Fix comment in AODELd

Add mdlp as library in lib/
Fix tests to reach 99.1% of coverage

Reviewed-on: #31

.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

@@ -1,31 +1,39 @@
-compilation_database_dir: build
-output_directory: puml
+compilation_database_dir: build_Debug
+output_directory: diagrams
 diagrams:
   BayesNet:
     type: class
     glob:
-      - src/BayesNet/*.cc
-      - src/Platform/*.cc
-    using_namespace: bayesnet
+      - 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
-        - platform
+    exclude:
+      access:
+        - private
     plantuml:
-      after:
-        - "note left of {{ alias(\"MyProjectMain\") }}: Main class of myproject library."
-  sequence:
-    type: sequence
-    glob:
-      - src/Platform/main.cc
-    combine_free_functions_into_file_participants: true
-    using_namespace:
-      - std
-      - bayesnet
-      - platform
-    include:
-      paths:
-        - src/BayesNet
-        - src/Platform
-    start_from:
-      - function: main(int,const char **)
+      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

@@ -32,10 +32,16 @@
 *.out
 *.app
 build/**
-build_debug/**
-build_release/**
+build_*/**
 *.dSYM/**
 cmake-build*/**
 .idea
 puml/**
 .vscode/settings.json
+sample/build
+**/.DS_Store
+docs/manual
+docs/man3
+docs/man
+docs/Doxyfile
+

.gitmodules

@@ -1,15 +1,21 @@
-[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
-[submodule "lib/libxlsxwriter"]
-	path = lib/libxlsxwriter
-	url = https://github.com/jmcnamara/libxlsxwriter.git
+    master = master
+	update = merge
+[submodule "lib/folding"]
+	path = lib/folding
+	url = https://github.com/rmontanana/folding
+	main = main
+	update = merge
+[submodule "tests/lib/catch2"]
+	path = tests/lib/catch2
+        url = https://github.com/catchorg/Catch2.git
+	main = main
+	update = merge
+[submodule "tests/lib/Files"]
+	path = tests/lib/Files
+	url = https://github.com/rmontanana/ArffFiles
+[submodule "lib/mdlp"]
+	path = lib/mdlp
+	url = https://github.com/rmontanana/mdlp

.sonarlint/connectedMode.json

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

.vscode/c_cpp_properties.json

@@ -3,15 +3,47 @@
         {
             "name": "Mac",
             "includePath": [
-                "${workspaceFolder}/**"
+                "/Users/rmontanana/Code/BayesNet/**"
             ],
             "defines": [],
             "macFrameworkPath": [
-                "/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk/System/Library/Frameworks"
+                "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include"
             ],
             "cStandard": "c17",
             "cppStandard": "c++17",
-            "compileCommands": "${workspaceFolder}/cmake-build-release/compile_commands.json"
+            "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

@@ -5,93 +5,44 @@
             "type": "lldb",
             "request": "launch",
             "name": "sample",
-            "program": "${workspaceFolder}/build/sample/BayesNetSample",
+            "program": "${workspaceFolder}/build_release/sample/bayesnet_sample",
             "args": [
-                "-d",
-                "iris",
-                "-m",
-                "TANLd",
-                "-s",
-                "271",
-                "-p",
-                "/Users/rmontanana/Code/discretizbench/datasets/",
-            ],
-            //"cwd": "${workspaceFolder}/build/sample/",
-        },
-        {
-            "type": "lldb",
-            "request": "launch",
-            "name": "experiment",
-            "program": "${workspaceFolder}/build/src/Platform/b_main",
-            "args": [
-                "-m",
-                "TAN",
-                "--stratified",
-                "-d",
-                "zoo",
-                "--discretize"
-                // "--hyperparameters",
-                // "{\"repeatSparent\": true, \"maxModels\": 12}"
-            ],
-            "cwd": "/Users/rmontanana/Code/odtebench",
-        },
-        {
-            "type": "lldb",
-            "request": "launch",
-            "name": "best",
-            "program": "${workspaceFolder}/build/src/Platform/b_best",
-            "args": [
-                "-m",
-                "BoostAODE",
-                "-s",
-                "accuracy",
-                "--build",
-            ],
-            "cwd": "/Users/rmontanana/Code/discretizbench",
-        },
-        {
-            "type": "lldb",
-            "request": "launch",
-            "name": "manage",
-            "program": "${workspaceFolder}/build/src/Platform/b_manage",
-            "args": [
-                "-n",
-                "20"
-            ],
-            "cwd": "/Users/rmontanana/Code/discretizbench",
-        },
-        {
-            "type": "lldb",
-            "request": "launch",
-            "name": "list",
-            "program": "${workspaceFolder}/build/src/Platform/b_list",
-            "args": [],
-            //"cwd": "/Users/rmontanana/Code/discretizbench",
-            "cwd": "/home/rmontanana/Code/covbench",
+                "${workspaceFolder}/tests/data/glass.arff"
+            ]
         },
         {
             "type": "lldb",
             "request": "launch",
             "name": "test",
-            "program": "${workspaceFolder}/build/tests/unit_tests",
+            "program": "${workspaceFolder}/build_Debug/tests/TestBayesNet",
             "args": [
-                "-c=\"Metrics Test\"",
-                // "-s",
+                "No features selected"
             ],
-            "cwd": "${workspaceFolder}/build/tests",
+            "cwd": "${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
+                }
+            ]
         }
     ]
 }

CHANGELOG.md

@@ -0,0 +1,129 @@
+# 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).
+
+## [Unreleased]
+
+## [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

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

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(BayesNet
-  VERSION 0.2.0
+  VERSION 1.0.6
   DESCRIPTION "Bayesian Network and basic classifiers Library."
   HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
   LANGUAGES CXX
@@ -24,34 +24,38 @@ 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_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -fno-elide-constructors")
+set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -Ofast")
+if (NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fno-default-inline")
+endif()
 
 # 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(INSTALL_GTEST "Enable installation of googletest."         OFF)
 
-# Boost Library
-set(Boost_USE_STATIC_LIBS OFF) 
-set(Boost_USE_MULTITHREADED ON)  
-set(Boost_USE_STATIC_RUNTIME OFF) 
-find_package(Boost 1.66.0 REQUIRED) 
-if(Boost_FOUND)
-    message("Boost_INCLUDE_DIRS=${Boost_INCLUDE_DIRS}")
-    include_directories(${Boost_INCLUDE_DIRS}) 
-endif()
-
-SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
 # CMakes modules
 # --------------
 set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules ${CMAKE_MODULE_PATH})
 include(AddGitSubmodule)
+
+if (CMAKE_BUILD_TYPE STREQUAL "Debug")
+  MESSAGE("Debug mode")
+  set(ENABLE_TESTING ON)
+  set(CODE_COVERAGE ON)
+endif (CMAKE_BUILD_TYPE STREQUAL "Debug")
+
+get_property(LANGUAGES GLOBAL PROPERTY ENABLED_LANGUAGES)
+message(STATUS "Languages=${LANGUAGES}")
 if (CODE_COVERAGE)
-    enable_testing()
-    include(CodeCoverage)
-    MESSAGE("Code coverage enabled")
-    set(CMAKE_CXX_FLAGS " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage -O0 -g")
-    SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
+  enable_testing()
+  include(CodeCoverage)
+  MESSAGE(STATUS "Code coverage enabled")
+  SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
 endif (CODE_COVERAGE)
 
 if (ENABLE_CLANG_TIDY)
@@ -60,34 +64,45 @@ 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")
-
-
-find_library(XLSXWRITER_LIB NAMES libxlsxwriter.dylib libxlsxwriter.so PATHS ${BayesNet_SOURCE_DIR}/lib/libxlsxwriter/lib)
-message("XLSXWRITER_LIB=${XLSXWRITER_LIB}")
-
+add_git_submodule("lib/mdlp")
 
 # 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/*.h)
-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_subdirectory(bayesnet)
 
 # Testing
 # -------
-
 if (ENABLE_TESTING)
-  MESSAGE("Testing enabled")
-  add_git_submodule("lib/catch2")  
+MESSAGE(STATUS "Testing enabled")
+  add_subdirectory(tests/lib/catch2)
   include(CTest)
   add_subdirectory(tests)
 endif (ENABLE_TESTING)
+
+# Installation
+# ------------
+install(TARGETS BayesNet
+        ARCHIVE DESTINATION lib
+        LIBRARY DESTINATION lib
+        CONFIGURATIONS Release)
+install(DIRECTORY bayesnet/ DESTINATION include/bayesnet FILES_MATCHING CONFIGURATIONS Release PATTERN "*.h")
+install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/bayesnet/config.h DESTINATION include/bayesnet CONFIGURATIONS Release)
+
+# 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)

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

@@ -1,12 +1,22 @@
 SHELL := /bin/bash
 .DEFAULT_GOAL := help
-.PHONY: coverage setup help build test clean debug release
+.PHONY: viewcoverage coverage setup help install uninstall diagrams buildr buildd test clean debug release sample updatebadge doc doc-install
 
-f_release = build_release
-f_debug = build_debug
-app_targets = b_best b_list b_main b_manage
-test_targets = unit_tests_bayesnet unit_tests_platform
-n_procs = -j 16
+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'
 
 define ClearTests
 	@for t in $(test_targets); do \
@@ -29,36 +39,45 @@ setup: ## Install dependencies for tests and coverage
 	fi
 	@if [ "$(shell uname)" = "Linux" ]; then \
 		pip install gcovr; \
+		sudo dnf install lcov;\
 	fi
+	@echo "* You should install plantuml & graphviz for the diagrams"
 
-dest ?= ${HOME}/bin
-install: ## Copy binary files to bin folder
-	@echo "Destination folder: $(dest)"
-	make buildr
-	@echo ">>> Copying files to $(dest)"
-	@cp $(f_release)/src/Platform/b_main $(dest)
-	@cp $(f_release)/src/Platform/b_list $(dest)
-	@cp $(f_release)/src/Platform/b_manage $(dest)
-	@cp $(f_release)/src/Platform/b_best $(dest)
-
-dependency: ## Create a dependency graph diagram of the project (build/dependency.png)
+diagrams: ## Create an UML class diagram & dependency of the project (diagrams/BayesNet.png)
+	@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 -Tpng dependency.dot -o dependency.png
+	cd $(f_debug) && cmake .. --graphviz=dependency.dot 
+	@$(dot) -Tsvg $(f_debug)/dependency.dot.BayesNet -o $(f_diagrams)/dependency.svg
 
 buildd: ## Build the debug targets
-	cmake --build $(f_debug) -t $(app_targets) $(n_procs)
+	cmake --build $(f_debug) -t $(app_targets) --parallel $(CMAKE_BUILD_PARALLEL_LEVEL)
 
 buildr: ## Build the release targets
-	cmake --build $(f_release) -t $(app_targets) $(n_procs)
+	cmake --build $(f_release) -t $(app_targets) --parallel $(CMAKE_BUILD_PARALLEL_LEVEL)
 
 clean: ## Clean the tests info
 	@echo ">>> Cleaning Debug BayesNet tests...";
 	$(call ClearTests)
 	@echo ">>> Done";
 
-clang-uml: ## Create uml class and sequence diagrams
-	clang-uml -p --add-compile-flag -I /usr/lib/gcc/x86_64-redhat-linux/8/include/
+uninstall: ## Uninstall library
+	@echo ">>> Uninstalling BayesNet...";
+	xargs rm < $(f_release)/install_manifest.txt
+	@echo ">>> Done";
+
+prefix = "/usr/local"
+install: ## Install library
+	@echo ">>> Installing BayesNet...";
+	@cmake --install $(f_release) --prefix $(prefix)
+	@echo ">>> Done";
 
 debug: ## Build a debug version of the project
 	@echo ">>> Building Debug BayesNet...";
@@ -72,44 +91,96 @@ release: ## Build a Release version of the project
 	@if [ -d ./$(f_release) ]; then rm -rf ./$(f_release); fi
 	@mkdir $(f_release); 
 	@cmake -S . -B $(f_release) -D CMAKE_BUILD_TYPE=Release
+	@echo ">>> Done";
+
+fname = "tests/data/iris.arff"
+sample: ## Build sample
+	@echo ">>> Building Sample...";
+	@if [ -d ./sample/build ]; then rm -rf ./sample/build; fi
+	@cd sample && cmake -B build -S . && cmake --build build -t bayesnet_sample
+	sample/build/bayesnet_sample $(fname)
 	@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 & Platform tests...";
+	@echo ">>> Running BayesNet tests...";
 	@$(MAKE) clean
-	@cmake --build $(f_debug) -t $(test_targets) $(n_procs)
+	@cmake --build $(f_debug) -t $(test_targets) --parallel $(CMAKE_BUILD_PARALLEL_LEVEL)
 	@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";
 
-opt = ""
-testp: ## Run platform tests (opt="-s") to verbose output the tests, (opt="-c='Stratified Fold Test'") to run only that section
-	@echo ">>> Running Platform tests...";
-	@$(MAKE) clean
-	@cmake --build $(f_debug) --target unit_tests_platform $(n_procs)
-	@if [ -f $(f_debug)/tests/unit_tests_platform ]; then cd $(f_debug)/tests ; ./unit_tests_platform $(opt) ; fi ; 
-	@echo ">>> Done";
-
-opt = ""
-testb: ## Run BayesNet tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section
-	@echo ">>> Running BayesNet tests...";
-	@$(MAKE) clean
-	@cmake --build $(f_debug) --target unit_tests_bayesnet $(n_procs)
-	@if [ -f $(f_debug)/tests/unit_tests_bayesnet ]; then cd $(f_debug)/tests ; ./unit_tests_bayesnet $(opt) ; fi ; 
-	@echo ">>> Done";
-
 coverage: ## Run tests and generate coverage report (build/index.html)
-	@echo ">>> Building tests with coverage...";
-	@$(MAKE) test
-	@cd $(f_debug) ; \
-	gcovr --config ../gcovr.cfg tests ;
+	@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 '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; \
+	$(lcov) --summary coverage.info
+	@$(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";
+
+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) $(docsrcdir)/index.html ; \
+	fi
+	@echo ">>> Done";
+
+docdir = ""
+doc-install: ## Install documentation
+	@echo ">>> Installing documentation..."
+	@if [ "$(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";
 
 help: ## Show help message
 	@IFS=$$'\n' ; \

README.md

@@ -1,51 +1,105 @@
-# 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:3000)
+[![Codacy Badge](https://app.codacy.com/project/badge/Grade/cf3e0ac71d764650b1bf4d8d00d303b1)](https://app.codacy.com/gh/Doctorado-ML/BayesNet/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
+[![Security Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_BayesNet&metric=security_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
+[![Reliability Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_BayesNet&metric=reliability_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
+![Gitea Last Commit](https://img.shields.io/gitea/last-commit/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es:3000&logo=gitea)
+[![Coverage Badge](https://img.shields.io/badge/Coverage-99,1%25-green)](html/index.html)
+[![DOI](https://zenodo.org/badge/667782806.svg)](https://doi.org/10.5281/zenodo.14210344)
 
-## 0. Setup
+Bayesian Network Classifiers library
 
-Before compiling BayesNet.
+## Dependencies
 
-### boost library
-
-[Getting Started](<https://www.boost.org/doc/libs/1_83_0/more/getting_started/index.html>)
-
-The best option is install the packages that the Linux distribution have in its repository. If this is the case:
+The only external dependency is [libtorch](https://pytorch.org/cppdocs/installing.html) which can be installed with the following commands:
 
 ```bash
-sudo dnf install boost-devel
+wget https://download.pytorch.org/libtorch/nightly/cpu/libtorch-shared-with-deps-latest.zip
+unzip libtorch-shared-with-deps-latest.zips
 ```
 
-If this is not possible and the compressed packaged is installed, the following environment variable has to be set:
+## Setup
+
+### Getting the code
 
 ```bash
-export BOOST_ROOT=/path/to/library/
+git clone --recurse-submodules https://github.com/doctorado-ml/bayesnet
 ```
 
-### libxlswriter
-
-```bash
-cd lib/libxlsxwriter
-make
-make install DESTDIR=/home/rmontanana/Code PREFIX=
-```
-
-Environment variable has to be set:
-
-```bash
- export LD_LIBRARY_PATH=/usr/local/lib
- ```
-
 ### Release
 
 ```bash
 make release
+make buildr
+sudo make install
 ```
 
 ### Debug & Tests
 
 ```bash
 make debug
+make test
 ```
 
-## 1. Introduction
+### Coverage
+
+```bash
+make coverage
+make viewcoverage
+```
+
+### Sample app
+
+After building and installing the release version, you can run the sample app with the following commands:
+
+```bash
+make sample
+make sample fname=tests/data/glass.arff
+```
+
+## Models
+
+#### - TAN
+
+#### - KDB
+
+#### - SPODE
+
+#### - SPnDE
+
+#### - AODE
+
+#### - A2DE
+
+#### - [BoostAODE](docs/BoostAODE.md)
+
+#### - BoostA2DE
+
+### 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)

bayesnet/BaseClassifier.h

@@ -0,0 +1,47 @@
+// ***************************************************************
+// 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:
+        // 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;
+        virtual ~BaseClassifier() = default;
+        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;
+    };
+}

bayesnet/CMakeLists.txt

@@ -0,0 +1,12 @@
+include_directories(
+    ${BayesNet_SOURCE_DIR}/lib/mdlp/src
+    ${BayesNet_SOURCE_DIR}/lib/folding
+    ${BayesNet_SOURCE_DIR}/lib/json/include
+    ${BayesNet_SOURCE_DIR}
+    ${CMAKE_BINARY_DIR}/configured_files/include
+)
+
+file(GLOB_RECURSE Sources "*.cc")
+
+add_library(BayesNet ${Sources})
+target_link_libraries(BayesNet fimdlp "${TORCH_LIBRARIES}")

bayesnet/classifiers/Classifier.cc

@@ -0,0 +1,194 @@
+// ***************************************************************
+// 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) {}
+    const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
+    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
+    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
+        status_t status = NORMAL;
+        std::vector<std::string> notes; // Used to store messages occurred during the fit process
+        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);
+    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

@@ -1,20 +1,29 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "KDB.h"
 
 namespace bayesnet {
-    using namespace torch;
-
-    KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta) {}
-    void KDB::setHyperparameters(nlohmann::json& hyperparameters)
+    KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta)
     {
-        // Check if hyperparameters are valid
-        const vector<string> validKeys = { "k", "theta" };
-        checkHyperparameters(validKeys, hyperparameters);
+        validHyperparameters = { "k", "theta" };
+
+    }
+    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)
     {
@@ -40,16 +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<double> 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, "..." });
+            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(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
@@ -67,9 +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;
@@ -81,7 +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
                 }
             }
@@ -91,11 +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 +1,27 @@
+// ***************************************************************
+// 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 "bayesnet/utils/bayesnetUtils.h"
+#include "Classifier.h"
+namespace bayesnet {
+    class KDB : public Classifier {
+    private:
+        int k;
+        float theta;
+        void add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights);
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    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;
+    };
+}
+#endif

bayesnet/classifiers/KDBLd.cc

@@ -1,29 +1,34 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "KDBLd.h"
 
 namespace bayesnet {
-    using namespace std;
     KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
-    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
+    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_);
         features = features_;
         className = className_;
         Xf = X_;
         y = y_;
-        // Fills vectors Xv & yv with the data from tensors X_ (discretized) & 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 KDB structure, KDB::fit initializes the base Bayesian network
-        KDB::fit(dataset, features, className, states);
+        KDB::fit(dataset, features, className, states, smoothing);
         states = localDiscretizationProposal(states, model);
         return *this;
     }
-    Tensor KDBLd::predict(Tensor& X)
+    torch::Tensor KDBLd::predict(torch::Tensor& X)
     {
         auto Xt = prepareX(X);
         return KDB::predict(Xt);
     }
-    vector<string> KDBLd::graph(const string& name) const
+    std::vector<std::string> KDBLd::graph(const std::string& name) const
     {
         return KDB::graph(name);
     }

bayesnet/classifiers/KDBLd.h

@@ -0,0 +1,24 @@
+// ***************************************************************
+// 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 {
+    private:
+    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;
+        std::vector<std::string> graph(const std::string& name = "KDB") const override;
+        torch::Tensor predict(torch::Tensor& X) override;
+        static inline std::string version() { return "0.0.1"; };
+    };
+}
+#endif // !KDBLD_H

bayesnet/classifiers/Proposal.cc

@@ -1,8 +1,13 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "Proposal.h"
-#include "ArffFiles.h"
 
 namespace bayesnet {
-    Proposal::Proposal(torch::Tensor& dataset_, vector<string>& features_, string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}
+    Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}
     Proposal::~Proposal()
     {
         for (auto& [key, value] : discretizers) {
@@ -18,14 +23,14 @@ namespace bayesnet {
             throw std::invalid_argument("y must be an integer tensor");
         }
     }
-    map<string, vector<int>> Proposal::localDiscretizationProposal(const map<string, vector<int>>& oldStates, Network& model)
+    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<string, vector<int>> states = oldStates;
-        vector<int> indicesToReDiscretize;
+        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();
@@ -33,65 +38,64 @@ namespace bayesnet {
             upgrade = true;
             int index = find(pFeatures.begin(), pFeatures.end(), feature) - pFeatures.begin();
             indicesToReDiscretize.push_back(index); // We need to re-discretize this feature
-            vector<string> parents;
+            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
-            vector<int> indices;
+            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)
-            vector<string> yJoinParents(Xf.size(1));
+            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 arff = ArffFiles();
-            auto yxv = arff.factorize(yJoinParents);
+            auto yxv = factorize(yJoinParents);
             auto xvf_ptr = Xf.index({ index }).data_ptr<float>();
-            auto xvf = vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));
+            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 = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
+                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 = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
+                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);
+            model.fit(pDataset, weights, pFeatures, pClassName, states, Smoothing_t::ORIGINAL);
         }
         return states;
     }
-    map<string, vector<int>> Proposal::fit_local_discretization(const torch::Tensor& y)
+    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<string, vector<int>> states;
-        pDataset = torch::zeros({ n + 1, m }, kInt32);
-        auto yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.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)
         for (auto i = 0; i < pFeatures.size(); ++i) {
             auto* discretizer = new mdlp::CPPFImdlp();
             auto Xt_ptr = Xf.index({ i }).data_ptr<float>();
-            auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
+            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 = vector<int>(discretizer->getCutPoints().size() + 1);
+            auto xStates = std::vector<int>(discretizer->getCutPoints().size() + 1);
             iota(xStates.begin(), xStates.end(), 0);
             states[pFeatures[i]] = xStates;
             discretizers[pFeatures[i]] = discretizer;
         }
         int n_classes = torch::max(y).item<int>() + 1;
-        auto yStates = vector<int>(n_classes);
+        auto yStates = std::vector<int>(n_classes);
         iota(yStates.begin(), yStates.end(), 0);
         states[pClassName] = yStates;
         pDataset.index_put_({ n, "..." }, y);
@@ -101,10 +105,25 @@ namespace bayesnet {
     {
         auto Xtd = torch::zeros_like(X, torch::kInt32);
         for (int i = 0; i < X.size(0); ++i) {
-            auto Xt = vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));
+            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;
+    }
 }

bayesnet/classifiers/Proposal.h

@@ -0,0 +1,37 @@
+// ***************************************************************
+// 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 <CPPFImdlp.h>
+#include "bayesnet/network/Network.h"
+#include "Classifier.h"
+
+namespace bayesnet {
+    class Proposal {
+    public:
+        Proposal(torch::Tensor& pDataset, std::vector<std::string>& features_, std::string& className_);
+        virtual ~Proposal();
+    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);
+        torch::Tensor Xf; // X continuous nxm tensor
+        torch::Tensor y; // y discrete nx1 tensor
+        map<std::string, mdlp::CPPFImdlp*> discretizers;
+    private:
+        std::vector<int> factorize(const std::vector<std::string>& labels_t);
+        torch::Tensor& pDataset; // (n+1)xm tensor
+        std::vector<std::string>& pFeatures;
+        std::string& pClassName;
+    };
+}
+
+#endif  

bayesnet/classifiers/SPODE.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "SPODE.h"
 
 namespace bayesnet {
@@ -17,7 +23,7 @@ namespace bayesnet {
             }
         }
     }
-    vector<string> SPODE::graph(const string& name) const
+    std::vector<std::string> SPODE::graph(const std::string& name) const
     {
         return model.graph(name);
     }

bayesnet/classifiers/SPODE.h

@@ -0,0 +1,23 @@
+// ***************************************************************
+// 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 {
+    private:
+        int root;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    public:
+        explicit SPODE(int root);
+        virtual ~SPODE() = default;
+        std::vector<std::string> graph(const std::string& name = "SPODE") const override;
+    };
+}
+#endif

bayesnet/classifiers/SPODELd.cc

@@ -0,0 +1,50 @@
+// ***************************************************************
+// 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) {}
+    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_;
+        // 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 SPODE structure, SPODE::fit initializes the base Bayesian network
+        SPODE::fit(dataset, features, className, states, smoothing);
+        states = localDiscretizationProposal(states, model);
+        return *this;
+    }
+    torch::Tensor SPODELd::predict(torch::Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return SPODE::predict(Xt);
+    }
+    std::vector<std::string> SPODELd::graph(const std::string& name) const
+    {
+        return SPODE::graph(name);
+    }
+}

bayesnet/classifiers/SPODELd.h

@@ -0,0 +1,25 @@
+// ***************************************************************
+// 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;
+        torch::Tensor predict(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

@@ -1,8 +1,12 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "TAN.h"
 
 namespace bayesnet {
-    using namespace torch;
-
     TAN::TAN() : Classifier(Network()) {}
 
     void TAN::buildModel(const torch::Tensor& weights)
@@ -11,10 +15,10 @@ namespace bayesnet {
         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 = vector <pair<int, float >>();
-        Tensor class_dataset = dataset.index({ -1, "..." });
+        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) {
-            Tensor feature_dataset = dataset.index({ i, "..." });
+            torch::Tensor feature_dataset = dataset.index({ i, "..." });
             auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset, weights);
             mi.push_back({ i, mi_value });
         }
@@ -34,7 +38,7 @@ namespace bayesnet {
             model.addEdge(className, feature);
         }
     }
-    vector<string> TAN::graph(const string& title) const
+    std::vector<std::string> TAN::graph(const std::string& title) const
     {
         return model.graph(title);
     }

bayesnet/classifiers/TAN.h

@@ -0,0 +1,21 @@
+// ***************************************************************
+// 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 {
+    private:
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    public:
+        TAN();
+        virtual ~TAN() = default;
+        std::vector<std::string> graph(const std::string& name = "TAN") const override;
+    };
+}
+#endif

bayesnet/classifiers/TANLd.cc

@@ -1,30 +1,35 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "TANLd.h"
 
 namespace bayesnet {
-    using namespace std;
     TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
-    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
+    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_);
         features = features_;
         className = className_;
         Xf = X_;
         y = y_;
-        // Fills vectors Xv & yv with the data from tensors X_ (discretized) & 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 TAN structure, TAN::fit initializes the base Bayesian network
-        TAN::fit(dataset, features, className, states);
+        TAN::fit(dataset, features, className, states, smoothing);
         states = localDiscretizationProposal(states, model);
         return *this;
 
     }
-    Tensor TANLd::predict(Tensor& X)
+    torch::Tensor TANLd::predict(torch::Tensor& X)
     {
         auto Xt = prepareX(X);
         return TAN::predict(Xt);
     }
-    vector<string> TANLd::graph(const string& name) const
+    std::vector<std::string> TANLd::graph(const std::string& name) const
     {
         return TAN::graph(name);
     }

bayesnet/classifiers/TANLd.h

@@ -0,0 +1,23 @@
+// ***************************************************************
+// 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;
+        std::vector<std::string> graph(const std::string& name = "TANLd") const override;
+        torch::Tensor predict(torch::Tensor& X) override;
+    };
+}
+#endif // !TANLD_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 +1,48 @@
+// ***************************************************************
+// 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)
+    {
+    }
+    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));
+        }
+        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 +1,25 @@
+// ***************************************************************
+// 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;
+    protected:
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
+        void buildModel(const torch::Tensor& weights) override;
+    };
+}
+#endif // !AODELD_H

bayesnet/ensembles/Boost.cc

@@ -0,0 +1,246 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#include <folding.hpp>
+#include "bayesnet/feature_selection/CFS.h"
+#include "bayesnet/feature_selection/FCBF.h"
+#include "bayesnet/feature_selection/IWSS.h"
+#include "Boost.h"
+
+namespace bayesnet {
+    Boost::Boost(bool predict_voting) : Ensemble(predict_voting)
+    {
+        validHyperparameters = { "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("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 > 4)
+                throw std::invalid_argument("Invalid maxTolerance value, must be greater in [1, 4]");
+            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");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
+    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>();
+        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 };
+    }
+}

bayesnet/ensembles/Boost.h

@@ -0,0 +1,52 @@
+// ***************************************************************
+// 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() = 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);
+        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; // 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 = Orders.DESC; // Selected feature selection algorithm
+        FeatureSelect* featureSelector = nullptr;
+        double threshold = -1;
+        bool block_update = false;
+
+    };
+}
+#endif

bayesnet/ensembles/BoostA2DE.cc

@@ -0,0 +1,170 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <set>
+#include <functional>
+#include <limits.h>
+#include <tuple>
+#include <folding.hpp>
+#include "bayesnet/feature_selection/CFS.h"
+#include "bayesnet/feature_selection/FCBF.h"
+#include "bayesnet/feature_selection/IWSS.h"
+#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 +1,161 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <random> 
+#include <set>
+#include <functional>
+#include <limits.h>
+#include <tuple>
+#include "BoostAODE.h"
+
+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;
+        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[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 };
+        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) {
+                    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++;
+                featuresUsed.push_back(feature);
+                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 || 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");
+                // 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 = 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 +1,26 @@
+// ***************************************************************
+// 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 "bayesnet/classifiers/SPODE.h"
+#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"
+#include "bayesnet/utils/CountingSemaphore.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);
+            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 +1,53 @@
+// ***************************************************************
+// 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
+        {
+            return "";
+        }
+    protected:
+        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);
+        unsigned n_models;
+        std::vector<std::unique_ptr<Classifier>> models;
+        std::vector<double> significanceModels;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
+        bool predict_voting;
+    };
+}
+#endif

bayesnet/feature_selection/CFS.cc

@@ -1,6 +1,12 @@
-#include "CFS.h"
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include <limits>
-#include "bayesnetUtils.h"
+#include "bayesnet/utils/bayesnetUtils.h"
+#include "CFS.h"
 namespace bayesnet {
     void CFS::fit()
     {
@@ -11,9 +17,9 @@ namespace bayesnet {
         auto feature = featureOrder[0];
         selectedFeatures.push_back(feature);
         selectedScores.push_back(suLabels[feature]);
-        selectedFeatures.erase(selectedFeatures.begin());
+        featureOrder.erase(featureOrder.begin());
         while (continueCondition) {
-            double merit = numeric_limits<double>::lowest();
+            double merit = std::numeric_limits<double>::lowest();
             int bestFeature = -1;
             for (auto feature : featureOrder) {
                 selectedFeatures.push_back(feature);
@@ -36,7 +42,7 @@ namespace bayesnet {
         }
         fitted = true;
     }
-    bool CFS::computeContinueCondition(const vector<int>& featureOrder)
+    bool CFS::computeContinueCondition(const std::vector<int>& featureOrder)
     {
         if (selectedFeatures.size() == maxFeatures || featureOrder.size() == 0) {
             return false;
@@ -49,11 +55,11 @@ namespace bayesnet {
             subsets show no improvement over the current best subset."
             as stated in Mark A.Hall Thesis
             */
-            double item_ant = numeric_limits<double>::lowest();
+            double item_ant = std::numeric_limits<double>::lowest();
             int num = 0;
-            vector<double> lastFive(selectedScores.end() - 5, selectedScores.end());
+            std::vector<double> lastFive(selectedScores.end() - 5, selectedScores.end());
             for (auto item : lastFive) {
-                if (item_ant == numeric_limits<double>::lowest()) {
+                if (item_ant == std::numeric_limits<double>::lowest()) {
                     item_ant = item;
                 }
                 if (item > item_ant) {

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

@@ -1,8 +1,14 @@
-#include "bayesnetUtils.h"
+// ***************************************************************
+// 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 vector<string>& features, const string& className, const int maxFeatures, const int classNumStates, const torch::Tensor& weights, const double threshold) :
+    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) {

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

@@ -1,8 +1,14 @@
-#include "FeatureSelect.h"
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include <limits>
-#include "bayesnetUtils.h"
+#include "bayesnet/utils/bayesnetUtils.h"
+#include "FeatureSelect.h"
 namespace bayesnet {
-    FeatureSelect::FeatureSelect(const torch::Tensor& samples, const vector<string>& features, const string& className, const int maxFeatures, const int classNumStates, const torch::Tensor& weights) :
+    FeatureSelect::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) :
         Metrics(samples, features, className, classNumStates), maxFeatures(maxFeatures == 0 ? samples.size(0) - 1 : maxFeatures), weights(weights)
 
     {
@@ -42,7 +48,7 @@ namespace bayesnet {
         try {
             return suFeatures.at({ firstFeature, secondFeature });
         }
-        catch (const out_of_range& e) {
+        catch (const std::out_of_range& e) {
             double result = symmetricalUncertainty(firstFeature, secondFeature);
             suFeatures[{firstFeature, secondFeature}] = result;
             return result;
@@ -50,7 +56,6 @@ namespace bayesnet {
     }
     double FeatureSelect::computeMeritCFS()
     {
-        double result;
         double rcf = 0;
         for (auto feature : selectedFeatures) {
             rcf += suLabels[feature];
@@ -62,17 +67,17 @@ namespace bayesnet {
         }
         return rcf / sqrt(n + (n * n - n) * rff);
     }
-    vector<int> FeatureSelect::getFeatures() const
+    std::vector<int> FeatureSelect::getFeatures() const
     {
         if (!fitted) {
-            throw runtime_error("FeatureSelect not fitted");
+            throw std::runtime_error("FeatureSelect not fitted");
         }
         return selectedFeatures;
     }
-    vector<double> FeatureSelect::getScores() const
+    std::vector<double> FeatureSelect::getScores() const
     {
         if (!fitted) {
-            throw runtime_error("FeatureSelect not 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

@@ -1,8 +1,14 @@
-#include "IWSS.h"
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include <limits>
-#include "bayesnetUtils.h"
+#include "bayesnet/utils/bayesnetUtils.h"
+#include "IWSS.h"
 namespace bayesnet {
-    IWSS::IWSS(const torch::Tensor& samples, const vector<string>& features, const string& className, const int maxFeatures, const int classNumStates, const torch::Tensor& weights, const double threshold) :
+    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) {
@@ -28,7 +34,7 @@ namespace bayesnet {
             selectedFeatures.push_back(feature);
             // Compute merit with selectedFeatures
             auto meritNew = computeMeritCFS();
-            double delta = merit != 0.0 ? abs(merit - meritNew) / merit : 0.0;
+            double delta = merit != 0.0 ? std::abs(merit - meritNew) / merit : 0.0;
             if (meritNew > merit || delta < threshold) {
                 if (meritNew > merit) {
                     merit = meritNew;

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/network/Network.cc

@@ -0,0 +1,506 @@
+// ***************************************************************
+// 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.getClassNumStates()),
+        fitted(other.fitted), samples(other.samples)
+    {
+        if (samples.defined())
+            samples = samples.clone();
+        for (const auto& node : other.nodes) {
+            nodes[node.first] = std::make_unique<Node>(*node.second);
+        }
+    }
+    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();
+    }
+}

bayesnet/network/Network.h

@@ -0,0 +1,70 @@
+// ***************************************************************
+// 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"
+
+namespace bayesnet {
+    enum class Smoothing_t {
+        NONE = -1,
+        ORIGINAL = 0,
+        LAPLACE,
+        CESTNIK
+    };
+    class Network {
+    public:
+        Network();
+        explicit Network(const Network&);
+        ~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() }; }
+    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

@@ -1,9 +1,15 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "Node.h"
 
 namespace bayesnet {
 
     Node::Node(const std::string& name)
-        : name(name), numStates(0), cpTable(torch::Tensor()), parents(vector<Node*>()), children(vector<Node*>())
+        : name(name)
     {
     }
     void Node::clear()
@@ -14,7 +20,7 @@ namespace bayesnet {
         dimensions.clear();
         numStates = 0;
     }
-    string Node::getName() const
+    std::string Node::getName() const
     {
         return name;
     }
@@ -34,11 +40,11 @@ namespace bayesnet {
     {
         children.push_back(child);
     }
-    vector<Node*>& Node::getParents()
+    std::vector<Node*>& Node::getParents()
     {
         return parents;
     }
-    vector<Node*>& Node::getChildren()
+    std::vector<Node*>& Node::getChildren()
     {
         return children;
     }
@@ -63,73 +69,75 @@ namespace bayesnet {
     */
     unsigned Node::minFill()
     {
-        unordered_set<string> neighbors;
+        std::unordered_set<std::string> neighbors;
         for (auto child : children) {
             neighbors.emplace(child->getName());
         }
         for (auto parent : parents) {
             neighbors.emplace(parent->getName());
         }
-        auto source = vector<string>(neighbors.begin(), neighbors.end());
+        auto source = std::vector<std::string>(neighbors.begin(), neighbors.end());
         return combinations(source).size();
     }
-    vector<pair<string, string>> Node::combinations(const vector<string>& source)
+    std::vector<std::pair<std::string, std::string>> Node::combinations(const std::vector<std::string>& source)
     {
-        vector<pair<string, string>> result;
+        std::vector<std::pair<std::string, std::string>> result;
         for (int i = 0; i < source.size(); ++i) {
-            string temp = source[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 vector<string>& features, const double laplaceSmoothing, const torch::Tensor& weights)
+    void Node::computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights)
     {
         dimensions.clear();
         // Get dimensions of the CPT
         dimensions.push_back(numStates);
         transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
-
         // Create a tensor of zeros with the dimensions of the CPT
-        cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
+        cpTable = torch::zeros(dimensions, torch::kDouble) + smoothing;
         // Fill table with counts
         auto pos = find(features.begin(), features.end(), name);
         if (pos == features.end()) {
-            throw logic_error("Feature " + name + " not found in dataset");
+            throw std::logic_error("Feature " + name + " not found in dataset");
         }
         int name_index = pos - features.begin();
+        c10::List<c10::optional<at::Tensor>> coordinates;
         for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
-            c10::List<c10::optional<at::Tensor>> coordinates;
-            coordinates.push_back(dataset.index({ name_index, n_sample }));
+            coordinates.clear();
+            auto sample = dataset.index({ "...", n_sample });
+            coordinates.push_back(sample[name_index]);
             for (auto parent : parents) {
                 pos = find(features.begin(), features.end(), parent->getName());
                 if (pos == features.end()) {
-                    throw logic_error("Feature parent " + parent->getName() + " not found in dataset");
+                    throw std::logic_error("Feature parent " + parent->getName() + " not found in dataset");
                 }
                 int parent_index = pos - features.begin();
-                coordinates.push_back(dataset.index({ parent_index, n_sample }));
+                coordinates.push_back(sample[parent_index]);
             }
             // Increment the count of the corresponding coordinate
-            cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + weights.index({ n_sample }).item<double>());
+            cpTable.index_put_({ coordinates }, weights.index({ n_sample }), true);
         }
         // Normalize the counts
+        // Divide each row by the sum of the row
         cpTable = cpTable / cpTable.sum(0);
     }
-    float Node::getFactorValue(map<string, int>& evidence)
+    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(), back_inserter(coordinates), [&evidence](const auto& parent) { return at::tensor(evidence[parent->getName()]); });
-        return cpTable.index({ coordinates }).item<float>();
+        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>();
     }
-    vector<string> Node::graph(const string& className)
+    std::vector<std::string> Node::graph(const std::string& className)
     {
-        auto output = vector<string>();
+        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(); });
+        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 +1,42 @@
+// ***************************************************************
+// 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&);
+        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/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 +1,46 @@
+#ifndef COUNTING_SEMAPHORE_H
+#define COUNTING_SEMAPHORE_H
+#include <mutex>
+#include <condition_variable>
+#include <algorithm>
+#include <thread>
+#include <mutex>
+#include <condition_variable>
+#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();
+        }
+    }
+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

@@ -1,14 +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;
@@ -41,35 +47,26 @@ namespace bayesnet {
             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()
-    {
-        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;
-        }
-    }
 
-    void insertElement(list<int>& variables, int variable)
+    void MST::insertElement(std::list<int>& variables, int variable)
     {
-        if (find(variables.begin(), variables.end(), variable) == variables.end()) {
+        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)
     {
         // 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 = vector<pair<int, int>>();
-        auto visited = vector<int>();
-        auto nextVariables = list<int>();
+        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.front();
@@ -104,8 +101,8 @@ 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);

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/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

cmake/modules/CodeCoverage.cmake

@@ -137,7 +137,7 @@
 
 include(CMakeParseArguments)
 
-option(CODE_COVERAGE_VERBOSE "Verbose information" FALSE)
+option(CODE_COVERAGE_VERBOSE "Verbose information" TRUE)
 
 # Check prereqs
 find_program( GCOV_PATH gcov )
@@ -160,7 +160,11 @@ foreach(LANG ${LANGUAGES})
     endif()
   elseif(NOT "${CMAKE_${LANG}_COMPILER_ID}" MATCHES "GNU"
          AND NOT "${CMAKE_${LANG}_COMPILER_ID}" MATCHES "(LLVM)?[Ff]lang")
-    message(FATAL_ERROR "Compiler is not GNU or Flang! Aborting...")
+         if ("${LANG}" MATCHES "CUDA")
+             message(STATUS "Ignoring CUDA")
+        else()
+            message(FATAL_ERROR "Compiler is not GNU or Flang! Aborting...")
+        endif()
   endif()
 endforeach()
 

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

@@ -7,7 +7,8 @@
 #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/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
-class att50
-class att153
-class att41
-class att204
-class att188
-class att26
-class att13
-class att117
-class att114
-class att10
-class att64
-class att200
-class att9
-class att3
-class att119
-class att45
-class att104
-class att140
-class att30
-class att183
-class att146
-class att141
-class att202
-class att194
-class att24
-class att147
-class att8
-class att212
-class att123
-class att166
-class att187
-class att127
-class att190
-class att105
-class att106
-class att184
-class att82
-class att2
-class att135
-class att154
-class att111
-class att115
-class att99
-class att22
-class att84
-class att207
-class att94
-class att177
-class att103
-class att93
-class att201
-class att43
-class att36
-class att12
-class att125
-class att165
-class att180
-class att195
-class att157
-class att48
-class att6
-class att113
-class att193
-class att91
-class att72
-class att31
-class att132
-class att33
-class att57
-class att144
-class att192
-class att185
-class att37
-class att53
-class att120
-class att186
-class att199
-class att65
-class att108
-class att133
-class att29
-class att19
-class att7
-class att97
-class att67
-class att55
-class att1
-class att109
-class att181
-att215 att25
-att215 att131
-att215 att95
-att25 att131
-att25 att121
-att25 att73
-att25 att61
-att25 att85
-att25 att169
-att25 att13
-att131 att95
-att131 att122
-att131 att17
-att131 att28
-att131 att121
-att131 att214
-att131 att116
-att131 att126
-att131 att143
-att95 att122
-att95 att17
-att95 att28
-att95 att5
-att95 att214
-att95 att116
-att95 att60
-att95 att143
-att95 att155
-att95 att71
-att122 att182
-att122 att170
-att17 att5
-att17 att197
-att17 att89
-att17 att77
-att17 att161
-att28 att206
-att28 att16
-att28 att76
-att28 att172
-att28 att124
-att28 att64
-att5 att197
-att5 att89
-att5 att209
-att121 att73
-att214 att178
-att214 att58
-att214 att142
-att197 att209
-att197 att101
-att116 att182
-att116 att60
-att116 att168
-att116 att178
-att116 att206
-att116 att126
-att116 att16
-att116 att27
-att116 att20
-att116 att211
-att116 att164
-att116 att128
-att182 att27
-att182 att14
-att60 att168
-att60 att156
-att168 att156
-att168 att96
-att178 att20
-att178 att58
-att178 att142
-att178 att130
-att206 att74
-att206 att170
-att206 att158
-att89 att77
-att89 att137
-att89 att149
-att89 att173
-att77 att137
-att77 att161
-att209 att101
-att209 att41
-att73 att61
-att73 att157
-att126 att162
-att126 att138
-att126 att150
-att16 att74
-att16 att76
-att16 att40
-att16 att4
-att74 att14
-att74 att62
-att27 att171
-att61 att85
-att61 att169
-att20 att211
-att20 att210
-att20 att164
-att20 att176
-att101 att41
-att85 att13
-att76 att40
-att76 att160
-att137 att149
-att211 att210
-att211 att162
-att211 att171
-att211 att163
-att211 att175
-att211 att79
-att143 att155
-att143 att23
-att143 att71
-att143 att83
-att143 att11
-att14 att98
-att40 att160
-att40 att4
-att40 att196
-att40 att52
-att210 att42
-att210 att114
-att155 att23
-att155 att203
-att155 att107
-att155 att11
-att170 att158
-att160 att52
-att23 att203
-att162 att138
-att162 att18
-att162 att150
-att162 att90
-att162 att174
-att203 att107
-att203 att49
-att203 att59
-att203 att191
-att203 att119
-att164 att62
-att164 att42
-att164 att128
-att164 att92
-att164 att163
-att164 att176
-att164 att145
-att164 att68
-att164 att80
-att164 att98
-att164 att110
-att164 att205
-att164 att21
-att164 att213
-att164 att112
-att164 att38
-att164 att56
-att164 att44
-att107 att59
-att107 att47
-att107 att191
-att71 att83
-att71 att167
-att71 att35
-att128 att92
-att138 att18
-att83 att167
-att171 att87
-att171 att159
-att171 att63
-att171 att51
-att171 att39
-att171 att75
-att163 att49
-att163 att175
-att163 att87
-att163 att79
-att163 att151
-att163 att139
-att163 att187
-att163 att91
-att161 att173
-att176 att145
-att176 att172
-att176 att68
-att176 att80
-att176 att32
-att176 att110
-att176 att205
-att176 att21
-att176 att134
-att176 att56
-att4 att196
-att4 att88
-att4 att136
-att4 att100
-att4 att148
-att4 att208
-att172 att112
-att172 att184
-att196 att88
-att196 att136
-att196 att100
-att196 att208
-att58 att46
-att68 att32
-att32 att200
-att87 att159
-att87 att63
-att87 att75
-att87 att15
-att87 att99
-att159 att195
-att18 att90
-att18 att102
-att18 att78
-att18 att198
-att52 att124
-att98 att86
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-att150 att66
-att156 att96
-att156 att216
-att156 att204
-att156 att24
-att156 att84
-att100 att148
-att63 att51
-att63 att3
-att63 att183
-att90 att102
-att90 att78
-att167 att35
-att167 att179
-att35 att179
-att51 att39
-att51 att3
-att21 att134
-att21 att213
-att21 att38
-att21 att189
-att21 att129
-att21 att81
-att21 att117
-att21 att9
-att142 att46
-att142 att130
-att142 att118
-att142 att10
-att142 att202
-att142 att190
-att142 att106
-att46 att70
-att46 att34
-att46 att166
-att134 att2
-att102 att54
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-att130 att10
-att130 att202
-att149 att125
-att96 att216
-att96 att24
-att75 att15
-att75 att99
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-att78 att198
-att213 att189
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-att38 att26
-att174 att54
-att174 att66
-att174 att30
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-att189 att129
-att189 att69
-att189 att81
-att189 att153
-att189 att117
-att189 att9
-att189 att45
-att189 att105
-att70 att34
-att59 att47
-att79 att151
-att79 att139
-att79 att187
-att79 att127
-att79 att103
-att79 att43
-att79 att91
-att79 att19
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-att86 att194
-att56 att44
-att56 att152
-att56 att50
-att56 att188
-att56 att26
-att56 att104
-att56 att140
-att56 att146
-att56 att194
-att56 att8
-att56 att2
-att56 att133
-att56 att1
-att173 att125
-att173 att113
-att44 att152
-att44 att188
-att44 att200
-att44 att212
-att44 att1
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-att139 att43
-att139 att31
-att139 att199
-att139 att7
-att216 att204
-att216 att36
-att216 att12
-att216 att180
-att216 att108
-att129 att69
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-att69 att153
-att81 att45
-att153 att141
-att41 att53
-att204 att12
-att13 att157
-att114 att6
-att114 att186
-att10 att190
-att64 att184
-att200 att104
-att9 att146
-att9 att141
-att9 att177
-att9 att37
-att9 att133
-att9 att109
-att9 att181
-att3 att183
-att3 att147
-att3 att123
-att3 att135
-att3 att111
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-att45 att177
-att45 att93
-att45 att201
-att45 att193
-att45 att37
-att45 att97
-att140 att8
-att30 att6
-att183 att147
-att183 att123
-att202 att166
-att202 att106
-att202 att82
-att24 att84
-att24 att36
-att147 att135
-att8 att212
-att166 att82
-att187 att127
-att187 att115
-att127 att115
-att105 att93
-att106 att154
-att82 att154
-att82 att22
-att135 att111
-att135 att207
-att154 att22
-att154 att94
-att111 att207
-att22 att94
-att84 att48
-att177 att165
-att103 att195
-att103 att109
-att93 att201
-att93 att165
-att93 att193
-att93 att33
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data/mfeat-factors-kdb3.net

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

data/mfeat-factors.net

@@ -1,859 +0,0 @@
-class att215
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-

diagrams/BayesNet.puml

@@ -0,0 +1,580 @@
+@startuml
+title clang-uml class diagram model
+class "bayesnet::Node" as C_0010428199432536647474
+class C_0010428199432536647474 #aliceblue;line:blue;line.dotted;text:blue {
++Node(const std::string &) : void
+..
++addChild(Node *) : void
++addParent(Node *) : void
++clear() : void
++computeCPT(const torch::Tensor & dataset, const std::vector<std::string> & features, const double smoothing, const torch::Tensor & weights) : void
++getCPT() : torch::Tensor &
++getChildren() : std::vector<Node *> &
++getFactorValue(std::map<std::string,int> &) : double
++getName() const : std::string
++getNumStates() const : int
++getParents() : std::vector<Node *> &
++graph(const std::string & clasName) : std::vector<std::string>
++minFill() : unsigned int
++removeChild(Node *) : void
++removeParent(Node *) : void
++setNumStates(int) : void
+__
+}
+enum "bayesnet::Smoothing_t" as C_0013393078277439680282
+enum C_0013393078277439680282 {
+NONE
+ORIGINAL
+LAPLACE
+CESTNIK
+}
+class "bayesnet::Network" as C_0009493661199123436603
+class C_0009493661199123436603 #aliceblue;line:blue;line.dotted;text:blue {
++Network() : void
++Network(const Network &) : void
++~Network() = default : void
+..
++addEdge(const std::string &, const std::string &) : void
++addNode(const std::string &) : void
++dump_cpt() const : std::string
++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) : 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 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
++getClassName() const : std::string
++getClassNumStates() const : int
++getEdges() const : std::vector<std::pair<std::string,std::string>>
++getFeatures() const : std::vector<std::string>
++getNodes() : std::map<std::string,std::unique_ptr<Node>> &
++getNumEdges() const : int
++getSamples() : torch::Tensor &
++getStates() const : int
++graph(const std::string & title) const : std::vector<std::string>
++initialize() : void
++predict(const std::vector<std::vector<int>> &) : std::vector<int>
++predict(const torch::Tensor &) : torch::Tensor
++predict_proba(const std::vector<std::vector<int>> &) : std::vector<std::vector<double>>
++predict_proba(const torch::Tensor &) : torch::Tensor
++predict_tensor(const torch::Tensor & samples, const bool proba) : torch::Tensor
++score(const std::vector<std::vector<int>> &, const std::vector<int> &) : double
++show() const : std::vector<std::string>
++topological_sort() : std::vector<std::string>
++version() : std::string
+__
+}
+enum "bayesnet::status_t" as C_0005907365846270811004
+enum C_0005907365846270811004 {
+NORMAL
+WARNING
+ERROR
+}
+abstract "bayesnet::BaseClassifier" as C_0002617087915615796317
+abstract C_0002617087915615796317 #aliceblue;line:blue;line.dotted;text:blue {
++~BaseClassifier() = default : void
+..
+{abstract} +dump_cpt() const = 0 : std::string
+{abstract} +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 : BaseClassifier &
+{abstract} +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 : BaseClassifier &
+{abstract} +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 : BaseClassifier &
+{abstract} +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 : BaseClassifier &
+{abstract} +getClassNumStates() const = 0 : int
+{abstract} +getNotes() const = 0 : std::vector<std::string>
+{abstract} +getNumberOfEdges() const = 0 : int
+{abstract} +getNumberOfNodes() const = 0 : int
+{abstract} +getNumberOfStates() const = 0 : int
+{abstract} +getStatus() const = 0 : status_t
++getValidHyperparameters() : std::vector<std::string> &
+{abstract} +getVersion() = 0 : std::string
+{abstract} +graph(const std::string & title = "") const = 0 : std::vector<std::string>
+{abstract} +predict(std::vector<std::vector<int>> & X) = 0 : std::vector<int>
+{abstract} +predict(torch::Tensor & X) = 0 : torch::Tensor
+{abstract} +predict_proba(std::vector<std::vector<int>> & X) = 0 : std::vector<std::vector<double>>
+{abstract} +predict_proba(torch::Tensor & X) = 0 : torch::Tensor
+{abstract} +score(std::vector<std::vector<int>> & X, std::vector<int> & y) = 0 : float
+{abstract} +score(torch::Tensor & X, torch::Tensor & y) = 0 : float
+{abstract} +setHyperparameters(const nlohmann::json & hyperparameters) = 0 : void
+{abstract} +show() const = 0 : std::vector<std::string>
+{abstract} +topological_order() = 0 : std::vector<std::string>
+{abstract} #trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) = 0 : void
+__
+#validHyperparameters : std::vector<std::string>
+}
+class "bayesnet::Metrics" as C_0005895723015084986588
+class C_0005895723015084986588 #aliceblue;line:blue;line.dotted;text:blue {
++Metrics() = default : void
++Metrics(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int classNumStates) : void
++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) : void
+..
++SelectKBestWeighted(const torch::Tensor & weights, bool ascending = false, unsigned int k = 0) : std::vector<int>
++SelectKPairs(const torch::Tensor & weights, std::vector<int> & featuresExcluded, bool ascending = false, unsigned int k = 0) : std::vector<std::pair<int,int>>
++conditionalEdge(const torch::Tensor & weights) : torch::Tensor
++conditionalEntropy(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & labels, const torch::Tensor & weights) : double
++conditionalMutualInformation(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & labels, const torch::Tensor & weights) : double
+#doCombinations<T>(const std::vector<T> & source) : std::vector<std::pair<T, T> >
++entropy(const torch::Tensor & feature, const torch::Tensor & weights) : double
++getScoresKBest() const : std::vector<double>
++getScoresKPairs() const : std::vector<std::pair<std::pair<int,int>,double>>
++maximumSpanningTree(const std::vector<std::string> & features, const torch::Tensor & weights, const int root) : std::vector<std::pair<int,int>>
++mutualInformation(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & weights) : double
+#pop_first<T>(std::vector<T> & v) : T
+__
+#className : std::string
+#features : std::vector<std::string>
+#samples : torch::Tensor
+}
+abstract "bayesnet::Classifier" as C_0016351972983202413152
+abstract C_0016351972983202413152 #aliceblue;line:blue;line.dotted;text:blue {
++Classifier(Network model) : void
++~Classifier() = default : void
+..
++addNodes() : void
+#buildDataset(torch::Tensor & y) : void
+{abstract} #buildModel(const torch::Tensor & weights) = 0 : void
+#checkFitParameters() : void
++dump_cpt() const : std::string
++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) : 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) : 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) : 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) : Classifier &
++getClassNumStates() const : int
++getNotes() const : std::vector<std::string>
++getNumberOfEdges() const : int
++getNumberOfNodes() const : int
++getNumberOfStates() const : int
++getStatus() const : status_t
++getVersion() : std::string
++predict(std::vector<std::vector<int>> & X) : std::vector<int>
++predict(torch::Tensor & X) : torch::Tensor
++predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
++predict_proba(torch::Tensor & X) : torch::Tensor
++score(torch::Tensor & X, torch::Tensor & y) : float
++score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
++setHyperparameters(const nlohmann::json & hyperparameters) : void
++show() const : std::vector<std::string>
++topological_order() : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+__
+#className : std::string
+#dataset : torch::Tensor
+#features : std::vector<std::string>
+#fitted : bool
+#m : unsigned int
+#metrics : Metrics
+#model : Network
+#n : unsigned int
+#notes : std::vector<std::string>
+#states : std::map<std::string,std::vector<int>>
+#status : status_t
+}
+class "bayesnet::KDB" as C_0008902920152122000044
+class C_0008902920152122000044 #aliceblue;line:blue;line.dotted;text:blue {
++KDB(int k, float theta = 0.03) : void
++~KDB() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & name = "KDB") const : std::vector<std::string>
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
+__
+}
+class "bayesnet::SPODE" as C_0004096182510460307610
+class C_0004096182510460307610 #aliceblue;line:blue;line.dotted;text:blue {
++SPODE(int root) : void
++~SPODE() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & name = "SPODE") const : std::vector<std::string>
+__
+}
+class "bayesnet::SPnDE" as C_0016268916386101512883
+class C_0016268916386101512883 #aliceblue;line:blue;line.dotted;text:blue {
++SPnDE(std::vector<int> parents) : void
++~SPnDE() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & name = "SPnDE") const : std::vector<std::string>
+__
+}
+class "bayesnet::TAN" as C_0014087955399074584137
+class C_0014087955399074584137 #aliceblue;line:blue;line.dotted;text:blue {
++TAN() : void
++~TAN() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & name = "TAN") const : std::vector<std::string>
+__
+}
+class "bayesnet::Proposal" as C_0017759964713298103839
+class C_0017759964713298103839 #aliceblue;line:blue;line.dotted;text:blue {
++Proposal(torch::Tensor & pDataset, std::vector<std::string> & features_, std::string & className_) : void
++~Proposal() : void
+..
+#checkInput(const torch::Tensor & X, const torch::Tensor & y) : void
+#fit_local_discretization(const torch::Tensor & y) : std::map<std::string,std::vector<int>>
+#localDiscretizationProposal(const std::map<std::string,std::vector<int>> & states, Network & model) : std::map<std::string,std::vector<int>>
+#prepareX(torch::Tensor & X) : torch::Tensor
+__
+#Xf : torch::Tensor
+#discretizers : map<std::string,mdlp::CPPFImdlp *>
+#y : torch::Tensor
+}
+class "bayesnet::KDBLd" as C_0002756018222998454702
+class C_0002756018222998454702 #aliceblue;line:blue;line.dotted;text:blue {
++KDBLd(int k) : void
++~KDBLd() = default : void
+..
++fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : KDBLd &
++graph(const std::string & name = "KDB") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+class "bayesnet::SPODELd" as C_0010957245114062042836
+class C_0010957245114062042836 #aliceblue;line:blue;line.dotted;text:blue {
++SPODELd(int root) : void
++~SPODELd() = default : void
+..
++commonFit(const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
++fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
++fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
++graph(const std::string & name = "SPODELd") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+class "bayesnet::TANLd" as C_0013350632773616302678
+class C_0013350632773616302678 #aliceblue;line:blue;line.dotted;text:blue {
++TANLd() : void
++~TANLd() = default : void
+..
++fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : TANLd &
++graph(const std::string & name = "TANLd") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+__
+}
+class "bayesnet::Ensemble" as C_0015881931090842884611
+class C_0015881931090842884611 #aliceblue;line:blue;line.dotted;text:blue {
++Ensemble(bool predict_voting = true) : void
++~Ensemble() = default : void
+..
+#compute_arg_max(std::vector<std::vector<double>> & X) : std::vector<int>
+#compute_arg_max(torch::Tensor & X) : torch::Tensor
++dump_cpt() const : std::string
++getNumberOfEdges() const : int
++getNumberOfNodes() const : int
++getNumberOfStates() const : int
++graph(const std::string & title) const : std::vector<std::string>
++predict(std::vector<std::vector<int>> & X) : std::vector<int>
++predict(torch::Tensor & X) : torch::Tensor
+#predict_average_proba(torch::Tensor & X) : torch::Tensor
+#predict_average_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
+#predict_average_voting(torch::Tensor & X) : torch::Tensor
+#predict_average_voting(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
++predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
++predict_proba(torch::Tensor & X) : torch::Tensor
++score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
++score(torch::Tensor & X, torch::Tensor & y) : float
++show() const : std::vector<std::string>
++topological_order() : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+#voting(torch::Tensor & votes) : torch::Tensor
+__
+#models : std::vector<std::unique_ptr<Classifier>>
+#n_models : unsigned int
+#predict_voting : bool
+#significanceModels : std::vector<double>
+}
+class "bayesnet::A2DE" as C_0001410789567057647859
+class C_0001410789567057647859 #aliceblue;line:blue;line.dotted;text:blue {
++A2DE(bool predict_voting = false) : void
++~A2DE() : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & title = "A2DE") const : std::vector<std::string>
++setHyperparameters(const nlohmann::json & hyperparameters) : void
+__
+}
+class "bayesnet::AODE" as C_0006288892608974306258
+class C_0006288892608974306258 #aliceblue;line:blue;line.dotted;text:blue {
++AODE(bool predict_voting = false) : void
++~AODE() : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & title = "AODE") const : std::vector<std::string>
++setHyperparameters(const nlohmann::json & hyperparameters) : void
+__
+}
+abstract "bayesnet::FeatureSelect" as C_0013562609546004646591
+abstract C_0013562609546004646591 #aliceblue;line:blue;line.dotted;text:blue {
++FeatureSelect(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
++~FeatureSelect() : void
+..
+#computeMeritCFS() : double
+#computeSuFeatures(const int a, const int b) : double
+#computeSuLabels() : void
+{abstract} +fit() = 0 : void
++getFeatures() const : std::vector<int>
++getScores() const : std::vector<double>
+#initialize() : void
+#symmetricalUncertainty(int a, int b) : double
+__
+#fitted : bool
+#maxFeatures : int
+#selectedFeatures : std::vector<int>
+#selectedScores : std::vector<double>
+#suFeatures : std::map<std::pair<int,int>,double>
+#suLabels : std::vector<double>
+#weights : const torch::Tensor &
+}
+class "bayesnet::(anonymous_60342586)" as C_0005584545181746538542
+class C_0005584545181746538542 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60343240)" as C_0016227156982041949444
+class C_0016227156982041949444 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::Boost" as C_0009819322948617116148
+class C_0009819322948617116148 #aliceblue;line:blue;line.dotted;text:blue {
++Boost(bool predict_voting = false) : void
++~Boost() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
+#featureSelection(torch::Tensor & weights_) : std::vector<int>
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
+#update_weights(torch::Tensor & ytrain, torch::Tensor & ypred, torch::Tensor & weights) : std::tuple<torch::Tensor &,double,bool>
+#update_weights_block(int k, torch::Tensor & ytrain, torch::Tensor & weights) : std::tuple<torch::Tensor &,double,bool>
+__
+#X_test : torch::Tensor
+#X_train : torch::Tensor
+#bisection : bool
+#block_update : bool
+#convergence : bool
+#convergence_best : bool
+#featureSelector : FeatureSelect *
+#maxTolerance : int
+#order_algorithm : std::string
+#selectFeatures : bool
+#select_features_algorithm : std::string
+#threshold : double
+#y_test : torch::Tensor
+#y_train : torch::Tensor
+}
+class "bayesnet::AODELd" as C_0003898187834670349177
+class C_0003898187834670349177 #aliceblue;line:blue;line.dotted;text:blue {
++AODELd(bool predict_voting = true) : void
++~AODELd() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++fit(torch::Tensor & X_, torch::Tensor & y_, const std::vector<std::string> & features_, const std::string & className_, std::map<std::string,std::vector<int>> & states_, const Smoothing_t smoothing) : AODELd &
++graph(const std::string & name = "AODELd") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::(anonymous_60275628)" as C_0009086919615463763584
+class C_0009086919615463763584 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60276282)" as C_0015251985607563196159
+class C_0015251985607563196159 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::BoostA2DE" as C_0000272055465257861326
+class C_0000272055465257861326 #aliceblue;line:blue;line.dotted;text:blue {
++BoostA2DE(bool predict_voting = false) : void
++~BoostA2DE() = default : void
+..
++graph(const std::string & title = "BoostA2DE") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::(anonymous_60275502)" as C_0016033655851510053155
+class C_0016033655851510053155 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60276156)" as C_0000379522761622473555
+class C_0000379522761622473555 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::BoostAODE" as C_0002867772739198819061
+class C_0002867772739198819061 #aliceblue;line:blue;line.dotted;text:blue {
++BoostAODE(bool predict_voting = false) : void
++~BoostAODE() = default : void
+..
++graph(const std::string & title = "BoostAODE") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::CFS" as C_0000093018845530739957
+class C_0000093018845530739957 #aliceblue;line:blue;line.dotted;text:blue {
++CFS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
++~CFS() : void
+..
++fit() : void
+__
+}
+class "bayesnet::FCBF" as C_0001157456122733975432
+class C_0001157456122733975432 #aliceblue;line:blue;line.dotted;text:blue {
++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) : void
++~FCBF() : void
+..
++fit() : void
+__
+}
+class "bayesnet::IWSS" as C_0000066148117395428429
+class C_0000066148117395428429 #aliceblue;line:blue;line.dotted;text:blue {
++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) : void
++~IWSS() : void
+..
++fit() : void
+__
+}
+class "bayesnet::(anonymous_60730495)" as C_0004857727320042830573
+class C_0004857727320042830573 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60731150)" as C_0000076541533312623385
+class C_0000076541533312623385 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::(anonymous_60653004)" as C_0001444063444142949758
+class C_0001444063444142949758 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60653658)" as C_0007139277546931322856
+class C_0007139277546931322856 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::(anonymous_60731375)" as C_0010493853592456211189
+class C_0010493853592456211189 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60732030)" as C_0007011438637915849564
+class C_0007011438637915849564 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::MST" as C_0001054867409378333602
+class C_0001054867409378333602 #aliceblue;line:blue;line.dotted;text:blue {
++MST() = default : void
++MST(const std::vector<std::string> & features, const torch::Tensor & weights, const int root) : void
+..
++insertElement(std::list<int> & variables, int variable) : void
++maximumSpanningTree() : 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>>
+__
+}
+class "bayesnet::Graph" as C_0009576333456015187741
+class C_0009576333456015187741 #aliceblue;line:blue;line.dotted;text:blue {
++Graph(int V) : void
+..
++addEdge(int u, int v, float wt) : void
++find_set(int i) : int
++get_mst() : std::vector<std::pair<float,std::pair<int,int>>>
++kruskal_algorithm() : void
++union_set(int u, int v) : void
+__
+}
+C_0010428199432536647474 --> C_0010428199432536647474 : -parents
+C_0010428199432536647474 --> C_0010428199432536647474 : -children
+C_0009493661199123436603 ..> C_0013393078277439680282
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+C_0015881931090842884611 o-- C_0016351972983202413152 : #models
+C_0016351972983202413152 <|-- C_0015881931090842884611
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+C_0015881931090842884611 <|-- C_0001410789567057647859
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+C_0015881931090842884611 <|-- C_0006288892608974306258
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+C_0015881931090842884611 <|-- C_0009819322948617116148
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+C_0003898187834670349177 ..> C_0013393078277439680282
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+C_0009819322948617116148 <|-- C_0002867772739198819061
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+C_0013562609546004646591 <|-- C_0001157456122733975432
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+C_0013562609546004646591 <|-- C_0000066148117395428429
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+
+'Generated with clang-uml, version 0.5.5
+'LLVM version clang version 18.1.8 (Fedora 18.1.8-5.fc41)
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docs/BoostAODE.md

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+# BoostAODE Algorithm Operation
+
+## Hyperparameters
+
+The hyperparameters defined in the algorithm are:
+
+- ***bisection*** (*boolean*): If set to true allows the algorithm to add *k* models at once (as specified in the algorithm) to the ensemble. Default value: *true*.
+- ***bisection_best*** (*boolean*): If set to *true*, the algorithm will take as *priorAccuracy* the best accuracy computed. If set to *false⁺ it will take the last accuracy as *priorAccuracy*. Default value: *false*.
+
+- ***order*** (*{"asc", "desc", "rand"}*): Sets the order (ascending/descending/random) in which dataset variables will be processed to choose the parents of the *SPODEs*. Default value: *"desc"*.
+
+- ***block_update*** (*boolean*): Sets whether the algorithm will update the weights of the models in blocks. If set to false, the algorithm will update the weights of the models one by one. Default value: *false*.
+
+- ***convergence*** (*boolean*): Sets whether the convergence of the result will be used as a termination condition. If this hyperparameter is set to true, the training dataset passed to the model is divided into two sets, one serving as training data and the other as a test set (so the original test partition will become a validation partition in this case). The partition is made by taking the first partition generated by a process of generating a 5 fold partition with stratification using a predetermined seed. The exit condition used in this *convergence* is that the difference between the accuracy obtained by the current model and that obtained by the previous model is greater than *1e-4*; otherwise, one will be added to the number of models that worsen the result (see next hyperparameter). Default value: *true*.
+
+- ***maxTolerance*** (*int*): Sets the maximum number of models that can worsen the result without constituting a termination condition. if ***bisection*** is set to *true*, the value of this hyperparameter will be exponent of base 2 to compute the number of models to insert at once. Default value: *3*
+
+- ***select_features*** (*{"IWSS", "FCBF", "CFS", ""}*): Selects the variable selection method to be used to build initial models for the ensemble that will be included without considering any of the other exit conditions. Once the models of the selected variables are built, the algorithm will update the weights using the ensemble and set the significance of all the models built with the same &alpha;<sub>t</sub>. Default value: *""*.
+
+- ***threshold*** (*double*): Sets the necessary value for the IWSS and FCBF algorithms to function. Accepted values are:
+  - IWSS: $threshold \in [0, 0.5]$
+  - FCBF: $threshold \in [10^{-7}, 1]$
+
+  Default value is *-1* so every time any of those algorithms are called, the threshold has to be set to the desired value.
+
+- ***predict_voting*** (*boolean*): Sets whether the algorithm will use *model voting* to predict the result. If set to false, the weighted average of the probabilities of each model's prediction will be used. Default value: *false*.
+
+## Operation
+
+### [Base Algorithm](./algorithm.md)

docs/algorithm.md

@@ -0,0 +1,117 @@
+# Algorithm
+
+- // notation
+
+- $n$ features ${\cal{X}} = \{X_1, \dots, X_n\}$ and the class $Y$
+
+- $m$ instances.
+
+- $D = \{ (x_1^i, \dots, x_n^i, y^i) \}_{i=1}^{m}$
+
+- $W$ a weights vector. $W_0$ are the initial weights.
+
+- $D[W]$ dataset with weights $W$ for the instances.
+
+1. // initialization
+
+2. $W_0 \leftarrow (w_1, \dots, w_m) \leftarrow 1/m$
+
+3. $W \leftarrow W_0$
+
+4. $Vars \leftarrow {\cal{X}}$
+
+5. $\delta \leftarrow 10^{-4}$
+
+6. $convergence \leftarrow True$ // hyperparameter
+
+7. $maxTolerancia \leftarrow 3$ // hyperparameter
+
+8. $bisection \leftarrow False$ // hyperparameter
+
+9. $finished \leftarrow False$
+
+10. $AODE \leftarrow \emptyset$ // the ensemble
+
+11. $tolerance \leftarrow 0$
+
+12. $numModelsInPack \leftarrow 0$
+
+13. $maxAccuracy \leftarrow -1$
+
+14.
+
+15. // main loop
+
+16. While $(\lnot finished)$
+
+    1. $\pi \leftarrow SortFeatures(Vars, criterio, D[W])$
+
+    2. $k \leftarrow 2^{tolerance}$
+
+    3. if ($tolerance == 0$) $numItemsPack \leftarrow0$
+
+    4. $P \leftarrow Head(\pi,k)$ // first k features in order
+
+    5. $spodes \leftarrow \emptyset$
+
+    6. $i \leftarrow 0$
+
+    7. While ($i < size(P)$)
+
+        1. $X \leftarrow P[i]$
+
+        2. $i \leftarrow i + 1$
+
+        3. $numItemsPack \leftarrow numItemsPack + 1$
+
+        4. $Vars.remove(X)$
+
+        5. $spode \leftarrow BuildSpode(X, {\cal{X}}, D[W])$
+
+        6. $\hat{y}[] \leftarrow spode.Predict(D)$
+
+        7. $\epsilon \leftarrow error(\hat{y}[], y[])$
+
+        8. $\alpha \leftarrow \frac{1}{2} ln \left ( \frac{1-\epsilon}{\epsilon} \right )$
+
+        9. if ($\epsilon > 0.5$)
+
+            1. $finished \leftarrow True$
+
+            2. break
+
+        10. $spodes.add( (spode,\alpha_t) )$
+
+        11. $W \leftarrow UpdateWeights(W,\alpha,y[],\hat{y}[])$
+
+    8. $AODE.add( spodes )$
+
+    9. if ($convergence \land \lnot finished$)
+
+        1. $\hat{y}[] \leftarrow AODE.Predict(D)$
+
+        2. $actualAccuracy \leftarrow accuracy(\hat{y}[], y[])$
+
+        3. $if (maxAccuracy == -1)\; maxAccuracy \leftarrow actualAccuracy$
+
+        4. if $((accuracy - maxAccuracy) < \delta)$ // result doesn't
+            improve enough
+
+            1. $tolerance \leftarrow tolerance + 1$
+
+        5. else
+
+            1. $tolerance \leftarrow 0$
+
+            2. $numItemsPack \leftarrow 0$
+
+    10. If $(Vars == \emptyset \lor tolerance>maxTolerance) \; finished \leftarrow True$
+
+    11. $lastAccuracy \leftarrow max(lastAccuracy, actualAccuracy)$
+
+17. if ($tolerance > maxTolerance$) // algorithm finished because of
+    lack of convergence
+
+    1. $removeModels(AODE, numItemsPack)$
+
+18. Return $AODE$

docs/algorithm.tex

@@ -0,0 +1,80 @@
+\section{Algorithm}
+\begin{itemize}
+\item[] // notation
+\item $n$ features ${\cal{X}} = \{X_1, \dots, X_n\}$ and the class $Y$
+\item $m$ instances. 
+\item $D = \{ (x_1^i, \dots, x_n^i, y^i) \}_{i=1}^{m}$
+\item $W$ a weights vector. $W_0$ are the initial weights.
+\item $D[W]$ dataset with weights $W$ for the instances.
+\end{itemize}
+\bigskip
+
+
+\begin{enumerate}
+\item[] // initialization
+\item $W_0 \leftarrow (w_1, \dots, w_m) \leftarrow 1/m$
+\item $W \leftarrow W_0$
+\item $Vars \leftarrow {\cal{X}}$
+\item $\delta \leftarrow 10^{-4}$
+\item $convergence \leftarrow True$ // hyperparameter
+\item $maxTolerancia \leftarrow 3$ // hyperparameter
+\item $bisection \leftarrow False$ // hyperparameter
+\item $finished \leftarrow False$
+\item $AODE \leftarrow \emptyset$ \hspace*{2cm} // the ensemble
+\item $tolerance \leftarrow 0$
+\item $numModelsInPack \leftarrow 0$
+\item $maxAccuracy \leftarrow -1$
+\item[] 
+\newpage
+\item[] // main loop
+\item While $(\lnot finished)$
+\begin{enumerate}
+    \item $\pi \leftarrow SortFeatures(Vars, criterio, D[W])$
+    \item $k \leftarrow 2^{tolerance}$
+    \item if ($tolerance == 0$) $numItemsPack \leftarrow0$
+    \item $P \leftarrow Head(\pi,k)$ \hspace*{2cm} //  first k features in order
+    \item $spodes \leftarrow \emptyset$
+    \item $i \leftarrow 0$
+    \item While ($ i < size(P)$)
+    \begin{enumerate}
+        \item $X \leftarrow P[i]$
+        \item $i \leftarrow i + 1$
+        \item $numItemsPack \leftarrow numItemsPack + 1$
+        \item $Vars.remove(X)$
+        \item $spode \leftarrow BuildSpode(X, {\cal{X}}, D[W])$
+        \item $\hat{y}[] \leftarrow spode.Predict(D)$
+        \item $\epsilon \leftarrow error(\hat{y}[], y[])$
+        \item $\alpha \leftarrow \frac{1}{2} ln \left ( \frac{1-\epsilon}{\epsilon} \right )$
+        \item if ($\epsilon > 0.5$)
+        \begin{enumerate}
+            \item $finished \leftarrow True$
+            \item break
+        \end{enumerate}
+        \item $spodes.add( (spode,\alpha_t) )$
+        \item $W \leftarrow UpdateWeights(W,\alpha,y[],\hat{y}[])$
+    \end{enumerate}       
+    \item $AODE.add( spodes )$
+    \item if ($convergence \land \lnot finished$) 
+    \begin{enumerate}       
+        \item $\hat{y}[] \leftarrow AODE.Predict(D)$
+        \item $actualAccuracy \leftarrow accuracy(\hat{y}[], y[])$
+        \item $if (maxAccuracy == -1)\; maxAccuracy \leftarrow actualAccuracy$
+        \item if $((accuracy - maxAccuracy) < \delta)$\hspace*{2cm} // result doesn't improve enough
+        \begin{enumerate}
+            \item $tolerance \leftarrow tolerance + 1$
+        \end{enumerate}
+        \item else
+        \begin{enumerate}
+            \item $tolerance \leftarrow 0$
+            \item $numItemsPack \leftarrow 0$
+         \end{enumerate}
+    \end{enumerate}
+    \item If $(Vars == \emptyset \lor tolerance>maxTolerance) \; finished \leftarrow True$
+    \item $lastAccuracy \leftarrow max(lastAccuracy, actualAccuracy)$
+\end{enumerate}
+\item if ($tolerance > maxTolerance$) \hspace*{1cm} // algorithm finished because of lack of convergence
+\begin{enumerate}
+    \item $removeModels(AODE, numItemsPack)$
+\end{enumerate}
+\item Return $AODE$
+\end{enumerate}

gcovr.cfg

@@ -1,4 +0,0 @@
-filter = src/
-exclude-directories = build/lib/
-print-summary = yes
-sort-percentage = yes

lib/Files/ArffFiles.cc

@@ -1,170 +0,0 @@
-#include "ArffFiles.h"
-#include <fstream>
-#include <sstream>
-#include <map>
-#include <iostream>
-
-using namespace std;
-
-ArffFiles::ArffFiles() = default;
-
-vector<string> ArffFiles::getLines() const
-{
-    return lines;
-}
-
-unsigned long int ArffFiles::getSize() const
-{
-    return lines.size();
-}
-
-vector<pair<string, string>> ArffFiles::getAttributes() const
-{
-    return attributes;
-}
-
-string ArffFiles::getClassName() const
-{
-    return className;
-}
-
-string ArffFiles::getClassType() const
-{
-    return classType;
-}
-
-vector<vector<float>>& ArffFiles::getX()
-{
-    return X;
-}
-
-vector<int>& ArffFiles::getY()
-{
-    return y;
-}
-
-void ArffFiles::loadCommon(string fileName)
-{
-    ifstream file(fileName);
-    if (!file.is_open()) {
-        throw invalid_argument("Unable to open file");
-    }
-    string line;
-    string keyword;
-    string attribute;
-    string type;
-    string type_w;
-    while (getline(file, line)) {
-        if (line.empty() || line[0] == '%' || line == "\r" || line == " ") {
-            continue;
-        }
-        if (line.find("@attribute") != string::npos || line.find("@ATTRIBUTE") != string::npos) {
-            stringstream ss(line);
-            ss >> keyword >> attribute;
-            type = "";
-            while (ss >> type_w)
-                type += type_w + " ";
-            attributes.emplace_back(trim(attribute), trim(type));
-            continue;
-        }
-        if (line[0] == '@') {
-            continue;
-        }
-        lines.push_back(line);
-    }
-    file.close();
-    if (attributes.empty())
-        throw invalid_argument("No attributes found");
-}
-
-void ArffFiles::load(const string& fileName, bool classLast)
-{
-    int labelIndex;
-    loadCommon(fileName);
-    if (classLast) {
-        className = get<0>(attributes.back());
-        classType = get<1>(attributes.back());
-        attributes.pop_back();
-        labelIndex = static_cast<int>(attributes.size());
-    } else {
-        className = get<0>(attributes.front());
-        classType = get<1>(attributes.front());
-        attributes.erase(attributes.begin());
-        labelIndex = 0;
-    }
-    generateDataset(labelIndex);
-}
-void ArffFiles::load(const string& fileName, const string& name)
-{
-    int labelIndex;
-    loadCommon(fileName);
-    bool found = false;
-    for (int i = 0; i < attributes.size(); ++i) {
-        if (attributes[i].first == name) {
-            className = get<0>(attributes[i]);
-            classType = get<1>(attributes[i]);
-            attributes.erase(attributes.begin() + i);
-            labelIndex = i;
-            found = true;
-            break;
-        }
-    }
-    if (!found) {
-        throw invalid_argument("Class name not found");
-    }
-    generateDataset(labelIndex);
-}
-
-void ArffFiles::generateDataset(int labelIndex)
-{
-    X = vector<vector<float>>(attributes.size(), vector<float>(lines.size()));
-    auto yy = vector<string>(lines.size(), "");
-    auto removeLines = vector<int>(); // Lines with missing values
-    for (size_t i = 0; i < lines.size(); i++) {
-        stringstream ss(lines[i]);
-        string value;
-        int pos = 0;
-        int xIndex = 0;
-        while (getline(ss, value, ',')) {
-            if (pos++ == labelIndex) {
-                yy[i] = value;
-            } else {
-                if (value == "?") {
-                    X[xIndex++][i] = -1;
-                    removeLines.push_back(i);
-                } else
-                    X[xIndex++][i] = stof(value);
-            }
-        }
-    }
-    for (auto i : removeLines) {
-        yy.erase(yy.begin() + i);
-        for (auto& x : X) {
-            x.erase(x.begin() + i);
-        }
-    }
-    y = factorize(yy);
-}
-
-string ArffFiles::trim(const string& source)
-{
-    string s(source);
-    s.erase(0, s.find_first_not_of(" '\n\r\t"));
-    s.erase(s.find_last_not_of(" '\n\r\t") + 1);
-    return s;
-}
-
-vector<int> ArffFiles::factorize(const vector<string>& labels_t)
-{
-    vector<int> yy;
-    yy.reserve(labels_t.size());
-    map<string, int> labelMap;
-    int i = 0;
-    for (const string& label : labels_t) {
-        if (labelMap.find(label) == labelMap.end()) {
-            labelMap[label] = i++;
-        }
-        yy.push_back(labelMap[label]);
-    }
-    return yy;
-}

lib/Files/ArffFiles.h

@@ -1,34 +0,0 @@
-#ifndef ARFFFILES_H
-#define ARFFFILES_H
-
-#include <string>
-#include <vector>
-
-using namespace std;
-
-class ArffFiles {
-private:
-    vector<string> lines;
-    vector<pair<string, string>> attributes;
-    string className;
-    string classType;
-    vector<vector<float>> X;
-    vector<int> y;
-    void generateDataset(int);
-    void loadCommon(string);
-public:
-    ArffFiles();
-    void load(const string&, bool = true);
-    void load(const string&, const string&);
-    vector<string> getLines() const;
-    unsigned long int getSize() const;
-    string getClassName() const;
-    string getClassType() const;
-    static string trim(const string&);
-    vector<vector<float>>& getX();
-    vector<int>& getY();
-    vector<pair<string, string>> getAttributes() const;
-    static vector<int> factorize(const vector<string>& labels_t);
-};
-
-#endif

lib/Files/CMakeLists.txt

@@ -1 +0,0 @@
-add_library(ArffFiles ArffFiles.cc)

lib/argparse

@@ -1 +0,0 @@
-Subproject commit b0930ab0288185815d6dc67af59de7014a6272f7

lib/catch2

@@ -1 +1 @@
-Subproject commit 9c541ca72e7857dec71d8a41b97e42c2f1c92602
+Subproject commit 029fe3b4609dd84cd939b73357f37bbb75bcf82f

lib/folding

@@ -0,0 +1 @@
+Subproject commit 2ac43e32ac1eac0c986702ec526cf5367a565ef0

lib/json

@@ -1 +1 @@
-Subproject commit 5d2754306d67d1e654a1a34e1d2e74439a9d53b3
+Subproject commit 378e091795a70fced276cd882bd8a6a428668fe5

lib/libxlsxwriter

@@ -1 +0,0 @@
-Subproject commit 29355a0887475488c7cc470ad43cc867fcfa92e2

lib/mdlp

@@ -1 +1 @@
-Subproject commit 5708dc3de944fc22d61a2dd071b63aa338e04db3
+Subproject commit 7d62d6af4a6ca944a3bbde0b61f651fd4b2d3f57