_back("Number of models: " + std::to_string(n_models));
+}
+std::vector<std::string> XBA2DE::graph(const std::string &title) const { return Ensemble::graph(title); }
+} // namespace bayesnet

bayesnet/ensembles/XBA2DE.h

@@ -0,0 +1,28 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef XBA2DE_H
+#define XBA2DE_H
+#include <string>
+#include <vector>
+#include "Boost.h"
+namespace bayesnet {
+    class XBA2DE : public Boost {
+    public:
+        explicit XBA2DE(bool predict_voting = false);
+        virtual ~XBA2DE() = default;
+        std::vector<std::string> graph(const std::string& title = "XBA2DE") const override;
+        std::string getVersion() override { return version; };
+    protected:
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
+    private:
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
+        std::vector<std::vector<int>> X_train_, X_test_;
+        std::vector<int> y_train_, y_test_;
+        std::string version = "0.9.7";
+    };
+}
+#endif

bayesnet/ensembles/XBAODE.cc

@@ -0,0 +1,184 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#include "XBAODE.h"
+#include "bayesnet/classifiers/XSPODE.h"
+#include "bayesnet/utils/TensorUtils.h"
+#include <limits.h>
+#include <random>
+#include <tuple>
+
+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) {
+            std::unique_ptr<Classifier> model = std::make_unique<XSpode>(feature);
+            model->fit(dataset, features, className, states, weights_, smoothing);
+            add_model(std::move(model), 1.0);
+        }
+        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)
+    {
+        X_train_ = TensorUtils::to_matrix(X_train);
+        y_train_ = TensorUtils::to_vector<int>(y_train);
+        if (convergence) {
+            X_test_ = TensorUtils::to_matrix(X_test);
+            y_test_ = TensorUtils::to_vector<int>(y_test);
+        }
+        fitted = true;
+        double alpha_t;
+        torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
+        bool finished = false;
+        std::vector<int> featuresUsed;
+        n_models = 0;
+        if (selectFeatures) {
+            featuresUsed = initializeModels(smoothing);
+            auto ypred = predict(X_train_);
+            auto ypred_t = torch::tensor(ypred);
+            std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
+            // Update significance of the models
+            for (const int &feature : featuresUsed) {
+                significanceModels.pop_back();
+            }
+            for (const int &feature : featuresUsed) {
+                significanceModels.push_back(alpha_t);
+            }
+            // VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t,
+            // n_models);
+            if (finished) {
+                return;
+            }
+        }
+        int numItemsPack = 0; // The counter of the models inserted in the current pack
+        // Variables to control the accuracy finish condition
+        double priorAccuracy = 0.0;
+        double improvement = 1.0;
+        double convergence_threshold = 1e-4;
+        int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
+        // Step 0: Set the finish condition
+        // epsilon sub t > 0.5 => inverse the weights_ policy
+        // validation error is not decreasing
+        // run out of features
+        bool ascending = order_algorithm == bayesnet::Orders.ASC;
+        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
+            if (order_algorithm == bayesnet::Orders.RAND) {
+                std::shuffle(featureSelection.begin(), featureSelection.end(), g);
+            }
+            // Remove used features
+            featureSelection.erase(remove_if(featureSelection.begin(), featureSelection.end(),
+                                             [&](auto x) {
+                                                 return std::find(featuresUsed.begin(), featuresUsed.end(), x) !=
+                                                        featuresUsed.end();
+                                             }),
+                                   featureSelection.end());
+            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());
+            while (counter++ < k && featureSelection.size() > 0) {
+                auto feature = featureSelection[0];
+                featureSelection.erase(featureSelection.begin());
+                std::unique_ptr<Classifier> model;
+                model = std::make_unique<XSpode>(feature);
+                model->fit(dataset, features, className, states, weights_, smoothing);
+                /*dynamic_cast<XSpode*>(model.get())->fitx(X_train, y_train, weights_,
+                 * smoothing); // using exclusive XSpode fit method*/
+                // DEBUG
+                /*std::cout << dynamic_cast<XSpode*>(model.get())->to_string() <<
+                 * std::endl;*/
+                // DEBUG
+                std::vector<int> ypred;
+                if (alpha_block) {
+                    //
+                    // Compute the prediction with the current ensemble + model
+                    //
+                    // Add the model to the ensemble
+                    add_model(std::move(model), 1.0);
+                    // Compute the prediction
+                    ypred = predict(X_train_);
+                    model = std::move(models.back());
+                    // Remove the model from the ensemble
+                    remove_last_model();
+                } else {
+                    ypred = model->predict(X_train_);
+                }
+                // Step 3.1: Compute the classifier amout of say
+                auto ypred_t = torch::tensor(ypred);
+                std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
+                // Step 3.4: Store classifier and its accuracy to weigh its future vote
+                numItemsPack++;
+                featuresUsed.push_back(feature);
+                add_model(std::move(model), alpha_t);
+                // VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d
+                // featuresUsed: %zu", finished, numItemsPack, n_models,
+                // featuresUsed.size());
+            } // End of the pack
+            if (convergence && !finished) {
+                auto y_val_predict = predict(X_test);
+                double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
+                if (priorAccuracy == 0) {
+                    priorAccuracy = accuracy;
+                } else {
+                    improvement = accuracy - priorAccuracy;
+                }
+                if (improvement < convergence_threshold) {
+                    // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d
+                    // numItemsPack: %d improvement: %f prior: %f current: %f", tolerance,
+                    // numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance++;
+                } else {
+                    // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d
+                    // numItemsPack: %d improvement: %f prior: %f current: %f", tolerance,
+                    // numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance = 0; // Reset the counter if the model performs better
+                    numItemsPack = 0;
+                }
+                if (convergence_best) {
+                    // Keep the best accuracy until now as the prior accuracy
+                    priorAccuracy = std::max(accuracy, priorAccuracy);
+                } else {
+                    // Keep the last accuray obtained as the prior accuracy
+                    priorAccuracy = accuracy;
+                }
+            }
+            // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size:
+            // %zu", tolerance, featuresUsed.size(), features.size());
+            finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
+        }
+        if (tolerance > maxTolerance) {
+            if (numItemsPack < n_models) {
+                notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated
+                // of %d", numItemsPack, n_models);
+                for (int i = featuresUsed.size() - 1; i >= featuresUsed.size() - numItemsPack; --i) {
+                    remove_last_model();
+                }
+                // VLOG_SCOPE_F(4, "*Convergence threshold %d models left & %d features
+                // used.", n_models, featuresUsed.size());
+            } else {
+                notes.push_back("Convergence threshold reached & 0 models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated
+                // n_models=%d numItemsPack=%d", n_models, numItemsPack);
+            }
+        }
+        if (featuresUsed.size() != features.size()) {
+            notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " +
+                            std::to_string(features.size()));
+            status = bayesnet::WARNING;
+        }
+        notes.push_back("Number of models: " + std::to_string(n_models));
+        return;
+    }
+} // namespace bayesnet

bayesnet/ensembles/XBAODE.h

@@ -0,0 +1,27 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef XBAODE_H
+#define XBAODE_H
+#include <vector>
+#include <cmath>
+#include "Boost.h"
+
+namespace bayesnet {
+    class XBAODE : public Boost {
+    public:
+        XBAODE();
+        std::string getVersion() override { return version; };
+    protected:
+        void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
+    private:
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
+        std::vector<std::vector<int>> X_train_, X_test_;
+        std::vector<int> y_train_, y_test_;
+        std::string version = "0.9.7";
+    };
+}
+#endif // XBAODE_H

bayesnet/feature_selection/CFS.cc

@@ -0,0 +1,78 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <limits>
+#include "bayesnet/utils/bayesnetUtils.h"
+#include "CFS.h"
+namespace bayesnet {
+    void CFS::fit()
+    {
+        initialize();
+        computeSuLabels();
+        auto featureOrder = argsort(suLabels); // sort descending order
+        auto continueCondition = true;
+        auto feature = featureOrder[0];
+        selectedFeatures.push_back(feature);
+        selectedScores.push_back(suLabels[feature]);
+        featureOrder.erase(featureOrder.begin());
+        while (continueCondition) {
+            double merit = std::numeric_limits<double>::lowest();
+            int bestFeature = -1;
+            for (auto feature : featureOrder) {
+                selectedFeatures.push_back(feature);
+                // Compute merit with selectedFeatures
+                auto meritNew = computeMeritCFS();
+                if (meritNew > merit) {
+                    merit = meritNew;
+                    bestFeature = feature;
+                }
+                selectedFeatures.pop_back();
+            }
+            if (bestFeature == -1) {
+                // meritNew has to be nan due to constant features
+                break;
+            }
+            selectedFeatures.push_back(bestFeature);
+            selectedScores.push_back(merit);
+            featureOrder.erase(remove(featureOrder.begin(), featureOrder.end(), bestFeature), featureOrder.end());
+            continueCondition = computeContinueCondition(featureOrder);
+        }
+        fitted = true;
+    }
+    bool CFS::computeContinueCondition(const std::vector<int>& featureOrder)
+    {
+        if (selectedFeatures.size() == maxFeatures || featureOrder.size() == 0) {
+            return false;
+        }
+        if (selectedScores.size() >= 5) {
+            /*
+            "To prevent the best first search from exploring the entire
+            feature subset search space, a stopping criterion is imposed.
+            The search will terminate if five consecutive fully expanded
+            subsets show no improvement over the current best subset."
+            as stated in Mark A.Hall Thesis
+            */
+            double item_ant = std::numeric_limits<double>::lowest();
+            int num = 0;
+            std::vector<double> lastFive(selectedScores.end() - 5, selectedScores.end());
+            for (auto item : lastFive) {
+                if (item_ant == std::numeric_limits<double>::lowest()) {
+                    item_ant = item;
+                }
+                if (item > item_ant) {
+                    break;
+                } else {
+                    num++;
+                    item_ant = item;
+                }
+            }
+            if (num == 5) {
+                return false;
+            }
+        }
+        return true;
+    }
+}

bayesnet/feature_selection/CFS.h

@@ -0,0 +1,26 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef CFS_H
+#define CFS_H
+#include <torch/torch.h>
+#include <vector>
+#include "bayesnet/feature_selection/FeatureSelect.h"
+namespace bayesnet {
+    class CFS : public FeatureSelect {
+    public:
+        // dataset is a n+1xm tensor of integers where dataset[-1] is the y std::vector
+        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) :
+            FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights)
+        {
+        }
+        virtual ~CFS() {};
+        void fit() override;
+    private:
+        bool computeContinueCondition(const std::vector<int>& featureOrder);
+    };
+}
+#endif

bayesnet/feature_selection/FCBF.cc

@@ -0,0 +1,50 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "bayesnet/utils/bayesnetUtils.h"
+#include "FCBF.h"
+namespace bayesnet {
+
+    FCBF::FCBF(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int maxFeatures, const int classNumStates, const torch::Tensor& weights, const double threshold) :
+        FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights), threshold(threshold)
+    {
+        if (threshold < 1e-7) {
+            throw std::invalid_argument("Threshold cannot be less than 1e-7");
+        }
+    }
+    void FCBF::fit()
+    {
+        initialize();
+        computeSuLabels();
+        auto featureOrder = argsort(suLabels); // sort descending order
+        auto featureOrderCopy = featureOrder;
+        for (const auto& feature : featureOrder) {
+            // Don't self compare
+            featureOrderCopy.erase(featureOrderCopy.begin());
+            if (suLabels.at(feature) == 0.0) {
+                // The feature has been removed from the list
+                continue;
+            }
+            if (suLabels.at(feature) < threshold) {
+                break;
+            }
+            // Remove redundant features
+            for (const auto& featureCopy : featureOrderCopy) {
+                double value = computeSuFeatures(feature, featureCopy);
+                if (value >= suLabels.at(featureCopy)) {
+                    // Remove feature from list
+                    suLabels[featureCopy] = 0.0;
+                }
+            }
+            selectedFeatures.push_back(feature);
+            selectedScores.push_back(suLabels[feature]);
+            if (selectedFeatures.size() == maxFeatures) {
+                break;
+            }
+        }
+        fitted = true;
+    }
+}

bayesnet/feature_selection/FCBF.h

@@ -0,0 +1,23 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef FCBF_H
+#define FCBF_H
+#include <torch/torch.h>
+#include <vector>
+#include "bayesnet/feature_selection/FeatureSelect.h"
+namespace bayesnet {
+    class FCBF : public FeatureSelect {
+    public:
+        // dataset is a n+1xm tensor of integers where dataset[-1] is the y std::vector
+        FCBF(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int maxFeatures, const int classNumStates, const torch::Tensor& weights, const double threshold);
+        virtual ~FCBF() {};
+        void fit() override;
+    private:
+        double threshold = -1;
+    };
+}
+#endif

bayesnet/feature_selection/FeatureSelect.cc

@@ -0,0 +1,139 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "bayesnet/utils/bayesnetUtils.h"
+#include "FeatureSelect.h"
+
+namespace bayesnet {
+
+    using namespace torch::indexing;          // for Ellipsis constant
+
+    //---------------------------------------------------------------------
+    // ctor
+    //---------------------------------------------------------------------
+    FeatureSelect::FeatureSelect(const torch::Tensor& samples,
+        const std::vector<std::string>& features,
+        const std::string& className,
+        int maxFeatures,
+        int classNumStates,
+        const torch::Tensor& weights)
+        : Metrics(samples, features, className, classNumStates),
+        maxFeatures(maxFeatures == 0 ? samples.size(0) - 1 : maxFeatures),
+        weights(weights)
+    {
+    }
+
+    //---------------------------------------------------------------------
+    // public helpers
+    //---------------------------------------------------------------------
+    void FeatureSelect::initialize()
+    {
+        selectedFeatures.clear();
+        selectedScores.clear();
+        suLabels.clear();
+        suFeatures.clear();
+
+        fitted = false;
+    }
+
+    //---------------------------------------------------------------------
+    // Symmetrical Uncertainty (SU)
+    //---------------------------------------------------------------------
+    double FeatureSelect::symmetricalUncertainty(int a, int b)
+    {
+        /*
+         * Compute symmetrical uncertainty. Normalises the information gain
+         * (mutual information) with the entropies of the variables to compensate
+         * the bias due to high‑cardinality features. Range: [0, 1]
+         * See: https://www.sciencedirect.com/science/article/pii/S0020025519303603
+         */
+
+        auto x = samples.index({ a, Ellipsis });             // row a => feature a
+        auto y = (b >= 0) ? samples.index({ b, Ellipsis })    // row b (>=0) => feature b
+            : samples.index({ -1, Ellipsis }); // ‑1 treated as last row = labels
+
+        double mu = mutualInformation(x, y, weights);
+        double hx = entropy(x, weights);
+        double hy = entropy(y, weights);
+
+        const double denom = hx + hy;
+        if (denom == 0.0) return 0.0;   // perfectly pure variables
+
+        return 2.0 * mu / denom;
+    }
+
+    //---------------------------------------------------------------------
+    // SU feature–class
+    //---------------------------------------------------------------------
+    void FeatureSelect::computeSuLabels()
+    {
+        // Compute Symmetrical Uncertainty between each feature and the class labels
+        // https://en.wikipedia.org/wiki/Symmetric_uncertainty
+        const int classIdx = static_cast<int>(samples.size(0)) - 1; // labels in last row
+        suLabels.reserve(features.size());
+        for (int i = 0; i < static_cast<int>(features.size()); ++i) {
+            suLabels.emplace_back(symmetricalUncertainty(i, classIdx));
+        }
+    }
+
+    //---------------------------------------------------------------------
+    // SU feature–feature with cache
+    //---------------------------------------------------------------------
+    double FeatureSelect::computeSuFeatures(int firstFeature, int secondFeature)
+    {
+        // Order the pair to exploit symmetry => only one entry in the map
+        auto ordered = std::minmax(firstFeature, secondFeature);
+        const std::pair<int, int> key{ ordered.first, ordered.second };
+
+        auto it = suFeatures.find(key);
+        if (it != suFeatures.end()) return it->second;
+
+        double result = symmetricalUncertainty(key.first, key.second);
+        suFeatures[key] = result;  // store once (symmetry handled by ordering)
+        return result;
+    }
+
+    //---------------------------------------------------------------------
+    // Correlation‑based Feature Selection (CFS) merit
+    //---------------------------------------------------------------------
+    double FeatureSelect::computeMeritCFS()
+    {
+        const int n = static_cast<int>(selectedFeatures.size());
+        if (n == 0) return 0.0;
+
+        // average r_cf (feature–class)
+        double rcf_sum = 0.0;
+        for (int f : selectedFeatures) rcf_sum += suLabels[f];
+        const double rcf_avg = rcf_sum / n;
+
+        // average r_ff (feature–feature)
+        double rff_sum = 0.0;
+        const auto& pairs = doCombinations(selectedFeatures); // generates each unordered pair once
+        for (const auto& p : pairs) rff_sum += computeSuFeatures(p.first, p.second);
+
+        const double numPairs = n * (n - 1) * 0.5;
+        const double rff_avg = (numPairs > 0) ? rff_sum / numPairs : 0.0;
+
+        // Merit_S = k * r_cf / sqrt( k + k*(k‑1) * r_ff )      (Hall, 1999)
+        const double k = static_cast<double>(n);
+        return (k * rcf_avg) / std::sqrt(k + k * (k - 1) * rff_avg);
+    }
+
+    //---------------------------------------------------------------------
+    // getters
+    //---------------------------------------------------------------------
+    std::vector<int> FeatureSelect::getFeatures() const
+    {
+        if (!fitted) throw std::runtime_error("FeatureSelect not fitted");
+        return selectedFeatures;
+    }
+
+    std::vector<double> FeatureSelect::getScores() const
+    {
+        if (!fitted) throw std::runtime_error("FeatureSelect not fitted");
+        return selectedScores;
+    }
+}

bayesnet/feature_selection/FeatureSelect.h

@@ -0,0 +1,36 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef FEATURE_SELECT_H
+#define FEATURE_SELECT_H
+#include <torch/torch.h>
+#include <vector>
+#include "bayesnet/utils/BayesMetrics.h"
+namespace bayesnet {
+    class FeatureSelect : public Metrics {
+    public:
+        // dataset is a n+1xm tensor of integers where dataset[-1] is the y std::vector
+        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);
+        virtual ~FeatureSelect() {};
+        virtual void fit() = 0;
+        std::vector<int> getFeatures() const;
+        std::vector<double> getScores() const;
+    protected:
+        void initialize();
+        void computeSuLabels();
+        double computeSuFeatures(const int a, const int b);
+        double symmetricalUncertainty(int a, int b);
+        double computeMeritCFS();
+        const torch::Tensor& weights;
+        int maxFeatures;
+        std::vector<int> selectedFeatures;
+        std::vector<double> selectedScores;
+        std::vector<double> suLabels;
+        std::map<std::pair<int, int>, double> suFeatures;
+        bool fitted = false;
+    };
+}
+#endif

bayesnet/feature_selection/IWSS.cc

@@ -0,0 +1,69 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <limits>
+#include "bayesnet/utils/bayesnetUtils.h"
+#include "IWSS.h"
+namespace bayesnet {
+    IWSS::IWSS(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int maxFeatures, const int classNumStates, const torch::Tensor& weights, const double threshold) :
+        FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights), threshold(threshold)
+    {
+        if (threshold < 0 || threshold > .5) {
+            throw std::invalid_argument("Threshold has to be in [0, 0.5]");
+        }
+    }
+    void IWSS::fit()
+    {
+        initialize();
+        computeSuLabels();
+        auto featureOrder = argsort(suLabels); // sort descending order
+        auto featureOrderCopy = featureOrder;
+        // Add first and second features to result
+        //     First with its own score
+        auto first_feature = pop_first(featureOrderCopy);
+        selectedFeatures.push_back(first_feature);
+        selectedScores.push_back(suLabels.at(first_feature));
+        // Select second feature that maximizes merit with first
+        double maxMerit = 0.0;
+        int secondFeature = -1;
+        for (const auto& candidate : featureOrderCopy) {
+            selectedFeatures.push_back(candidate);
+            double candidateMerit = computeMeritCFS();
+            if (candidateMerit > maxMerit) {
+                maxMerit = candidateMerit;
+                secondFeature = candidate;
+            }
+            selectedFeatures.pop_back();
+        }
+
+        if (secondFeature != -1) {
+            selectedFeatures.push_back(secondFeature);
+            selectedScores.push_back(maxMerit);
+            // Remove from featureOrderCopy
+            featureOrderCopy.erase(std::remove(featureOrderCopy.begin(), featureOrderCopy.end(), secondFeature), featureOrderCopy.end());
+        }
+        double merit = maxMerit;
+        for (const auto feature : featureOrderCopy) {
+            selectedFeatures.push_back(feature);
+            // Compute merit with selectedFeatures
+            auto meritNew = computeMeritCFS();
+            double delta = merit != 0.0 ? std::abs(merit - meritNew) / merit : 0.0;
+            if (meritNew > merit || delta < threshold) {
+                if (meritNew > merit) {
+                    merit = meritNew;
+                }
+                selectedScores.push_back(meritNew);
+            } else {
+                selectedFeatures.pop_back();
+                break;
+            }
+            if (selectedFeatures.size() == maxFeatures) {
+                break;
+            }
+        }
+        fitted = true;
+    }
+}

bayesnet/feature_selection/IWSS.h

@@ -0,0 +1,23 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef IWSS_H
+#define IWSS_H
+#include <vector>
+#include <torch/torch.h>
+#include "FeatureSelect.h"
+namespace bayesnet {
+    class IWSS : public FeatureSelect {
+    public:
+        // dataset is a n+1xm tensor of integers where dataset[-1] is the y std::vector
+        IWSS(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int maxFeatures, const int classNumStates, const torch::Tensor& weights, const double threshold);
+        virtual ~IWSS() {};
+        void fit() override;
+    private:
+        double threshold = -1;
+    };
+}
+#endif

bayesnet/feature_selection/L1FS.cc

@@ -0,0 +1,279 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <algorithm>
+#include <cmath>
+#include <numeric>
+#include "bayesnet/utils/bayesnetUtils.h"
+#include "L1FS.h"
+
+namespace bayesnet {
+    using namespace torch::indexing;
+
+    L1FS::L1FS(const torch::Tensor& samples,
+        const std::vector<std::string>& features,
+        const std::string& className,
+        const int maxFeatures,
+        const int classNumStates,
+        const torch::Tensor& weights,
+        const double alpha,
+        const int maxIter,
+        const double tolerance,
+        const bool fitIntercept)
+        : FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights),
+        alpha(alpha), maxIter(maxIter), tolerance(tolerance), fitIntercept(fitIntercept)
+    {
+        if (alpha < 0) {
+            throw std::invalid_argument("Alpha (regularization strength) must be non-negative");
+        }
+        if (maxIter < 1) {
+            throw std::invalid_argument("Maximum iterations must be positive");
+        }
+        if (tolerance <= 0) {
+            throw std::invalid_argument("Tolerance must be positive");
+        }
+
+        // Determine if this is a regression or classification task
+        // For simplicity, assume binary classification if classNumStates == 2
+        // and regression otherwise (this can be refined based on your needs)
+        isRegression = (classNumStates > 2 || classNumStates == 0);
+    }
+
+    void L1FS::fit()
+    {
+        initialize();
+
+        // Prepare data
+        int n_samples = samples.size(1);
+        int n_features = features.size();
+
+        // Extract features (all rows except last)
+        auto X = samples.index({ Slice(0, n_features), Slice() }).t().contiguous();
+
+        // Extract labels (last row)
+        auto y = samples.index({ -1, Slice() }).contiguous();
+
+        // Convert to float for numerical operations
+        X = X.to(torch::kFloat32);
+        y = y.to(torch::kFloat32);
+
+        // Normalize features for better convergence
+        auto X_mean = X.mean(0);
+        auto X_std = X.std(0);
+        X_std = torch::where(X_std == 0, torch::ones_like(X_std), X_std);
+        X = (X - X_mean) / X_std;
+
+        if (isRegression) {
+            // Normalize y for regression
+            auto y_mean = y.mean();
+            auto y_std = y.std();
+            if (y_std.item<double>() > 0) {
+                y = (y - y_mean) / y_std;
+            }
+            fitLasso(X, y, weights);
+        } else {
+            // For binary classification
+            fitL1Logistic(X, y, weights);
+        }
+
+        // Select features based on non-zero coefficients
+        std::vector<std::pair<int, double>> featureImportance;
+        for (int i = 0; i < n_features; ++i) {
+            double coef_magnitude = std::abs(coefficients[i]);
+            if (coef_magnitude > 1e-10) {  // Threshold for numerical zero
+                featureImportance.push_back({ i, coef_magnitude });
+            }
+        }
+
+        // If all coefficients are zero (high regularization), select based on original feature-class correlation
+        if (featureImportance.empty() && maxFeatures > 0) {
+            // Compute SU with labels as fallback
+            computeSuLabels();
+            auto featureOrder = argsort(suLabels);
+
+            // Select top features by SU score
+            int numToSelect = std::min(static_cast<int>(featureOrder.size()),
+                std::min(maxFeatures, 3)); // At most 3 features as fallback
+
+            for (int i = 0; i < numToSelect; ++i) {
+                selectedFeatures.push_back(featureOrder[i]);
+                selectedScores.push_back(suLabels[featureOrder[i]]);
+            }
+        } else {
+            // Sort by importance (absolute coefficient value)
+            std::sort(featureImportance.begin(), featureImportance.end(),
+                [](const auto& a, const auto& b) { return a.second > b.second; });
+
+            // Select top features up to maxFeatures
+            int numToSelect = std::min(static_cast<int>(featureImportance.size()),
+                maxFeatures);
+
+            for (int i = 0; i < numToSelect; ++i) {
+                selectedFeatures.push_back(featureImportance[i].first);
+                selectedScores.push_back(featureImportance[i].second);
+            }
+        }
+
+        fitted = true;
+    }
+
+    void L1FS::fitLasso(const torch::Tensor& X, const torch::Tensor& y,
+        const torch::Tensor& sampleWeights)
+    {
+        int n_samples = X.size(0);
+        int n_features = X.size(1);
+
+        // Initialize coefficients
+        coefficients.resize(n_features, 0.0);
+        double intercept = 0.0;
+
+        // Ensure consistent types
+        torch::Tensor weights = sampleWeights.to(torch::kFloat32);
+
+        // Coordinate descent for Lasso
+        torch::Tensor residuals = y.clone();
+        if (fitIntercept) {
+            intercept = (y * weights).sum().item<float>() / weights.sum().item<float>();
+            residuals = y - intercept;
+        }
+
+        // Precompute feature norms
+        std::vector<double> featureNorms(n_features);
+        for (int j = 0; j < n_features; ++j) {
+            auto Xj = X.index({ Slice(), j });
+            featureNorms[j] = (Xj * Xj * weights).sum().item<float>();
+        }
+
+        // Coordinate descent iterations
+        for (int iter = 0; iter < maxIter; ++iter) {
+            double maxChange = 0.0;
+
+            // Update each coordinate
+            for (int j = 0; j < n_features; ++j) {
+                auto Xj = X.index({ Slice(), j });
+
+                // Compute partial residuals (excluding feature j)
+                torch::Tensor partialResiduals = residuals + coefficients[j] * Xj;
+
+                // Compute rho (correlation with residuals)
+                double rho = (Xj * partialResiduals * weights).sum().item<float>();
+
+                // Soft thresholding
+                double oldCoef = coefficients[j];
+                coefficients[j] = softThreshold(rho, alpha) / featureNorms[j];
+
+                // Update residuals
+                residuals = partialResiduals - coefficients[j] * Xj;
+
+                maxChange = std::max(maxChange, std::abs(coefficients[j] - oldCoef));
+            }
+
+            // Update intercept if needed
+            if (fitIntercept) {
+                double oldIntercept = intercept;
+                intercept = (residuals * weights).sum().item<float>() /
+                    weights.sum().item<float>();
+                residuals = residuals - (intercept - oldIntercept);
+                maxChange = std::max(maxChange, std::abs(intercept - oldIntercept));
+            }
+
+            // Check convergence
+            if (maxChange < tolerance) {
+                break;
+            }
+        }
+    }
+
+    void L1FS::fitL1Logistic(const torch::Tensor& X, const torch::Tensor& y,
+        const torch::Tensor& sampleWeights)
+    {
+        int n_samples = X.size(0);
+        int n_features = X.size(1);
+
+        // Initialize coefficients
+        torch::Tensor coef = torch::zeros({ n_features }, torch::kFloat32);
+        double intercept = 0.0;
+
+        // Ensure consistent types
+        torch::Tensor weights = sampleWeights.to(torch::kFloat32);
+
+        // Learning rate (can be adaptive)
+        double learningRate = 0.01;
+
+        // Proximal gradient descent
+        for (int iter = 0; iter < maxIter; ++iter) {
+            // Compute predictions
+            torch::Tensor linearPred = X.matmul(coef);
+            if (fitIntercept) {
+                linearPred = linearPred + intercept;
+            }
+            torch::Tensor pred = sigmoid(linearPred);
+
+            // Compute gradient
+            torch::Tensor diff = pred - y;
+            torch::Tensor grad = X.t().matmul(diff * weights) / n_samples;
+
+            // Gradient descent step
+            torch::Tensor coef_new = coef - learningRate * grad;
+
+            // Proximal step (soft thresholding)
+            for (int j = 0; j < n_features; ++j) {
+                coef_new[j] = softThreshold(coef_new[j].item<float>(),
+                    learningRate * alpha);
+            }
+
+            // Update intercept if needed
+            if (fitIntercept) {
+                double grad_intercept = (diff * weights).sum().item<float>() / n_samples;
+                intercept -= learningRate * grad_intercept;
+            }
+
+            // Check convergence
+            double change = (coef_new - coef).abs().max().item<float>();
+            coef = coef_new;
+
+            if (change < tolerance) {
+                break;
+            }
+
+            // Adaptive learning rate (optional)
+            if (iter % 100 == 0) {
+                learningRate *= 0.9;
+            }
+        }
+
+        // Store final coefficients
+        coefficients.resize(n_features);
+        for (int j = 0; j < n_features; ++j) {
+            coefficients[j] = coef[j].item<float>();
+        }
+    }
+
+    double L1FS::softThreshold(double x, double lambda) const
+    {
+        if (x > lambda) {
+            return x - lambda;
+        } else if (x < -lambda) {
+            return x + lambda;
+        } else {
+            return 0.0;
+        }
+    }
+
+    torch::Tensor L1FS::sigmoid(const torch::Tensor& z) const
+    {
+        return 1.0 / (1.0 + torch::exp(-z));
+    }
+
+    std::vector<double> L1FS::getCoefficients() const
+    {
+        if (!fitted) {
+            throw std::runtime_error("L1FS not fitted");
+        }
+        return coefficients;
+    }
+
+} // namespace bayesnet

bayesnet/feature_selection/L1FS.h

@@ -0,0 +1,83 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef L1FS_H
+#define L1FS_H
+#include <torch/torch.h>
+#include <vector>
+#include "bayesnet/feature_selection/FeatureSelect.h"
+
+namespace bayesnet {
+    /**
+     * L1-Regularized Feature Selection (L1FS)
+     *
+     * This class implements feature selection using L1-regularized linear models.
+     * For classification tasks, it uses one-vs-rest logistic regression with L1 penalty.
+     * For regression tasks, it uses Lasso regression.
+     *
+     * The L1 penalty induces sparsity in the model coefficients, effectively
+     * performing feature selection by setting irrelevant feature weights to zero.
+     */
+    class L1FS : public FeatureSelect {
+    public:
+        /**
+         * Constructor for L1FS
+         * @param samples n+1xm tensor where samples[-1] is the target variable
+         * @param features vector of feature names
+         * @param className name of the class/target variable
+         * @param maxFeatures maximum number of features to select (0 = all)
+         * @param classNumStates number of states for classification (ignored for regression)
+         * @param weights sample weights
+         * @param alpha L1 regularization strength (higher = more sparsity)
+         * @param maxIter maximum iterations for optimization
+         * @param tolerance convergence tolerance
+         * @param fitIntercept whether to fit an intercept term
+         */
+        L1FS(const torch::Tensor& samples,
+            const std::vector<std::string>& features,
+            const std::string& className,
+            const int maxFeatures,
+            const int classNumStates,
+            const torch::Tensor& weights,
+            const double alpha = 1.0,
+            const int maxIter = 1000,
+            const double tolerance = 1e-4,
+            const bool fitIntercept = true);
+
+        virtual ~L1FS() {};
+
+        void fit() override;
+
+        // Get the learned coefficients for each feature
+        std::vector<double> getCoefficients() const;
+
+    private:
+        double alpha;        // L1 regularization strength
+        int maxIter;         // Maximum iterations for optimization
+        double tolerance;    // Convergence tolerance
+        bool fitIntercept;   // Whether to fit intercept
+        bool isRegression;   // Task type (regression vs classification)
+
+        std::vector<double> coefficients;  // Learned coefficients
+
+        // Coordinate descent for Lasso regression
+        void fitLasso(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& sampleWeights);
+
+        // Proximal gradient descent for L1-regularized logistic regression
+        void fitL1Logistic(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& sampleWeights);
+
+        // Soft thresholding operator for L1 regularization
+        double softThreshold(double x, double lambda) const;
+
+        // Logistic function
+        torch::Tensor sigmoid(const torch::Tensor& z) const;
+
+        // Compute logistic loss
+        double logisticLoss(const torch::Tensor& X, const torch::Tensor& y,
+            const torch::Tensor& coef, const torch::Tensor& sampleWeights) const;
+    };
+}
+#endif

bayesnet/network/Network.cc

@@ -0,0 +1,619 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <thread>
+#include <sstream>
+#include <numeric>
+#include <algorithm>
+#include "Network.h"
+#include "bayesnet/utils/bayesnetUtils.h"
+#include "bayesnet/utils/CountingSemaphore.h"
+#include <pthread.h>
+#include <fstream>
+namespace bayesnet {
+    Network::Network() : fitted{ false }, classNumStates{ 0 }
+    {
+    }
+    Network::Network(const Network& other) 
+        : features(other.features), className(other.className), classNumStates(other.classNumStates),
+          fitted(other.fitted)
+    {
+        // Deep copy the samples tensor
+        if (other.samples.defined()) {
+            samples = other.samples.clone();
+        }
+        
+        // First, create all nodes (without relationships)
+        for (const auto& node : other.nodes) {
+            nodes[node.first] = std::make_unique<Node>(*node.second);
+        }
+        
+        // Second, reconstruct the relationships between nodes
+        for (const auto& node : other.nodes) {
+            const std::string& nodeName = node.first;
+            Node* originalNode = node.second.get();
+            Node* newNode = nodes[nodeName].get();
+            
+            // Reconstruct parent relationships
+            for (Node* parent : originalNode->getParents()) {
+                const std::string& parentName = parent->getName();
+                if (nodes.find(parentName) != nodes.end()) {
+                    newNode->addParent(nodes[parentName].get());
+                }
+            }
+            
+            // Reconstruct child relationships
+            for (Node* child : originalNode->getChildren()) {
+                const std::string& childName = child->getName();
+                if (nodes.find(childName) != nodes.end()) {
+                    newNode->addChild(nodes[childName].get());
+                }
+            }
+        }
+    }
+    
+    Network& Network::operator=(const Network& other)
+    {
+        if (this != &other) {
+            // Clear existing state
+            nodes.clear();
+            features = other.features;
+            className = other.className;
+            classNumStates = other.classNumStates;
+            fitted = other.fitted;
+            
+            // Deep copy the samples tensor
+            if (other.samples.defined()) {
+                samples = other.samples.clone();
+            } else {
+                samples = torch::Tensor();
+            }
+            
+            // First, create all nodes (without relationships)
+            for (const auto& node : other.nodes) {
+                nodes[node.first] = std::make_unique<Node>(*node.second);
+            }
+            
+            // Second, reconstruct the relationships between nodes
+            for (const auto& node : other.nodes) {
+                const std::string& nodeName = node.first;
+                Node* originalNode = node.second.get();
+                Node* newNode = nodes[nodeName].get();
+                
+                // Reconstruct parent relationships
+                for (Node* parent : originalNode->getParents()) {
+                    const std::string& parentName = parent->getName();
+                    if (nodes.find(parentName) != nodes.end()) {
+                        newNode->addParent(nodes[parentName].get());
+                    }
+                }
+                
+                // Reconstruct child relationships
+                for (Node* child : originalNode->getChildren()) {
+                    const std::string& childName = child->getName();
+                    if (nodes.find(childName) != nodes.end()) {
+                        newNode->addChild(nodes[childName].get());
+                    }
+                }
+            }
+        }
+        return *this;
+    }
+    void Network::initialize()
+    {
+        features.clear();
+        className = "";
+        classNumStates = 0;
+        fitted = false;
+        nodes.clear();
+        samples = torch::Tensor();
+    }
+    torch::Tensor& Network::getSamples()
+    {
+        return samples;
+    }
+    void Network::addNode(const std::string& name)
+    {
+        if (fitted) {
+            throw std::invalid_argument("Cannot add node to a fitted network. Initialize first.");
+        }
+        if (name == "") {
+            throw std::invalid_argument("Node name cannot be empty");
+        }
+        if (nodes.find(name) != nodes.end()) {
+            return;
+        }
+        if (find(features.begin(), features.end(), name) == features.end()) {
+            features.push_back(name);
+        }
+        nodes[name] = std::make_unique<Node>(name);
+    }
+    std::vector<std::string> Network::getFeatures() const
+    {
+        return features;
+    }
+    int Network::getClassNumStates() const
+    {
+        return classNumStates;
+    }
+    int Network::getStates() const
+    {
+        int result = 0;
+        for (auto& node : nodes) {
+            result += node.second->getNumStates();
+        }
+        return result;
+    }
+    std::string Network::getClassName() const
+    {
+        return className;
+    }
+    bool Network::isCyclic(const std::string& nodeId, std::unordered_set<std::string>& visited, std::unordered_set<std::string>& recStack)
+    {
+        if (visited.find(nodeId) == visited.end()) // if node hasn't been visited yet
+        {
+            visited.insert(nodeId);
+            recStack.insert(nodeId);
+            for (Node* child : nodes[nodeId]->getChildren()) {
+                if (visited.find(child->getName()) == visited.end() && isCyclic(child->getName(), visited, recStack))
+                    return true;
+                if (recStack.find(child->getName()) != recStack.end())
+                    return true;
+            }
+        }
+        recStack.erase(nodeId); // remove node from recursion stack before function ends
+        return false;
+    }
+    void Network::addEdge(const std::string& parent, const std::string& child)
+    {
+        if (fitted) {
+            throw std::invalid_argument("Cannot add edge to a fitted network. Initialize first.");
+        }
+        if (nodes.find(parent) == nodes.end()) {
+            throw std::invalid_argument("Parent node " + parent + " does not exist");
+        }
+        if (nodes.find(child) == nodes.end()) {
+            throw std::invalid_argument("Child node " + child + " does not exist");
+        }
+        // Check if the edge is already in the graph
+        for (auto& node : nodes[parent]->getChildren()) {
+            if (node->getName() == child) {
+                throw std::invalid_argument("Edge " + parent + " -> " + child + " already exists");
+            }
+        }
+        // Temporarily add edge to check for cycles
+        nodes[parent]->addChild(nodes[child].get());
+        nodes[child]->addParent(nodes[parent].get());
+        std::unordered_set<std::string> visited;
+        std::unordered_set<std::string> recStack;
+        if (isCyclic(nodes[child]->getName(), visited, recStack)) // if adding this edge forms a cycle
+        {
+            // remove problematic edge
+            nodes[parent]->removeChild(nodes[child].get());
+            nodes[child]->removeParent(nodes[parent].get());
+            throw std::invalid_argument("Adding this edge forms a cycle in the graph.");
+        }
+    }
+    std::map<std::string, std::unique_ptr<Node>>& Network::getNodes()
+    {
+        return nodes;
+    }
+    void Network::checkFitData(int n_samples, int n_features, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
+    {
+        if (weights.size(0) != n_samples) {
+            throw std::invalid_argument("Weights (" + std::to_string(weights.size(0)) + ") must have the same number of elements as samples (" + std::to_string(n_samples) + ") in Network::fit");
+        }
+        if (n_samples != n_samples_y) {
+            throw std::invalid_argument("X and y must have the same number of samples in Network::fit (" + std::to_string(n_samples) + " != " + std::to_string(n_samples_y) + ")");
+        }
+        if (n_features != featureNames.size()) {
+            throw std::invalid_argument("X and features must have the same number of features in Network::fit (" + std::to_string(n_features) + " != " + std::to_string(featureNames.size()) + ")");
+        }
+        if (features.size() == 0) {
+            throw std::invalid_argument("The network has not been initialized. You must call addNode() before calling fit()");
+        }
+        if (n_features != features.size() - 1) {
+            throw std::invalid_argument("X and local features must have the same number of features in Network::fit (" + std::to_string(n_features) + " != " + std::to_string(features.size() - 1) + ")");
+        }
+        if (find(features.begin(), features.end(), className) == features.end()) {
+            throw std::invalid_argument("Class Name not found in Network::features");
+        }
+        for (auto& feature : featureNames) {
+            if (find(features.begin(), features.end(), feature) == features.end()) {
+                throw std::invalid_argument("Feature " + feature + " not found in Network::features");
+            }
+            if (states.find(feature) == states.end()) {
+                throw std::invalid_argument("Feature " + feature + " not found in states");
+            }
+        }
+    }
+    void Network::setStates(const std::map<std::string, std::vector<int>>& states)
+    {
+        // Set states to every Node in the network
+        for_each(features.begin(), features.end(), [this, &states](const std::string& feature) {
+            nodes.at(feature)->setNumStates(states.at(feature).size());
+            });
+        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, const Smoothing_t smoothing)
+    {
+        checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states, weights);
+        this->className = className;
+        torch::Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
+        samples = torch::cat({ X , ytmp }, 0);
+        for (int i = 0; i < featureNames.size(); ++i) {
+            auto row_feature = X.index({ i, "..." });
+        }
+        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, 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, 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, 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);
+        this->className = className;
+        // Build tensor of samples (nxm) (n+1 because of the class)
+        samples = torch::zeros({ static_cast<int>(input_data.size() + 1), static_cast<int>(input_data[0].size()) }, torch::kInt32);
+        for (int i = 0; i < featureNames.size(); ++i) {
+            samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
+        }
+        samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
+        completeFit(states, weights, smoothing);
+    }
+    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;
+        auto& semaphore = CountingSemaphore::getInstance();
+        const double n_samples = static_cast<double>(samples.size(1));
+        auto worker = [&](std::pair<const std::string, std::unique_ptr<Node>>& node, int i) {
+            std::string threadName = "FitWorker-" + std::to_string(i);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            double numStates = static_cast<double>(node.second->getNumStates());
+            double smoothing_factor;
+            switch (smoothing) {
+                case Smoothing_t::ORIGINAL:
+                    smoothing_factor = 1.0 / n_samples;
+                    break;
+                case Smoothing_t::LAPLACE:
+                    smoothing_factor = 1.0;
+                    break;
+                case Smoothing_t::CESTNIK:
+                    smoothing_factor = 1 / numStates;
+                    break;
+                default:
+                    smoothing_factor = 0.0; // No smoothing 
+            }
+            node.second->computeCPT(samples, features, smoothing_factor, weights);
+            semaphore.release();
+            };
+        int i = 0;
+        for (auto& node : nodes) {
+            semaphore.acquire();
+            threads.emplace_back(worker, std::ref(node), i++);
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        fitted = true;
+    }
+    torch::Tensor Network::predict_tensor(const torch::Tensor& samples, const bool proba)
+    {
+        if (!fitted) {
+            throw std::logic_error("You must call fit() before calling predict()");
+        }
+        // Ensure the sample size is equal to the number of features
+        if (samples.size(0) != features.size() - 1) {
+            throw std::invalid_argument("(T) Sample size (" + std::to_string(samples.size(0)) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
+        torch::Tensor result;
+        std::vector<std::thread> threads;
+        std::mutex mtx;
+        auto& semaphore = CountingSemaphore::getInstance();
+        result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);
+        auto worker = [&](const torch::Tensor& sample, int i) {
+            std::string threadName = "PredictWorker-" + std::to_string(i);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            auto psample = predict_sample(sample);
+            auto temp = torch::tensor(psample, torch::kFloat64);
+            {
+                std::lock_guard<std::mutex> lock(mtx);
+                result.index_put_({ i, "..." }, temp);
+            }
+            semaphore.release();
+            };
+        for (int i = 0; i < samples.size(1); ++i) {
+            semaphore.acquire();
+            const torch::Tensor sample = samples.index({ "...", i });
+            threads.emplace_back(worker, sample, i);
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        if (proba)
+            return result;
+        return result.argmax(1);
+    }
+    // Return mxn tensor of probabilities
+    torch::Tensor Network::predict_proba(const torch::Tensor& samples)
+    {
+        return predict_tensor(samples, true);
+    }
+
+    // Return mxn tensor of probabilities
+    torch::Tensor Network::predict(const torch::Tensor& samples)
+    {
+        return predict_tensor(samples, false);
+    }
+
+    // Return mx1 std::vector of predictions
+    // tsamples is nxm std::vector of samples
+    std::vector<int> Network::predict(const std::vector<std::vector<int>>& tsamples)
+    {
+        if (!fitted) {
+            throw std::logic_error("You must call fit() before calling predict()");
+        }
+        // Ensure the sample size is equal to the number of features
+        if (tsamples.size() != features.size() - 1) {
+            throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
+        std::vector<int> predictions(tsamples[0].size(), 0);
+        std::vector<int> sample;
+        std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        auto worker = [&](const std::vector<int>& sample, const int row, int& prediction) {
+            std::string threadName = "(V)PWorker-" + std::to_string(row);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            auto classProbabilities = predict_sample(sample);
+            auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
+            int predictedClass = distance(classProbabilities.begin(), maxElem);
+            prediction = predictedClass;
+            semaphore.release();
+            };
+        for (int row = 0; row < tsamples[0].size(); ++row) {
+            sample.clear();
+            for (int col = 0; col < tsamples.size(); ++col) {
+                sample.push_back(tsamples[col][row]);
+            }
+            semaphore.acquire();
+            threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        return predictions;
+    }
+    // Return mxn std::vector of probabilities
+    // tsamples is nxm std::vector of samples
+    std::vector<std::vector<double>> Network::predict_proba(const std::vector<std::vector<int>>& tsamples)
+    {
+        if (!fitted) {
+            throw std::logic_error("You must call fit() before calling predict_proba()");
+        }
+        // Ensure the sample size is equal to the number of features
+        if (tsamples.size() != features.size() - 1) {
+            throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
+        std::vector<std::vector<double>> predictions(tsamples[0].size(), std::vector<double>(classNumStates, 0.0));
+        std::vector<int> sample;
+        std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        auto worker = [&](const std::vector<int>& sample, int row, std::vector<double>& predictions) {
+            std::string threadName = "(V)PWorker-" + std::to_string(row);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            std::vector<double> classProbabilities = predict_sample(sample);
+            predictions = classProbabilities;
+            semaphore.release();
+            };
+        for (int row = 0; row < tsamples[0].size(); ++row) {
+            sample.clear();
+            for (int col = 0; col < tsamples.size(); ++col) {
+                sample.push_back(tsamples[col][row]);
+            }
+            semaphore.acquire();
+            threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        return predictions;
+    }
+    double Network::score(const std::vector<std::vector<int>>& tsamples, const std::vector<int>& labels)
+    {
+        std::vector<int> y_pred = predict(tsamples);
+        int correct = 0;
+        for (int i = 0; i < y_pred.size(); ++i) {
+            if (y_pred[i] == labels[i]) {
+                correct++;
+            }
+        }
+        return (double)correct / y_pred.size();
+    }
+    // Return 1xn std::vector of probabilities
+    std::vector<double> Network::predict_sample(const std::vector<int>& sample)
+    {
+        std::map<std::string, int> evidence;
+        for (int i = 0; i < sample.size(); ++i) {
+            evidence[features[i]] = sample[i];
+        }
+        return exactInference(evidence);
+    }
+    // Return 1xn std::vector of probabilities
+    std::vector<double> Network::predict_sample(const torch::Tensor& sample)
+    {
+        std::map<std::string, int> evidence;
+        for (int i = 0; i < sample.size(0); ++i) {
+            evidence[features[i]] = sample[i].item<int>();
+        }
+        return exactInference(evidence);
+    }
+    std::vector<double> Network::exactInference(std::map<std::string, int>& evidence)
+    {
+        std::vector<double> result(classNumStates, 0.0);
+        auto completeEvidence = std::map<std::string, int>(evidence);
+        for (int i = 0; i < classNumStates; ++i) {
+            completeEvidence[getClassName()] = i;
+            double partial = 1.0;
+            for (auto& node : getNodes()) {
+                partial *= node.second->getFactorValue(completeEvidence);
+            }
+            result[i] = partial;
+        }
+        // Normalize result
+        double sum = std::accumulate(result.begin(), result.end(), 0.0);
+        transform(result.begin(), result.end(), result.begin(), [sum](const double& value) { return value / sum; });
+        return result;
+    }
+    std::vector<std::string> Network::show() const
+    {
+        std::vector<std::string> result;
+        // Draw the network
+        for (auto& node : nodes) {
+            std::string line = node.first + " -> ";
+            for (auto child : node.second->getChildren()) {
+                line += child->getName() + ", ";
+            }
+            result.push_back(line);
+        }
+        return result;
+    }
+    std::vector<std::string> Network::graph(const std::string& title) const
+    {
+        auto output = std::vector<std::string>();
+        auto prefix = "digraph BayesNet {\nlabel=<BayesNet ";
+        auto suffix = ">\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n";
+        std::string header = prefix + title + suffix;
+        output.push_back(header);
+        for (auto& node : nodes) {
+            auto result = node.second->graph(className);
+            output.insert(output.end(), result.begin(), result.end());
+        }
+        output.push_back("}\n");
+        return output;
+    }
+    std::vector<std::pair<std::string, std::string>> Network::getEdges() const
+    {
+        auto edges = std::vector<std::pair<std::string, std::string>>();
+        for (const auto& node : nodes) {
+            auto head = node.first;
+            for (const auto& child : node.second->getChildren()) {
+                auto tail = child->getName();
+                edges.push_back({ head, tail });
+            }
+        }
+        return edges;
+    }
+    int Network::getNumEdges() const
+    {
+        return getEdges().size();
+    }
+    std::vector<std::string> Network::topological_sort()
+    {
+        /* Check if al the fathers of every node are before the node */
+        auto result = features;
+        result.erase(remove(result.begin(), result.end(), className), result.end());
+        bool ending{ false };
+        while (!ending) {
+            ending = true;
+            for (auto feature : features) {
+                auto fathers = nodes[feature]->getParents();
+                for (const auto& father : fathers) {
+                    auto fatherName = father->getName();
+                    if (fatherName == className) {
+                        continue;
+                    }
+                    // Check if father is placed before the actual feature
+                    auto it = find(result.begin(), result.end(), fatherName);
+                    if (it != result.end()) {
+                        auto it2 = find(result.begin(), result.end(), feature);
+                        if (it2 != result.end()) {
+                            if (distance(it, it2) < 0) {
+                                // if it is not, insert it before the feature
+                                result.erase(remove(result.begin(), result.end(), fatherName), result.end());
+                                result.insert(it2, fatherName);
+                                ending = false;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        return result;
+    }
+    std::string Network::dump_cpt() const
+    {
+        std::stringstream oss;
+        for (auto& node : nodes) {
+            oss << "* " << node.first << ": (" << node.second->getNumStates() << ") : " << node.second->getCPT().sizes() << std::endl;
+            oss << node.second->getCPT() << std::endl;
+        }
+        return oss.str();
+    }
+    
+    bool Network::operator==(const Network& other) const
+    {
+        // Compare number of nodes
+        if (nodes.size() != other.nodes.size()) {
+            return false;
+        }
+        
+        // Compare if all node names exist in both networks
+        for (const auto& node : nodes) {
+            if (other.nodes.find(node.first) == other.nodes.end()) {
+                return false;
+            }
+        }
+        
+        // Compare edges (topology)
+        auto thisEdges = getEdges();
+        auto otherEdges = other.getEdges();
+        
+        // Compare number of edges
+        if (thisEdges.size() != otherEdges.size()) {
+            return false;
+        }
+        
+        // Sort both edge lists for comparison
+        std::sort(thisEdges.begin(), thisEdges.end());
+        std::sort(otherEdges.begin(), otherEdges.end());
+        
+        // Compare each edge
+        for (size_t i = 0; i < thisEdges.size(); ++i) {
+            if (thisEdges[i] != otherEdges[i]) {
+                return false;
+            }
+        }
+        
+        return true;
+    }
+}

bayesnet/network/Network.h

@@ -0,0 +1,68 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef NETWORK_H
+#define NETWORK_H
+#include <map>
+#include <vector>
+#include "bayesnet/config.h"
+#include "Node.h"
+#include "Smoothing.h"
+
+namespace bayesnet {
+
+    class Network {
+    public:
+        Network();
+        Network(const Network& other);
+        Network& operator=(const Network& other);
+        ~Network() = default;
+        torch::Tensor& getSamples();
+        void addNode(const std::string&);
+        void addEdge(const std::string&, const std::string&);
+        std::map<std::string, std::unique_ptr<Node>>& getNodes();
+        std::vector<std::string> getFeatures() const;
+        int getStates() const;
+        std::vector<std::pair<std::string, std::string>> getEdges() const;
+        int getNumEdges() const;
+        int getClassNumStates() const;
+        std::string getClassName() const;
+        /*
+        Notice: Nodes have to be inserted in the same order as they are in the dataset, i.e., first node is first column and so on.
+        */
+        void 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);
+        void 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);
+        void 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);
+        std::vector<int> predict(const std::vector<std::vector<int>>&); // Return mx1 std::vector of predictions
+        torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
+        torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);
+        std::vector<std::vector<double>> predict_proba(const std::vector<std::vector<int>>&); // Return mxn std::vector of probabilities
+        torch::Tensor predict_proba(const torch::Tensor&); // Return mxn tensor of probabilities
+        double score(const std::vector<std::vector<int>>&, const std::vector<int>&);
+        std::vector<std::string> topological_sort();
+        std::vector<std::string> show() const;
+        std::vector<std::string> graph(const std::string& title) const; // Returns a std::vector of std::strings representing the graph in graphviz format
+        void initialize();
+        std::string dump_cpt() const;
+        inline std::string version() { return  { project_version.begin(), project_version.end() }; }
+        bool operator==(const Network& other) const;
+    private:
+        std::map<std::string, std::unique_ptr<Node>> nodes;
+        bool fitted;
+        int classNumStates;
+        std::vector<std::string> features; // Including classname
+        std::string className;
+        torch::Tensor samples; // n+1xm tensor used to fit the model
+        bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
+        std::vector<double> predict_sample(const std::vector<int>&);
+        std::vector<double> predict_sample(const torch::Tensor&);
+        std::vector<double> exactInference(std::map<std::string, int>&);
+        void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
+        void checkFitData(int n_samples, int n_features, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
+        void setStates(const std::map<std::string, std::vector<int>>&);
+    };
+}
+#endif

bayesnet/network/Node.cc

@@ -0,0 +1,197 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "Node.h"
+#include <iterator>
+
+namespace bayesnet {
+
+    Node::Node(const std::string& name)
+        : name(name)
+    {
+    }
+    
+    Node::Node(const Node& other)
+        : name(other.name), numStates(other.numStates), dimensions(other.dimensions)
+    {
+        // Deep copy the CPT tensor
+        if (other.cpTable.defined()) {
+            cpTable = other.cpTable.clone();
+        }
+        // Note: parent and children pointers are NOT copied here
+        // They will be reconstructed by the Network copy constructor
+        // to maintain proper object relationships
+    }
+    
+    Node& Node::operator=(const Node& other)
+    {
+        if (this != &other) {
+            name = other.name;
+            numStates = other.numStates;
+            dimensions = other.dimensions;
+            
+            // Deep copy the CPT tensor
+            if (other.cpTable.defined()) {
+                cpTable = other.cpTable.clone();
+            } else {
+                cpTable = torch::Tensor();
+            }
+            
+            // Clear existing relationships
+            parents.clear();
+            children.clear();
+            // Note: parent and children pointers are NOT copied here
+            // They must be reconstructed to maintain proper object relationships
+        }
+        return *this;
+    }
+    void Node::clear()
+    {
+        parents.clear();
+        children.clear();
+        cpTable = torch::Tensor();
+        dimensions.clear();
+        numStates = 0;
+    }
+    std::string Node::getName() const
+    {
+        return name;
+    }
+    void Node::addParent(Node* parent)
+    {
+        parents.push_back(parent);
+    }
+    void Node::removeParent(Node* parent)
+    {
+        parents.erase(std::remove(parents.begin(), parents.end(), parent), parents.end());
+    }
+    void Node::removeChild(Node* child)
+    {
+        children.erase(std::remove(children.begin(), children.end(), child), children.end());
+    }
+    void Node::addChild(Node* child)
+    {
+        children.push_back(child);
+    }
+    std::vector<Node*>& Node::getParents()
+    {
+        return parents;
+    }
+    std::vector<Node*>& Node::getChildren()
+    {
+        return children;
+    }
+    int Node::getNumStates() const
+    {
+        return numStates;
+    }
+    void Node::setNumStates(int numStates)
+    {
+        this->numStates = numStates;
+    }
+    torch::Tensor& Node::getCPT()
+    {
+        return cpTable;
+    }
+    /*
+     The MinFill criterion is a heuristic for variable elimination.
+     The variable that minimizes the number of edges that need to be added to the graph to make it triangulated.
+     This is done by counting the number of edges that need to be added to the graph if the variable is eliminated.
+     The variable with the minimum number of edges is chosen.
+     Here this is done computing the length of the combinations of the node neighbors taken 2 by 2.
+    */
+    unsigned Node::minFill()
+    {
+        std::unordered_set<std::string> neighbors;
+        for (auto child : children) {
+            neighbors.emplace(child->getName());
+        }
+        for (auto parent : parents) {
+            neighbors.emplace(parent->getName());
+        }
+        auto source = std::vector<std::string>(neighbors.begin(), neighbors.end());
+        return combinations(source).size();
+    }
+    std::vector<std::pair<std::string, std::string>> Node::combinations(const std::vector<std::string>& source)
+    {
+        std::vector<std::pair<std::string, std::string>> result;
+        for (int i = 0; i < source.size(); ++i) {
+            std::string temp = source[i];
+            for (int j = i + 1; j < source.size(); ++j) {
+                result.push_back({ temp, source[j] });
+            }
+        }
+        return result;
+    }
+    void Node::computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights)
+    {
+        dimensions.clear();
+        dimensions.reserve(parents.size() + 1);
+        dimensions.push_back(numStates);
+        for (const auto& parent : parents) {
+            dimensions.push_back(parent->getNumStates());
+        }
+        cpTable = torch::full(dimensions, smoothing, torch::kDouble);
+
+        // Build feature index map
+        std::unordered_map<std::string, int> featureIndexMap;
+        for (size_t i = 0; i < features.size(); ++i) {
+            featureIndexMap[features[i]] = i;
+        }
+
+        // Gather indices for node and parents
+        std::vector<int64_t> all_indices;
+        all_indices.push_back(featureIndexMap[name]);
+        for (const auto& parent : parents) {
+            all_indices.push_back(featureIndexMap[parent->getName()]);
+        }
+
+        // Extract relevant columns: shape (num_features, num_samples)
+        auto indices_tensor = dataset.index_select(0, torch::tensor(all_indices, torch::kLong));
+        indices_tensor = indices_tensor.transpose(0, 1).to(torch::kLong); // (num_samples, num_features)
+
+        // Manual flattening of indices
+        std::vector<int64_t> strides(all_indices.size(), 1);
+        for (int i = strides.size() - 2; i >= 0; --i) {
+            strides[i] = strides[i + 1] * cpTable.size(i + 1);
+        }
+        auto indices_tensor_cpu = indices_tensor.cpu();
+        auto indices_accessor = indices_tensor_cpu.accessor<int64_t, 2>();
+        std::vector<int64_t> flat_indices(indices_tensor.size(0));
+        for (int64_t i = 0; i < indices_tensor.size(0); ++i) {
+            int64_t idx = 0;
+            for (size_t j = 0; j < strides.size(); ++j) {
+                idx += indices_accessor[i][j] * strides[j];
+            }
+            flat_indices[i] = idx;
+        }
+
+        // Accumulate weights into flat CPT
+        auto flat_cpt = cpTable.flatten();
+        auto flat_indices_tensor = torch::from_blob(flat_indices.data(), { (int64_t)flat_indices.size() }, torch::kLong).clone();
+        flat_cpt.index_add_(0, flat_indices_tensor, weights.cpu());
+        cpTable = flat_cpt.view(cpTable.sizes());
+
+        // Normalize the counts (dividing each row by the sum of the row)
+        cpTable /= cpTable.sum(0, true);
+    }
+    double Node::getFactorValue(std::map<std::string, int>& evidence)
+    {
+        c10::List<c10::optional<at::Tensor>> coordinates;
+        // following predetermined order of indices in the cpTable (see Node.h)
+        coordinates.push_back(at::tensor(evidence[name]));
+        transform(parents.begin(), parents.end(), std::back_inserter(coordinates), [&evidence](const auto& parent) { return at::tensor(evidence[parent->getName()]); });
+        return cpTable.index({ coordinates }).item<double>();
+    }
+    std::vector<std::string> Node::graph(const std::string& className)
+    {
+        auto output = std::vector<std::string>();
+        auto suffix = name == className ? ", fontcolor=red, fillcolor=lightblue, style=filled " : "";
+        output.push_back("\"" + name + "\" [shape=circle" + suffix + "] \n");
+        transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return "\"" + name + "\" -> \"" + child->getName() + "\""; });
+        return output;
+    }
+}

bayesnet/network/Node.h

@@ -0,0 +1,45 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef NODE_H
+#define NODE_H
+#include <unordered_set>
+#include <vector>
+#include <string>
+#include <torch/torch.h>
+namespace bayesnet {
+    class Node {
+    public:
+        explicit Node(const std::string&);
+        Node(const Node& other);
+        Node& operator=(const Node& other);
+        ~Node() = default;
+        void clear();
+        void addParent(Node*);
+        void addChild(Node*);
+        void removeParent(Node*);
+        void removeChild(Node*);
+        std::string getName() const;
+        std::vector<Node*>& getParents();
+        std::vector<Node*>& getChildren();
+        torch::Tensor& getCPT();
+        void computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights);
+        int getNumStates() const;
+        void setNumStates(int);
+        unsigned minFill();
+        std::vector<std::string> graph(const std::string& clasName); // Returns a std::vector of std::strings representing the graph in graphviz format
+        double getFactorValue(std::map<std::string, int>&);
+    private:
+        std::string name;
+        std::vector<Node*> parents;
+        std::vector<Node*> children;
+        int numStates = 0; // number of states of the variable
+        torch::Tensor cpTable; // Order of indices is 0-> node variable, 1-> 1st parent, 2-> 2nd parent, ...
+        std::vector<int64_t> dimensions; // dimensions of the cpTable
+        std::vector<std::pair<std::string, std::string>> combinations(const std::vector<std::string>&);
+    };
+}
+#endif

bayesnet/network/Smoothing.h

@@ -0,0 +1,17 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef SMOOTHING_H
+#define SMOOTHING_H
+namespace bayesnet {
+    enum class Smoothing_t {
+        NONE = -1,
+        ORIGINAL = 0,
+        LAPLACE,
+        CESTNIK
+    };
+}
+#endif // SMOOTHING_H

bayesnet/utils/BayesMetrics.cc

@@ -0,0 +1,260 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <map>
+#include <unordered_map>
+#include <tuple>
+#include "Mst.h"
+#include "BayesMetrics.h"
+namespace bayesnet {
+    //samples is n+1xm tensor used to fit the model
+    Metrics::Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates)
+        : samples(samples)
+        , className(className)
+        , features(features)
+        , classNumStates(classNumStates)
+    {
+    }
+    //samples is n+1xm std::vector used to fit the model
+    Metrics::Metrics(const std::vector<std::vector<int>>& vsamples, const std::vector<int>& labels, const std::vector<std::string>& features, const std::string& className, const int classNumStates)
+        : samples(torch::zeros({ static_cast<int>(vsamples.size() + 1), static_cast<int>(vsamples[0].size()) }, torch::kInt32))
+        , className(className)
+        , features(features)
+        , classNumStates(classNumStates)
+    {
+        for (int i = 0; i < vsamples.size(); ++i) {
+            samples.index_put_({ i,  "..." }, torch::tensor(vsamples[i], torch::kInt32));
+        }
+        samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
+    }
+    std::vector<std::pair<int, int>> Metrics::SelectKPairs(const torch::Tensor& weights, std::vector<int>& featuresExcluded, bool ascending, unsigned k)
+    {
+        // Return the K Best features 
+        auto n = features.size();
+        // compute scores
+        scoresKPairs.clear();
+        pairsKBest.clear();
+        auto labels = samples.index({ -1, "..." });
+        for (int i = 0; i < n - 1; ++i) {
+            if (std::find(featuresExcluded.begin(), featuresExcluded.end(), i) != featuresExcluded.end()) {
+                continue;
+            }
+            for (int j = i + 1; j < n; ++j) {
+                if (std::find(featuresExcluded.begin(), featuresExcluded.end(), j) != featuresExcluded.end()) {
+                    continue;
+                }
+                auto key = std::make_pair(i, j);
+                auto value = conditionalMutualInformation(samples.index({ i, "..." }), samples.index({ j, "..." }), labels, weights);
+                scoresKPairs.push_back({ key, value });
+            }
+        }
+        // sort scores
+        if (ascending) {
+            sort(scoresKPairs.begin(), scoresKPairs.end(), [](auto& a, auto& b)
+                { return a.second < b.second; });
+
+        } else {
+            sort(scoresKPairs.begin(), scoresKPairs.end(), [](auto& a, auto& b)
+                { return a.second > b.second; });
+        }
+        for (auto& [pairs, score] : scoresKPairs) {
+            pairsKBest.push_back(pairs);
+        }
+        if (k != 0 && k < pairsKBest.size()) {
+            if (ascending) {
+                int limit = pairsKBest.size() - k;
+                for (int i = 0; i < limit; i++) {
+                    pairsKBest.erase(pairsKBest.begin());
+                    scoresKPairs.erase(scoresKPairs.begin());
+                }
+            } else {
+                pairsKBest.resize(k);
+                scoresKPairs.resize(k);
+            }
+        }
+        return pairsKBest;
+    }
+    std::vector<int> Metrics::SelectKBestWeighted(const torch::Tensor& weights, bool ascending, unsigned k)
+    {
+        // Return the K Best features 
+        auto n = features.size();
+        if (k == 0) {
+            k = n;
+        }
+        // compute scores
+        scoresKBest.clear();
+        featuresKBest.clear();
+        auto label = samples.index({ -1, "..." });
+        for (int i = 0; i < n; ++i) {
+            scoresKBest.push_back(mutualInformation(label, samples.index({ i, "..." }), weights));
+            featuresKBest.push_back(i);
+        }
+        // sort & reduce scores and features
+        if (ascending) {
+            sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j)
+                { return scoresKBest[i] < scoresKBest[j]; });
+            sort(scoresKBest.begin(), scoresKBest.end(), std::less<double>());
+            if (k < n) {
+                for (int i = 0; i < n - k; ++i) {
+                    featuresKBest.erase(featuresKBest.begin());
+                    scoresKBest.erase(scoresKBest.begin());
+                }
+            }
+        } else {
+            sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j)
+                { return scoresKBest[i] > scoresKBest[j]; });
+            sort(scoresKBest.begin(), scoresKBest.end(), std::greater<double>());
+            featuresKBest.resize(k);
+            scoresKBest.resize(k);
+        }
+        return featuresKBest;
+    }
+    std::vector<double> Metrics::getScoresKBest() const
+    {
+        return scoresKBest;
+    }
+    std::vector<std::pair<std::pair<int, int>, double>> Metrics::getScoresKPairs() const
+    {
+        return scoresKPairs;
+    }
+    torch::Tensor Metrics::conditionalEdge(const torch::Tensor& weights)
+    {
+        auto result = std::vector<double>();
+        auto source = std::vector<std::string>(features);
+        source.push_back(className);
+        auto combinations = doCombinations(source);
+        // Compute class prior
+        auto margin = torch::zeros({ classNumStates }, torch::kFloat);
+        for (int value = 0; value < classNumStates; ++value) {
+            auto mask = samples.index({ -1,  "..." }) == value;
+            margin[value] = mask.sum().item<double>() / samples.size(1);
+        }
+        for (auto [first, second] : combinations) {
+            int index_first = find(features.begin(), features.end(), first) - features.begin();
+            int index_second = find(features.begin(), features.end(), second) - features.begin();
+            double accumulated = 0;
+            for (int value = 0; value < classNumStates; ++value) {
+                auto mask = samples.index({ -1, "..." }) == value;
+                auto first_dataset = samples.index({ index_first, mask });
+                auto second_dataset = samples.index({ index_second, mask });
+                auto weights_dataset = weights.index({ mask });
+                auto mi = mutualInformation(first_dataset, second_dataset, weights_dataset);
+                auto pb = margin[value].item<double>();
+                accumulated += pb * mi;
+            }
+            result.push_back(accumulated);
+        }
+        long n_vars = source.size();
+        auto matrix = torch::zeros({ n_vars, n_vars });
+        auto indices = torch::triu_indices(n_vars, n_vars, 1);
+        for (auto i = 0; i < result.size(); ++i) {
+            auto x = indices[0][i];
+            auto y = indices[1][i];
+            matrix[x][y] = result[i];
+            matrix[y][x] = result[i];
+        }
+        return matrix;
+    }
+    // Measured in nats (natural logarithm (log) base e)
+    // Elements of Information Theory, 2nd Edition, Thomas M. Cover, Joy A. Thomas p. 14
+    double Metrics::entropy(const torch::Tensor& feature, const torch::Tensor& weights)
+    {
+        torch::Tensor counts = feature.bincount(weights);
+        double totalWeight = counts.sum().item<double>();
+        torch::Tensor probs = counts.to(torch::kFloat) / totalWeight;
+        torch::Tensor logProbs = torch::log(probs);
+        torch::Tensor entropy = -probs * logProbs;
+        return entropy.nansum().item<double>();
+    }
+    // H(Y|X) = sum_{x in X} p(x) H(Y|X=x)
+    double Metrics::conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights)
+    {
+        int numSamples = firstFeature.sizes()[0];
+        torch::Tensor featureCounts = secondFeature.bincount(weights);
+        std::unordered_map<int, std::unordered_map<int, double>> jointCounts;
+        double totalWeight = 0;
+        for (auto i = 0; i < numSamples; i++) {
+            jointCounts[secondFeature[i].item<int>()][firstFeature[i].item<int>()] += weights[i].item<double>();
+            totalWeight += weights[i].item<float>();
+        }
+        if (totalWeight == 0)
+            return 0;
+        double entropyValue = 0;
+        for (int value = 0; value < featureCounts.sizes()[0]; ++value) {
+            double p_f = featureCounts[value].item<double>() / totalWeight;
+            double entropy_f = 0;
+            for (auto& [label, jointCount] : jointCounts[value]) {
+                double p_l_f = jointCount / featureCounts[value].item<double>();
+                if (p_l_f > 0) {
+                    entropy_f -= p_l_f * log(p_l_f);
+                } else {
+                    entropy_f = 0;
+                }
+            }
+            entropyValue += p_f * entropy_f;
+        }
+        return entropyValue;
+    }
+    // H(X|Y,C) = sum_{y in Y, c in C} p(x,c) H(X|Y=y,C=c)
+    double Metrics::conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights)
+    {
+        // Ensure the tensors are of the same length
+        assert(firstFeature.size(0) == secondFeature.size(0) && firstFeature.size(0) == labels.size(0) && firstFeature.size(0) == weights.size(0));
+        // Convert tensors to vectors for easier processing
+        auto firstFeatureData = firstFeature.accessor<int, 1>();
+        auto secondFeatureData = secondFeature.accessor<int, 1>();
+        auto labelsData = labels.accessor<int, 1>();
+        auto weightsData = weights.accessor<double, 1>();
+        int numSamples = firstFeature.size(0);
+        // Maps for joint and marginal probabilities
+        std::map<std::tuple<int, int, int>, double> jointCount;
+        std::map<std::tuple<int, int>, double> marginalCount;
+        // Compute joint and marginal counts
+        for (int i = 0; i < numSamples; ++i) {
+            auto keyJoint = std::make_tuple(firstFeatureData[i], labelsData[i], secondFeatureData[i]);
+            auto keyMarginal = std::make_tuple(firstFeatureData[i], labelsData[i]);
+
+            jointCount[keyJoint] += weightsData[i];
+            marginalCount[keyMarginal] += weightsData[i];
+        }
+        // Total weight sum
+        double totalWeight = torch::sum(weights).item<double>();
+        if (totalWeight == 0)
+            return 0;
+        // Compute the conditional entropy
+        double conditionalEntropy = 0.0;
+        for (const auto& [keyJoint, jointFreq] : jointCount) {
+            auto [x, c, y] = keyJoint;
+            auto keyMarginal = std::make_tuple(x, c);
+            //double p_xc = marginalCount[keyMarginal] / totalWeight;
+            double p_y_given_xc = jointFreq / marginalCount[keyMarginal];
+            if (p_y_given_xc > 0) {
+                conditionalEntropy -= (jointFreq / totalWeight) * std::log(p_y_given_xc);
+            }
+        }
+        return conditionalEntropy;
+    }
+    // I(X;Y) = H(Y) - H(Y|X) ; I(X;Y) >= 0
+    double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights)
+    {
+        return std::max(entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights), 0.0);
+    }
+    // I(X;Y|C) = H(X|C) - H(X|Y,C) >= 0
+    double Metrics::conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights)
+    {
+        return std::max(conditionalEntropy(firstFeature, labels, weights) - conditionalEntropy(firstFeature, secondFeature, labels, weights), 0.0);
+    }
+    /*
+    Compute the maximum spanning tree considering the weights as distances
+    and the indices of the weights as nodes of this square matrix using
+    Kruskal algorithm
+    */
+    std::vector<std::pair<int, int>> Metrics::maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root)
+    {
+        auto mst = MST(features, weights, root);
+        return mst.maximumSpanningTree();
+    }
+}

bayesnet/utils/BayesMetrics.h

@@ -0,0 +1,62 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef BAYESNET_METRICS_H
+#define BAYESNET_METRICS_H
+#include <vector>
+#include <string>
+#include <torch/torch.h>
+namespace bayesnet {
+    class Metrics {
+    public:
+        Metrics() = default;
+        Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates);
+        Metrics(const std::vector<std::vector<int>>& vsamples, const std::vector<int>& labels, const std::vector<std::string>& features, const std::string& className, const int classNumStates);
+        std::vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);
+        std::vector<std::pair<int, int>> SelectKPairs(const torch::Tensor& weights, std::vector<int>& featuresExcluded, bool ascending = false, unsigned k = 0);
+        std::vector<double> getScoresKBest() const;
+        std::vector<std::pair<std::pair<int, int>, double>> getScoresKPairs() const;
+        double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
+        double conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
+        torch::Tensor conditionalEdge(const torch::Tensor& weights);
+        std::vector<std::pair<int, int>> maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
+        // Measured in nats (natural logarithm (log) base e)
+        // Elements of Information Theory, 2nd Edition, Thomas M. Cover, Joy A. Thomas p. 14
+        double entropy(const torch::Tensor& feature, const torch::Tensor& weights);
+        double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
+    protected:
+        torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector
+        std::string className;
+        std::vector<std::string> features;
+        template <class T>
+        std::vector<std::pair<T, T>> doCombinations(const std::vector<T>& source)
+        {
+            std::vector<std::pair<T, T>> result;
+            for (int i = 0; i < source.size() - 1; ++i) {
+                T temp = source[i];
+                for (int j = i + 1; j < source.size(); ++j) {
+                    result.push_back({ temp, source[j] });
+                }
+            }
+            return result;
+        }
+        template <class T>
+        T pop_first(std::vector<T>& v)
+        {
+            T temp = v[0];
+            v.erase(v.begin());
+            return temp;
+        }
+    private:
+        int classNumStates = 0;
+        std::vector<double> scoresKBest;
+        std::vector<int> featuresKBest; // sorted indices of the features
+        std::vector<std::pair<int, int>> pairsKBest; // sorted indices of the pairs
+        std::vector<std::pair<std::pair<int, int>, double>> scoresKPairs;
+        double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
+    };
+}
+#endif

bayesnet/utils/CountingSemaphore.h

@@ -0,0 +1,51 @@
+#ifndef COUNTING_SEMAPHORE_H
+#define COUNTING_SEMAPHORE_H
+#include <mutex>
+#include <condition_variable>
+#include <algorithm>
+#include <thread>
+
+class CountingSemaphore {
+public:
+    static CountingSemaphore& getInstance()
+    {
+        static CountingSemaphore instance;
+        return instance;
+    }
+    // Delete copy constructor and assignment operator
+    CountingSemaphore(const CountingSemaphore&) = delete;
+    CountingSemaphore& operator=(const CountingSemaphore&) = delete;
+    void acquire()
+    {
+        std::unique_lock<std::mutex> lock(mtx_);
+        cv_.wait(lock, [this]() { return count_ > 0; });
+        --count_;
+    }
+    void release()
+    {
+        std::lock_guard<std::mutex> lock(mtx_);
+        ++count_;
+        if (count_ <= max_count_) {
+            cv_.notify_one();
+        }
+    }
+    uint getCount() const
+    {
+        return count_;
+    }
+    uint getMaxCount() const
+    {
+        return max_count_;
+    }
+private:
+    CountingSemaphore()
+        : max_count_(std::max(1u, static_cast<uint>(0.95 * std::thread::hardware_concurrency()))),
+        count_(max_count_)
+    {
+    }
+    std::mutex mtx_;
+    std::condition_variable cv_;
+    const uint max_count_;
+    uint count_;
+};
+#endif

bayesnet/utils/Mst.cc

@@ -1,13 +1,20 @@
-#include "Mst.h"
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <sstream>
 #include <vector>
+#include <list>
+#include "Mst.h"
 /*
     Based on the code from https://www.softwaretestinghelp.com/minimum-spanning-tree-tutorial/
 
 */
 
 namespace bayesnet {
-    using namespace std;
-    Graph::Graph(int V) : V(V), parent(vector<int>(V))
+    Graph::Graph(int V) : V(V), parent(std::vector<int>(V))
     {
         for (int i = 0; i < V; i++)
             parent[i] = i;
@@ -34,36 +41,36 @@ namespace bayesnet {
     void Graph::kruskal_algorithm()
     {
         // sort the edges ordered on decreasing weight
-        sort(G.begin(), G.end(), [](const auto& left, const auto& right) {return left.first > right.first;});
+        stable_sort(G.begin(), G.end(), [](const auto& left, const auto& right) {return left.first > right.first;});
         for (int i = 0; i < G.size(); i++) {
             int uSt, vEd;
             uSt = find_set(G[i].second.first);
             vEd = find_set(G[i].second.second);
             if (uSt != vEd) {
-                T.push_back(G[i]); // add to mst vector
+                T.push_back(G[i]); // add to mst std::vector
                 union_set(uSt, vEd);
             }
         }
     }
-    void Graph::display_mst()
+
+    void MST::insertElement(std::list<int>& variables, int variable)
     {
-        cout << "Edge :" << " Weight" << endl;
-        for (int i = 0; i < T.size(); i++) {
-            cout << T[i].second.first << " - " << T[i].second.second << " : "
-                << T[i].first;
-            cout << endl;
+        if (std::find(variables.begin(), variables.end(), variable) == variables.end()) {
+            variables.push_front(variable);
         }
     }
 
-    vector<pair<int, int>> reorder(vector<pair<float, pair<int, int>>> T, int root_original)
+    std::vector<std::pair<int, int>> MST::reorder(std::vector<std::pair<float, std::pair<int, int>>> T, int root_original)
     {
-        auto result = vector<pair<int, int>>();
-        auto visited = vector<int>();
-        auto nextVariables = unordered_set<int>();
-        nextVariables.emplace(root_original);
+        // Create the edges of a DAG from the MST
+        // replacing unordered_set with list because unordered_set cannot guarantee the order of the elements inserted
+        auto result = std::vector<std::pair<int, int>>();
+        auto visited = std::vector<int>();
+        auto nextVariables = std::list<int>();
+        nextVariables.push_front(root_original);
         while (nextVariables.size() > 0) {
-            int root = *nextVariables.begin();
-            nextVariables.erase(nextVariables.begin());
+            int root = nextVariables.front();
+            nextVariables.pop_front();
             for (int i = 0; i < T.size(); ++i) {
                 auto [weight, edge] = T[i];
                 auto [from, to] = edge;
@@ -71,10 +78,10 @@ namespace bayesnet {
                     visited.insert(visited.begin(), i);
                     if (from == root) {
                         result.push_back({ from, to });
-                        nextVariables.emplace(to);
+                        insertElement(nextVariables, to);
                     } else {
                         result.push_back({ to, from });
-                        nextVariables.emplace(from);
+                        insertElement(nextVariables, from);
                     }
                 }
             }
@@ -94,12 +101,11 @@ namespace bayesnet {
         return result;
     }
 
-    MST::MST(const vector<string>& features, const Tensor& weights, const int root) : features(features), weights(weights), root(root) {}
-    vector<pair<int, int>> MST::maximumSpanningTree()
+    MST::MST(const std::vector<std::string>& features, const torch::Tensor& weights, const int root) : features(features), weights(weights), root(root) {}
+    std::vector<std::pair<int, int>> MST::maximumSpanningTree()
     {
         auto num_features = features.size();
         Graph g(num_features);
-
         // Make a complete graph
         for (int i = 0; i < num_features - 1; ++i) {
             for (int j = i + 1; j < num_features; ++j) {

bayesnet/utils/Mst.h

@@ -0,0 +1,40 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef MST_H
+#define MST_H
+#include <vector>
+#include <string>
+#include <torch/torch.h>
+namespace bayesnet {
+    class MST {
+    public:
+        MST() = default;
+        MST(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
+        void insertElement(std::list<int>& variables, int variable);
+        std::vector<std::pair<int, int>> reorder(std::vector<std::pair<float, std::pair<int, int>>> T, int root_original);
+        std::vector<std::pair<int, int>> maximumSpanningTree();
+    private:
+        torch::Tensor weights;
+        std::vector<std::string> features;
+        int root = 0;
+    };
+    class Graph {
+    public:
+        explicit Graph(int V);
+        void addEdge(int u, int v, float wt);
+        int find_set(int i);
+        void union_set(int u, int v);
+        void kruskal_algorithm();
+        std::vector <std::pair<float, std::pair<int, int>>> get_mst() { return T; }
+    private:
+        int V;      // number of nodes in graph
+        std::vector <std::pair<float, std::pair<int, int>>> G; // std::vector for graph
+        std::vector <std::pair<float, std::pair<int, int>>> T; // std::vector for mst
+        std::vector<int> parent;
+    };
+}
+#endif

bayesnet/utils/TensorUtils.h

@@ -0,0 +1,51 @@
+#ifndef TENSORUTILS_H
+#define TENSORUTILS_H
+#include <torch/torch.h>
+#include <vector>
+namespace bayesnet {
+    class TensorUtils {
+    public:
+        static std::vector<std::vector<int>> to_matrix(const torch::Tensor& X)
+        {
+            // Ensure tensor is contiguous in memory
+            auto X_contig = X.contiguous();
+
+            // Access tensor data pointer directly
+            auto data_ptr = X_contig.data_ptr<int>();
+
+            // IF you are using int64_t as the data type, use the following line
+            //auto data_ptr = X_contig.data_ptr<int64_t>();
+            //std::vector<std::vector<int64_t>> data(X.size(0), std::vector<int64_t>(X.size(1)));
+
+            // Prepare output container
+            std::vector<std::vector<int>> data(X.size(0), std::vector<int>(X.size(1)));
+
+            // Fill the 2D vector in a single loop using pointer arithmetic
+            int rows = X.size(0);
+            int cols = X.size(1);
+            for (int i = 0; i < rows; ++i) {
+                std::copy(data_ptr + i * cols, data_ptr + (i + 1) * cols, data[i].begin());
+            }
+            return data;
+        }
+        template <typename T>
+        static std::vector<T> to_vector(const torch::Tensor& y)
+        {
+            // Ensure the tensor is contiguous in memory
+            auto y_contig = y.contiguous();
+
+            // Access data pointer
+            auto data_ptr = y_contig.data_ptr<T>();
+
+            // Prepare output container
+            std::vector<T> data(y.size(0));
+
+            // Copy data efficiently
+            std::copy(data_ptr, data_ptr + y.size(0), data.begin());
+
+            return data;
+        }
+    };
+}
+
+#endif // TENSORUTILS_H

bayesnet/utils/bayesnetUtils.cc

@@ -0,0 +1,44 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+
+#include "bayesnetUtils.h"
+namespace bayesnet {
+    // Return the indices in descending order
+    std::vector<int> argsort(std::vector<double>& nums)
+    {
+        int n = nums.size();
+        std::vector<int> indices(n);
+        iota(indices.begin(), indices.end(), 0);
+        sort(indices.begin(), indices.end(), [&nums](int i, int j) {return nums[i] > nums[j];});
+        return indices;
+    }
+    std::vector<std::vector<double>> tensorToVectorDouble(torch::Tensor& dtensor)
+    {
+        // convert mxn tensor to mxn std::vector
+        std::vector<std::vector<double>> result;
+        // Iterate over cols
+        for (int i = 0; i < dtensor.size(0); ++i) {
+            auto col_tensor = dtensor.index({ i, "..." });
+            auto col = std::vector<double>(col_tensor.data_ptr<float>(), col_tensor.data_ptr<float>() + dtensor.size(1));
+            result.push_back(col);
+        }
+        return result;
+    }
+    torch::Tensor vectorToTensor(std::vector<std::vector<int>>& vector, bool transpose)
+    {
+        // convert nxm std::vector to mxn tensor if transpose
+        long int m = transpose ? vector[0].size() : vector.size();
+        long int n = transpose ? vector.size() : vector[0].size();
+        auto tensor = torch::zeros({ m, n }, torch::kInt32);
+        for (int i = 0; i < m; ++i) {
+            for (int j = 0; j < n; ++j) {
+                tensor[i][j] = transpose ? vector[j][i] : vector[i][j];
+            }
+        }
+        return tensor;
+    }
+}

bayesnet/utils/bayesnetUtils.h

@@ -0,0 +1,16 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef BAYESNET_UTILS_H
+#define BAYESNET_UTILS_H
+#include <vector>
+#include <torch/torch.h>
+namespace bayesnet {
+    std::vector<int> argsort(std::vector<double>& nums);
+    std::vector<std::vector<double>> tensorToVectorDouble(torch::Tensor& dtensor);
+    torch::Tensor vectorToTensor(std::vector<std::vector<int>>& vector, bool transpose = true);
+}
+#endif //BAYESNET_UTILS_H

bayesnetConfig.cmake.in

@@ -0,0 +1,4 @@
+@PACKAGE_INIT@
+
+include("${CMAKE_CURRENT_LIST_DIR}/bayesnetTargets.cmake")
+

cmake/modules/AddGitSubmodule.cmake

@@ -1,12 +0,0 @@
-
-function(add_git_submodule dir)
-  find_package(Git REQUIRED) 
-
-  if(NOT EXISTS ${dir}/CMakeLists.txt)
-    message(STATUS "🚨 Adding git submodule => ${dir}")
-    execute_process(COMMAND ${GIT_EXECUTABLE}
-      submodule update --init --recursive -- ${dir}
-      WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}) 
-  endif()
-  add_subdirectory(${dir})
-endfunction(add_git_submodule)

cmake/modules/CodeCoverage.cmake

@@ -1,742 +0,0 @@
-# Copyright (c) 2012 - 2017, Lars Bilke
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without modification,
-# are permitted provided that the following conditions are met:
-#
-# 1. Redistributions of source code must retain the above copyright notice, this
-#    list of conditions and the following disclaimer.
-#
-# 2. Redistributions in binary form must reproduce the above copyright notice,
-#    this list of conditions and the following disclaimer in the documentation
-#    and/or other materials provided with the distribution.
-#
-# 3. Neither the name of the copyright holder nor the names of its contributors
-#    may be used to endorse or promote products derived from this software without
-#    specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
-# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
-# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-#
-# CHANGES:
-#
-# 2012-01-31, Lars Bilke
-# - Enable Code Coverage
-#
-# 2013-09-17, Joakim Söderberg
-# - Added support for Clang.
-# - Some additional usage instructions.
-#
-# 2016-02-03, Lars Bilke
-# - Refactored functions to use named parameters
-#
-# 2017-06-02, Lars Bilke
-# - Merged with modified version from github.com/ufz/ogs
-#
-# 2019-05-06, Anatolii Kurotych
-# - Remove unnecessary --coverage flag
-#
-# 2019-12-13, FeRD (Frank Dana)
-# - Deprecate COVERAGE_LCOVR_EXCLUDES and COVERAGE_GCOVR_EXCLUDES lists in favor
-#   of tool-agnostic COVERAGE_EXCLUDES variable, or EXCLUDE setup arguments.
-# - CMake 3.4+: All excludes can be specified relative to BASE_DIRECTORY
-# - All setup functions: accept BASE_DIRECTORY, EXCLUDE list
-# - Set lcov basedir with -b argument
-# - Add automatic --demangle-cpp in lcovr, if 'c++filt' is available (can be
-#   overridden with NO_DEMANGLE option in setup_target_for_coverage_lcovr().)
-# - Delete output dir, .info file on 'make clean'
-# - Remove Python detection, since version mismatches will break gcovr
-# - Minor cleanup (lowercase function names, update examples...)
-#
-# 2019-12-19, FeRD (Frank Dana)
-# - Rename Lcov outputs, make filtered file canonical, fix cleanup for targets
-#
-# 2020-01-19, Bob Apthorpe
-# - Added gfortran support
-#
-# 2020-02-17, FeRD (Frank Dana)
-# - Make all add_custom_target()s VERBATIM to auto-escape wildcard characters
-#   in EXCLUDEs, and remove manual escaping from gcovr targets
-#
-# 2021-01-19, Robin Mueller
-# - Add CODE_COVERAGE_VERBOSE option which will allow to print out commands which are run
-# - Added the option for users to set the GCOVR_ADDITIONAL_ARGS variable to supply additional
-#   flags to the gcovr command
-#
-# 2020-05-04, Mihchael Davis
-#     - Add -fprofile-abs-path to make gcno files contain absolute paths
-#     - Fix BASE_DIRECTORY not working when defined
-#     - Change BYPRODUCT from folder to index.html to stop ninja from complaining about double defines
-#
-# 2021-05-10, Martin Stump
-#     - Check if the generator is multi-config before warning about non-Debug builds
-#
-# 2022-02-22, Marko Wehle
-#     - Change gcovr output from -o <filename> for --xml <filename> and --html <filename> output respectively.
-#       This will allow for Multiple Output Formats at the same time by making use of GCOVR_ADDITIONAL_ARGS, e.g. GCOVR_ADDITIONAL_ARGS "--txt".
-#
-# 2022-09-28, Sebastian Mueller
-#     - fix append_coverage_compiler_flags_to_target to correctly add flags
-#     - replace "-fprofile-arcs -ftest-coverage" with "--coverage" (equivalent)
-#
-# USAGE:
-#
-# 1. Copy this file into your cmake modules path.
-#
-# 2. Add the following line to your CMakeLists.txt (best inside an if-condition
-#    using a CMake option() to enable it just optionally):
-#      include(CodeCoverage)
-#
-# 3. Append necessary compiler flags for all supported source files:
-#      append_coverage_compiler_flags()
-#    Or for specific target:
-#      append_coverage_compiler_flags_to_target(YOUR_TARGET_NAME)
-#
-# 3.a (OPTIONAL) Set appropriate optimization flags, e.g. -O0, -O1 or -Og
-#
-# 4. If you need to exclude additional directories from the report, specify them
-#    using full paths in the COVERAGE_EXCLUDES variable before calling
-#    setup_target_for_coverage_*().
-#    Example:
-#      set(COVERAGE_EXCLUDES
-#          '${PROJECT_SOURCE_DIR}/src/dir1/*'
-#          '/path/to/my/src/dir2/*')
-#    Or, use the EXCLUDE argument to setup_target_for_coverage_*().
-#    Example:
-#      setup_target_for_coverage_lcov(
-#          NAME coverage
-#          EXECUTABLE testrunner
-#          EXCLUDE "${PROJECT_SOURCE_DIR}/src/dir1/*" "/path/to/my/src/dir2/*")
-#
-# 4.a NOTE: With CMake 3.4+, COVERAGE_EXCLUDES or EXCLUDE can also be set
-#     relative to the BASE_DIRECTORY (default: PROJECT_SOURCE_DIR)
-#     Example:
-#       set(COVERAGE_EXCLUDES "dir1/*")
-#       setup_target_for_coverage_gcovr_html(
-#           NAME coverage
-#           EXECUTABLE testrunner
-#           BASE_DIRECTORY "${PROJECT_SOURCE_DIR}/src"
-#           EXCLUDE "dir2/*")
-#
-# 5. Use the functions described below to create a custom make target which
-#    runs your test executable and produces a code coverage report.
-#
-# 6. Build a Debug build:
-#      cmake -DCMAKE_BUILD_TYPE=Debug ..
-#      make
-#      make my_coverage_target
-#
-
-include(CMakeParseArguments)
-
-option(CODE_COVERAGE_VERBOSE "Verbose information" FALSE)
-
-# Check prereqs
-find_program( GCOV_PATH gcov )
-find_program( LCOV_PATH  NAMES lcov lcov.bat lcov.exe lcov.perl)
-find_program( FASTCOV_PATH NAMES fastcov fastcov.py )
-find_program( GENHTML_PATH NAMES genhtml genhtml.perl genhtml.bat )
-find_program( GCOVR_PATH gcovr PATHS ${CMAKE_SOURCE_DIR}/scripts/test)
-find_program( CPPFILT_PATH NAMES c++filt )
-
-if(NOT GCOV_PATH)
-    message(FATAL_ERROR "gcov not found! Aborting...")
-endif() # NOT GCOV_PATH
-
-# Check supported compiler (Clang, GNU and Flang)
-get_property(LANGUAGES GLOBAL PROPERTY ENABLED_LANGUAGES)
-foreach(LANG ${LANGUAGES})
-  if("${CMAKE_${LANG}_COMPILER_ID}" MATCHES "(Apple)?[Cc]lang")
-    if("${CMAKE_${LANG}_COMPILER_VERSION}" VERSION_LESS 3)
-      message(FATAL_ERROR "Clang version must be 3.0.0 or greater! Aborting...")
-    endif()
-  elseif(NOT "${CMAKE_${LANG}_COMPILER_ID}" MATCHES "GNU"
-         AND NOT "${CMAKE_${LANG}_COMPILER_ID}" MATCHES "(LLVM)?[Ff]lang")
-    message(FATAL_ERROR "Compiler is not GNU or Flang! Aborting...")
-  endif()
-endforeach()
-
-set(COVERAGE_COMPILER_FLAGS "-g --coverage"
-    CACHE INTERNAL "")
-if(CMAKE_CXX_COMPILER_ID MATCHES "(GNU|Clang)")
-    include(CheckCXXCompilerFlag)
-    check_cxx_compiler_flag(-fprofile-abs-path HAVE_fprofile_abs_path)
-    if(HAVE_fprofile_abs_path)
-        set(COVERAGE_COMPILER_FLAGS "${COVERAGE_COMPILER_FLAGS} -fprofile-abs-path")
-    endif()
-endif()
-
-set(CMAKE_Fortran_FLAGS_COVERAGE
-    ${COVERAGE_COMPILER_FLAGS}
-    CACHE STRING "Flags used by the Fortran compiler during coverage builds."
-    FORCE )
-set(CMAKE_CXX_FLAGS_COVERAGE
-    ${COVERAGE_COMPILER_FLAGS}
-    CACHE STRING "Flags used by the C++ compiler during coverage builds."
-    FORCE )
-set(CMAKE_C_FLAGS_COVERAGE
-    ${COVERAGE_COMPILER_FLAGS}
-    CACHE STRING "Flags used by the C compiler during coverage builds."
-    FORCE )
-set(CMAKE_EXE_LINKER_FLAGS_COVERAGE
-    ""
-    CACHE STRING "Flags used for linking binaries during coverage builds."
-    FORCE )
-set(CMAKE_SHARED_LINKER_FLAGS_COVERAGE
-    ""
-    CACHE STRING "Flags used by the shared libraries linker during coverage builds."
-    FORCE )
-mark_as_advanced(
-    CMAKE_Fortran_FLAGS_COVERAGE
-    CMAKE_CXX_FLAGS_COVERAGE
-    CMAKE_C_FLAGS_COVERAGE
-    CMAKE_EXE_LINKER_FLAGS_COVERAGE
-    CMAKE_SHARED_LINKER_FLAGS_COVERAGE )
-
-get_property(GENERATOR_IS_MULTI_CONFIG GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG)
-if(NOT (CMAKE_BUILD_TYPE STREQUAL "Debug" OR GENERATOR_IS_MULTI_CONFIG))
-    message(WARNING "Code coverage results with an optimised (non-Debug) build may be misleading")
-endif() # NOT (CMAKE_BUILD_TYPE STREQUAL "Debug" OR GENERATOR_IS_MULTI_CONFIG)
-
-if(CMAKE_C_COMPILER_ID STREQUAL "GNU" OR CMAKE_Fortran_COMPILER_ID STREQUAL "GNU")
-    link_libraries(gcov)
-endif()
-
-# Defines a target for running and collection code coverage information
-# Builds dependencies, runs the given executable and outputs reports.
-# NOTE! The executable should always have a ZERO as exit code otherwise
-# the coverage generation will not complete.
-#
-# setup_target_for_coverage_lcov(
-#     NAME testrunner_coverage                    # New target name
-#     EXECUTABLE testrunner -j ${PROCESSOR_COUNT} # Executable in PROJECT_BINARY_DIR
-#     DEPENDENCIES testrunner                     # Dependencies to build first
-#     BASE_DIRECTORY "../"                        # Base directory for report
-#                                                 #  (defaults to PROJECT_SOURCE_DIR)
-#     EXCLUDE "src/dir1/*" "src/dir2/*"           # Patterns to exclude (can be relative
-#                                                 #  to BASE_DIRECTORY, with CMake 3.4+)
-#     NO_DEMANGLE                                 # Don't demangle C++ symbols
-#                                                 #  even if c++filt is found
-# )
-function(setup_target_for_coverage_lcov)
-
-    set(options NO_DEMANGLE SONARQUBE)
-    set(oneValueArgs BASE_DIRECTORY NAME)
-    set(multiValueArgs EXCLUDE EXECUTABLE EXECUTABLE_ARGS DEPENDENCIES LCOV_ARGS GENHTML_ARGS)
-    cmake_parse_arguments(Coverage "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
-
-    if(NOT LCOV_PATH)
-        message(FATAL_ERROR "lcov not found! Aborting...")
-    endif() # NOT LCOV_PATH
-
-    if(NOT GENHTML_PATH)
-        message(FATAL_ERROR "genhtml not found! Aborting...")
-    endif() # NOT GENHTML_PATH
-
-    # Set base directory (as absolute path), or default to PROJECT_SOURCE_DIR
-    if(DEFINED Coverage_BASE_DIRECTORY)
-        get_filename_component(BASEDIR ${Coverage_BASE_DIRECTORY} ABSOLUTE)
-    else()
-        set(BASEDIR ${PROJECT_SOURCE_DIR})
-    endif()
-
-    # Collect excludes (CMake 3.4+: Also compute absolute paths)
-    set(LCOV_EXCLUDES "")
-    foreach(EXCLUDE ${Coverage_EXCLUDE} ${COVERAGE_EXCLUDES} ${COVERAGE_LCOV_EXCLUDES})
-        if(CMAKE_VERSION VERSION_GREATER 3.4)
-            get_filename_component(EXCLUDE ${EXCLUDE} ABSOLUTE BASE_DIR ${BASEDIR})
-        endif()
-        list(APPEND LCOV_EXCLUDES "${EXCLUDE}")
-    endforeach()
-    list(REMOVE_DUPLICATES LCOV_EXCLUDES)
-
-    # Conditional arguments
-    if(CPPFILT_PATH AND NOT ${Coverage_NO_DEMANGLE})
-      set(GENHTML_EXTRA_ARGS "--demangle-cpp")
-    endif()
-
-    # Setting up commands which will be run to generate coverage data.
-    # Cleanup lcov
-    set(LCOV_CLEAN_CMD
-        ${LCOV_PATH} ${Coverage_LCOV_ARGS} --gcov-tool ${GCOV_PATH} -directory .
-        -b ${BASEDIR} --zerocounters
-    )
-    # Create baseline to make sure untouched files show up in the report
-    set(LCOV_BASELINE_CMD
-        ${LCOV_PATH} ${Coverage_LCOV_ARGS} --gcov-tool ${GCOV_PATH} -c -i -d . -b
-        ${BASEDIR} -o ${Coverage_NAME}.base
-    )
-    # Run tests
-    set(LCOV_EXEC_TESTS_CMD
-        ${Coverage_EXECUTABLE} ${Coverage_EXECUTABLE_ARGS}
-    )
-    # Capturing lcov counters and generating report
-    set(LCOV_CAPTURE_CMD
-        ${LCOV_PATH} ${Coverage_LCOV_ARGS} --gcov-tool ${GCOV_PATH} --directory . -b
-        ${BASEDIR} --capture --output-file ${Coverage_NAME}.capture
-    )
-    # add baseline counters
-    set(LCOV_BASELINE_COUNT_CMD
-        ${LCOV_PATH} ${Coverage_LCOV_ARGS} --gcov-tool ${GCOV_PATH} -a ${Coverage_NAME}.base
-        -a ${Coverage_NAME}.capture --output-file ${Coverage_NAME}.total
-    )
-    # filter collected data to final coverage report
-    set(LCOV_FILTER_CMD
-        ${LCOV_PATH} ${Coverage_LCOV_ARGS} --gcov-tool ${GCOV_PATH} --remove
-        ${Coverage_NAME}.total ${LCOV_EXCLUDES} --output-file ${Coverage_NAME}.info
-    )
-    # Generate HTML output
-    set(LCOV_GEN_HTML_CMD
-        ${GENHTML_PATH} ${GENHTML_EXTRA_ARGS} ${Coverage_GENHTML_ARGS} -o
-        ${Coverage_NAME} ${Coverage_NAME}.info
-    )
-    if(${Coverage_SONARQUBE})
-        # Generate SonarQube output
-        set(GCOVR_XML_CMD
-            ${GCOVR_PATH} --sonarqube ${Coverage_NAME}_sonarqube.xml -r ${BASEDIR} ${GCOVR_ADDITIONAL_ARGS}
-            ${GCOVR_EXCLUDE_ARGS} --object-directory=${PROJECT_BINARY_DIR}
-        )
-        set(GCOVR_XML_CMD_COMMAND
-            COMMAND ${GCOVR_XML_CMD}
-        )
-        set(GCOVR_XML_CMD_BYPRODUCTS ${Coverage_NAME}_sonarqube.xml)
-        set(GCOVR_XML_CMD_COMMENT COMMENT "SonarQube code coverage info report saved in ${Coverage_NAME}_sonarqube.xml.")
-    endif()
-
-
-    if(CODE_COVERAGE_VERBOSE)
-        message(STATUS "Executed command report")
-        message(STATUS "Command to clean up lcov: ")
-        string(REPLACE ";" " " LCOV_CLEAN_CMD_SPACED "${LCOV_CLEAN_CMD}")
-        message(STATUS "${LCOV_CLEAN_CMD_SPACED}")
-
-        message(STATUS "Command to create baseline: ")
-        string(REPLACE ";" " " LCOV_BASELINE_CMD_SPACED "${LCOV_BASELINE_CMD}")
-        message(STATUS "${LCOV_BASELINE_CMD_SPACED}")
-
-        message(STATUS "Command to run the tests: ")
-        string(REPLACE ";" " " LCOV_EXEC_TESTS_CMD_SPACED "${LCOV_EXEC_TESTS_CMD}")
-        message(STATUS "${LCOV_EXEC_TESTS_CMD_SPACED}")
-
-        message(STATUS "Command to capture counters and generate report: ")
-        string(REPLACE ";" " " LCOV_CAPTURE_CMD_SPACED "${LCOV_CAPTURE_CMD}")
-        message(STATUS "${LCOV_CAPTURE_CMD_SPACED}")
-
-        message(STATUS "Command to add baseline counters: ")
-        string(REPLACE ";" " " LCOV_BASELINE_COUNT_CMD_SPACED "${LCOV_BASELINE_COUNT_CMD}")
-        message(STATUS "${LCOV_BASELINE_COUNT_CMD_SPACED}")
-
-        message(STATUS "Command to filter collected data: ")
-        string(REPLACE ";" " " LCOV_FILTER_CMD_SPACED "${LCOV_FILTER_CMD}")
-        message(STATUS "${LCOV_FILTER_CMD_SPACED}")
-
-        message(STATUS "Command to generate lcov HTML output: ")
-        string(REPLACE ";" " " LCOV_GEN_HTML_CMD_SPACED "${LCOV_GEN_HTML_CMD}")
-        message(STATUS "${LCOV_GEN_HTML_CMD_SPACED}")
-
-        if(${Coverage_SONARQUBE})
-            message(STATUS "Command to generate SonarQube XML output: ")
-            string(REPLACE ";" " " GCOVR_XML_CMD_SPACED "${GCOVR_XML_CMD}")
-            message(STATUS "${GCOVR_XML_CMD_SPACED}")
-        endif()
-    endif()
-
-    # Setup target
-    add_custom_target(${Coverage_NAME}
-        COMMAND ${LCOV_CLEAN_CMD}
-        COMMAND ${LCOV_BASELINE_CMD}
-        COMMAND ${LCOV_EXEC_TESTS_CMD}
-        COMMAND ${LCOV_CAPTURE_CMD}
-        COMMAND ${LCOV_BASELINE_COUNT_CMD}
-        COMMAND ${LCOV_FILTER_CMD}
-        COMMAND ${LCOV_GEN_HTML_CMD}
-        ${GCOVR_XML_CMD_COMMAND}
-
-        # Set output files as GENERATED (will be removed on 'make clean')
-        BYPRODUCTS
-            ${Coverage_NAME}.base
-            ${Coverage_NAME}.capture
-            ${Coverage_NAME}.total
-            ${Coverage_NAME}.info
-            ${GCOVR_XML_CMD_BYPRODUCTS}
-            ${Coverage_NAME}/index.html
-        WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-        DEPENDS ${Coverage_DEPENDENCIES}
-        VERBATIM # Protect arguments to commands
-        COMMENT "Resetting code coverage counters to zero.\nProcessing code coverage counters and generating report."
-    )
-
-    # Show where to find the lcov info report
-    add_custom_command(TARGET ${Coverage_NAME} POST_BUILD
-        COMMAND ;
-        COMMENT "Lcov code coverage info report saved in ${Coverage_NAME}.info."
-        ${GCOVR_XML_CMD_COMMENT}
-    )
-
-    # Show info where to find the report
-    add_custom_command(TARGET ${Coverage_NAME} POST_BUILD
-        COMMAND ;
-        COMMENT "Open ./${Coverage_NAME}/index.html in your browser to view the coverage report."
-    )
-
-endfunction() # setup_target_for_coverage_lcov
-
-# Defines a target for running and collection code coverage information
-# Builds dependencies, runs the given executable and outputs reports.
-# NOTE! The executable should always have a ZERO as exit code otherwise
-# the coverage generation will not complete.
-#
-# setup_target_for_coverage_gcovr_xml(
-#     NAME ctest_coverage                    # New target name
-#     EXECUTABLE ctest -j ${PROCESSOR_COUNT} # Executable in PROJECT_BINARY_DIR
-#     DEPENDENCIES executable_target         # Dependencies to build first
-#     BASE_DIRECTORY "../"                   # Base directory for report
-#                                            #  (defaults to PROJECT_SOURCE_DIR)
-#     EXCLUDE "src/dir1/*" "src/dir2/*"      # Patterns to exclude (can be relative
-#                                            #  to BASE_DIRECTORY, with CMake 3.4+)
-# )
-# The user can set the variable GCOVR_ADDITIONAL_ARGS to supply additional flags to the
-# GCVOR command.
-function(setup_target_for_coverage_gcovr_xml)
-
-    set(options NONE)
-    set(oneValueArgs BASE_DIRECTORY NAME)
-    set(multiValueArgs EXCLUDE EXECUTABLE EXECUTABLE_ARGS DEPENDENCIES)
-    cmake_parse_arguments(Coverage "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
-
-    if(NOT GCOVR_PATH)
-        message(FATAL_ERROR "gcovr not found! Aborting...")
-    endif() # NOT GCOVR_PATH
-
-    # Set base directory (as absolute path), or default to PROJECT_SOURCE_DIR
-    if(DEFINED Coverage_BASE_DIRECTORY)
-        get_filename_component(BASEDIR ${Coverage_BASE_DIRECTORY} ABSOLUTE)
-    else()
-        set(BASEDIR ${PROJECT_SOURCE_DIR})
-    endif()
-
-    # Collect excludes (CMake 3.4+: Also compute absolute paths)
-    set(GCOVR_EXCLUDES "")
-    foreach(EXCLUDE ${Coverage_EXCLUDE} ${COVERAGE_EXCLUDES} ${COVERAGE_GCOVR_EXCLUDES})
-        if(CMAKE_VERSION VERSION_GREATER 3.4)
-            get_filename_component(EXCLUDE ${EXCLUDE} ABSOLUTE BASE_DIR ${BASEDIR})
-        endif()
-        list(APPEND GCOVR_EXCLUDES "${EXCLUDE}")
-    endforeach()
-    list(REMOVE_DUPLICATES GCOVR_EXCLUDES)
-
-    # Combine excludes to several -e arguments
-    set(GCOVR_EXCLUDE_ARGS "")
-    foreach(EXCLUDE ${GCOVR_EXCLUDES})
-        list(APPEND GCOVR_EXCLUDE_ARGS "-e")
-        list(APPEND GCOVR_EXCLUDE_ARGS "${EXCLUDE}")
-    endforeach()
-
-    # Set up commands which will be run to generate coverage data
-    # Run tests
-    set(GCOVR_XML_EXEC_TESTS_CMD
-        ${Coverage_EXECUTABLE} ${Coverage_EXECUTABLE_ARGS}
-    )
-    # Running gcovr
-    set(GCOVR_XML_CMD
-        ${GCOVR_PATH} --xml ${Coverage_NAME}.xml -r ${BASEDIR} ${GCOVR_ADDITIONAL_ARGS}
-        ${GCOVR_EXCLUDE_ARGS} --object-directory=${PROJECT_BINARY_DIR}
-    )
-
-    if(CODE_COVERAGE_VERBOSE)
-        message(STATUS "Executed command report")
-
-        message(STATUS "Command to run tests: ")
-        string(REPLACE ";" " " GCOVR_XML_EXEC_TESTS_CMD_SPACED "${GCOVR_XML_EXEC_TESTS_CMD}")
-        message(STATUS "${GCOVR_XML_EXEC_TESTS_CMD_SPACED}")
-
-        message(STATUS "Command to generate gcovr XML coverage data: ")
-        string(REPLACE ";" " " GCOVR_XML_CMD_SPACED "${GCOVR_XML_CMD}")
-        message(STATUS "${GCOVR_XML_CMD_SPACED}")
-    endif()
-
-    add_custom_target(${Coverage_NAME}
-        COMMAND ${GCOVR_XML_EXEC_TESTS_CMD}
-        COMMAND ${GCOVR_XML_CMD}
-
-        BYPRODUCTS ${Coverage_NAME}.xml
-        WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-        DEPENDS ${Coverage_DEPENDENCIES}
-        VERBATIM # Protect arguments to commands
-        COMMENT "Running gcovr to produce Cobertura code coverage report."
-    )
-
-    # Show info where to find the report
-    add_custom_command(TARGET ${Coverage_NAME} POST_BUILD
-        COMMAND ;
-        COMMENT "Cobertura code coverage report saved in ${Coverage_NAME}.xml."
-    )
-endfunction() # setup_target_for_coverage_gcovr_xml
-
-# Defines a target for running and collection code coverage information
-# Builds dependencies, runs the given executable and outputs reports.
-# NOTE! The executable should always have a ZERO as exit code otherwise
-# the coverage generation will not complete.
-#
-# setup_target_for_coverage_gcovr_html(
-#     NAME ctest_coverage                    # New target name
-#     EXECUTABLE ctest -j ${PROCESSOR_COUNT} # Executable in PROJECT_BINARY_DIR
-#     DEPENDENCIES executable_target         # Dependencies to build first
-#     BASE_DIRECTORY "../"                   # Base directory for report
-#                                            #  (defaults to PROJECT_SOURCE_DIR)
-#     EXCLUDE "src/dir1/*" "src/dir2/*"      # Patterns to exclude (can be relative
-#                                            #  to BASE_DIRECTORY, with CMake 3.4+)
-# )
-# The user can set the variable GCOVR_ADDITIONAL_ARGS to supply additional flags to the
-# GCVOR command.
-function(setup_target_for_coverage_gcovr_html)
-
-    set(options NONE)
-    set(oneValueArgs BASE_DIRECTORY NAME)
-    set(multiValueArgs EXCLUDE EXECUTABLE EXECUTABLE_ARGS DEPENDENCIES)
-    cmake_parse_arguments(Coverage "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
-
-    if(NOT GCOVR_PATH)
-        message(FATAL_ERROR "gcovr not found! Aborting...")
-    endif() # NOT GCOVR_PATH
-
-    # Set base directory (as absolute path), or default to PROJECT_SOURCE_DIR
-    if(DEFINED Coverage_BASE_DIRECTORY)
-        get_filename_component(BASEDIR ${Coverage_BASE_DIRECTORY} ABSOLUTE)
-    else()
-        set(BASEDIR ${PROJECT_SOURCE_DIR})
-    endif()
-
-    # Collect excludes (CMake 3.4+: Also compute absolute paths)
-    set(GCOVR_EXCLUDES "")
-    foreach(EXCLUDE ${Coverage_EXCLUDE} ${COVERAGE_EXCLUDES} ${COVERAGE_GCOVR_EXCLUDES})
-        if(CMAKE_VERSION VERSION_GREATER 3.4)
-            get_filename_component(EXCLUDE ${EXCLUDE} ABSOLUTE BASE_DIR ${BASEDIR})
-        endif()
-        list(APPEND GCOVR_EXCLUDES "${EXCLUDE}")
-    endforeach()
-    list(REMOVE_DUPLICATES GCOVR_EXCLUDES)
-
-    # Combine excludes to several -e arguments
-    set(GCOVR_EXCLUDE_ARGS "")
-    foreach(EXCLUDE ${GCOVR_EXCLUDES})
-        list(APPEND GCOVR_EXCLUDE_ARGS "-e")
-        list(APPEND GCOVR_EXCLUDE_ARGS "${EXCLUDE}")
-    endforeach()
-
-    # Set up commands which will be run to generate coverage data
-    # Run tests
-    set(GCOVR_HTML_EXEC_TESTS_CMD
-        ${Coverage_EXECUTABLE} ${Coverage_EXECUTABLE_ARGS}
-    )
-    # Create folder
-    set(GCOVR_HTML_FOLDER_CMD
-        ${CMAKE_COMMAND} -E make_directory ${PROJECT_BINARY_DIR}/${Coverage_NAME}
-    )
-    # Running gcovr
-    set(GCOVR_HTML_CMD
-        ${GCOVR_PATH} --html ${Coverage_NAME}/index.html --html-details -r ${BASEDIR} ${GCOVR_ADDITIONAL_ARGS}
-        ${GCOVR_EXCLUDE_ARGS} --object-directory=${PROJECT_BINARY_DIR}
-    )
-
-    if(CODE_COVERAGE_VERBOSE)
-        message(STATUS "Executed command report")
-
-        message(STATUS "Command to run tests: ")
-        string(REPLACE ";" " " GCOVR_HTML_EXEC_TESTS_CMD_SPACED "${GCOVR_HTML_EXEC_TESTS_CMD}")
-        message(STATUS "${GCOVR_HTML_EXEC_TESTS_CMD_SPACED}")
-
-        message(STATUS "Command to create a folder: ")
-        string(REPLACE ";" " " GCOVR_HTML_FOLDER_CMD_SPACED "${GCOVR_HTML_FOLDER_CMD}")
-        message(STATUS "${GCOVR_HTML_FOLDER_CMD_SPACED}")
-
-        message(STATUS "Command to generate gcovr HTML coverage data: ")
-        string(REPLACE ";" " " GCOVR_HTML_CMD_SPACED "${GCOVR_HTML_CMD}")
-        message(STATUS "${GCOVR_HTML_CMD_SPACED}")
-    endif()
-
-    add_custom_target(${Coverage_NAME}
-        COMMAND ${GCOVR_HTML_EXEC_TESTS_CMD}
-        COMMAND ${GCOVR_HTML_FOLDER_CMD}
-        COMMAND ${GCOVR_HTML_CMD}
-
-        BYPRODUCTS ${PROJECT_BINARY_DIR}/${Coverage_NAME}/index.html  # report directory
-        WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-        DEPENDS ${Coverage_DEPENDENCIES}
-        VERBATIM # Protect arguments to commands
-        COMMENT "Running gcovr to produce HTML code coverage report."
-    )
-
-    # Show info where to find the report
-    add_custom_command(TARGET ${Coverage_NAME} POST_BUILD
-        COMMAND ;
-        COMMENT "Open ./${Coverage_NAME}/index.html in your browser to view the coverage report."
-    )
-
-endfunction() # setup_target_for_coverage_gcovr_html
-
-# Defines a target for running and collection code coverage information
-# Builds dependencies, runs the given executable and outputs reports.
-# NOTE! The executable should always have a ZERO as exit code otherwise
-# the coverage generation will not complete.
-#
-# setup_target_for_coverage_fastcov(
-#     NAME testrunner_coverage                    # New target name
-#     EXECUTABLE testrunner -j ${PROCESSOR_COUNT} # Executable in PROJECT_BINARY_DIR
-#     DEPENDENCIES testrunner                     # Dependencies to build first
-#     BASE_DIRECTORY "../"                        # Base directory for report
-#                                                 #  (defaults to PROJECT_SOURCE_DIR)
-#     EXCLUDE "src/dir1/" "src/dir2/"             # Patterns to exclude.
-#     NO_DEMANGLE                                 # Don't demangle C++ symbols
-#                                                 #  even if c++filt is found
-#     SKIP_HTML                                   # Don't create html report
-#     POST_CMD perl -i -pe s!${PROJECT_SOURCE_DIR}/!!g ctest_coverage.json  # E.g. for stripping source dir from file paths
-# )
-function(setup_target_for_coverage_fastcov)
-
-    set(options NO_DEMANGLE SKIP_HTML)
-    set(oneValueArgs BASE_DIRECTORY NAME)
-    set(multiValueArgs EXCLUDE EXECUTABLE EXECUTABLE_ARGS DEPENDENCIES FASTCOV_ARGS GENHTML_ARGS POST_CMD)
-    cmake_parse_arguments(Coverage "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
-
-    if(NOT FASTCOV_PATH)
-        message(FATAL_ERROR "fastcov not found! Aborting...")
-    endif()
-
-    if(NOT Coverage_SKIP_HTML AND NOT GENHTML_PATH)
-        message(FATAL_ERROR "genhtml not found! Aborting...")
-    endif()
-
-    # Set base directory (as absolute path), or default to PROJECT_SOURCE_DIR
-    if(Coverage_BASE_DIRECTORY)
-        get_filename_component(BASEDIR ${Coverage_BASE_DIRECTORY} ABSOLUTE)
-    else()
-        set(BASEDIR ${PROJECT_SOURCE_DIR})
-    endif()
-
-    # Collect excludes (Patterns, not paths, for fastcov)
-    set(FASTCOV_EXCLUDES "")
-    foreach(EXCLUDE ${Coverage_EXCLUDE} ${COVERAGE_EXCLUDES} ${COVERAGE_FASTCOV_EXCLUDES})
-        list(APPEND FASTCOV_EXCLUDES "${EXCLUDE}")
-    endforeach()
-    list(REMOVE_DUPLICATES FASTCOV_EXCLUDES)
-
-    # Conditional arguments
-    if(CPPFILT_PATH AND NOT ${Coverage_NO_DEMANGLE})
-        set(GENHTML_EXTRA_ARGS "--demangle-cpp")
-    endif()
-
-    # Set up commands which will be run to generate coverage data
-    set(FASTCOV_EXEC_TESTS_CMD ${Coverage_EXECUTABLE} ${Coverage_EXECUTABLE_ARGS})
-
-    set(FASTCOV_CAPTURE_CMD ${FASTCOV_PATH} ${Coverage_FASTCOV_ARGS} --gcov ${GCOV_PATH}
-        --search-directory ${BASEDIR}
-        --process-gcno
-        --output ${Coverage_NAME}.json
-        --exclude ${FASTCOV_EXCLUDES}
-    )
-
-    set(FASTCOV_CONVERT_CMD ${FASTCOV_PATH}
-        -C ${Coverage_NAME}.json --lcov --output ${Coverage_NAME}.info
-    )
-
-    if(Coverage_SKIP_HTML)
-        set(FASTCOV_HTML_CMD ";")
-    else()
-        set(FASTCOV_HTML_CMD ${GENHTML_PATH} ${GENHTML_EXTRA_ARGS} ${Coverage_GENHTML_ARGS}
-            -o ${Coverage_NAME} ${Coverage_NAME}.info
-        )
-    endif()
-
-    set(FASTCOV_POST_CMD ";")
-    if(Coverage_POST_CMD)
-        set(FASTCOV_POST_CMD ${Coverage_POST_CMD})
-    endif()
-
-    if(CODE_COVERAGE_VERBOSE)
-        message(STATUS "Code coverage commands for target ${Coverage_NAME} (fastcov):")
-
-        message("   Running tests:")
-        string(REPLACE ";" " " FASTCOV_EXEC_TESTS_CMD_SPACED "${FASTCOV_EXEC_TESTS_CMD}")
-        message("     ${FASTCOV_EXEC_TESTS_CMD_SPACED}")
-
-        message("   Capturing fastcov counters and generating report:")
-        string(REPLACE ";" " " FASTCOV_CAPTURE_CMD_SPACED "${FASTCOV_CAPTURE_CMD}")
-        message("     ${FASTCOV_CAPTURE_CMD_SPACED}")
-
-        message("   Converting fastcov .json to lcov .info:")
-        string(REPLACE ";" " " FASTCOV_CONVERT_CMD_SPACED "${FASTCOV_CONVERT_CMD}")
-        message("     ${FASTCOV_CONVERT_CMD_SPACED}")
-
-        if(NOT Coverage_SKIP_HTML)
-            message("   Generating HTML report: ")
-            string(REPLACE ";" " " FASTCOV_HTML_CMD_SPACED "${FASTCOV_HTML_CMD}")
-            message("     ${FASTCOV_HTML_CMD_SPACED}")
-        endif()
-        if(Coverage_POST_CMD)
-            message("   Running post command: ")
-            string(REPLACE ";" " " FASTCOV_POST_CMD_SPACED "${FASTCOV_POST_CMD}")
-            message("     ${FASTCOV_POST_CMD_SPACED}")
-        endif()
-    endif()
-
-    # Setup target
-    add_custom_target(${Coverage_NAME}
-
-        # Cleanup fastcov
-        COMMAND ${FASTCOV_PATH} ${Coverage_FASTCOV_ARGS} --gcov ${GCOV_PATH}
-            --search-directory ${BASEDIR}
-            --zerocounters
-
-        COMMAND ${FASTCOV_EXEC_TESTS_CMD}
-        COMMAND ${FASTCOV_CAPTURE_CMD}
-        COMMAND ${FASTCOV_CONVERT_CMD}
-        COMMAND ${FASTCOV_HTML_CMD}
-        COMMAND ${FASTCOV_POST_CMD}
-
-        # Set output files as GENERATED (will be removed on 'make clean')
-        BYPRODUCTS
-             ${Coverage_NAME}.info
-             ${Coverage_NAME}.json
-             ${Coverage_NAME}/index.html  # report directory
-
-        WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-        DEPENDS ${Coverage_DEPENDENCIES}
-        VERBATIM # Protect arguments to commands
-        COMMENT "Resetting code coverage counters to zero. Processing code coverage counters and generating report."
-    )
-
-    set(INFO_MSG "fastcov code coverage info report saved in ${Coverage_NAME}.info and ${Coverage_NAME}.json.")
-    if(NOT Coverage_SKIP_HTML)
-        string(APPEND INFO_MSG " Open ${PROJECT_BINARY_DIR}/${Coverage_NAME}/index.html in your browser to view the coverage report.")
-    endif()
-    # Show where to find the fastcov info report
-    add_custom_command(TARGET ${Coverage_NAME} POST_BUILD
-        COMMAND ${CMAKE_COMMAND} -E echo ${INFO_MSG}
-    )
-
-endfunction() # setup_target_for_coverage_fastcov
-
-function(append_coverage_compiler_flags)
-    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${COVERAGE_COMPILER_FLAGS}" PARENT_SCOPE)
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${COVERAGE_COMPILER_FLAGS}" PARENT_SCOPE)
-    set(CMAKE_Fortran_FLAGS "${CMAKE_Fortran_FLAGS} ${COVERAGE_COMPILER_FLAGS}" PARENT_SCOPE)
-    message(STATUS "Appending code coverage compiler flags: ${COVERAGE_COMPILER_FLAGS}")
-endfunction() # append_coverage_compiler_flags
-
-# Setup coverage for specific library
-function(append_coverage_compiler_flags_to_target name)
-    separate_arguments(_flag_list NATIVE_COMMAND "${COVERAGE_COMPILER_FLAGS}")
-    target_compile_options(${name} PRIVATE ${_flag_list})
-    if(CMAKE_C_COMPILER_ID STREQUAL "GNU" OR CMAKE_Fortran_COMPILER_ID STREQUAL "GNU")
-        target_link_libraries(${name} PRIVATE gcov)
-    endif()
-endfunction()

cmake/modules/StaticAnalyzers.cmake

@@ -1,22 +0,0 @@
-if(ENABLE_CLANG_TIDY)
-    find_program(CLANG_TIDY_COMMAND NAMES clang-tidy)
-
-    if(NOT CLANG_TIDY_COMMAND)
-        message(WARNING "🔴 CMake_RUN_CLANG_TIDY is ON but clang-tidy is not found!")
-        set(CMAKE_CXX_CLANG_TIDY "" CACHE STRING "" FORCE)
-    else()
-    
-        message(STATUS "🟢 CMake_RUN_CLANG_TIDY is ON")
-        set(CLANGTIDY_EXTRA_ARGS
-            "-extra-arg=-Wno-unknown-warning-option"
-        )
-        set(CMAKE_CXX_CLANG_TIDY "${CLANG_TIDY_COMMAND};-p=${CMAKE_BINARY_DIR};${CLANGTIDY_EXTRA_ARGS}" CACHE STRING "" FORCE)
-
-        add_custom_target(clang-tidy
-            COMMAND ${CMAKE_COMMAND} --build ${CMAKE_BINARY_DIR} --target ${CMAKE_PROJECT_NAME}
-            COMMAND ${CMAKE_COMMAND} --build ${CMAKE_BINARY_DIR} --target clang-tidy
-            COMMENT "Running clang-tidy..."
-        )
-       set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
-    endif()
-endif(ENABLE_CLANG_TIDY)

conandata.yml

@@ -0,0 +1,10 @@
+sources:
+  "1.1.2":
+    url: "https://github.com/rmontanana/BayesNet/archive/v1.1.2.tar.gz"
+    sha256: "placeholder_sha256"  # Replace with actual SHA256 when releasing
+  "1.0.7":
+    url: "https://github.com/rmontanana/BayesNet/archive/v1.0.7.tar.gz"
+    sha256: "placeholder_sha256"  # Replace with actual SHA256 when releasing
+
+patches:
+  # Add patches here if needed for specific versions

conanfile.py

@@ -0,0 +1,108 @@
+import os, re, pathlib
+from conan import ConanFile
+from conan.tools.cmake import CMakeToolchain, CMake, cmake_layout, CMakeDeps
+from conan.tools.files import copy
+
+
+class BayesNetConan(ConanFile):
+    name = "bayesnet"
+    settings = "os", "compiler", "build_type", "arch"
+    options = {
+        "shared": [True, False],
+        "fPIC": [True, False],
+        "enable_testing": [True, False],
+        "enable_coverage": [True, False],
+    }
+    default_options = {
+        "shared": False,
+        "fPIC": True,
+        "enable_testing": False,
+        "enable_coverage": False,
+    }
+
+    # Sources are located in the same place as this recipe, copy them to the recipe
+    exports_sources = (
+        "CMakeLists.txt",
+        "bayesnet/*",
+        "config/*",
+        "cmake/*",
+        "docs/*",
+        "tests/*",
+        "bayesnetConfig.cmake.in",
+    )
+
+    def set_version(self) -> None:
+        cmake = pathlib.Path(self.recipe_folder) / "CMakeLists.txt"
+        text = cmake.read_text(encoding="utf-8")
+
+        # Accept either: project(foo VERSION 1.2.3)  or  set(foo_VERSION 1.2.3)
+        match = re.search(
+            r"""project\s*\([^\)]*VERSION\s+([0-9]+\.[0-9]+\.[0-9]+)""",
+            text,
+            re.IGNORECASE | re.VERBOSE,
+        )
+        if match:
+            self.version = match.group(1)
+        else:
+            raise Exception("Version not found in CMakeLists.txt")
+        self.version = match.group(1)
+
+    def config_options(self):
+        if self.settings.os == "Windows":
+            del self.options.fPIC
+
+    def configure(self):
+        if self.options.shared:
+            self.options.rm_safe("fPIC")
+
+    def requirements(self):
+        # Core dependencies
+        self.requires("libtorch/2.7.1")
+        self.requires("nlohmann_json/3.11.3")
+        self.requires("folding/1.1.2")  # Custom package
+        self.requires("fimdlp/2.1.1")  # Custom package
+
+    def build_requirements(self):
+        self.build_requires("cmake/[>=3.27]")
+        self.test_requires("arff-files/1.2.1")  # Custom package
+        self.test_requires("catch2/3.8.1")
+
+    def layout(self):
+        cmake_layout(self)
+
+    def generate(self):
+        deps = CMakeDeps(self)
+        deps.generate()
+        tc = CMakeToolchain(self)
+        tc.variables["ENABLE_TESTING"] = self.options.enable_testing
+        tc.variables["CODE_COVERAGE"] = self.options.enable_coverage
+        tc.generate()
+
+    def build(self):
+        cmake = CMake(self)
+        cmake.configure()
+        cmake.build()
+
+        if self.options.enable_testing:
+            # Run tests only if we're building with testing enabled
+            self.run("ctest --output-on-failure", cwd=self.build_folder)
+
+    def package(self):
+        copy(
+            self,
+            "LICENSE",
+            src=self.source_folder,
+            dst=os.path.join(self.package_folder, "licenses"),
+        )
+        cmake = CMake(self)
+        cmake.install()
+
+    def package_info(self):
+        self.cpp_info.libs = ["bayesnet"]
+        self.cpp_info.includedirs = ["include"]
+        self.cpp_info.set_property("cmake_find_mode", "both")
+        self.cpp_info.set_property("cmake_target_name", "bayesnet::bayesnet")
+
+        # Add compiler flags that might be needed
+        if self.settings.os == "Linux":
+            self.cpp_info.system_libs = ["pthread"]

config/CMakeLists.txt

@@ -1,4 +1,4 @@
 configure_file(
   "config.h.in"
-  "${CMAKE_BINARY_DIR}/configured_files/include/config.h" ESCAPE_QUOTES
+  "${CMAKE_BINARY_DIR}/configured_files/include/bayesnet/config.h" ESCAPE_QUOTES
 )

config/config.h.in

@@ -3,11 +3,8 @@
 #include <string>
 #include <string_view>
 
-#define PROJECT_VERSION_MAJOR @PROJECT_VERSION_MAJOR @
-#define PROJECT_VERSION_MINOR @PROJECT_VERSION_MINOR @
-#define PROJECT_VERSION_PATCH @PROJECT_VERSION_PATCH @
-
-static constexpr std::string_view project_name = " @PROJECT_NAME@ ";
+static constexpr std::string_view project_name = "@PROJECT_NAME@";
 static constexpr std::string_view project_version = "@PROJECT_VERSION@";
 static constexpr std::string_view project_description = "@PROJECT_DESCRIPTION@";
 static constexpr std::string_view git_sha = "@GIT_SHA@";
+static constexpr std::string_view data_path = "@bayesnet_SOURCE_DIR@/tests/data/";

data/_TAN_cpp_accuracy__.json

@@ -1 +0,0 @@
-null

data/glass.net

@@ -1,25 +0,0 @@
-Type Si
-Type Fe
-Type RI
-Type Na
-Type Ba
-Type Ca
-Type Al
-Type K
-Type Mg
-Fe RI
-Fe Ba
-Fe Ca
-RI Na
-RI Ba
-RI Ca
-RI Al
-RI K
-RI Mg
-Ba Ca
-Ba Al
-Ca Al
-Ca K
-Ca Mg
-Al K
-K Mg

data/mfeat-factors-kdb2.net

@@ -1,645 +0,0 @@
-class att215
-class att25
-class att131
-class att95
-class att122
-class att17
-class att28
-class att5
-class att121
-class att214
-class att197
-class att116
-class att182
-class att60
-class att168
-class att178
-class att206
-class att89
-class att77
-class att209
-class att73
-class att126
-class att16
-class att74
-class att27
-class att61
-class att20
-class att101
-class att85
-class att76
-class att137
-class att211
-class att143
-class att14
-class att40
-class att210
-class att155
-class att170
-class att160
-class att23
-class att162
-class att203
-class att164
-class att107
-class att62
-class att42
-class att71
-class att128
-class att138
-class att83
-class att171
-class att92
-class att163
-class att49
-class att161
-class att158
-class att176
-class att11
-class att145
-class att4
-class att172
-class att196
-class att58
-class att68
-class att169
-class att80
-class att32
-class att175
-class att87
-class att88
-class att159
-class att18
-class att52
-class att98
-class att136
-class att150
-class att156
-class att110
-class att100
-class att63
-class att148
-class att90
-class att167
-class att35
-class att205
-class att51
-class att21
-class att142
-class att46
-class att134
-class att39
-class att102
-class att208
-class att130
-class att149
-class att96
-class att75
-class att118
-class att78
-class att213
-class att112
-class att38
-class att174
-class att189
-class att70
-class att179
-class att59
-class att79
-class att15
-class att47
-class att124
-class att34
-class att54
-class att191
-class att86
-class att56
-class att151
-class att66
-class att173
-class att44
-class att198
-class att139
-class att216
-class att129
-class att152
-class att69
-class att81
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data/mfeat-factors-kdb3.net

@@ -1,859 +0,0 @@
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