Enhance tests coverage and report output

Add link to pdf coverage report

.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'

.gitignore

@@ -38,3 +38,5 @@ cmake-build*/**
 .idea
 puml/**
 .vscode/settings.json
+sample/build
+

.gitmodules

@@ -3,23 +3,13 @@
 	url = https://github.com/rmontanana/mdlp
 	main = main
 	update = merge
-[submodule "lib/catch2"]
-	path = lib/catch2
-        main  = v2.x
-	update = merge
-	url = https://github.com/catchorg/Catch2.git
-[submodule "lib/argparse"]
-	path = lib/argparse
-	url = https://github.com/p-ranav/argparse
-	master = master
-	update = merge
 [submodule "lib/json"]
 	path = lib/json
 	url = https://github.com/nlohmann/json.git
     master = master
 	update = merge
-[submodule "lib/libxlsxwriter"]
-	path = lib/libxlsxwriter
-	url = https://github.com/jmcnamara/libxlsxwriter.git
+[submodule "lib/folding"]
+	path = lib/folding
+	url = https://github.com/rmontanana/folding
 	main = main
 	update = merge

.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

@@ -5,126 +5,44 @@
             "type": "lldb",
             "request": "launch",
             "name": "sample",
-            "program": "${workspaceFolder}/build_debug/sample/BayesNetSample",
+            "program": "${workspaceFolder}/build_release/sample/bayesnet_sample",
             "args": [
-                "-d",
-                "iris",
-                "-m",
-                "TANLd",
-                "-s",
-                "271",
-                "-p",
-                "/Users/rmontanana/Code/discretizbench/datasets/",
-            ],
-            //"cwd": "${workspaceFolder}/build/sample/",
-        },
-        {
-            "type": "lldb",
-            "request": "launch",
-            "name": "experimentPy",
-            "program": "${workspaceFolder}/build_debug/src/Platform/b_main",
-            "args": [
-                "-m",
-                "STree",
-                "--stratified",
-                "-d",
-                "iris",
-                //"--discretize"
-                // "--hyperparameters",
-                // "{\"repeatSparent\": true, \"maxModels\": 12}"
-            ],
-            "cwd": "${workspaceFolder}/../discretizbench",
-        },
-        {
-            "type": "lldb",
-            "request": "launch",
-            "name": "gridsearch",
-            "program": "${workspaceFolder}/build_debug/src/Platform/b_grid",
-            "args": [
-                "-m",
-                "KDB",
-                "--discretize",
-                "--continue",
-                "glass",
-                "--only",
-                "--compute"
-            ],
-            "cwd": "${workspaceFolder}/../discretizbench",
-        },
-        {
-            "type": "lldb",
-            "request": "launch",
-            "name": "experimentBayes",
-            "program": "${workspaceFolder}/build_debug/src/Platform/b_main",
-            "args": [
-                "-m",
-                "TAN",
-                "--stratified",
-                "--discretize",
-                "-d",
-                "iris",
-                "--hyperparameters",
-                "{\"repeatSparent\": true, \"maxModels\": 12}"
-            ],
-            "cwd": "/home/rmontanana/Code/discretizbench",
-        },
-        {
-            "type": "lldb",
-            "request": "launch",
-            "name": "best",
-            "program": "${workspaceFolder}/build_debug/src/Platform/b_best",
-            "args": [
-                "-m",
-                "BoostAODE",
-                "-s",
-                "accuracy",
-                "--build",
-            ],
-            "cwd": "${workspaceFolder}/../discretizbench",
-        },
-        {
-            "type": "lldb",
-            "request": "launch",
-            "name": "manage",
-            "program": "${workspaceFolder}/build_debug/src/Platform/b_manage",
-            "args": [
-                "-n",
-                "20"
-            ],
-            "cwd": "${workspaceFolder}/../discretizbench",
-        },
-        {
-            "type": "lldb",
-            "request": "launch",
-            "name": "list",
-            "program": "${workspaceFolder}/build_debug/src/Platform/b_list",
-            "args": [],
-            //"cwd": "/Users/rmontanana/Code/discretizbench",
-            "cwd": "${workspaceFolder}/../discretizbench",
+                "${workspaceFolder}/tests/data/glass.arff"
+            ]
         },
         {
             "type": "lldb",
             "request": "launch",
             "name": "test",
-            "program": "${workspaceFolder}/build_debug/tests/unit_tests",
+            "program": "${workspaceFolder}/build_debug/tests/TestBayesNet",
             "args": [
-                "-c=\"Metrics Test\"",
-                // "-s",
+                "\"Bisection Best\""
             ],
-            "cwd": "${workspaceFolder}/build/tests",
+            "cwd": "${workspaceFolder}/build_debug/tests"
         },
         {
-            "name": "Build & debug active file",
+            "name": "(gdb) Launch",
             "type": "cppdbg",
             "request": "launch",
-            "program": "${workspaceFolder}/build_debug/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 +1,94 @@
+# 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]
+
+### Added
+
+- Add the Library logo generated with <https://openart.ai> to README.md
+- Add link to the coverage report in the README.md coverage label.
+- Add the *convergence_best* hyperparameter to the BoostAODE class, to control the way the prior accuracy is computed if convergence is set. Default value is *false*.
+
+### Internal
+
+- Refactor library ArffFile to limit the number of samples with a parameter.
+- Refactor tests libraries location to test/lib
+- Refactor loadDataset function in tests.
+- Remove conditionalEdgeWeights method in BayesMetrics.
+
+## [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 1.0.5
   DESCRIPTION "Bayesian Network and basic classifiers Library."
   HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
   LANGUAGES CXX
@@ -25,43 +25,31 @@ 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 "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -O0 -g")
+set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
 # 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(MPI_ENABLED "Enable MPI options"                           ON)
-
-if (MPI_ENABLED)
-  find_package(MPI REQUIRED)
-  message("MPI_CXX_LIBRARIES=${MPI_CXX_LIBRARIES}")
-  message("MPI_CXX_INCLUDE_DIRS=${MPI_CXX_INCLUDE_DIRS}")
-endif (MPI_ENABLED)
-
-# 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 COMPONENTS python3 numpy3) 
-if(Boost_FOUND)
-    message("Boost_INCLUDE_DIRS=${Boost_INCLUDE_DIRS}")
-    include_directories(${Boost_INCLUDE_DIRS}) 
-endif()
-
-# Python
-find_package(Python3 3.11...3.11.9 COMPONENTS Interpreter Development REQUIRED)
-message("Python3_LIBRARIES=${Python3_LIBRARIES}")
+option(INSTALL_GTEST "Enable installation of googletest."         OFF)
 
 # 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")
+
+
 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")
 endif (CODE_COVERAGE)
 
@@ -73,33 +61,28 @@ endif (ENABLE_CLANG_TIDY)
 # ---------------------------------------------
 # 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}")
-
-
 # Subdirectories
 # --------------
 add_subdirectory(config)
-add_subdirectory(lib/Files)
-add_subdirectory(src/BayesNet)
-add_subdirectory(src/Platform)
-add_subdirectory(src/PyClassifiers)
-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("Testing enabled")
+  add_subdirectory(tests/lib/catch2)
+  add_subdirectory(tests/lib/Files)
   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)

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

@@ -1,11 +1,18 @@
 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
 
 f_release = build_release
 f_debug = build_debug
-app_targets = b_best b_list b_main b_manage b_grid
-test_targets = unit_tests_bayesnet unit_tests_platform
+f_diagrams = diagrams
+app_targets = BayesNet
+test_targets = TestBayesNet
+clang-uml = clang-uml
+plantuml = plantuml
+gcovr = gcovr
+lcov = lcov
+genhtml = genhtml
+dot = dot
 n_procs = -j 16
 
 define ClearTests
@@ -29,38 +36,45 @@ 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 "*******************************************"
-	@echo ">>> Copying files to $(dest)"
-	@echo "*******************************************"
-	@for item in $(app_targets); do \
-		echo ">>> Copying $$item" ; \
-		cp $(f_release)/src/Platform/$$item $(dest) ; \
-	done
-
-dependency: ## Create a dependency graph diagram of the project (build/dependency.png)
+diagrams: ## Create an UML class diagram & depnendency 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) BayesNetSample $(n_procs)
+	cmake --build $(f_debug) -t $(app_targets) $(n_procs)
 
 buildr: ## Build the release targets
-	cmake --build $(f_release) -t $(app_targets) BayesNetSample $(n_procs)
+	cmake --build $(f_release) -t $(app_targets) $(n_procs)
 
 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...";
@@ -76,42 +90,61 @@ release: ## Build a Release version of the project
 	@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)
 	@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...";
+	@echo ">>> Building tests with coverage..."
+	@which $(gcovr) || (echo ">>> Please install gcovr"; exit 1)
+	@which $(lcov) || (echo ">>> Please install lcov"; exit 1)
+	@which $(genhtml) || (echo ">>> Please install lcov"; exit 1)
 	@$(MAKE) test
-	@cd $(f_debug) ; \
-	gcovr --config ../gcovr.cfg tests ;
+	@$(gcovr) $(f_debug)/tests
+	@echo ">>> Building report..."
+	@cd $(f_debug)/tests; \
+	$(lcov) --directory CMakeFiles --capture --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; \
+	$(lcov) --remove coverage.info 'bayesnet/utils/loguru.*' --ignore-errors unused --output-file coverage.info >/dev/null 2>&1
+	@$(genhtml) $(f_debug)/tests/coverage.info --demangle-cpp --output-directory html >/dev/null 2>&1;
+	@$(MAKE) updatebadge
 	@echo ">>> Done";	
 
+viewcoverage: ## View the html coverage report
+	@xdg-open html/index.html || open html/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";
 
 help: ## Show help message
 	@IFS=$$'\n' ; \

README.md

@@ -1,91 +1,78 @@
-# BayesNet
+# <img src="logo.png" alt="logo" width="50"/>  BayesNet
 
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+![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-97,8%25-green)](html/index.html)
 
-Bayesian Network Classifier with libtorch from scratch
+Bayesian Network Classifiers using libtorch from scratch
 
-## 0. Setup
+## Dependencies
 
-Before compiling BayesNet.
-
-### Miniconda
-
-To be able to run Python Classifiers such as STree, ODTE, SVC, etc. it is needed to install Miniconda. To do so, download the installer from [Miniconda](https://docs.conda.io/en/latest/miniconda.html) and run it. It is recommended to install it in the home folder.
-
-In Linux sometimes the library libstdc++ is mistaken from the miniconda installation and produces the next message when running the b_xxxx executables:
+The only external dependency is [libtorch](https://pytorch.org/cppdocs/installing.html) which can be installed with the following commands:
 
 ```bash
-libstdc++.so.6: version `GLIBCXX_3.4.32' not found (required by b_xxxx)
+wget https://download.pytorch.org/libtorch/nightly/cpu/libtorch-shared-with-deps-latest.zip
+unzip libtorch-shared-with-deps-latest.zips
 ```
 
-The solution is to erase the libstdc++ library from the miniconda installation:
+## Setup
 
-### MPI
-
-In Linux just install openmpi & openmpi-devel packages. Only if cmake can't find openmpi installation (like in Oracle Linux) set the following variable:
+### Getting the code
 
 ```bash
-export MPI_HOME="/usr/lib64/openmpi"
+git clone --recurse-submodules https://github.com/doctorado-ml/bayesnet
 ```
 
-In Mac OS X, install mpich with brew and if cmake doesn't find it, edit mpicxx wrapper to remove the ",-commons,use_dylibs" from final_ldflags
-
-```bash
-vi /opt/homebrew/bin/mpicx
-```
-
-### 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:
-
-```bash
-sudo dnf install boost-devel
-```
-
-If this is not possible and the compressed packaged is installed, the following environment variable has to be set pointing to the folder where it was unzipped to:
-
-```bash
-export BOOST_ROOT=/path/to/library/
-```
-
-In some cases, it is needed to build the library, to do so:
-
-```bash
-cd /path/to/library
-mkdir own
-./bootstrap.sh --prefix=/path/to/library/own
-./b2 install
-export BOOST_ROOT=/path/to/library/own/
-```
-
-Don't forget to add the export BOOST_ROOT statement to .bashrc or wherever it is meant to be.
-
-### 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
+
+### [BoostAODE](docs/BoostAODE.md)
+
+## Diagrams
+
+### UML Class Diagram
+
+![BayesNet UML Class Diagram](diagrams/BayesNet.svg)
+
+### Dependency Diagram
+
+![BayesNet Dependency Diagram](diagrams/dependency.svg)
+
+## Coverage report
+
+### [Coverage report](docs/coverage.pdf)

bayesnet/BaseClassifier.h

@@ -1,8 +1,13 @@
-#ifndef BASE_H
-#define BASE_H
+// ***************************************************************
+// 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 <vector>
 namespace bayesnet {
     enum status_t { NORMAL, WARNING, ERROR };
     class BaseClassifier {
@@ -16,17 +21,21 @@ namespace bayesnet {
         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;
-        void virtual dump_cpt()const = 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:
@@ -34,4 +43,3 @@ namespace bayesnet {
         std::vector<std::string> validHyperparameters;
     };
 }
-#endif

bayesnet/CMakeLists.txt

@@ -0,0 +1,13 @@
+include_directories(
+    ${BayesNet_SOURCE_DIR}/lib/mdlp
+    ${BayesNet_SOURCE_DIR}/lib/Files
+    ${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 ${Sources})
+target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}")

bayesnet/classifiers/Classifier.cc

@@ -1,15 +1,23 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <sstream>
+#include "bayesnet/utils/bayesnetUtils.h"
 #include "Classifier.h"
-#include "bayesnetUtils.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)
     {
         this->features = features;
         this->className = className;
         this->states = states;
         m = dataset.size(1);
-        n = dataset.size(0) - 1;
+        n = features.size();
         checkFitParameters();
         auto n_classes = states.at(className).size();
         metrics = Metrics(dataset, features, className, n_classes);
@@ -26,10 +34,11 @@ namespace bayesnet {
             dataset = torch::cat({ dataset, yresized }, 0);
         }
         catch (const std::exception& e) {
-            std::cerr << e.what() << '\n';
-            std::cout << "X dimensions: " << dataset.sizes() << "\n";
-            std::cout << "y dimensions: " << ytmp.sizes() << "\n";
-            exit(1);
+            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)
@@ -72,11 +81,11 @@ namespace bayesnet {
         if (torch::is_floating_point(dataset)) {
             throw std::invalid_argument("dataset (X, y) must be of type Integer");
         }
-        if (n != features.size()) {
-            throw std::invalid_argument("Classifier: X " + std::to_string(n) + " and features " + std::to_string(features.size()) + " must have the same number of features");
+        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("className not found in states");
+            throw std::invalid_argument("class name not found in states");
         }
         for (auto feature : features) {
             if (states.find(feature) == states.end()) {
@@ -87,14 +96,14 @@ namespace bayesnet {
     torch::Tensor Classifier::predict(torch::Tensor& X)
     {
         if (!fitted) {
-            throw std::logic_error("Classifier has not been 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 has not been fitted");
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
         }
         auto m_ = X[0].size();
         auto n_ = X.size();
@@ -105,18 +114,37 @@ namespace bayesnet {
         auto yp = model.predict(Xd);
         return yp;
     }
-    float Classifier::score(torch::Tensor& X, torch::Tensor& y)
+    torch::Tensor Classifier::predict_proba(torch::Tensor& X)
     {
         if (!fitted) {
-            throw std::logic_error("Classifier has not been 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 has not been fitted");
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
         }
         return model.score(X, y);
     }
@@ -145,16 +173,22 @@ namespace bayesnet {
     {
         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();
     }
-    void Classifier::dump_cpt() const
+    std::string Classifier::dump_cpt() const
     {
-        model.dump_cpt();
+        return model.dump_cpt();
     }
     void Classifier::setHyperparameters(const nlohmann::json& hyperparameters)
     {
-        //For classifiers that don't have hyperparameters
+        if (!hyperparameters.empty()) {
+            throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
+        }
     }
 }

bayesnet/classifiers/Classifier.h

@@ -1,28 +1,18 @@
+// ***************************************************************
+// 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 "BaseClassifier.h"
-#include "Network.h"
-#include "BayesMetrics.h"
+#include "bayesnet/utils/BayesMetrics.h"
+#include "bayesnet/network/Network.h"
+#include "bayesnet/BaseClassifier.h"
 
 namespace bayesnet {
     class Classifier : public BaseClassifier {
-    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);
-    protected:
-        bool fitted;
-        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;
-        void checkFitParameters();
-        virtual void buildModel(const torch::Tensor& weights) = 0;
-        void trainModel(const torch::Tensor& weights) override;
-        void buildDataset(torch::Tensor& y);
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;
@@ -34,16 +24,37 @@ namespace bayesnet {
         int getNumberOfNodes() const override;
         int getNumberOfEdges() const override;
         int getNumberOfStates() const override;
+        int getClassNumStates() const override;
         torch::Tensor predict(torch::Tensor& X) override;
-        status_t getStatus() const override { return status; }
-        std::string getVersion() override { return "0.2.0"; };
         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;
-        void dump_cpt() const 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) 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);
     };
 }
 #endif

bayesnet/classifiers/KDB.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "KDB.h"
 
 namespace bayesnet {
@@ -6,14 +12,18 @@ namespace bayesnet {
         validHyperparameters = { "k", "theta" };
 
     }
-    void KDB::setHyperparameters(const nlohmann::json& hyperparameters)
+    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)
     {

bayesnet/classifiers/KDB.h

@@ -1,8 +1,14 @@
+// ***************************************************************
+// 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"
-#include "bayesnetUtils.h"
 namespace bayesnet {
     class KDB : public Classifier {
     private:
@@ -14,7 +20,7 @@ namespace bayesnet {
     public:
         explicit KDB(int k, float theta = 0.03);
         virtual ~KDB() = default;
-        void setHyperparameters(const nlohmann::json& hyperparameters) override;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
         std::vector<std::string> graph(const std::string& name = "KDB") const override;
     };
 }

bayesnet/classifiers/KDBLd.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "KDBLd.h"
 
 namespace bayesnet {

bayesnet/classifiers/KDBLd.h

@@ -1,7 +1,13 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef KDBLD_H
 #define KDBLD_H
-#include "KDB.h"
 #include "Proposal.h"
+#include "KDB.h"
 
 namespace bayesnet {
     class KDBLd : public KDB, public Proposal {

bayesnet/classifiers/Proposal.cc

@@ -1,5 +1,11 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <ArffFiles.h>
 #include "Proposal.h"
-#include "ArffFiles.h"
 
 namespace bayesnet {
     Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}

bayesnet/classifiers/Proposal.h

@@ -1,10 +1,16 @@
+// ***************************************************************
+// 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 "Network.h"
-#include "CPPFImdlp.h"
+#include <CPPFImdlp.h>
+#include "bayesnet/network/Network.h"
 #include "Classifier.h"
 
 namespace bayesnet {

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 {

bayesnet/classifiers/SPODE.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef SPODE_H
 #define SPODE_H
 #include "Classifier.h"

bayesnet/classifiers/SPODELd.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "SPODELd.h"
 
 namespace bayesnet {
@@ -5,25 +11,23 @@ namespace bayesnet {
     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_)
     {
         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 SPODE structure, SPODE::fit initializes the base Bayesian network
-        SPODE::fit(dataset, features, className, states);
-        states = localDiscretizationProposal(states, model);
-        return *this;
+        return commonFit(features_, className_, states_);
     }
+
     SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
     {
         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();
+        y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);
+        return commonFit(features_, className_, states_);
+    }
+
+    SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    {
         features = features_;
         className = className_;
         // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
@@ -34,7 +38,6 @@ namespace bayesnet {
         states = localDiscretizationProposal(states, model);
         return *this;
     }
-
     torch::Tensor SPODELd::predict(torch::Tensor& X)
     {
         auto Xt = prepareX(X);

bayesnet/classifiers/SPODELd.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef SPODELD_H
 #define SPODELD_H
 #include "SPODE.h"
@@ -10,6 +16,7 @@ namespace bayesnet {
         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) override;
         SPODELd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
+        SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states);
         std::vector<std::string> graph(const std::string& name = "SPODE") const override;
         torch::Tensor predict(torch::Tensor& X) override;
         static inline std::string version() { return "0.0.1"; };

bayesnet/classifiers/TAN.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "TAN.h"
 
 namespace bayesnet {

bayesnet/classifiers/TAN.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef TAN_H
 #define TAN_H
 #include "Classifier.h"

bayesnet/classifiers/TANLd.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "TANLd.h"
 
 namespace bayesnet {

bayesnet/classifiers/TANLd.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef TANLD_H
 #define TANLD_H
 #include "TAN.h"

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

@@ -1,7 +1,15 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "AODELd.h"
 
 namespace bayesnet {
-    AODELd::AODELd() : Ensemble(), Proposal(dataset, features, className) {}
+    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_)
     {
         checkInput(X_, y_);

bayesnet/ensembles/AODELd.h

@@ -1,20 +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"
-#include "Proposal.h"
-#include "SPODELd.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_) override;
+        std::vector<std::string> graph(const std::string& name = "AODELd") const override;
     protected:
         void trainModel(const torch::Tensor& weights) override;
         void buildModel(const torch::Tensor& weights) override;
-    public:
-        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_) override;
-        virtual ~AODELd() = default;
-        std::vector<std::string> graph(const std::string& name = "AODELd") const override;
-        static inline std::string version() { return "0.0.1"; };
     };
 }
 #endif // !AODELD_H

bayesnet/ensembles/BoostAODE.cc

@@ -0,0 +1,396 @@
+// ***************************************************************
+// 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 "BoostAODE.h"
+#include "lib/log/loguru.cpp"
+
+namespace bayesnet {
+
+    BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)
+    {
+        validHyperparameters = {
+            "maxModels", "bisection", "order", "convergence", "convergence_best", "threshold",
+            "select_features", "maxTolerance", "predict_voting", "block_update"
+        };
+
+    }
+    void BoostAODE::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_;
+        }
+    }
+    void BoostAODE::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);
+    }
+    std::tuple<torch::Tensor&, double, bool> 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> BoostAODE::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 };
+    }
+    std::vector<int> BoostAODE::initializeModels()
+    {
+        std::vector<int> featuresUsed;
+        torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
+        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 cfsFeatures = featureSelector->getFeatures();
+        auto scores = featureSelector->getScores();
+        for (const int& feature : cfsFeatures) {
+            featuresUsed.push_back(feature);
+            std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
+            model->fit(dataset, features, className, states, weights_);
+            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(featuresUsed.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
+        delete featureSelector;
+        return featuresUsed;
+    }
+    void BoostAODE::trainModel(const torch::Tensor& weights)
+    {
+        //
+        // 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();
+            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_);
+                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 +1,50 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef BOOSTAODE_H
+#define BOOSTAODE_H
+#include <map>
+#include "bayesnet/classifiers/SPODE.h"
+#include "bayesnet/feature_selection/FeatureSelect.h"
+#include "Ensemble.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 BoostAODE : public Ensemble {
+    public:
+        explicit BoostAODE(bool predict_voting = false);
+        virtual ~BoostAODE() = default;
+        std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights) override;
+    private:
+        std::tuple<torch::Tensor&, double, bool> update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights);
+        std::vector<int> initializeModels();
+        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/Ensemble.cc

@@ -0,0 +1,222 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "Ensemble.h"
+
+namespace bayesnet {
+
+    Ensemble::Ensemble(bool predict_voting) : Classifier(Network()), n_models(0), predict_voting(predict_voting)
+    {
+
+    };
+    const std::string ENSEMBLE_NOT_FITTED = "Ensemble has not been fitted";
+    void Ensemble::trainModel(const torch::Tensor& weights)
+    {
+        n_models = models.size();
+        for (auto i = 0; i < n_models; ++i) {
+            // fit with std::vectors
+            models[i]->fit(dataset, features, className, states);
+        }
+    }
+    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);
+        auto threads{ std::vector<std::thread>() };
+        std::mutex mtx;
+        for (auto i = 0; i < n_models; ++i) {
+            threads.push_back(std::thread([&, i]() {
+                auto ypredict = models[i]->predict_proba(X);
+                std::lock_guard<std::mutex> lock(mtx);
+                y_pred += ypredict * significanceModels[i];
+                }));
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        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));
+        auto threads{ std::vector<std::thread>() };
+        std::mutex mtx;
+        for (auto i = 0; i < n_models; ++i) {
+            threads.push_back(std::thread([&, i]() {
+                auto ypredict = models[i]->predict_proba(X);
+                assert(ypredict.size() == y_pred.size());
+                assert(ypredict[0].size() == y_pred[0].size());
+                std::lock_guard<std::mutex> lock(mtx);
+                // 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; });
+                }
+                }));
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        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);
+        auto threads{ std::vector<std::thread>() };
+        std::mutex mtx;
+        for (auto i = 0; i < n_models; ++i) {
+            threads.push_back(std::thread([&, i]() {
+                auto ypredict = models[i]->predict(X);
+                std::lock_guard<std::mutex> lock(mtx);
+                y_pred.index_put_({ "...", i }, ypredict);
+                }));
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        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) 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()
     {
@@ -11,7 +17,7 @@ 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 = std::numeric_limits<double>::lowest();
             int bestFeature = -1;

bayesnet/feature_selection/CFS.h

@@ -1,8 +1,14 @@
+// ***************************************************************
+// 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 "FeatureSelect.h"
+#include "bayesnet/feature_selection/FeatureSelect.h"
 namespace bayesnet {
     class CFS : public FeatureSelect {
     public:

bayesnet/feature_selection/FCBF.cc

@@ -1,4 +1,10 @@
-#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 {
 

bayesnet/feature_selection/FCBF.h

@@ -1,8 +1,14 @@
+// ***************************************************************
+// 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 "FeatureSelect.h"
+#include "bayesnet/feature_selection/FeatureSelect.h"
 namespace bayesnet {
     class FCBF : public FeatureSelect {
     public:

bayesnet/feature_selection/FeatureSelect.cc

@@ -1,6 +1,12 @@
-#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 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)
@@ -50,7 +56,6 @@ namespace bayesnet {
     }
     double FeatureSelect::computeMeritCFS()
     {
-        double result;
         double rcf = 0;
         for (auto feature : selectedFeatures) {
             rcf += suLabels[feature];

bayesnet/feature_selection/FeatureSelect.h

@@ -1,8 +1,14 @@
+// ***************************************************************
+// 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 "BayesMetrics.h"
+#include "bayesnet/utils/BayesMetrics.h"
 namespace bayesnet {
     class FeatureSelect : public Metrics {
     public:

bayesnet/feature_selection/IWSS.cc

@@ -1,6 +1,12 @@
-#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 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)
@@ -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

@@ -1,7 +1,13 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef IWSS_H
 #define IWSS_H
-#include <torch/torch.h>
 #include <vector>
+#include <torch/torch.h>
 #include "FeatureSelect.h"
 namespace bayesnet {
     class IWSS : public FeatureSelect {

bayesnet/network/Network.cc

@@ -1,27 +1,41 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include <thread>
 #include <mutex>
+#include <sstream>
 #include "Network.h"
-#include "bayesnetUtils.h"
+#include "bayesnet/utils/bayesnetUtils.h"
 namespace bayesnet {
-    Network::Network() : features(std::vector<std::string>()), className(""), classNumStates(0), fitted(false), laplaceSmoothing(0) {}
-    Network::Network(float maxT) : features(std::vector<std::string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false), laplaceSmoothing(0) {}
-    Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other.
-        getmaxThreads()), fitted(other.fitted)
+    Network::Network() : fitted{ false }, maxThreads{ 0.95 }, classNumStates{ 0 }, laplaceSmoothing{ 0 }
     {
+    }
+    Network::Network(float maxT) : fitted{ false }, maxThreads{ maxT }, classNumStates{ 0 }, laplaceSmoothing{ 0 }
+    {
+
+    }
+    Network::Network(const Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
+        maxThreads(other.getMaxThreads()), 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 = std::vector<std::string>();
+        features.clear();
         className = "";
         classNumStates = 0;
         fitted = false;
         nodes.clear();
         samples = torch::Tensor();
     }
-    float Network::getmaxThreads()
+    float Network::getMaxThreads() const
     {
         return maxThreads;
     }
@@ -71,7 +85,7 @@ namespace bayesnet {
             for (Node* child : nodes[nodeId]->getChildren()) {
                 if (visited.find(child->getName()) == visited.end() && isCyclic(child->getName(), visited, recStack))
                     return true;
-                else if (recStack.find(child->getName()) != recStack.end())
+                if (recStack.find(child->getName()) != recStack.end())
                     return true;
             }
         }
@@ -114,11 +128,14 @@ namespace bayesnet {
         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("className not found in Network::features");
+            throw std::invalid_argument("Class Name not found in Network::features");
         }
         for (auto& feature : featureNames) {
             if (find(features.begin(), features.end(), feature) == features.end()) {
@@ -238,6 +255,7 @@ namespace bayesnet {
         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) {
@@ -392,22 +410,20 @@ namespace bayesnet {
                                 result.insert(it2, fatherName);
                                 ending = false;
                             }
-                        } else {
-                            throw std::logic_error("Error in topological sort because of node " + feature + " is not in result");
                         }
-                    } else {
-                        throw std::logic_error("Error in topological sort because of node father " + fatherName + " is not in result");
                     }
                 }
             }
         }
         return result;
     }
-    void Network::dump_cpt() const
+    std::string Network::dump_cpt() const
     {
+        std::stringstream oss;
         for (auto& node : nodes) {
-            std::cout << "* " << node.first << ": (" << node.second->getNumStates() << ") : " << node.second->getCPT().sizes() << std::endl;
-            std::cout << node.second->getCPT() << std::endl;
+            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

@@ -1,35 +1,25 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef NETWORK_H
 #define NETWORK_H
-#include "Node.h"
 #include <map>
 #include <vector>
+#include "bayesnet/config.h"
+#include "Node.h"
 
 namespace bayesnet {
     class Network {
-    private:
-        std::map<std::string, std::unique_ptr<Node>> nodes;
-        bool fitted;
-        float maxThreads = 0.95;
-        int classNumStates;
-        std::vector<std::string> features; // Including classname
-        std::string className;
-        double laplaceSmoothing;
-        torch::Tensor samples; // nxm 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>&);
-        double computeFactor(std::map<std::string, int>&);
-        void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
-        void checkFitData(int n_features, int n_samples, 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>>&);
     public:
         Network();
         explicit Network(float);
-        explicit Network(Network&);
+        explicit Network(const Network&);
         ~Network() = default;
         torch::Tensor& getSamples();
-        float getmaxThreads();
+        float getMaxThreads() const;
         void addNode(const std::string&);
         void addEdge(const std::string&, const std::string&);
         std::map<std::string, std::unique_ptr<Node>>& getNodes();
@@ -55,8 +45,25 @@ namespace bayesnet {
         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();
-        void dump_cpt() const;
-        inline std::string version() { return "0.2.0"; }
+        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;
+        float maxThreads = 0.95;
+        int classNumStates;
+        std::vector<std::string> features; // Including classname
+        std::string className;
+        double laplaceSmoothing;
+        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>&);
+        double computeFactor(std::map<std::string, int>&);
+        void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
+        void checkFitData(int n_features, int n_samples, 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

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "Node.h"
 
 namespace bayesnet {

bayesnet/network/Node.h

@@ -1,9 +1,15 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef NODE_H
 #define NODE_H
-#include <torch/torch.h>
 #include <unordered_set>
 #include <vector>
 #include <string>
+#include <torch/torch.h>
 namespace bayesnet {
     class Node {
     private:

bayesnet/utils/BayesMetrics.cc

@@ -1,20 +1,26 @@
-#include "BayesMetrics.h"
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #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)
-        , features(features)
         , className(className)
+        , features(features)
         , classNumStates(classNumStates)
     {
     }
-    //samples is nxm std::vector used to fit the model
+    //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)
-        : features(features)
+        : samples(torch::zeros({ static_cast<int>(vsamples.size() + 1), static_cast<int>(vsamples[0].size()) }, torch::kInt32))
         , className(className)
+        , features(features)
         , classNumStates(classNumStates)
-        , samples(torch::zeros({ static_cast<int>(vsamples[0].size()), static_cast<int>(vsamples.size() + 1) }, torch::kInt32))
     {
         for (int i = 0; i < vsamples.size(); ++i) {
             samples.index_put_({ i,  "..." }, torch::tensor(vsamples[i], torch::kInt32));
@@ -24,7 +30,7 @@ namespace bayesnet {
     std::vector<int> Metrics::SelectKBestWeighted(const torch::Tensor& weights, bool ascending, unsigned k)
     {
         // Return the K Best features 
-        auto n = samples.size(0) - 1;
+        auto n = features.size();
         if (k == 0) {
             k = n;
         }
@@ -99,14 +105,6 @@ namespace bayesnet {
         }
         return matrix;
     }
-    // To use in Python
-    std::vector<float> Metrics::conditionalEdgeWeights(std::vector<float>& weights_)
-    {
-        const torch::Tensor weights = torch::tensor(weights_);
-        auto matrix = conditionalEdge(weights);
-        std::vector<float> v(matrix.data_ptr<float>(), matrix.data_ptr<float>() + matrix.numel());
-        return v;
-    }
     double Metrics::entropy(const torch::Tensor& feature, const torch::Tensor& weights)
     {
         torch::Tensor counts = feature.bincount(weights);

bayesnet/utils/BayesMetrics.h

@@ -1,15 +1,25 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef BAYESNET_METRICS_H
 #define BAYESNET_METRICS_H
-#include <torch/torch.h>
 #include <vector>
 #include <string>
+#include <torch/torch.h>
 namespace bayesnet {
     class Metrics {
-    private:
-        int classNumStates = 0;
-        std::vector<double> scoresKBest;
-        std::vector<int> featuresKBest; // sorted indices of the features
-        double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
+    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<double> getScoresKBest() const;
+        double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, 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);
     protected:
         torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector
         std::string className;
@@ -34,16 +44,11 @@ namespace bayesnet {
             v.erase(v.begin());
             return temp;
         }
-    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<double> getScoresKBest() const;
-        double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
-        std::vector<float> conditionalEdgeWeights(std::vector<float>& weights); // To use in Python
-        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);
+    private:
+        int classNumStates = 0;
+        std::vector<double> scoresKBest;
+        std::vector<int> featuresKBest; // sorted indices of the features
+        double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
     };
 }
 #endif

bayesnet/utils/Mst.cc

@@ -1,6 +1,13 @@
-#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/
 
@@ -45,15 +52,6 @@ namespace bayesnet {
             }
         }
     }
-    void Graph::display_mst()
-    {
-        std::cout << "Edge :" << " Weight" << std::endl;
-        for (int i = 0; i < T.size(); i++) {
-            std::cout << T[i].second.first << " - " << T[i].second.second << " : "
-                << T[i].first;
-            std::cout << std::endl;
-        }
-    }
 
     void insertElement(std::list<int>& variables, int variable)
     {

bayesnet/utils/Mst.h

@@ -1,33 +1,38 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef MST_H
 #define MST_H
-#include <torch/torch.h>
 #include <vector>
 #include <string>
+#include <torch/torch.h>
 namespace bayesnet {
     class MST {
-    private:
-        torch::Tensor weights;
-        std::vector<std::string> features;
-        int root = 0;
     public:
         MST() = default;
         MST(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
         std::vector<std::pair<int, int>> maximumSpanningTree();
+    private:
+        torch::Tensor weights;
+        std::vector<std::string> features;
+        int root = 0;
     };
     class Graph {
-    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;
     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();
-        void display_mst();
         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

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
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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
-class att25
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-class att95
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-class att17
-class att28
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-class att214
-class att197
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-class att168
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-class att89
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-class att74
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-class att101
-class att85
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-class att137
-class att211
-class att143
-class att14
-class att40
-class att210
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-class att170
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-class att23
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-

diagrams/BayesNet.puml

@@ -0,0 +1,412 @@
+@startuml
+title clang-uml class diagram model
+class "bayesnet::Metrics" as C_0000736965376885623323
+class C_0000736965376885623323 #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>
++conditionalEdge(const torch::Tensor & weights) : torch::Tensor
++conditionalEdgeWeights(std::vector<float> & weights) : std::vector<float>
+#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>
++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
+}
+class "bayesnet::Node" as C_0001303524929067080934
+class C_0001303524929067080934 #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 laplaceSmoothing, const torch::Tensor & weights) : void
++getCPT() : torch::Tensor &
++getChildren() : std::vector<Node *> &
++getFactorValue(std::map<std::string,int> &) : float
++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
+__
+}
+class "bayesnet::Network" as C_0001186707649890429575
+class C_0001186707649890429575 #aliceblue;line:blue;line.dotted;text:blue {
++Network() : void
++Network(float) : 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) : 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) : 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) : void
++getClassName() const : std::string
++getClassNumStates() const : int
++getEdges() const : std::vector<std::pair<std::string,std::string>>
++getFeatures() const : std::vector<std::string>
++getMaxThreads() const : float
++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_0000738420730783851375
+enum C_0000738420730783851375 {
+NORMAL
+WARNING
+ERROR
+}
+abstract "bayesnet::BaseClassifier" as C_0000327135989451974539
+abstract C_0000327135989451974539 #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) = 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) = 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) = 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) = 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) = 0 : void
+__
+#validHyperparameters : std::vector<std::string>
+}
+abstract "bayesnet::Classifier" as C_0002043996622900301644
+abstract C_0002043996622900301644 #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) : 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) : Classifier &
++fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : 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) : 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) : 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_0001112865019015250005
+class C_0001112865019015250005 #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::TAN" as C_0001760994424884323017
+class C_0001760994424884323017 #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_0002219995589162262979
+class C_0002219995589162262979 #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::TANLd" as C_0001668829096702037834
+class C_0001668829096702037834 #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) : TANLd &
++graph(const std::string & name = "TAN") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+abstract "bayesnet::FeatureSelect" as C_0001695326193250580823
+abstract C_0001695326193250580823 #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::CFS" as C_0000011627355691342494
+class C_0000011627355691342494 #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_0000144682015341746929
+class C_0000144682015341746929 #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_0000008268514674428553
+class C_0000008268514674428553 #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::SPODE" as C_0000512022813807538451
+class C_0000512022813807538451 #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::Ensemble" as C_0001985241386355360576
+class C_0001985241386355360576 #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) : 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::(anonymous_45089536)" as C_0001186398587753535158
+class C_0001186398587753535158 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_45090163)" as C_0000602764946063116717
+class C_0000602764946063116717 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::BoostAODE" as C_0000358471592399852382
+class C_0000358471592399852382 #aliceblue;line:blue;line.dotted;text:blue {
++BoostAODE(bool predict_voting = false) : void
++~BoostAODE() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & title = "BoostAODE") const : std::vector<std::string>
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
+#trainModel(const torch::Tensor & weights) : void
+__
+}
+class "bayesnet::MST" as C_0000131858426172291700
+class C_0000131858426172291700 #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
+..
++maximumSpanningTree() : std::vector<std::pair<int,int>>
+__
+}
+class "bayesnet::Graph" as C_0001197041682001898467
+class C_0001197041682001898467 #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
+__
+}
+class "bayesnet::KDBLd" as C_0000344502277874806837
+class C_0000344502277874806837 #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) : KDBLd &
++graph(const std::string & name = "KDB") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+class "bayesnet::AODE" as C_0000786111576121788282
+class C_0000786111576121788282 #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
+__
+}
+class "bayesnet::SPODELd" as C_0001369655639257755354
+class C_0001369655639257755354 #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) : 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) : SPODELd &
++fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : SPODELd &
++graph(const std::string & name = "SPODE") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+class "bayesnet::AODELd" as C_0000487273479333793647
+class C_0000487273479333793647 #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_) : AODELd &
++graph(const std::string & name = "AODELd") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights) : void
+__
+}
+C_0001303524929067080934 --> C_0001303524929067080934 : -parents
+C_0001303524929067080934 --> C_0001303524929067080934 : -children
+C_0001186707649890429575 o-- C_0001303524929067080934 : -nodes
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+C_0002043996622900301644 <|-- C_0000512022813807538451
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+C_0002043996622900301644 <|-- C_0001985241386355360576
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+C_0001985241386355360576 <|-- C_0000358471592399852382
+C_0001112865019015250005 <|-- C_0000344502277874806837
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+C_0001985241386355360576 <|-- C_0000487273479333793647
+C_0002219995589162262979 <|-- C_0000487273479333793647
+
+'Generated with clang-uml, version 0.5.1
+'LLVM version clang version 17.0.6 (Fedora 17.0.6-2.fc39)
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docs/BoostAODE.md

@@ -0,0 +1,30 @@
+# 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*.
+- ***biesection_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
+
+### [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 +1,6 @@
-filter = src/
-exclude-directories = build/lib/
+filter = bayesnet/
+exclude-directories = build_debug/lib/
+exclude-directories = build_debug/tests/lib
+exclude = bayesnet/utils/loguru.*
 print-summary = yes
-sort-percentage = yes
+sort = uncovered-percent

grid_stree.json

@@ -1,162 +0,0 @@
-{
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-        "gamma": 0.1,
-        "kernel": "rbf",
-        "max_iter": 10000
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-    "balloons": {
-        "C": 7,
-        "gamma": 0.1,
-        "kernel": "rbf",
-        "max_iter": 10000
-    },
-    "breast-cancer-wisc-diag": {
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-        "max_iter": 10000
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-    "breast-cancer": {},
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-    "cardiotocography-3clases": {},
-    "conn-bench-sonar-mines-rocks": {},
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-        "max_iter": 10000
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-    "echocardiogram": {
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-        "gamma": 0.1,
-        "kernel": "poly",
-        "max_features": "auto",
-        "max_iter": 10000
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-        "max_features": "auto",
-        "max_iter": 10000
-    },
-    "haberman-survival": {},
-    "heart-hungarian": {
-        "C": 0.05,
-        "max_iter": 10000
-    },
-    "hepatitis": {
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-        "gamma": 0.1,
-        "kernel": "rbf",
-        "max_iter": 10000
-    },
-    "ilpd-indian-liver": {},
-    "ionosphere": {
-        "C": 7,
-        "gamma": 0.1,
-        "kernel": "rbf",
-        "max_iter": 10000
-    },
-    "iris": {},
-    "led-display": {},
-    "libras": {
-        "C": 0.08,
-        "max_iter": 10000
-    },
-    "low-res-spect": {
-        "C": 0.05,
-        "max_iter": 10000
-    },
-    "lymphography": {
-        "C": 0.05,
-        "max_iter": 10000
-    },
-    "mammographic": {},
-    "molec-biol-promoter": {
-        "C": 0.05,
-        "gamma": 0.1,
-        "kernel": "poly",
-        "max_iter": 10000
-    },
-    "musk-1": {
-        "C": 0.05,
-        "gamma": 0.1,
-        "kernel": "poly",
-        "max_iter": 10000
-    },
-    "oocytes_merluccius_nucleus_4d": {
-        "C": 8.25,
-        "gamma": 0.1,
-        "kernel": "poly"
-    },
-    "oocytes_merluccius_states_2f": {},
-    "oocytes_trisopterus_nucleus_2f": {},
-    "oocytes_trisopterus_states_5b": {
-        "C": 0.11,
-        "max_iter": 10000
-    },
-    "parkinsons": {},
-    "pima": {},
-    "pittsburg-bridges-MATERIAL": {
-        "C": 7,
-        "gamma": 0.1,
-        "kernel": "rbf",
-        "max_iter": 10000
-    },
-    "pittsburg-bridges-REL-L": {},
-    "pittsburg-bridges-SPAN": {
-        "C": 0.05,
-        "max_iter": 10000
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-    "pittsburg-bridges-T-OR-D": {},
-    "planning": {
-        "C": 7,
-        "gamma": 10.0,
-        "kernel": "rbf",
-        "max_iter": 10000
-    },
-    "post-operative": {
-        "C": 55,
-        "degree": 5,
-        "gamma": 0.1,
-        "kernel": "poly",
-        "max_iter": 10000
-    },
-    "seeds": {
-        "C": 10000.0,
-        "max_iter": 10000
-    },
-    "statlog-australian-credit": {
-        "C": 0.05,
-        "max_features": "auto",
-        "max_iter": 10000
-    },
-    "statlog-german-credit": {},
-    "statlog-heart": {},
-    "statlog-image": {
-        "C": 7,
-        "max_iter": 10000
-    },
-    "statlog-vehicle": {},
-    "synthetic-control": {
-        "C": 0.55,
-        "max_iter": 10000
-    },
-    "tic-tac-toe": {
-        "C": 0.2,
-        "gamma": 0.1,
-        "kernel": "poly",
-        "max_iter": 10000
-    },
-    "vertebral-column-2clases": {},
-    "wine": {
-        "C": 0.55,
-        "max_iter": 10000
-    },
-    "zoo": {
-        "C": 0.1,
-        "max_iter": 10000
-    }
-}

html/BayesNet/bayesnet/BaseClassifier.h.func-c.html

@@ -0,0 +1,96 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/BaseClassifier.h - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet</a> - BaseClassifier.h<span style="font-size: 80%;"> (<a href="BaseClassifier.h.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">1</td>
+            <td class="headerCovTableEntry">1</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryLo">50.0&nbsp;%</td>
+            <td class="headerCovTableEntry">2</td>
+            <td class="headerCovTableEntry">1</td>
+          </tr>
+                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="BaseClassifier.h.func.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="BaseClassifier.h.gcov.html#L19">_ZN8bayesnet14BaseClassifierD0Ev</a></td>
+              
+              <td class="coverFnHi">606</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="BaseClassifier.h.gcov.html#L19">_ZN8bayesnet14BaseClassifierD0Ev</a></td>
+              
+              <td class="coverFnAliasLo">0</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="BaseClassifier.h.gcov.html#L19">_ZN8bayesnet14BaseClassifierD2Ev</a></td>
+              
+              <td class="coverFnAliasHi">606</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/BaseClassifier.h.func.html

@@ -0,0 +1,96 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/BaseClassifier.h - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet</a> - BaseClassifier.h<span style="font-size: 80%;"> (<a href="BaseClassifier.h.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">1</td>
+            <td class="headerCovTableEntry">1</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryLo">50.0&nbsp;%</td>
+            <td class="headerCovTableEntry">2</td>
+            <td class="headerCovTableEntry">1</td>
+          </tr>
+                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><a href="BaseClassifier.h.func-c.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="BaseClassifier.h.gcov.html#L19">_ZN8bayesnet14BaseClassifierD0Ev</a></td>
+              
+              <td class="coverFnHi">606</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="BaseClassifier.h.gcov.html#L19">_ZN8bayesnet14BaseClassifierD0Ev</a></td>
+              
+              <td class="coverFnAliasLo">0</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="BaseClassifier.h.gcov.html#L19">_ZN8bayesnet14BaseClassifierD2Ev</a></td>
+              
+              <td class="coverFnAliasHi">606</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/BaseClassifier.h.gcov.html

@@ -0,0 +1,121 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/BaseClassifier.h</title>
+  <link rel="stylesheet" type="text/css" href="../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet</a> - BaseClassifier.h<span style="font-size: 80%;"> (source / <a href="BaseClassifier.h.func-c.html">functions</a>)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">1</td>
+            <td class="headerCovTableEntry">1</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryLo">50.0&nbsp;%</td>
+            <td class="headerCovTableEntry">2</td>
+            <td class="headerCovTableEntry">1</td>
+          </tr>
+                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <table cellpadding=0 cellspacing=0 border=0>
+            <tr>
+              <td><br></td>
+            </tr>
+            <tr>
+              <td>
+<pre class="sourceHeading">            Line data    Source code</pre>
+<pre class="source">
+<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
+<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
+<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
+<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
+<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
+<span id="L6"><span class="lineNum">       6</span>              : </span>
+<span id="L7"><span class="lineNum">       7</span>              : #pragma once</span>
+<span id="L8"><span class="lineNum">       8</span>              : #include &lt;vector&gt;</span>
+<span id="L9"><span class="lineNum">       9</span>              : #include &lt;torch/torch.h&gt;</span>
+<span id="L10"><span class="lineNum">      10</span>              : #include &lt;nlohmann/json.hpp&gt;</span>
+<span id="L11"><span class="lineNum">      11</span>              : namespace bayesnet {</span>
+<span id="L12"><span class="lineNum">      12</span>              :     enum status_t { NORMAL, WARNING, ERROR };</span>
+<span id="L13"><span class="lineNum">      13</span>              :     class BaseClassifier {</span>
+<span id="L14"><span class="lineNum">      14</span>              :     public:</span>
+<span id="L15"><span class="lineNum">      15</span>              :         // X is nxm std::vector, y is nx1 std::vector</span>
+<span id="L16"><span class="lineNum">      16</span>              :         virtual BaseClassifier&amp; fit(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) = 0;</span>
+<span id="L17"><span class="lineNum">      17</span>              :         // X is nxm tensor, y is nx1 tensor</span>
+<span id="L18"><span class="lineNum">      18</span>              :         virtual BaseClassifier&amp; fit(torch::Tensor&amp; X, torch::Tensor&amp; y, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) = 0;</span>
+<span id="L19"><span class="lineNum">      19</span>              :         virtual BaseClassifier&amp; fit(torch::Tensor&amp; dataset, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) = 0;</span>
+<span id="L20"><span class="lineNum">      20</span>              :         virtual BaseClassifier&amp; fit(torch::Tensor&amp; dataset, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, const torch::Tensor&amp; weights) = 0;</span>
+<span id="L21"><span class="lineNum">      21</span> <span class="tlaGNC tlaBgGNC">         606 :         virtual ~BaseClassifier() = default;</span></span>
+<span id="L22"><span class="lineNum">      22</span>              :         torch::Tensor virtual predict(torch::Tensor&amp; X) = 0;</span>
+<span id="L23"><span class="lineNum">      23</span>              :         std::vector&lt;int&gt; virtual predict(std::vector&lt;std::vector&lt;int &gt;&gt;&amp; X) = 0;</span>
+<span id="L24"><span class="lineNum">      24</span>              :         torch::Tensor virtual predict_proba(torch::Tensor&amp; X) = 0;</span>
+<span id="L25"><span class="lineNum">      25</span>              :         std::vector&lt;std::vector&lt;double&gt;&gt; virtual predict_proba(std::vector&lt;std::vector&lt;int &gt;&gt;&amp; X) = 0;</span>
+<span id="L26"><span class="lineNum">      26</span>              :         status_t virtual getStatus() const = 0;</span>
+<span id="L27"><span class="lineNum">      27</span>              :         float virtual score(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y) = 0;</span>
+<span id="L28"><span class="lineNum">      28</span>              :         float virtual score(torch::Tensor&amp; X, torch::Tensor&amp; y) = 0;</span>
+<span id="L29"><span class="lineNum">      29</span>              :         int virtual getNumberOfNodes()const = 0;</span>
+<span id="L30"><span class="lineNum">      30</span>              :         int virtual getNumberOfEdges()const = 0;</span>
+<span id="L31"><span class="lineNum">      31</span>              :         int virtual getNumberOfStates() const = 0;</span>
+<span id="L32"><span class="lineNum">      32</span>              :         int virtual getClassNumStates() const = 0;</span>
+<span id="L33"><span class="lineNum">      33</span>              :         std::vector&lt;std::string&gt; virtual show() const = 0;</span>
+<span id="L34"><span class="lineNum">      34</span>              :         std::vector&lt;std::string&gt; virtual graph(const std::string&amp; title = &quot;&quot;) const = 0;</span>
+<span id="L35"><span class="lineNum">      35</span>              :         virtual std::string getVersion() = 0;</span>
+<span id="L36"><span class="lineNum">      36</span>              :         std::vector&lt;std::string&gt; virtual topological_order() = 0;</span>
+<span id="L37"><span class="lineNum">      37</span>              :         std::vector&lt;std::string&gt; virtual getNotes() const = 0;</span>
+<span id="L38"><span class="lineNum">      38</span>              :         std::string virtual dump_cpt()const = 0;</span>
+<span id="L39"><span class="lineNum">      39</span>              :         virtual void setHyperparameters(const nlohmann::json&amp; hyperparameters) = 0;</span>
+<span id="L40"><span class="lineNum">      40</span>              :         std::vector&lt;std::string&gt;&amp; getValidHyperparameters() { return validHyperparameters; }</span>
+<span id="L41"><span class="lineNum">      41</span>              :     protected:</span>
+<span id="L42"><span class="lineNum">      42</span>              :         virtual void trainModel(const torch::Tensor&amp; weights) = 0;</span>
+<span id="L43"><span class="lineNum">      43</span>              :         std::vector&lt;std::string&gt; validHyperparameters;</span>
+<span id="L44"><span class="lineNum">      44</span>              :     };</span>
+<span id="L45"><span class="lineNum">      45</span>              : }</span>
+        </pre>
+              </td>
+            </tr>
+          </table>
+          <br>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/Classifier.cc.func-c.html

@@ -0,0 +1,243 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/Classifier.cc - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - Classifier.cc<span style="font-size: 80%;"> (<a href="Classifier.cc.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">126</td>
+            <td class="headerCovTableEntry">126</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">24</td>
+            <td class="headerCovTableEntry">24</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="Classifier.cc.func.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
+              
+              
+            </tr>
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+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L10">_ZN8bayesnet10ClassifierC2ENS_7NetworkE</a></td>
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+              <td class="coverFnHi">886</td>
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+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
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+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/Classifier.cc.func.html

@@ -0,0 +1,243 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/Classifier.cc - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - Classifier.cc<span style="font-size: 80%;"> (<a href="Classifier.cc.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">126</td>
+            <td class="headerCovTableEntry">126</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">24</td>
+            <td class="headerCovTableEntry">24</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><a href="Classifier.cc.func-c.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L42">_ZN8bayesnet10Classifier10trainModelERKN2at6TensorE</a></td>
+              
+              <td class="coverFnHi">560</td>
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+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L28">_ZN8bayesnet10Classifier12buildDatasetERN2at6TensorE</a></td>
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+              <td class="coverFnHi">162</td>
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+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L115">_ZN8bayesnet10Classifier13predict_probaERN2at6TensorE</a></td>
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+              <td class="coverFnHi">742</td>
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+              
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+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L77">_ZN8bayesnet10Classifier18checkFitParametersEv</a></td>
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+              <td class="coverFnHi">644</td>
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+              <td class="coverFnHi">330</td>
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+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L137">_ZN8bayesnet10Classifier5scoreERN2at6TensorES3_</a></td>
+              
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+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L142">_ZN8bayesnet10Classifier5scoreERSt6vectorIS1_IiSaIiEESaIS3_EERS3_</a></td>
+              
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+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L94">_ZN8bayesnet10Classifier7predictERN2at6TensorE</a></td>
+              
+              <td class="coverFnHi">850</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L101">_ZN8bayesnet10Classifier7predictERSt6vectorIS1_IiSaIiEESaIS3_EE</a></td>
+              
+              <td class="coverFnHi">8</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L153">_ZN8bayesnet10Classifier8addNodesEv</a></td>
+              
+              <td class="coverFnHi">560</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L10">_ZN8bayesnet10ClassifierC2ENS_7NetworkE</a></td>
+              
+              <td class="coverFnHi">886</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L166">_ZNK8bayesnet10Classifier16getNumberOfEdgesEv</a></td>
+              
+              <td class="coverFnHi">94</td>
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+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L161">_ZNK8bayesnet10Classifier16getNumberOfNodesEv</a></td>
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+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L174">_ZNK8bayesnet10Classifier17getClassNumStatesEv</a></td>
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+              <td class="coverFnHi">170</td>
+              
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+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L170">_ZNK8bayesnet10Classifier17getNumberOfStatesEv</a></td>
+              
+              <td class="coverFnHi">12</td>
+              
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+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L149">_ZNK8bayesnet10Classifier4showB5cxx11Ev</a></td>
+              
+              <td class="coverFnHi">12</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L182">_ZNK8bayesnet10Classifier8dump_cptB5cxx11Ev</a></td>
+              
+              <td class="coverFnHi">2</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/Classifier.cc.gcov.html

@@ -0,0 +1,270 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/Classifier.cc</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - Classifier.cc<span style="font-size: 80%;"> (source / <a href="Classifier.cc.func-c.html">functions</a>)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">126</td>
+            <td class="headerCovTableEntry">126</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">24</td>
+            <td class="headerCovTableEntry">24</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <table cellpadding=0 cellspacing=0 border=0>
+            <tr>
+              <td><br></td>
+            </tr>
+            <tr>
+              <td>
+<pre class="sourceHeading">            Line data    Source code</pre>
+<pre class="source">
+<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
+<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
+<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
+<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
+<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
+<span id="L6"><span class="lineNum">       6</span>              : </span>
+<span id="L7"><span class="lineNum">       7</span>              : #include &lt;sstream&gt;</span>
+<span id="L8"><span class="lineNum">       8</span>              : #include &quot;bayesnet/utils/bayesnetUtils.h&quot;</span>
+<span id="L9"><span class="lineNum">       9</span>              : #include &quot;Classifier.h&quot;</span>
+<span id="L10"><span class="lineNum">      10</span>              : </span>
+<span id="L11"><span class="lineNum">      11</span>              : namespace bayesnet {</span>
+<span id="L12"><span class="lineNum">      12</span> <span class="tlaGNC tlaBgGNC">         886 :     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}</span></span>
+<span id="L13"><span class="lineNum">      13</span>              :     const std::string CLASSIFIER_NOT_FITTED = &quot;Classifier has not been fitted&quot;;</span>
+<span id="L14"><span class="lineNum">      14</span> <span class="tlaGNC">         644 :     Classifier&amp; Classifier::build(const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, const torch::Tensor&amp; weights)</span></span>
+<span id="L15"><span class="lineNum">      15</span>              :     {</span>
+<span id="L16"><span class="lineNum">      16</span> <span class="tlaGNC">         644 :         this-&gt;features = features;</span></span>
+<span id="L17"><span class="lineNum">      17</span> <span class="tlaGNC">         644 :         this-&gt;className = className;</span></span>
+<span id="L18"><span class="lineNum">      18</span> <span class="tlaGNC">         644 :         this-&gt;states = states;</span></span>
+<span id="L19"><span class="lineNum">      19</span> <span class="tlaGNC">         644 :         m = dataset.size(1);</span></span>
+<span id="L20"><span class="lineNum">      20</span> <span class="tlaGNC">         644 :         n = features.size();</span></span>
+<span id="L21"><span class="lineNum">      21</span> <span class="tlaGNC">         644 :         checkFitParameters();</span></span>
+<span id="L22"><span class="lineNum">      22</span> <span class="tlaGNC">         628 :         auto n_classes = states.at(className).size();</span></span>
+<span id="L23"><span class="lineNum">      23</span> <span class="tlaGNC">         628 :         metrics = Metrics(dataset, features, className, n_classes);</span></span>
+<span id="L24"><span class="lineNum">      24</span> <span class="tlaGNC">         628 :         model.initialize();</span></span>
+<span id="L25"><span class="lineNum">      25</span> <span class="tlaGNC">         628 :         buildModel(weights);</span></span>
+<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC">         628 :         trainModel(weights);</span></span>
+<span id="L27"><span class="lineNum">      27</span> <span class="tlaGNC">         620 :         fitted = true;</span></span>
+<span id="L28"><span class="lineNum">      28</span> <span class="tlaGNC">         620 :         return *this;</span></span>
+<span id="L29"><span class="lineNum">      29</span>              :     }</span>
+<span id="L30"><span class="lineNum">      30</span> <span class="tlaGNC">         162 :     void Classifier::buildDataset(torch::Tensor&amp; ytmp)</span></span>
+<span id="L31"><span class="lineNum">      31</span>              :     {</span>
+<span id="L32"><span class="lineNum">      32</span>              :         try {</span>
+<span id="L33"><span class="lineNum">      33</span> <span class="tlaGNC">         162 :             auto yresized = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);</span></span>
+<span id="L34"><span class="lineNum">      34</span> <span class="tlaGNC">         502 :             dataset = torch::cat({ dataset, yresized }, 0);</span></span>
+<span id="L35"><span class="lineNum">      35</span> <span class="tlaGNC">         162 :         }</span></span>
+<span id="L36"><span class="lineNum">      36</span> <span class="tlaGNC">           8 :         catch (const std::exception&amp; e) {</span></span>
+<span id="L37"><span class="lineNum">      37</span> <span class="tlaGNC">           8 :             std::stringstream oss;</span></span>
+<span id="L38"><span class="lineNum">      38</span> <span class="tlaGNC">           8 :             oss &lt;&lt; &quot;* Error in X and y dimensions *\n&quot;;</span></span>
+<span id="L39"><span class="lineNum">      39</span> <span class="tlaGNC">           8 :             oss &lt;&lt; &quot;X dimensions: &quot; &lt;&lt; dataset.sizes() &lt;&lt; &quot;\n&quot;;</span></span>
+<span id="L40"><span class="lineNum">      40</span> <span class="tlaGNC">           8 :             oss &lt;&lt; &quot;y dimensions: &quot; &lt;&lt; ytmp.sizes();</span></span>
+<span id="L41"><span class="lineNum">      41</span> <span class="tlaGNC">           8 :             throw std::runtime_error(oss.str());</span></span>
+<span id="L42"><span class="lineNum">      42</span> <span class="tlaGNC">          16 :         }</span></span>
+<span id="L43"><span class="lineNum">      43</span> <span class="tlaGNC">         324 :     }</span></span>
+<span id="L44"><span class="lineNum">      44</span> <span class="tlaGNC">         560 :     void Classifier::trainModel(const torch::Tensor&amp; weights)</span></span>
+<span id="L45"><span class="lineNum">      45</span>              :     {</span>
+<span id="L46"><span class="lineNum">      46</span> <span class="tlaGNC">         560 :         model.fit(dataset, weights, features, className, states);</span></span>
+<span id="L47"><span class="lineNum">      47</span> <span class="tlaGNC">         560 :     }</span></span>
+<span id="L48"><span class="lineNum">      48</span>              :     // X is nxm where n is the number of features and m the number of samples</span>
+<span id="L49"><span class="lineNum">      49</span> <span class="tlaGNC">          64 :     Classifier&amp; Classifier::fit(torch::Tensor&amp; X, torch::Tensor&amp; y, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states)</span></span>
+<span id="L50"><span class="lineNum">      50</span>              :     {</span>
+<span id="L51"><span class="lineNum">      51</span> <span class="tlaGNC">          64 :         dataset = X;</span></span>
+<span id="L52"><span class="lineNum">      52</span> <span class="tlaGNC">          64 :         buildDataset(y);</span></span>
+<span id="L53"><span class="lineNum">      53</span> <span class="tlaGNC">          60 :         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</span></span>
+<span id="L54"><span class="lineNum">      54</span> <span class="tlaGNC">         104 :         return build(features, className, states, weights);</span></span>
+<span id="L55"><span class="lineNum">      55</span> <span class="tlaGNC">          60 :     }</span></span>
+<span id="L56"><span class="lineNum">      56</span>              :     // X is nxm where n is the number of features and m the number of samples</span>
+<span id="L57"><span class="lineNum">      57</span> <span class="tlaGNC">          60 :     Classifier&amp; Classifier::fit(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states)</span></span>
+<span id="L58"><span class="lineNum">      58</span>              :     {</span>
+<span id="L59"><span class="lineNum">      59</span> <span class="tlaGNC">          60 :         dataset = torch::zeros({ static_cast&lt;int&gt;(X.size()), static_cast&lt;int&gt;(X[0].size()) }, torch::kInt32);</span></span>
+<span id="L60"><span class="lineNum">      60</span> <span class="tlaGNC">         418 :         for (int i = 0; i &lt; X.size(); ++i) {</span></span>
+<span id="L61"><span class="lineNum">      61</span> <span class="tlaGNC">        1432 :             dataset.index_put_({ i, &quot;...&quot; }, torch::tensor(X[i], torch::kInt32));</span></span>
+<span id="L62"><span class="lineNum">      62</span>              :         }</span>
+<span id="L63"><span class="lineNum">      63</span> <span class="tlaGNC">          60 :         auto ytmp = torch::tensor(y, torch::kInt32);</span></span>
+<span id="L64"><span class="lineNum">      64</span> <span class="tlaGNC">          60 :         buildDataset(ytmp);</span></span>
+<span id="L65"><span class="lineNum">      65</span> <span class="tlaGNC">          56 :         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</span></span>
+<span id="L66"><span class="lineNum">      66</span> <span class="tlaGNC">         104 :         return build(features, className, states, weights);</span></span>
+<span id="L67"><span class="lineNum">      67</span> <span class="tlaGNC">         426 :     }</span></span>
+<span id="L68"><span class="lineNum">      68</span> <span class="tlaGNC">         198 :     Classifier&amp; Classifier::fit(torch::Tensor&amp; dataset, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states)</span></span>
+<span id="L69"><span class="lineNum">      69</span>              :     {</span>
+<span id="L70"><span class="lineNum">      70</span> <span class="tlaGNC">         198 :         this-&gt;dataset = dataset;</span></span>
+<span id="L71"><span class="lineNum">      71</span> <span class="tlaGNC">         198 :         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</span></span>
+<span id="L72"><span class="lineNum">      72</span> <span class="tlaGNC">         396 :         return build(features, className, states, weights);</span></span>
+<span id="L73"><span class="lineNum">      73</span> <span class="tlaGNC">         198 :     }</span></span>
+<span id="L74"><span class="lineNum">      74</span> <span class="tlaGNC">         330 :     Classifier&amp; Classifier::fit(torch::Tensor&amp; dataset, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, const torch::Tensor&amp; weights)</span></span>
+<span id="L75"><span class="lineNum">      75</span>              :     {</span>
+<span id="L76"><span class="lineNum">      76</span> <span class="tlaGNC">         330 :         this-&gt;dataset = dataset;</span></span>
+<span id="L77"><span class="lineNum">      77</span> <span class="tlaGNC">         330 :         return build(features, className, states, weights);</span></span>
+<span id="L78"><span class="lineNum">      78</span>              :     }</span>
+<span id="L79"><span class="lineNum">      79</span> <span class="tlaGNC">         644 :     void Classifier::checkFitParameters()</span></span>
+<span id="L80"><span class="lineNum">      80</span>              :     {</span>
+<span id="L81"><span class="lineNum">      81</span> <span class="tlaGNC">         644 :         if (torch::is_floating_point(dataset)) {</span></span>
+<span id="L82"><span class="lineNum">      82</span> <span class="tlaGNC">           4 :             throw std::invalid_argument(&quot;dataset (X, y) must be of type Integer&quot;);</span></span>
+<span id="L83"><span class="lineNum">      83</span>              :         }</span>
+<span id="L84"><span class="lineNum">      84</span> <span class="tlaGNC">         640 :         if (dataset.size(0) - 1 != features.size()) {</span></span>
+<span id="L85"><span class="lineNum">      85</span> <span class="tlaGNC">           4 :             throw std::invalid_argument(&quot;Classifier: X &quot; + std::to_string(dataset.size(0) - 1) + &quot; and features &quot; + std::to_string(features.size()) + &quot; must have the same number of features&quot;);</span></span>
+<span id="L86"><span class="lineNum">      86</span>              :         }</span>
+<span id="L87"><span class="lineNum">      87</span> <span class="tlaGNC">         636 :         if (states.find(className) == states.end()) {</span></span>
+<span id="L88"><span class="lineNum">      88</span> <span class="tlaGNC">           4 :             throw std::invalid_argument(&quot;class name not found in states&quot;);</span></span>
+<span id="L89"><span class="lineNum">      89</span>              :         }</span>
+<span id="L90"><span class="lineNum">      90</span> <span class="tlaGNC">       14208 :         for (auto feature : features) {</span></span>
+<span id="L91"><span class="lineNum">      91</span> <span class="tlaGNC">       13580 :             if (states.find(feature) == states.end()) {</span></span>
+<span id="L92"><span class="lineNum">      92</span> <span class="tlaGNC">           4 :                 throw std::invalid_argument(&quot;feature [&quot; + feature + &quot;] not found in states&quot;);</span></span>
+<span id="L93"><span class="lineNum">      93</span>              :             }</span>
+<span id="L94"><span class="lineNum">      94</span> <span class="tlaGNC">       13580 :         }</span></span>
+<span id="L95"><span class="lineNum">      95</span> <span class="tlaGNC">         628 :     }</span></span>
+<span id="L96"><span class="lineNum">      96</span> <span class="tlaGNC">         850 :     torch::Tensor Classifier::predict(torch::Tensor&amp; X)</span></span>
+<span id="L97"><span class="lineNum">      97</span>              :     {</span>
+<span id="L98"><span class="lineNum">      98</span> <span class="tlaGNC">         850 :         if (!fitted) {</span></span>
+<span id="L99"><span class="lineNum">      99</span> <span class="tlaGNC">           8 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
+<span id="L100"><span class="lineNum">     100</span>              :         }</span>
+<span id="L101"><span class="lineNum">     101</span> <span class="tlaGNC">         842 :         return model.predict(X);</span></span>
+<span id="L102"><span class="lineNum">     102</span>              :     }</span>
+<span id="L103"><span class="lineNum">     103</span> <span class="tlaGNC">           8 :     std::vector&lt;int&gt; Classifier::predict(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</span></span>
+<span id="L104"><span class="lineNum">     104</span>              :     {</span>
+<span id="L105"><span class="lineNum">     105</span> <span class="tlaGNC">           8 :         if (!fitted) {</span></span>
+<span id="L106"><span class="lineNum">     106</span> <span class="tlaGNC">           4 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
+<span id="L107"><span class="lineNum">     107</span>              :         }</span>
+<span id="L108"><span class="lineNum">     108</span> <span class="tlaGNC">           4 :         auto m_ = X[0].size();</span></span>
+<span id="L109"><span class="lineNum">     109</span> <span class="tlaGNC">           4 :         auto n_ = X.size();</span></span>
+<span id="L110"><span class="lineNum">     110</span> <span class="tlaGNC">           4 :         std::vector&lt;std::vector&lt;int&gt;&gt; Xd(n_, std::vector&lt;int&gt;(m_, 0));</span></span>
+<span id="L111"><span class="lineNum">     111</span> <span class="tlaGNC">          20 :         for (auto i = 0; i &lt; n_; i++) {</span></span>
+<span id="L112"><span class="lineNum">     112</span> <span class="tlaGNC">          32 :             Xd[i] = std::vector&lt;int&gt;(X[i].begin(), X[i].end());</span></span>
+<span id="L113"><span class="lineNum">     113</span>              :         }</span>
+<span id="L114"><span class="lineNum">     114</span> <span class="tlaGNC">           4 :         auto yp = model.predict(Xd);</span></span>
+<span id="L115"><span class="lineNum">     115</span> <span class="tlaGNC">           8 :         return yp;</span></span>
+<span id="L116"><span class="lineNum">     116</span> <span class="tlaGNC">           4 :     }</span></span>
+<span id="L117"><span class="lineNum">     117</span> <span class="tlaGNC">         742 :     torch::Tensor Classifier::predict_proba(torch::Tensor&amp; X)</span></span>
+<span id="L118"><span class="lineNum">     118</span>              :     {</span>
+<span id="L119"><span class="lineNum">     119</span> <span class="tlaGNC">         742 :         if (!fitted) {</span></span>
+<span id="L120"><span class="lineNum">     120</span> <span class="tlaGNC">           4 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
+<span id="L121"><span class="lineNum">     121</span>              :         }</span>
+<span id="L122"><span class="lineNum">     122</span> <span class="tlaGNC">         738 :         return model.predict_proba(X);</span></span>
+<span id="L123"><span class="lineNum">     123</span>              :     }</span>
+<span id="L124"><span class="lineNum">     124</span> <span class="tlaGNC">         130 :     std::vector&lt;std::vector&lt;double&gt;&gt; Classifier::predict_proba(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</span></span>
+<span id="L125"><span class="lineNum">     125</span>              :     {</span>
+<span id="L126"><span class="lineNum">     126</span> <span class="tlaGNC">         130 :         if (!fitted) {</span></span>
+<span id="L127"><span class="lineNum">     127</span> <span class="tlaGNC">           4 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
+<span id="L128"><span class="lineNum">     128</span>              :         }</span>
+<span id="L129"><span class="lineNum">     129</span> <span class="tlaGNC">         126 :         auto m_ = X[0].size();</span></span>
+<span id="L130"><span class="lineNum">     130</span> <span class="tlaGNC">         126 :         auto n_ = X.size();</span></span>
+<span id="L131"><span class="lineNum">     131</span> <span class="tlaGNC">         126 :         std::vector&lt;std::vector&lt;int&gt;&gt; Xd(n_, std::vector&lt;int&gt;(m_, 0));</span></span>
+<span id="L132"><span class="lineNum">     132</span>              :         // Convert to nxm vector</span>
+<span id="L133"><span class="lineNum">     133</span> <span class="tlaGNC">        1080 :         for (auto i = 0; i &lt; n_; i++) {</span></span>
+<span id="L134"><span class="lineNum">     134</span> <span class="tlaGNC">        1908 :             Xd[i] = std::vector&lt;int&gt;(X[i].begin(), X[i].end());</span></span>
+<span id="L135"><span class="lineNum">     135</span>              :         }</span>
+<span id="L136"><span class="lineNum">     136</span> <span class="tlaGNC">         126 :         auto yp = model.predict_proba(Xd);</span></span>
+<span id="L137"><span class="lineNum">     137</span> <span class="tlaGNC">         252 :         return yp;</span></span>
+<span id="L138"><span class="lineNum">     138</span> <span class="tlaGNC">         126 :     }</span></span>
+<span id="L139"><span class="lineNum">     139</span> <span class="tlaGNC">          56 :     float Classifier::score(torch::Tensor&amp; X, torch::Tensor&amp; y)</span></span>
+<span id="L140"><span class="lineNum">     140</span>              :     {</span>
+<span id="L141"><span class="lineNum">     141</span> <span class="tlaGNC">          56 :         torch::Tensor y_pred = predict(X);</span></span>
+<span id="L142"><span class="lineNum">     142</span> <span class="tlaGNC">         104 :         return (y_pred == y).sum().item&lt;float&gt;() / y.size(0);</span></span>
+<span id="L143"><span class="lineNum">     143</span> <span class="tlaGNC">          52 :     }</span></span>
+<span id="L144"><span class="lineNum">     144</span> <span class="tlaGNC">           8 :     float Classifier::score(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y)</span></span>
+<span id="L145"><span class="lineNum">     145</span>              :     {</span>
+<span id="L146"><span class="lineNum">     146</span> <span class="tlaGNC">           8 :         if (!fitted) {</span></span>
+<span id="L147"><span class="lineNum">     147</span> <span class="tlaGNC">           4 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
+<span id="L148"><span class="lineNum">     148</span>              :         }</span>
+<span id="L149"><span class="lineNum">     149</span> <span class="tlaGNC">           4 :         return model.score(X, y);</span></span>
+<span id="L150"><span class="lineNum">     150</span>              :     }</span>
+<span id="L151"><span class="lineNum">     151</span> <span class="tlaGNC">          12 :     std::vector&lt;std::string&gt; Classifier::show() const</span></span>
+<span id="L152"><span class="lineNum">     152</span>              :     {</span>
+<span id="L153"><span class="lineNum">     153</span> <span class="tlaGNC">          12 :         return model.show();</span></span>
+<span id="L154"><span class="lineNum">     154</span>              :     }</span>
+<span id="L155"><span class="lineNum">     155</span> <span class="tlaGNC">         560 :     void Classifier::addNodes()</span></span>
+<span id="L156"><span class="lineNum">     156</span>              :     {</span>
+<span id="L157"><span class="lineNum">     157</span>              :         // Add all nodes to the network</span>
+<span id="L158"><span class="lineNum">     158</span> <span class="tlaGNC">       13216 :         for (const auto&amp; feature : features) {</span></span>
+<span id="L159"><span class="lineNum">     159</span> <span class="tlaGNC">       12656 :             model.addNode(feature);</span></span>
+<span id="L160"><span class="lineNum">     160</span>              :         }</span>
+<span id="L161"><span class="lineNum">     161</span> <span class="tlaGNC">         560 :         model.addNode(className);</span></span>
+<span id="L162"><span class="lineNum">     162</span> <span class="tlaGNC">         560 :     }</span></span>
+<span id="L163"><span class="lineNum">     163</span> <span class="tlaGNC">          94 :     int Classifier::getNumberOfNodes() const</span></span>
+<span id="L164"><span class="lineNum">     164</span>              :     {</span>
+<span id="L165"><span class="lineNum">     165</span>              :         // Features does not include class</span>
+<span id="L166"><span class="lineNum">     166</span> <span class="tlaGNC">          94 :         return fitted ? model.getFeatures().size() : 0;</span></span>
+<span id="L167"><span class="lineNum">     167</span>              :     }</span>
+<span id="L168"><span class="lineNum">     168</span> <span class="tlaGNC">          94 :     int Classifier::getNumberOfEdges() const</span></span>
+<span id="L169"><span class="lineNum">     169</span>              :     {</span>
+<span id="L170"><span class="lineNum">     170</span> <span class="tlaGNC">          94 :         return fitted ? model.getNumEdges() : 0;</span></span>
+<span id="L171"><span class="lineNum">     171</span>              :     }</span>
+<span id="L172"><span class="lineNum">     172</span> <span class="tlaGNC">          12 :     int Classifier::getNumberOfStates() const</span></span>
+<span id="L173"><span class="lineNum">     173</span>              :     {</span>
+<span id="L174"><span class="lineNum">     174</span> <span class="tlaGNC">          12 :         return fitted ? model.getStates() : 0;</span></span>
+<span id="L175"><span class="lineNum">     175</span>              :     }</span>
+<span id="L176"><span class="lineNum">     176</span> <span class="tlaGNC">         170 :     int Classifier::getClassNumStates() const</span></span>
+<span id="L177"><span class="lineNum">     177</span>              :     {</span>
+<span id="L178"><span class="lineNum">     178</span> <span class="tlaGNC">         170 :         return fitted ? model.getClassNumStates() : 0;</span></span>
+<span id="L179"><span class="lineNum">     179</span>              :     }</span>
+<span id="L180"><span class="lineNum">     180</span> <span class="tlaGNC">           2 :     std::vector&lt;std::string&gt; Classifier::topological_order()</span></span>
+<span id="L181"><span class="lineNum">     181</span>              :     {</span>
+<span id="L182"><span class="lineNum">     182</span> <span class="tlaGNC">           2 :         return model.topological_sort();</span></span>
+<span id="L183"><span class="lineNum">     183</span>              :     }</span>
+<span id="L184"><span class="lineNum">     184</span> <span class="tlaGNC">           2 :     std::string Classifier::dump_cpt() const</span></span>
+<span id="L185"><span class="lineNum">     185</span>              :     {</span>
+<span id="L186"><span class="lineNum">     186</span> <span class="tlaGNC">           2 :         return model.dump_cpt();</span></span>
+<span id="L187"><span class="lineNum">     187</span>              :     }</span>
+<span id="L188"><span class="lineNum">     188</span> <span class="tlaGNC">          42 :     void Classifier::setHyperparameters(const nlohmann::json&amp; hyperparameters)</span></span>
+<span id="L189"><span class="lineNum">     189</span>              :     {</span>
+<span id="L190"><span class="lineNum">     190</span> <span class="tlaGNC">          42 :         if (!hyperparameters.empty()) {</span></span>
+<span id="L191"><span class="lineNum">     191</span> <span class="tlaGNC">           4 :             throw std::invalid_argument(&quot;Invalid hyperparameters&quot; + hyperparameters.dump());</span></span>
+<span id="L192"><span class="lineNum">     192</span>              :         }</span>
+<span id="L193"><span class="lineNum">     193</span> <span class="tlaGNC">          38 :     }</span></span>
+<span id="L194"><span class="lineNum">     194</span>              : }</span>
+        </pre>
+              </td>
+            </tr>
+          </table>
+          <br>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/Classifier.h.func-c.html

@@ -0,0 +1,117 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/Classifier.h - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - Classifier.h<span style="font-size: 80%;"> (<a href="Classifier.h.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">4</td>
+            <td class="headerCovTableEntry">4</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryMed">80.0&nbsp;%</td>
+            <td class="headerCovTableEntry">5</td>
+            <td class="headerCovTableEntry">4</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="Classifier.h.func.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.h.gcov.html#L31">_ZN8bayesnet10Classifier10getVersionB5cxx11Ev</a></td>
+              
+              <td class="coverFnHi">16</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.h.gcov.html#L36">_ZNK8bayesnet10Classifier8getNotesB5cxx11Ev</a></td>
+              
+              <td class="coverFnHi">38</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.h.gcov.html#L30">_ZNK8bayesnet10Classifier9getStatusEv</a></td>
+              
+              <td class="coverFnHi">64</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.h.gcov.html#L16">_ZN8bayesnet10ClassifierD0Ev</a></td>
+              
+              <td class="coverFnHi">606</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="Classifier.h.gcov.html#L16">_ZN8bayesnet10ClassifierD0Ev</a></td>
+              
+              <td class="coverFnAliasLo">0</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="Classifier.h.gcov.html#L16">_ZN8bayesnet10ClassifierD2Ev</a></td>
+              
+              <td class="coverFnAliasHi">606</td>
+              
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+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/Classifier.h.func.html

@@ -0,0 +1,117 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/Classifier.h - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - Classifier.h<span style="font-size: 80%;"> (<a href="Classifier.h.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">4</td>
+            <td class="headerCovTableEntry">4</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryMed">80.0&nbsp;%</td>
+            <td class="headerCovTableEntry">5</td>
+            <td class="headerCovTableEntry">4</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><a href="Classifier.h.func-c.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.h.gcov.html#L31">_ZN8bayesnet10Classifier10getVersionB5cxx11Ev</a></td>
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+            <tr>
+              <td class="coverFn"><a href="Classifier.h.gcov.html#L16">_ZN8bayesnet10ClassifierD0Ev</a></td>
+              
+              <td class="coverFnHi">606</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="Classifier.h.gcov.html#L16">_ZN8bayesnet10ClassifierD0Ev</a></td>
+              
+              <td class="coverFnAliasLo">0</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="Classifier.h.gcov.html#L16">_ZN8bayesnet10ClassifierD2Ev</a></td>
+              
+              <td class="coverFnAliasHi">606</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.h.gcov.html#L36">_ZNK8bayesnet10Classifier8getNotesB5cxx11Ev</a></td>
+              
+              <td class="coverFnHi">38</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Classifier.h.gcov.html#L30">_ZNK8bayesnet10Classifier9getStatusEv</a></td>
+              
+              <td class="coverFnHi">64</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/Classifier.h.gcov.html

@@ -0,0 +1,141 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/Classifier.h</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - Classifier.h<span style="font-size: 80%;"> (source / <a href="Classifier.h.func-c.html">functions</a>)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">4</td>
+            <td class="headerCovTableEntry">4</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryMed">80.0&nbsp;%</td>
+            <td class="headerCovTableEntry">5</td>
+            <td class="headerCovTableEntry">4</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <table cellpadding=0 cellspacing=0 border=0>
+            <tr>
+              <td><br></td>
+            </tr>
+            <tr>
+              <td>
+<pre class="sourceHeading">            Line data    Source code</pre>
+<pre class="source">
+<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
+<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
+<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
+<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
+<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
+<span id="L6"><span class="lineNum">       6</span>              : </span>
+<span id="L7"><span class="lineNum">       7</span>              : #ifndef CLASSIFIER_H</span>
+<span id="L8"><span class="lineNum">       8</span>              : #define CLASSIFIER_H</span>
+<span id="L9"><span class="lineNum">       9</span>              : #include &lt;torch/torch.h&gt;</span>
+<span id="L10"><span class="lineNum">      10</span>              : #include &quot;bayesnet/utils/BayesMetrics.h&quot;</span>
+<span id="L11"><span class="lineNum">      11</span>              : #include &quot;bayesnet/network/Network.h&quot;</span>
+<span id="L12"><span class="lineNum">      12</span>              : #include &quot;bayesnet/BaseClassifier.h&quot;</span>
+<span id="L13"><span class="lineNum">      13</span>              : </span>
+<span id="L14"><span class="lineNum">      14</span>              : namespace bayesnet {</span>
+<span id="L15"><span class="lineNum">      15</span>              :     class Classifier : public BaseClassifier {</span>
+<span id="L16"><span class="lineNum">      16</span>              :     public:</span>
+<span id="L17"><span class="lineNum">      17</span>              :         Classifier(Network model);</span>
+<span id="L18"><span class="lineNum">      18</span> <span class="tlaGNC tlaBgGNC">         606 :         virtual ~Classifier() = default;</span></span>
+<span id="L19"><span class="lineNum">      19</span>              :         Classifier&amp; fit(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) override;</span>
+<span id="L20"><span class="lineNum">      20</span>              :         Classifier&amp; fit(torch::Tensor&amp; X, torch::Tensor&amp; y, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) override;</span>
+<span id="L21"><span class="lineNum">      21</span>              :         Classifier&amp; fit(torch::Tensor&amp; dataset, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) override;</span>
+<span id="L22"><span class="lineNum">      22</span>              :         Classifier&amp; fit(torch::Tensor&amp; dataset, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, const torch::Tensor&amp; weights) override;</span>
+<span id="L23"><span class="lineNum">      23</span>              :         void addNodes();</span>
+<span id="L24"><span class="lineNum">      24</span>              :         int getNumberOfNodes() const override;</span>
+<span id="L25"><span class="lineNum">      25</span>              :         int getNumberOfEdges() const override;</span>
+<span id="L26"><span class="lineNum">      26</span>              :         int getNumberOfStates() const override;</span>
+<span id="L27"><span class="lineNum">      27</span>              :         int getClassNumStates() const override;</span>
+<span id="L28"><span class="lineNum">      28</span>              :         torch::Tensor predict(torch::Tensor&amp; X) override;</span>
+<span id="L29"><span class="lineNum">      29</span>              :         std::vector&lt;int&gt; predict(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X) override;</span>
+<span id="L30"><span class="lineNum">      30</span>              :         torch::Tensor predict_proba(torch::Tensor&amp; X) override;</span>
+<span id="L31"><span class="lineNum">      31</span>              :         std::vector&lt;std::vector&lt;double&gt;&gt; predict_proba(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X) override;</span>
+<span id="L32"><span class="lineNum">      32</span> <span class="tlaGNC">          64 :         status_t getStatus() const override { return status; }</span></span>
+<span id="L33"><span class="lineNum">      33</span> <span class="tlaGNC">          48 :         std::string getVersion() override { return { project_version.begin(), project_version.end() }; };</span></span>
+<span id="L34"><span class="lineNum">      34</span>              :         float score(torch::Tensor&amp; X, torch::Tensor&amp; y) override;</span>
+<span id="L35"><span class="lineNum">      35</span>              :         float score(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y) override;</span>
+<span id="L36"><span class="lineNum">      36</span>              :         std::vector&lt;std::string&gt; show() const override;</span>
+<span id="L37"><span class="lineNum">      37</span>              :         std::vector&lt;std::string&gt; topological_order()  override;</span>
+<span id="L38"><span class="lineNum">      38</span> <span class="tlaGNC">          38 :         std::vector&lt;std::string&gt; getNotes() const override { return notes; }</span></span>
+<span id="L39"><span class="lineNum">      39</span>              :         std::string dump_cpt() const override;</span>
+<span id="L40"><span class="lineNum">      40</span>              :         void setHyperparameters(const nlohmann::json&amp; hyperparameters) override; //For classifiers that don't have hyperparameters</span>
+<span id="L41"><span class="lineNum">      41</span>              :     protected:</span>
+<span id="L42"><span class="lineNum">      42</span>              :         bool fitted;</span>
+<span id="L43"><span class="lineNum">      43</span>              :         unsigned int m, n; // m: number of samples, n: number of features</span>
+<span id="L44"><span class="lineNum">      44</span>              :         Network model;</span>
+<span id="L45"><span class="lineNum">      45</span>              :         Metrics metrics;</span>
+<span id="L46"><span class="lineNum">      46</span>              :         std::vector&lt;std::string&gt; features;</span>
+<span id="L47"><span class="lineNum">      47</span>              :         std::string className;</span>
+<span id="L48"><span class="lineNum">      48</span>              :         std::map&lt;std::string, std::vector&lt;int&gt;&gt; states;</span>
+<span id="L49"><span class="lineNum">      49</span>              :         torch::Tensor dataset; // (n+1)xm tensor</span>
+<span id="L50"><span class="lineNum">      50</span>              :         status_t status = NORMAL;</span>
+<span id="L51"><span class="lineNum">      51</span>              :         std::vector&lt;std::string&gt; notes; // Used to store messages occurred during the fit process</span>
+<span id="L52"><span class="lineNum">      52</span>              :         void checkFitParameters();</span>
+<span id="L53"><span class="lineNum">      53</span>              :         virtual void buildModel(const torch::Tensor&amp; weights) = 0;</span>
+<span id="L54"><span class="lineNum">      54</span>              :         void trainModel(const torch::Tensor&amp; weights) override;</span>
+<span id="L55"><span class="lineNum">      55</span>              :         void buildDataset(torch::Tensor&amp; y);</span>
+<span id="L56"><span class="lineNum">      56</span>              :     private:</span>
+<span id="L57"><span class="lineNum">      57</span>              :         Classifier&amp; build(const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, const torch::Tensor&amp; weights);</span>
+<span id="L58"><span class="lineNum">      58</span>              :     };</span>
+<span id="L59"><span class="lineNum">      59</span>              : }</span>
+<span id="L60"><span class="lineNum">      60</span>              : #endif</span>
+<span id="L61"><span class="lineNum">      61</span>              : </span>
+<span id="L62"><span class="lineNum">      62</span>              : </span>
+<span id="L63"><span class="lineNum">      63</span>              : </span>
+<span id="L64"><span class="lineNum">      64</span>              : </span>
+<span id="L65"><span class="lineNum">      65</span>              : </span>
+        </pre>
+              </td>
+            </tr>
+          </table>
+          <br>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/KDB.cc.func-c.html

@@ -0,0 +1,110 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/KDB.cc - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - KDB.cc<span style="font-size: 80%;"> (<a href="KDB.cc.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">96.3&nbsp;%</td>
+            <td class="headerCovTableEntry">54</td>
+            <td class="headerCovTableEntry">52</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">5</td>
+            <td class="headerCovTableEntry">5</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="KDB.cc.func.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDB.cc.gcov.html#L101">_ZNK8bayesnet3KDB5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
+              
+              <td class="coverFnHi">4</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDB.cc.gcov.html#L13">_ZN8bayesnet3KDB18setHyperparametersERKN8nlohmann16json_abi_v3_11_310basic_jsonISt3mapSt6vectorNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEblmdSaNS2_14adl_serializerES5_IhSaIhEEvEE</a></td>
+              
+              <td class="coverFnHi">6</td>
+              
+              
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+            <tr>
+              <td class="coverFn"><a href="KDB.cc.gcov.html#L26">_ZN8bayesnet3KDB10buildModelERKN2at6TensorE</a></td>
+              
+              <td class="coverFnHi">26</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDB.cc.gcov.html#L8">_ZN8bayesnet3KDBC2Eif</a></td>
+              
+              <td class="coverFnHi">74</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDB.cc.gcov.html#L77">_ZN8bayesnet3KDB11add_m_edgesEiRSt6vectorIiSaIiEERN2at6TensorE</a></td>
+              
+              <td class="coverFnHi">172</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/KDB.cc.func.html

@@ -0,0 +1,110 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/KDB.cc - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - KDB.cc<span style="font-size: 80%;"> (<a href="KDB.cc.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">96.3&nbsp;%</td>
+            <td class="headerCovTableEntry">54</td>
+            <td class="headerCovTableEntry">52</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">5</td>
+            <td class="headerCovTableEntry">5</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><a href="KDB.cc.func-c.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDB.cc.gcov.html#L26">_ZN8bayesnet3KDB10buildModelERKN2at6TensorE</a></td>
+              
+              <td class="coverFnHi">26</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDB.cc.gcov.html#L77">_ZN8bayesnet3KDB11add_m_edgesEiRSt6vectorIiSaIiEERN2at6TensorE</a></td>
+              
+              <td class="coverFnHi">172</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDB.cc.gcov.html#L13">_ZN8bayesnet3KDB18setHyperparametersERKN8nlohmann16json_abi_v3_11_310basic_jsonISt3mapSt6vectorNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEblmdSaNS2_14adl_serializerES5_IhSaIhEEvEE</a></td>
+              
+              <td class="coverFnHi">6</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDB.cc.gcov.html#L8">_ZN8bayesnet3KDBC2Eif</a></td>
+              
+              <td class="coverFnHi">74</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDB.cc.gcov.html#L101">_ZNK8bayesnet3KDB5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
+              
+              <td class="coverFnHi">4</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/KDB.cc.gcov.html

@@ -0,0 +1,187 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/KDB.cc</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - KDB.cc<span style="font-size: 80%;"> (source / <a href="KDB.cc.func-c.html">functions</a>)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">96.3&nbsp;%</td>
+            <td class="headerCovTableEntry">54</td>
+            <td class="headerCovTableEntry">52</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">5</td>
+            <td class="headerCovTableEntry">5</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <table cellpadding=0 cellspacing=0 border=0>
+            <tr>
+              <td><br></td>
+            </tr>
+            <tr>
+              <td>
+<pre class="sourceHeading">            Line data    Source code</pre>
+<pre class="source">
+<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
+<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
+<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
+<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
+<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
+<span id="L6"><span class="lineNum">       6</span>              : </span>
+<span id="L7"><span class="lineNum">       7</span>              : #include &quot;KDB.h&quot;</span>
+<span id="L8"><span class="lineNum">       8</span>              : </span>
+<span id="L9"><span class="lineNum">       9</span>              : namespace bayesnet {</span>
+<span id="L10"><span class="lineNum">      10</span> <span class="tlaGNC tlaBgGNC">          74 :     KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta)</span></span>
+<span id="L11"><span class="lineNum">      11</span>              :     {</span>
+<span id="L12"><span class="lineNum">      12</span> <span class="tlaGNC">         222 :         validHyperparameters = { &quot;k&quot;, &quot;theta&quot; };</span></span>
+<span id="L13"><span class="lineNum">      13</span>              : </span>
+<span id="L14"><span class="lineNum">      14</span> <span class="tlaGNC">         222 :     }</span></span>
+<span id="L15"><span class="lineNum">      15</span> <span class="tlaGNC">           6 :     void KDB::setHyperparameters(const nlohmann::json&amp; hyperparameters_)</span></span>
+<span id="L16"><span class="lineNum">      16</span>              :     {</span>
+<span id="L17"><span class="lineNum">      17</span> <span class="tlaGNC">           6 :         auto hyperparameters = hyperparameters_;</span></span>
+<span id="L18"><span class="lineNum">      18</span> <span class="tlaGNC">           6 :         if (hyperparameters.contains(&quot;k&quot;)) {</span></span>
+<span id="L19"><span class="lineNum">      19</span> <span class="tlaGNC">           2 :             k = hyperparameters[&quot;k&quot;];</span></span>
+<span id="L20"><span class="lineNum">      20</span> <span class="tlaGNC">           2 :             hyperparameters.erase(&quot;k&quot;);</span></span>
+<span id="L21"><span class="lineNum">      21</span>              :         }</span>
+<span id="L22"><span class="lineNum">      22</span> <span class="tlaGNC">           6 :         if (hyperparameters.contains(&quot;theta&quot;)) {</span></span>
+<span id="L23"><span class="lineNum">      23</span> <span class="tlaGNC">           2 :             theta = hyperparameters[&quot;theta&quot;];</span></span>
+<span id="L24"><span class="lineNum">      24</span> <span class="tlaGNC">           2 :             hyperparameters.erase(&quot;theta&quot;);</span></span>
+<span id="L25"><span class="lineNum">      25</span>              :         }</span>
+<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC">           6 :         Classifier::setHyperparameters(hyperparameters);</span></span>
+<span id="L27"><span class="lineNum">      27</span> <span class="tlaGNC">           6 :     }</span></span>
+<span id="L28"><span class="lineNum">      28</span> <span class="tlaGNC">          26 :     void KDB::buildModel(const torch::Tensor&amp; weights)</span></span>
+<span id="L29"><span class="lineNum">      29</span>              :     {</span>
+<span id="L30"><span class="lineNum">      30</span>              :         /*</span>
+<span id="L31"><span class="lineNum">      31</span>              :         1. For each feature Xi, compute mutual information, I(X;C),</span>
+<span id="L32"><span class="lineNum">      32</span>              :         where C is the class.</span>
+<span id="L33"><span class="lineNum">      33</span>              :         2. Compute class conditional mutual information I(Xi;XjIC), f or each</span>
+<span id="L34"><span class="lineNum">      34</span>              :         pair of features Xi and Xj, where i#j.</span>
+<span id="L35"><span class="lineNum">      35</span>              :         3. Let the used variable list, S, be empty.</span>
+<span id="L36"><span class="lineNum">      36</span>              :         4. Let the DAG network being constructed, BN, begin with a single</span>
+<span id="L37"><span class="lineNum">      37</span>              :         class node, C.</span>
+<span id="L38"><span class="lineNum">      38</span>              :         5. Repeat until S includes all domain features</span>
+<span id="L39"><span class="lineNum">      39</span>              :         5.1. Select feature Xmax which is not in S and has the largest value</span>
+<span id="L40"><span class="lineNum">      40</span>              :         I(Xmax;C).</span>
+<span id="L41"><span class="lineNum">      41</span>              :         5.2. Add a node to BN representing Xmax.</span>
+<span id="L42"><span class="lineNum">      42</span>              :         5.3. Add an arc from C to Xmax in BN.</span>
+<span id="L43"><span class="lineNum">      43</span>              :         5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with</span>
+<span id="L44"><span class="lineNum">      44</span>              :         the highest value for I(Xmax;X,jC).</span>
+<span id="L45"><span class="lineNum">      45</span>              :         5.5. Add Xmax to S.</span>
+<span id="L46"><span class="lineNum">      46</span>              :         Compute the conditional probabilility infered by the structure of BN by</span>
+<span id="L47"><span class="lineNum">      47</span>              :         using counts from DB, and output BN.</span>
+<span id="L48"><span class="lineNum">      48</span>              :         */</span>
+<span id="L49"><span class="lineNum">      49</span>              :         // 1. For each feature Xi, compute mutual information, I(X;C),</span>
+<span id="L50"><span class="lineNum">      50</span>              :         // where C is the class.</span>
+<span id="L51"><span class="lineNum">      51</span> <span class="tlaGNC">          26 :         addNodes();</span></span>
+<span id="L52"><span class="lineNum">      52</span> <span class="tlaGNC">          78 :         const torch::Tensor&amp; y = dataset.index({ -1, &quot;...&quot; });</span></span>
+<span id="L53"><span class="lineNum">      53</span> <span class="tlaGNC">          26 :         std::vector&lt;double&gt; mi;</span></span>
+<span id="L54"><span class="lineNum">      54</span> <span class="tlaGNC">         198 :         for (auto i = 0; i &lt; features.size(); i++) {</span></span>
+<span id="L55"><span class="lineNum">      55</span> <span class="tlaGNC">         516 :             torch::Tensor firstFeature = dataset.index({ i, &quot;...&quot; });</span></span>
+<span id="L56"><span class="lineNum">      56</span> <span class="tlaGNC">         172 :             mi.push_back(metrics.mutualInformation(firstFeature, y, weights));</span></span>
+<span id="L57"><span class="lineNum">      57</span> <span class="tlaGNC">         172 :         }</span></span>
+<span id="L58"><span class="lineNum">      58</span>              :         // 2. Compute class conditional mutual information I(Xi;XjIC), f or each</span>
+<span id="L59"><span class="lineNum">      59</span> <span class="tlaGNC">          26 :         auto conditionalEdgeWeights = metrics.conditionalEdge(weights);</span></span>
+<span id="L60"><span class="lineNum">      60</span>              :         // 3. Let the used variable list, S, be empty.</span>
+<span id="L61"><span class="lineNum">      61</span> <span class="tlaGNC">          26 :         std::vector&lt;int&gt; S;</span></span>
+<span id="L62"><span class="lineNum">      62</span>              :         // 4. Let the DAG network being constructed, BN, begin with a single</span>
+<span id="L63"><span class="lineNum">      63</span>              :         // class node, C.</span>
+<span id="L64"><span class="lineNum">      64</span>              :         // 5. Repeat until S includes all domain features</span>
+<span id="L65"><span class="lineNum">      65</span>              :         // 5.1. Select feature Xmax which is not in S and has the largest value</span>
+<span id="L66"><span class="lineNum">      66</span>              :         // I(Xmax;C).</span>
+<span id="L67"><span class="lineNum">      67</span> <span class="tlaGNC">          26 :         auto order = argsort(mi);</span></span>
+<span id="L68"><span class="lineNum">      68</span> <span class="tlaGNC">         198 :         for (auto idx : order) {</span></span>
+<span id="L69"><span class="lineNum">      69</span>              :             // 5.2. Add a node to BN representing Xmax.</span>
+<span id="L70"><span class="lineNum">      70</span>              :             // 5.3. Add an arc from C to Xmax in BN.</span>
+<span id="L71"><span class="lineNum">      71</span> <span class="tlaGNC">         172 :             model.addEdge(className, features[idx]);</span></span>
+<span id="L72"><span class="lineNum">      72</span>              :             // 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with</span>
+<span id="L73"><span class="lineNum">      73</span>              :             // the highest value for I(Xmax;X,jC).</span>
+<span id="L74"><span class="lineNum">      74</span> <span class="tlaGNC">         172 :             add_m_edges(idx, S, conditionalEdgeWeights);</span></span>
+<span id="L75"><span class="lineNum">      75</span>              :             // 5.5. Add Xmax to S.</span>
+<span id="L76"><span class="lineNum">      76</span> <span class="tlaGNC">         172 :             S.push_back(idx);</span></span>
+<span id="L77"><span class="lineNum">      77</span>              :         }</span>
+<span id="L78"><span class="lineNum">      78</span> <span class="tlaGNC">         224 :     }</span></span>
+<span id="L79"><span class="lineNum">      79</span> <span class="tlaGNC">         172 :     void KDB::add_m_edges(int idx, std::vector&lt;int&gt;&amp; S, torch::Tensor&amp; weights)</span></span>
+<span id="L80"><span class="lineNum">      80</span>              :     {</span>
+<span id="L81"><span class="lineNum">      81</span> <span class="tlaGNC">         172 :         auto n_edges = std::min(k, static_cast&lt;int&gt;(S.size()));</span></span>
+<span id="L82"><span class="lineNum">      82</span> <span class="tlaGNC">         172 :         auto cond_w = clone(weights);</span></span>
+<span id="L83"><span class="lineNum">      83</span> <span class="tlaGNC">         172 :         bool exit_cond = k == 0;</span></span>
+<span id="L84"><span class="lineNum">      84</span> <span class="tlaGNC">         172 :         int num = 0;</span></span>
+<span id="L85"><span class="lineNum">      85</span> <span class="tlaGNC">         502 :         while (!exit_cond) {</span></span>
+<span id="L86"><span class="lineNum">      86</span> <span class="tlaGNC">        1320 :             auto max_minfo = argmax(cond_w.index({ idx, &quot;...&quot; })).item&lt;int&gt;();</span></span>
+<span id="L87"><span class="lineNum">      87</span> <span class="tlaGNC">         330 :             auto belongs = find(S.begin(), S.end(), max_minfo) != S.end();</span></span>
+<span id="L88"><span class="lineNum">      88</span> <span class="tlaGNC">         882 :             if (belongs &amp;&amp; cond_w.index({ idx, max_minfo }).item&lt;float&gt;() &gt; theta) {</span></span>
+<span id="L89"><span class="lineNum">      89</span>              :                 try {</span>
+<span id="L90"><span class="lineNum">      90</span> <span class="tlaGNC">         160 :                     model.addEdge(features[max_minfo], features[idx]);</span></span>
+<span id="L91"><span class="lineNum">      91</span> <span class="tlaGNC">         160 :                     num++;</span></span>
+<span id="L92"><span class="lineNum">      92</span>              :                 }</span>
+<span id="L93"><span class="lineNum">      93</span> <span class="tlaUNC tlaBgUNC">           0 :                 catch (const std::invalid_argument&amp; e) {</span></span>
+<span id="L94"><span class="lineNum">      94</span>              :                     // Loops are not allowed</span>
+<span id="L95"><span class="lineNum">      95</span> <span class="tlaUNC">           0 :                 }</span></span>
+<span id="L96"><span class="lineNum">      96</span>              :             }</span>
+<span id="L97"><span class="lineNum">      97</span> <span class="tlaGNC tlaBgGNC">        1320 :             cond_w.index_put_({ idx, max_minfo }, -1);</span></span>
+<span id="L98"><span class="lineNum">      98</span> <span class="tlaGNC">         990 :             auto candidates_mask = cond_w.index({ idx, &quot;...&quot; }).gt(theta);</span></span>
+<span id="L99"><span class="lineNum">      99</span> <span class="tlaGNC">         330 :             auto candidates = candidates_mask.nonzero();</span></span>
+<span id="L100"><span class="lineNum">     100</span> <span class="tlaGNC">         330 :             exit_cond = num == n_edges || candidates.size(0) == 0;</span></span>
+<span id="L101"><span class="lineNum">     101</span> <span class="tlaGNC">         330 :         }</span></span>
+<span id="L102"><span class="lineNum">     102</span> <span class="tlaGNC">        1346 :     }</span></span>
+<span id="L103"><span class="lineNum">     103</span> <span class="tlaGNC">           4 :     std::vector&lt;std::string&gt; KDB::graph(const std::string&amp; title) const</span></span>
+<span id="L104"><span class="lineNum">     104</span>              :     {</span>
+<span id="L105"><span class="lineNum">     105</span> <span class="tlaGNC">           4 :         std::string header{ title };</span></span>
+<span id="L106"><span class="lineNum">     106</span> <span class="tlaGNC">           4 :         if (title == &quot;KDB&quot;) {</span></span>
+<span id="L107"><span class="lineNum">     107</span> <span class="tlaGNC">           4 :             header += &quot; (k=&quot; + std::to_string(k) + &quot;, theta=&quot; + std::to_string(theta) + &quot;)&quot;;</span></span>
+<span id="L108"><span class="lineNum">     108</span>              :         }</span>
+<span id="L109"><span class="lineNum">     109</span> <span class="tlaGNC">           8 :         return model.graph(header);</span></span>
+<span id="L110"><span class="lineNum">     110</span> <span class="tlaGNC">           4 :     }</span></span>
+<span id="L111"><span class="lineNum">     111</span>              : }</span>
+        </pre>
+              </td>
+            </tr>
+          </table>
+          <br>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/KDB.h.func-c.html

@@ -0,0 +1,96 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/KDB.h - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - KDB.h<span style="font-size: 80%;"> (<a href="KDB.h.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">1</td>
+            <td class="headerCovTableEntry">1</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">2</td>
+            <td class="headerCovTableEntry">2</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="KDB.h.func.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDB.h.gcov.html#L20">_ZN8bayesnet3KDBD0Ev</a></td>
+              
+              <td class="coverFnHi">22</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="KDB.h.gcov.html#L20">_ZN8bayesnet3KDBD0Ev</a></td>
+              
+              <td class="coverFnAliasHi">4</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="KDB.h.gcov.html#L20">_ZN8bayesnet3KDBD2Ev</a></td>
+              
+              <td class="coverFnAliasHi">18</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/KDB.h.func.html

@@ -0,0 +1,96 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/KDB.h - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - KDB.h<span style="font-size: 80%;"> (<a href="KDB.h.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">1</td>
+            <td class="headerCovTableEntry">1</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">2</td>
+            <td class="headerCovTableEntry">2</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><a href="KDB.h.func-c.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDB.h.gcov.html#L20">_ZN8bayesnet3KDBD0Ev</a></td>
+              
+              <td class="coverFnHi">22</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="KDB.h.gcov.html#L20">_ZN8bayesnet3KDBD0Ev</a></td>
+              
+              <td class="coverFnAliasHi">4</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="KDB.h.gcov.html#L20">_ZN8bayesnet3KDBD2Ev</a></td>
+              
+              <td class="coverFnAliasHi">18</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/KDB.h.gcov.html

@@ -0,0 +1,103 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/KDB.h</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - KDB.h<span style="font-size: 80%;"> (source / <a href="KDB.h.func-c.html">functions</a>)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">1</td>
+            <td class="headerCovTableEntry">1</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">2</td>
+            <td class="headerCovTableEntry">2</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <table cellpadding=0 cellspacing=0 border=0>
+            <tr>
+              <td><br></td>
+            </tr>
+            <tr>
+              <td>
+<pre class="sourceHeading">            Line data    Source code</pre>
+<pre class="source">
+<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
+<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
+<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
+<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
+<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
+<span id="L6"><span class="lineNum">       6</span>              : </span>
+<span id="L7"><span class="lineNum">       7</span>              : #ifndef KDB_H</span>
+<span id="L8"><span class="lineNum">       8</span>              : #define KDB_H</span>
+<span id="L9"><span class="lineNum">       9</span>              : #include &lt;torch/torch.h&gt;</span>
+<span id="L10"><span class="lineNum">      10</span>              : #include &quot;bayesnet/utils/bayesnetUtils.h&quot;</span>
+<span id="L11"><span class="lineNum">      11</span>              : #include &quot;Classifier.h&quot;</span>
+<span id="L12"><span class="lineNum">      12</span>              : namespace bayesnet {</span>
+<span id="L13"><span class="lineNum">      13</span>              :     class KDB : public Classifier {</span>
+<span id="L14"><span class="lineNum">      14</span>              :     private:</span>
+<span id="L15"><span class="lineNum">      15</span>              :         int k;</span>
+<span id="L16"><span class="lineNum">      16</span>              :         float theta;</span>
+<span id="L17"><span class="lineNum">      17</span>              :         void add_m_edges(int idx, std::vector&lt;int&gt;&amp; S, torch::Tensor&amp; weights);</span>
+<span id="L18"><span class="lineNum">      18</span>              :     protected:</span>
+<span id="L19"><span class="lineNum">      19</span>              :         void buildModel(const torch::Tensor&amp; weights) override;</span>
+<span id="L20"><span class="lineNum">      20</span>              :     public:</span>
+<span id="L21"><span class="lineNum">      21</span>              :         explicit KDB(int k, float theta = 0.03);</span>
+<span id="L22"><span class="lineNum">      22</span> <span class="tlaGNC tlaBgGNC">          22 :         virtual ~KDB() = default;</span></span>
+<span id="L23"><span class="lineNum">      23</span>              :         void setHyperparameters(const nlohmann::json&amp; hyperparameters_) override;</span>
+<span id="L24"><span class="lineNum">      24</span>              :         std::vector&lt;std::string&gt; graph(const std::string&amp; name = &quot;KDB&quot;) const override;</span>
+<span id="L25"><span class="lineNum">      25</span>              :     };</span>
+<span id="L26"><span class="lineNum">      26</span>              : }</span>
+<span id="L27"><span class="lineNum">      27</span>              : #endif</span>
+        </pre>
+              </td>
+            </tr>
+          </table>
+          <br>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/KDBLd.cc.func-c.html

@@ -0,0 +1,103 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/KDBLd.cc - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - KDBLd.cc<span style="font-size: 80%;"> (<a href="KDBLd.cc.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">17</td>
+            <td class="headerCovTableEntry">17</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">4</td>
+            <td class="headerCovTableEntry">4</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="KDBLd.cc.func.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L29">_ZNK8bayesnet5KDBLd5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
+              
+              <td class="coverFnHi">2</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L24">_ZN8bayesnet5KDBLd7predictERN2at6TensorE</a></td>
+              
+              <td class="coverFnHi">8</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L9">_ZN8bayesnet5KDBLd3fitERN2at6TensorES3_RKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISA_EERKSA_RSt3mapISA_S4_IiSaIiEESt4lessISA_ESaISt4pairISF_SJ_EEE</a></td>
+              
+              <td class="coverFnHi">10</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L8">_ZN8bayesnet5KDBLdC2Ei</a></td>
+              
+              <td class="coverFnHi">34</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/KDBLd.cc.func.html

@@ -0,0 +1,103 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/KDBLd.cc - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - KDBLd.cc<span style="font-size: 80%;"> (<a href="KDBLd.cc.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">17</td>
+            <td class="headerCovTableEntry">17</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">4</td>
+            <td class="headerCovTableEntry">4</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><a href="KDBLd.cc.func-c.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L9">_ZN8bayesnet5KDBLd3fitERN2at6TensorES3_RKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISA_EERKSA_RSt3mapISA_S4_IiSaIiEESt4lessISA_ESaISt4pairISF_SJ_EEE</a></td>
+              
+              <td class="coverFnHi">10</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L24">_ZN8bayesnet5KDBLd7predictERN2at6TensorE</a></td>
+              
+              <td class="coverFnHi">8</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L8">_ZN8bayesnet5KDBLdC2Ei</a></td>
+              
+              <td class="coverFnHi">34</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L29">_ZNK8bayesnet5KDBLd5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
+              
+              <td class="coverFnHi">2</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/KDBLd.cc.gcov.html

@@ -0,0 +1,111 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/KDBLd.cc</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - KDBLd.cc<span style="font-size: 80%;"> (source / <a href="KDBLd.cc.func-c.html">functions</a>)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">17</td>
+            <td class="headerCovTableEntry">17</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">4</td>
+            <td class="headerCovTableEntry">4</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <table cellpadding=0 cellspacing=0 border=0>
+            <tr>
+              <td><br></td>
+            </tr>
+            <tr>
+              <td>
+<pre class="sourceHeading">            Line data    Source code</pre>
+<pre class="source">
+<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
+<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
+<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
+<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
+<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
+<span id="L6"><span class="lineNum">       6</span>              : </span>
+<span id="L7"><span class="lineNum">       7</span>              : #include &quot;KDBLd.h&quot;</span>
+<span id="L8"><span class="lineNum">       8</span>              : </span>
+<span id="L9"><span class="lineNum">       9</span>              : namespace bayesnet {</span>
+<span id="L10"><span class="lineNum">      10</span> <span class="tlaGNC tlaBgGNC">          34 :     KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}</span></span>
+<span id="L11"><span class="lineNum">      11</span> <span class="tlaGNC">          10 :     KDBLd&amp; KDBLd::fit(torch::Tensor&amp; X_, torch::Tensor&amp; y_, const std::vector&lt;std::string&gt;&amp; features_, const std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</span></span>
+<span id="L12"><span class="lineNum">      12</span>              :     {</span>
+<span id="L13"><span class="lineNum">      13</span> <span class="tlaGNC">          10 :         checkInput(X_, y_);</span></span>
+<span id="L14"><span class="lineNum">      14</span> <span class="tlaGNC">          10 :         features = features_;</span></span>
+<span id="L15"><span class="lineNum">      15</span> <span class="tlaGNC">          10 :         className = className_;</span></span>
+<span id="L16"><span class="lineNum">      16</span> <span class="tlaGNC">          10 :         Xf = X_;</span></span>
+<span id="L17"><span class="lineNum">      17</span> <span class="tlaGNC">          10 :         y = y_;</span></span>
+<span id="L18"><span class="lineNum">      18</span>              :         // Fills std::vectors Xv &amp; yv with the data from tensors X_ (discretized) &amp; y</span>
+<span id="L19"><span class="lineNum">      19</span> <span class="tlaGNC">          10 :         states = fit_local_discretization(y);</span></span>
+<span id="L20"><span class="lineNum">      20</span>              :         // We have discretized the input data</span>
+<span id="L21"><span class="lineNum">      21</span>              :         // 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network</span>
+<span id="L22"><span class="lineNum">      22</span> <span class="tlaGNC">          10 :         KDB::fit(dataset, features, className, states);</span></span>
+<span id="L23"><span class="lineNum">      23</span> <span class="tlaGNC">          10 :         states = localDiscretizationProposal(states, model);</span></span>
+<span id="L24"><span class="lineNum">      24</span> <span class="tlaGNC">          10 :         return *this;</span></span>
+<span id="L25"><span class="lineNum">      25</span>              :     }</span>
+<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC">           8 :     torch::Tensor KDBLd::predict(torch::Tensor&amp; X)</span></span>
+<span id="L27"><span class="lineNum">      27</span>              :     {</span>
+<span id="L28"><span class="lineNum">      28</span> <span class="tlaGNC">           8 :         auto Xt = prepareX(X);</span></span>
+<span id="L29"><span class="lineNum">      29</span> <span class="tlaGNC">          16 :         return KDB::predict(Xt);</span></span>
+<span id="L30"><span class="lineNum">      30</span> <span class="tlaGNC">           8 :     }</span></span>
+<span id="L31"><span class="lineNum">      31</span> <span class="tlaGNC">           2 :     std::vector&lt;std::string&gt; KDBLd::graph(const std::string&amp; name) const</span></span>
+<span id="L32"><span class="lineNum">      32</span>              :     {</span>
+<span id="L33"><span class="lineNum">      33</span> <span class="tlaGNC">           2 :         return KDB::graph(name);</span></span>
+<span id="L34"><span class="lineNum">      34</span>              :     }</span>
+<span id="L35"><span class="lineNum">      35</span>              : }</span>
+        </pre>
+              </td>
+            </tr>
+          </table>
+          <br>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/KDBLd.h.func-c.html

@@ -0,0 +1,96 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/KDBLd.h - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - KDBLd.h<span style="font-size: 80%;"> (<a href="KDBLd.h.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">1</td>
+            <td class="headerCovTableEntry">1</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">2</td>
+            <td class="headerCovTableEntry">2</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="KDBLd.h.func.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDBLd.h.gcov.html#L15">_ZN8bayesnet5KDBLdD0Ev</a></td>
+              
+              <td class="coverFnHi">10</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="KDBLd.h.gcov.html#L15">_ZN8bayesnet5KDBLdD0Ev</a></td>
+              
+              <td class="coverFnAliasHi">4</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="KDBLd.h.gcov.html#L15">_ZN8bayesnet5KDBLdD2Ev</a></td>
+              
+              <td class="coverFnAliasHi">6</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/KDBLd.h.func.html

@@ -0,0 +1,96 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/KDBLd.h - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - KDBLd.h<span style="font-size: 80%;"> (<a href="KDBLd.h.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">1</td>
+            <td class="headerCovTableEntry">1</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">2</td>
+            <td class="headerCovTableEntry">2</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><a href="KDBLd.h.func-c.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="KDBLd.h.gcov.html#L15">_ZN8bayesnet5KDBLdD0Ev</a></td>
+              
+              <td class="coverFnHi">10</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="KDBLd.h.gcov.html#L15">_ZN8bayesnet5KDBLdD0Ev</a></td>
+              
+              <td class="coverFnAliasHi">4</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="KDBLd.h.gcov.html#L15">_ZN8bayesnet5KDBLdD2Ev</a></td>
+              
+              <td class="coverFnAliasHi">6</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/KDBLd.h.gcov.html

@@ -0,0 +1,100 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/KDBLd.h</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - KDBLd.h<span style="font-size: 80%;"> (source / <a href="KDBLd.h.func-c.html">functions</a>)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">1</td>
+            <td class="headerCovTableEntry">1</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">2</td>
+            <td class="headerCovTableEntry">2</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <table cellpadding=0 cellspacing=0 border=0>
+            <tr>
+              <td><br></td>
+            </tr>
+            <tr>
+              <td>
+<pre class="sourceHeading">            Line data    Source code</pre>
+<pre class="source">
+<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
+<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
+<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
+<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
+<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
+<span id="L6"><span class="lineNum">       6</span>              : </span>
+<span id="L7"><span class="lineNum">       7</span>              : #ifndef KDBLD_H</span>
+<span id="L8"><span class="lineNum">       8</span>              : #define KDBLD_H</span>
+<span id="L9"><span class="lineNum">       9</span>              : #include &quot;Proposal.h&quot;</span>
+<span id="L10"><span class="lineNum">      10</span>              : #include &quot;KDB.h&quot;</span>
+<span id="L11"><span class="lineNum">      11</span>              : </span>
+<span id="L12"><span class="lineNum">      12</span>              : namespace bayesnet {</span>
+<span id="L13"><span class="lineNum">      13</span>              :     class KDBLd : public KDB, public Proposal {</span>
+<span id="L14"><span class="lineNum">      14</span>              :     private:</span>
+<span id="L15"><span class="lineNum">      15</span>              :     public:</span>
+<span id="L16"><span class="lineNum">      16</span>              :         explicit KDBLd(int k);</span>
+<span id="L17"><span class="lineNum">      17</span> <span class="tlaGNC tlaBgGNC">          10 :         virtual ~KDBLd() = default;</span></span>
+<span id="L18"><span class="lineNum">      18</span>              :         KDBLd&amp; fit(torch::Tensor&amp; X, torch::Tensor&amp; y, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) override;</span>
+<span id="L19"><span class="lineNum">      19</span>              :         std::vector&lt;std::string&gt; graph(const std::string&amp; name = &quot;KDB&quot;) const override;</span>
+<span id="L20"><span class="lineNum">      20</span>              :         torch::Tensor predict(torch::Tensor&amp; X) override;</span>
+<span id="L21"><span class="lineNum">      21</span>              :         static inline std::string version() { return &quot;0.0.1&quot;; };</span>
+<span id="L22"><span class="lineNum">      22</span>              :     };</span>
+<span id="L23"><span class="lineNum">      23</span>              : }</span>
+<span id="L24"><span class="lineNum">      24</span>              : #endif // !KDBLD_H</span>
+        </pre>
+              </td>
+            </tr>
+          </table>
+          <br>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/Proposal.cc.func-c.html

@@ -0,0 +1,145 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/Proposal.cc - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - Proposal.cc<span style="font-size: 80%;"> (<a href="Proposal.cc.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">97.7&nbsp;%</td>
+            <td class="headerCovTableEntry">86</td>
+            <td class="headerCovTableEntry">84</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryMed">88.9&nbsp;%</td>
+            <td class="headerCovTableEntry">9</td>
+            <td class="headerCovTableEntry">8</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="Proposal.cc.func.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L104">_ZN8bayesnet8Proposal8prepareXERN2at6TensorE</a></td>
+              
+              <td class="coverFnHi">84</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L10">_ZN8bayesnet8ProposalD0Ev</a></td>
+              
+              <td class="coverFnHi">100</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="Proposal.cc.gcov.html#L10">_ZN8bayesnet8ProposalD0Ev</a></td>
+              
+              <td class="coverFnAliasLo">0</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="Proposal.cc.gcov.html#L10">_ZN8bayesnet8ProposalD2Ev</a></td>
+              
+              <td class="coverFnAliasHi">100</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L25">_ZN8bayesnet8Proposal27localDiscretizationProposalERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIiSaIiEESt4lessIS7_ESaISt4pairIKS7_SA_EEERNS_7NetworkE</a></td>
+              
+              <td class="coverFnHi">106</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L16">_ZN8bayesnet8Proposal10checkInputERKN2at6TensorES4_</a></td>
+              
+              <td class="coverFnHi">114</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L77">_ZN8bayesnet8Proposal24fit_local_discretizationB5cxx11ERKN2at6TensorE</a></td>
+              
+              <td class="coverFnHi">116</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L9">_ZN8bayesnet8ProposalC2ERN2at6TensorERSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISA_EERSA_</a></td>
+              
+              <td class="coverFnHi">212</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L47">_ZZN8bayesnet8Proposal27localDiscretizationProposalERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIiSaIiEESt4lessIS7_ESaISt4pairIKS7_SA_EEERNS_7NetworkEENKUlRKT_E0_clIS7_EEDaSO_</a></td>
+              
+              <td class="coverFnHi">686</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L41">_ZZN8bayesnet8Proposal27localDiscretizationProposalERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIiSaIiEESt4lessIS7_ESaISt4pairIKS7_SA_EEERNS_7NetworkEENKUlRKT_E_clIPNS_4NodeEEEDaSO_</a></td>
+              
+              <td class="coverFnHi">1348</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/Proposal.cc.func.html

@@ -0,0 +1,145 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/Proposal.cc - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - Proposal.cc<span style="font-size: 80%;"> (<a href="Proposal.cc.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">97.7&nbsp;%</td>
+            <td class="headerCovTableEntry">86</td>
+            <td class="headerCovTableEntry">84</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryMed">88.9&nbsp;%</td>
+            <td class="headerCovTableEntry">9</td>
+            <td class="headerCovTableEntry">8</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><a href="Proposal.cc.func-c.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L16">_ZN8bayesnet8Proposal10checkInputERKN2at6TensorES4_</a></td>
+              
+              <td class="coverFnHi">114</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L77">_ZN8bayesnet8Proposal24fit_local_discretizationB5cxx11ERKN2at6TensorE</a></td>
+              
+              <td class="coverFnHi">116</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L25">_ZN8bayesnet8Proposal27localDiscretizationProposalERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIiSaIiEESt4lessIS7_ESaISt4pairIKS7_SA_EEERNS_7NetworkE</a></td>
+              
+              <td class="coverFnHi">106</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L104">_ZN8bayesnet8Proposal8prepareXERN2at6TensorE</a></td>
+              
+              <td class="coverFnHi">84</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L9">_ZN8bayesnet8ProposalC2ERN2at6TensorERSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISA_EERSA_</a></td>
+              
+              <td class="coverFnHi">212</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L10">_ZN8bayesnet8ProposalD0Ev</a></td>
+              
+              <td class="coverFnHi">100</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="Proposal.cc.gcov.html#L10">_ZN8bayesnet8ProposalD0Ev</a></td>
+              
+              <td class="coverFnAliasLo">0</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFnAlias"><a href="Proposal.cc.gcov.html#L10">_ZN8bayesnet8ProposalD2Ev</a></td>
+              
+              <td class="coverFnAliasHi">100</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L47">_ZZN8bayesnet8Proposal27localDiscretizationProposalERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIiSaIiEESt4lessIS7_ESaISt4pairIKS7_SA_EEERNS_7NetworkEENKUlRKT_E0_clIS7_EEDaSO_</a></td>
+              
+              <td class="coverFnHi">686</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L41">_ZZN8bayesnet8Proposal27localDiscretizationProposalERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIiSaIiEESt4lessIS7_ESaISt4pairIKS7_SA_EEERNS_7NetworkEENKUlRKT_E_clIPNS_4NodeEEEDaSO_</a></td>
+              
+              <td class="coverFnHi">1348</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/Proposal.cc.gcov.html

@@ -0,0 +1,192 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/Proposal.cc</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - Proposal.cc<span style="font-size: 80%;"> (source / <a href="Proposal.cc.func-c.html">functions</a>)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">97.7&nbsp;%</td>
+            <td class="headerCovTableEntry">86</td>
+            <td class="headerCovTableEntry">84</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryMed">88.9&nbsp;%</td>
+            <td class="headerCovTableEntry">9</td>
+            <td class="headerCovTableEntry">8</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <table cellpadding=0 cellspacing=0 border=0>
+            <tr>
+              <td><br></td>
+            </tr>
+            <tr>
+              <td>
+<pre class="sourceHeading">            Line data    Source code</pre>
+<pre class="source">
+<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
+<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
+<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
+<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
+<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
+<span id="L6"><span class="lineNum">       6</span>              : </span>
+<span id="L7"><span class="lineNum">       7</span>              : #include &lt;ArffFiles.h&gt;</span>
+<span id="L8"><span class="lineNum">       8</span>              : #include &quot;Proposal.h&quot;</span>
+<span id="L9"><span class="lineNum">       9</span>              : </span>
+<span id="L10"><span class="lineNum">      10</span>              : namespace bayesnet {</span>
+<span id="L11"><span class="lineNum">      11</span> <span class="tlaGNC tlaBgGNC">         212 :     Proposal::Proposal(torch::Tensor&amp; dataset_, std::vector&lt;std::string&gt;&amp; features_, std::string&amp; className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}</span></span>
+<span id="L12"><span class="lineNum">      12</span> <span class="tlaGNC">         100 :     Proposal::~Proposal()</span></span>
+<span id="L13"><span class="lineNum">      13</span>              :     {</span>
+<span id="L14"><span class="lineNum">      14</span> <span class="tlaGNC">         948 :         for (auto&amp; [key, value] : discretizers) {</span></span>
+<span id="L15"><span class="lineNum">      15</span> <span class="tlaGNC">         848 :             delete value;</span></span>
+<span id="L16"><span class="lineNum">      16</span>              :         }</span>
+<span id="L17"><span class="lineNum">      17</span> <span class="tlaGNC">         100 :     }</span></span>
+<span id="L18"><span class="lineNum">      18</span> <span class="tlaGNC">         114 :     void Proposal::checkInput(const torch::Tensor&amp; X, const torch::Tensor&amp; y)</span></span>
+<span id="L19"><span class="lineNum">      19</span>              :     {</span>
+<span id="L20"><span class="lineNum">      20</span> <span class="tlaGNC">         114 :         if (!torch::is_floating_point(X)) {</span></span>
+<span id="L21"><span class="lineNum">      21</span> <span class="tlaUNC tlaBgUNC">           0 :             throw std::invalid_argument(&quot;X must be a floating point tensor&quot;);</span></span>
+<span id="L22"><span class="lineNum">      22</span>              :         }</span>
+<span id="L23"><span class="lineNum">      23</span> <span class="tlaGNC tlaBgGNC">         114 :         if (torch::is_floating_point(y)) {</span></span>
+<span id="L24"><span class="lineNum">      24</span> <span class="tlaUNC tlaBgUNC">           0 :             throw std::invalid_argument(&quot;y must be an integer tensor&quot;);</span></span>
+<span id="L25"><span class="lineNum">      25</span>              :         }</span>
+<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC tlaBgGNC">         114 :     }</span></span>
+<span id="L27"><span class="lineNum">      27</span> <span class="tlaGNC">         106 :     map&lt;std::string, std::vector&lt;int&gt;&gt; Proposal::localDiscretizationProposal(const map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; oldStates, Network&amp; model)</span></span>
+<span id="L28"><span class="lineNum">      28</span>              :     {</span>
+<span id="L29"><span class="lineNum">      29</span>              :         // order of local discretization is important. no good 0, 1, 2...</span>
+<span id="L30"><span class="lineNum">      30</span>              :         // although we rediscretize features after the local discretization of every feature</span>
+<span id="L31"><span class="lineNum">      31</span> <span class="tlaGNC">         106 :         auto order = model.topological_sort();</span></span>
+<span id="L32"><span class="lineNum">      32</span> <span class="tlaGNC">         106 :         auto&amp; nodes = model.getNodes();</span></span>
+<span id="L33"><span class="lineNum">      33</span> <span class="tlaGNC">         106 :         map&lt;std::string, std::vector&lt;int&gt;&gt; states = oldStates;</span></span>
+<span id="L34"><span class="lineNum">      34</span> <span class="tlaGNC">         106 :         std::vector&lt;int&gt; indicesToReDiscretize;</span></span>
+<span id="L35"><span class="lineNum">      35</span> <span class="tlaGNC">         106 :         bool upgrade = false; // Flag to check if we need to upgrade the model</span></span>
+<span id="L36"><span class="lineNum">      36</span> <span class="tlaGNC">         888 :         for (auto feature : order) {</span></span>
+<span id="L37"><span class="lineNum">      37</span> <span class="tlaGNC">         782 :             auto nodeParents = nodes[feature]-&gt;getParents();</span></span>
+<span id="L38"><span class="lineNum">      38</span> <span class="tlaGNC">         782 :             if (nodeParents.size() &lt; 2) continue; // Only has class as parent</span></span>
+<span id="L39"><span class="lineNum">      39</span> <span class="tlaGNC">         662 :             upgrade = true;</span></span>
+<span id="L40"><span class="lineNum">      40</span> <span class="tlaGNC">         662 :             int index = find(pFeatures.begin(), pFeatures.end(), feature) - pFeatures.begin();</span></span>
+<span id="L41"><span class="lineNum">      41</span> <span class="tlaGNC">         662 :             indicesToReDiscretize.push_back(index); // We need to re-discretize this feature</span></span>
+<span id="L42"><span class="lineNum">      42</span> <span class="tlaGNC">         662 :             std::vector&lt;std::string&gt; parents;</span></span>
+<span id="L43"><span class="lineNum">      43</span> <span class="tlaGNC">        2010 :             transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](const auto&amp; p) { return p-&gt;getName(); });</span></span>
+<span id="L44"><span class="lineNum">      44</span>              :             // Remove class as parent as it will be added later</span>
+<span id="L45"><span class="lineNum">      45</span> <span class="tlaGNC">         662 :             parents.erase(remove(parents.begin(), parents.end(), pClassName), parents.end());</span></span>
+<span id="L46"><span class="lineNum">      46</span>              :             // Get the indices of the parents</span>
+<span id="L47"><span class="lineNum">      47</span> <span class="tlaGNC">         662 :             std::vector&lt;int&gt; indices;</span></span>
+<span id="L48"><span class="lineNum">      48</span> <span class="tlaGNC">         662 :             indices.push_back(-1); // Add class index</span></span>
+<span id="L49"><span class="lineNum">      49</span> <span class="tlaGNC">        1348 :             transform(parents.begin(), parents.end(), back_inserter(indices), [&amp;](const auto&amp; p) {return find(pFeatures.begin(), pFeatures.end(), p) - pFeatures.begin(); });</span></span>
+<span id="L50"><span class="lineNum">      50</span>              :             // Now we fit the discretizer of the feature, conditioned on its parents and the class i.e. discretizer.fit(X[index], X[indices] + y)</span>
+<span id="L51"><span class="lineNum">      51</span> <span class="tlaGNC">         662 :             std::vector&lt;std::string&gt; yJoinParents(Xf.size(1));</span></span>
+<span id="L52"><span class="lineNum">      52</span> <span class="tlaGNC">        2010 :             for (auto idx : indices) {</span></span>
+<span id="L53"><span class="lineNum">      53</span> <span class="tlaGNC">      479320 :                 for (int i = 0; i &lt; Xf.size(1); ++i) {</span></span>
+<span id="L54"><span class="lineNum">      54</span> <span class="tlaGNC">     1433916 :                     yJoinParents[i] += to_string(pDataset.index({ idx, i }).item&lt;int&gt;());</span></span>
+<span id="L55"><span class="lineNum">      55</span>              :                 }</span>
+<span id="L56"><span class="lineNum">      56</span>              :             }</span>
+<span id="L57"><span class="lineNum">      57</span> <span class="tlaGNC">         662 :             auto arff = ArffFiles();</span></span>
+<span id="L58"><span class="lineNum">      58</span> <span class="tlaGNC">         662 :             auto yxv = arff.factorize(yJoinParents);</span></span>
+<span id="L59"><span class="lineNum">      59</span> <span class="tlaGNC">        1324 :             auto xvf_ptr = Xf.index({ index }).data_ptr&lt;float&gt;();</span></span>
+<span id="L60"><span class="lineNum">      60</span> <span class="tlaGNC">         662 :             auto xvf = std::vector&lt;mdlp::precision_t&gt;(xvf_ptr, xvf_ptr + Xf.size(1));</span></span>
+<span id="L61"><span class="lineNum">      61</span> <span class="tlaGNC">         662 :             discretizers[feature]-&gt;fit(xvf, yxv);</span></span>
+<span id="L62"><span class="lineNum">      62</span> <span class="tlaGNC">         902 :         }</span></span>
+<span id="L63"><span class="lineNum">      63</span> <span class="tlaGNC">         106 :         if (upgrade) {</span></span>
+<span id="L64"><span class="lineNum">      64</span>              :             // Discretize again X (only the affected indices) with the new fitted discretizers</span>
+<span id="L65"><span class="lineNum">      65</span> <span class="tlaGNC">         768 :             for (auto index : indicesToReDiscretize) {</span></span>
+<span id="L66"><span class="lineNum">      66</span> <span class="tlaGNC">        1324 :                 auto Xt_ptr = Xf.index({ index }).data_ptr&lt;float&gt;();</span></span>
+<span id="L67"><span class="lineNum">      67</span> <span class="tlaGNC">         662 :                 auto Xt = std::vector&lt;float&gt;(Xt_ptr, Xt_ptr + Xf.size(1));</span></span>
+<span id="L68"><span class="lineNum">      68</span> <span class="tlaGNC">        2648 :                 pDataset.index_put_({ index, &quot;...&quot; }, torch::tensor(discretizers[pFeatures[index]]-&gt;transform(Xt)));</span></span>
+<span id="L69"><span class="lineNum">      69</span> <span class="tlaGNC">         662 :                 auto xStates = std::vector&lt;int&gt;(discretizers[pFeatures[index]]-&gt;getCutPoints().size() + 1);</span></span>
+<span id="L70"><span class="lineNum">      70</span> <span class="tlaGNC">         662 :                 iota(xStates.begin(), xStates.end(), 0);</span></span>
+<span id="L71"><span class="lineNum">      71</span>              :                 //Update new states of the feature/node</span>
+<span id="L72"><span class="lineNum">      72</span> <span class="tlaGNC">         662 :                 states[pFeatures[index]] = xStates;</span></span>
+<span id="L73"><span class="lineNum">      73</span> <span class="tlaGNC">         662 :             }</span></span>
+<span id="L74"><span class="lineNum">      74</span> <span class="tlaGNC">         106 :             const torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);</span></span>
+<span id="L75"><span class="lineNum">      75</span> <span class="tlaGNC">         106 :             model.fit(pDataset, weights, pFeatures, pClassName, states);</span></span>
+<span id="L76"><span class="lineNum">      76</span> <span class="tlaGNC">         106 :         }</span></span>
+<span id="L77"><span class="lineNum">      77</span> <span class="tlaGNC">         212 :         return states;</span></span>
+<span id="L78"><span class="lineNum">      78</span> <span class="tlaGNC">      480064 :     }</span></span>
+<span id="L79"><span class="lineNum">      79</span> <span class="tlaGNC">         116 :     map&lt;std::string, std::vector&lt;int&gt;&gt; Proposal::fit_local_discretization(const torch::Tensor&amp; y)</span></span>
+<span id="L80"><span class="lineNum">      80</span>              :     {</span>
+<span id="L81"><span class="lineNum">      81</span>              :         // Discretize the continuous input data and build pDataset (Classifier::dataset)</span>
+<span id="L82"><span class="lineNum">      82</span> <span class="tlaGNC">         116 :         int m = Xf.size(1);</span></span>
+<span id="L83"><span class="lineNum">      83</span> <span class="tlaGNC">         116 :         int n = Xf.size(0);</span></span>
+<span id="L84"><span class="lineNum">      84</span> <span class="tlaGNC">         116 :         map&lt;std::string, std::vector&lt;int&gt;&gt; states;</span></span>
+<span id="L85"><span class="lineNum">      85</span> <span class="tlaGNC">         116 :         pDataset = torch::zeros({ n + 1, m }, torch::kInt32);</span></span>
+<span id="L86"><span class="lineNum">      86</span> <span class="tlaGNC">         116 :         auto yv = std::vector&lt;int&gt;(y.data_ptr&lt;int&gt;(), y.data_ptr&lt;int&gt;() + y.size(0));</span></span>
+<span id="L87"><span class="lineNum">      87</span>              :         // discretize input data by feature(row)</span>
+<span id="L88"><span class="lineNum">      88</span> <span class="tlaGNC">         972 :         for (auto i = 0; i &lt; pFeatures.size(); ++i) {</span></span>
+<span id="L89"><span class="lineNum">      89</span> <span class="tlaGNC">         856 :             auto* discretizer = new mdlp::CPPFImdlp();</span></span>
+<span id="L90"><span class="lineNum">      90</span> <span class="tlaGNC">        1712 :             auto Xt_ptr = Xf.index({ i }).data_ptr&lt;float&gt;();</span></span>
+<span id="L91"><span class="lineNum">      91</span> <span class="tlaGNC">         856 :             auto Xt = std::vector&lt;float&gt;(Xt_ptr, Xt_ptr + Xf.size(1));</span></span>
+<span id="L92"><span class="lineNum">      92</span> <span class="tlaGNC">         856 :             discretizer-&gt;fit(Xt, yv);</span></span>
+<span id="L93"><span class="lineNum">      93</span> <span class="tlaGNC">        3424 :             pDataset.index_put_({ i, &quot;...&quot; }, torch::tensor(discretizer-&gt;transform(Xt)));</span></span>
+<span id="L94"><span class="lineNum">      94</span> <span class="tlaGNC">         856 :             auto xStates = std::vector&lt;int&gt;(discretizer-&gt;getCutPoints().size() + 1);</span></span>
+<span id="L95"><span class="lineNum">      95</span> <span class="tlaGNC">         856 :             iota(xStates.begin(), xStates.end(), 0);</span></span>
+<span id="L96"><span class="lineNum">      96</span> <span class="tlaGNC">         856 :             states[pFeatures[i]] = xStates;</span></span>
+<span id="L97"><span class="lineNum">      97</span> <span class="tlaGNC">         856 :             discretizers[pFeatures[i]] = discretizer;</span></span>
+<span id="L98"><span class="lineNum">      98</span> <span class="tlaGNC">         856 :         }</span></span>
+<span id="L99"><span class="lineNum">      99</span> <span class="tlaGNC">         116 :         int n_classes = torch::max(y).item&lt;int&gt;() + 1;</span></span>
+<span id="L100"><span class="lineNum">     100</span> <span class="tlaGNC">         116 :         auto yStates = std::vector&lt;int&gt;(n_classes);</span></span>
+<span id="L101"><span class="lineNum">     101</span> <span class="tlaGNC">         116 :         iota(yStates.begin(), yStates.end(), 0);</span></span>
+<span id="L102"><span class="lineNum">     102</span> <span class="tlaGNC">         116 :         states[pClassName] = yStates;</span></span>
+<span id="L103"><span class="lineNum">     103</span> <span class="tlaGNC">         348 :         pDataset.index_put_({ n, &quot;...&quot; }, y);</span></span>
+<span id="L104"><span class="lineNum">     104</span> <span class="tlaGNC">         232 :         return states;</span></span>
+<span id="L105"><span class="lineNum">     105</span> <span class="tlaGNC">        1944 :     }</span></span>
+<span id="L106"><span class="lineNum">     106</span> <span class="tlaGNC">          84 :     torch::Tensor Proposal::prepareX(torch::Tensor&amp; X)</span></span>
+<span id="L107"><span class="lineNum">     107</span>              :     {</span>
+<span id="L108"><span class="lineNum">     108</span> <span class="tlaGNC">          84 :         auto Xtd = torch::zeros_like(X, torch::kInt32);</span></span>
+<span id="L109"><span class="lineNum">     109</span> <span class="tlaGNC">         688 :         for (int i = 0; i &lt; X.size(0); ++i) {</span></span>
+<span id="L110"><span class="lineNum">     110</span> <span class="tlaGNC">         604 :             auto Xt = std::vector&lt;float&gt;(X[i].data_ptr&lt;float&gt;(), X[i].data_ptr&lt;float&gt;() + X.size(1));</span></span>
+<span id="L111"><span class="lineNum">     111</span> <span class="tlaGNC">         604 :             auto Xd = discretizers[pFeatures[i]]-&gt;transform(Xt);</span></span>
+<span id="L112"><span class="lineNum">     112</span> <span class="tlaGNC">        1812 :             Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));</span></span>
+<span id="L113"><span class="lineNum">     113</span> <span class="tlaGNC">         604 :         }</span></span>
+<span id="L114"><span class="lineNum">     114</span> <span class="tlaGNC">          84 :         return Xtd;</span></span>
+<span id="L115"><span class="lineNum">     115</span> <span class="tlaGNC">         604 :     }</span></span>
+<span id="L116"><span class="lineNum">     116</span>              : }</span>
+        </pre>
+              </td>
+            </tr>
+          </table>
+          <br>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>

html/BayesNet/bayesnet/classifiers/SPODE.cc.func-c.html

@@ -0,0 +1,96 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+<html lang="en">
+
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - coverage.info - BayesNet/bayesnet/classifiers/SPODE.cc - functions</title>
+  <link rel="stylesheet" type="text/css" href="../../../gcov.css">
+</head>
+
+<body>
+
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="title">LCOV - code coverage report</td></tr>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+
+            <tr>
+              <td width="100%">
+                <table cellpadding=1 border=0 width="100%">
+          <tr>
+            <td width="10%" class="headerItem">Current view:</td>
+            <td width="10%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">BayesNet/bayesnet/classifiers</a> - SPODE.cc<span style="font-size: 80%;"> (<a href="SPODE.cc.gcov.html">source</a> / functions)</span></td>
+            <td width="5%"></td>
+            <td width="5%"></td>
+            <td width="5%" class="headerCovTableHead">Coverage</td>
+            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
+            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test:</td>
+            <td class="headerValue">coverage.info</td>
+            <td></td>
+            <td class="headerItem">Lines:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">10</td>
+            <td class="headerCovTableEntry">10</td>
+          </tr>
+          <tr>
+            <td class="headerItem">Test Date:</td>
+            <td class="headerValue">2024-04-30 13:17:26</td>
+            <td></td>
+            <td class="headerItem">Functions:</td>
+            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
+            <td class="headerCovTableEntry">3</td>
+            <td class="headerCovTableEntry">3</td>
+          </tr>
+                  <tr><td><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+                </table>
+              </td>
+            </tr>
+
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+          </table>
+
+          <center>
+          <table cellpadding=1 cellspacing=1 border=0>
+            <tr><td><br></td></tr>
+            <tr>
+              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="SPODE.cc.func.html"><img src="../../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
+              
+              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="SPODE.cc.gcov.html#L24">_ZNK8bayesnet5SPODE5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
+              
+              <td class="coverFnHi">34</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="SPODE.cc.gcov.html#L11">_ZN8bayesnet5SPODE10buildModelERKN2at6TensorE</a></td>
+              
+              <td class="coverFnHi">508</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="SPODE.cc.gcov.html#L9">_ZN8bayesnet5SPODEC2Ei</a></td>
+              
+              <td class="coverFnHi">562</td>
+              
+              
+            </tr>
+          </table>
+          <br>
+          </center>
+          <table width="100%" border=0 cellspacing=0 cellpadding=0>
+            <tr><td class="ruler"><img src="../../../glass.png" width=3 height=3 alt=""></td></tr>
+            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
+          </table>
+          <br>
+
+</body>
+</html>