Merge pull request 'Replace git submodule dependencies for vcpg dependencies' (#35) from vcpkg into main

Reviewed-on: #35

.clang-format

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

.clang-uml

@@ -1,4 +1,4 @@
-compilation_database_dir: build_debug
+compilation_database_dir: build_Debug
 output_directory: diagrams
 diagrams:
   BayesNet:

.devcontainer/Dockerfile

@@ -1,6 +1,6 @@
 FROM mcr.microsoft.com/devcontainers/cpp:ubuntu22.04
 
-ARG REINSTALL_CMAKE_VERSION_FROM_SOURCE="3.22.2"
+ARG REINSTALL_CMAKE_VERSION_FROM_SOURCE="3.29.3"
 
 # Optionally install the cmake for vcpkg
 COPY ./reinstall-cmake.sh /tmp/
@@ -23,7 +23,7 @@ RUN add-apt-repository ppa:ubuntu-toolchain-r/test
 RUN apt-get update
 
 # Install GCC 13.1
-RUN apt-get install -y gcc-13 g++-13
+RUN apt-get install -y gcc-13 g++-13 doxygen
 
 # Install lcov 2.1
 RUN wget --quiet https://github.com/linux-test-project/lcov/releases/download/v2.1/lcov-2.1.tar.gz && \

.gitignore

@@ -44,4 +44,5 @@ docs/manual
 docs/man3
 docs/man
 docs/Doxyfile
-
+.cache
+vcpkg_installed

.gitmodules

@@ -1,23 +0,0 @@
-[submodule "lib/mdlp"]
-	path = lib/mdlp
-	url = https://github.com/rmontanana/mdlp
-	main = main
-	update = merge
-[submodule "lib/json"]
-	path = lib/json
-	url = https://github.com/nlohmann/json.git
-    master = master
-	update = merge
-[submodule "lib/folding"]
-	path = lib/folding
-	url = https://github.com/rmontanana/folding
-	main = main
-	update = merge
-[submodule "tests/lib/catch2"]
-	path = tests/lib/catch2
-        url = https://github.com/catchorg/Catch2.git
-	main = main
-	update = merge
-[submodule "tests/lib/Files"]
-	path = tests/lib/Files
-	url = https://github.com/rmontanana/ArffFiles

.vscode/launch.json

@@ -5,7 +5,7 @@
             "type": "lldb",
             "request": "launch",
             "name": "sample",
-            "program": "${workspaceFolder}/build_release/sample/bayesnet_sample",
+            "program": "${workspaceFolder}/sample/build/bayesnet_sample",
             "args": [
                 "${workspaceFolder}/tests/data/glass.arff"
             ]
@@ -14,11 +14,11 @@
             "type": "lldb",
             "request": "launch",
             "name": "test",
-            "program": "${workspaceFolder}/build_debug/tests/TestBayesNet",
+            "program": "${workspaceFolder}/build_Debug/tests/TestBayesNet",
             "args": [
-                "[Node]"
+                "[XBAODE]"
             ],
-            "cwd": "${workspaceFolder}/build_debug/tests"
+            "cwd": "${workspaceFolder}/build_Debug/tests"
         },
         {
             "name": "(gdb) Launch",

CHANGELOG.md

@@ -7,6 +7,44 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [1.1.0] - 2025-04-27
+
+### Internal
+
+- Add changes to .clang-format to ajust to vscode format style thanks to <https://clang-format-configurator.site/>
+- Remove all the dependencies as git submodules and add them as vcpkg dependencies.
+- Fix the dependencies versions for this specific BayesNet version.
+
+## [1.0.7] 2025-03-16
+
+### Added
+
+- A new hyperparameter to the BoostAODE class, *alphablock*, to control the way &alpha; is computed, with the last model or with the ensmble built so far. Default value is *false*.
+- A new hyperparameter to the SPODE class, *parent*, to set the root node of the model. If no value is set the root parameter of the constructor is used.
+- A new hyperparameter to the TAN class, *parent*, to set the root node of the model. If not set the first feature is used as root.
+- A new model named XSPODE, an optimized for speed averaged one dependence estimator.
+- A new model named XSP2DE, an optimized for speed averaged two dependence estimator.
+- A new model named XBAODE, an optimized for speed BoostAODE model.
+- A new model named XBA2DE, an optimized for speed BoostA2DE model.
+
+### Internal
+
+- Optimize ComputeCPT method in the Node class.
+- Add methods getCount and getMaxCount to the CountingSemaphore class, returning the current count and the maximum count of threads respectively.
+
+### Changed
+
+- Hyperparameter *maxTolerance* in the BoostAODE class is now in [1, 6] range (it was in [1, 4] range before).
+
+## [1.0.6] 2024-11-23
+
+### Fixed
+
+- Prevent existing edges to be added to the network in the `add_edge` method.
+- Don't allow to add nodes or edges on already fiited networks.
+- Number of threads spawned
+- Network class tests
+
 ### Added
 
 - Library logo generated with <https://openart.ai> to README.md
@@ -14,14 +52,21 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - *convergence_best* hyperparameter to the BoostAODE class, to control the way the prior accuracy is computed if convergence is set. Default value is *false*.
 - SPnDE model.
 - A2DE model.
+- BoostA2DE model.
 - A2DE & SPnDE tests.
 - Add tests to reach 99% of coverage.
 - Add tests to check the correct version of the mdlp, folding and json libraries.
 - Library documentation generated with Doxygen.
 - Link to documentation in the README.md.
+- Three types of smoothing the Bayesian Network ORIGINAL, LAPLACE and CESTNIK.
 
 ### Internal
 
+- Fixed doxygen optional dependency
+- Add env parallel variable to Makefile
+- Add CountingSemaphore class to manage the number of threads spawned.
+- Ignore CUDA language in CMake CodeCoverage module.
+- Update mdlp library as a git submodule.
 - Create library ShuffleArffFile to limit the number of samples with a parameter and shuffle them.
 - Refactor catch2 library location to test/lib
 - Refactor loadDataset function in tests.
@@ -32,6 +77,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Add a Makefile target (doc) to generate the documentation.
 - Add a Makefile target (doc-install) to install the documentation.
 
+### Libraries versions
+
+- mdlp: 2.0.1
+- Folding: 1.1.0
+- json: 3.11
+- ArffFiles: 1.1.0
+
 ## [1.0.5] 2024-04-20
 
 ### Added

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(BayesNet
-  VERSION 1.0.5.1
+  VERSION 1.1.0
   DESCRIPTION "Bayesian Network and basic classifiers Library."
   HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
   LANGUAGES CXX
@@ -26,7 +26,7 @@ set(CMAKE_EXPORT_COMPILE_COMMANDS          ON)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
 SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
 set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -fno-elide-constructors")
-set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
+set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -Ofast")
 if (NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
     set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fno-default-inline")
 endif()
@@ -41,7 +41,6 @@ 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")
@@ -49,11 +48,12 @@ if (CMAKE_BUILD_TYPE STREQUAL "Debug")
   set(CODE_COVERAGE ON)
 endif (CMAKE_BUILD_TYPE STREQUAL "Debug")
 
-
+get_property(LANGUAGES GLOBAL PROPERTY ENABLED_LANGUAGES)
+message(STATUS "Languages=${LANGUAGES}")
 if (CODE_COVERAGE)
   enable_testing()
   include(CodeCoverage)
-  MESSAGE("Code coverage enabled")
+  MESSAGE(STATUS "Code coverage enabled")
   SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
 endif (CODE_COVERAGE)
 
@@ -63,9 +63,11 @@ endif (ENABLE_CLANG_TIDY)
 
 # External libraries - dependencies of BayesNet
 # ---------------------------------------------
-# include(FetchContent)
+
-add_git_submodule("lib/json")
+find_package(Torch CONFIG REQUIRED)
-add_git_submodule("lib/mdlp")
+find_package(fimdlp CONFIG REQUIRED)
+find_package(nlohmann_json CONFIG REQUIRED)
+find_package(folding CONFIG REQUIRED)
 
 # Subdirectories
 # --------------
@@ -75,8 +77,8 @@ add_subdirectory(bayesnet)
 # Testing
 # -------
 if (ENABLE_TESTING)
-MESSAGE("Testing enabled")
+MESSAGE(STATUS "Testing enabled")
-  add_subdirectory(tests/lib/catch2)
+  find_package(Catch2 CONFIG REQUIRED)
   include(CTest)
   add_subdirectory(tests)
 endif (ENABLE_TESTING)
@@ -93,10 +95,14 @@ install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/bayesnet/config.h DES
 # Documentation
 # -------------
 find_package(Doxygen)
-set(DOC_DIR ${CMAKE_CURRENT_SOURCE_DIR}/docs)
+if (Doxygen_FOUND)
-set(doxyfile_in ${DOC_DIR}/Doxyfile.in)
+  set(DOC_DIR ${CMAKE_CURRENT_SOURCE_DIR}/docs)
-set(doxyfile ${DOC_DIR}/Doxyfile)
+  set(doxyfile_in ${DOC_DIR}/Doxyfile.in)
-configure_file(${doxyfile_in} ${doxyfile} @ONLY)
+  set(doxyfile ${DOC_DIR}/Doxyfile)
-doxygen_add_docs(doxygen
+  configure_file(${doxyfile_in} ${doxyfile} @ONLY)
-    WORKING_DIRECTORY ${DOC_DIR}
+  doxygen_add_docs(doxygen
+      WORKING_DIRECTORY ${DOC_DIR}
     CONFIG_FILE ${doxyfile})
+else (Doxygen_FOUND) 
+  MESSAGE("* Doxygen not found")
+endif (Doxygen_FOUND)

Makefile

@@ -1,6 +1,6 @@
 SHELL := /bin/bash
 .DEFAULT_GOAL := help
-.PHONY: viewcoverage coverage setup help install uninstall diagrams buildr buildd test clean debug release sample updatebadge doc doc-install
+.PHONY: viewcoverage coverage setup help install uninstall diagrams buildr buildd test clean debug release sample updatebadge doc doc-install init clean-test
 
 f_release = build_Release
 f_debug = build_Debug
@@ -12,7 +12,6 @@ plantuml = plantuml
 lcov = lcov
 genhtml = genhtml
 dot = dot
-n_procs = -j 16
 docsrcdir = docs/manual
 mansrcdir = docs/man3
 mandestdir = /usr/local/share/man
@@ -44,7 +43,7 @@ setup: ## Install dependencies for tests and coverage
 	fi
 	@echo "* You should install plantuml & graphviz for the diagrams"
 
-diagrams: ## Create an UML class diagram & depnendency of the project (diagrams/BayesNet.png)
+diagrams: ## Create an UML class diagram & dependency of the project (diagrams/BayesNet.png)
 	@which $(plantuml) || (echo ">>> Please install plantuml"; exit 1)
 	@which $(dot) || (echo ">>> Please install graphviz"; exit 1)
 	@which $(clang-uml) || (echo ">>> Please install clang-uml"; exit 1)
@@ -59,12 +58,12 @@ diagrams: ## Create an UML class diagram & depnendency of the project (diagrams/
 	@$(dot) -Tsvg $(f_debug)/dependency.dot.BayesNet -o $(f_diagrams)/dependency.svg
 
 buildd: ## Build the debug targets
-	cmake --build $(f_debug) -t $(app_targets) $(n_procs)
+	cmake --build $(f_debug) -t $(app_targets) --parallel $(CMAKE_BUILD_PARALLEL_LEVEL)
 
 buildr: ## Build the release targets
-	cmake --build $(f_release) -t $(app_targets) $(n_procs)
+	cmake --build $(f_release) -t $(app_targets) --parallel $(CMAKE_BUILD_PARALLEL_LEVEL)
 
-clean: ## Clean the tests info
+clean-test: ## Clean the tests info
 	@echo ">>> Cleaning Debug BayesNet tests...";
 	$(call ClearTests)
 	@echo ">>> Done";
@@ -80,33 +79,56 @@ install: ## Install library
 	@cmake --install $(f_release) --prefix $(prefix)
 	@echo ">>> Done";
 
+init: ## Initialize the project installing dependencies
+	@echo ">>> Installing dependencies"
+	@vcpkg install
+	@echo ">>> Done";
+
+clean: ## Clean the project
+	@echo ">>> Cleaning the project..."
+	@if test -d build_Debug ; then echo "- Deleting build_Debug folder" ; rm -rf build_Debug; fi
+	@if test -d build_Release ; then echo "- Deleting build_Release folder" ; rm -rf build_Release; fi
+	@if test -f CMakeCache.txt ; then echo "- Deleting CMakeCache.txt"; rm -f CMakeCache.txt; fi
+	@if test -d vcpkg_installed ; then echo "- Deleting vcpkg_installed folder" ; rm -rf vcpkg_installed; fi
+	@$(MAKE) clean-test
+	@echo ">>> Done";
+
 debug: ## Build a debug version of the project
 	@echo ">>> Building Debug BayesNet...";
 	@if [ -d ./$(f_debug) ]; then rm -rf ./$(f_debug); fi
 	@mkdir $(f_debug); 
-	@cmake -S . -B $(f_debug) -D CMAKE_BUILD_TYPE=Debug -D ENABLE_TESTING=ON -D CODE_COVERAGE=ON
+	@cmake -S . -B $(f_debug) -D CMAKE_BUILD_TYPE=Debug -D ENABLE_TESTING=ON -D CODE_COVERAGE=ON -DCMAKE_TOOLCHAIN_FILE=${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake
 	@echo ">>> Done";
 
 release: ## Build a Release version of the project
 	@echo ">>> Building Release BayesNet...";
 	@if [ -d ./$(f_release) ]; then rm -rf ./$(f_release); fi
 	@mkdir $(f_release); 
-	@cmake -S . -B $(f_release) -D CMAKE_BUILD_TYPE=Release
+	@cmake -S . -B $(f_release) -D CMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake
 	@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
+	@cd sample && cmake -B build -S . -D CMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake && \
+	cmake --build build -t bayesnet_sample
 	sample/build/bayesnet_sample $(fname)
-	@echo ">>> Done";	
+	@echo ">>> Done";
+
+fname = "tests/data/iris.arff"
+sample2: ## Build sample2
+	@echo ">>> Building Sample...";
+	@if [ -d ./sample/build ]; then rm -rf ./sample/build; fi
+	@cd sample && cmake -B build -S . -D CMAKE_BUILD_TYPE=Debug && cmake --build build -t bayesnet_sample_xspode
+	sample/build/bayesnet_sample_xspode $(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 tests...";
-	@$(MAKE) clean
+	@$(MAKE) clean-test
-	@cmake --build $(f_debug) -t $(test_targets) $(n_procs)
+	@cmake --build $(f_debug) -t $(test_targets) --parallel $(CMAKE_BUILD_PARALLEL_LEVEL)
 	@for t in $(test_targets); do \
 		echo ">>> Running $$t...";\
 		if [ -f $(f_debug)/tests/$$t ]; then \
@@ -119,13 +141,14 @@ test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximu
 
 coverage: ## Run tests and generate coverage report (build/index.html)
 	@echo ">>> Building tests with coverage..."
-	@which $(lcov) || (echo ">>> Please install lcov"; exit 1)
+	@which $(lcov) || (echo ">>ease install lcov"; exit 1)
 	@if [ ! -f $(f_debug)/tests/coverage.info ] ; then $(MAKE) test ; fi
 	@echo ">>> Building report..."
 	@cd $(f_debug)/tests; \
 	$(lcov) --directory CMakeFiles --capture --demangle-cpp --ignore-errors source,source --output-file coverage.info >/dev/null 2>&1; \
 	$(lcov) --remove coverage.info '/usr/*' --output-file coverage.info >/dev/null 2>&1; \
 	$(lcov) --remove coverage.info 'lib/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'include/*' --output-file coverage.info >/dev/null 2>&1; \
 	$(lcov) --remove coverage.info 'libtorch/*' --output-file coverage.info >/dev/null 2>&1; \
 	$(lcov) --remove coverage.info 'tests/*' --output-file coverage.info >/dev/null 2>&1; \
 	$(lcov) --remove coverage.info 'bayesnet/utils/loguru.*' --ignore-errors unused --output-file coverage.info >/dev/null 2>&1; \
@@ -173,7 +196,7 @@ docdir = ""
 doc-install: ## Install documentation
 	@echo ">>> Installing documentation..."
 	@if [ "$(docdir)" = "" ]; then \
-		echo "docdir parameter has to be set when calling doc-install"; \
+		echo "docdir parameter has to be set when calling doc-install, i.e. docdir=../bayesnet_help"; \
 		exit 1; \
 	fi
 	@if [ ! -d $(docdir) ]; then \

README.md

@@ -2,52 +2,113 @@
 
 ![C++](https://img.shields.io/badge/c++-%2300599C.svg?style=flat&logo=c%2B%2B&logoColor=white)
 [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](<https://opensource.org/licenses/MIT>)
-![Gitea Release](https://img.shields.io/gitea/v/release/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es:3000)
+![Gitea Release](https://img.shields.io/gitea/v/release/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es)
 [![Codacy Badge](https://app.codacy.com/project/badge/Grade/cf3e0ac71d764650b1bf4d8d00d303b1)](https://app.codacy.com/gh/Doctorado-ML/BayesNet/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
 [![Security Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_BayesNet&metric=security_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
 [![Reliability Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_BayesNet&metric=reliability_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
-![Gitea Last Commit](https://img.shields.io/gitea/last-commit/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es:3000&logo=gitea)
+![Gitea Last Commit](https://img.shields.io/gitea/last-commit/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es&logo=gitea)
-[![Coverage Badge](https://img.shields.io/badge/Coverage-97,3%25-green)](html/index.html)
+[![Coverage Badge](https://img.shields.io/badge/Coverage-99,1%25-green)](https://gitea.rmontanana.es/rmontanana/BayesNet)
+[![DOI](https://zenodo.org/badge/667782806.svg)](https://doi.org/10.5281/zenodo.14210344)
 
-Bayesian Network Classifiers using libtorch from scratch
+Bayesian Network Classifiers library
-
-## Dependencies
-
-The only external dependency is [libtorch](https://pytorch.org/cppdocs/installing.html) which can be installed with the following commands:
-
-```bash
-wget https://download.pytorch.org/libtorch/nightly/cpu/libtorch-shared-with-deps-latest.zip
-unzip libtorch-shared-with-deps-latest.zips
-```
 
 ## Setup
 
+### Using the vcpkg library
+
+You can use the library with the vcpkg library manager. In your project you have to add the following files:
+
+#### vcpkg.json
+
+```json
+{
+  "name": "sample-project",
+  "version-string": "0.1.0",
+  "dependencies": [
+    "bayesnet"
+  ]
+}
+```
+
+#### vcpkg-configuration.json
+
+```json
+{
+  "registries": [
+    {
+      "kind": "git",
+      "repository": "https://github.com/rmontanana/vcpkg-stash",
+      "baseline": "393efa4e74e053b6f02c4ab03738c8fe796b28e5",
+      "packages": [
+        "folding",
+        "bayesnet",
+        "arff-files",
+        "fimdlp",
+        "libtorch-bin"
+      ]
+    }
+  ],
+  "default-registry": {
+    "kind": "git",
+    "repository": "https://github.com/microsoft/vcpkg",
+    "baseline": "760bfd0c8d7c89ec640aec4df89418b7c2745605"
+  }
+}
+```
+
+#### CMakeLists.txt
+
+You have to include the following lines in your `CMakeLists.txt` file:
+
+```cmake
+find_package(bayesnet CONFIG REQUIRED)
+
+add_executable(myapp main.cpp)
+
+target_link_libraries(myapp PRIVATE bayesnet::bayesnet)
+```
+
+After that, you can use the `vcpkg` command to install the dependencies:
+
+```bash
+vcpkg install
+```
+
+**Note: In the `sample` folder you can find a sample application that uses the library. You can use it as a reference to create your own application.**
+
+## Playing with the library
+
+The dependencies are managed with [vcpkg](https://vcpkg.io/) and supported by a private vcpkg repository in [https://github.com/rmontanana/vcpkg-stash](https://github.com/rmontanana/vcpkg-stash).
+
 ### Getting the code
 
 ```bash
-git clone --recurse-submodules https://github.com/doctorado-ml/bayesnet
+git clone https://github.com/doctorado-ml/bayesnet
 ```
 
+Once you have the code, you can use the `make` command to build the project. The `Makefile` is used to manage the build process and it will automatically download and install the dependencies.
+
 ### Release
 
 ```bash
-make release
+make init # Install dependencies
-make buildr
+make release # Build the release version
-sudo make install
+make buildr # compile and link the release version
 ```
 
 ### Debug & Tests
 
 ```bash
-make debug
+make init # Install dependencies
-make test
+make debug # Build the debug version
+make test # Run the tests
 ```
 
 ### Coverage
 
 ```bash
-make coverage
+make coverage # Run the tests with coverage
-make viewcoverage
+make viewcoverage # View the coverage report in the browser
 ```
 
 ### Sample app
@@ -71,10 +132,16 @@ make sample fname=tests/data/glass.arff
 
 #### - AODE
 
+#### - A2DE
+
 #### - [BoostAODE](docs/BoostAODE.md)
 
+#### - XBAODE
+
 #### - BoostA2DE
 
+#### - XBA2DE
+
 ### With Local Discretization
 
 #### - TANLd

bayesnet/BaseClassifier.h

@@ -8,17 +8,19 @@
 #include <vector>
 #include <torch/torch.h>
 #include <nlohmann/json.hpp>
+#include "bayesnet/network/Network.h"
+
 namespace bayesnet {
     enum status_t { NORMAL, WARNING, ERROR };
     class BaseClassifier {
     public:
-        // X is nxm std::vector, y is nx1 std::vector
-        virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
-        // X is nxm tensor, y is nx1 tensor
-        virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
-        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
-        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) = 0;
         virtual ~BaseClassifier() = default;
+        // X is nxm std::vector, y is nx1 std::vector
+        virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
+        // X is nxm tensor, y is nx1 tensor
+        virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
         torch::Tensor virtual predict(torch::Tensor& X) = 0;
         std::vector<int> virtual predict(std::vector<std::vector<int >>& X) = 0;
         torch::Tensor virtual predict_proba(torch::Tensor& X) = 0;
@@ -26,8 +28,8 @@ namespace bayesnet {
         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 getNumberOfNodes() const = 0;
-        int virtual getNumberOfEdges()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;
@@ -35,11 +37,13 @@ namespace bayesnet {
         virtual std::string getVersion() = 0;
         std::vector<std::string> virtual topological_order() = 0;
         std::vector<std::string> virtual getNotes() const = 0;
-        std::string virtual dump_cpt()const = 0;
+        std::string virtual dump_cpt() const = 0;
         virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
         std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
     protected:
-        virtual void trainModel(const torch::Tensor& weights) = 0;
+        virtual void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
         std::vector<std::string> validHyperparameters;
+        std::vector<std::string> notes; // Used to store messages occurred during the fit process
+        status_t status = NORMAL;
     };
 }

bayesnet/CMakeLists.txt

@@ -1,5 +1,6 @@
 include_directories(
-    ${BayesNet_SOURCE_DIR}/lib/mdlp
+    ${BayesNet_SOURCE_DIR}/lib/log
+    ${BayesNet_SOURCE_DIR}/lib/mdlp/src
     ${BayesNet_SOURCE_DIR}/lib/folding
     ${BayesNet_SOURCE_DIR}/lib/json/include
     ${BayesNet_SOURCE_DIR}
@@ -9,4 +10,4 @@ include_directories(
 file(GLOB_RECURSE Sources "*.cc")
 
 add_library(BayesNet ${Sources})
-target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}")
+target_link_libraries(BayesNet fimdlp "${TORCH_LIBRARIES}")

bayesnet/classifiers/Classifier.cc

@@ -10,8 +10,7 @@
 
 namespace bayesnet {
     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
-    const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
+    Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
-    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;
@@ -23,7 +22,7 @@ namespace bayesnet {
         metrics = Metrics(dataset, features, className, n_classes);
         model.initialize();
         buildModel(weights);
-        trainModel(weights);
+        trainModel(weights, smoothing);
         fitted = true;
         return *this;
     }
@@ -41,20 +40,20 @@ namespace bayesnet {
             throw std::runtime_error(oss.str());
         }
     }
-    void Classifier::trainModel(const torch::Tensor& weights)
+    void Classifier::trainModel(const torch::Tensor& weights, Smoothing_t smoothing)
     {
-        model.fit(dataset, weights, features, className, states);
+        model.fit(dataset, weights, features, className, states, smoothing);
     }
     // X is nxm where n is the number of features and m the number of samples
-    Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
+    Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         dataset = X;
         buildDataset(y);
         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
-        return build(features, className, states, weights);
+        return build(features, className, states, weights, smoothing);
     }
     // X is nxm where n is the number of features and m the number of samples
-    Classifier& Classifier::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
+    Classifier& Classifier::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kInt32);
         for (int i = 0; i < X.size(); ++i) {
@@ -63,18 +62,18 @@ namespace bayesnet {
         auto ytmp = torch::tensor(y, torch::kInt32);
         buildDataset(ytmp);
         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
-        return build(features, className, states, weights);
+        return build(features, className, states, weights, smoothing);
     }
-    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
+    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         this->dataset = dataset;
         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
-        return build(features, className, states, weights);
+        return build(features, className, states, weights, smoothing);
     }
-    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
+    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         this->dataset = dataset;
-        return build(features, className, states, weights);
+        return build(features, className, states, weights, smoothing);
     }
     void Classifier::checkFitParameters()
     {
@@ -191,4 +190,4 @@ namespace bayesnet {
             throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
         }
     }
-}
+}

bayesnet/classifiers/Classifier.h

@@ -8,7 +8,6 @@
 #define CLASSIFIER_H
 #include <torch/torch.h>
 #include "bayesnet/utils/BayesMetrics.h"
-#include "bayesnet/network/Network.h"
 #include "bayesnet/BaseClassifier.h"
 
 namespace bayesnet {
@@ -16,10 +15,10 @@ namespace bayesnet {
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;
-        Classifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
+        Classifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        Classifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) override;
+        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void addNodes();
         int getNumberOfNodes() const override;
         int getNumberOfEdges() const override;
@@ -47,14 +46,13 @@ namespace bayesnet {
         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 trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void buildDataset(torch::Tensor& y);
+        const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
     private:
-        Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
+        Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
     };
 }
 #endif

bayesnet/classifiers/KDB.cc

@@ -3,7 +3,7 @@
 // SPDX-FileType: SOURCE
 // SPDX-License-Identifier: MIT
 // ***************************************************************
-
+#include "bayesnet/utils/bayesnetUtils.h"
 #include "KDB.h"
 
 namespace bayesnet {

bayesnet/classifiers/KDB.h

@@ -7,15 +7,14 @@
 #ifndef KDB_H
 #define KDB_H
 #include <torch/torch.h>
-#include "bayesnet/utils/bayesnetUtils.h"
 #include "Classifier.h"
 namespace bayesnet {
     class KDB : public Classifier {
     private:
         int k;
         float theta;
-        void add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights);
     protected:
+        void add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights);
         void buildModel(const torch::Tensor& weights) override;
     public:
         explicit KDB(int k, float theta = 0.03);
@@ -24,4 +23,4 @@ namespace bayesnet {
         std::vector<std::string> graph(const std::string& name = "KDB") const override;
     };
 }
-#endif
+#endif

bayesnet/classifiers/KDBLd.cc

@@ -8,7 +8,7 @@
 
 namespace bayesnet {
     KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
-    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         features = features_;
@@ -19,7 +19,7 @@ namespace bayesnet {
         states = fit_local_discretization(y);
         // We have discretized the input data
         // 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network
-        KDB::fit(dataset, features, className, states);
+        KDB::fit(dataset, features, className, states, smoothing);
         states = localDiscretizationProposal(states, model);
         return *this;
     }

bayesnet/classifiers/KDBLd.h

@@ -15,7 +15,7 @@ namespace bayesnet {
     public:
         explicit KDBLd(int k);
         virtual ~KDBLd() = default;
-        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
+        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
         std::vector<std::string> graph(const std::string& name = "KDB") const override;
         torch::Tensor predict(torch::Tensor& X) override;
         static inline std::string version() { return "0.0.1"; };

bayesnet/classifiers/Proposal.cc

@@ -11,7 +11,7 @@ namespace bayesnet {
     Proposal::~Proposal()
     {
         for (auto& [key, value] : discretizers) {
-            delete value;
+            delete value; 
         }
     }
     void Proposal::checkInput(const torch::Tensor& X, const torch::Tensor& y)
@@ -70,7 +70,7 @@ namespace bayesnet {
                 states[pFeatures[index]] = xStates;
             }
             const torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);
-            model.fit(pDataset, weights, pFeatures, pClassName, states);
+            model.fit(pDataset, weights, pFeatures, pClassName, states, Smoothing_t::ORIGINAL);
         }
         return states;
     }
@@ -126,4 +126,4 @@ namespace bayesnet {
         }
         return yy;
     }
-}
+}

bayesnet/classifiers/Proposal.h

@@ -9,7 +9,7 @@
 #include <string>
 #include <map>
 #include <torch/torch.h>
-#include <CPPFImdlp.h>
+#include <fimdlp/CPPFImdlp.h>
 #include "bayesnet/network/Network.h"
 #include "Classifier.h"
 

bayesnet/classifiers/SPODE.cc

@@ -8,14 +8,29 @@
 
 namespace bayesnet {
 
-    SPODE::SPODE(int root) : Classifier(Network()), root(root) {}
+    SPODE::SPODE(int root) : Classifier(Network()), root(root)
+    {
+        validHyperparameters = { "parent" };
+    }
 
+    void SPODE::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("parent")) {
+            root = hyperparameters["parent"];
+            hyperparameters.erase("parent");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
     void SPODE::buildModel(const torch::Tensor& weights)
     {
         // 0. Add all nodes to the model
         addNodes();
         // 1. Add edges from the class node to all other nodes
         // 2. Add edges from the root node to all other nodes
+        if (root >= static_cast<int>(features.size())) {
+            throw std::invalid_argument("The parent node is not in the dataset");
+        }
         for (int i = 0; i < static_cast<int>(features.size()); ++i) {
             model.addEdge(className, features[i]);
             if (i != root) {

bayesnet/classifiers/SPODE.h

@@ -10,14 +10,15 @@
 
 namespace bayesnet {
     class SPODE : public Classifier {
-    private:
-        int root;
-    protected:
-        void buildModel(const torch::Tensor& weights) override;
     public:
         explicit SPODE(int root);
         virtual ~SPODE() = default;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
         std::vector<std::string> graph(const std::string& name = "SPODE") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    private:
+        int root;
     };
 }
 #endif

bayesnet/classifiers/SPODELd.cc

@@ -8,25 +8,25 @@
 
 namespace bayesnet {
     SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
-    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         Xf = X_;
         y = y_;
-        return commonFit(features_, className_, states_);
+        return commonFit(features_, className_, states_, smoothing);
     }
 
-    SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         if (!torch::is_floating_point(dataset)) {
             throw std::runtime_error("Dataset must be a floating point tensor");
         }
         Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
         y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);
-        return commonFit(features_, className_, states_);
+        return commonFit(features_, className_, states_, smoothing);
     }
 
-    SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         features = features_;
         className = className_;
@@ -34,7 +34,7 @@ namespace bayesnet {
         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);
+        SPODE::fit(dataset, features, className, states, smoothing);
         states = localDiscretizationProposal(states, model);
         return *this;
     }

bayesnet/classifiers/SPODELd.h

@@ -14,10 +14,10 @@ namespace bayesnet {
     public:
         explicit SPODELd(int root);
         virtual ~SPODELd() = default;
-        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
+        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        SPODELd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
+        SPODELd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states);
+        SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
-        std::vector<std::string> graph(const std::string& name = "SPODE") const override;
+        std::vector<std::string> graph(const std::string& name = "SPODELd") const override;
         torch::Tensor predict(torch::Tensor& X) override;
         static inline std::string version() { return "0.0.1"; };
     };

bayesnet/classifiers/SPnDE.cc

@@ -35,4 +35,4 @@ namespace bayesnet {
         return model.graph(name);
     }
 
-}
+}

bayesnet/classifiers/TAN.cc

@@ -7,8 +7,20 @@
 #include "TAN.h"
 
 namespace bayesnet {
-    TAN::TAN() : Classifier(Network()) {}
+    TAN::TAN() : Classifier(Network())
+    {
+        validHyperparameters = { "parent" };
+    }
 
+    void TAN::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("parent")) {
+            parent = hyperparameters["parent"];
+            hyperparameters.erase("parent");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
     void TAN::buildModel(const torch::Tensor& weights)
     {
         // 0. Add all nodes to the model
@@ -23,7 +35,10 @@ namespace bayesnet {
             mi.push_back({ i, mi_value });
         }
         sort(mi.begin(), mi.end(), [](const auto& left, const auto& right) {return left.second < right.second;});
-        auto root = mi[mi.size() - 1].first;
+        auto root = parent == -1 ? mi[mi.size() - 1].first : parent;
+        if (root >= static_cast<int>(features.size())) {
+            throw std::invalid_argument("The parent node is not in the dataset");
+        }
         // 2. Compute mutual information between each feature and the class
         auto weights_matrix = metrics.conditionalEdge(weights);
         // 3. Compute the maximum spanning tree

bayesnet/classifiers/TAN.h

@@ -9,13 +9,15 @@
 #include "Classifier.h"
 namespace bayesnet {
     class TAN : public Classifier {
-    private:
-    protected:
-        void buildModel(const torch::Tensor& weights) override;
     public:
         TAN();
         virtual ~TAN() = default;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
         std::vector<std::string> graph(const std::string& name = "TAN") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    private:
+        int parent = -1;
     };
 }
 #endif

bayesnet/classifiers/TANLd.cc

@@ -8,7 +8,7 @@
 
 namespace bayesnet {
     TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
-    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         features = features_;
@@ -19,7 +19,7 @@ namespace bayesnet {
         states = fit_local_discretization(y);
         // We have discretized the input data
         // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
-        TAN::fit(dataset, features, className, states);
+        TAN::fit(dataset, features, className, states, smoothing);
         states = localDiscretizationProposal(states, model);
         return *this;
 

bayesnet/classifiers/TANLd.h

@@ -15,10 +15,9 @@ namespace bayesnet {
     public:
         TANLd();
         virtual ~TANLd() = default;
-        TANLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
+        TANLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        std::vector<std::string> graph(const std::string& name = "TAN") const override;
+        std::vector<std::string> graph(const std::string& name = "TANLd") const override;
         torch::Tensor predict(torch::Tensor& X) override;
-        static inline std::string version() { return "0.0.1"; };
     };
 }
 #endif // !TANLD_H

bayesnet/classifiers/XSP2DE.cc

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

bayesnet/classifiers/XSP2DE.h

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

bayesnet/classifiers/XSPODE.cc

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

bayesnet/classifiers/XSPODE.h

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

bayesnet/ensembles/AODELd.cc

@@ -10,7 +10,7 @@ namespace bayesnet {
     AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
     {
     }
-    AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         features = features_;
@@ -20,8 +20,9 @@ namespace bayesnet {
         // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
         states = fit_local_discretization(y);
         // We have discretized the input data
-        // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
+        // 1st we need to fit the model to build the normal AODE structure, Ensemble::fit  
-        Ensemble::fit(dataset, features, className, states);
+        // calls buildModel to initialize the base models
+        Ensemble::fit(dataset, features, className, states, smoothing);
         return *this;
 
     }
@@ -34,10 +35,10 @@ namespace bayesnet {
         n_models = models.size();
         significanceModels = std::vector<double>(n_models, 1.0);
     }
-    void AODELd::trainModel(const torch::Tensor& weights)
+    void AODELd::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         for (const auto& model : models) {
-            model->fit(Xf, y, features, className, states);
+            model->fit(Xf, y, features, className, states, smoothing);
         }
     }
     std::vector<std::string> AODELd::graph(const std::string& name) const

bayesnet/ensembles/AODELd.h

@@ -15,10 +15,10 @@ namespace bayesnet {
     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;
+        AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing) override;
         std::vector<std::string> graph(const std::string& name = "AODELd") const override;
     protected:
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void buildModel(const torch::Tensor& weights) override;
     };
 }

bayesnet/ensembles/Boost.cc

@@ -3,244 +3,266 @@
 // SPDX-FileType: SOURCE
 // SPDX-License-Identifier: MIT
 // ***************************************************************
-#include <folding.hpp>
+#include "Boost.h"
 #include "bayesnet/feature_selection/CFS.h"
 #include "bayesnet/feature_selection/FCBF.h"
 #include "bayesnet/feature_selection/IWSS.h"
-#include "Boost.h"
+#include <folding.hpp>
 
 namespace bayesnet {
-    Boost::Boost(bool predict_voting) : Ensemble(predict_voting)
+Boost::Boost(bool predict_voting) : Ensemble(predict_voting) {
-    {
+    validHyperparameters = {"alpha_block", "order",        "convergence",    "convergence_best", "bisection",
-        validHyperparameters = { "order", "convergence", "convergence_best", "bisection", "threshold", "maxTolerance",
+                            "threshold",   "maxTolerance", "predict_voting", "select_features",  "block_update"};
-        "predict_voting", "select_features", "block_update" };
+}
+void Boost::setHyperparameters(const nlohmann::json &hyperparameters_) {
+    auto hyperparameters = hyperparameters_;
+    if (hyperparameters.contains("order")) {
+        std::vector<std::string> algos = {Orders.ASC, Orders.DESC, Orders.RAND};
+        order_algorithm = hyperparameters["order"];
+        if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {
+            throw std::invalid_argument("Invalid order algorithm, valid values [" + Orders.ASC + ", " + Orders.DESC +
+                                        ", " + Orders.RAND + "]");
+        }
+        hyperparameters.erase("order");
     }
-    void Boost::setHyperparameters(const nlohmann::json& hyperparameters_)
+    if (hyperparameters.contains("alpha_block")) {
-    {
+        alpha_block = hyperparameters["alpha_block"];
-        auto hyperparameters = hyperparameters_;
+        hyperparameters.erase("alpha_block");
-        if (hyperparameters.contains("order")) {
-            std::vector<std::string> algos = { Orders.ASC, Orders.DESC, Orders.RAND };
-            order_algorithm = hyperparameters["order"];
-            if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {
-                throw std::invalid_argument("Invalid order algorithm, valid values [" + Orders.ASC + ", " + Orders.DESC + ", " + Orders.RAND + "]");
-            }
-            hyperparameters.erase("order");
-        }
-        if (hyperparameters.contains("convergence")) {
-            convergence = hyperparameters["convergence"];
-            hyperparameters.erase("convergence");
-        }
-        if (hyperparameters.contains("convergence_best")) {
-            convergence_best = hyperparameters["convergence_best"];
-            hyperparameters.erase("convergence_best");
-        }
-        if (hyperparameters.contains("bisection")) {
-            bisection = hyperparameters["bisection"];
-            hyperparameters.erase("bisection");
-        }
-        if (hyperparameters.contains("threshold")) {
-            threshold = hyperparameters["threshold"];
-            hyperparameters.erase("threshold");
-        }
-        if (hyperparameters.contains("maxTolerance")) {
-            maxTolerance = hyperparameters["maxTolerance"];
-            if (maxTolerance < 1 || maxTolerance > 4)
-                throw std::invalid_argument("Invalid maxTolerance value, must be greater in [1, 4]");
-            hyperparameters.erase("maxTolerance");
-        }
-        if (hyperparameters.contains("predict_voting")) {
-            predict_voting = hyperparameters["predict_voting"];
-            hyperparameters.erase("predict_voting");
-        }
-        if (hyperparameters.contains("select_features")) {
-            auto selectedAlgorithm = hyperparameters["select_features"];
-            std::vector<std::string> algos = { SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF };
-            selectFeatures = true;
-            select_features_algorithm = selectedAlgorithm;
-            if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
-                throw std::invalid_argument("Invalid selectFeatures value, valid values [" + SelectFeatures.IWSS + ", " + SelectFeatures.CFS + ", " + SelectFeatures.FCBF + "]");
-            }
-            hyperparameters.erase("select_features");
-        }
-        if (hyperparameters.contains("block_update")) {
-            block_update = hyperparameters["block_update"];
-            hyperparameters.erase("block_update");
-        }
-        Classifier::setHyperparameters(hyperparameters);
     }
-    void Boost::buildModel(const torch::Tensor& weights)
+    if (hyperparameters.contains("convergence")) {
-    {
+        convergence = hyperparameters["convergence"];
-        // Models shall be built in trainModel
+        hyperparameters.erase("convergence");
-        models.clear();
-        significanceModels.clear();
-        n_models = 0;
-        // Prepare the validation dataset
-        auto y_ = dataset.index({ -1, "..." });
-        if (convergence) {
-            // Prepare train & validation sets from train data
-            auto fold = folding::StratifiedKFold(5, y_, 271);
-            auto [train, test] = fold.getFold(0);
-            auto train_t = torch::tensor(train);
-            auto test_t = torch::tensor(test);
-            // Get train and validation sets
-            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
-            y_train = dataset.index({ -1, train_t });
-            X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
-            y_test = dataset.index({ -1, test_t });
-            dataset = X_train;
-            m = X_train.size(1);
-            auto n_classes = states.at(className).size();
-            // Build dataset with train data
-            buildDataset(y_train);
-            metrics = Metrics(dataset, features, className, n_classes);
-        } else {
-            // Use all data to train
-            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
-            y_train = y_;
-        }
     }
-    std::vector<int> Boost::featureSelection(torch::Tensor& weights_)
+    if (hyperparameters.contains("convergence_best")) {
-    {
+        convergence_best = hyperparameters["convergence_best"];
-        int maxFeatures = 0;
+        hyperparameters.erase("convergence_best");
-        if (select_features_algorithm == SelectFeatures.CFS) {
-            featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);
-        } else if (select_features_algorithm == SelectFeatures.IWSS) {
-            if (threshold < 0 || threshold >0.5) {
-                throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.IWSS + " [0, 0.5]");
-            }
-            featureSelector = new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
-        } else if (select_features_algorithm == SelectFeatures.FCBF) {
-            if (threshold < 1e-7 || threshold > 1) {
-                throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.FCBF + " [1e-7, 1]");
-            }
-            featureSelector = new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
-        }
-        featureSelector->fit();
-        auto featuresUsed = featureSelector->getFeatures();
-        delete featureSelector;
-        return featuresUsed;
     }
-    std::tuple<torch::Tensor&, double, bool> Boost::update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights)
+    if (hyperparameters.contains("bisection")) {
-    {
+        bisection = hyperparameters["bisection"];
-        bool terminate = false;
+        hyperparameters.erase("bisection");
-        double alpha_t = 0;
-        auto mask_wrong = ypred != ytrain;
-        auto mask_right = ypred == ytrain;
-        auto masked_weights = weights * mask_wrong.to(weights.dtype());
-        double epsilon_t = masked_weights.sum().item<double>();
-        if (epsilon_t > 0.5) {
-            // Inverse the weights policy (plot ln(wt))
-            // "In each round of AdaBoost, there is a sanity check to ensure that the current base 
-            // learner is better than random guess" (Zhi-Hua Zhou, 2012)
-            terminate = true;
-        } else {
-            double wt = (1 - epsilon_t) / epsilon_t;
-            alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
-            // Step 3.2: Update weights for next classifier
-            // Step 3.2.1: Update weights of wrong samples
-            weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;
-            // Step 3.2.2: Update weights of right samples
-            weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;
-            // Step 3.3: Normalise the weights
-            double totalWeights = torch::sum(weights).item<double>();
-            weights = weights / totalWeights;
-        }
-        return { weights, alpha_t, terminate };
     }
-    std::tuple<torch::Tensor&, double, bool> Boost::update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights)
+    if (hyperparameters.contains("threshold")) {
-    {
+        threshold = hyperparameters["threshold"];
-        /* Update Block algorithm
+        hyperparameters.erase("threshold");
-            k = # of models in block
+    }
-            n_models = # of models in ensemble to make predictions
+    if (hyperparameters.contains("maxTolerance")) {
-            n_models_bak = # models saved
+        maxTolerance = hyperparameters["maxTolerance"];
-            models = vector of models to make predictions
+        if (maxTolerance < 1 || maxTolerance > 6)
-            models_bak = models not used to make predictions
+            throw std::invalid_argument("Invalid maxTolerance value, must be greater in [1, 6]");
-            significances_bak = backup of significances vector
+        hyperparameters.erase("maxTolerance");
+    }
+    if (hyperparameters.contains("predict_voting")) {
+        predict_voting = hyperparameters["predict_voting"];
+        hyperparameters.erase("predict_voting");
+    }
+    if (hyperparameters.contains("select_features")) {
+        auto selectedAlgorithm = hyperparameters["select_features"];
+        std::vector<std::string> algos = {SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF};
+        selectFeatures = true;
+        select_features_algorithm = selectedAlgorithm;
+        if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
+            throw std::invalid_argument("Invalid selectFeatures value, valid values [" + SelectFeatures.IWSS + ", " +
+                                        SelectFeatures.CFS + ", " + SelectFeatures.FCBF + "]");
+        }
+        hyperparameters.erase("select_features");
+    }
+    if (hyperparameters.contains("block_update")) {
+        block_update = hyperparameters["block_update"];
+        hyperparameters.erase("block_update");
+    }
+    if (block_update && alpha_block) {
+        throw std::invalid_argument("alpha_block and block_update cannot be true at the same time");
+    }
+    if (block_update && !bisection) {
+        throw std::invalid_argument("block_update needs bisection to be true");
+    }
+    Classifier::setHyperparameters(hyperparameters);
+}
+void Boost::add_model(std::unique_ptr<Classifier> model, double significance) {
+    models.push_back(std::move(model));
+    n_models++;
+    significanceModels.push_back(significance);
+}
+void Boost::remove_last_model() {
+    models.pop_back();
+    significanceModels.pop_back();
+    n_models--;
+}
+void Boost::buildModel(const torch::Tensor &weights) {
+    // Models shall be built in trainModel
+    models.clear();
+    significanceModels.clear();
+    n_models = 0;
+    // Prepare the validation dataset
+    auto y_ = dataset.index({-1, "..."});
+    if (convergence) {
+        // Prepare train & validation sets from train data
+        auto fold = folding::StratifiedKFold(5, y_, 271);
+        auto [train, test] = fold.getFold(0);
+        auto train_t = torch::tensor(train);
+        auto test_t = torch::tensor(test);
+        // Get train and validation sets
+        X_train = dataset.index({torch::indexing::Slice(0, dataset.size(0) - 1), train_t});
+        y_train = dataset.index({-1, train_t});
+        X_test = dataset.index({torch::indexing::Slice(0, dataset.size(0) - 1), test_t});
+        y_test = dataset.index({-1, test_t});
+        dataset = X_train;
+        m = X_train.size(1);
+        auto n_classes = states.at(className).size();
+        // Build dataset with train data
+        buildDataset(y_train);
+        metrics = Metrics(dataset, features, className, n_classes);
+    } else {
+        // Use all data to train
+        X_train = dataset.index({torch::indexing::Slice(0, dataset.size(0) - 1), "..."});
+        y_train = y_;
+    }
+}
+std::vector<int> Boost::featureSelection(torch::Tensor &weights_) {
+    int maxFeatures = 0;
+    if (select_features_algorithm == SelectFeatures.CFS) {
+        featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);
+    } else if (select_features_algorithm == SelectFeatures.IWSS) {
+        if (threshold < 0 || threshold > 0.5) {
+            throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.IWSS + " [0, 0.5]");
+        }
+        featureSelector =
+            new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
+    } else if (select_features_algorithm == SelectFeatures.FCBF) {
+        if (threshold < 1e-7 || threshold > 1) {
+            throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.FCBF + " [1e-7, 1]");
+        }
+        featureSelector =
+            new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
+    }
+    featureSelector->fit();
+    auto featuresUsed = featureSelector->getFeatures();
+    delete featureSelector;
+    return featuresUsed;
+}
+std::tuple<torch::Tensor &, double, bool> Boost::update_weights(torch::Tensor &ytrain, torch::Tensor &ypred,
+                                                                torch::Tensor &weights) {
+    bool terminate = false;
+    double alpha_t = 0;
+    auto mask_wrong = ypred != ytrain;
+    auto mask_right = ypred == ytrain;
+    auto masked_weights = weights * mask_wrong.to(weights.dtype());
+    double epsilon_t = masked_weights.sum().item<double>();
+    // std::cout << "epsilon_t: " << epsilon_t << " count wrong: " << mask_wrong.sum().item<int>() << " count right: "
+    // << mask_right.sum().item<int>() << std::endl;
+    if (epsilon_t > 0.5) {
+        // Inverse the weights policy (plot ln(wt))
+        // "In each round of AdaBoost, there is a sanity check to ensure that the current base
+        // learner is better than random guess" (Zhi-Hua Zhou, 2012)
+        terminate = true;
+    } else {
+        double wt = (1 - epsilon_t) / epsilon_t;
+        alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
+        // Step 3.2: Update weights for next classifier
+        // Step 3.2.1: Update weights of wrong samples
+        weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;
+        // Step 3.2.2: Update weights of right samples
+        weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;
+        // Step 3.3: Normalise the weights
+        double totalWeights = torch::sum(weights).item<double>();
+        weights = weights / totalWeights;
+    }
+    return {weights, alpha_t, terminate};
+}
+std::tuple<torch::Tensor &, double, bool> Boost::update_weights_block(int k, torch::Tensor &ytrain,
+                                                                      torch::Tensor &weights) {
+    /* Update Block algorithm
+        k = # of models in block
+        n_models = # of models in ensemble to make predictions
+        n_models_bak = # models saved
+        models = vector of models to make predictions
+        models_bak = models not used to make predictions
+        significances_bak = backup of significances vector
 
-            Case list
+        Case list
-            A) k = 1, n_models = 1		=> n = 0 , n_models = n + k
+        A) k = 1, n_models = 1		=> n = 0 , n_models = n + k
-            B) k = 1, n_models = n + 1	=> 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
+        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
+        D) k > 1, n_models = k		=> n = 0, n_models = n + k
-            E) k > 1, n_models = k + n	=> n_models = n + k
+        E) k > 1, n_models = k + n	=> n_models = n + k
 
-            A, D) n=0, k > 0, n_models == k
+        A, D) n=0, k > 0, n_models == k
-            1. n_models_bak <- n_models
+        1. n_models_bak <- n_models
-            2. significances_bak <- significances
+        2. significances_bak <- significances
-            3. significances = vector(k, 1)
+        3. significances = vector(k, 1)
-            4. Don’t move any classifiers out of models
+        4. Don’t move any classifiers out of models
-            5. n_models <- k
+        5. n_models <- k
-            6. Make prediction, compute alpha, update weights
+        6. Make prediction, compute alpha, update weights
-            7. Don’t restore any classifiers to models
+        7. Don’t restore any classifiers to models
-            8. significances <- significances_bak
+        8. significances <- significances_bak
-            9. Update last k significances
+        9. Update last k significances
-            10. n_models <- n_models_bak
+        10. n_models <- n_models_bak
 
-            B, C, E) n > 0, k > 0, n_models == n + k
+        B, C, E) n > 0, k > 0, n_models == n + k
-            1. n_models_bak <- n_models
+        1. n_models_bak <- n_models
-            2. significances_bak <- significances
+        2. significances_bak <- significances
-            3. significances = vector(k, 1)
+        3. significances = vector(k, 1)
-            4. Move first n classifiers to models_bak
+        4. Move first n classifiers to models_bak
-            5. n_models <- k
+        5. n_models <- k
-            6. Make prediction, compute alpha, update weights
+        6. Make prediction, compute alpha, update weights
-            7. Insert classifiers in models_bak to be the first n models
+        7. Insert classifiers in models_bak to be the first n models
-            8. significances <- significances_bak
+        8. significances <- significances_bak
-            9. Update last k significances
+        9. Update last k significances
-            10. n_models <- n_models_bak
+        10. n_models <- n_models_bak
-        */
+    */
-        //
+    //
-        // Make predict with only the last k models
+    // Make predict with only the last k models
-        //
+    //
-        std::unique_ptr<Classifier> model;
+    std::unique_ptr<Classifier> model;
-        std::vector<std::unique_ptr<Classifier>> models_bak;
+    std::vector<std::unique_ptr<Classifier>> models_bak;
-        // 1. n_models_bak <- n_models 2. significances_bak <- significances
+    // 1. n_models_bak <- n_models 2. significances_bak <- significances
-        auto significance_bak = significanceModels;
+    auto significance_bak = significanceModels;
-        auto n_models_bak = n_models;
+    auto n_models_bak = n_models;
-        // 3. significances = vector(k, 1)
+    // 3. significances = vector(k, 1)
-        significanceModels = std::vector<double>(k, 1.0);
+    significanceModels = std::vector<double>(k, 1.0);
-        // 4. Move first n classifiers to models_bak
+    // 4. Move first n classifiers to models_bak
-        // backup the first n_models - k models (if n_models == k, don't backup any)
+    // backup the first n_models - k models (if n_models == k, don't backup any)
-        for (int i = 0; i < n_models - k; ++i) {
+    for (int i = 0; i < n_models - k; ++i) {
-            model = std::move(models[0]);
+        model = std::move(models[0]);
-            models.erase(models.begin());
+        models.erase(models.begin());
-            models_bak.push_back(std::move(model));
+        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 };
     }
-}
+    assert(models.size() == k);
+    // 5. n_models <- k
+    n_models = k;
+    // 6. Make prediction, compute alpha, update weights
+    auto ypred = predict(X_train);
+    //
+    // Update weights
+    //
+    double alpha_t;
+    bool terminate;
+    std::tie(weights, alpha_t, terminate) = update_weights(y_train, ypred, weights);
+    //
+    // Restore the models if needed
+    //
+    // 7. Insert classifiers in models_bak to be the first n models
+    // if n_models_bak == k, don't restore any, because none of them were moved
+    if (k != n_models_bak) {
+        // Insert in the same order as they were extracted
+        int bak_size = models_bak.size();
+        for (int i = 0; i < bak_size; ++i) {
+            model = std::move(models_bak[bak_size - 1 - i]);
+            models_bak.erase(models_bak.end() - 1);
+            models.insert(models.begin(), std::move(model));
+        }
+    }
+    // 8. significances <- significances_bak
+    significanceModels = significance_bak;
+    //
+    // Update the significance of the last k models
+    //
+    // 9. Update last k significances
+    for (int i = 0; i < k; ++i) {
+        significanceModels[n_models_bak - k + i] = alpha_t;
+    }
+    // 10. n_models <- n_models_bak
+    n_models = n_models_bak;
+    return {weights, alpha_t, terminate};
+}
+} // namespace bayesnet

bayesnet/ensembles/Boost.h

@@ -27,26 +27,31 @@ namespace bayesnet {
     class Boost : public Ensemble {
     public:
         explicit Boost(bool predict_voting = false);
-        virtual ~Boost() = default;
+        virtual ~Boost() override = default;
         void setHyperparameters(const nlohmann::json& hyperparameters_) override;
     protected:
         std::vector<int> featureSelection(torch::Tensor& weights_);
         void buildModel(const torch::Tensor& weights) override;
         std::tuple<torch::Tensor&, double, bool> update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights);
         std::tuple<torch::Tensor&, double, bool> update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights);
+        void add_model(std::unique_ptr<Classifier> model, double significance);
+        void remove_last_model();
+        //
+        // Attributes
+        //
         torch::Tensor X_train, y_train, X_test, y_test;
         // Hyperparameters
         bool bisection = true; // if true, use bisection stratety to add k models at once to the ensemble
         int maxTolerance = 3;
-        std::string order_algorithm; // order to process the KBest features asc, desc, rand
+        std::string order_algorithm = Orders.DESC; // order to process the KBest features asc, desc, rand
         bool convergence = true; //if true, stop when the model does not improve
         bool convergence_best = false; // wether to keep the best accuracy to the moment or the last accuracy as prior accuracy
         bool selectFeatures = false; // if true, use feature selection
-        std::string select_features_algorithm = Orders.DESC; // Selected feature selection algorithm
+        std::string select_features_algorithm; // Selected feature selection algorithm
         FeatureSelect* featureSelector = nullptr;
         double threshold = -1;
-        bool block_update = false;
+        bool block_update = false; // if true, use block update algorithm, only meaningful if bisection is true
-
+        bool alpha_block = false; // if true, the alpha is computed with the ensemble built so far and the new model
     };
 }
-#endif
+#endif

bayesnet/ensembles/BoostA2DE.cc

@@ -4,14 +4,9 @@
 // SPDX-License-Identifier: MIT
 // ***************************************************************
 
-#include <set>
-#include <functional>
 #include <limits.h>
 #include <tuple>
 #include <folding.hpp>
-#include "bayesnet/feature_selection/CFS.h"
-#include "bayesnet/feature_selection/FCBF.h"
-#include "bayesnet/feature_selection/IWSS.h"
 #include "BoostA2DE.h"
 
 namespace bayesnet {
@@ -19,7 +14,7 @@ namespace bayesnet {
     BoostA2DE::BoostA2DE(bool predict_voting) : Boost(predict_voting)
     {
     }
-    std::vector<int> BoostA2DE::initializeModels()
+    std::vector<int> BoostA2DE::initializeModels(const Smoothing_t smoothing)
     {
         torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
         std::vector<int> featuresSelected = featureSelection(weights_);
@@ -32,7 +27,7 @@ namespace bayesnet {
             for (int j = i + 1; j < featuresSelected.size(); j++) {
                 auto parents = { featuresSelected[i], featuresSelected[j] };
                 std::unique_ptr<Classifier> model = std::make_unique<SPnDE>(parents);
-                model->fit(dataset, features, className, states, weights_);
+                model->fit(dataset, features, className, states, weights_, smoothing);
                 models.push_back(std::move(model));
                 significanceModels.push_back(1.0); // They will be updated later in trainModel
                 n_models++;
@@ -41,7 +36,7 @@ namespace bayesnet {
         notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
         return featuresSelected;
     }
-    void BoostA2DE::trainModel(const torch::Tensor& weights)
+    void BoostA2DE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         //
         // Logging setup
@@ -58,7 +53,10 @@ namespace bayesnet {
         bool finished = false;
         std::vector<int> featuresUsed;
         if (selectFeatures) {
-            featuresUsed = initializeModels();
+            featuresUsed = initializeModels(smoothing);
+            if (featuresUsed.size() == 0) {
+                return;
+            }
             auto ypred = predict(X_train);
             std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
             // Update significance of the models
@@ -96,7 +94,7 @@ namespace bayesnet {
                 pairSelection.erase(pairSelection.begin());
                 std::unique_ptr<Classifier> model;
                 model = std::make_unique<SPnDE>(std::vector<int>({ feature_pair.first, feature_pair.second }));
-                model->fit(dataset, features, className, states, weights_);
+                model->fit(dataset, features, className, states, weights_, smoothing);
                 alpha_t = 0.0;
                 if (!block_update) {
                     auto ypred = model->predict(X_train);
@@ -164,4 +162,4 @@ namespace bayesnet {
     {
         return Ensemble::graph(title);
     }
-}
+}

bayesnet/ensembles/BoostA2DE.h

@@ -17,9 +17,9 @@ namespace bayesnet {
         virtual ~BoostA2DE() = default;
         std::vector<std::string> graph(const std::string& title = "BoostA2DE") const override;
     protected:
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
     private:
-        std::vector<int> initializeModels();
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
     };
 }
 #endif

bayesnet/ensembles/BoostAODE.cc

@@ -4,25 +4,27 @@
 // SPDX-License-Identifier: MIT
 // ***************************************************************
 
-#include <random> 
-#include <set>
-#include <functional>
-#include <limits.h>
-#include <tuple>
 #include "BoostAODE.h"
+#include "bayesnet/classifiers/SPODE.h"
+#include <limits.h>
+// #include <loguru.cpp>
+// #include <loguru.hpp>
+#include <random>
+#include <set>
+#include <tuple>
 
 namespace bayesnet {
 
     BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting)
     {
     }
-    std::vector<int> BoostAODE::initializeModels()
+    std::vector<int> BoostAODE::initializeModels(const Smoothing_t smoothing)
     {
         torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
         std::vector<int> featuresSelected = featureSelection(weights_);
         for (const int& feature : featuresSelected) {
             std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
-            model->fit(dataset, features, className, states, weights_);
+            model->fit(dataset, features, className, states, weights_, smoothing);
             models.push_back(std::move(model));
             significanceModels.push_back(1.0); // They will be updated later in trainModel
             n_models++;
@@ -30,7 +32,7 @@ namespace bayesnet {
         notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
         return featuresSelected;
     }
-    void BoostAODE::trainModel(const torch::Tensor& weights)
+    void BoostAODE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         //
         // Logging setup
@@ -46,14 +48,16 @@ namespace bayesnet {
         torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
         bool finished = false;
         std::vector<int> featuresUsed;
+        n_models = 0;
         if (selectFeatures) {
-            featuresUsed = initializeModels();
+            featuresUsed = initializeModels(smoothing);
             auto ypred = predict(X_train);
             std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
             // Update significance of the models
             for (int i = 0; i < n_models; ++i) {
-                significanceModels[i] = alpha_t;
+                significanceModels.push_back(alpha_t);
             }
+            // VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t, n_models);
             if (finished) {
                 return;
             }
@@ -77,10 +81,8 @@ namespace bayesnet {
                 std::shuffle(featureSelection.begin(), featureSelection.end(), g);
             }
             // Remove used features
-            featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x)
+            featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x) { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed); }),
-                { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed);}),
+                end(featureSelection));
-                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());
@@ -89,10 +91,28 @@ namespace bayesnet {
                 featureSelection.erase(featureSelection.begin());
                 std::unique_ptr<Classifier> model;
                 model = std::make_unique<SPODE>(feature);
-                model->fit(dataset, features, className, states, weights_);
+                model->fit(dataset, features, className, states, weights_, smoothing);
                 alpha_t = 0.0;
                 if (!block_update) {
-                    auto ypred = model->predict(X_train);
+                    torch::Tensor ypred;
+                    if (alpha_block) {
+                        //
+                        // Compute the prediction with the current ensemble + model
+                        //
+                        // Add the model to the ensemble
+                        n_models++;
+                        models.push_back(std::move(model));
+                        significanceModels.push_back(1);
+                        // Compute the prediction
+                        ypred = predict(X_train);
+                        // Remove the model from the ensemble
+                        model = std::move(models.back());
+                        models.pop_back();
+                        significanceModels.pop_back();
+                        n_models--;
+                    } else {
+                        ypred = model->predict(X_train);
+                    }
                     // Step 3.1: Compute the classifier amout of say
                     std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
                 }
@@ -102,7 +122,7 @@ namespace bayesnet {
                 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());
+                // VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d featuresUsed: %zu", finished, numItemsPack, n_models, featuresUsed.size());
             }
             if (block_update) {
                 std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
@@ -145,7 +165,7 @@ namespace bayesnet {
                 }
             } 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);
+                // VLG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
             }
         }
         if (featuresUsed.size() != features.size()) {
@@ -158,4 +178,4 @@ namespace bayesnet {
     {
         return Ensemble::graph(title);
     }
-}
+}

bayesnet/ensembles/BoostAODE.h

@@ -8,7 +8,6 @@
 #define BOOSTAODE_H
 #include <string>
 #include <vector>
-#include "bayesnet/classifiers/SPODE.h"
 #include "Boost.h"
 
 namespace bayesnet {
@@ -18,9 +17,9 @@ namespace bayesnet {
         virtual ~BoostAODE() = default;
         std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
     protected:
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
     private:
-        std::vector<int> initializeModels();
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
     };
 }
-#endif
+#endif

bayesnet/ensembles/Ensemble.cc

@@ -3,22 +3,20 @@
 // 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)
+    void Ensemble::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         n_models = models.size();
         for (auto i = 0; i < n_models; ++i) {
             // fit with std::vectors
-            models[i]->fit(dataset, features, className, states);
+            models[i]->fit(dataset, features, className, states, smoothing);
         }
     }
     std::vector<int> Ensemble::compute_arg_max(std::vector<std::vector<double>>& X)
@@ -85,17 +83,10 @@ namespace bayesnet {
     {
         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);
-                auto ypredict = models[i]->predict_proba(X);
+            /*std::cout << "model " << i << " prediction: " << ypredict << " significance " << significanceModels[i] << std::endl;*/
-                std::lock_guard<std::mutex> lock(mtx);
+            y_pred += ypredict * significanceModels[i];
-                y_pred += ypredict * significanceModels[i];
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
         }
         auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
         y_pred /= sum;
@@ -105,23 +96,15 @@ namespace bayesnet {
     {
         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);
-                auto ypredict = models[i]->predict_proba(X);
+            assert(ypredict.size() == y_pred.size());
-                assert(ypredict.size() == y_pred.size());
+            assert(ypredict[0].size() == y_pred[0].size());
-                assert(ypredict[0].size() == y_pred[0].size());
+            // Multiply each prediction by the significance of the model and then add it to the final prediction
-                std::lock_guard<std::mutex> lock(mtx);
+            for (auto j = 0; j < ypredict.size(); ++j) {
-                // Multiply each prediction by the significance of the model and then add it to the final prediction
+                std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),
-                for (auto j = 0; j < ypredict.size(); ++j) {
+                    [significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });
-                    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
@@ -141,17 +124,9 @@ namespace bayesnet {
     {
         // 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);
-                auto ypredict = models[i]->predict(X);
+            y_pred.index_put_({ "...", i }, ypredict);
-                std::lock_guard<std::mutex> lock(mtx);
-                y_pred.index_put_({ "...", i }, ypredict);
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
         }
         return voting(y_pred);
     }
@@ -219,4 +194,4 @@ namespace bayesnet {
         }
         return nstates;
     }
-}
+}

bayesnet/ensembles/Ensemble.h

@@ -33,9 +33,15 @@ namespace bayesnet {
         }
         std::string dump_cpt() const override
         {
-            return "";
+            std::string output;
+            for (auto& model : models) {
+                output += model->dump_cpt();
+                output += std::string(80, '-') + "\n";
+            }
+            return output;
         }
     protected:
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         torch::Tensor predict_average_voting(torch::Tensor& X);
         std::vector<std::vector<double>> predict_average_voting(std::vector<std::vector<int>>& X);
         torch::Tensor predict_average_proba(torch::Tensor& X);
@@ -43,10 +49,10 @@ namespace bayesnet {
         torch::Tensor compute_arg_max(torch::Tensor& X);
         std::vector<int> compute_arg_max(std::vector<std::vector<double>>& X);
         torch::Tensor voting(torch::Tensor& votes);
+        // Attributes
         unsigned n_models;
         std::vector<std::unique_ptr<Classifier>> models;
         std::vector<double> significanceModels;
-        void trainModel(const torch::Tensor& weights) override;
         bool predict_voting;
     };
 }

bayesnet/ensembles/XBA2DE.cc

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

bayesnet/ensembles/XBA2DE.h

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

bayesnet/ensembles/XBAODE.cc

@@ -0,0 +1,184 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#include "XBAODE.h"
+#include "bayesnet/classifiers/XSPODE.h"
+#include "bayesnet/utils/TensorUtils.h"
+#include <limits.h>
+#include <random>
+#include <tuple>
+
+namespace bayesnet
+{
+    XBAODE::XBAODE() : Boost(false) {}
+    std::vector<int> XBAODE::initializeModels(const Smoothing_t smoothing)
+    {
+        torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
+        std::vector<int> featuresSelected = featureSelection(weights_);
+        for (const int &feature : featuresSelected) {
+            std::unique_ptr<Classifier> model = std::make_unique<XSpode>(feature);
+            model->fit(dataset, features, className, states, weights_, smoothing);
+            add_model(std::move(model), 1.0);
+        }
+        notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " +
+                        std::to_string(features.size()) + " with " + select_features_algorithm);
+        return featuresSelected;
+    }
+    void XBAODE::trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing)
+    {
+        X_train_ = TensorUtils::to_matrix(X_train);
+        y_train_ = TensorUtils::to_vector<int>(y_train);
+        if (convergence) {
+            X_test_ = TensorUtils::to_matrix(X_test);
+            y_test_ = TensorUtils::to_vector<int>(y_test);
+        }
+        fitted = true;
+        double alpha_t;
+        torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
+        bool finished = false;
+        std::vector<int> featuresUsed;
+        n_models = 0;
+        if (selectFeatures) {
+            featuresUsed = initializeModels(smoothing);
+            auto ypred = predict(X_train_);
+            auto ypred_t = torch::tensor(ypred);
+            std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
+            // Update significance of the models
+            for (const int &feature : featuresUsed) {
+                significanceModels.pop_back();
+            }
+            for (const int &feature : featuresUsed) {
+                significanceModels.push_back(alpha_t);
+            }
+            // VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t,
+            // n_models);
+            if (finished) {
+                return;
+            }
+        }
+        int numItemsPack = 0; // The counter of the models inserted in the current pack
+        // Variables to control the accuracy finish condition
+        double priorAccuracy = 0.0;
+        double improvement = 1.0;
+        double convergence_threshold = 1e-4;
+        int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
+        // Step 0: Set the finish condition
+        // epsilon sub t > 0.5 => inverse the weights_ policy
+        // validation error is not decreasing
+        // run out of features
+        bool ascending = order_algorithm == bayesnet::Orders.ASC;
+        std::mt19937 g{173};
+        while (!finished) {
+            // Step 1: Build ranking with mutual information
+            auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
+            if (order_algorithm == bayesnet::Orders.RAND) {
+                std::shuffle(featureSelection.begin(), featureSelection.end(), g);
+            }
+            // Remove used features
+            featureSelection.erase(remove_if(featureSelection.begin(), featureSelection.end(),
+                                             [&](auto x) {
+                                                 return std::find(featuresUsed.begin(), featuresUsed.end(), x) !=
+                                                        featuresUsed.end();
+                                             }),
+                                   featureSelection.end());
+            int k = bisection ? pow(2, tolerance) : 1;
+            int counter = 0; // The model counter of the current pack
+            // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k,
+            // featureSelection.size());
+            while (counter++ < k && featureSelection.size() > 0) {
+                auto feature = featureSelection[0];
+                featureSelection.erase(featureSelection.begin());
+                std::unique_ptr<Classifier> model;
+                model = std::make_unique<XSpode>(feature);
+                model->fit(dataset, features, className, states, weights_, smoothing);
+                /*dynamic_cast<XSpode*>(model.get())->fitx(X_train, y_train, weights_,
+                 * smoothing); // using exclusive XSpode fit method*/
+                // DEBUG
+                /*std::cout << dynamic_cast<XSpode*>(model.get())->to_string() <<
+                 * std::endl;*/
+                // DEBUG
+                std::vector<int> ypred;
+                if (alpha_block) {
+                    //
+                    // Compute the prediction with the current ensemble + model
+                    //
+                    // Add the model to the ensemble
+                    add_model(std::move(model), 1.0);
+                    // Compute the prediction
+                    ypred = predict(X_train_);
+                    model = std::move(models.back());
+                    // Remove the model from the ensemble
+                    remove_last_model();
+                } else {
+                    ypred = model->predict(X_train_);
+                }
+                // Step 3.1: Compute the classifier amout of say
+                auto ypred_t = torch::tensor(ypred);
+                std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
+                // Step 3.4: Store classifier and its accuracy to weigh its future vote
+                numItemsPack++;
+                featuresUsed.push_back(feature);
+                add_model(std::move(model), alpha_t);
+                // VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d
+                // featuresUsed: %zu", finished, numItemsPack, n_models,
+                // featuresUsed.size());
+            } // End of the pack
+            if (convergence && !finished) {
+                auto y_val_predict = predict(X_test);
+                double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
+                if (priorAccuracy == 0) {
+                    priorAccuracy = accuracy;
+                } else {
+                    improvement = accuracy - priorAccuracy;
+                }
+                if (improvement < convergence_threshold) {
+                    // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d
+                    // numItemsPack: %d improvement: %f prior: %f current: %f", tolerance,
+                    // numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance++;
+                } else {
+                    // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d
+                    // numItemsPack: %d improvement: %f prior: %f current: %f", tolerance,
+                    // numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance = 0; // Reset the counter if the model performs better
+                    numItemsPack = 0;
+                }
+                if (convergence_best) {
+                    // Keep the best accuracy until now as the prior accuracy
+                    priorAccuracy = std::max(accuracy, priorAccuracy);
+                } else {
+                    // Keep the last accuray obtained as the prior accuracy
+                    priorAccuracy = accuracy;
+                }
+            }
+            // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size:
+            // %zu", tolerance, featuresUsed.size(), features.size());
+            finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
+        }
+        if (tolerance > maxTolerance) {
+            if (numItemsPack < n_models) {
+                notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated
+                // of %d", numItemsPack, n_models);
+                for (int i = featuresUsed.size() - 1; i >= featuresUsed.size() - numItemsPack; --i) {
+                    remove_last_model();
+                }
+                // VLOG_SCOPE_F(4, "*Convergence threshold %d models left & %d features
+                // used.", n_models, featuresUsed.size());
+            } else {
+                notes.push_back("Convergence threshold reached & 0 models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated
+                // n_models=%d numItemsPack=%d", n_models, numItemsPack);
+            }
+        }
+        if (featuresUsed.size() != features.size()) {
+            notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " +
+                            std::to_string(features.size()));
+            status = bayesnet::WARNING;
+        }
+        notes.push_back("Number of models: " + std::to_string(n_models));
+        return;
+    }
+} // namespace bayesnet

bayesnet/ensembles/XBAODE.h

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

bayesnet/network/Network.cc

@@ -5,20 +5,20 @@
 // ***************************************************************
 
 #include <thread>
-#include <mutex>
 #include <sstream>
+#include <numeric>
+#include <algorithm>
 #include "Network.h"
 #include "bayesnet/utils/bayesnetUtils.h"
+#include "bayesnet/utils/CountingSemaphore.h"
+#include <pthread.h>
+#include <fstream>
 namespace bayesnet {
-    Network::Network() : fitted{ false }, maxThreads{ 0.95 }, classNumStates{ 0 }, laplaceSmoothing{ 0 }
+    Network::Network() : fitted{ false }, classNumStates{ 0 }
     {
     }
-    Network::Network(float maxT) : fitted{ false }, maxThreads{ maxT }, classNumStates{ 0 }, laplaceSmoothing{ 0 }
+    Network::Network(const Network& other) : features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
-    {
+        fitted(other.fitted), samples(other.samples)
-
-    }
-    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();
@@ -35,16 +35,15 @@ namespace bayesnet {
         nodes.clear();
         samples = torch::Tensor();
     }
-    float Network::getMaxThreads() const
-    {
-        return maxThreads;
-    }
     torch::Tensor& Network::getSamples()
     {
         return samples;
     }
     void Network::addNode(const std::string& name)
     {
+        if (fitted) {
+            throw std::invalid_argument("Cannot add node to a fitted network. Initialize first.");
+        }
         if (name == "") {
             throw std::invalid_argument("Node name cannot be empty");
         }
@@ -94,12 +93,21 @@ namespace bayesnet {
     }
     void Network::addEdge(const std::string& parent, const std::string& child)
     {
+        if (fitted) {
+            throw std::invalid_argument("Cannot add edge to a fitted network. Initialize first.");
+        }
         if (nodes.find(parent) == nodes.end()) {
             throw std::invalid_argument("Parent node " + parent + " does not exist");
         }
         if (nodes.find(child) == nodes.end()) {
             throw std::invalid_argument("Child node " + child + " does not exist");
         }
+        // Check if the edge is already in the graph
+        for (auto& node : nodes[parent]->getChildren()) {
+            if (node->getName() == child) {
+                throw std::invalid_argument("Edge " + parent + " -> " + child + " already exists");
+            }
+        }
         // Temporarily add edge to check for cycles
         nodes[parent]->addChild(nodes[child].get());
         nodes[child]->addParent(nodes[parent].get());
@@ -155,7 +163,7 @@ namespace bayesnet {
         classNumStates = nodes.at(className)->getNumStates();
     }
     // X comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
+    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states, weights);
         this->className = className;
@@ -164,17 +172,17 @@ namespace bayesnet {
         for (int i = 0; i < featureNames.size(); ++i) {
             auto row_feature = X.index({ i, "..." });
         }
-        completeFit(states, weights);
+        completeFit(states, weights, smoothing);
     }
-    void Network::fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
+    void Network::fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states, weights);
         this->className = className;
         this->samples = samples;
-        completeFit(states, weights);
+        completeFit(states, weights, smoothing);
     }
     // input_data comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights_, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
+    void Network::fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights_, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         const torch::Tensor weights = torch::tensor(weights_, torch::kFloat64);
         checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states, weights);
@@ -185,17 +193,43 @@ namespace bayesnet {
             samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
         }
         samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
-        completeFit(states, weights);
+        completeFit(states, weights, smoothing);
     }
-    void Network::completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
+    void Network::completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         setStates(states);
-        laplaceSmoothing = 1.0 / samples.size(1); // To use in CPT computation
         std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        const double n_samples = static_cast<double>(samples.size(1));
+        auto worker = [&](std::pair<const std::string, std::unique_ptr<Node>>& node, int i) {
+            std::string threadName = "FitWorker-" + std::to_string(i);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            double numStates = static_cast<double>(node.second->getNumStates());
+            double smoothing_factor;
+            switch (smoothing) {
+                case Smoothing_t::ORIGINAL:
+                    smoothing_factor = 1.0 / n_samples;
+                    break;
+                case Smoothing_t::LAPLACE:
+                    smoothing_factor = 1.0;
+                    break;
+                case Smoothing_t::CESTNIK:
+                    smoothing_factor = 1 / numStates;
+                    break;
+                default:
+                    smoothing_factor = 0.0; // No smoothing 
+            }
+            node.second->computeCPT(samples, features, smoothing_factor, weights);
+            semaphore.release();
+            };
+        int i = 0;
         for (auto& node : nodes) {
-            threads.emplace_back([this, &node, &weights]() {
+            semaphore.acquire();
-                node.second->computeCPT(samples, features, laplaceSmoothing, weights);
+            threads.emplace_back(worker, std::ref(node), i++);
-                });
         }
         for (auto& thread : threads) {
             thread.join();
@@ -207,14 +241,38 @@ namespace bayesnet {
         if (!fitted) {
             throw std::logic_error("You must call fit() before calling predict()");
         }
+        // Ensure the sample size is equal to the number of features
+        if (samples.size(0) != features.size() - 1) {
+            throw std::invalid_argument("(T) Sample size (" + std::to_string(samples.size(0)) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
         torch::Tensor result;
+        std::vector<std::thread> threads;
+        std::mutex mtx;
+        auto& semaphore = CountingSemaphore::getInstance();
         result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);
-        for (int i = 0; i < samples.size(1); ++i) {
+        auto worker = [&](const torch::Tensor& sample, int i) {
-            const torch::Tensor sample = samples.index({ "...", i });
+            std::string threadName = "PredictWorker-" + std::to_string(i);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
             auto psample = predict_sample(sample);
             auto temp = torch::tensor(psample, torch::kFloat64);
-            //            result.index_put_({ i, "..." }, torch::tensor(predict_sample(sample), torch::kFloat64));
+            {
-            result.index_put_({ i, "..." }, temp);
+                std::lock_guard<std::mutex> lock(mtx);
+                result.index_put_({ i, "..." }, temp);
+            }
+            semaphore.release();
+            };
+        for (int i = 0; i < samples.size(1); ++i) {
+            semaphore.acquire();
+            const torch::Tensor sample = samples.index({ "...", i });
+            threads.emplace_back(worker, sample, i);
+        }
+        for (auto& thread : threads) {
+            thread.join();
         }
         if (proba)
             return result;
@@ -239,18 +297,38 @@ namespace bayesnet {
         if (!fitted) {
             throw std::logic_error("You must call fit() before calling predict()");
         }
-        std::vector<int> predictions;
+        // Ensure the sample size is equal to the number of features
+        if (tsamples.size() != features.size() - 1) {
+            throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
+        std::vector<int> predictions(tsamples[0].size(), 0);
         std::vector<int> sample;
+        std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        auto worker = [&](const std::vector<int>& sample, const int row, int& prediction) {
+            std::string threadName = "(V)PWorker-" + std::to_string(row);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            auto classProbabilities = predict_sample(sample);
+            auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
+            int predictedClass = distance(classProbabilities.begin(), maxElem);
+            prediction = predictedClass;
+            semaphore.release();
+            };
         for (int row = 0; row < tsamples[0].size(); ++row) {
             sample.clear();
             for (int col = 0; col < tsamples.size(); ++col) {
                 sample.push_back(tsamples[col][row]);
             }
-            std::vector<double> classProbabilities = predict_sample(sample);
+            semaphore.acquire();
-            // Find the class with the maximum posterior probability
+            threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
-            auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
+        }
-            int predictedClass = distance(classProbabilities.begin(), maxElem);
+        for (auto& thread : threads) {
-            predictions.push_back(predictedClass);
+            thread.join();
         }
         return predictions;
     }
@@ -261,14 +339,36 @@ namespace bayesnet {
         if (!fitted) {
             throw std::logic_error("You must call fit() before calling predict_proba()");
         }
-        std::vector<std::vector<double>> predictions;
+        // Ensure the sample size is equal to the number of features
+        if (tsamples.size() != features.size() - 1) {
+            throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
+        std::vector<std::vector<double>> predictions(tsamples[0].size(), std::vector<double>(classNumStates, 0.0));
         std::vector<int> sample;
+        std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        auto worker = [&](const std::vector<int>& sample, int row, std::vector<double>& predictions) {
+            std::string threadName = "(V)PWorker-" + std::to_string(row);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            std::vector<double> classProbabilities = predict_sample(sample);
+            predictions = classProbabilities;
+            semaphore.release();
+            };
         for (int row = 0; row < tsamples[0].size(); ++row) {
             sample.clear();
             for (int col = 0; col < tsamples.size(); ++col) {
                 sample.push_back(tsamples[col][row]);
             }
-            predictions.push_back(predict_sample(sample));
+            semaphore.acquire();
+            threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
+        }
+        for (auto& thread : threads) {
+            thread.join();
         }
         return predictions;
     }
@@ -286,11 +386,6 @@ namespace bayesnet {
     // Return 1xn std::vector of probabilities
     std::vector<double> Network::predict_sample(const std::vector<int>& sample)
     {
-        // Ensure the sample size is equal to the number of features
-        if (sample.size() != features.size() - 1) {
-            throw std::invalid_argument("Sample size (" + std::to_string(sample.size()) +
-                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
-        }
         std::map<std::string, int> evidence;
         for (int i = 0; i < sample.size(); ++i) {
             evidence[features[i]] = sample[i];
@@ -300,44 +395,26 @@ namespace bayesnet {
     // Return 1xn std::vector of probabilities
     std::vector<double> Network::predict_sample(const torch::Tensor& sample)
     {
-        // Ensure the sample size is equal to the number of features
-        if (sample.size(0) != features.size() - 1) {
-            throw std::invalid_argument("Sample size (" + std::to_string(sample.size(0)) +
-                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
-        }
         std::map<std::string, int> evidence;
         for (int i = 0; i < sample.size(0); ++i) {
             evidence[features[i]] = sample[i].item<int>();
         }
         return exactInference(evidence);
     }
-    double Network::computeFactor(std::map<std::string, int>& completeEvidence)
-    {
-        double result = 1.0;
-        for (auto& node : getNodes()) {
-            result *= node.second->getFactorValue(completeEvidence);
-        }
-        return result;
-    }
     std::vector<double> Network::exactInference(std::map<std::string, int>& evidence)
     {
         std::vector<double> result(classNumStates, 0.0);
-        std::vector<std::thread> threads;
+        auto completeEvidence = std::map<std::string, int>(evidence);
-        std::mutex mtx;
         for (int i = 0; i < classNumStates; ++i) {
-            threads.emplace_back([this, &result, &evidence, i, &mtx]() {
+            completeEvidence[getClassName()] = i;
-                auto completeEvidence = std::map<std::string, int>(evidence);
+            double partial = 1.0;
-                completeEvidence[getClassName()] = i;
+            for (auto& node : getNodes()) {
-                double factor = computeFactor(completeEvidence);
+                partial *= node.second->getFactorValue(completeEvidence);
-                std::lock_guard<std::mutex> lock(mtx);
+            }
-                result[i] = factor;
+            result[i] = partial;
-                });
-        }
-        for (auto& thread : threads) {
-            thread.join();
         }
         // Normalize result
-        double sum = accumulate(result.begin(), result.end(), 0.0);
+        double sum = std::accumulate(result.begin(), result.end(), 0.0);
         transform(result.begin(), result.end(), result.begin(), [sum](const double& value) { return value / sum; });
         return result;
     }

bayesnet/network/Network.h

@@ -10,16 +10,16 @@
 #include <vector>
 #include "bayesnet/config.h"
 #include "Node.h"
+#include "Smoothing.h"
 
 namespace bayesnet {
+
     class Network {
     public:
         Network();
-        explicit Network(float);
         explicit Network(const Network&);
         ~Network() = default;
         torch::Tensor& getSamples();
-        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();
@@ -32,9 +32,9 @@ namespace bayesnet {
         /*
         Notice: Nodes have to be inserted in the same order as they are in the dataset, i.e., first node is first column and so on.
         */
-        void fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
+        void fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
-        void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
+        void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
-        void fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
+        void fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
         std::vector<int> predict(const std::vector<std::vector<int>>&); // Return mx1 std::vector of predictions
         torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
         torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);
@@ -50,19 +50,16 @@ namespace bayesnet {
     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, const Smoothing_t smoothing);
-        void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
+        void checkFitData(int n_samples, int n_features, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
-        void 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>>&);
     };
 }

bayesnet/network/Node.cc

@@ -90,51 +90,60 @@ namespace bayesnet {
         }
         return result;
     }
-    void Node::computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double laplaceSmoothing, const torch::Tensor& weights)
+    void Node::computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights)
     {
         dimensions.clear();
+        dimensions.reserve(parents.size() + 1);
         // Get dimensions of the CPT
         dimensions.push_back(numStates);
-        transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
+        for (const auto& parent : parents) {
-        // Create a tensor of zeros with the dimensions of the CPT
+            dimensions.push_back(parent->getNumStates());
-        cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
-        // Fill table with counts
-        auto pos = find(features.begin(), features.end(), name);
-        if (pos == features.end()) {
-            throw std::logic_error("Feature " + name + " not found in dataset");
         }
-        int name_index = pos - features.begin();
+        //transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
+        // Create a tensor initialized with smoothing
+        cpTable = torch::full(dimensions, smoothing, torch::kDouble);
+        // Create a map for quick feature index lookup
+        std::unordered_map<std::string, int> featureIndexMap;
+        for (size_t i = 0; i < features.size(); ++i) {
+            featureIndexMap[features[i]] = i;
+        }
+        // Fill table with counts
+        // Get the index of this node's feature
+        int name_index = featureIndexMap[name];
+        // Get parent indices in dataset
+        std::vector<int> parent_indices;
+        parent_indices.reserve(parents.size());
+        for (const auto& parent : parents) {
+            parent_indices.push_back(featureIndexMap[parent->getName()]);
+        }
+        c10::List<c10::optional<at::Tensor>> coordinates;
         for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
-            c10::List<c10::optional<at::Tensor>> coordinates;
+            coordinates.clear();
-            coordinates.push_back(dataset.index({ name_index, n_sample }));
+            auto sample = dataset.index({ "...", n_sample });
-            for (auto parent : parents) {
+            coordinates.push_back(sample[name_index]);
-                pos = find(features.begin(), features.end(), parent->getName());
+            for (size_t i = 0; i < parent_indices.size(); ++i) {
-                if (pos == features.end()) {
+                coordinates.push_back(sample[parent_indices[i]]);
-                    throw std::logic_error("Feature parent " + parent->getName() + " not found in dataset");
-                }
-                int parent_index = pos - features.begin();
-                coordinates.push_back(dataset.index({ parent_index, n_sample }));
             }
             // Increment the count of the corresponding coordinate
-            cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + weights.index({ n_sample }).item<double>());
+            cpTable.index_put_({ coordinates }, weights.index({ n_sample }), true);
         }
-        // Normalize the counts
+        // Normalize the counts (dividing each row by the sum of the row)
-        cpTable = cpTable / cpTable.sum(0);
+        cpTable /= cpTable.sum(0, true);
     }
-    float Node::getFactorValue(std::map<std::string, int>& evidence)
+    double Node::getFactorValue(std::map<std::string, int>& evidence)
     {
         c10::List<c10::optional<at::Tensor>> coordinates;
         // following predetermined order of indices in the cpTable (see Node.h)
         coordinates.push_back(at::tensor(evidence[name]));
         transform(parents.begin(), parents.end(), std::back_inserter(coordinates), [&evidence](const auto& parent) { return at::tensor(evidence[parent->getName()]); });
-        return cpTable.index({ coordinates }).item<float>();
+        return cpTable.index({ coordinates }).item<double>();
     }
     std::vector<std::string> Node::graph(const std::string& className)
     {
         auto output = std::vector<std::string>();
         auto suffix = name == className ? ", fontcolor=red, fillcolor=lightblue, style=filled " : "";
-        output.push_back(name + " [shape=circle" + suffix + "] \n");
+        output.push_back("\"" + name + "\" [shape=circle" + suffix + "] \n");
-        transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return name + " -> " + child->getName(); });
+        transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return "\"" + name + "\" -> \"" + child->getName() + "\""; });
         return output;
     }
 }

bayesnet/network/Node.h

@@ -23,12 +23,12 @@ namespace bayesnet {
         std::vector<Node*>& getParents();
         std::vector<Node*>& getChildren();
         torch::Tensor& getCPT();
-        void computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double laplaceSmoothing, const torch::Tensor& weights);
+        void computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights);
         int getNumStates() const;
         void setNumStates(int);
         unsigned minFill();
         std::vector<std::string> graph(const std::string& clasName); // Returns a std::vector of std::strings representing the graph in graphviz format
-        float getFactorValue(std::map<std::string, int>&);
+        double getFactorValue(std::map<std::string, int>&);
     private:
         std::string name;
         std::vector<Node*> parents;

bayesnet/network/Smoothing.h

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

bayesnet/utils/CountingSemaphore.h

@@ -0,0 +1,54 @@
+#ifndef COUNTING_SEMAPHORE_H
+#define COUNTING_SEMAPHORE_H
+#include <mutex>
+#include <condition_variable>
+#include <algorithm>
+#include <thread>
+#include <mutex>
+#include <condition_variable>
+#include <thread>
+
+class CountingSemaphore {
+public:
+    static CountingSemaphore& getInstance()
+    {
+        static CountingSemaphore instance;
+        return instance;
+    }
+    // Delete copy constructor and assignment operator
+    CountingSemaphore(const CountingSemaphore&) = delete;
+    CountingSemaphore& operator=(const CountingSemaphore&) = delete;
+    void acquire()
+    {
+        std::unique_lock<std::mutex> lock(mtx_);
+        cv_.wait(lock, [this]() { return count_ > 0; });
+        --count_;
+    }
+    void release()
+    {
+        std::lock_guard<std::mutex> lock(mtx_);
+        ++count_;
+        if (count_ <= max_count_) {
+            cv_.notify_one();
+        }
+    }
+    uint getCount() const
+    {
+        return count_;
+    }
+    uint getMaxCount() const
+    {
+        return max_count_;
+    }
+private:
+    CountingSemaphore()
+        : max_count_(std::max(1u, static_cast<uint>(0.95 * std::thread::hardware_concurrency()))),
+        count_(max_count_)
+    {
+    }
+    std::mutex mtx_;
+    std::condition_variable cv_;
+    const uint max_count_;
+    uint count_;
+};
+#endif

bayesnet/utils/Mst.cc

@@ -53,14 +53,14 @@ namespace bayesnet {
         }
     }
 
-    void insertElement(std::list<int>& variables, int variable)
+    void MST::insertElement(std::list<int>& variables, int variable)
     {
         if (std::find(variables.begin(), variables.end(), variable) == variables.end()) {
             variables.push_front(variable);
         }
     }
 
-    std::vector<std::pair<int, int>> reorder(std::vector<std::pair<float, std::pair<int, int>>> T, int root_original)
+    std::vector<std::pair<int, int>> MST::reorder(std::vector<std::pair<float, std::pair<int, int>>> T, int root_original)
     {
         // Create the edges of a DAG from the MST
         // replacing unordered_set with list because unordered_set cannot guarantee the order of the elements inserted

bayesnet/utils/Mst.h

@@ -14,6 +14,8 @@ namespace bayesnet {
     public:
         MST() = default;
         MST(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
+        void insertElement(std::list<int>& variables, int variable);
+        std::vector<std::pair<int, int>> reorder(std::vector<std::pair<float, std::pair<int, int>>> T, int root_original);
         std::vector<std::pair<int, int>> maximumSpanningTree();
     private:
         torch::Tensor weights;

bayesnet/utils/TensorUtils.h

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

cmake/modules/CodeCoverage.cmake

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

diagrams/BayesNet.puml

@@ -1,36 +1,16 @@
 @startuml
 title clang-uml class diagram model
-class "bayesnet::Metrics" as C_0000736965376885623323
+class "bayesnet::Node" as C_0010428199432536647474
-class C_0000736965376885623323 #aliceblue;line:blue;line.dotted;text:blue {
+class C_0010428199432536647474 #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
++computeCPT(const torch::Tensor & dataset, const std::vector<std::string> & features, const double smoothing, const torch::Tensor & weights) : void
 +getCPT() : torch::Tensor &
 +getChildren() : std::vector<Node *> &
-+getFactorValue(std::map<std::string,int> &) : float
++getFactorValue(std::map<std::string,int> &) : double
 +getName() const : std::string
 +getNumStates() const : int
 +getParents() : std::vector<Node *> &
@@ -41,24 +21,29 @@ class C_0001303524929067080934 #aliceblue;line:blue;line.dotted;text:blue {
 +setNumStates(int) : void
 __
 }
-class "bayesnet::Network" as C_0001186707649890429575
+enum "bayesnet::Smoothing_t" as C_0013393078277439680282
-class C_0001186707649890429575 #aliceblue;line:blue;line.dotted;text:blue {
+enum C_0013393078277439680282 {
+NONE
+ORIGINAL
+LAPLACE
+CESTNIK
+}
+class "bayesnet::Network" as C_0009493661199123436603
+class C_0009493661199123436603 #aliceblue;line:blue;line.dotted;text:blue {
 +Network() : void
-+Network(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 & samples, const torch::Tensor & weights, const std::vector<std::string> & featureNames, const std::string & className, const std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : void
-+fit(const torch::Tensor & X, const torch::Tensor & y, const torch::Tensor & weights, const std::vector<std::string> & featureNames, const std::string & className, const std::map<std::string,std::vector<int>> & states) : void
++fit(const torch::Tensor & X, const torch::Tensor & y, const torch::Tensor & weights, const std::vector<std::string> & featureNames, const std::string & className, const std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : void
-+fit(const std::vector<std::vector<int>> & input_data, const std::vector<int> & labels, const std::vector<double> & weights, const std::vector<std::string> & featureNames, const std::string & className, const std::map<std::string,std::vector<int>> & states) : void
++fit(const std::vector<std::vector<int>> & input_data, const std::vector<int> & labels, const std::vector<double> & weights, const std::vector<std::string> & featureNames, const std::string & className, const std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : void
 +getClassName() const : std::string
 +getClassNumStates() const : int
 +getEdges() const : std::vector<std::pair<std::string,std::string>>
 +getFeatures() const : std::vector<std::string>
-+getMaxThreads() const : float
 +getNodes() : std::map<std::string,std::unique_ptr<Node>> &
 +getNumEdges() const : int
 +getSamples() : torch::Tensor &
@@ -76,21 +61,21 @@ class C_0001186707649890429575 #aliceblue;line:blue;line.dotted;text:blue {
 +version() : std::string
 __
 }
-enum "bayesnet::status_t" as C_0000738420730783851375
+enum "bayesnet::status_t" as C_0005907365846270811004
-enum C_0000738420730783851375 {
+enum C_0005907365846270811004 {
 NORMAL
 WARNING
 ERROR
 }
-abstract "bayesnet::BaseClassifier" as C_0000327135989451974539
+abstract "bayesnet::BaseClassifier" as C_0002617087915615796317
-abstract C_0000327135989451974539 #aliceblue;line:blue;line.dotted;text:blue {
+abstract C_0002617087915615796317 #aliceblue;line:blue;line.dotted;text:blue {
 +~BaseClassifier() = default : void
 ..
 {abstract} +dump_cpt() const = 0 : std::string
-{abstract} +fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) = 0 : BaseClassifier &
+{abstract} +fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) = 0 : BaseClassifier &
-{abstract} +fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) = 0 : BaseClassifier &
+{abstract} +fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) = 0 : BaseClassifier &
-{abstract} +fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const torch::Tensor & weights) = 0 : BaseClassifier &
+{abstract} +fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const torch::Tensor & weights, const Smoothing_t smoothing) = 0 : BaseClassifier &
-{abstract} +fit(std::vector<std::vector<int>> & X, std::vector<int> & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) = 0 : BaseClassifier &
+{abstract} +fit(std::vector<std::vector<int>> & X, std::vector<int> & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) = 0 : BaseClassifier &
 {abstract} +getClassNumStates() const = 0 : int
 {abstract} +getNotes() const = 0 : std::vector<std::string>
 {abstract} +getNumberOfEdges() const = 0 : int
@@ -109,12 +94,37 @@ abstract C_0000327135989451974539 #aliceblue;line:blue;line.dotted;text:blue {
 {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
+{abstract} #trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) = 0 : void
 __
+#notes : std::vector<std::string>
+#status : status_t
 #validHyperparameters : std::vector<std::string>
 }
-abstract "bayesnet::Classifier" as C_0002043996622900301644
+class "bayesnet::Metrics" as C_0005895723015084986588
-abstract C_0002043996622900301644 #aliceblue;line:blue;line.dotted;text:blue {
+class C_0005895723015084986588 #aliceblue;line:blue;line.dotted;text:blue {
++Metrics() = default : void
++Metrics(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int classNumStates) : void
++Metrics(const std::vector<std::vector<int>> & vsamples, const std::vector<int> & labels, const std::vector<std::string> & features, const std::string & className, const int classNumStates) : void
+..
++SelectKBestWeighted(const torch::Tensor & weights, bool ascending = false, unsigned int k = 0) : std::vector<int>
++SelectKPairs(const torch::Tensor & weights, std::vector<int> & featuresExcluded, bool ascending = false, unsigned int k = 0) : std::vector<std::pair<int,int>>
++conditionalEdge(const torch::Tensor & weights) : torch::Tensor
++conditionalEntropy(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & labels, const torch::Tensor & weights) : double
++conditionalMutualInformation(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & labels, const torch::Tensor & weights) : double
+#doCombinations<T>(const std::vector<T> & source) : std::vector<std::pair<T, T> >
++entropy(const torch::Tensor & feature, const torch::Tensor & weights) : double
++getScoresKBest() const : std::vector<double>
++getScoresKPairs() const : std::vector<std::pair<std::pair<int,int>,double>>
++maximumSpanningTree(const std::vector<std::string> & features, const torch::Tensor & weights, const int root) : std::vector<std::pair<int,int>>
++mutualInformation(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & weights) : double
+#pop_first<T>(std::vector<T> & v) : T
+__
+#className : std::string
+#features : std::vector<std::string>
+#samples : torch::Tensor
+}
+abstract "bayesnet::Classifier" as C_0016351972983202413152
+abstract C_0016351972983202413152 #aliceblue;line:blue;line.dotted;text:blue {
 +Classifier(Network model) : void
 +~Classifier() = default : void
 ..
@@ -123,10 +133,10 @@ abstract C_0002043996622900301644 #aliceblue;line:blue;line.dotted;text:blue {
 {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(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : Classifier &
-+fit(std::vector<std::vector<int>> & X, std::vector<int> & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : Classifier &
++fit(std::vector<std::vector<int>> & X, std::vector<int> & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : Classifier &
-+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : Classifier &
++fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : Classifier &
-+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const torch::Tensor & weights) : Classifier &
++fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const torch::Tensor & weights, const Smoothing_t smoothing) : Classifier &
 +getClassNumStates() const : int
 +getNotes() const : std::vector<std::string>
 +getNumberOfEdges() const : int
@@ -143,8 +153,9 @@ abstract C_0002043996622900301644 #aliceblue;line:blue;line.dotted;text:blue {
 +setHyperparameters(const nlohmann::json & hyperparameters) : void
 +show() const : std::vector<std::string>
 +topological_order() : std::vector<std::string>
-#trainModel(const torch::Tensor & weights) : void
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
 __
+#CLASSIFIER_NOT_FITTED : const std::string
 #className : std::string
 #dataset : torch::Tensor
 #features : std::vector<std::string>
@@ -153,31 +164,10 @@ __
 #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 "bayesnet::Proposal" as C_0017759964713298103839
-class C_0001112865019015250005 #aliceblue;line:blue;line.dotted;text:blue {
+class C_0017759964713298103839 #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
 ..
@@ -190,74 +180,140 @@ __
 #discretizers : map<std::string,mdlp::CPPFImdlp *>
 #y : torch::Tensor
 }
-class "bayesnet::TANLd" as C_0001668829096702037834
+class "bayesnet::KDB" as C_0008902920152122000044
-class C_0001668829096702037834 #aliceblue;line:blue;line.dotted;text:blue {
+class C_0008902920152122000044 #aliceblue;line:blue;line.dotted;text:blue {
-+TANLd() : void
++KDB(int k, float theta = 0.03) : void
-+~TANLd() = default : void
++~KDB() = 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 &
+#add_m_edges(int idx, std::vector<int> & S, torch::Tensor & weights) : void
-+graph(const std::string & name = "TAN") const : std::vector<std::string>
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & name = "KDB") const : std::vector<std::string>
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
+__
+}
+class "bayesnet::KDBLd" as C_0002756018222998454702
+class C_0002756018222998454702 #aliceblue;line:blue;line.dotted;text:blue {
++KDBLd(int k) : void
++~KDBLd() = default : void
+..
++fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : KDBLd &
++graph(const std::string & name = "KDB") const : std::vector<std::string>
 +predict(torch::Tensor & X) : torch::Tensor
 {static} +version() : std::string
 __
 }
-abstract "bayesnet::FeatureSelect" as C_0001695326193250580823
+class "bayesnet::SPODE" as C_0004096182510460307610
-abstract C_0001695326193250580823 #aliceblue;line:blue;line.dotted;text:blue {
+class C_0004096182510460307610 #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>
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
 __
 }
-class "bayesnet::Ensemble" as C_0001985241386355360576
+class "bayesnet::SPODELd" as C_0010957245114062042836
-class C_0001985241386355360576 #aliceblue;line:blue;line.dotted;text:blue {
+class C_0010957245114062042836 #aliceblue;line:blue;line.dotted;text:blue {
++SPODELd(int root) : void
++~SPODELd() = default : void
+..
++commonFit(const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
++fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
++fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
++graph(const std::string & name = "SPODELd") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+class "bayesnet::SPnDE" as C_0016268916386101512883
+class C_0016268916386101512883 #aliceblue;line:blue;line.dotted;text:blue {
++SPnDE(std::vector<int> parents) : void
++~SPnDE() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & name = "SPnDE") const : std::vector<std::string>
+__
+}
+class "bayesnet::TAN" as C_0014087955399074584137
+class C_0014087955399074584137 #aliceblue;line:blue;line.dotted;text:blue {
++TAN() : void
++~TAN() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & name = "TAN") const : std::vector<std::string>
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
+__
+}
+class "bayesnet::TANLd" as C_0013350632773616302678
+class C_0013350632773616302678 #aliceblue;line:blue;line.dotted;text:blue {
++TANLd() : void
++~TANLd() = default : void
+..
++fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : TANLd &
++graph(const std::string & name = "TANLd") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+__
+}
+class "bayesnet::XSp2de" as C_0007640742442325463418
+class C_0007640742442325463418 #aliceblue;line:blue;line.dotted;text:blue {
++XSp2de(int spIndex1, int spIndex2) : void
+..
+#buildModel(const torch::Tensor & weights) : void
++fitx(torch::Tensor & X, torch::Tensor & y, torch::Tensor & weights_, const Smoothing_t smoothing) : void
++getClassNumStates() const : int
++getNFeatures() const : int
++getNumberOfEdges() const : int
++getNumberOfNodes() const : int
++getNumberOfStates() const : int
++graph(const std::string & title) const : std::vector<std::string>
++predict(const std::vector<int> & instance) const : int
++predict(std::vector<std::vector<int>> & test_data) : std::vector<int>
++predict(torch::Tensor & X) : torch::Tensor
++predict_proba(const std::vector<int> & instance) const : std::vector<double>
++predict_proba(std::vector<std::vector<int>> & test_data) : std::vector<std::vector<double>>
++predict_proba(torch::Tensor & X) : torch::Tensor
++score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
++score(torch::Tensor & X, torch::Tensor & y) : float
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
++to_string() const : std::string
+#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::XSpode" as C_0015654113248178830206
+class C_0015654113248178830206 #aliceblue;line:blue;line.dotted;text:blue {
++XSpode(int spIndex) : void
+..
+#buildModel(const torch::Tensor & weights) : void
++fitx(torch::Tensor & X, torch::Tensor & y, torch::Tensor & weights_, const Smoothing_t smoothing) : void
++getClassNumStates() const : int
++getNFeatures() const : int
++getNumberOfEdges() const : int
++getNumberOfNodes() const : int
++getNumberOfStates() const : int
++getStates() : std::vector<int> &
++graph(const std::string & title) const : std::vector<std::string>
++normalize(std::vector<double> & v) const : void
++predict(const std::vector<int> & instance) const : int
++predict(std::vector<std::vector<int>> & X) : std::vector<int>
++predict(torch::Tensor & X) : torch::Tensor
++predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
++predict_proba(torch::Tensor & X) : torch::Tensor
++predict_proba(const std::vector<int> & instance) const : std::vector<double>
++score(torch::Tensor & X, torch::Tensor & y) : float
++score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
++to_string() const : std::string
+#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::TensorUtils" as C_0010304804115474100819
+class C_0010304804115474100819 #aliceblue;line:blue;line.dotted;text:blue {
+{static} +to_matrix(const torch::Tensor & X) : std::vector<std::vector<int>>
+{static} +to_vector<T>(const torch::Tensor & y) : std::vector<T>
+__
+}
+class "bayesnet::Ensemble" as C_0015881931090842884611
+class C_0015881931090842884611 #aliceblue;line:blue;line.dotted;text:blue {
 +Ensemble(bool predict_voting = true) : void
 +~Ensemble() = default : void
 ..
@@ -280,7 +336,7 @@ class C_0001985241386355360576 #aliceblue;line:blue;line.dotted;text:blue {
 +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
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
 #voting(torch::Tensor & votes) : torch::Tensor
 __
 #models : std::vector<std::unique_ptr<Classifier>>
@@ -288,41 +344,244 @@ __
 #predict_voting : bool
 #significanceModels : std::vector<double>
 }
-class "bayesnet::(anonymous_45089536)" as C_0001186398587753535158
+class "bayesnet::A2DE" as C_0001410789567057647859
-class C_0001186398587753535158 #aliceblue;line:blue;line.dotted;text:blue {
+class C_0001410789567057647859 #aliceblue;line:blue;line.dotted;text:blue {
++A2DE(bool predict_voting = false) : void
++~A2DE() : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & title = "A2DE") const : std::vector<std::string>
++setHyperparameters(const nlohmann::json & hyperparameters) : void
+__
+}
+class "bayesnet::AODE" as C_0006288892608974306258
+class C_0006288892608974306258 #aliceblue;line:blue;line.dotted;text:blue {
++AODE(bool predict_voting = false) : void
++~AODE() : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & title = "AODE") const : std::vector<std::string>
++setHyperparameters(const nlohmann::json & hyperparameters) : void
+__
+}
+class "bayesnet::AODELd" as C_0003898187834670349177
+class C_0003898187834670349177 #aliceblue;line:blue;line.dotted;text:blue {
++AODELd(bool predict_voting = true) : void
++~AODELd() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++fit(torch::Tensor & X_, torch::Tensor & y_, const std::vector<std::string> & features_, const std::string & className_, std::map<std::string,std::vector<int>> & states_, const Smoothing_t smoothing) : AODELd &
++graph(const std::string & name = "AODELd") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+__
+}
+abstract "bayesnet::FeatureSelect" as C_0013562609546004646591
+abstract C_0013562609546004646591 #aliceblue;line:blue;line.dotted;text:blue {
++FeatureSelect(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
++~FeatureSelect() : void
+..
+#computeMeritCFS() : double
+#computeSuFeatures(const int a, const int b) : double
+#computeSuLabels() : void
+{abstract} +fit() = 0 : void
++getFeatures() const : std::vector<int>
++getScores() const : std::vector<double>
+#initialize() : void
+#symmetricalUncertainty(int a, int b) : double
+__
+#fitted : bool
+#maxFeatures : int
+#selectedFeatures : std::vector<int>
+#selectedScores : std::vector<double>
+#suFeatures : std::map<std::pair<int,int>,double>
+#suLabels : std::vector<double>
+#weights : const torch::Tensor &
+}
+class "bayesnet::(anonymous_60357672)" as C_0006397015156479549697
+class C_0006397015156479549697 #aliceblue;line:blue;line.dotted;text:blue {
 __
 +CFS : std::string
 +FCBF : std::string
 +IWSS : std::string
 }
-class "bayesnet::(anonymous_45090163)" as C_0000602764946063116717
+class "bayesnet::(anonymous_60358326)" as C_0013066254331852347304
-class C_0000602764946063116717 #aliceblue;line:blue;line.dotted;text:blue {
+class C_0013066254331852347304 #aliceblue;line:blue;line.dotted;text:blue {
 __
 +ASC : std::string
 +DESC : std::string
 +RAND : std::string
 }
-class "bayesnet::BoostAODE" as C_0000358471592399852382
+class "bayesnet::Boost" as C_0009819322948617116148
-class C_0000358471592399852382 #aliceblue;line:blue;line.dotted;text:blue {
+class C_0009819322948617116148 #aliceblue;line:blue;line.dotted;text:blue {
++Boost(bool predict_voting = false) : void
++~Boost() = default : void
+..
+#add_model(std::unique_ptr<Classifier> model, double significance) : void
+#buildModel(const torch::Tensor & weights) : void
+#featureSelection(torch::Tensor & weights_) : std::vector<int>
+#remove_last_model() : void
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
+#update_weights(torch::Tensor & ytrain, torch::Tensor & ypred, torch::Tensor & weights) : std::tuple<torch::Tensor &,double,bool>
+#update_weights_block(int k, torch::Tensor & ytrain, torch::Tensor & weights) : std::tuple<torch::Tensor &,double,bool>
+__
+#X_test : torch::Tensor
+#X_train : torch::Tensor
+#alpha_block : bool
+#bisection : bool
+#block_update : bool
+#convergence : bool
+#convergence_best : bool
+#featureSelector : FeatureSelect *
+#maxTolerance : int
+#order_algorithm : std::string
+#selectFeatures : bool
+#select_features_algorithm : std::string
+#threshold : double
+#y_test : torch::Tensor
+#y_train : torch::Tensor
+}
+class "bayesnet::BoostA2DE" as C_0000272055465257861326
+class C_0000272055465257861326 #aliceblue;line:blue;line.dotted;text:blue {
++BoostA2DE(bool predict_voting = false) : void
++~BoostA2DE() = default : void
+..
++graph(const std::string & title = "BoostA2DE") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::(anonymous_60425028)" as C_0000461144706913711531
+class C_0000461144706913711531 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60425682)" as C_0014849589915262463453
+class C_0014849589915262463453 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::BoostAODE" as C_0002867772739198819061
+class C_0002867772739198819061 #aliceblue;line:blue;line.dotted;text:blue {
 +BoostAODE(bool predict_voting = false) : void
 +~BoostAODE() = default : void
 ..
-#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, const Smoothing_t smoothing) : void
-#trainModel(const torch::Tensor & weights) : void
 __
 }
-class "bayesnet::MST" as C_0000131858426172291700
+class "bayesnet::XBA2DE" as C_0008480973840710001141
-class C_0000131858426172291700 #aliceblue;line:blue;line.dotted;text:blue {
+class C_0008480973840710001141 #aliceblue;line:blue;line.dotted;text:blue {
++XBA2DE(bool predict_voting = false) : void
++~XBA2DE() = default : void
+..
++getVersion() : std::string
++graph(const std::string & title = "XBA2DE") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::(anonymous_60414016)" as C_0008746994658440620779
+class C_0008746994658440620779 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60414670)" as C_0008030559132212449356
+class C_0008030559132212449356 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::XBAODE" as C_0005198482342493966768
+class C_0005198482342493966768 #aliceblue;line:blue;line.dotted;text:blue {
++XBAODE() : void
+..
++getVersion() : std::string
+#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::CFS" as C_0000093018845530739957
+class C_0000093018845530739957 #aliceblue;line:blue;line.dotted;text:blue {
++CFS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
++~CFS() : void
+..
++fit() : void
+__
+}
+class "bayesnet::FCBF" as C_0001157456122733975432
+class C_0001157456122733975432 #aliceblue;line:blue;line.dotted;text:blue {
++FCBF(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights, const double threshold) : void
++~FCBF() : void
+..
++fit() : void
+__
+}
+class "bayesnet::IWSS" as C_0000066148117395428429
+class C_0000066148117395428429 #aliceblue;line:blue;line.dotted;text:blue {
++IWSS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights, const double threshold) : void
++~IWSS() : void
+..
++fit() : void
+__
+}
+class "bayesnet::(anonymous_60810808)" as C_0012002108046995621535
+class C_0012002108046995621535 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60811462)" as C_0004735044229422764240
+class C_0004735044229422764240 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::(anonymous_60804220)" as C_0007082100550474633839
+class C_0007082100550474633839 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60804874)" as C_0003669430095936529648
+class C_0003669430095936529648 #aliceblue;line:blue;line.dotted;text:blue {
+__
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++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::(anonymous_60809706)" as C_0012336951062058157227
+class C_0012336951062058157227 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60810360)" as C_0002435892998884329673
+class C_0002435892998884329673 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
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+class C_0001054867409378333602 #aliceblue;line:blue;line.dotted;text:blue {
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 +MST(const std::vector<std::string> & features, const torch::Tensor & weights, const int root) : void
 ..
++insertElement(std::list<int> & variables, int variable) : void
 +maximumSpanningTree() : std::vector<std::pair<int,int>>
++reorder(std::vector<std::pair<float,std::pair<int,int>>> T, int root_original) : std::vector<std::pair<int,int>>
 __
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+class C_0009576333456015187741 #aliceblue;line:blue;line.dotted;text:blue {
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 ..
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+C_0016351972983202413152 o-- C_0005895723015084986588 : #metrics
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-class C_0001369655639257755354 #aliceblue;line:blue;line.dotted;text:blue {
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+<title>node17&#45;&gt;node18</title>
+<path fill="none" stroke="black" stroke-dasharray="5,2" d="M3624.66,-102.95C3624.66,-91.68 3624.66,-75.4 3624.66,-61.37"/>
+<polygon fill="black" stroke="black" points="3628.16,-61.78 3624.66,-51.78 3621.16,-61.78 3628.16,-61.78"/>
 </g>
 </g>
 </svg>

sample/CMakeLists.txt

@@ -4,16 +4,19 @@ project(bayesnet_sample)
 
 set(CMAKE_CXX_STANDARD 17)
 
-find_package(Torch REQUIRED)
+find_package(Torch CONFIG REQUIRED)
-find_library(BayesNet NAMES BayesNet.a libBayesNet.a REQUIRED)
+find_package(bayesnet CONFIG REQUIRED)
+find_package(fimdlp CONFIG REQUIRED)
+find_package(folding CONFIG REQUIRED)
+find_package(arff-files CONFIG REQUIRED)
+find_package(nlohman_json CONFIG REQUIRED)
 
-include_directories(
-    lib/Files
-    lib/mdlp
-    lib/json/include
-    /usr/local/include
-)
-
-add_subdirectory(lib/mdlp)
 add_executable(bayesnet_sample sample.cc) 
-target_link_libraries(bayesnet_sample mdlp "${TORCH_LIBRARIES}" "${BayesNet}")
+target_link_libraries(bayesnet_sample PRIVATE 
+    fimdlp::fimdlp 
+    arff-files::arff-files 
+    "${TORCH_LIBRARIES}" 
+    bayesnet::bayesnet 
+    nlohmann_json::nlohmann_json
+    folding::folding
+)

sample/lib/json/include/nlohmann/adl_serializer.hpp

@@ -1,55 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <utility>
-
-#include <nlohmann/detail/abi_macros.hpp>
-#include <nlohmann/detail/conversions/from_json.hpp>
-#include <nlohmann/detail/conversions/to_json.hpp>
-#include <nlohmann/detail/meta/identity_tag.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-/// @sa https://json.nlohmann.me/api/adl_serializer/
-template<typename ValueType, typename>
-struct adl_serializer
-{
-    /// @brief convert a JSON value to any value type
-    /// @sa https://json.nlohmann.me/api/adl_serializer/from_json/
-    template<typename BasicJsonType, typename TargetType = ValueType>
-    static auto from_json(BasicJsonType && j, TargetType& val) noexcept(
-        noexcept(::nlohmann::from_json(std::forward<BasicJsonType>(j), val)))
-    -> decltype(::nlohmann::from_json(std::forward<BasicJsonType>(j), val), void())
-    {
-        ::nlohmann::from_json(std::forward<BasicJsonType>(j), val);
-    }
-
-    /// @brief convert a JSON value to any value type
-    /// @sa https://json.nlohmann.me/api/adl_serializer/from_json/
-    template<typename BasicJsonType, typename TargetType = ValueType>
-    static auto from_json(BasicJsonType && j) noexcept(
-    noexcept(::nlohmann::from_json(std::forward<BasicJsonType>(j), detail::identity_tag<TargetType> {})))
-    -> decltype(::nlohmann::from_json(std::forward<BasicJsonType>(j), detail::identity_tag<TargetType> {}))
-    {
-        return ::nlohmann::from_json(std::forward<BasicJsonType>(j), detail::identity_tag<TargetType> {});
-    }
-
-    /// @brief convert any value type to a JSON value
-    /// @sa https://json.nlohmann.me/api/adl_serializer/to_json/
-    template<typename BasicJsonType, typename TargetType = ValueType>
-    static auto to_json(BasicJsonType& j, TargetType && val) noexcept(
-        noexcept(::nlohmann::to_json(j, std::forward<TargetType>(val))))
-    -> decltype(::nlohmann::to_json(j, std::forward<TargetType>(val)), void())
-    {
-        ::nlohmann::to_json(j, std::forward<TargetType>(val));
-    }
-};
-
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/byte_container_with_subtype.hpp

@@ -1,103 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <cstdint> // uint8_t, uint64_t
-#include <tuple> // tie
-#include <utility> // move
-
-#include <nlohmann/detail/abi_macros.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-/// @brief an internal type for a backed binary type
-/// @sa https://json.nlohmann.me/api/byte_container_with_subtype/
-template<typename BinaryType>
-class byte_container_with_subtype : public BinaryType
-{
-  public:
-    using container_type = BinaryType;
-    using subtype_type = std::uint64_t;
-
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
-    byte_container_with_subtype() noexcept(noexcept(container_type()))
-        : container_type()
-    {}
-
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
-    byte_container_with_subtype(const container_type& b) noexcept(noexcept(container_type(b)))
-        : container_type(b)
-    {}
-
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
-    byte_container_with_subtype(container_type&& b) noexcept(noexcept(container_type(std::move(b))))
-        : container_type(std::move(b))
-    {}
-
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
-    byte_container_with_subtype(const container_type& b, subtype_type subtype_) noexcept(noexcept(container_type(b)))
-        : container_type(b)
-        , m_subtype(subtype_)
-        , m_has_subtype(true)
-    {}
-
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
-    byte_container_with_subtype(container_type&& b, subtype_type subtype_) noexcept(noexcept(container_type(std::move(b))))
-        : container_type(std::move(b))
-        , m_subtype(subtype_)
-        , m_has_subtype(true)
-    {}
-
-    bool operator==(const byte_container_with_subtype& rhs) const
-    {
-        return std::tie(static_cast<const BinaryType&>(*this), m_subtype, m_has_subtype) ==
-               std::tie(static_cast<const BinaryType&>(rhs), rhs.m_subtype, rhs.m_has_subtype);
-    }
-
-    bool operator!=(const byte_container_with_subtype& rhs) const
-    {
-        return !(rhs == *this);
-    }
-
-    /// @brief sets the binary subtype
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/set_subtype/
-    void set_subtype(subtype_type subtype_) noexcept
-    {
-        m_subtype = subtype_;
-        m_has_subtype = true;
-    }
-
-    /// @brief return the binary subtype
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/subtype/
-    constexpr subtype_type subtype() const noexcept
-    {
-        return m_has_subtype ? m_subtype : static_cast<subtype_type>(-1);
-    }
-
-    /// @brief return whether the value has a subtype
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/has_subtype/
-    constexpr bool has_subtype() const noexcept
-    {
-        return m_has_subtype;
-    }
-
-    /// @brief clears the binary subtype
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/clear_subtype/
-    void clear_subtype() noexcept
-    {
-        m_subtype = 0;
-        m_has_subtype = false;
-    }
-
-  private:
-    subtype_type m_subtype = 0;
-    bool m_has_subtype = false;
-};
-
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/abi_macros.hpp

@@ -1,100 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-// This file contains all macro definitions affecting or depending on the ABI
-
-#ifndef JSON_SKIP_LIBRARY_VERSION_CHECK
-    #if defined(NLOHMANN_JSON_VERSION_MAJOR) && defined(NLOHMANN_JSON_VERSION_MINOR) && defined(NLOHMANN_JSON_VERSION_PATCH)
-        #if NLOHMANN_JSON_VERSION_MAJOR != 3 || NLOHMANN_JSON_VERSION_MINOR != 11 || NLOHMANN_JSON_VERSION_PATCH != 3
-            #warning "Already included a different version of the library!"
-        #endif
-    #endif
-#endif
-
-#define NLOHMANN_JSON_VERSION_MAJOR 3   // NOLINT(modernize-macro-to-enum)
-#define NLOHMANN_JSON_VERSION_MINOR 11  // NOLINT(modernize-macro-to-enum)
-#define NLOHMANN_JSON_VERSION_PATCH 3   // NOLINT(modernize-macro-to-enum)
-
-#ifndef JSON_DIAGNOSTICS
-    #define JSON_DIAGNOSTICS 0
-#endif
-
-#ifndef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
-    #define JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON 0
-#endif
-
-#if JSON_DIAGNOSTICS
-    #define NLOHMANN_JSON_ABI_TAG_DIAGNOSTICS _diag
-#else
-    #define NLOHMANN_JSON_ABI_TAG_DIAGNOSTICS
-#endif
-
-#if JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
-    #define NLOHMANN_JSON_ABI_TAG_LEGACY_DISCARDED_VALUE_COMPARISON _ldvcmp
-#else
-    #define NLOHMANN_JSON_ABI_TAG_LEGACY_DISCARDED_VALUE_COMPARISON
-#endif
-
-#ifndef NLOHMANN_JSON_NAMESPACE_NO_VERSION
-    #define NLOHMANN_JSON_NAMESPACE_NO_VERSION 0
-#endif
-
-// Construct the namespace ABI tags component
-#define NLOHMANN_JSON_ABI_TAGS_CONCAT_EX(a, b) json_abi ## a ## b
-#define NLOHMANN_JSON_ABI_TAGS_CONCAT(a, b) \
-    NLOHMANN_JSON_ABI_TAGS_CONCAT_EX(a, b)
-
-#define NLOHMANN_JSON_ABI_TAGS                                       \
-    NLOHMANN_JSON_ABI_TAGS_CONCAT(                                   \
-            NLOHMANN_JSON_ABI_TAG_DIAGNOSTICS,                       \
-            NLOHMANN_JSON_ABI_TAG_LEGACY_DISCARDED_VALUE_COMPARISON)
-
-// Construct the namespace version component
-#define NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT_EX(major, minor, patch) \
-    _v ## major ## _ ## minor ## _ ## patch
-#define NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT(major, minor, patch) \
-    NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT_EX(major, minor, patch)
-
-#if NLOHMANN_JSON_NAMESPACE_NO_VERSION
-#define NLOHMANN_JSON_NAMESPACE_VERSION
-#else
-#define NLOHMANN_JSON_NAMESPACE_VERSION                                 \
-    NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT(NLOHMANN_JSON_VERSION_MAJOR, \
-                                           NLOHMANN_JSON_VERSION_MINOR, \
-                                           NLOHMANN_JSON_VERSION_PATCH)
-#endif
-
-// Combine namespace components
-#define NLOHMANN_JSON_NAMESPACE_CONCAT_EX(a, b) a ## b
-#define NLOHMANN_JSON_NAMESPACE_CONCAT(a, b) \
-    NLOHMANN_JSON_NAMESPACE_CONCAT_EX(a, b)
-
-#ifndef NLOHMANN_JSON_NAMESPACE
-#define NLOHMANN_JSON_NAMESPACE               \
-    nlohmann::NLOHMANN_JSON_NAMESPACE_CONCAT( \
-            NLOHMANN_JSON_ABI_TAGS,           \
-            NLOHMANN_JSON_NAMESPACE_VERSION)
-#endif
-
-#ifndef NLOHMANN_JSON_NAMESPACE_BEGIN
-#define NLOHMANN_JSON_NAMESPACE_BEGIN                \
-    namespace nlohmann                               \
-    {                                                \
-    inline namespace NLOHMANN_JSON_NAMESPACE_CONCAT( \
-                NLOHMANN_JSON_ABI_TAGS,              \
-                NLOHMANN_JSON_NAMESPACE_VERSION)     \
-    {
-#endif
-
-#ifndef NLOHMANN_JSON_NAMESPACE_END
-#define NLOHMANN_JSON_NAMESPACE_END                                     \
-    }  /* namespace (inline namespace) NOLINT(readability/namespace) */ \
-    }  // namespace nlohmann
-#endif

sample/lib/json/include/nlohmann/detail/conversions/from_json.hpp

@@ -1,497 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <algorithm> // transform
-#include <array> // array
-#include <forward_list> // forward_list
-#include <iterator> // inserter, front_inserter, end
-#include <map> // map
-#include <string> // string
-#include <tuple> // tuple, make_tuple
-#include <type_traits> // is_arithmetic, is_same, is_enum, underlying_type, is_convertible
-#include <unordered_map> // unordered_map
-#include <utility> // pair, declval
-#include <valarray> // valarray
-
-#include <nlohmann/detail/exceptions.hpp>
-#include <nlohmann/detail/macro_scope.hpp>
-#include <nlohmann/detail/meta/cpp_future.hpp>
-#include <nlohmann/detail/meta/identity_tag.hpp>
-#include <nlohmann/detail/meta/std_fs.hpp>
-#include <nlohmann/detail/meta/type_traits.hpp>
-#include <nlohmann/detail/string_concat.hpp>
-#include <nlohmann/detail/value_t.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename std::nullptr_t& n)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_null()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be null, but is ", j.type_name()), &j));
-    }
-    n = nullptr;
-}
-
-// overloads for basic_json template parameters
-template < typename BasicJsonType, typename ArithmeticType,
-           enable_if_t < std::is_arithmetic<ArithmeticType>::value&&
-                         !std::is_same<ArithmeticType, typename BasicJsonType::boolean_t>::value,
-                         int > = 0 >
-void get_arithmetic_value(const BasicJsonType& j, ArithmeticType& val)
-{
-    switch (static_cast<value_t>(j))
-    {
-        case value_t::number_unsigned:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_unsigned_t*>());
-            break;
-        }
-        case value_t::number_integer:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_integer_t*>());
-            break;
-        }
-        case value_t::number_float:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_float_t*>());
-            break;
-        }
-
-        case value_t::null:
-        case value_t::object:
-        case value_t::array:
-        case value_t::string:
-        case value_t::boolean:
-        case value_t::binary:
-        case value_t::discarded:
-        default:
-            JSON_THROW(type_error::create(302, concat("type must be number, but is ", j.type_name()), &j));
-    }
-}
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename BasicJsonType::boolean_t& b)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_boolean()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be boolean, but is ", j.type_name()), &j));
-    }
-    b = *j.template get_ptr<const typename BasicJsonType::boolean_t*>();
-}
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename BasicJsonType::string_t& s)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_string()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be string, but is ", j.type_name()), &j));
-    }
-    s = *j.template get_ptr<const typename BasicJsonType::string_t*>();
-}
-
-template <
-    typename BasicJsonType, typename StringType,
-    enable_if_t <
-        std::is_assignable<StringType&, const typename BasicJsonType::string_t>::value
-        && is_detected_exact<typename BasicJsonType::string_t::value_type, value_type_t, StringType>::value
-        && !std::is_same<typename BasicJsonType::string_t, StringType>::value
-        && !is_json_ref<StringType>::value, int > = 0 >
-inline void from_json(const BasicJsonType& j, StringType& s)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_string()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be string, but is ", j.type_name()), &j));
-    }
-
-    s = *j.template get_ptr<const typename BasicJsonType::string_t*>();
-}
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename BasicJsonType::number_float_t& val)
-{
-    get_arithmetic_value(j, val);
-}
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename BasicJsonType::number_unsigned_t& val)
-{
-    get_arithmetic_value(j, val);
-}
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename BasicJsonType::number_integer_t& val)
-{
-    get_arithmetic_value(j, val);
-}
-
-#if !JSON_DISABLE_ENUM_SERIALIZATION
-template<typename BasicJsonType, typename EnumType,
-         enable_if_t<std::is_enum<EnumType>::value, int> = 0>
-inline void from_json(const BasicJsonType& j, EnumType& e)
-{
-    typename std::underlying_type<EnumType>::type val;
-    get_arithmetic_value(j, val);
-    e = static_cast<EnumType>(val);
-}
-#endif  // JSON_DISABLE_ENUM_SERIALIZATION
-
-// forward_list doesn't have an insert method
-template<typename BasicJsonType, typename T, typename Allocator,
-         enable_if_t<is_getable<BasicJsonType, T>::value, int> = 0>
-inline void from_json(const BasicJsonType& j, std::forward_list<T, Allocator>& l)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-    l.clear();
-    std::transform(j.rbegin(), j.rend(),
-                   std::front_inserter(l), [](const BasicJsonType & i)
-    {
-        return i.template get<T>();
-    });
-}
-
-// valarray doesn't have an insert method
-template<typename BasicJsonType, typename T,
-         enable_if_t<is_getable<BasicJsonType, T>::value, int> = 0>
-inline void from_json(const BasicJsonType& j, std::valarray<T>& l)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-    l.resize(j.size());
-    std::transform(j.begin(), j.end(), std::begin(l),
-                   [](const BasicJsonType & elem)
-    {
-        return elem.template get<T>();
-    });
-}
-
-template<typename BasicJsonType, typename T, std::size_t N>
-auto from_json(const BasicJsonType& j, T (&arr)[N])  // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
--> decltype(j.template get<T>(), void())
-{
-    for (std::size_t i = 0; i < N; ++i)
-    {
-        arr[i] = j.at(i).template get<T>();
-    }
-}
-
-template<typename BasicJsonType>
-inline void from_json_array_impl(const BasicJsonType& j, typename BasicJsonType::array_t& arr, priority_tag<3> /*unused*/)
-{
-    arr = *j.template get_ptr<const typename BasicJsonType::array_t*>();
-}
-
-template<typename BasicJsonType, typename T, std::size_t N>
-auto from_json_array_impl(const BasicJsonType& j, std::array<T, N>& arr,
-                          priority_tag<2> /*unused*/)
--> decltype(j.template get<T>(), void())
-{
-    for (std::size_t i = 0; i < N; ++i)
-    {
-        arr[i] = j.at(i).template get<T>();
-    }
-}
-
-template<typename BasicJsonType, typename ConstructibleArrayType,
-         enable_if_t<
-             std::is_assignable<ConstructibleArrayType&, ConstructibleArrayType>::value,
-             int> = 0>
-auto from_json_array_impl(const BasicJsonType& j, ConstructibleArrayType& arr, priority_tag<1> /*unused*/)
--> decltype(
-    arr.reserve(std::declval<typename ConstructibleArrayType::size_type>()),
-    j.template get<typename ConstructibleArrayType::value_type>(),
-    void())
-{
-    using std::end;
-
-    ConstructibleArrayType ret;
-    ret.reserve(j.size());
-    std::transform(j.begin(), j.end(),
-                   std::inserter(ret, end(ret)), [](const BasicJsonType & i)
-    {
-        // get<BasicJsonType>() returns *this, this won't call a from_json
-        // method when value_type is BasicJsonType
-        return i.template get<typename ConstructibleArrayType::value_type>();
-    });
-    arr = std::move(ret);
-}
-
-template<typename BasicJsonType, typename ConstructibleArrayType,
-         enable_if_t<
-             std::is_assignable<ConstructibleArrayType&, ConstructibleArrayType>::value,
-             int> = 0>
-inline void from_json_array_impl(const BasicJsonType& j, ConstructibleArrayType& arr,
-                                 priority_tag<0> /*unused*/)
-{
-    using std::end;
-
-    ConstructibleArrayType ret;
-    std::transform(
-        j.begin(), j.end(), std::inserter(ret, end(ret)),
-        [](const BasicJsonType & i)
-    {
-        // get<BasicJsonType>() returns *this, this won't call a from_json
-        // method when value_type is BasicJsonType
-        return i.template get<typename ConstructibleArrayType::value_type>();
-    });
-    arr = std::move(ret);
-}
-
-template < typename BasicJsonType, typename ConstructibleArrayType,
-           enable_if_t <
-               is_constructible_array_type<BasicJsonType, ConstructibleArrayType>::value&&
-               !is_constructible_object_type<BasicJsonType, ConstructibleArrayType>::value&&
-               !is_constructible_string_type<BasicJsonType, ConstructibleArrayType>::value&&
-               !std::is_same<ConstructibleArrayType, typename BasicJsonType::binary_t>::value&&
-               !is_basic_json<ConstructibleArrayType>::value,
-               int > = 0 >
-auto from_json(const BasicJsonType& j, ConstructibleArrayType& arr)
--> decltype(from_json_array_impl(j, arr, priority_tag<3> {}),
-j.template get<typename ConstructibleArrayType::value_type>(),
-void())
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-
-    from_json_array_impl(j, arr, priority_tag<3> {});
-}
-
-template < typename BasicJsonType, typename T, std::size_t... Idx >
-std::array<T, sizeof...(Idx)> from_json_inplace_array_impl(BasicJsonType&& j,
-        identity_tag<std::array<T, sizeof...(Idx)>> /*unused*/, index_sequence<Idx...> /*unused*/)
-{
-    return { { std::forward<BasicJsonType>(j).at(Idx).template get<T>()... } };
-}
-
-template < typename BasicJsonType, typename T, std::size_t N >
-auto from_json(BasicJsonType&& j, identity_tag<std::array<T, N>> tag)
--> decltype(from_json_inplace_array_impl(std::forward<BasicJsonType>(j), tag, make_index_sequence<N> {}))
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-
-    return from_json_inplace_array_impl(std::forward<BasicJsonType>(j), tag, make_index_sequence<N> {});
-}
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename BasicJsonType::binary_t& bin)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_binary()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be binary, but is ", j.type_name()), &j));
-    }
-
-    bin = *j.template get_ptr<const typename BasicJsonType::binary_t*>();
-}
-
-template<typename BasicJsonType, typename ConstructibleObjectType,
-         enable_if_t<is_constructible_object_type<BasicJsonType, ConstructibleObjectType>::value, int> = 0>
-inline void from_json(const BasicJsonType& j, ConstructibleObjectType& obj)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_object()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be object, but is ", j.type_name()), &j));
-    }
-
-    ConstructibleObjectType ret;
-    const auto* inner_object = j.template get_ptr<const typename BasicJsonType::object_t*>();
-    using value_type = typename ConstructibleObjectType::value_type;
-    std::transform(
-        inner_object->begin(), inner_object->end(),
-        std::inserter(ret, ret.begin()),
-        [](typename BasicJsonType::object_t::value_type const & p)
-    {
-        return value_type(p.first, p.second.template get<typename ConstructibleObjectType::mapped_type>());
-    });
-    obj = std::move(ret);
-}
-
-// overload for arithmetic types, not chosen for basic_json template arguments
-// (BooleanType, etc..); note: Is it really necessary to provide explicit
-// overloads for boolean_t etc. in case of a custom BooleanType which is not
-// an arithmetic type?
-template < typename BasicJsonType, typename ArithmeticType,
-           enable_if_t <
-               std::is_arithmetic<ArithmeticType>::value&&
-               !std::is_same<ArithmeticType, typename BasicJsonType::number_unsigned_t>::value&&
-               !std::is_same<ArithmeticType, typename BasicJsonType::number_integer_t>::value&&
-               !std::is_same<ArithmeticType, typename BasicJsonType::number_float_t>::value&&
-               !std::is_same<ArithmeticType, typename BasicJsonType::boolean_t>::value,
-               int > = 0 >
-inline void from_json(const BasicJsonType& j, ArithmeticType& val)
-{
-    switch (static_cast<value_t>(j))
-    {
-        case value_t::number_unsigned:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_unsigned_t*>());
-            break;
-        }
-        case value_t::number_integer:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_integer_t*>());
-            break;
-        }
-        case value_t::number_float:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_float_t*>());
-            break;
-        }
-        case value_t::boolean:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::boolean_t*>());
-            break;
-        }
-
-        case value_t::null:
-        case value_t::object:
-        case value_t::array:
-        case value_t::string:
-        case value_t::binary:
-        case value_t::discarded:
-        default:
-            JSON_THROW(type_error::create(302, concat("type must be number, but is ", j.type_name()), &j));
-    }
-}
-
-template<typename BasicJsonType, typename... Args, std::size_t... Idx>
-std::tuple<Args...> from_json_tuple_impl_base(BasicJsonType&& j, index_sequence<Idx...> /*unused*/)
-{
-    return std::make_tuple(std::forward<BasicJsonType>(j).at(Idx).template get<Args>()...);
-}
-
-template < typename BasicJsonType, class A1, class A2 >
-std::pair<A1, A2> from_json_tuple_impl(BasicJsonType&& j, identity_tag<std::pair<A1, A2>> /*unused*/, priority_tag<0> /*unused*/)
-{
-    return {std::forward<BasicJsonType>(j).at(0).template get<A1>(),
-            std::forward<BasicJsonType>(j).at(1).template get<A2>()};
-}
-
-template<typename BasicJsonType, typename A1, typename A2>
-inline void from_json_tuple_impl(BasicJsonType&& j, std::pair<A1, A2>& p, priority_tag<1> /*unused*/)
-{
-    p = from_json_tuple_impl(std::forward<BasicJsonType>(j), identity_tag<std::pair<A1, A2>> {}, priority_tag<0> {});
-}
-
-template<typename BasicJsonType, typename... Args>
-std::tuple<Args...> from_json_tuple_impl(BasicJsonType&& j, identity_tag<std::tuple<Args...>> /*unused*/, priority_tag<2> /*unused*/)
-{
-    return from_json_tuple_impl_base<BasicJsonType, Args...>(std::forward<BasicJsonType>(j), index_sequence_for<Args...> {});
-}
-
-template<typename BasicJsonType, typename... Args>
-inline void from_json_tuple_impl(BasicJsonType&& j, std::tuple<Args...>& t, priority_tag<3> /*unused*/)
-{
-    t = from_json_tuple_impl_base<BasicJsonType, Args...>(std::forward<BasicJsonType>(j), index_sequence_for<Args...> {});
-}
-
-template<typename BasicJsonType, typename TupleRelated>
-auto from_json(BasicJsonType&& j, TupleRelated&& t)
--> decltype(from_json_tuple_impl(std::forward<BasicJsonType>(j), std::forward<TupleRelated>(t), priority_tag<3> {}))
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-
-    return from_json_tuple_impl(std::forward<BasicJsonType>(j), std::forward<TupleRelated>(t), priority_tag<3> {});
-}
-
-template < typename BasicJsonType, typename Key, typename Value, typename Compare, typename Allocator,
-           typename = enable_if_t < !std::is_constructible <
-                                        typename BasicJsonType::string_t, Key >::value >>
-inline void from_json(const BasicJsonType& j, std::map<Key, Value, Compare, Allocator>& m)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-    m.clear();
-    for (const auto& p : j)
-    {
-        if (JSON_HEDLEY_UNLIKELY(!p.is_array()))
-        {
-            JSON_THROW(type_error::create(302, concat("type must be array, but is ", p.type_name()), &j));
-        }
-        m.emplace(p.at(0).template get<Key>(), p.at(1).template get<Value>());
-    }
-}
-
-template < typename BasicJsonType, typename Key, typename Value, typename Hash, typename KeyEqual, typename Allocator,
-           typename = enable_if_t < !std::is_constructible <
-                                        typename BasicJsonType::string_t, Key >::value >>
-inline void from_json(const BasicJsonType& j, std::unordered_map<Key, Value, Hash, KeyEqual, Allocator>& m)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-    m.clear();
-    for (const auto& p : j)
-    {
-        if (JSON_HEDLEY_UNLIKELY(!p.is_array()))
-        {
-            JSON_THROW(type_error::create(302, concat("type must be array, but is ", p.type_name()), &j));
-        }
-        m.emplace(p.at(0).template get<Key>(), p.at(1).template get<Value>());
-    }
-}
-
-#if JSON_HAS_FILESYSTEM || JSON_HAS_EXPERIMENTAL_FILESYSTEM
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, std_fs::path& p)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_string()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be string, but is ", j.type_name()), &j));
-    }
-    p = *j.template get_ptr<const typename BasicJsonType::string_t*>();
-}
-#endif
-
-struct from_json_fn
-{
-    template<typename BasicJsonType, typename T>
-    auto operator()(const BasicJsonType& j, T&& val) const
-    noexcept(noexcept(from_json(j, std::forward<T>(val))))
-    -> decltype(from_json(j, std::forward<T>(val)))
-    {
-        return from_json(j, std::forward<T>(val));
-    }
-};
-
-}  // namespace detail
-
-#ifndef JSON_HAS_CPP_17
-/// namespace to hold default `from_json` function
-/// to see why this is required:
-/// http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2015/n4381.html
-namespace // NOLINT(cert-dcl59-cpp,fuchsia-header-anon-namespaces,google-build-namespaces)
-{
-#endif
-JSON_INLINE_VARIABLE constexpr const auto& from_json = // NOLINT(misc-definitions-in-headers)
-    detail::static_const<detail::from_json_fn>::value;
-#ifndef JSON_HAS_CPP_17
-}  // namespace
-#endif
-
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/conversions/to_json.hpp

@@ -1,447 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <algorithm> // copy
-#include <iterator> // begin, end
-#include <string> // string
-#include <tuple> // tuple, get
-#include <type_traits> // is_same, is_constructible, is_floating_point, is_enum, underlying_type
-#include <utility> // move, forward, declval, pair
-#include <valarray> // valarray
-#include <vector> // vector
-
-#include <nlohmann/detail/iterators/iteration_proxy.hpp>
-#include <nlohmann/detail/macro_scope.hpp>
-#include <nlohmann/detail/meta/cpp_future.hpp>
-#include <nlohmann/detail/meta/std_fs.hpp>
-#include <nlohmann/detail/meta/type_traits.hpp>
-#include <nlohmann/detail/value_t.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-//////////////////
-// constructors //
-//////////////////
-
-/*
- * Note all external_constructor<>::construct functions need to call
- * j.m_data.m_value.destroy(j.m_data.m_type) to avoid a memory leak in case j contains an
- * allocated value (e.g., a string). See bug issue
- * https://github.com/nlohmann/json/issues/2865 for more information.
- */
-
-template<value_t> struct external_constructor;
-
-template<>
-struct external_constructor<value_t::boolean>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::boolean_t b) noexcept
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::boolean;
-        j.m_data.m_value = b;
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::string>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, const typename BasicJsonType::string_t& s)
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::string;
-        j.m_data.m_value = s;
-        j.assert_invariant();
-    }
-
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::string_t&& s)
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::string;
-        j.m_data.m_value = std::move(s);
-        j.assert_invariant();
-    }
-
-    template < typename BasicJsonType, typename CompatibleStringType,
-               enable_if_t < !std::is_same<CompatibleStringType, typename BasicJsonType::string_t>::value,
-                             int > = 0 >
-    static void construct(BasicJsonType& j, const CompatibleStringType& str)
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::string;
-        j.m_data.m_value.string = j.template create<typename BasicJsonType::string_t>(str);
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::binary>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, const typename BasicJsonType::binary_t& b)
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::binary;
-        j.m_data.m_value = typename BasicJsonType::binary_t(b);
-        j.assert_invariant();
-    }
-
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::binary_t&& b)
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::binary;
-        j.m_data.m_value = typename BasicJsonType::binary_t(std::move(b));
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::number_float>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::number_float_t val) noexcept
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::number_float;
-        j.m_data.m_value = val;
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::number_unsigned>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::number_unsigned_t val) noexcept
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::number_unsigned;
-        j.m_data.m_value = val;
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::number_integer>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::number_integer_t val) noexcept
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::number_integer;
-        j.m_data.m_value = val;
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::array>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, const typename BasicJsonType::array_t& arr)
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::array;
-        j.m_data.m_value = arr;
-        j.set_parents();
-        j.assert_invariant();
-    }
-
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::array_t&& arr)
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::array;
-        j.m_data.m_value = std::move(arr);
-        j.set_parents();
-        j.assert_invariant();
-    }
-
-    template < typename BasicJsonType, typename CompatibleArrayType,
-               enable_if_t < !std::is_same<CompatibleArrayType, typename BasicJsonType::array_t>::value,
-                             int > = 0 >
-    static void construct(BasicJsonType& j, const CompatibleArrayType& arr)
-    {
-        using std::begin;
-        using std::end;
-
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::array;
-        j.m_data.m_value.array = j.template create<typename BasicJsonType::array_t>(begin(arr), end(arr));
-        j.set_parents();
-        j.assert_invariant();
-    }
-
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, const std::vector<bool>& arr)
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::array;
-        j.m_data.m_value = value_t::array;
-        j.m_data.m_value.array->reserve(arr.size());
-        for (const bool x : arr)
-        {
-            j.m_data.m_value.array->push_back(x);
-            j.set_parent(j.m_data.m_value.array->back());
-        }
-        j.assert_invariant();
-    }
-
-    template<typename BasicJsonType, typename T,
-             enable_if_t<std::is_convertible<T, BasicJsonType>::value, int> = 0>
-    static void construct(BasicJsonType& j, const std::valarray<T>& arr)
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::array;
-        j.m_data.m_value = value_t::array;
-        j.m_data.m_value.array->resize(arr.size());
-        if (arr.size() > 0)
-        {
-            std::copy(std::begin(arr), std::end(arr), j.m_data.m_value.array->begin());
-        }
-        j.set_parents();
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::object>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, const typename BasicJsonType::object_t& obj)
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::object;
-        j.m_data.m_value = obj;
-        j.set_parents();
-        j.assert_invariant();
-    }
-
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::object_t&& obj)
-    {
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::object;
-        j.m_data.m_value = std::move(obj);
-        j.set_parents();
-        j.assert_invariant();
-    }
-
-    template < typename BasicJsonType, typename CompatibleObjectType,
-               enable_if_t < !std::is_same<CompatibleObjectType, typename BasicJsonType::object_t>::value, int > = 0 >
-    static void construct(BasicJsonType& j, const CompatibleObjectType& obj)
-    {
-        using std::begin;
-        using std::end;
-
-        j.m_data.m_value.destroy(j.m_data.m_type);
-        j.m_data.m_type = value_t::object;
-        j.m_data.m_value.object = j.template create<typename BasicJsonType::object_t>(begin(obj), end(obj));
-        j.set_parents();
-        j.assert_invariant();
-    }
-};
-
-/////////////
-// to_json //
-/////////////
-
-template<typename BasicJsonType, typename T,
-         enable_if_t<std::is_same<T, typename BasicJsonType::boolean_t>::value, int> = 0>
-inline void to_json(BasicJsonType& j, T b) noexcept
-{
-    external_constructor<value_t::boolean>::construct(j, b);
-}
-
-template < typename BasicJsonType, typename BoolRef,
-           enable_if_t <
-               ((std::is_same<std::vector<bool>::reference, BoolRef>::value
-                 && !std::is_same <std::vector<bool>::reference, typename BasicJsonType::boolean_t&>::value)
-                || (std::is_same<std::vector<bool>::const_reference, BoolRef>::value
-                    && !std::is_same <detail::uncvref_t<std::vector<bool>::const_reference>,
-                                      typename BasicJsonType::boolean_t >::value))
-               && std::is_convertible<const BoolRef&, typename BasicJsonType::boolean_t>::value, int > = 0 >
-inline void to_json(BasicJsonType& j, const BoolRef& b) noexcept
-{
-    external_constructor<value_t::boolean>::construct(j, static_cast<typename BasicJsonType::boolean_t>(b));
-}
-
-template<typename BasicJsonType, typename CompatibleString,
-         enable_if_t<std::is_constructible<typename BasicJsonType::string_t, CompatibleString>::value, int> = 0>
-inline void to_json(BasicJsonType& j, const CompatibleString& s)
-{
-    external_constructor<value_t::string>::construct(j, s);
-}
-
-template<typename BasicJsonType>
-inline void to_json(BasicJsonType& j, typename BasicJsonType::string_t&& s)
-{
-    external_constructor<value_t::string>::construct(j, std::move(s));
-}
-
-template<typename BasicJsonType, typename FloatType,
-         enable_if_t<std::is_floating_point<FloatType>::value, int> = 0>
-inline void to_json(BasicJsonType& j, FloatType val) noexcept
-{
-    external_constructor<value_t::number_float>::construct(j, static_cast<typename BasicJsonType::number_float_t>(val));
-}
-
-template<typename BasicJsonType, typename CompatibleNumberUnsignedType,
-         enable_if_t<is_compatible_integer_type<typename BasicJsonType::number_unsigned_t, CompatibleNumberUnsignedType>::value, int> = 0>
-inline void to_json(BasicJsonType& j, CompatibleNumberUnsignedType val) noexcept
-{
-    external_constructor<value_t::number_unsigned>::construct(j, static_cast<typename BasicJsonType::number_unsigned_t>(val));
-}
-
-template<typename BasicJsonType, typename CompatibleNumberIntegerType,
-         enable_if_t<is_compatible_integer_type<typename BasicJsonType::number_integer_t, CompatibleNumberIntegerType>::value, int> = 0>
-inline void to_json(BasicJsonType& j, CompatibleNumberIntegerType val) noexcept
-{
-    external_constructor<value_t::number_integer>::construct(j, static_cast<typename BasicJsonType::number_integer_t>(val));
-}
-
-#if !JSON_DISABLE_ENUM_SERIALIZATION
-template<typename BasicJsonType, typename EnumType,
-         enable_if_t<std::is_enum<EnumType>::value, int> = 0>
-inline void to_json(BasicJsonType& j, EnumType e) noexcept
-{
-    using underlying_type = typename std::underlying_type<EnumType>::type;
-    static constexpr value_t integral_value_t = std::is_unsigned<underlying_type>::value ? value_t::number_unsigned : value_t::number_integer;
-    external_constructor<integral_value_t>::construct(j, static_cast<underlying_type>(e));
-}
-#endif  // JSON_DISABLE_ENUM_SERIALIZATION
-
-template<typename BasicJsonType>
-inline void to_json(BasicJsonType& j, const std::vector<bool>& e)
-{
-    external_constructor<value_t::array>::construct(j, e);
-}
-
-template < typename BasicJsonType, typename CompatibleArrayType,
-           enable_if_t < is_compatible_array_type<BasicJsonType,
-                         CompatibleArrayType>::value&&
-                         !is_compatible_object_type<BasicJsonType, CompatibleArrayType>::value&&
-                         !is_compatible_string_type<BasicJsonType, CompatibleArrayType>::value&&
-                         !std::is_same<typename BasicJsonType::binary_t, CompatibleArrayType>::value&&
-                         !is_basic_json<CompatibleArrayType>::value,
-                         int > = 0 >
-inline void to_json(BasicJsonType& j, const CompatibleArrayType& arr)
-{
-    external_constructor<value_t::array>::construct(j, arr);
-}
-
-template<typename BasicJsonType>
-inline void to_json(BasicJsonType& j, const typename BasicJsonType::binary_t& bin)
-{
-    external_constructor<value_t::binary>::construct(j, bin);
-}
-
-template<typename BasicJsonType, typename T,
-         enable_if_t<std::is_convertible<T, BasicJsonType>::value, int> = 0>
-inline void to_json(BasicJsonType& j, const std::valarray<T>& arr)
-{
-    external_constructor<value_t::array>::construct(j, std::move(arr));
-}
-
-template<typename BasicJsonType>
-inline void to_json(BasicJsonType& j, typename BasicJsonType::array_t&& arr)
-{
-    external_constructor<value_t::array>::construct(j, std::move(arr));
-}
-
-template < typename BasicJsonType, typename CompatibleObjectType,
-           enable_if_t < is_compatible_object_type<BasicJsonType, CompatibleObjectType>::value&& !is_basic_json<CompatibleObjectType>::value, int > = 0 >
-inline void to_json(BasicJsonType& j, const CompatibleObjectType& obj)
-{
-    external_constructor<value_t::object>::construct(j, obj);
-}
-
-template<typename BasicJsonType>
-inline void to_json(BasicJsonType& j, typename BasicJsonType::object_t&& obj)
-{
-    external_constructor<value_t::object>::construct(j, std::move(obj));
-}
-
-template <
-    typename BasicJsonType, typename T, std::size_t N,
-    enable_if_t < !std::is_constructible<typename BasicJsonType::string_t,
-                  const T(&)[N]>::value, // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
-                  int > = 0 >
-inline void to_json(BasicJsonType& j, const T(&arr)[N]) // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
-{
-    external_constructor<value_t::array>::construct(j, arr);
-}
-
-template < typename BasicJsonType, typename T1, typename T2, enable_if_t < std::is_constructible<BasicJsonType, T1>::value&& std::is_constructible<BasicJsonType, T2>::value, int > = 0 >
-inline void to_json(BasicJsonType& j, const std::pair<T1, T2>& p)
-{
-    j = { p.first, p.second };
-}
-
-// for https://github.com/nlohmann/json/pull/1134
-template<typename BasicJsonType, typename T,
-         enable_if_t<std::is_same<T, iteration_proxy_value<typename BasicJsonType::iterator>>::value, int> = 0>
-inline void to_json(BasicJsonType& j, const T& b)
-{
-    j = { {b.key(), b.value()} };
-}
-
-template<typename BasicJsonType, typename Tuple, std::size_t... Idx>
-inline void to_json_tuple_impl(BasicJsonType& j, const Tuple& t, index_sequence<Idx...> /*unused*/)
-{
-    j = { std::get<Idx>(t)... };
-}
-
-template<typename BasicJsonType, typename T, enable_if_t<is_constructible_tuple<BasicJsonType, T>::value, int > = 0>
-inline void to_json(BasicJsonType& j, const T& t)
-{
-    to_json_tuple_impl(j, t, make_index_sequence<std::tuple_size<T>::value> {});
-}
-
-#if JSON_HAS_FILESYSTEM || JSON_HAS_EXPERIMENTAL_FILESYSTEM
-template<typename BasicJsonType>
-inline void to_json(BasicJsonType& j, const std_fs::path& p)
-{
-    j = p.string();
-}
-#endif
-
-struct to_json_fn
-{
-    template<typename BasicJsonType, typename T>
-    auto operator()(BasicJsonType& j, T&& val) const noexcept(noexcept(to_json(j, std::forward<T>(val))))
-    -> decltype(to_json(j, std::forward<T>(val)), void())
-    {
-        return to_json(j, std::forward<T>(val));
-    }
-};
-}  // namespace detail
-
-#ifndef JSON_HAS_CPP_17
-/// namespace to hold default `to_json` function
-/// to see why this is required:
-/// http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2015/n4381.html
-namespace // NOLINT(cert-dcl59-cpp,fuchsia-header-anon-namespaces,google-build-namespaces)
-{
-#endif
-JSON_INLINE_VARIABLE constexpr const auto& to_json = // NOLINT(misc-definitions-in-headers)
-    detail::static_const<detail::to_json_fn>::value;
-#ifndef JSON_HAS_CPP_17
-}  // namespace
-#endif
-
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/exceptions.hpp

@@ -1,257 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <cstddef> // nullptr_t
-#include <exception> // exception
-#if JSON_DIAGNOSTICS
-    #include <numeric> // accumulate
-#endif
-#include <stdexcept> // runtime_error
-#include <string> // to_string
-#include <vector> // vector
-
-#include <nlohmann/detail/value_t.hpp>
-#include <nlohmann/detail/string_escape.hpp>
-#include <nlohmann/detail/input/position_t.hpp>
-#include <nlohmann/detail/macro_scope.hpp>
-#include <nlohmann/detail/meta/cpp_future.hpp>
-#include <nlohmann/detail/meta/type_traits.hpp>
-#include <nlohmann/detail/string_concat.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-////////////////
-// exceptions //
-////////////////
-
-/// @brief general exception of the @ref basic_json class
-/// @sa https://json.nlohmann.me/api/basic_json/exception/
-class exception : public std::exception
-{
-  public:
-    /// returns the explanatory string
-    const char* what() const noexcept override
-    {
-        return m.what();
-    }
-
-    /// the id of the exception
-    const int id; // NOLINT(cppcoreguidelines-non-private-member-variables-in-classes)
-
-  protected:
-    JSON_HEDLEY_NON_NULL(3)
-    exception(int id_, const char* what_arg) : id(id_), m(what_arg) {} // NOLINT(bugprone-throw-keyword-missing)
-
-    static std::string name(const std::string& ename, int id_)
-    {
-        return concat("[json.exception.", ename, '.', std::to_string(id_), "] ");
-    }
-
-    static std::string diagnostics(std::nullptr_t /*leaf_element*/)
-    {
-        return "";
-    }
-
-    template<typename BasicJsonType>
-    static std::string diagnostics(const BasicJsonType* leaf_element)
-    {
-#if JSON_DIAGNOSTICS
-        std::vector<std::string> tokens;
-        for (const auto* current = leaf_element; current != nullptr && current->m_parent != nullptr; current = current->m_parent)
-        {
-            switch (current->m_parent->type())
-            {
-                case value_t::array:
-                {
-                    for (std::size_t i = 0; i < current->m_parent->m_data.m_value.array->size(); ++i)
-                    {
-                        if (&current->m_parent->m_data.m_value.array->operator[](i) == current)
-                        {
-                            tokens.emplace_back(std::to_string(i));
-                            break;
-                        }
-                    }
-                    break;
-                }
-
-                case value_t::object:
-                {
-                    for (const auto& element : *current->m_parent->m_data.m_value.object)
-                    {
-                        if (&element.second == current)
-                        {
-                            tokens.emplace_back(element.first.c_str());
-                            break;
-                        }
-                    }
-                    break;
-                }
-
-                case value_t::null: // LCOV_EXCL_LINE
-                case value_t::string: // LCOV_EXCL_LINE
-                case value_t::boolean: // LCOV_EXCL_LINE
-                case value_t::number_integer: // LCOV_EXCL_LINE
-                case value_t::number_unsigned: // LCOV_EXCL_LINE
-                case value_t::number_float: // LCOV_EXCL_LINE
-                case value_t::binary: // LCOV_EXCL_LINE
-                case value_t::discarded: // LCOV_EXCL_LINE
-                default:   // LCOV_EXCL_LINE
-                    break; // LCOV_EXCL_LINE
-            }
-        }
-
-        if (tokens.empty())
-        {
-            return "";
-        }
-
-        auto str = std::accumulate(tokens.rbegin(), tokens.rend(), std::string{},
-                                   [](const std::string & a, const std::string & b)
-        {
-            return concat(a, '/', detail::escape(b));
-        });
-        return concat('(', str, ") ");
-#else
-        static_cast<void>(leaf_element);
-        return "";
-#endif
-    }
-
-  private:
-    /// an exception object as storage for error messages
-    std::runtime_error m;
-};
-
-/// @brief exception indicating a parse error
-/// @sa https://json.nlohmann.me/api/basic_json/parse_error/
-class parse_error : public exception
-{
-  public:
-    /*!
-    @brief create a parse error exception
-    @param[in] id_       the id of the exception
-    @param[in] pos       the position where the error occurred (or with
-                         chars_read_total=0 if the position cannot be
-                         determined)
-    @param[in] what_arg  the explanatory string
-    @return parse_error object
-    */
-    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
-    static parse_error create(int id_, const position_t& pos, const std::string& what_arg, BasicJsonContext context)
-    {
-        const std::string w = concat(exception::name("parse_error", id_), "parse error",
-                                     position_string(pos), ": ", exception::diagnostics(context), what_arg);
-        return {id_, pos.chars_read_total, w.c_str()};
-    }
-
-    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
-    static parse_error create(int id_, std::size_t byte_, const std::string& what_arg, BasicJsonContext context)
-    {
-        const std::string w = concat(exception::name("parse_error", id_), "parse error",
-                                     (byte_ != 0 ? (concat(" at byte ", std::to_string(byte_))) : ""),
-                                     ": ", exception::diagnostics(context), what_arg);
-        return {id_, byte_, w.c_str()};
-    }
-
-    /*!
-    @brief byte index of the parse error
-
-    The byte index of the last read character in the input file.
-
-    @note For an input with n bytes, 1 is the index of the first character and
-          n+1 is the index of the terminating null byte or the end of file.
-          This also holds true when reading a byte vector (CBOR or MessagePack).
-    */
-    const std::size_t byte;
-
-  private:
-    parse_error(int id_, std::size_t byte_, const char* what_arg)
-        : exception(id_, what_arg), byte(byte_) {}
-
-    static std::string position_string(const position_t& pos)
-    {
-        return concat(" at line ", std::to_string(pos.lines_read + 1),
-                      ", column ", std::to_string(pos.chars_read_current_line));
-    }
-};
-
-/// @brief exception indicating errors with iterators
-/// @sa https://json.nlohmann.me/api/basic_json/invalid_iterator/
-class invalid_iterator : public exception
-{
-  public:
-    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
-    static invalid_iterator create(int id_, const std::string& what_arg, BasicJsonContext context)
-    {
-        const std::string w = concat(exception::name("invalid_iterator", id_), exception::diagnostics(context), what_arg);
-        return {id_, w.c_str()};
-    }
-
-  private:
-    JSON_HEDLEY_NON_NULL(3)
-    invalid_iterator(int id_, const char* what_arg)
-        : exception(id_, what_arg) {}
-};
-
-/// @brief exception indicating executing a member function with a wrong type
-/// @sa https://json.nlohmann.me/api/basic_json/type_error/
-class type_error : public exception
-{
-  public:
-    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
-    static type_error create(int id_, const std::string& what_arg, BasicJsonContext context)
-    {
-        const std::string w = concat(exception::name("type_error", id_), exception::diagnostics(context), what_arg);
-        return {id_, w.c_str()};
-    }
-
-  private:
-    JSON_HEDLEY_NON_NULL(3)
-    type_error(int id_, const char* what_arg) : exception(id_, what_arg) {}
-};
-
-/// @brief exception indicating access out of the defined range
-/// @sa https://json.nlohmann.me/api/basic_json/out_of_range/
-class out_of_range : public exception
-{
-  public:
-    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
-    static out_of_range create(int id_, const std::string& what_arg, BasicJsonContext context)
-    {
-        const std::string w = concat(exception::name("out_of_range", id_), exception::diagnostics(context), what_arg);
-        return {id_, w.c_str()};
-    }
-
-  private:
-    JSON_HEDLEY_NON_NULL(3)
-    out_of_range(int id_, const char* what_arg) : exception(id_, what_arg) {}
-};
-
-/// @brief exception indicating other library errors
-/// @sa https://json.nlohmann.me/api/basic_json/other_error/
-class other_error : public exception
-{
-  public:
-    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
-    static other_error create(int id_, const std::string& what_arg, BasicJsonContext context)
-    {
-        const std::string w = concat(exception::name("other_error", id_), exception::diagnostics(context), what_arg);
-        return {id_, w.c_str()};
-    }
-
-  private:
-    JSON_HEDLEY_NON_NULL(3)
-    other_error(int id_, const char* what_arg) : exception(id_, what_arg) {}
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/hash.hpp

@@ -1,129 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <cstdint> // uint8_t
-#include <cstddef> // size_t
-#include <functional> // hash
-
-#include <nlohmann/detail/abi_macros.hpp>
-#include <nlohmann/detail/value_t.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-// boost::hash_combine
-inline std::size_t combine(std::size_t seed, std::size_t h) noexcept
-{
-    seed ^= h + 0x9e3779b9 + (seed << 6U) + (seed >> 2U);
-    return seed;
-}
-
-/*!
-@brief hash a JSON value
-
-The hash function tries to rely on std::hash where possible. Furthermore, the
-type of the JSON value is taken into account to have different hash values for
-null, 0, 0U, and false, etc.
-
-@tparam BasicJsonType basic_json specialization
-@param j JSON value to hash
-@return hash value of j
-*/
-template<typename BasicJsonType>
-std::size_t hash(const BasicJsonType& j)
-{
-    using string_t = typename BasicJsonType::string_t;
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-
-    const auto type = static_cast<std::size_t>(j.type());
-    switch (j.type())
-    {
-        case BasicJsonType::value_t::null:
-        case BasicJsonType::value_t::discarded:
-        {
-            return combine(type, 0);
-        }
-
-        case BasicJsonType::value_t::object:
-        {
-            auto seed = combine(type, j.size());
-            for (const auto& element : j.items())
-            {
-                const auto h = std::hash<string_t> {}(element.key());
-                seed = combine(seed, h);
-                seed = combine(seed, hash(element.value()));
-            }
-            return seed;
-        }
-
-        case BasicJsonType::value_t::array:
-        {
-            auto seed = combine(type, j.size());
-            for (const auto& element : j)
-            {
-                seed = combine(seed, hash(element));
-            }
-            return seed;
-        }
-
-        case BasicJsonType::value_t::string:
-        {
-            const auto h = std::hash<string_t> {}(j.template get_ref<const string_t&>());
-            return combine(type, h);
-        }
-
-        case BasicJsonType::value_t::boolean:
-        {
-            const auto h = std::hash<bool> {}(j.template get<bool>());
-            return combine(type, h);
-        }
-
-        case BasicJsonType::value_t::number_integer:
-        {
-            const auto h = std::hash<number_integer_t> {}(j.template get<number_integer_t>());
-            return combine(type, h);
-        }
-
-        case BasicJsonType::value_t::number_unsigned:
-        {
-            const auto h = std::hash<number_unsigned_t> {}(j.template get<number_unsigned_t>());
-            return combine(type, h);
-        }
-
-        case BasicJsonType::value_t::number_float:
-        {
-            const auto h = std::hash<number_float_t> {}(j.template get<number_float_t>());
-            return combine(type, h);
-        }
-
-        case BasicJsonType::value_t::binary:
-        {
-            auto seed = combine(type, j.get_binary().size());
-            const auto h = std::hash<bool> {}(j.get_binary().has_subtype());
-            seed = combine(seed, h);
-            seed = combine(seed, static_cast<std::size_t>(j.get_binary().subtype()));
-            for (const auto byte : j.get_binary())
-            {
-                seed = combine(seed, std::hash<std::uint8_t> {}(byte));
-            }
-            return seed;
-        }
-
-        default:                   // LCOV_EXCL_LINE
-            JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
-            return 0;              // LCOV_EXCL_LINE
-    }
-}
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/input/input_adapters.hpp

@@ -1,492 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <array> // array
-#include <cstddef> // size_t
-#include <cstring> // strlen
-#include <iterator> // begin, end, iterator_traits, random_access_iterator_tag, distance, next
-#include <memory> // shared_ptr, make_shared, addressof
-#include <numeric> // accumulate
-#include <string> // string, char_traits
-#include <type_traits> // enable_if, is_base_of, is_pointer, is_integral, remove_pointer
-#include <utility> // pair, declval
-
-#ifndef JSON_NO_IO
-    #include <cstdio>   // FILE *
-    #include <istream>  // istream
-#endif                  // JSON_NO_IO
-
-#include <nlohmann/detail/iterators/iterator_traits.hpp>
-#include <nlohmann/detail/macro_scope.hpp>
-#include <nlohmann/detail/meta/type_traits.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-/// the supported input formats
-enum class input_format_t { json, cbor, msgpack, ubjson, bson, bjdata };
-
-////////////////////
-// input adapters //
-////////////////////
-
-#ifndef JSON_NO_IO
-/*!
-Input adapter for stdio file access. This adapter read only 1 byte and do not use any
- buffer. This adapter is a very low level adapter.
-*/
-class file_input_adapter
-{
-  public:
-    using char_type = char;
-
-    JSON_HEDLEY_NON_NULL(2)
-    explicit file_input_adapter(std::FILE* f) noexcept
-        : m_file(f)
-    {
-        JSON_ASSERT(m_file != nullptr);
-    }
-
-    // make class move-only
-    file_input_adapter(const file_input_adapter&) = delete;
-    file_input_adapter(file_input_adapter&&) noexcept = default;
-    file_input_adapter& operator=(const file_input_adapter&) = delete;
-    file_input_adapter& operator=(file_input_adapter&&) = delete;
-    ~file_input_adapter() = default;
-
-    std::char_traits<char>::int_type get_character() noexcept
-    {
-        return std::fgetc(m_file);
-    }
-
-  private:
-    /// the file pointer to read from
-    std::FILE* m_file;
-};
-
-/*!
-Input adapter for a (caching) istream. Ignores a UFT Byte Order Mark at
-beginning of input. Does not support changing the underlying std::streambuf
-in mid-input. Maintains underlying std::istream and std::streambuf to support
-subsequent use of standard std::istream operations to process any input
-characters following those used in parsing the JSON input.  Clears the
-std::istream flags; any input errors (e.g., EOF) will be detected by the first
-subsequent call for input from the std::istream.
-*/
-class input_stream_adapter
-{
-  public:
-    using char_type = char;
-
-    ~input_stream_adapter()
-    {
-        // clear stream flags; we use underlying streambuf I/O, do not
-        // maintain ifstream flags, except eof
-        if (is != nullptr)
-        {
-            is->clear(is->rdstate() & std::ios::eofbit);
-        }
-    }
-
-    explicit input_stream_adapter(std::istream& i)
-        : is(&i), sb(i.rdbuf())
-    {}
-
-    // delete because of pointer members
-    input_stream_adapter(const input_stream_adapter&) = delete;
-    input_stream_adapter& operator=(input_stream_adapter&) = delete;
-    input_stream_adapter& operator=(input_stream_adapter&&) = delete;
-
-    input_stream_adapter(input_stream_adapter&& rhs) noexcept
-        : is(rhs.is), sb(rhs.sb)
-    {
-        rhs.is = nullptr;
-        rhs.sb = nullptr;
-    }
-
-    // std::istream/std::streambuf use std::char_traits<char>::to_int_type, to
-    // ensure that std::char_traits<char>::eof() and the character 0xFF do not
-    // end up as the same value, e.g. 0xFFFFFFFF.
-    std::char_traits<char>::int_type get_character()
-    {
-        auto res = sb->sbumpc();
-        // set eof manually, as we don't use the istream interface.
-        if (JSON_HEDLEY_UNLIKELY(res == std::char_traits<char>::eof()))
-        {
-            is->clear(is->rdstate() | std::ios::eofbit);
-        }
-        return res;
-    }
-
-  private:
-    /// the associated input stream
-    std::istream* is = nullptr;
-    std::streambuf* sb = nullptr;
-};
-#endif  // JSON_NO_IO
-
-// General-purpose iterator-based adapter. It might not be as fast as
-// theoretically possible for some containers, but it is extremely versatile.
-template<typename IteratorType>
-class iterator_input_adapter
-{
-  public:
-    using char_type = typename std::iterator_traits<IteratorType>::value_type;
-
-    iterator_input_adapter(IteratorType first, IteratorType last)
-        : current(std::move(first)), end(std::move(last))
-    {}
-
-    typename char_traits<char_type>::int_type get_character()
-    {
-        if (JSON_HEDLEY_LIKELY(current != end))
-        {
-            auto result = char_traits<char_type>::to_int_type(*current);
-            std::advance(current, 1);
-            return result;
-        }
-
-        return char_traits<char_type>::eof();
-    }
-
-  private:
-    IteratorType current;
-    IteratorType end;
-
-    template<typename BaseInputAdapter, size_t T>
-    friend struct wide_string_input_helper;
-
-    bool empty() const
-    {
-        return current == end;
-    }
-};
-
-template<typename BaseInputAdapter, size_t T>
-struct wide_string_input_helper;
-
-template<typename BaseInputAdapter>
-struct wide_string_input_helper<BaseInputAdapter, 4>
-{
-    // UTF-32
-    static void fill_buffer(BaseInputAdapter& input,
-                            std::array<std::char_traits<char>::int_type, 4>& utf8_bytes,
-                            size_t& utf8_bytes_index,
-                            size_t& utf8_bytes_filled)
-    {
-        utf8_bytes_index = 0;
-
-        if (JSON_HEDLEY_UNLIKELY(input.empty()))
-        {
-            utf8_bytes[0] = std::char_traits<char>::eof();
-            utf8_bytes_filled = 1;
-        }
-        else
-        {
-            // get the current character
-            const auto wc = input.get_character();
-
-            // UTF-32 to UTF-8 encoding
-            if (wc < 0x80)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(wc);
-                utf8_bytes_filled = 1;
-            }
-            else if (wc <= 0x7FF)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xC0u | ((static_cast<unsigned int>(wc) >> 6u) & 0x1Fu));
-                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
-                utf8_bytes_filled = 2;
-            }
-            else if (wc <= 0xFFFF)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xE0u | ((static_cast<unsigned int>(wc) >> 12u) & 0x0Fu));
-                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | ((static_cast<unsigned int>(wc) >> 6u) & 0x3Fu));
-                utf8_bytes[2] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
-                utf8_bytes_filled = 3;
-            }
-            else if (wc <= 0x10FFFF)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xF0u | ((static_cast<unsigned int>(wc) >> 18u) & 0x07u));
-                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | ((static_cast<unsigned int>(wc) >> 12u) & 0x3Fu));
-                utf8_bytes[2] = static_cast<std::char_traits<char>::int_type>(0x80u | ((static_cast<unsigned int>(wc) >> 6u) & 0x3Fu));
-                utf8_bytes[3] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
-                utf8_bytes_filled = 4;
-            }
-            else
-            {
-                // unknown character
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(wc);
-                utf8_bytes_filled = 1;
-            }
-        }
-    }
-};
-
-template<typename BaseInputAdapter>
-struct wide_string_input_helper<BaseInputAdapter, 2>
-{
-    // UTF-16
-    static void fill_buffer(BaseInputAdapter& input,
-                            std::array<std::char_traits<char>::int_type, 4>& utf8_bytes,
-                            size_t& utf8_bytes_index,
-                            size_t& utf8_bytes_filled)
-    {
-        utf8_bytes_index = 0;
-
-        if (JSON_HEDLEY_UNLIKELY(input.empty()))
-        {
-            utf8_bytes[0] = std::char_traits<char>::eof();
-            utf8_bytes_filled = 1;
-        }
-        else
-        {
-            // get the current character
-            const auto wc = input.get_character();
-
-            // UTF-16 to UTF-8 encoding
-            if (wc < 0x80)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(wc);
-                utf8_bytes_filled = 1;
-            }
-            else if (wc <= 0x7FF)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xC0u | ((static_cast<unsigned int>(wc) >> 6u)));
-                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
-                utf8_bytes_filled = 2;
-            }
-            else if (0xD800 > wc || wc >= 0xE000)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xE0u | ((static_cast<unsigned int>(wc) >> 12u)));
-                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | ((static_cast<unsigned int>(wc) >> 6u) & 0x3Fu));
-                utf8_bytes[2] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
-                utf8_bytes_filled = 3;
-            }
-            else
-            {
-                if (JSON_HEDLEY_UNLIKELY(!input.empty()))
-                {
-                    const auto wc2 = static_cast<unsigned int>(input.get_character());
-                    const auto charcode = 0x10000u + (((static_cast<unsigned int>(wc) & 0x3FFu) << 10u) | (wc2 & 0x3FFu));
-                    utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xF0u | (charcode >> 18u));
-                    utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | ((charcode >> 12u) & 0x3Fu));
-                    utf8_bytes[2] = static_cast<std::char_traits<char>::int_type>(0x80u | ((charcode >> 6u) & 0x3Fu));
-                    utf8_bytes[3] = static_cast<std::char_traits<char>::int_type>(0x80u | (charcode & 0x3Fu));
-                    utf8_bytes_filled = 4;
-                }
-                else
-                {
-                    utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(wc);
-                    utf8_bytes_filled = 1;
-                }
-            }
-        }
-    }
-};
-
-// Wraps another input adapter to convert wide character types into individual bytes.
-template<typename BaseInputAdapter, typename WideCharType>
-class wide_string_input_adapter
-{
-  public:
-    using char_type = char;
-
-    wide_string_input_adapter(BaseInputAdapter base)
-        : base_adapter(base) {}
-
-    typename std::char_traits<char>::int_type get_character() noexcept
-    {
-        // check if buffer needs to be filled
-        if (utf8_bytes_index == utf8_bytes_filled)
-        {
-            fill_buffer<sizeof(WideCharType)>();
-
-            JSON_ASSERT(utf8_bytes_filled > 0);
-            JSON_ASSERT(utf8_bytes_index == 0);
-        }
-
-        // use buffer
-        JSON_ASSERT(utf8_bytes_filled > 0);
-        JSON_ASSERT(utf8_bytes_index < utf8_bytes_filled);
-        return utf8_bytes[utf8_bytes_index++];
-    }
-
-  private:
-    BaseInputAdapter base_adapter;
-
-    template<size_t T>
-    void fill_buffer()
-    {
-        wide_string_input_helper<BaseInputAdapter, T>::fill_buffer(base_adapter, utf8_bytes, utf8_bytes_index, utf8_bytes_filled);
-    }
-
-    /// a buffer for UTF-8 bytes
-    std::array<std::char_traits<char>::int_type, 4> utf8_bytes = {{0, 0, 0, 0}};
-
-    /// index to the utf8_codes array for the next valid byte
-    std::size_t utf8_bytes_index = 0;
-    /// number of valid bytes in the utf8_codes array
-    std::size_t utf8_bytes_filled = 0;
-};
-
-template<typename IteratorType, typename Enable = void>
-struct iterator_input_adapter_factory
-{
-    using iterator_type = IteratorType;
-    using char_type = typename std::iterator_traits<iterator_type>::value_type;
-    using adapter_type = iterator_input_adapter<iterator_type>;
-
-    static adapter_type create(IteratorType first, IteratorType last)
-    {
-        return adapter_type(std::move(first), std::move(last));
-    }
-};
-
-template<typename T>
-struct is_iterator_of_multibyte
-{
-    using value_type = typename std::iterator_traits<T>::value_type;
-    enum
-    {
-        value = sizeof(value_type) > 1
-    };
-};
-
-template<typename IteratorType>
-struct iterator_input_adapter_factory<IteratorType, enable_if_t<is_iterator_of_multibyte<IteratorType>::value>>
-{
-    using iterator_type = IteratorType;
-    using char_type = typename std::iterator_traits<iterator_type>::value_type;
-    using base_adapter_type = iterator_input_adapter<iterator_type>;
-    using adapter_type = wide_string_input_adapter<base_adapter_type, char_type>;
-
-    static adapter_type create(IteratorType first, IteratorType last)
-    {
-        return adapter_type(base_adapter_type(std::move(first), std::move(last)));
-    }
-};
-
-// General purpose iterator-based input
-template<typename IteratorType>
-typename iterator_input_adapter_factory<IteratorType>::adapter_type input_adapter(IteratorType first, IteratorType last)
-{
-    using factory_type = iterator_input_adapter_factory<IteratorType>;
-    return factory_type::create(first, last);
-}
-
-// Convenience shorthand from container to iterator
-// Enables ADL on begin(container) and end(container)
-// Encloses the using declarations in namespace for not to leak them to outside scope
-
-namespace container_input_adapter_factory_impl
-{
-
-using std::begin;
-using std::end;
-
-template<typename ContainerType, typename Enable = void>
-struct container_input_adapter_factory {};
-
-template<typename ContainerType>
-struct container_input_adapter_factory< ContainerType,
-       void_t<decltype(begin(std::declval<ContainerType>()), end(std::declval<ContainerType>()))>>
-       {
-           using adapter_type = decltype(input_adapter(begin(std::declval<ContainerType>()), end(std::declval<ContainerType>())));
-
-           static adapter_type create(const ContainerType& container)
-{
-    return input_adapter(begin(container), end(container));
-}
-       };
-
-}  // namespace container_input_adapter_factory_impl
-
-template<typename ContainerType>
-typename container_input_adapter_factory_impl::container_input_adapter_factory<ContainerType>::adapter_type input_adapter(const ContainerType& container)
-{
-    return container_input_adapter_factory_impl::container_input_adapter_factory<ContainerType>::create(container);
-}
-
-#ifndef JSON_NO_IO
-// Special cases with fast paths
-inline file_input_adapter input_adapter(std::FILE* file)
-{
-    return file_input_adapter(file);
-}
-
-inline input_stream_adapter input_adapter(std::istream& stream)
-{
-    return input_stream_adapter(stream);
-}
-
-inline input_stream_adapter input_adapter(std::istream&& stream)
-{
-    return input_stream_adapter(stream);
-}
-#endif  // JSON_NO_IO
-
-using contiguous_bytes_input_adapter = decltype(input_adapter(std::declval<const char*>(), std::declval<const char*>()));
-
-// Null-delimited strings, and the like.
-template < typename CharT,
-           typename std::enable_if <
-               std::is_pointer<CharT>::value&&
-               !std::is_array<CharT>::value&&
-               std::is_integral<typename std::remove_pointer<CharT>::type>::value&&
-               sizeof(typename std::remove_pointer<CharT>::type) == 1,
-               int >::type = 0 >
-contiguous_bytes_input_adapter input_adapter(CharT b)
-{
-    auto length = std::strlen(reinterpret_cast<const char*>(b));
-    const auto* ptr = reinterpret_cast<const char*>(b);
-    return input_adapter(ptr, ptr + length);
-}
-
-template<typename T, std::size_t N>
-auto input_adapter(T (&array)[N]) -> decltype(input_adapter(array, array + N)) // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
-{
-    return input_adapter(array, array + N);
-}
-
-// This class only handles inputs of input_buffer_adapter type.
-// It's required so that expressions like {ptr, len} can be implicitly cast
-// to the correct adapter.
-class span_input_adapter
-{
-  public:
-    template < typename CharT,
-               typename std::enable_if <
-                   std::is_pointer<CharT>::value&&
-                   std::is_integral<typename std::remove_pointer<CharT>::type>::value&&
-                   sizeof(typename std::remove_pointer<CharT>::type) == 1,
-                   int >::type = 0 >
-    span_input_adapter(CharT b, std::size_t l)
-        : ia(reinterpret_cast<const char*>(b), reinterpret_cast<const char*>(b) + l) {}
-
-    template<class IteratorType,
-             typename std::enable_if<
-                 std::is_same<typename iterator_traits<IteratorType>::iterator_category, std::random_access_iterator_tag>::value,
-                 int>::type = 0>
-    span_input_adapter(IteratorType first, IteratorType last)
-        : ia(input_adapter(first, last)) {}
-
-    contiguous_bytes_input_adapter&& get()
-    {
-        return std::move(ia); // NOLINT(hicpp-move-const-arg,performance-move-const-arg)
-    }
-
-  private:
-    contiguous_bytes_input_adapter ia;
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/input/json_sax.hpp

@@ -1,727 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <cstddef>
-#include <string> // string
-#include <utility> // move
-#include <vector> // vector
-
-#include <nlohmann/detail/exceptions.hpp>
-#include <nlohmann/detail/macro_scope.hpp>
-#include <nlohmann/detail/string_concat.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-/*!
-@brief SAX interface
-
-This class describes the SAX interface used by @ref nlohmann::json::sax_parse.
-Each function is called in different situations while the input is parsed. The
-boolean return value informs the parser whether to continue processing the
-input.
-*/
-template<typename BasicJsonType>
-struct json_sax
-{
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-
-    /*!
-    @brief a null value was read
-    @return whether parsing should proceed
-    */
-    virtual bool null() = 0;
-
-    /*!
-    @brief a boolean value was read
-    @param[in] val  boolean value
-    @return whether parsing should proceed
-    */
-    virtual bool boolean(bool val) = 0;
-
-    /*!
-    @brief an integer number was read
-    @param[in] val  integer value
-    @return whether parsing should proceed
-    */
-    virtual bool number_integer(number_integer_t val) = 0;
-
-    /*!
-    @brief an unsigned integer number was read
-    @param[in] val  unsigned integer value
-    @return whether parsing should proceed
-    */
-    virtual bool number_unsigned(number_unsigned_t val) = 0;
-
-    /*!
-    @brief a floating-point number was read
-    @param[in] val  floating-point value
-    @param[in] s    raw token value
-    @return whether parsing should proceed
-    */
-    virtual bool number_float(number_float_t val, const string_t& s) = 0;
-
-    /*!
-    @brief a string value was read
-    @param[in] val  string value
-    @return whether parsing should proceed
-    @note It is safe to move the passed string value.
-    */
-    virtual bool string(string_t& val) = 0;
-
-    /*!
-    @brief a binary value was read
-    @param[in] val  binary value
-    @return whether parsing should proceed
-    @note It is safe to move the passed binary value.
-    */
-    virtual bool binary(binary_t& val) = 0;
-
-    /*!
-    @brief the beginning of an object was read
-    @param[in] elements  number of object elements or -1 if unknown
-    @return whether parsing should proceed
-    @note binary formats may report the number of elements
-    */
-    virtual bool start_object(std::size_t elements) = 0;
-
-    /*!
-    @brief an object key was read
-    @param[in] val  object key
-    @return whether parsing should proceed
-    @note It is safe to move the passed string.
-    */
-    virtual bool key(string_t& val) = 0;
-
-    /*!
-    @brief the end of an object was read
-    @return whether parsing should proceed
-    */
-    virtual bool end_object() = 0;
-
-    /*!
-    @brief the beginning of an array was read
-    @param[in] elements  number of array elements or -1 if unknown
-    @return whether parsing should proceed
-    @note binary formats may report the number of elements
-    */
-    virtual bool start_array(std::size_t elements) = 0;
-
-    /*!
-    @brief the end of an array was read
-    @return whether parsing should proceed
-    */
-    virtual bool end_array() = 0;
-
-    /*!
-    @brief a parse error occurred
-    @param[in] position    the position in the input where the error occurs
-    @param[in] last_token  the last read token
-    @param[in] ex          an exception object describing the error
-    @return whether parsing should proceed (must return false)
-    */
-    virtual bool parse_error(std::size_t position,
-                             const std::string& last_token,
-                             const detail::exception& ex) = 0;
-
-    json_sax() = default;
-    json_sax(const json_sax&) = default;
-    json_sax(json_sax&&) noexcept = default;
-    json_sax& operator=(const json_sax&) = default;
-    json_sax& operator=(json_sax&&) noexcept = default;
-    virtual ~json_sax() = default;
-};
-
-namespace detail
-{
-/*!
-@brief SAX implementation to create a JSON value from SAX events
-
-This class implements the @ref json_sax interface and processes the SAX events
-to create a JSON value which makes it basically a DOM parser. The structure or
-hierarchy of the JSON value is managed by the stack `ref_stack` which contains
-a pointer to the respective array or object for each recursion depth.
-
-After successful parsing, the value that is passed by reference to the
-constructor contains the parsed value.
-
-@tparam BasicJsonType  the JSON type
-*/
-template<typename BasicJsonType>
-class json_sax_dom_parser
-{
-  public:
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-
-    /*!
-    @param[in,out] r  reference to a JSON value that is manipulated while
-                       parsing
-    @param[in] allow_exceptions_  whether parse errors yield exceptions
-    */
-    explicit json_sax_dom_parser(BasicJsonType& r, const bool allow_exceptions_ = true)
-        : root(r), allow_exceptions(allow_exceptions_)
-    {}
-
-    // make class move-only
-    json_sax_dom_parser(const json_sax_dom_parser&) = delete;
-    json_sax_dom_parser(json_sax_dom_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
-    json_sax_dom_parser& operator=(const json_sax_dom_parser&) = delete;
-    json_sax_dom_parser& operator=(json_sax_dom_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
-    ~json_sax_dom_parser() = default;
-
-    bool null()
-    {
-        handle_value(nullptr);
-        return true;
-    }
-
-    bool boolean(bool val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool number_integer(number_integer_t val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool number_unsigned(number_unsigned_t val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool number_float(number_float_t val, const string_t& /*unused*/)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool string(string_t& val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool binary(binary_t& val)
-    {
-        handle_value(std::move(val));
-        return true;
-    }
-
-    bool start_object(std::size_t len)
-    {
-        ref_stack.push_back(handle_value(BasicJsonType::value_t::object));
-
-        if (JSON_HEDLEY_UNLIKELY(len != static_cast<std::size_t>(-1) && len > ref_stack.back()->max_size()))
-        {
-            JSON_THROW(out_of_range::create(408, concat("excessive object size: ", std::to_string(len)), ref_stack.back()));
-        }
-
-        return true;
-    }
-
-    bool key(string_t& val)
-    {
-        JSON_ASSERT(!ref_stack.empty());
-        JSON_ASSERT(ref_stack.back()->is_object());
-
-        // add null at given key and store the reference for later
-        object_element = &(ref_stack.back()->m_data.m_value.object->operator[](val));
-        return true;
-    }
-
-    bool end_object()
-    {
-        JSON_ASSERT(!ref_stack.empty());
-        JSON_ASSERT(ref_stack.back()->is_object());
-
-        ref_stack.back()->set_parents();
-        ref_stack.pop_back();
-        return true;
-    }
-
-    bool start_array(std::size_t len)
-    {
-        ref_stack.push_back(handle_value(BasicJsonType::value_t::array));
-
-        if (JSON_HEDLEY_UNLIKELY(len != static_cast<std::size_t>(-1) && len > ref_stack.back()->max_size()))
-        {
-            JSON_THROW(out_of_range::create(408, concat("excessive array size: ", std::to_string(len)), ref_stack.back()));
-        }
-
-        return true;
-    }
-
-    bool end_array()
-    {
-        JSON_ASSERT(!ref_stack.empty());
-        JSON_ASSERT(ref_stack.back()->is_array());
-
-        ref_stack.back()->set_parents();
-        ref_stack.pop_back();
-        return true;
-    }
-
-    template<class Exception>
-    bool parse_error(std::size_t /*unused*/, const std::string& /*unused*/,
-                     const Exception& ex)
-    {
-        errored = true;
-        static_cast<void>(ex);
-        if (allow_exceptions)
-        {
-            JSON_THROW(ex);
-        }
-        return false;
-    }
-
-    constexpr bool is_errored() const
-    {
-        return errored;
-    }
-
-  private:
-    /*!
-    @invariant If the ref stack is empty, then the passed value will be the new
-               root.
-    @invariant If the ref stack contains a value, then it is an array or an
-               object to which we can add elements
-    */
-    template<typename Value>
-    JSON_HEDLEY_RETURNS_NON_NULL
-    BasicJsonType* handle_value(Value&& v)
-    {
-        if (ref_stack.empty())
-        {
-            root = BasicJsonType(std::forward<Value>(v));
-            return &root;
-        }
-
-        JSON_ASSERT(ref_stack.back()->is_array() || ref_stack.back()->is_object());
-
-        if (ref_stack.back()->is_array())
-        {
-            ref_stack.back()->m_data.m_value.array->emplace_back(std::forward<Value>(v));
-            return &(ref_stack.back()->m_data.m_value.array->back());
-        }
-
-        JSON_ASSERT(ref_stack.back()->is_object());
-        JSON_ASSERT(object_element);
-        *object_element = BasicJsonType(std::forward<Value>(v));
-        return object_element;
-    }
-
-    /// the parsed JSON value
-    BasicJsonType& root;
-    /// stack to model hierarchy of values
-    std::vector<BasicJsonType*> ref_stack {};
-    /// helper to hold the reference for the next object element
-    BasicJsonType* object_element = nullptr;
-    /// whether a syntax error occurred
-    bool errored = false;
-    /// whether to throw exceptions in case of errors
-    const bool allow_exceptions = true;
-};
-
-template<typename BasicJsonType>
-class json_sax_dom_callback_parser
-{
-  public:
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-    using parser_callback_t = typename BasicJsonType::parser_callback_t;
-    using parse_event_t = typename BasicJsonType::parse_event_t;
-
-    json_sax_dom_callback_parser(BasicJsonType& r,
-                                 const parser_callback_t cb,
-                                 const bool allow_exceptions_ = true)
-        : root(r), callback(cb), allow_exceptions(allow_exceptions_)
-    {
-        keep_stack.push_back(true);
-    }
-
-    // make class move-only
-    json_sax_dom_callback_parser(const json_sax_dom_callback_parser&) = delete;
-    json_sax_dom_callback_parser(json_sax_dom_callback_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
-    json_sax_dom_callback_parser& operator=(const json_sax_dom_callback_parser&) = delete;
-    json_sax_dom_callback_parser& operator=(json_sax_dom_callback_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
-    ~json_sax_dom_callback_parser() = default;
-
-    bool null()
-    {
-        handle_value(nullptr);
-        return true;
-    }
-
-    bool boolean(bool val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool number_integer(number_integer_t val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool number_unsigned(number_unsigned_t val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool number_float(number_float_t val, const string_t& /*unused*/)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool string(string_t& val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool binary(binary_t& val)
-    {
-        handle_value(std::move(val));
-        return true;
-    }
-
-    bool start_object(std::size_t len)
-    {
-        // check callback for object start
-        const bool keep = callback(static_cast<int>(ref_stack.size()), parse_event_t::object_start, discarded);
-        keep_stack.push_back(keep);
-
-        auto val = handle_value(BasicJsonType::value_t::object, true);
-        ref_stack.push_back(val.second);
-
-        // check object limit
-        if (ref_stack.back() && JSON_HEDLEY_UNLIKELY(len != static_cast<std::size_t>(-1) && len > ref_stack.back()->max_size()))
-        {
-            JSON_THROW(out_of_range::create(408, concat("excessive object size: ", std::to_string(len)), ref_stack.back()));
-        }
-
-        return true;
-    }
-
-    bool key(string_t& val)
-    {
-        BasicJsonType k = BasicJsonType(val);
-
-        // check callback for key
-        const bool keep = callback(static_cast<int>(ref_stack.size()), parse_event_t::key, k);
-        key_keep_stack.push_back(keep);
-
-        // add discarded value at given key and store the reference for later
-        if (keep && ref_stack.back())
-        {
-            object_element = &(ref_stack.back()->m_data.m_value.object->operator[](val) = discarded);
-        }
-
-        return true;
-    }
-
-    bool end_object()
-    {
-        if (ref_stack.back())
-        {
-            if (!callback(static_cast<int>(ref_stack.size()) - 1, parse_event_t::object_end, *ref_stack.back()))
-            {
-                // discard object
-                *ref_stack.back() = discarded;
-            }
-            else
-            {
-                ref_stack.back()->set_parents();
-            }
-        }
-
-        JSON_ASSERT(!ref_stack.empty());
-        JSON_ASSERT(!keep_stack.empty());
-        ref_stack.pop_back();
-        keep_stack.pop_back();
-
-        if (!ref_stack.empty() && ref_stack.back() && ref_stack.back()->is_structured())
-        {
-            // remove discarded value
-            for (auto it = ref_stack.back()->begin(); it != ref_stack.back()->end(); ++it)
-            {
-                if (it->is_discarded())
-                {
-                    ref_stack.back()->erase(it);
-                    break;
-                }
-            }
-        }
-
-        return true;
-    }
-
-    bool start_array(std::size_t len)
-    {
-        const bool keep = callback(static_cast<int>(ref_stack.size()), parse_event_t::array_start, discarded);
-        keep_stack.push_back(keep);
-
-        auto val = handle_value(BasicJsonType::value_t::array, true);
-        ref_stack.push_back(val.second);
-
-        // check array limit
-        if (ref_stack.back() && JSON_HEDLEY_UNLIKELY(len != static_cast<std::size_t>(-1) && len > ref_stack.back()->max_size()))
-        {
-            JSON_THROW(out_of_range::create(408, concat("excessive array size: ", std::to_string(len)), ref_stack.back()));
-        }
-
-        return true;
-    }
-
-    bool end_array()
-    {
-        bool keep = true;
-
-        if (ref_stack.back())
-        {
-            keep = callback(static_cast<int>(ref_stack.size()) - 1, parse_event_t::array_end, *ref_stack.back());
-            if (keep)
-            {
-                ref_stack.back()->set_parents();
-            }
-            else
-            {
-                // discard array
-                *ref_stack.back() = discarded;
-            }
-        }
-
-        JSON_ASSERT(!ref_stack.empty());
-        JSON_ASSERT(!keep_stack.empty());
-        ref_stack.pop_back();
-        keep_stack.pop_back();
-
-        // remove discarded value
-        if (!keep && !ref_stack.empty() && ref_stack.back()->is_array())
-        {
-            ref_stack.back()->m_data.m_value.array->pop_back();
-        }
-
-        return true;
-    }
-
-    template<class Exception>
-    bool parse_error(std::size_t /*unused*/, const std::string& /*unused*/,
-                     const Exception& ex)
-    {
-        errored = true;
-        static_cast<void>(ex);
-        if (allow_exceptions)
-        {
-            JSON_THROW(ex);
-        }
-        return false;
-    }
-
-    constexpr bool is_errored() const
-    {
-        return errored;
-    }
-
-  private:
-    /*!
-    @param[in] v  value to add to the JSON value we build during parsing
-    @param[in] skip_callback  whether we should skip calling the callback
-               function; this is required after start_array() and
-               start_object() SAX events, because otherwise we would call the
-               callback function with an empty array or object, respectively.
-
-    @invariant If the ref stack is empty, then the passed value will be the new
-               root.
-    @invariant If the ref stack contains a value, then it is an array or an
-               object to which we can add elements
-
-    @return pair of boolean (whether value should be kept) and pointer (to the
-            passed value in the ref_stack hierarchy; nullptr if not kept)
-    */
-    template<typename Value>
-    std::pair<bool, BasicJsonType*> handle_value(Value&& v, const bool skip_callback = false)
-    {
-        JSON_ASSERT(!keep_stack.empty());
-
-        // do not handle this value if we know it would be added to a discarded
-        // container
-        if (!keep_stack.back())
-        {
-            return {false, nullptr};
-        }
-
-        // create value
-        auto value = BasicJsonType(std::forward<Value>(v));
-
-        // check callback
-        const bool keep = skip_callback || callback(static_cast<int>(ref_stack.size()), parse_event_t::value, value);
-
-        // do not handle this value if we just learnt it shall be discarded
-        if (!keep)
-        {
-            return {false, nullptr};
-        }
-
-        if (ref_stack.empty())
-        {
-            root = std::move(value);
-            return {true, & root};
-        }
-
-        // skip this value if we already decided to skip the parent
-        // (https://github.com/nlohmann/json/issues/971#issuecomment-413678360)
-        if (!ref_stack.back())
-        {
-            return {false, nullptr};
-        }
-
-        // we now only expect arrays and objects
-        JSON_ASSERT(ref_stack.back()->is_array() || ref_stack.back()->is_object());
-
-        // array
-        if (ref_stack.back()->is_array())
-        {
-            ref_stack.back()->m_data.m_value.array->emplace_back(std::move(value));
-            return {true, & (ref_stack.back()->m_data.m_value.array->back())};
-        }
-
-        // object
-        JSON_ASSERT(ref_stack.back()->is_object());
-        // check if we should store an element for the current key
-        JSON_ASSERT(!key_keep_stack.empty());
-        const bool store_element = key_keep_stack.back();
-        key_keep_stack.pop_back();
-
-        if (!store_element)
-        {
-            return {false, nullptr};
-        }
-
-        JSON_ASSERT(object_element);
-        *object_element = std::move(value);
-        return {true, object_element};
-    }
-
-    /// the parsed JSON value
-    BasicJsonType& root;
-    /// stack to model hierarchy of values
-    std::vector<BasicJsonType*> ref_stack {};
-    /// stack to manage which values to keep
-    std::vector<bool> keep_stack {}; // NOLINT(readability-redundant-member-init)
-    /// stack to manage which object keys to keep
-    std::vector<bool> key_keep_stack {}; // NOLINT(readability-redundant-member-init)
-    /// helper to hold the reference for the next object element
-    BasicJsonType* object_element = nullptr;
-    /// whether a syntax error occurred
-    bool errored = false;
-    /// callback function
-    const parser_callback_t callback = nullptr;
-    /// whether to throw exceptions in case of errors
-    const bool allow_exceptions = true;
-    /// a discarded value for the callback
-    BasicJsonType discarded = BasicJsonType::value_t::discarded;
-};
-
-template<typename BasicJsonType>
-class json_sax_acceptor
-{
-  public:
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-
-    bool null()
-    {
-        return true;
-    }
-
-    bool boolean(bool /*unused*/)
-    {
-        return true;
-    }
-
-    bool number_integer(number_integer_t /*unused*/)
-    {
-        return true;
-    }
-
-    bool number_unsigned(number_unsigned_t /*unused*/)
-    {
-        return true;
-    }
-
-    bool number_float(number_float_t /*unused*/, const string_t& /*unused*/)
-    {
-        return true;
-    }
-
-    bool string(string_t& /*unused*/)
-    {
-        return true;
-    }
-
-    bool binary(binary_t& /*unused*/)
-    {
-        return true;
-    }
-
-    bool start_object(std::size_t /*unused*/ = static_cast<std::size_t>(-1))
-    {
-        return true;
-    }
-
-    bool key(string_t& /*unused*/)
-    {
-        return true;
-    }
-
-    bool end_object()
-    {
-        return true;
-    }
-
-    bool start_array(std::size_t /*unused*/ = static_cast<std::size_t>(-1))
-    {
-        return true;
-    }
-
-    bool end_array()
-    {
-        return true;
-    }
-
-    bool parse_error(std::size_t /*unused*/, const std::string& /*unused*/, const detail::exception& /*unused*/)
-    {
-        return false;
-    }
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/input/parser.hpp

@@ -1,519 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <cmath> // isfinite
-#include <cstdint> // uint8_t
-#include <functional> // function
-#include <string> // string
-#include <utility> // move
-#include <vector> // vector
-
-#include <nlohmann/detail/exceptions.hpp>
-#include <nlohmann/detail/input/input_adapters.hpp>
-#include <nlohmann/detail/input/json_sax.hpp>
-#include <nlohmann/detail/input/lexer.hpp>
-#include <nlohmann/detail/macro_scope.hpp>
-#include <nlohmann/detail/meta/is_sax.hpp>
-#include <nlohmann/detail/string_concat.hpp>
-#include <nlohmann/detail/value_t.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-////////////
-// parser //
-////////////
-
-enum class parse_event_t : std::uint8_t
-{
-    /// the parser read `{` and started to process a JSON object
-    object_start,
-    /// the parser read `}` and finished processing a JSON object
-    object_end,
-    /// the parser read `[` and started to process a JSON array
-    array_start,
-    /// the parser read `]` and finished processing a JSON array
-    array_end,
-    /// the parser read a key of a value in an object
-    key,
-    /// the parser finished reading a JSON value
-    value
-};
-
-template<typename BasicJsonType>
-using parser_callback_t =
-    std::function<bool(int /*depth*/, parse_event_t /*event*/, BasicJsonType& /*parsed*/)>;
-
-/*!
-@brief syntax analysis
-
-This class implements a recursive descent parser.
-*/
-template<typename BasicJsonType, typename InputAdapterType>
-class parser
-{
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using lexer_t = lexer<BasicJsonType, InputAdapterType>;
-    using token_type = typename lexer_t::token_type;
-
-  public:
-    /// a parser reading from an input adapter
-    explicit parser(InputAdapterType&& adapter,
-                    const parser_callback_t<BasicJsonType> cb = nullptr,
-                    const bool allow_exceptions_ = true,
-                    const bool skip_comments = false)
-        : callback(cb)
-        , m_lexer(std::move(adapter), skip_comments)
-        , allow_exceptions(allow_exceptions_)
-    {
-        // read first token
-        get_token();
-    }
-
-    /*!
-    @brief public parser interface
-
-    @param[in] strict      whether to expect the last token to be EOF
-    @param[in,out] result  parsed JSON value
-
-    @throw parse_error.101 in case of an unexpected token
-    @throw parse_error.102 if to_unicode fails or surrogate error
-    @throw parse_error.103 if to_unicode fails
-    */
-    void parse(const bool strict, BasicJsonType& result)
-    {
-        if (callback)
-        {
-            json_sax_dom_callback_parser<BasicJsonType> sdp(result, callback, allow_exceptions);
-            sax_parse_internal(&sdp);
-
-            // in strict mode, input must be completely read
-            if (strict && (get_token() != token_type::end_of_input))
-            {
-                sdp.parse_error(m_lexer.get_position(),
-                                m_lexer.get_token_string(),
-                                parse_error::create(101, m_lexer.get_position(),
-                                                    exception_message(token_type::end_of_input, "value"), nullptr));
-            }
-
-            // in case of an error, return discarded value
-            if (sdp.is_errored())
-            {
-                result = value_t::discarded;
-                return;
-            }
-
-            // set top-level value to null if it was discarded by the callback
-            // function
-            if (result.is_discarded())
-            {
-                result = nullptr;
-            }
-        }
-        else
-        {
-            json_sax_dom_parser<BasicJsonType> sdp(result, allow_exceptions);
-            sax_parse_internal(&sdp);
-
-            // in strict mode, input must be completely read
-            if (strict && (get_token() != token_type::end_of_input))
-            {
-                sdp.parse_error(m_lexer.get_position(),
-                                m_lexer.get_token_string(),
-                                parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_of_input, "value"), nullptr));
-            }
-
-            // in case of an error, return discarded value
-            if (sdp.is_errored())
-            {
-                result = value_t::discarded;
-                return;
-            }
-        }
-
-        result.assert_invariant();
-    }
-
-    /*!
-    @brief public accept interface
-
-    @param[in] strict  whether to expect the last token to be EOF
-    @return whether the input is a proper JSON text
-    */
-    bool accept(const bool strict = true)
-    {
-        json_sax_acceptor<BasicJsonType> sax_acceptor;
-        return sax_parse(&sax_acceptor, strict);
-    }
-
-    template<typename SAX>
-    JSON_HEDLEY_NON_NULL(2)
-    bool sax_parse(SAX* sax, const bool strict = true)
-    {
-        (void)detail::is_sax_static_asserts<SAX, BasicJsonType> {};
-        const bool result = sax_parse_internal(sax);
-
-        // strict mode: next byte must be EOF
-        if (result && strict && (get_token() != token_type::end_of_input))
-        {
-            return sax->parse_error(m_lexer.get_position(),
-                                    m_lexer.get_token_string(),
-                                    parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_of_input, "value"), nullptr));
-        }
-
-        return result;
-    }
-
-  private:
-    template<typename SAX>
-    JSON_HEDLEY_NON_NULL(2)
-    bool sax_parse_internal(SAX* sax)
-    {
-        // stack to remember the hierarchy of structured values we are parsing
-        // true = array; false = object
-        std::vector<bool> states;
-        // value to avoid a goto (see comment where set to true)
-        bool skip_to_state_evaluation = false;
-
-        while (true)
-        {
-            if (!skip_to_state_evaluation)
-            {
-                // invariant: get_token() was called before each iteration
-                switch (last_token)
-                {
-                    case token_type::begin_object:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->start_object(static_cast<std::size_t>(-1))))
-                        {
-                            return false;
-                        }
-
-                        // closing } -> we are done
-                        if (get_token() == token_type::end_object)
-                        {
-                            if (JSON_HEDLEY_UNLIKELY(!sax->end_object()))
-                            {
-                                return false;
-                            }
-                            break;
-                        }
-
-                        // parse key
-                        if (JSON_HEDLEY_UNLIKELY(last_token != token_type::value_string))
-                        {
-                            return sax->parse_error(m_lexer.get_position(),
-                                                    m_lexer.get_token_string(),
-                                                    parse_error::create(101, m_lexer.get_position(), exception_message(token_type::value_string, "object key"), nullptr));
-                        }
-                        if (JSON_HEDLEY_UNLIKELY(!sax->key(m_lexer.get_string())))
-                        {
-                            return false;
-                        }
-
-                        // parse separator (:)
-                        if (JSON_HEDLEY_UNLIKELY(get_token() != token_type::name_separator))
-                        {
-                            return sax->parse_error(m_lexer.get_position(),
-                                                    m_lexer.get_token_string(),
-                                                    parse_error::create(101, m_lexer.get_position(), exception_message(token_type::name_separator, "object separator"), nullptr));
-                        }
-
-                        // remember we are now inside an object
-                        states.push_back(false);
-
-                        // parse values
-                        get_token();
-                        continue;
-                    }
-
-                    case token_type::begin_array:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->start_array(static_cast<std::size_t>(-1))))
-                        {
-                            return false;
-                        }
-
-                        // closing ] -> we are done
-                        if (get_token() == token_type::end_array)
-                        {
-                            if (JSON_HEDLEY_UNLIKELY(!sax->end_array()))
-                            {
-                                return false;
-                            }
-                            break;
-                        }
-
-                        // remember we are now inside an array
-                        states.push_back(true);
-
-                        // parse values (no need to call get_token)
-                        continue;
-                    }
-
-                    case token_type::value_float:
-                    {
-                        const auto res = m_lexer.get_number_float();
-
-                        if (JSON_HEDLEY_UNLIKELY(!std::isfinite(res)))
-                        {
-                            return sax->parse_error(m_lexer.get_position(),
-                                                    m_lexer.get_token_string(),
-                                                    out_of_range::create(406, concat("number overflow parsing '", m_lexer.get_token_string(), '\''), nullptr));
-                        }
-
-                        if (JSON_HEDLEY_UNLIKELY(!sax->number_float(res, m_lexer.get_string())))
-                        {
-                            return false;
-                        }
-
-                        break;
-                    }
-
-                    case token_type::literal_false:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->boolean(false)))
-                        {
-                            return false;
-                        }
-                        break;
-                    }
-
-                    case token_type::literal_null:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->null()))
-                        {
-                            return false;
-                        }
-                        break;
-                    }
-
-                    case token_type::literal_true:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->boolean(true)))
-                        {
-                            return false;
-                        }
-                        break;
-                    }
-
-                    case token_type::value_integer:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->number_integer(m_lexer.get_number_integer())))
-                        {
-                            return false;
-                        }
-                        break;
-                    }
-
-                    case token_type::value_string:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->string(m_lexer.get_string())))
-                        {
-                            return false;
-                        }
-                        break;
-                    }
-
-                    case token_type::value_unsigned:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->number_unsigned(m_lexer.get_number_unsigned())))
-                        {
-                            return false;
-                        }
-                        break;
-                    }
-
-                    case token_type::parse_error:
-                    {
-                        // using "uninitialized" to avoid "expected" message
-                        return sax->parse_error(m_lexer.get_position(),
-                                                m_lexer.get_token_string(),
-                                                parse_error::create(101, m_lexer.get_position(), exception_message(token_type::uninitialized, "value"), nullptr));
-                    }
-                    case token_type::end_of_input:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(m_lexer.get_position().chars_read_total == 1))
-                        {
-                            return sax->parse_error(m_lexer.get_position(),
-                                                    m_lexer.get_token_string(),
-                                                    parse_error::create(101, m_lexer.get_position(),
-                                                            "attempting to parse an empty input; check that your input string or stream contains the expected JSON", nullptr));
-                        }
-
-                        return sax->parse_error(m_lexer.get_position(),
-                                                m_lexer.get_token_string(),
-                                                parse_error::create(101, m_lexer.get_position(), exception_message(token_type::literal_or_value, "value"), nullptr));
-                    }
-                    case token_type::uninitialized:
-                    case token_type::end_array:
-                    case token_type::end_object:
-                    case token_type::name_separator:
-                    case token_type::value_separator:
-                    case token_type::literal_or_value:
-                    default: // the last token was unexpected
-                    {
-                        return sax->parse_error(m_lexer.get_position(),
-                                                m_lexer.get_token_string(),
-                                                parse_error::create(101, m_lexer.get_position(), exception_message(token_type::literal_or_value, "value"), nullptr));
-                    }
-                }
-            }
-            else
-            {
-                skip_to_state_evaluation = false;
-            }
-
-            // we reached this line after we successfully parsed a value
-            if (states.empty())
-            {
-                // empty stack: we reached the end of the hierarchy: done
-                return true;
-            }
-
-            if (states.back())  // array
-            {
-                // comma -> next value
-                if (get_token() == token_type::value_separator)
-                {
-                    // parse a new value
-                    get_token();
-                    continue;
-                }
-
-                // closing ]
-                if (JSON_HEDLEY_LIKELY(last_token == token_type::end_array))
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!sax->end_array()))
-                    {
-                        return false;
-                    }
-
-                    // We are done with this array. Before we can parse a
-                    // new value, we need to evaluate the new state first.
-                    // By setting skip_to_state_evaluation to false, we
-                    // are effectively jumping to the beginning of this if.
-                    JSON_ASSERT(!states.empty());
-                    states.pop_back();
-                    skip_to_state_evaluation = true;
-                    continue;
-                }
-
-                return sax->parse_error(m_lexer.get_position(),
-                                        m_lexer.get_token_string(),
-                                        parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_array, "array"), nullptr));
-            }
-
-            // states.back() is false -> object
-
-            // comma -> next value
-            if (get_token() == token_type::value_separator)
-            {
-                // parse key
-                if (JSON_HEDLEY_UNLIKELY(get_token() != token_type::value_string))
-                {
-                    return sax->parse_error(m_lexer.get_position(),
-                                            m_lexer.get_token_string(),
-                                            parse_error::create(101, m_lexer.get_position(), exception_message(token_type::value_string, "object key"), nullptr));
-                }
-
-                if (JSON_HEDLEY_UNLIKELY(!sax->key(m_lexer.get_string())))
-                {
-                    return false;
-                }
-
-                // parse separator (:)
-                if (JSON_HEDLEY_UNLIKELY(get_token() != token_type::name_separator))
-                {
-                    return sax->parse_error(m_lexer.get_position(),
-                                            m_lexer.get_token_string(),
-                                            parse_error::create(101, m_lexer.get_position(), exception_message(token_type::name_separator, "object separator"), nullptr));
-                }
-
-                // parse values
-                get_token();
-                continue;
-            }
-
-            // closing }
-            if (JSON_HEDLEY_LIKELY(last_token == token_type::end_object))
-            {
-                if (JSON_HEDLEY_UNLIKELY(!sax->end_object()))
-                {
-                    return false;
-                }
-
-                // We are done with this object. Before we can parse a
-                // new value, we need to evaluate the new state first.
-                // By setting skip_to_state_evaluation to false, we
-                // are effectively jumping to the beginning of this if.
-                JSON_ASSERT(!states.empty());
-                states.pop_back();
-                skip_to_state_evaluation = true;
-                continue;
-            }
-
-            return sax->parse_error(m_lexer.get_position(),
-                                    m_lexer.get_token_string(),
-                                    parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_object, "object"), nullptr));
-        }
-    }
-
-    /// get next token from lexer
-    token_type get_token()
-    {
-        return last_token = m_lexer.scan();
-    }
-
-    std::string exception_message(const token_type expected, const std::string& context)
-    {
-        std::string error_msg = "syntax error ";
-
-        if (!context.empty())
-        {
-            error_msg += concat("while parsing ", context, ' ');
-        }
-
-        error_msg += "- ";
-
-        if (last_token == token_type::parse_error)
-        {
-            error_msg += concat(m_lexer.get_error_message(), "; last read: '",
-                                m_lexer.get_token_string(), '\'');
-        }
-        else
-        {
-            error_msg += concat("unexpected ", lexer_t::token_type_name(last_token));
-        }
-
-        if (expected != token_type::uninitialized)
-        {
-            error_msg += concat("; expected ", lexer_t::token_type_name(expected));
-        }
-
-        return error_msg;
-    }
-
-  private:
-    /// callback function
-    const parser_callback_t<BasicJsonType> callback = nullptr;
-    /// the type of the last read token
-    token_type last_token = token_type::uninitialized;
-    /// the lexer
-    lexer_t m_lexer;
-    /// whether to throw exceptions in case of errors
-    const bool allow_exceptions = true;
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/input/position_t.hpp

@@ -1,37 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <cstddef> // size_t
-
-#include <nlohmann/detail/abi_macros.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-/// struct to capture the start position of the current token
-struct position_t
-{
-    /// the total number of characters read
-    std::size_t chars_read_total = 0;
-    /// the number of characters read in the current line
-    std::size_t chars_read_current_line = 0;
-    /// the number of lines read
-    std::size_t lines_read = 0;
-
-    /// conversion to size_t to preserve SAX interface
-    constexpr operator size_t() const
-    {
-        return chars_read_total;
-    }
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/iterators/internal_iterator.hpp

@@ -1,35 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <nlohmann/detail/abi_macros.hpp>
-#include <nlohmann/detail/iterators/primitive_iterator.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-/*!
-@brief an iterator value
-
-@note This structure could easily be a union, but MSVC currently does not allow
-unions members with complex constructors, see https://github.com/nlohmann/json/pull/105.
-*/
-template<typename BasicJsonType> struct internal_iterator
-{
-    /// iterator for JSON objects
-    typename BasicJsonType::object_t::iterator object_iterator {};
-    /// iterator for JSON arrays
-    typename BasicJsonType::array_t::iterator array_iterator {};
-    /// generic iterator for all other types
-    primitive_iterator_t primitive_iterator {};
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/iterators/iter_impl.hpp

@@ -1,751 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <iterator> // iterator, random_access_iterator_tag, bidirectional_iterator_tag, advance, next
-#include <type_traits> // conditional, is_const, remove_const
-
-#include <nlohmann/detail/exceptions.hpp>
-#include <nlohmann/detail/iterators/internal_iterator.hpp>
-#include <nlohmann/detail/iterators/primitive_iterator.hpp>
-#include <nlohmann/detail/macro_scope.hpp>
-#include <nlohmann/detail/meta/cpp_future.hpp>
-#include <nlohmann/detail/meta/type_traits.hpp>
-#include <nlohmann/detail/value_t.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-// forward declare, to be able to friend it later on
-template<typename IteratorType> class iteration_proxy;
-template<typename IteratorType> class iteration_proxy_value;
-
-/*!
-@brief a template for a bidirectional iterator for the @ref basic_json class
-This class implements a both iterators (iterator and const_iterator) for the
-@ref basic_json class.
-@note An iterator is called *initialized* when a pointer to a JSON value has
-      been set (e.g., by a constructor or a copy assignment). If the iterator is
-      default-constructed, it is *uninitialized* and most methods are undefined.
-      **The library uses assertions to detect calls on uninitialized iterators.**
-@requirement The class satisfies the following concept requirements:
--
-[BidirectionalIterator](https://en.cppreference.com/w/cpp/named_req/BidirectionalIterator):
-  The iterator that can be moved can be moved in both directions (i.e.
-  incremented and decremented).
-@since version 1.0.0, simplified in version 2.0.9, change to bidirectional
-       iterators in version 3.0.0 (see https://github.com/nlohmann/json/issues/593)
-*/
-template<typename BasicJsonType>
-class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-special-member-functions)
-{
-    /// the iterator with BasicJsonType of different const-ness
-    using other_iter_impl = iter_impl<typename std::conditional<std::is_const<BasicJsonType>::value, typename std::remove_const<BasicJsonType>::type, const BasicJsonType>::type>;
-    /// allow basic_json to access private members
-    friend other_iter_impl;
-    friend BasicJsonType;
-    friend iteration_proxy<iter_impl>;
-    friend iteration_proxy_value<iter_impl>;
-
-    using object_t = typename BasicJsonType::object_t;
-    using array_t = typename BasicJsonType::array_t;
-    // make sure BasicJsonType is basic_json or const basic_json
-    static_assert(is_basic_json<typename std::remove_const<BasicJsonType>::type>::value,
-                  "iter_impl only accepts (const) basic_json");
-    // superficial check for the LegacyBidirectionalIterator named requirement
-    static_assert(std::is_base_of<std::bidirectional_iterator_tag, std::bidirectional_iterator_tag>::value
-                  &&  std::is_base_of<std::bidirectional_iterator_tag, typename std::iterator_traits<typename array_t::iterator>::iterator_category>::value,
-                  "basic_json iterator assumes array and object type iterators satisfy the LegacyBidirectionalIterator named requirement.");
-
-  public:
-    /// The std::iterator class template (used as a base class to provide typedefs) is deprecated in C++17.
-    /// The C++ Standard has never required user-defined iterators to derive from std::iterator.
-    /// A user-defined iterator should provide publicly accessible typedefs named
-    /// iterator_category, value_type, difference_type, pointer, and reference.
-    /// Note that value_type is required to be non-const, even for constant iterators.
-    using iterator_category = std::bidirectional_iterator_tag;
-
-    /// the type of the values when the iterator is dereferenced
-    using value_type = typename BasicJsonType::value_type;
-    /// a type to represent differences between iterators
-    using difference_type = typename BasicJsonType::difference_type;
-    /// defines a pointer to the type iterated over (value_type)
-    using pointer = typename std::conditional<std::is_const<BasicJsonType>::value,
-          typename BasicJsonType::const_pointer,
-          typename BasicJsonType::pointer>::type;
-    /// defines a reference to the type iterated over (value_type)
-    using reference =
-        typename std::conditional<std::is_const<BasicJsonType>::value,
-        typename BasicJsonType::const_reference,
-        typename BasicJsonType::reference>::type;
-
-    iter_impl() = default;
-    ~iter_impl() = default;
-    iter_impl(iter_impl&&) noexcept = default;
-    iter_impl& operator=(iter_impl&&) noexcept = default;
-
-    /*!
-    @brief constructor for a given JSON instance
-    @param[in] object  pointer to a JSON object for this iterator
-    @pre object != nullptr
-    @post The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    explicit iter_impl(pointer object) noexcept : m_object(object)
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_data.m_type)
-        {
-            case value_t::object:
-            {
-                m_it.object_iterator = typename object_t::iterator();
-                break;
-            }
-
-            case value_t::array:
-            {
-                m_it.array_iterator = typename array_t::iterator();
-                break;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                m_it.primitive_iterator = primitive_iterator_t();
-                break;
-            }
-        }
-    }
-
-    /*!
-    @note The conventional copy constructor and copy assignment are implicitly
-          defined. Combined with the following converting constructor and
-          assignment, they support: (1) copy from iterator to iterator, (2)
-          copy from const iterator to const iterator, and (3) conversion from
-          iterator to const iterator. However conversion from const iterator
-          to iterator is not defined.
-    */
-
-    /*!
-    @brief const copy constructor
-    @param[in] other const iterator to copy from
-    @note This copy constructor had to be defined explicitly to circumvent a bug
-          occurring on msvc v19.0 compiler (VS 2015) debug build. For more
-          information refer to: https://github.com/nlohmann/json/issues/1608
-    */
-    iter_impl(const iter_impl<const BasicJsonType>& other) noexcept
-        : m_object(other.m_object), m_it(other.m_it)
-    {}
-
-    /*!
-    @brief converting assignment
-    @param[in] other const iterator to copy from
-    @return const/non-const iterator
-    @note It is not checked whether @a other is initialized.
-    */
-    iter_impl& operator=(const iter_impl<const BasicJsonType>& other) noexcept
-    {
-        if (&other != this)
-        {
-            m_object = other.m_object;
-            m_it = other.m_it;
-        }
-        return *this;
-    }
-
-    /*!
-    @brief converting constructor
-    @param[in] other  non-const iterator to copy from
-    @note It is not checked whether @a other is initialized.
-    */
-    iter_impl(const iter_impl<typename std::remove_const<BasicJsonType>::type>& other) noexcept
-        : m_object(other.m_object), m_it(other.m_it)
-    {}
-
-    /*!
-    @brief converting assignment
-    @param[in] other  non-const iterator to copy from
-    @return const/non-const iterator
-    @note It is not checked whether @a other is initialized.
-    */
-    iter_impl& operator=(const iter_impl<typename std::remove_const<BasicJsonType>::type>& other) noexcept // NOLINT(cert-oop54-cpp)
-    {
-        m_object = other.m_object;
-        m_it = other.m_it;
-        return *this;
-    }
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    /*!
-    @brief set the iterator to the first value
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    void set_begin() noexcept
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_data.m_type)
-        {
-            case value_t::object:
-            {
-                m_it.object_iterator = m_object->m_data.m_value.object->begin();
-                break;
-            }
-
-            case value_t::array:
-            {
-                m_it.array_iterator = m_object->m_data.m_value.array->begin();
-                break;
-            }
-
-            case value_t::null:
-            {
-                // set to end so begin()==end() is true: null is empty
-                m_it.primitive_iterator.set_end();
-                break;
-            }
-
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                m_it.primitive_iterator.set_begin();
-                break;
-            }
-        }
-    }
-
-    /*!
-    @brief set the iterator past the last value
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    void set_end() noexcept
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_data.m_type)
-        {
-            case value_t::object:
-            {
-                m_it.object_iterator = m_object->m_data.m_value.object->end();
-                break;
-            }
-
-            case value_t::array:
-            {
-                m_it.array_iterator = m_object->m_data.m_value.array->end();
-                break;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                m_it.primitive_iterator.set_end();
-                break;
-            }
-        }
-    }
-
-  public:
-    /*!
-    @brief return a reference to the value pointed to by the iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    reference operator*() const
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_data.m_type)
-        {
-            case value_t::object:
-            {
-                JSON_ASSERT(m_it.object_iterator != m_object->m_data.m_value.object->end());
-                return m_it.object_iterator->second;
-            }
-
-            case value_t::array:
-            {
-                JSON_ASSERT(m_it.array_iterator != m_object->m_data.m_value.array->end());
-                return *m_it.array_iterator;
-            }
-
-            case value_t::null:
-                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
-
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                if (JSON_HEDLEY_LIKELY(m_it.primitive_iterator.is_begin()))
-                {
-                    return *m_object;
-                }
-
-                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
-            }
-        }
-    }
-
-    /*!
-    @brief dereference the iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    pointer operator->() const
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_data.m_type)
-        {
-            case value_t::object:
-            {
-                JSON_ASSERT(m_it.object_iterator != m_object->m_data.m_value.object->end());
-                return &(m_it.object_iterator->second);
-            }
-
-            case value_t::array:
-            {
-                JSON_ASSERT(m_it.array_iterator != m_object->m_data.m_value.array->end());
-                return &*m_it.array_iterator;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                if (JSON_HEDLEY_LIKELY(m_it.primitive_iterator.is_begin()))
-                {
-                    return m_object;
-                }
-
-                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
-            }
-        }
-    }
-
-    /*!
-    @brief post-increment (it++)
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl operator++(int)& // NOLINT(cert-dcl21-cpp)
-    {
-        auto result = *this;
-        ++(*this);
-        return result;
-    }
-
-    /*!
-    @brief pre-increment (++it)
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl& operator++()
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_data.m_type)
-        {
-            case value_t::object:
-            {
-                std::advance(m_it.object_iterator, 1);
-                break;
-            }
-
-            case value_t::array:
-            {
-                std::advance(m_it.array_iterator, 1);
-                break;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                ++m_it.primitive_iterator;
-                break;
-            }
-        }
-
-        return *this;
-    }
-
-    /*!
-    @brief post-decrement (it--)
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl operator--(int)& // NOLINT(cert-dcl21-cpp)
-    {
-        auto result = *this;
-        --(*this);
-        return result;
-    }
-
-    /*!
-    @brief pre-decrement (--it)
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl& operator--()
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_data.m_type)
-        {
-            case value_t::object:
-            {
-                std::advance(m_it.object_iterator, -1);
-                break;
-            }
-
-            case value_t::array:
-            {
-                std::advance(m_it.array_iterator, -1);
-                break;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                --m_it.primitive_iterator;
-                break;
-            }
-        }
-
-        return *this;
-    }
-
-    /*!
-    @brief comparison: equal
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    template < typename IterImpl, detail::enable_if_t < (std::is_same<IterImpl, iter_impl>::value || std::is_same<IterImpl, other_iter_impl>::value), std::nullptr_t > = nullptr >
-    bool operator==(const IterImpl& other) const
-    {
-        // if objects are not the same, the comparison is undefined
-        if (JSON_HEDLEY_UNLIKELY(m_object != other.m_object))
-        {
-            JSON_THROW(invalid_iterator::create(212, "cannot compare iterators of different containers", m_object));
-        }
-
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_data.m_type)
-        {
-            case value_t::object:
-                return (m_it.object_iterator == other.m_it.object_iterator);
-
-            case value_t::array:
-                return (m_it.array_iterator == other.m_it.array_iterator);
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-                return (m_it.primitive_iterator == other.m_it.primitive_iterator);
-        }
-    }
-
-    /*!
-    @brief comparison: not equal
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    template < typename IterImpl, detail::enable_if_t < (std::is_same<IterImpl, iter_impl>::value || std::is_same<IterImpl, other_iter_impl>::value), std::nullptr_t > = nullptr >
-    bool operator!=(const IterImpl& other) const
-    {
-        return !operator==(other);
-    }
-
-    /*!
-    @brief comparison: smaller
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    bool operator<(const iter_impl& other) const
-    {
-        // if objects are not the same, the comparison is undefined
-        if (JSON_HEDLEY_UNLIKELY(m_object != other.m_object))
-        {
-            JSON_THROW(invalid_iterator::create(212, "cannot compare iterators of different containers", m_object));
-        }
-
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_data.m_type)
-        {
-            case value_t::object:
-                JSON_THROW(invalid_iterator::create(213, "cannot compare order of object iterators", m_object));
-
-            case value_t::array:
-                return (m_it.array_iterator < other.m_it.array_iterator);
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-                return (m_it.primitive_iterator < other.m_it.primitive_iterator);
-        }
-    }
-
-    /*!
-    @brief comparison: less than or equal
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    bool operator<=(const iter_impl& other) const
-    {
-        return !other.operator < (*this);
-    }
-
-    /*!
-    @brief comparison: greater than
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    bool operator>(const iter_impl& other) const
-    {
-        return !operator<=(other);
-    }
-
-    /*!
-    @brief comparison: greater than or equal
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    bool operator>=(const iter_impl& other) const
-    {
-        return !operator<(other);
-    }
-
-    /*!
-    @brief add to iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl& operator+=(difference_type i)
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_data.m_type)
-        {
-            case value_t::object:
-                JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object));
-
-            case value_t::array:
-            {
-                std::advance(m_it.array_iterator, i);
-                break;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                m_it.primitive_iterator += i;
-                break;
-            }
-        }
-
-        return *this;
-    }
-
-    /*!
-    @brief subtract from iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl& operator-=(difference_type i)
-    {
-        return operator+=(-i);
-    }
-
-    /*!
-    @brief add to iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl operator+(difference_type i) const
-    {
-        auto result = *this;
-        result += i;
-        return result;
-    }
-
-    /*!
-    @brief addition of distance and iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    friend iter_impl operator+(difference_type i, const iter_impl& it)
-    {
-        auto result = it;
-        result += i;
-        return result;
-    }
-
-    /*!
-    @brief subtract from iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl operator-(difference_type i) const
-    {
-        auto result = *this;
-        result -= i;
-        return result;
-    }
-
-    /*!
-    @brief return difference
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    difference_type operator-(const iter_impl& other) const
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_data.m_type)
-        {
-            case value_t::object:
-                JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object));
-
-            case value_t::array:
-                return m_it.array_iterator - other.m_it.array_iterator;
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-                return m_it.primitive_iterator - other.m_it.primitive_iterator;
-        }
-    }
-
-    /*!
-    @brief access to successor
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    reference operator[](difference_type n) const
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_data.m_type)
-        {
-            case value_t::object:
-                JSON_THROW(invalid_iterator::create(208, "cannot use operator[] for object iterators", m_object));
-
-            case value_t::array:
-                return *std::next(m_it.array_iterator, n);
-
-            case value_t::null:
-                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
-
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                if (JSON_HEDLEY_LIKELY(m_it.primitive_iterator.get_value() == -n))
-                {
-                    return *m_object;
-                }
-
-                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
-            }
-        }
-    }
-
-    /*!
-    @brief return the key of an object iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    const typename object_t::key_type& key() const
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        if (JSON_HEDLEY_LIKELY(m_object->is_object()))
-        {
-            return m_it.object_iterator->first;
-        }
-
-        JSON_THROW(invalid_iterator::create(207, "cannot use key() for non-object iterators", m_object));
-    }
-
-    /*!
-    @brief return the value of an iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    reference value() const
-    {
-        return operator*();
-    }
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    /// associated JSON instance
-    pointer m_object = nullptr;
-    /// the actual iterator of the associated instance
-    internal_iterator<typename std::remove_const<BasicJsonType>::type> m_it {};
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/iterators/iteration_proxy.hpp

@@ -1,242 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <cstddef> // size_t
-#include <iterator> // input_iterator_tag
-#include <string> // string, to_string
-#include <tuple> // tuple_size, get, tuple_element
-#include <utility> // move
-
-#if JSON_HAS_RANGES
-    #include <ranges> // enable_borrowed_range
-#endif
-
-#include <nlohmann/detail/abi_macros.hpp>
-#include <nlohmann/detail/meta/type_traits.hpp>
-#include <nlohmann/detail/value_t.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-template<typename string_type>
-void int_to_string( string_type& target, std::size_t value )
-{
-    // For ADL
-    using std::to_string;
-    target = to_string(value);
-}
-template<typename IteratorType> class iteration_proxy_value
-{
-  public:
-    using difference_type = std::ptrdiff_t;
-    using value_type = iteration_proxy_value;
-    using pointer = value_type *;
-    using reference = value_type &;
-    using iterator_category = std::input_iterator_tag;
-    using string_type = typename std::remove_cv< typename std::remove_reference<decltype( std::declval<IteratorType>().key() ) >::type >::type;
-
-  private:
-    /// the iterator
-    IteratorType anchor{};
-    /// an index for arrays (used to create key names)
-    std::size_t array_index = 0;
-    /// last stringified array index
-    mutable std::size_t array_index_last = 0;
-    /// a string representation of the array index
-    mutable string_type array_index_str = "0";
-    /// an empty string (to return a reference for primitive values)
-    string_type empty_str{};
-
-  public:
-    explicit iteration_proxy_value() = default;
-    explicit iteration_proxy_value(IteratorType it, std::size_t array_index_ = 0)
-    noexcept(std::is_nothrow_move_constructible<IteratorType>::value
-             && std::is_nothrow_default_constructible<string_type>::value)
-        : anchor(std::move(it))
-        , array_index(array_index_)
-    {}
-
-    iteration_proxy_value(iteration_proxy_value const&) = default;
-    iteration_proxy_value& operator=(iteration_proxy_value const&) = default;
-    // older GCCs are a bit fussy and require explicit noexcept specifiers on defaulted functions
-    iteration_proxy_value(iteration_proxy_value&&)
-    noexcept(std::is_nothrow_move_constructible<IteratorType>::value
-             && std::is_nothrow_move_constructible<string_type>::value) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor,cppcoreguidelines-noexcept-move-operations)
-    iteration_proxy_value& operator=(iteration_proxy_value&&)
-    noexcept(std::is_nothrow_move_assignable<IteratorType>::value
-             && std::is_nothrow_move_assignable<string_type>::value) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor,cppcoreguidelines-noexcept-move-operations)
-    ~iteration_proxy_value() = default;
-
-    /// dereference operator (needed for range-based for)
-    const iteration_proxy_value& operator*() const
-    {
-        return *this;
-    }
-
-    /// increment operator (needed for range-based for)
-    iteration_proxy_value& operator++()
-    {
-        ++anchor;
-        ++array_index;
-
-        return *this;
-    }
-
-    iteration_proxy_value operator++(int)& // NOLINT(cert-dcl21-cpp)
-    {
-        auto tmp = iteration_proxy_value(anchor, array_index);
-        ++anchor;
-        ++array_index;
-        return tmp;
-    }
-
-    /// equality operator (needed for InputIterator)
-    bool operator==(const iteration_proxy_value& o) const
-    {
-        return anchor == o.anchor;
-    }
-
-    /// inequality operator (needed for range-based for)
-    bool operator!=(const iteration_proxy_value& o) const
-    {
-        return anchor != o.anchor;
-    }
-
-    /// return key of the iterator
-    const string_type& key() const
-    {
-        JSON_ASSERT(anchor.m_object != nullptr);
-
-        switch (anchor.m_object->type())
-        {
-            // use integer array index as key
-            case value_t::array:
-            {
-                if (array_index != array_index_last)
-                {
-                    int_to_string( array_index_str, array_index );
-                    array_index_last = array_index;
-                }
-                return array_index_str;
-            }
-
-            // use key from the object
-            case value_t::object:
-                return anchor.key();
-
-            // use an empty key for all primitive types
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-                return empty_str;
-        }
-    }
-
-    /// return value of the iterator
-    typename IteratorType::reference value() const
-    {
-        return anchor.value();
-    }
-};
-
-/// proxy class for the items() function
-template<typename IteratorType> class iteration_proxy
-{
-  private:
-    /// the container to iterate
-    typename IteratorType::pointer container = nullptr;
-
-  public:
-    explicit iteration_proxy() = default;
-
-    /// construct iteration proxy from a container
-    explicit iteration_proxy(typename IteratorType::reference cont) noexcept
-        : container(&cont) {}
-
-    iteration_proxy(iteration_proxy const&) = default;
-    iteration_proxy& operator=(iteration_proxy const&) = default;
-    iteration_proxy(iteration_proxy&&) noexcept = default;
-    iteration_proxy& operator=(iteration_proxy&&) noexcept = default;
-    ~iteration_proxy() = default;
-
-    /// return iterator begin (needed for range-based for)
-    iteration_proxy_value<IteratorType> begin() const noexcept
-    {
-        return iteration_proxy_value<IteratorType>(container->begin());
-    }
-
-    /// return iterator end (needed for range-based for)
-    iteration_proxy_value<IteratorType> end() const noexcept
-    {
-        return iteration_proxy_value<IteratorType>(container->end());
-    }
-};
-
-// Structured Bindings Support
-// For further reference see https://blog.tartanllama.xyz/structured-bindings/
-// And see https://github.com/nlohmann/json/pull/1391
-template<std::size_t N, typename IteratorType, enable_if_t<N == 0, int> = 0>
-auto get(const nlohmann::detail::iteration_proxy_value<IteratorType>& i) -> decltype(i.key())
-{
-    return i.key();
-}
-// Structured Bindings Support
-// For further reference see https://blog.tartanllama.xyz/structured-bindings/
-// And see https://github.com/nlohmann/json/pull/1391
-template<std::size_t N, typename IteratorType, enable_if_t<N == 1, int> = 0>
-auto get(const nlohmann::detail::iteration_proxy_value<IteratorType>& i) -> decltype(i.value())
-{
-    return i.value();
-}
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// The Addition to the STD Namespace is required to add
-// Structured Bindings Support to the iteration_proxy_value class
-// For further reference see https://blog.tartanllama.xyz/structured-bindings/
-// And see https://github.com/nlohmann/json/pull/1391
-namespace std
-{
-
-#if defined(__clang__)
-    // Fix: https://github.com/nlohmann/json/issues/1401
-    #pragma clang diagnostic push
-    #pragma clang diagnostic ignored "-Wmismatched-tags"
-#endif
-template<typename IteratorType>
-class tuple_size<::nlohmann::detail::iteration_proxy_value<IteratorType>> // NOLINT(cert-dcl58-cpp)
-            : public std::integral_constant<std::size_t, 2> {};
-
-template<std::size_t N, typename IteratorType>
-class tuple_element<N, ::nlohmann::detail::iteration_proxy_value<IteratorType >> // NOLINT(cert-dcl58-cpp)
-{
-  public:
-    using type = decltype(
-                     get<N>(std::declval <
-                            ::nlohmann::detail::iteration_proxy_value<IteratorType >> ()));
-};
-#if defined(__clang__)
-    #pragma clang diagnostic pop
-#endif
-
-}  // namespace std
-
-#if JSON_HAS_RANGES
-    template <typename IteratorType>
-    inline constexpr bool ::std::ranges::enable_borrowed_range<::nlohmann::detail::iteration_proxy<IteratorType>> = true;
-#endif

sample/lib/json/include/nlohmann/detail/iterators/iterator_traits.hpp

@@ -1,61 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <iterator> // random_access_iterator_tag
-
-#include <nlohmann/detail/abi_macros.hpp>
-#include <nlohmann/detail/meta/void_t.hpp>
-#include <nlohmann/detail/meta/cpp_future.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-template<typename It, typename = void>
-struct iterator_types {};
-
-template<typename It>
-struct iterator_types <
-    It,
-    void_t<typename It::difference_type, typename It::value_type, typename It::pointer,
-    typename It::reference, typename It::iterator_category >>
-{
-    using difference_type = typename It::difference_type;
-    using value_type = typename It::value_type;
-    using pointer = typename It::pointer;
-    using reference = typename It::reference;
-    using iterator_category = typename It::iterator_category;
-};
-
-// This is required as some compilers implement std::iterator_traits in a way that
-// doesn't work with SFINAE. See https://github.com/nlohmann/json/issues/1341.
-template<typename T, typename = void>
-struct iterator_traits
-{
-};
-
-template<typename T>
-struct iterator_traits < T, enable_if_t < !std::is_pointer<T>::value >>
-            : iterator_types<T>
-{
-};
-
-template<typename T>
-struct iterator_traits<T*, enable_if_t<std::is_object<T>::value>>
-{
-    using iterator_category = std::random_access_iterator_tag;
-    using value_type = T;
-    using difference_type = ptrdiff_t;
-    using pointer = T*;
-    using reference = T&;
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/iterators/json_reverse_iterator.hpp

@@ -1,130 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <cstddef> // ptrdiff_t
-#include <iterator> // reverse_iterator
-#include <utility> // declval
-
-#include <nlohmann/detail/abi_macros.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-//////////////////////
-// reverse_iterator //
-//////////////////////
-
-/*!
-@brief a template for a reverse iterator class
-
-@tparam Base the base iterator type to reverse. Valid types are @ref
-iterator (to create @ref reverse_iterator) and @ref const_iterator (to
-create @ref const_reverse_iterator).
-
-@requirement The class satisfies the following concept requirements:
--
-[BidirectionalIterator](https://en.cppreference.com/w/cpp/named_req/BidirectionalIterator):
-  The iterator that can be moved can be moved in both directions (i.e.
-  incremented and decremented).
-- [OutputIterator](https://en.cppreference.com/w/cpp/named_req/OutputIterator):
-  It is possible to write to the pointed-to element (only if @a Base is
-  @ref iterator).
-
-@since version 1.0.0
-*/
-template<typename Base>
-class json_reverse_iterator : public std::reverse_iterator<Base>
-{
-  public:
-    using difference_type = std::ptrdiff_t;
-    /// shortcut to the reverse iterator adapter
-    using base_iterator = std::reverse_iterator<Base>;
-    /// the reference type for the pointed-to element
-    using reference = typename Base::reference;
-
-    /// create reverse iterator from iterator
-    explicit json_reverse_iterator(const typename base_iterator::iterator_type& it) noexcept
-        : base_iterator(it) {}
-
-    /// create reverse iterator from base class
-    explicit json_reverse_iterator(const base_iterator& it) noexcept : base_iterator(it) {}
-
-    /// post-increment (it++)
-    json_reverse_iterator operator++(int)& // NOLINT(cert-dcl21-cpp)
-    {
-        return static_cast<json_reverse_iterator>(base_iterator::operator++(1));
-    }
-
-    /// pre-increment (++it)
-    json_reverse_iterator& operator++()
-    {
-        return static_cast<json_reverse_iterator&>(base_iterator::operator++());
-    }
-
-    /// post-decrement (it--)
-    json_reverse_iterator operator--(int)& // NOLINT(cert-dcl21-cpp)
-    {
-        return static_cast<json_reverse_iterator>(base_iterator::operator--(1));
-    }
-
-    /// pre-decrement (--it)
-    json_reverse_iterator& operator--()
-    {
-        return static_cast<json_reverse_iterator&>(base_iterator::operator--());
-    }
-
-    /// add to iterator
-    json_reverse_iterator& operator+=(difference_type i)
-    {
-        return static_cast<json_reverse_iterator&>(base_iterator::operator+=(i));
-    }
-
-    /// add to iterator
-    json_reverse_iterator operator+(difference_type i) const
-    {
-        return static_cast<json_reverse_iterator>(base_iterator::operator+(i));
-    }
-
-    /// subtract from iterator
-    json_reverse_iterator operator-(difference_type i) const
-    {
-        return static_cast<json_reverse_iterator>(base_iterator::operator-(i));
-    }
-
-    /// return difference
-    difference_type operator-(const json_reverse_iterator& other) const
-    {
-        return base_iterator(*this) - base_iterator(other);
-    }
-
-    /// access to successor
-    reference operator[](difference_type n) const
-    {
-        return *(this->operator+(n));
-    }
-
-    /// return the key of an object iterator
-    auto key() const -> decltype(std::declval<Base>().key())
-    {
-        auto it = --this->base();
-        return it.key();
-    }
-
-    /// return the value of an iterator
-    reference value() const
-    {
-        auto it = --this->base();
-        return it.operator * ();
-    }
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/iterators/primitive_iterator.hpp

@@ -1,132 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <cstddef> // ptrdiff_t
-#include <limits>  // numeric_limits
-
-#include <nlohmann/detail/macro_scope.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-/*
-@brief an iterator for primitive JSON types
-
-This class models an iterator for primitive JSON types (boolean, number,
-string). It's only purpose is to allow the iterator/const_iterator classes
-to "iterate" over primitive values. Internally, the iterator is modeled by
-a `difference_type` variable. Value begin_value (`0`) models the begin,
-end_value (`1`) models past the end.
-*/
-class primitive_iterator_t
-{
-  private:
-    using difference_type = std::ptrdiff_t;
-    static constexpr difference_type begin_value = 0;
-    static constexpr difference_type end_value = begin_value + 1;
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    /// iterator as signed integer type
-    difference_type m_it = (std::numeric_limits<std::ptrdiff_t>::min)();
-
-  public:
-    constexpr difference_type get_value() const noexcept
-    {
-        return m_it;
-    }
-
-    /// set iterator to a defined beginning
-    void set_begin() noexcept
-    {
-        m_it = begin_value;
-    }
-
-    /// set iterator to a defined past the end
-    void set_end() noexcept
-    {
-        m_it = end_value;
-    }
-
-    /// return whether the iterator can be dereferenced
-    constexpr bool is_begin() const noexcept
-    {
-        return m_it == begin_value;
-    }
-
-    /// return whether the iterator is at end
-    constexpr bool is_end() const noexcept
-    {
-        return m_it == end_value;
-    }
-
-    friend constexpr bool operator==(primitive_iterator_t lhs, primitive_iterator_t rhs) noexcept
-    {
-        return lhs.m_it == rhs.m_it;
-    }
-
-    friend constexpr bool operator<(primitive_iterator_t lhs, primitive_iterator_t rhs) noexcept
-    {
-        return lhs.m_it < rhs.m_it;
-    }
-
-    primitive_iterator_t operator+(difference_type n) noexcept
-    {
-        auto result = *this;
-        result += n;
-        return result;
-    }
-
-    friend constexpr difference_type operator-(primitive_iterator_t lhs, primitive_iterator_t rhs) noexcept
-    {
-        return lhs.m_it - rhs.m_it;
-    }
-
-    primitive_iterator_t& operator++() noexcept
-    {
-        ++m_it;
-        return *this;
-    }
-
-    primitive_iterator_t operator++(int)& noexcept // NOLINT(cert-dcl21-cpp)
-    {
-        auto result = *this;
-        ++m_it;
-        return result;
-    }
-
-    primitive_iterator_t& operator--() noexcept
-    {
-        --m_it;
-        return *this;
-    }
-
-    primitive_iterator_t operator--(int)& noexcept // NOLINT(cert-dcl21-cpp)
-    {
-        auto result = *this;
-        --m_it;
-        return result;
-    }
-
-    primitive_iterator_t& operator+=(difference_type n) noexcept
-    {
-        m_it += n;
-        return *this;
-    }
-
-    primitive_iterator_t& operator-=(difference_type n) noexcept
-    {
-        m_it -= n;
-        return *this;
-    }
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/json_custom_base_class.hpp

@@ -1,39 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <type_traits> // conditional, is_same
-
-#include <nlohmann/detail/abi_macros.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-/*!
-@brief Default base class of the @ref basic_json class.
-
-So that the correct implementations of the copy / move ctors / assign operators
-of @ref basic_json do not require complex case distinctions
-(no base class / custom base class used as customization point),
-@ref basic_json always has a base class.
-By default, this class is used because it is empty and thus has no effect
-on the behavior of @ref basic_json.
-*/
-struct json_default_base {};
-
-template<class T>
-using json_base_class = typename std::conditional <
-                        std::is_same<T, void>::value,
-                        json_default_base,
-                        T
-                        >::type;
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/json_pointer.hpp

@@ -1,988 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <algorithm> // all_of
-#include <cctype> // isdigit
-#include <cerrno> // errno, ERANGE
-#include <cstdlib> // strtoull
-#ifndef JSON_NO_IO
-    #include <iosfwd> // ostream
-#endif  // JSON_NO_IO
-#include <limits> // max
-#include <numeric> // accumulate
-#include <string> // string
-#include <utility> // move
-#include <vector> // vector
-
-#include <nlohmann/detail/exceptions.hpp>
-#include <nlohmann/detail/macro_scope.hpp>
-#include <nlohmann/detail/string_concat.hpp>
-#include <nlohmann/detail/string_escape.hpp>
-#include <nlohmann/detail/value_t.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-/// @brief JSON Pointer defines a string syntax for identifying a specific value within a JSON document
-/// @sa https://json.nlohmann.me/api/json_pointer/
-template<typename RefStringType>
-class json_pointer
-{
-    // allow basic_json to access private members
-    NLOHMANN_BASIC_JSON_TPL_DECLARATION
-    friend class basic_json;
-
-    template<typename>
-    friend class json_pointer;
-
-    template<typename T>
-    struct string_t_helper
-    {
-        using type = T;
-    };
-
-    NLOHMANN_BASIC_JSON_TPL_DECLARATION
-    struct string_t_helper<NLOHMANN_BASIC_JSON_TPL>
-    {
-        using type = StringType;
-    };
-
-  public:
-    // for backwards compatibility accept BasicJsonType
-    using string_t = typename string_t_helper<RefStringType>::type;
-
-    /// @brief create JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/json_pointer/
-    explicit json_pointer(const string_t& s = "")
-        : reference_tokens(split(s))
-    {}
-
-    /// @brief return a string representation of the JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/to_string/
-    string_t to_string() const
-    {
-        return std::accumulate(reference_tokens.begin(), reference_tokens.end(),
-                               string_t{},
-                               [](const string_t& a, const string_t& b)
-        {
-            return detail::concat(a, '/', detail::escape(b));
-        });
-    }
-
-    /// @brief return a string representation of the JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_string/
-    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, to_string())
-    operator string_t() const
-    {
-        return to_string();
-    }
-
-#ifndef JSON_NO_IO
-    /// @brief write string representation of the JSON pointer to stream
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_ltlt/
-    friend std::ostream& operator<<(std::ostream& o, const json_pointer& ptr)
-    {
-        o << ptr.to_string();
-        return o;
-    }
-#endif
-
-    /// @brief append another JSON pointer at the end of this JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slasheq/
-    json_pointer& operator/=(const json_pointer& ptr)
-    {
-        reference_tokens.insert(reference_tokens.end(),
-                                ptr.reference_tokens.begin(),
-                                ptr.reference_tokens.end());
-        return *this;
-    }
-
-    /// @brief append an unescaped reference token at the end of this JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slasheq/
-    json_pointer& operator/=(string_t token)
-    {
-        push_back(std::move(token));
-        return *this;
-    }
-
-    /// @brief append an array index at the end of this JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slasheq/
-    json_pointer& operator/=(std::size_t array_idx)
-    {
-        return *this /= std::to_string(array_idx);
-    }
-
-    /// @brief create a new JSON pointer by appending the right JSON pointer at the end of the left JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slash/
-    friend json_pointer operator/(const json_pointer& lhs,
-                                  const json_pointer& rhs)
-    {
-        return json_pointer(lhs) /= rhs;
-    }
-
-    /// @brief create a new JSON pointer by appending the unescaped token at the end of the JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slash/
-    friend json_pointer operator/(const json_pointer& lhs, string_t token) // NOLINT(performance-unnecessary-value-param)
-    {
-        return json_pointer(lhs) /= std::move(token);
-    }
-
-    /// @brief create a new JSON pointer by appending the array-index-token at the end of the JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slash/
-    friend json_pointer operator/(const json_pointer& lhs, std::size_t array_idx)
-    {
-        return json_pointer(lhs) /= array_idx;
-    }
-
-    /// @brief returns the parent of this JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/parent_pointer/
-    json_pointer parent_pointer() const
-    {
-        if (empty())
-        {
-            return *this;
-        }
-
-        json_pointer res = *this;
-        res.pop_back();
-        return res;
-    }
-
-    /// @brief remove last reference token
-    /// @sa https://json.nlohmann.me/api/json_pointer/pop_back/
-    void pop_back()
-    {
-        if (JSON_HEDLEY_UNLIKELY(empty()))
-        {
-            JSON_THROW(detail::out_of_range::create(405, "JSON pointer has no parent", nullptr));
-        }
-
-        reference_tokens.pop_back();
-    }
-
-    /// @brief return last reference token
-    /// @sa https://json.nlohmann.me/api/json_pointer/back/
-    const string_t& back() const
-    {
-        if (JSON_HEDLEY_UNLIKELY(empty()))
-        {
-            JSON_THROW(detail::out_of_range::create(405, "JSON pointer has no parent", nullptr));
-        }
-
-        return reference_tokens.back();
-    }
-
-    /// @brief append an unescaped token at the end of the reference pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/push_back/
-    void push_back(const string_t& token)
-    {
-        reference_tokens.push_back(token);
-    }
-
-    /// @brief append an unescaped token at the end of the reference pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/push_back/
-    void push_back(string_t&& token)
-    {
-        reference_tokens.push_back(std::move(token));
-    }
-
-    /// @brief return whether pointer points to the root document
-    /// @sa https://json.nlohmann.me/api/json_pointer/empty/
-    bool empty() const noexcept
-    {
-        return reference_tokens.empty();
-    }
-
-  private:
-    /*!
-    @param[in] s  reference token to be converted into an array index
-
-    @return integer representation of @a s
-
-    @throw parse_error.106  if an array index begins with '0'
-    @throw parse_error.109  if an array index begins not with a digit
-    @throw out_of_range.404 if string @a s could not be converted to an integer
-    @throw out_of_range.410 if an array index exceeds size_type
-    */
-    template<typename BasicJsonType>
-    static typename BasicJsonType::size_type array_index(const string_t& s)
-    {
-        using size_type = typename BasicJsonType::size_type;
-
-        // error condition (cf. RFC 6901, Sect. 4)
-        if (JSON_HEDLEY_UNLIKELY(s.size() > 1 && s[0] == '0'))
-        {
-            JSON_THROW(detail::parse_error::create(106, 0, detail::concat("array index '", s, "' must not begin with '0'"), nullptr));
-        }
-
-        // error condition (cf. RFC 6901, Sect. 4)
-        if (JSON_HEDLEY_UNLIKELY(s.size() > 1 && !(s[0] >= '1' && s[0] <= '9')))
-        {
-            JSON_THROW(detail::parse_error::create(109, 0, detail::concat("array index '", s, "' is not a number"), nullptr));
-        }
-
-        const char* p = s.c_str();
-        char* p_end = nullptr;
-        errno = 0; // strtoull doesn't reset errno
-        const unsigned long long res = std::strtoull(p, &p_end, 10); // NOLINT(runtime/int)
-        if (p == p_end // invalid input or empty string
-                || errno == ERANGE // out of range
-                || JSON_HEDLEY_UNLIKELY(static_cast<std::size_t>(p_end - p) != s.size())) // incomplete read
-        {
-            JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", s, "'"), nullptr));
-        }
-
-        // only triggered on special platforms (like 32bit), see also
-        // https://github.com/nlohmann/json/pull/2203
-        if (res >= static_cast<unsigned long long>((std::numeric_limits<size_type>::max)()))  // NOLINT(runtime/int)
-        {
-            JSON_THROW(detail::out_of_range::create(410, detail::concat("array index ", s, " exceeds size_type"), nullptr));   // LCOV_EXCL_LINE
-        }
-
-        return static_cast<size_type>(res);
-    }
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    json_pointer top() const
-    {
-        if (JSON_HEDLEY_UNLIKELY(empty()))
-        {
-            JSON_THROW(detail::out_of_range::create(405, "JSON pointer has no parent", nullptr));
-        }
-
-        json_pointer result = *this;
-        result.reference_tokens = {reference_tokens[0]};
-        return result;
-    }
-
-  private:
-    /*!
-    @brief create and return a reference to the pointed to value
-
-    @complexity Linear in the number of reference tokens.
-
-    @throw parse_error.109 if array index is not a number
-    @throw type_error.313 if value cannot be unflattened
-    */
-    template<typename BasicJsonType>
-    BasicJsonType& get_and_create(BasicJsonType& j) const
-    {
-        auto* result = &j;
-
-        // in case no reference tokens exist, return a reference to the JSON value
-        // j which will be overwritten by a primitive value
-        for (const auto& reference_token : reference_tokens)
-        {
-            switch (result->type())
-            {
-                case detail::value_t::null:
-                {
-                    if (reference_token == "0")
-                    {
-                        // start a new array if reference token is 0
-                        result = &result->operator[](0);
-                    }
-                    else
-                    {
-                        // start a new object otherwise
-                        result = &result->operator[](reference_token);
-                    }
-                    break;
-                }
-
-                case detail::value_t::object:
-                {
-                    // create an entry in the object
-                    result = &result->operator[](reference_token);
-                    break;
-                }
-
-                case detail::value_t::array:
-                {
-                    // create an entry in the array
-                    result = &result->operator[](array_index<BasicJsonType>(reference_token));
-                    break;
-                }
-
-                /*
-                The following code is only reached if there exists a reference
-                token _and_ the current value is primitive. In this case, we have
-                an error situation, because primitive values may only occur as
-                single value; that is, with an empty list of reference tokens.
-                */
-                case detail::value_t::string:
-                case detail::value_t::boolean:
-                case detail::value_t::number_integer:
-                case detail::value_t::number_unsigned:
-                case detail::value_t::number_float:
-                case detail::value_t::binary:
-                case detail::value_t::discarded:
-                default:
-                    JSON_THROW(detail::type_error::create(313, "invalid value to unflatten", &j));
-            }
-        }
-
-        return *result;
-    }
-
-    /*!
-    @brief return a reference to the pointed to value
-
-    @note This version does not throw if a value is not present, but tries to
-          create nested values instead. For instance, calling this function
-          with pointer `"/this/that"` on a null value is equivalent to calling
-          `operator[]("this").operator[]("that")` on that value, effectively
-          changing the null value to an object.
-
-    @param[in] ptr  a JSON value
-
-    @return reference to the JSON value pointed to by the JSON pointer
-
-    @complexity Linear in the length of the JSON pointer.
-
-    @throw parse_error.106   if an array index begins with '0'
-    @throw parse_error.109   if an array index was not a number
-    @throw out_of_range.404  if the JSON pointer can not be resolved
-    */
-    template<typename BasicJsonType>
-    BasicJsonType& get_unchecked(BasicJsonType* ptr) const
-    {
-        for (const auto& reference_token : reference_tokens)
-        {
-            // convert null values to arrays or objects before continuing
-            if (ptr->is_null())
-            {
-                // check if reference token is a number
-                const bool nums =
-                    std::all_of(reference_token.begin(), reference_token.end(),
-                                [](const unsigned char x)
-                {
-                    return std::isdigit(x);
-                });
-
-                // change value to array for numbers or "-" or to object otherwise
-                *ptr = (nums || reference_token == "-")
-                       ? detail::value_t::array
-                       : detail::value_t::object;
-            }
-
-            switch (ptr->type())
-            {
-                case detail::value_t::object:
-                {
-                    // use unchecked object access
-                    ptr = &ptr->operator[](reference_token);
-                    break;
-                }
-
-                case detail::value_t::array:
-                {
-                    if (reference_token == "-")
-                    {
-                        // explicitly treat "-" as index beyond the end
-                        ptr = &ptr->operator[](ptr->m_data.m_value.array->size());
-                    }
-                    else
-                    {
-                        // convert array index to number; unchecked access
-                        ptr = &ptr->operator[](array_index<BasicJsonType>(reference_token));
-                    }
-                    break;
-                }
-
-                case detail::value_t::null:
-                case detail::value_t::string:
-                case detail::value_t::boolean:
-                case detail::value_t::number_integer:
-                case detail::value_t::number_unsigned:
-                case detail::value_t::number_float:
-                case detail::value_t::binary:
-                case detail::value_t::discarded:
-                default:
-                    JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", reference_token, "'"), ptr));
-            }
-        }
-
-        return *ptr;
-    }
-
-    /*!
-    @throw parse_error.106   if an array index begins with '0'
-    @throw parse_error.109   if an array index was not a number
-    @throw out_of_range.402  if the array index '-' is used
-    @throw out_of_range.404  if the JSON pointer can not be resolved
-    */
-    template<typename BasicJsonType>
-    BasicJsonType& get_checked(BasicJsonType* ptr) const
-    {
-        for (const auto& reference_token : reference_tokens)
-        {
-            switch (ptr->type())
-            {
-                case detail::value_t::object:
-                {
-                    // note: at performs range check
-                    ptr = &ptr->at(reference_token);
-                    break;
-                }
-
-                case detail::value_t::array:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
-                    {
-                        // "-" always fails the range check
-                        JSON_THROW(detail::out_of_range::create(402, detail::concat(
-                                "array index '-' (", std::to_string(ptr->m_data.m_value.array->size()),
-                                ") is out of range"), ptr));
-                    }
-
-                    // note: at performs range check
-                    ptr = &ptr->at(array_index<BasicJsonType>(reference_token));
-                    break;
-                }
-
-                case detail::value_t::null:
-                case detail::value_t::string:
-                case detail::value_t::boolean:
-                case detail::value_t::number_integer:
-                case detail::value_t::number_unsigned:
-                case detail::value_t::number_float:
-                case detail::value_t::binary:
-                case detail::value_t::discarded:
-                default:
-                    JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", reference_token, "'"), ptr));
-            }
-        }
-
-        return *ptr;
-    }
-
-    /*!
-    @brief return a const reference to the pointed to value
-
-    @param[in] ptr  a JSON value
-
-    @return const reference to the JSON value pointed to by the JSON
-    pointer
-
-    @throw parse_error.106   if an array index begins with '0'
-    @throw parse_error.109   if an array index was not a number
-    @throw out_of_range.402  if the array index '-' is used
-    @throw out_of_range.404  if the JSON pointer can not be resolved
-    */
-    template<typename BasicJsonType>
-    const BasicJsonType& get_unchecked(const BasicJsonType* ptr) const
-    {
-        for (const auto& reference_token : reference_tokens)
-        {
-            switch (ptr->type())
-            {
-                case detail::value_t::object:
-                {
-                    // use unchecked object access
-                    ptr = &ptr->operator[](reference_token);
-                    break;
-                }
-
-                case detail::value_t::array:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
-                    {
-                        // "-" cannot be used for const access
-                        JSON_THROW(detail::out_of_range::create(402, detail::concat("array index '-' (", std::to_string(ptr->m_data.m_value.array->size()), ") is out of range"), ptr));
-                    }
-
-                    // use unchecked array access
-                    ptr = &ptr->operator[](array_index<BasicJsonType>(reference_token));
-                    break;
-                }
-
-                case detail::value_t::null:
-                case detail::value_t::string:
-                case detail::value_t::boolean:
-                case detail::value_t::number_integer:
-                case detail::value_t::number_unsigned:
-                case detail::value_t::number_float:
-                case detail::value_t::binary:
-                case detail::value_t::discarded:
-                default:
-                    JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", reference_token, "'"), ptr));
-            }
-        }
-
-        return *ptr;
-    }
-
-    /*!
-    @throw parse_error.106   if an array index begins with '0'
-    @throw parse_error.109   if an array index was not a number
-    @throw out_of_range.402  if the array index '-' is used
-    @throw out_of_range.404  if the JSON pointer can not be resolved
-    */
-    template<typename BasicJsonType>
-    const BasicJsonType& get_checked(const BasicJsonType* ptr) const
-    {
-        for (const auto& reference_token : reference_tokens)
-        {
-            switch (ptr->type())
-            {
-                case detail::value_t::object:
-                {
-                    // note: at performs range check
-                    ptr = &ptr->at(reference_token);
-                    break;
-                }
-
-                case detail::value_t::array:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
-                    {
-                        // "-" always fails the range check
-                        JSON_THROW(detail::out_of_range::create(402, detail::concat(
-                                "array index '-' (", std::to_string(ptr->m_data.m_value.array->size()),
-                                ") is out of range"), ptr));
-                    }
-
-                    // note: at performs range check
-                    ptr = &ptr->at(array_index<BasicJsonType>(reference_token));
-                    break;
-                }
-
-                case detail::value_t::null:
-                case detail::value_t::string:
-                case detail::value_t::boolean:
-                case detail::value_t::number_integer:
-                case detail::value_t::number_unsigned:
-                case detail::value_t::number_float:
-                case detail::value_t::binary:
-                case detail::value_t::discarded:
-                default:
-                    JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", reference_token, "'"), ptr));
-            }
-        }
-
-        return *ptr;
-    }
-
-    /*!
-    @throw parse_error.106   if an array index begins with '0'
-    @throw parse_error.109   if an array index was not a number
-    */
-    template<typename BasicJsonType>
-    bool contains(const BasicJsonType* ptr) const
-    {
-        for (const auto& reference_token : reference_tokens)
-        {
-            switch (ptr->type())
-            {
-                case detail::value_t::object:
-                {
-                    if (!ptr->contains(reference_token))
-                    {
-                        // we did not find the key in the object
-                        return false;
-                    }
-
-                    ptr = &ptr->operator[](reference_token);
-                    break;
-                }
-
-                case detail::value_t::array:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
-                    {
-                        // "-" always fails the range check
-                        return false;
-                    }
-                    if (JSON_HEDLEY_UNLIKELY(reference_token.size() == 1 && !("0" <= reference_token && reference_token <= "9")))
-                    {
-                        // invalid char
-                        return false;
-                    }
-                    if (JSON_HEDLEY_UNLIKELY(reference_token.size() > 1))
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!('1' <= reference_token[0] && reference_token[0] <= '9')))
-                        {
-                            // first char should be between '1' and '9'
-                            return false;
-                        }
-                        for (std::size_t i = 1; i < reference_token.size(); i++)
-                        {
-                            if (JSON_HEDLEY_UNLIKELY(!('0' <= reference_token[i] && reference_token[i] <= '9')))
-                            {
-                                // other char should be between '0' and '9'
-                                return false;
-                            }
-                        }
-                    }
-
-                    const auto idx = array_index<BasicJsonType>(reference_token);
-                    if (idx >= ptr->size())
-                    {
-                        // index out of range
-                        return false;
-                    }
-
-                    ptr = &ptr->operator[](idx);
-                    break;
-                }
-
-                case detail::value_t::null:
-                case detail::value_t::string:
-                case detail::value_t::boolean:
-                case detail::value_t::number_integer:
-                case detail::value_t::number_unsigned:
-                case detail::value_t::number_float:
-                case detail::value_t::binary:
-                case detail::value_t::discarded:
-                default:
-                {
-                    // we do not expect primitive values if there is still a
-                    // reference token to process
-                    return false;
-                }
-            }
-        }
-
-        // no reference token left means we found a primitive value
-        return true;
-    }
-
-    /*!
-    @brief split the string input to reference tokens
-
-    @note This function is only called by the json_pointer constructor.
-          All exceptions below are documented there.
-
-    @throw parse_error.107  if the pointer is not empty or begins with '/'
-    @throw parse_error.108  if character '~' is not followed by '0' or '1'
-    */
-    static std::vector<string_t> split(const string_t& reference_string)
-    {
-        std::vector<string_t> result;
-
-        // special case: empty reference string -> no reference tokens
-        if (reference_string.empty())
-        {
-            return result;
-        }
-
-        // check if nonempty reference string begins with slash
-        if (JSON_HEDLEY_UNLIKELY(reference_string[0] != '/'))
-        {
-            JSON_THROW(detail::parse_error::create(107, 1, detail::concat("JSON pointer must be empty or begin with '/' - was: '", reference_string, "'"), nullptr));
-        }
-
-        // extract the reference tokens:
-        // - slash: position of the last read slash (or end of string)
-        // - start: position after the previous slash
-        for (
-            // search for the first slash after the first character
-            std::size_t slash = reference_string.find_first_of('/', 1),
-            // set the beginning of the first reference token
-            start = 1;
-            // we can stop if start == 0 (if slash == string_t::npos)
-            start != 0;
-            // set the beginning of the next reference token
-            // (will eventually be 0 if slash == string_t::npos)
-            start = (slash == string_t::npos) ? 0 : slash + 1,
-            // find next slash
-            slash = reference_string.find_first_of('/', start))
-        {
-            // use the text between the beginning of the reference token
-            // (start) and the last slash (slash).
-            auto reference_token = reference_string.substr(start, slash - start);
-
-            // check reference tokens are properly escaped
-            for (std::size_t pos = reference_token.find_first_of('~');
-                    pos != string_t::npos;
-                    pos = reference_token.find_first_of('~', pos + 1))
-            {
-                JSON_ASSERT(reference_token[pos] == '~');
-
-                // ~ must be followed by 0 or 1
-                if (JSON_HEDLEY_UNLIKELY(pos == reference_token.size() - 1 ||
-                                         (reference_token[pos + 1] != '0' &&
-                                          reference_token[pos + 1] != '1')))
-                {
-                    JSON_THROW(detail::parse_error::create(108, 0, "escape character '~' must be followed with '0' or '1'", nullptr));
-                }
-            }
-
-            // finally, store the reference token
-            detail::unescape(reference_token);
-            result.push_back(reference_token);
-        }
-
-        return result;
-    }
-
-  private:
-    /*!
-    @param[in] reference_string  the reference string to the current value
-    @param[in] value             the value to consider
-    @param[in,out] result        the result object to insert values to
-
-    @note Empty objects or arrays are flattened to `null`.
-    */
-    template<typename BasicJsonType>
-    static void flatten(const string_t& reference_string,
-                        const BasicJsonType& value,
-                        BasicJsonType& result)
-    {
-        switch (value.type())
-        {
-            case detail::value_t::array:
-            {
-                if (value.m_data.m_value.array->empty())
-                {
-                    // flatten empty array as null
-                    result[reference_string] = nullptr;
-                }
-                else
-                {
-                    // iterate array and use index as reference string
-                    for (std::size_t i = 0; i < value.m_data.m_value.array->size(); ++i)
-                    {
-                        flatten(detail::concat(reference_string, '/', std::to_string(i)),
-                                value.m_data.m_value.array->operator[](i), result);
-                    }
-                }
-                break;
-            }
-
-            case detail::value_t::object:
-            {
-                if (value.m_data.m_value.object->empty())
-                {
-                    // flatten empty object as null
-                    result[reference_string] = nullptr;
-                }
-                else
-                {
-                    // iterate object and use keys as reference string
-                    for (const auto& element : *value.m_data.m_value.object)
-                    {
-                        flatten(detail::concat(reference_string, '/', detail::escape(element.first)), element.second, result);
-                    }
-                }
-                break;
-            }
-
-            case detail::value_t::null:
-            case detail::value_t::string:
-            case detail::value_t::boolean:
-            case detail::value_t::number_integer:
-            case detail::value_t::number_unsigned:
-            case detail::value_t::number_float:
-            case detail::value_t::binary:
-            case detail::value_t::discarded:
-            default:
-            {
-                // add primitive value with its reference string
-                result[reference_string] = value;
-                break;
-            }
-        }
-    }
-
-    /*!
-    @param[in] value  flattened JSON
-
-    @return unflattened JSON
-
-    @throw parse_error.109 if array index is not a number
-    @throw type_error.314  if value is not an object
-    @throw type_error.315  if object values are not primitive
-    @throw type_error.313  if value cannot be unflattened
-    */
-    template<typename BasicJsonType>
-    static BasicJsonType
-    unflatten(const BasicJsonType& value)
-    {
-        if (JSON_HEDLEY_UNLIKELY(!value.is_object()))
-        {
-            JSON_THROW(detail::type_error::create(314, "only objects can be unflattened", &value));
-        }
-
-        BasicJsonType result;
-
-        // iterate the JSON object values
-        for (const auto& element : *value.m_data.m_value.object)
-        {
-            if (JSON_HEDLEY_UNLIKELY(!element.second.is_primitive()))
-            {
-                JSON_THROW(detail::type_error::create(315, "values in object must be primitive", &element.second));
-            }
-
-            // assign value to reference pointed to by JSON pointer; Note that if
-            // the JSON pointer is "" (i.e., points to the whole value), function
-            // get_and_create returns a reference to result itself. An assignment
-            // will then create a primitive value.
-            json_pointer(element.first).get_and_create(result) = element.second;
-        }
-
-        return result;
-    }
-
-    // can't use conversion operator because of ambiguity
-    json_pointer<string_t> convert() const&
-    {
-        json_pointer<string_t> result;
-        result.reference_tokens = reference_tokens;
-        return result;
-    }
-
-    json_pointer<string_t> convert()&&
-    {
-        json_pointer<string_t> result;
-        result.reference_tokens = std::move(reference_tokens);
-        return result;
-    }
-
-  public:
-#if JSON_HAS_THREE_WAY_COMPARISON
-    /// @brief compares two JSON pointers for equality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
-    template<typename RefStringTypeRhs>
-    bool operator==(const json_pointer<RefStringTypeRhs>& rhs) const noexcept
-    {
-        return reference_tokens == rhs.reference_tokens;
-    }
-
-    /// @brief compares JSON pointer and string for equality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
-    JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator==(json_pointer))
-    bool operator==(const string_t& rhs) const
-    {
-        return *this == json_pointer(rhs);
-    }
-
-    /// @brief 3-way compares two JSON pointers
-    template<typename RefStringTypeRhs>
-    std::strong_ordering operator<=>(const json_pointer<RefStringTypeRhs>& rhs) const noexcept // *NOPAD*
-    {
-        return  reference_tokens <=> rhs.reference_tokens; // *NOPAD*
-    }
-#else
-    /// @brief compares two JSON pointers for equality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
-    template<typename RefStringTypeLhs, typename RefStringTypeRhs>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator==(const json_pointer<RefStringTypeLhs>& lhs,
-                           const json_pointer<RefStringTypeRhs>& rhs) noexcept;
-
-    /// @brief compares JSON pointer and string for equality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
-    template<typename RefStringTypeLhs, typename StringType>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator==(const json_pointer<RefStringTypeLhs>& lhs,
-                           const StringType& rhs);
-
-    /// @brief compares string and JSON pointer for equality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
-    template<typename RefStringTypeRhs, typename StringType>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator==(const StringType& lhs,
-                           const json_pointer<RefStringTypeRhs>& rhs);
-
-    /// @brief compares two JSON pointers for inequality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_ne/
-    template<typename RefStringTypeLhs, typename RefStringTypeRhs>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator!=(const json_pointer<RefStringTypeLhs>& lhs,
-                           const json_pointer<RefStringTypeRhs>& rhs) noexcept;
-
-    /// @brief compares JSON pointer and string for inequality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_ne/
-    template<typename RefStringTypeLhs, typename StringType>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator!=(const json_pointer<RefStringTypeLhs>& lhs,
-                           const StringType& rhs);
-
-    /// @brief compares string and JSON pointer for inequality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_ne/
-    template<typename RefStringTypeRhs, typename StringType>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator!=(const StringType& lhs,
-                           const json_pointer<RefStringTypeRhs>& rhs);
-
-    /// @brief compares two JSON pointer for less-than
-    template<typename RefStringTypeLhs, typename RefStringTypeRhs>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator<(const json_pointer<RefStringTypeLhs>& lhs,
-                          const json_pointer<RefStringTypeRhs>& rhs) noexcept;
-#endif
-
-  private:
-    /// the reference tokens
-    std::vector<string_t> reference_tokens;
-};
-
-#if !JSON_HAS_THREE_WAY_COMPARISON
-// functions cannot be defined inside class due to ODR violations
-template<typename RefStringTypeLhs, typename RefStringTypeRhs>
-inline bool operator==(const json_pointer<RefStringTypeLhs>& lhs,
-                       const json_pointer<RefStringTypeRhs>& rhs) noexcept
-{
-    return lhs.reference_tokens == rhs.reference_tokens;
-}
-
-template<typename RefStringTypeLhs,
-         typename StringType = typename json_pointer<RefStringTypeLhs>::string_t>
-JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator==(json_pointer, json_pointer))
-inline bool operator==(const json_pointer<RefStringTypeLhs>& lhs,
-                       const StringType& rhs)
-{
-    return lhs == json_pointer<RefStringTypeLhs>(rhs);
-}
-
-template<typename RefStringTypeRhs,
-         typename StringType = typename json_pointer<RefStringTypeRhs>::string_t>
-JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator==(json_pointer, json_pointer))
-inline bool operator==(const StringType& lhs,
-                       const json_pointer<RefStringTypeRhs>& rhs)
-{
-    return json_pointer<RefStringTypeRhs>(lhs) == rhs;
-}
-
-template<typename RefStringTypeLhs, typename RefStringTypeRhs>
-inline bool operator!=(const json_pointer<RefStringTypeLhs>& lhs,
-                       const json_pointer<RefStringTypeRhs>& rhs) noexcept
-{
-    return !(lhs == rhs);
-}
-
-template<typename RefStringTypeLhs,
-         typename StringType = typename json_pointer<RefStringTypeLhs>::string_t>
-JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator!=(json_pointer, json_pointer))
-inline bool operator!=(const json_pointer<RefStringTypeLhs>& lhs,
-                       const StringType& rhs)
-{
-    return !(lhs == rhs);
-}
-
-template<typename RefStringTypeRhs,
-         typename StringType = typename json_pointer<RefStringTypeRhs>::string_t>
-JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator!=(json_pointer, json_pointer))
-inline bool operator!=(const StringType& lhs,
-                       const json_pointer<RefStringTypeRhs>& rhs)
-{
-    return !(lhs == rhs);
-}
-
-template<typename RefStringTypeLhs, typename RefStringTypeRhs>
-inline bool operator<(const json_pointer<RefStringTypeLhs>& lhs,
-                      const json_pointer<RefStringTypeRhs>& rhs) noexcept
-{
-    return lhs.reference_tokens < rhs.reference_tokens;
-}
-#endif
-
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/json_ref.hpp

@@ -1,78 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <initializer_list>
-#include <utility>
-
-#include <nlohmann/detail/abi_macros.hpp>
-#include <nlohmann/detail/meta/type_traits.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-template<typename BasicJsonType>
-class json_ref
-{
-  public:
-    using value_type = BasicJsonType;
-
-    json_ref(value_type&& value)
-        : owned_value(std::move(value))
-    {}
-
-    json_ref(const value_type& value)
-        : value_ref(&value)
-    {}
-
-    json_ref(std::initializer_list<json_ref> init)
-        : owned_value(init)
-    {}
-
-    template <
-        class... Args,
-        enable_if_t<std::is_constructible<value_type, Args...>::value, int> = 0 >
-    json_ref(Args && ... args)
-        : owned_value(std::forward<Args>(args)...)
-    {}
-
-    // class should be movable only
-    json_ref(json_ref&&) noexcept = default;
-    json_ref(const json_ref&) = delete;
-    json_ref& operator=(const json_ref&) = delete;
-    json_ref& operator=(json_ref&&) = delete;
-    ~json_ref() = default;
-
-    value_type moved_or_copied() const
-    {
-        if (value_ref == nullptr)
-        {
-            return std::move(owned_value);
-        }
-        return *value_ref;
-    }
-
-    value_type const& operator*() const
-    {
-        return value_ref ? *value_ref : owned_value;
-    }
-
-    value_type const* operator->() const
-    {
-        return &** this;
-    }
-
-  private:
-    mutable value_type owned_value = nullptr;
-    value_type const* value_ref = nullptr;
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/macro_scope.hpp

@@ -1,482 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <utility> // declval, pair
-#include <nlohmann/detail/meta/detected.hpp>
-#include <nlohmann/thirdparty/hedley/hedley.hpp>
-
-// This file contains all internal macro definitions (except those affecting ABI)
-// You MUST include macro_unscope.hpp at the end of json.hpp to undef all of them
-
-#include <nlohmann/detail/abi_macros.hpp>
-
-// exclude unsupported compilers
-#if !defined(JSON_SKIP_UNSUPPORTED_COMPILER_CHECK)
-    #if defined(__clang__)
-        #if (__clang_major__ * 10000 + __clang_minor__ * 100 + __clang_patchlevel__) < 30400
-            #error "unsupported Clang version - see https://github.com/nlohmann/json#supported-compilers"
-        #endif
-    #elif defined(__GNUC__) && !(defined(__ICC) || defined(__INTEL_COMPILER))
-        #if (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__) < 40800
-            #error "unsupported GCC version - see https://github.com/nlohmann/json#supported-compilers"
-        #endif
-    #endif
-#endif
-
-// C++ language standard detection
-// if the user manually specified the used c++ version this is skipped
-#if !defined(JSON_HAS_CPP_20) && !defined(JSON_HAS_CPP_17) && !defined(JSON_HAS_CPP_14) && !defined(JSON_HAS_CPP_11)
-    #if (defined(__cplusplus) && __cplusplus >= 202002L) || (defined(_MSVC_LANG) && _MSVC_LANG >= 202002L)
-        #define JSON_HAS_CPP_20
-        #define JSON_HAS_CPP_17
-        #define JSON_HAS_CPP_14
-    #elif (defined(__cplusplus) && __cplusplus >= 201703L) || (defined(_HAS_CXX17) && _HAS_CXX17 == 1) // fix for issue #464
-        #define JSON_HAS_CPP_17
-        #define JSON_HAS_CPP_14
-    #elif (defined(__cplusplus) && __cplusplus >= 201402L) || (defined(_HAS_CXX14) && _HAS_CXX14 == 1)
-        #define JSON_HAS_CPP_14
-    #endif
-    // the cpp 11 flag is always specified because it is the minimal required version
-    #define JSON_HAS_CPP_11
-#endif
-
-#ifdef __has_include
-    #if __has_include(<version>)
-        #include <version>
-    #endif
-#endif
-
-#if !defined(JSON_HAS_FILESYSTEM) && !defined(JSON_HAS_EXPERIMENTAL_FILESYSTEM)
-    #ifdef JSON_HAS_CPP_17
-        #if defined(__cpp_lib_filesystem)
-            #define JSON_HAS_FILESYSTEM 1
-        #elif defined(__cpp_lib_experimental_filesystem)
-            #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 1
-        #elif !defined(__has_include)
-            #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 1
-        #elif __has_include(<filesystem>)
-            #define JSON_HAS_FILESYSTEM 1
-        #elif __has_include(<experimental/filesystem>)
-            #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 1
-        #endif
-
-        // std::filesystem does not work on MinGW GCC 8: https://sourceforge.net/p/mingw-w64/bugs/737/
-        #if defined(__MINGW32__) && defined(__GNUC__) && __GNUC__ == 8
-            #undef JSON_HAS_FILESYSTEM
-            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-        #endif
-
-        // no filesystem support before GCC 8: https://en.cppreference.com/w/cpp/compiler_support
-        #if defined(__GNUC__) && !defined(__clang__) && __GNUC__ < 8
-            #undef JSON_HAS_FILESYSTEM
-            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-        #endif
-
-        // no filesystem support before Clang 7: https://en.cppreference.com/w/cpp/compiler_support
-        #if defined(__clang_major__) && __clang_major__ < 7
-            #undef JSON_HAS_FILESYSTEM
-            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-        #endif
-
-        // no filesystem support before MSVC 19.14: https://en.cppreference.com/w/cpp/compiler_support
-        #if defined(_MSC_VER) && _MSC_VER < 1914
-            #undef JSON_HAS_FILESYSTEM
-            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-        #endif
-
-        // no filesystem support before iOS 13
-        #if defined(__IPHONE_OS_VERSION_MIN_REQUIRED) && __IPHONE_OS_VERSION_MIN_REQUIRED < 130000
-            #undef JSON_HAS_FILESYSTEM
-            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-        #endif
-
-        // no filesystem support before macOS Catalina
-        #if defined(__MAC_OS_X_VERSION_MIN_REQUIRED) && __MAC_OS_X_VERSION_MIN_REQUIRED < 101500
-            #undef JSON_HAS_FILESYSTEM
-            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-        #endif
-    #endif
-#endif
-
-#ifndef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-    #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 0
-#endif
-
-#ifndef JSON_HAS_FILESYSTEM
-    #define JSON_HAS_FILESYSTEM 0
-#endif
-
-#ifndef JSON_HAS_THREE_WAY_COMPARISON
-    #if defined(__cpp_impl_three_way_comparison) && __cpp_impl_three_way_comparison >= 201907L \
-        && defined(__cpp_lib_three_way_comparison) && __cpp_lib_three_way_comparison >= 201907L
-        #define JSON_HAS_THREE_WAY_COMPARISON 1
-    #else
-        #define JSON_HAS_THREE_WAY_COMPARISON 0
-    #endif
-#endif
-
-#ifndef JSON_HAS_RANGES
-    // ranges header shipping in GCC 11.1.0 (released 2021-04-27) has syntax error
-    #if defined(__GLIBCXX__) && __GLIBCXX__ == 20210427
-        #define JSON_HAS_RANGES 0
-    #elif defined(__cpp_lib_ranges)
-        #define JSON_HAS_RANGES 1
-    #else
-        #define JSON_HAS_RANGES 0
-    #endif
-#endif
-
-#ifndef JSON_HAS_STATIC_RTTI
-    #if !defined(_HAS_STATIC_RTTI) || _HAS_STATIC_RTTI != 0
-        #define JSON_HAS_STATIC_RTTI 1
-    #else
-        #define JSON_HAS_STATIC_RTTI 0
-    #endif
-#endif
-
-#ifdef JSON_HAS_CPP_17
-    #define JSON_INLINE_VARIABLE inline
-#else
-    #define JSON_INLINE_VARIABLE
-#endif
-
-#if JSON_HEDLEY_HAS_ATTRIBUTE(no_unique_address)
-    #define JSON_NO_UNIQUE_ADDRESS [[no_unique_address]]
-#else
-    #define JSON_NO_UNIQUE_ADDRESS
-#endif
-
-// disable documentation warnings on clang
-#if defined(__clang__)
-    #pragma clang diagnostic push
-    #pragma clang diagnostic ignored "-Wdocumentation"
-    #pragma clang diagnostic ignored "-Wdocumentation-unknown-command"
-#endif
-
-// allow disabling exceptions
-#if (defined(__cpp_exceptions) || defined(__EXCEPTIONS) || defined(_CPPUNWIND)) && !defined(JSON_NOEXCEPTION)
-    #define JSON_THROW(exception) throw exception
-    #define JSON_TRY try
-    #define JSON_CATCH(exception) catch(exception)
-    #define JSON_INTERNAL_CATCH(exception) catch(exception)
-#else
-    #include <cstdlib>
-    #define JSON_THROW(exception) std::abort()
-    #define JSON_TRY if(true)
-    #define JSON_CATCH(exception) if(false)
-    #define JSON_INTERNAL_CATCH(exception) if(false)
-#endif
-
-// override exception macros
-#if defined(JSON_THROW_USER)
-    #undef JSON_THROW
-    #define JSON_THROW JSON_THROW_USER
-#endif
-#if defined(JSON_TRY_USER)
-    #undef JSON_TRY
-    #define JSON_TRY JSON_TRY_USER
-#endif
-#if defined(JSON_CATCH_USER)
-    #undef JSON_CATCH
-    #define JSON_CATCH JSON_CATCH_USER
-    #undef JSON_INTERNAL_CATCH
-    #define JSON_INTERNAL_CATCH JSON_CATCH_USER
-#endif
-#if defined(JSON_INTERNAL_CATCH_USER)
-    #undef JSON_INTERNAL_CATCH
-    #define JSON_INTERNAL_CATCH JSON_INTERNAL_CATCH_USER
-#endif
-
-// allow overriding assert
-#if !defined(JSON_ASSERT)
-    #include <cassert> // assert
-    #define JSON_ASSERT(x) assert(x)
-#endif
-
-// allow to access some private functions (needed by the test suite)
-#if defined(JSON_TESTS_PRIVATE)
-    #define JSON_PRIVATE_UNLESS_TESTED public
-#else
-    #define JSON_PRIVATE_UNLESS_TESTED private
-#endif
-
-/*!
-@brief macro to briefly define a mapping between an enum and JSON
-@def NLOHMANN_JSON_SERIALIZE_ENUM
-@since version 3.4.0
-*/
-#define NLOHMANN_JSON_SERIALIZE_ENUM(ENUM_TYPE, ...)                                            \
-    template<typename BasicJsonType>                                                            \
-    inline void to_json(BasicJsonType& j, const ENUM_TYPE& e)                                   \
-    {                                                                                           \
-        static_assert(std::is_enum<ENUM_TYPE>::value, #ENUM_TYPE " must be an enum!");          \
-        static const std::pair<ENUM_TYPE, BasicJsonType> m[] = __VA_ARGS__;                     \
-        auto it = std::find_if(std::begin(m), std::end(m),                                      \
-                               [e](const std::pair<ENUM_TYPE, BasicJsonType>& ej_pair) -> bool  \
-        {                                                                                       \
-            return ej_pair.first == e;                                                          \
-        });                                                                                     \
-        j = ((it != std::end(m)) ? it : std::begin(m))->second;                                 \
-    }                                                                                           \
-    template<typename BasicJsonType>                                                            \
-    inline void from_json(const BasicJsonType& j, ENUM_TYPE& e)                                 \
-    {                                                                                           \
-        static_assert(std::is_enum<ENUM_TYPE>::value, #ENUM_TYPE " must be an enum!");          \
-        static const std::pair<ENUM_TYPE, BasicJsonType> m[] = __VA_ARGS__;                     \
-        auto it = std::find_if(std::begin(m), std::end(m),                                      \
-                               [&j](const std::pair<ENUM_TYPE, BasicJsonType>& ej_pair) -> bool \
-        {                                                                                       \
-            return ej_pair.second == j;                                                         \
-        });                                                                                     \
-        e = ((it != std::end(m)) ? it : std::begin(m))->first;                                  \
-    }
-
-// Ugly macros to avoid uglier copy-paste when specializing basic_json. They
-// may be removed in the future once the class is split.
-
-#define NLOHMANN_BASIC_JSON_TPL_DECLARATION                                \
-    template<template<typename, typename, typename...> class ObjectType,   \
-             template<typename, typename...> class ArrayType,              \
-             class StringType, class BooleanType, class NumberIntegerType, \
-             class NumberUnsignedType, class NumberFloatType,              \
-             template<typename> class AllocatorType,                       \
-             template<typename, typename = void> class JSONSerializer,     \
-             class BinaryType,                                             \
-             class CustomBaseClass>
-
-#define NLOHMANN_BASIC_JSON_TPL                                            \
-    basic_json<ObjectType, ArrayType, StringType, BooleanType,             \
-    NumberIntegerType, NumberUnsignedType, NumberFloatType,                \
-    AllocatorType, JSONSerializer, BinaryType, CustomBaseClass>
-
-// Macros to simplify conversion from/to types
-
-#define NLOHMANN_JSON_EXPAND( x ) x
-#define NLOHMANN_JSON_GET_MACRO(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55, _56, _57, _58, _59, _60, _61, _62, _63, _64, NAME,...) NAME
-#define NLOHMANN_JSON_PASTE(...) NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_GET_MACRO(__VA_ARGS__, \
-        NLOHMANN_JSON_PASTE64, \
-        NLOHMANN_JSON_PASTE63, \
-        NLOHMANN_JSON_PASTE62, \
-        NLOHMANN_JSON_PASTE61, \
-        NLOHMANN_JSON_PASTE60, \
-        NLOHMANN_JSON_PASTE59, \
-        NLOHMANN_JSON_PASTE58, \
-        NLOHMANN_JSON_PASTE57, \
-        NLOHMANN_JSON_PASTE56, \
-        NLOHMANN_JSON_PASTE55, \
-        NLOHMANN_JSON_PASTE54, \
-        NLOHMANN_JSON_PASTE53, \
-        NLOHMANN_JSON_PASTE52, \
-        NLOHMANN_JSON_PASTE51, \
-        NLOHMANN_JSON_PASTE50, \
-        NLOHMANN_JSON_PASTE49, \
-        NLOHMANN_JSON_PASTE48, \
-        NLOHMANN_JSON_PASTE47, \
-        NLOHMANN_JSON_PASTE46, \
-        NLOHMANN_JSON_PASTE45, \
-        NLOHMANN_JSON_PASTE44, \
-        NLOHMANN_JSON_PASTE43, \
-        NLOHMANN_JSON_PASTE42, \
-        NLOHMANN_JSON_PASTE41, \
-        NLOHMANN_JSON_PASTE40, \
-        NLOHMANN_JSON_PASTE39, \
-        NLOHMANN_JSON_PASTE38, \
-        NLOHMANN_JSON_PASTE37, \
-        NLOHMANN_JSON_PASTE36, \
-        NLOHMANN_JSON_PASTE35, \
-        NLOHMANN_JSON_PASTE34, \
-        NLOHMANN_JSON_PASTE33, \
-        NLOHMANN_JSON_PASTE32, \
-        NLOHMANN_JSON_PASTE31, \
-        NLOHMANN_JSON_PASTE30, \
-        NLOHMANN_JSON_PASTE29, \
-        NLOHMANN_JSON_PASTE28, \
-        NLOHMANN_JSON_PASTE27, \
-        NLOHMANN_JSON_PASTE26, \
-        NLOHMANN_JSON_PASTE25, \
-        NLOHMANN_JSON_PASTE24, \
-        NLOHMANN_JSON_PASTE23, \
-        NLOHMANN_JSON_PASTE22, \
-        NLOHMANN_JSON_PASTE21, \
-        NLOHMANN_JSON_PASTE20, \
-        NLOHMANN_JSON_PASTE19, \
-        NLOHMANN_JSON_PASTE18, \
-        NLOHMANN_JSON_PASTE17, \
-        NLOHMANN_JSON_PASTE16, \
-        NLOHMANN_JSON_PASTE15, \
-        NLOHMANN_JSON_PASTE14, \
-        NLOHMANN_JSON_PASTE13, \
-        NLOHMANN_JSON_PASTE12, \
-        NLOHMANN_JSON_PASTE11, \
-        NLOHMANN_JSON_PASTE10, \
-        NLOHMANN_JSON_PASTE9, \
-        NLOHMANN_JSON_PASTE8, \
-        NLOHMANN_JSON_PASTE7, \
-        NLOHMANN_JSON_PASTE6, \
-        NLOHMANN_JSON_PASTE5, \
-        NLOHMANN_JSON_PASTE4, \
-        NLOHMANN_JSON_PASTE3, \
-        NLOHMANN_JSON_PASTE2, \
-        NLOHMANN_JSON_PASTE1)(__VA_ARGS__))
-#define NLOHMANN_JSON_PASTE2(func, v1) func(v1)
-#define NLOHMANN_JSON_PASTE3(func, v1, v2) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE2(func, v2)
-#define NLOHMANN_JSON_PASTE4(func, v1, v2, v3) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE3(func, v2, v3)
-#define NLOHMANN_JSON_PASTE5(func, v1, v2, v3, v4) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE4(func, v2, v3, v4)
-#define NLOHMANN_JSON_PASTE6(func, v1, v2, v3, v4, v5) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE5(func, v2, v3, v4, v5)
-#define NLOHMANN_JSON_PASTE7(func, v1, v2, v3, v4, v5, v6) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE6(func, v2, v3, v4, v5, v6)
-#define NLOHMANN_JSON_PASTE8(func, v1, v2, v3, v4, v5, v6, v7) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE7(func, v2, v3, v4, v5, v6, v7)
-#define NLOHMANN_JSON_PASTE9(func, v1, v2, v3, v4, v5, v6, v7, v8) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE8(func, v2, v3, v4, v5, v6, v7, v8)
-#define NLOHMANN_JSON_PASTE10(func, v1, v2, v3, v4, v5, v6, v7, v8, v9) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE9(func, v2, v3, v4, v5, v6, v7, v8, v9)
-#define NLOHMANN_JSON_PASTE11(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE10(func, v2, v3, v4, v5, v6, v7, v8, v9, v10)
-#define NLOHMANN_JSON_PASTE12(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE11(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11)
-#define NLOHMANN_JSON_PASTE13(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE12(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12)
-#define NLOHMANN_JSON_PASTE14(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE13(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13)
-#define NLOHMANN_JSON_PASTE15(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE14(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14)
-#define NLOHMANN_JSON_PASTE16(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE15(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15)
-#define NLOHMANN_JSON_PASTE17(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE16(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16)
-#define NLOHMANN_JSON_PASTE18(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE17(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17)
-#define NLOHMANN_JSON_PASTE19(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE18(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18)
-#define NLOHMANN_JSON_PASTE20(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE19(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19)
-#define NLOHMANN_JSON_PASTE21(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE20(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20)
-#define NLOHMANN_JSON_PASTE22(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE21(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21)
-#define NLOHMANN_JSON_PASTE23(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE22(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22)
-#define NLOHMANN_JSON_PASTE24(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE23(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23)
-#define NLOHMANN_JSON_PASTE25(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE24(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24)
-#define NLOHMANN_JSON_PASTE26(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE25(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25)
-#define NLOHMANN_JSON_PASTE27(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE26(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26)
-#define NLOHMANN_JSON_PASTE28(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE27(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27)
-#define NLOHMANN_JSON_PASTE29(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE28(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28)
-#define NLOHMANN_JSON_PASTE30(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE29(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29)
-#define NLOHMANN_JSON_PASTE31(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE30(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30)
-#define NLOHMANN_JSON_PASTE32(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE31(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31)
-#define NLOHMANN_JSON_PASTE33(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE32(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32)
-#define NLOHMANN_JSON_PASTE34(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE33(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33)
-#define NLOHMANN_JSON_PASTE35(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE34(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34)
-#define NLOHMANN_JSON_PASTE36(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE35(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35)
-#define NLOHMANN_JSON_PASTE37(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE36(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36)
-#define NLOHMANN_JSON_PASTE38(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE37(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37)
-#define NLOHMANN_JSON_PASTE39(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE38(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38)
-#define NLOHMANN_JSON_PASTE40(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE39(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39)
-#define NLOHMANN_JSON_PASTE41(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE40(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40)
-#define NLOHMANN_JSON_PASTE42(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE41(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41)
-#define NLOHMANN_JSON_PASTE43(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE42(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42)
-#define NLOHMANN_JSON_PASTE44(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE43(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43)
-#define NLOHMANN_JSON_PASTE45(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE44(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44)
-#define NLOHMANN_JSON_PASTE46(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE45(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45)
-#define NLOHMANN_JSON_PASTE47(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE46(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46)
-#define NLOHMANN_JSON_PASTE48(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE47(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47)
-#define NLOHMANN_JSON_PASTE49(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE48(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48)
-#define NLOHMANN_JSON_PASTE50(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE49(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49)
-#define NLOHMANN_JSON_PASTE51(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE50(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50)
-#define NLOHMANN_JSON_PASTE52(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE51(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51)
-#define NLOHMANN_JSON_PASTE53(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE52(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52)
-#define NLOHMANN_JSON_PASTE54(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE53(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53)
-#define NLOHMANN_JSON_PASTE55(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE54(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54)
-#define NLOHMANN_JSON_PASTE56(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE55(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55)
-#define NLOHMANN_JSON_PASTE57(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE56(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56)
-#define NLOHMANN_JSON_PASTE58(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE57(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57)
-#define NLOHMANN_JSON_PASTE59(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE58(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58)
-#define NLOHMANN_JSON_PASTE60(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE59(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59)
-#define NLOHMANN_JSON_PASTE61(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE60(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60)
-#define NLOHMANN_JSON_PASTE62(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE61(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61)
-#define NLOHMANN_JSON_PASTE63(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE62(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62)
-#define NLOHMANN_JSON_PASTE64(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62, v63) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE63(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62, v63)
-
-#define NLOHMANN_JSON_TO(v1) nlohmann_json_j[#v1] = nlohmann_json_t.v1;
-#define NLOHMANN_JSON_FROM(v1) nlohmann_json_j.at(#v1).get_to(nlohmann_json_t.v1);
-#define NLOHMANN_JSON_FROM_WITH_DEFAULT(v1) nlohmann_json_t.v1 = nlohmann_json_j.value(#v1, nlohmann_json_default_obj.v1);
-
-/*!
-@brief macro
-@def NLOHMANN_DEFINE_TYPE_INTRUSIVE
-@since version 3.9.0
-*/
-#define NLOHMANN_DEFINE_TYPE_INTRUSIVE(Type, ...)  \
-    friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
-    friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) }
-
-#define NLOHMANN_DEFINE_TYPE_INTRUSIVE_WITH_DEFAULT(Type, ...)  \
-    friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
-    friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { const Type nlohmann_json_default_obj{}; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
-
-#define NLOHMANN_DEFINE_TYPE_INTRUSIVE_ONLY_SERIALIZE(Type, ...)  \
-    friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) }
-
-/*!
-@brief macro
-@def NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE
-@since version 3.9.0
-*/
-#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Type, ...)  \
-    inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
-    inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) }
-
-#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_ONLY_SERIALIZE(Type, ...)  \
-    inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) }
-
-#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_WITH_DEFAULT(Type, ...)  \
-    inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
-    inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { const Type nlohmann_json_default_obj{}; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
-
-// inspired from https://stackoverflow.com/a/26745591
-// allows to call any std function as if (e.g. with begin):
-// using std::begin; begin(x);
-//
-// it allows using the detected idiom to retrieve the return type
-// of such an expression
-#define NLOHMANN_CAN_CALL_STD_FUNC_IMPL(std_name)                                 \
-    namespace detail {                                                            \
-    using std::std_name;                                                          \
-    \
-    template<typename... T>                                                       \
-    using result_of_##std_name = decltype(std_name(std::declval<T>()...));        \
-    }                                                                             \
-    \
-    namespace detail2 {                                                           \
-    struct std_name##_tag                                                         \
-    {                                                                             \
-    };                                                                            \
-    \
-    template<typename... T>                                                       \
-    std_name##_tag std_name(T&&...);                                              \
-    \
-    template<typename... T>                                                       \
-    using result_of_##std_name = decltype(std_name(std::declval<T>()...));        \
-    \
-    template<typename... T>                                                       \
-    struct would_call_std_##std_name                                              \
-    {                                                                             \
-        static constexpr auto const value = ::nlohmann::detail::                  \
-                                            is_detected_exact<std_name##_tag, result_of_##std_name, T...>::value; \
-    };                                                                            \
-    } /* namespace detail2 */ \
-    \
-    template<typename... T>                                                       \
-    struct would_call_std_##std_name : detail2::would_call_std_##std_name<T...>   \
-    {                                                                             \
-    }
-
-#ifndef JSON_USE_IMPLICIT_CONVERSIONS
-    #define JSON_USE_IMPLICIT_CONVERSIONS 1
-#endif
-
-#if JSON_USE_IMPLICIT_CONVERSIONS
-    #define JSON_EXPLICIT
-#else
-    #define JSON_EXPLICIT explicit
-#endif
-
-#ifndef JSON_DISABLE_ENUM_SERIALIZATION
-    #define JSON_DISABLE_ENUM_SERIALIZATION 0
-#endif
-
-#ifndef JSON_USE_GLOBAL_UDLS
-    #define JSON_USE_GLOBAL_UDLS 1
-#endif

sample/lib/json/include/nlohmann/detail/macro_unscope.hpp

@@ -1,45 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-// restore clang diagnostic settings
-#if defined(__clang__)
-    #pragma clang diagnostic pop
-#endif
-
-// clean up
-#undef JSON_ASSERT
-#undef JSON_INTERNAL_CATCH
-#undef JSON_THROW
-#undef JSON_PRIVATE_UNLESS_TESTED
-#undef NLOHMANN_BASIC_JSON_TPL_DECLARATION
-#undef NLOHMANN_BASIC_JSON_TPL
-#undef JSON_EXPLICIT
-#undef NLOHMANN_CAN_CALL_STD_FUNC_IMPL
-#undef JSON_INLINE_VARIABLE
-#undef JSON_NO_UNIQUE_ADDRESS
-#undef JSON_DISABLE_ENUM_SERIALIZATION
-#undef JSON_USE_GLOBAL_UDLS
-
-#ifndef JSON_TEST_KEEP_MACROS
-    #undef JSON_CATCH
-    #undef JSON_TRY
-    #undef JSON_HAS_CPP_11
-    #undef JSON_HAS_CPP_14
-    #undef JSON_HAS_CPP_17
-    #undef JSON_HAS_CPP_20
-    #undef JSON_HAS_FILESYSTEM
-    #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-    #undef JSON_HAS_THREE_WAY_COMPARISON
-    #undef JSON_HAS_RANGES
-    #undef JSON_HAS_STATIC_RTTI
-    #undef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
-#endif
-
-#include <nlohmann/thirdparty/hedley/hedley_undef.hpp>

sample/lib/json/include/nlohmann/detail/meta/call_std/begin.hpp

@@ -1,17 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <nlohmann/detail/macro_scope.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-NLOHMANN_CAN_CALL_STD_FUNC_IMPL(begin);
-
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/meta/call_std/end.hpp

@@ -1,17 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <nlohmann/detail/macro_scope.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-NLOHMANN_CAN_CALL_STD_FUNC_IMPL(end);
-
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/meta/cpp_future.hpp

@@ -1,171 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-FileCopyrightText: 2018 The Abseil Authors
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <array> // array
-#include <cstddef> // size_t
-#include <type_traits> // conditional, enable_if, false_type, integral_constant, is_constructible, is_integral, is_same, remove_cv, remove_reference, true_type
-#include <utility> // index_sequence, make_index_sequence, index_sequence_for
-
-#include <nlohmann/detail/macro_scope.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-template<typename T>
-using uncvref_t = typename std::remove_cv<typename std::remove_reference<T>::type>::type;
-
-#ifdef JSON_HAS_CPP_14
-
-// the following utilities are natively available in C++14
-using std::enable_if_t;
-using std::index_sequence;
-using std::make_index_sequence;
-using std::index_sequence_for;
-
-#else
-
-// alias templates to reduce boilerplate
-template<bool B, typename T = void>
-using enable_if_t = typename std::enable_if<B, T>::type;
-
-// The following code is taken from https://github.com/abseil/abseil-cpp/blob/10cb35e459f5ecca5b2ff107635da0bfa41011b4/absl/utility/utility.h
-// which is part of Google Abseil (https://github.com/abseil/abseil-cpp), licensed under the Apache License 2.0.
-
-//// START OF CODE FROM GOOGLE ABSEIL
-
-// integer_sequence
-//
-// Class template representing a compile-time integer sequence. An instantiation
-// of `integer_sequence<T, Ints...>` has a sequence of integers encoded in its
-// type through its template arguments (which is a common need when
-// working with C++11 variadic templates). `absl::integer_sequence` is designed
-// to be a drop-in replacement for C++14's `std::integer_sequence`.
-//
-// Example:
-//
-//   template< class T, T... Ints >
-//   void user_function(integer_sequence<T, Ints...>);
-//
-//   int main()
-//   {
-//     // user_function's `T` will be deduced to `int` and `Ints...`
-//     // will be deduced to `0, 1, 2, 3, 4`.
-//     user_function(make_integer_sequence<int, 5>());
-//   }
-template <typename T, T... Ints>
-struct integer_sequence
-{
-    using value_type = T;
-    static constexpr std::size_t size() noexcept
-    {
-        return sizeof...(Ints);
-    }
-};
-
-// index_sequence
-//
-// A helper template for an `integer_sequence` of `size_t`,
-// `absl::index_sequence` is designed to be a drop-in replacement for C++14's
-// `std::index_sequence`.
-template <size_t... Ints>
-using index_sequence = integer_sequence<size_t, Ints...>;
-
-namespace utility_internal
-{
-
-template <typename Seq, size_t SeqSize, size_t Rem>
-struct Extend;
-
-// Note that SeqSize == sizeof...(Ints). It's passed explicitly for efficiency.
-template <typename T, T... Ints, size_t SeqSize>
-struct Extend<integer_sequence<T, Ints...>, SeqSize, 0>
-{
-    using type = integer_sequence < T, Ints..., (Ints + SeqSize)... >;
-};
-
-template <typename T, T... Ints, size_t SeqSize>
-struct Extend<integer_sequence<T, Ints...>, SeqSize, 1>
-{
-    using type = integer_sequence < T, Ints..., (Ints + SeqSize)..., 2 * SeqSize >;
-};
-
-// Recursion helper for 'make_integer_sequence<T, N>'.
-// 'Gen<T, N>::type' is an alias for 'integer_sequence<T, 0, 1, ... N-1>'.
-template <typename T, size_t N>
-struct Gen
-{
-    using type =
-        typename Extend < typename Gen < T, N / 2 >::type, N / 2, N % 2 >::type;
-};
-
-template <typename T>
-struct Gen<T, 0>
-{
-    using type = integer_sequence<T>;
-};
-
-}  // namespace utility_internal
-
-// Compile-time sequences of integers
-
-// make_integer_sequence
-//
-// This template alias is equivalent to
-// `integer_sequence<int, 0, 1, ..., N-1>`, and is designed to be a drop-in
-// replacement for C++14's `std::make_integer_sequence`.
-template <typename T, T N>
-using make_integer_sequence = typename utility_internal::Gen<T, N>::type;
-
-// make_index_sequence
-//
-// This template alias is equivalent to `index_sequence<0, 1, ..., N-1>`,
-// and is designed to be a drop-in replacement for C++14's
-// `std::make_index_sequence`.
-template <size_t N>
-using make_index_sequence = make_integer_sequence<size_t, N>;
-
-// index_sequence_for
-//
-// Converts a typename pack into an index sequence of the same length, and
-// is designed to be a drop-in replacement for C++14's
-// `std::index_sequence_for()`
-template <typename... Ts>
-using index_sequence_for = make_index_sequence<sizeof...(Ts)>;
-
-//// END OF CODE FROM GOOGLE ABSEIL
-
-#endif
-
-// dispatch utility (taken from ranges-v3)
-template<unsigned N> struct priority_tag : priority_tag < N - 1 > {};
-template<> struct priority_tag<0> {};
-
-// taken from ranges-v3
-template<typename T>
-struct static_const
-{
-    static JSON_INLINE_VARIABLE constexpr T value{};
-};
-
-#ifndef JSON_HAS_CPP_17
-    template<typename T>
-    constexpr T static_const<T>::value;
-#endif
-
-template<typename T, typename... Args>
-inline constexpr std::array<T, sizeof...(Args)> make_array(Args&& ... args)
-{
-    return std::array<T, sizeof...(Args)> {{static_cast<T>(std::forward<Args>(args))...}};
-}
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/meta/detected.hpp

@@ -1,70 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <type_traits>
-
-#include <nlohmann/detail/meta/void_t.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-// https://en.cppreference.com/w/cpp/experimental/is_detected
-struct nonesuch
-{
-    nonesuch() = delete;
-    ~nonesuch() = delete;
-    nonesuch(nonesuch const&) = delete;
-    nonesuch(nonesuch const&&) = delete;
-    void operator=(nonesuch const&) = delete;
-    void operator=(nonesuch&&) = delete;
-};
-
-template<class Default,
-         class AlwaysVoid,
-         template<class...> class Op,
-         class... Args>
-struct detector
-{
-    using value_t = std::false_type;
-    using type = Default;
-};
-
-template<class Default, template<class...> class Op, class... Args>
-struct detector<Default, void_t<Op<Args...>>, Op, Args...>
-{
-    using value_t = std::true_type;
-    using type = Op<Args...>;
-};
-
-template<template<class...> class Op, class... Args>
-using is_detected = typename detector<nonesuch, void, Op, Args...>::value_t;
-
-template<template<class...> class Op, class... Args>
-struct is_detected_lazy : is_detected<Op, Args...> { };
-
-template<template<class...> class Op, class... Args>
-using detected_t = typename detector<nonesuch, void, Op, Args...>::type;
-
-template<class Default, template<class...> class Op, class... Args>
-using detected_or = detector<Default, void, Op, Args...>;
-
-template<class Default, template<class...> class Op, class... Args>
-using detected_or_t = typename detected_or<Default, Op, Args...>::type;
-
-template<class Expected, template<class...> class Op, class... Args>
-using is_detected_exact = std::is_same<Expected, detected_t<Op, Args...>>;
-
-template<class To, template<class...> class Op, class... Args>
-using is_detected_convertible =
-    std::is_convertible<detected_t<Op, Args...>, To>;
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/meta/identity_tag.hpp

@@ -1,21 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <nlohmann/detail/abi_macros.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-// dispatching helper struct
-template <class T> struct identity_tag {};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/meta/is_sax.hpp

@@ -1,159 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <cstdint> // size_t
-#include <utility> // declval
-#include <string> // string
-
-#include <nlohmann/detail/abi_macros.hpp>
-#include <nlohmann/detail/meta/detected.hpp>
-#include <nlohmann/detail/meta/type_traits.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-template<typename T>
-using null_function_t = decltype(std::declval<T&>().null());
-
-template<typename T>
-using boolean_function_t =
-    decltype(std::declval<T&>().boolean(std::declval<bool>()));
-
-template<typename T, typename Integer>
-using number_integer_function_t =
-    decltype(std::declval<T&>().number_integer(std::declval<Integer>()));
-
-template<typename T, typename Unsigned>
-using number_unsigned_function_t =
-    decltype(std::declval<T&>().number_unsigned(std::declval<Unsigned>()));
-
-template<typename T, typename Float, typename String>
-using number_float_function_t = decltype(std::declval<T&>().number_float(
-                                    std::declval<Float>(), std::declval<const String&>()));
-
-template<typename T, typename String>
-using string_function_t =
-    decltype(std::declval<T&>().string(std::declval<String&>()));
-
-template<typename T, typename Binary>
-using binary_function_t =
-    decltype(std::declval<T&>().binary(std::declval<Binary&>()));
-
-template<typename T>
-using start_object_function_t =
-    decltype(std::declval<T&>().start_object(std::declval<std::size_t>()));
-
-template<typename T, typename String>
-using key_function_t =
-    decltype(std::declval<T&>().key(std::declval<String&>()));
-
-template<typename T>
-using end_object_function_t = decltype(std::declval<T&>().end_object());
-
-template<typename T>
-using start_array_function_t =
-    decltype(std::declval<T&>().start_array(std::declval<std::size_t>()));
-
-template<typename T>
-using end_array_function_t = decltype(std::declval<T&>().end_array());
-
-template<typename T, typename Exception>
-using parse_error_function_t = decltype(std::declval<T&>().parse_error(
-        std::declval<std::size_t>(), std::declval<const std::string&>(),
-        std::declval<const Exception&>()));
-
-template<typename SAX, typename BasicJsonType>
-struct is_sax
-{
-  private:
-    static_assert(is_basic_json<BasicJsonType>::value,
-                  "BasicJsonType must be of type basic_json<...>");
-
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-    using exception_t = typename BasicJsonType::exception;
-
-  public:
-    static constexpr bool value =
-        is_detected_exact<bool, null_function_t, SAX>::value &&
-        is_detected_exact<bool, boolean_function_t, SAX>::value &&
-        is_detected_exact<bool, number_integer_function_t, SAX, number_integer_t>::value &&
-        is_detected_exact<bool, number_unsigned_function_t, SAX, number_unsigned_t>::value &&
-        is_detected_exact<bool, number_float_function_t, SAX, number_float_t, string_t>::value &&
-        is_detected_exact<bool, string_function_t, SAX, string_t>::value &&
-        is_detected_exact<bool, binary_function_t, SAX, binary_t>::value &&
-        is_detected_exact<bool, start_object_function_t, SAX>::value &&
-        is_detected_exact<bool, key_function_t, SAX, string_t>::value &&
-        is_detected_exact<bool, end_object_function_t, SAX>::value &&
-        is_detected_exact<bool, start_array_function_t, SAX>::value &&
-        is_detected_exact<bool, end_array_function_t, SAX>::value &&
-        is_detected_exact<bool, parse_error_function_t, SAX, exception_t>::value;
-};
-
-template<typename SAX, typename BasicJsonType>
-struct is_sax_static_asserts
-{
-  private:
-    static_assert(is_basic_json<BasicJsonType>::value,
-                  "BasicJsonType must be of type basic_json<...>");
-
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-    using exception_t = typename BasicJsonType::exception;
-
-  public:
-    static_assert(is_detected_exact<bool, null_function_t, SAX>::value,
-                  "Missing/invalid function: bool null()");
-    static_assert(is_detected_exact<bool, boolean_function_t, SAX>::value,
-                  "Missing/invalid function: bool boolean(bool)");
-    static_assert(is_detected_exact<bool, boolean_function_t, SAX>::value,
-                  "Missing/invalid function: bool boolean(bool)");
-    static_assert(
-        is_detected_exact<bool, number_integer_function_t, SAX,
-        number_integer_t>::value,
-        "Missing/invalid function: bool number_integer(number_integer_t)");
-    static_assert(
-        is_detected_exact<bool, number_unsigned_function_t, SAX,
-        number_unsigned_t>::value,
-        "Missing/invalid function: bool number_unsigned(number_unsigned_t)");
-    static_assert(is_detected_exact<bool, number_float_function_t, SAX,
-                  number_float_t, string_t>::value,
-                  "Missing/invalid function: bool number_float(number_float_t, const string_t&)");
-    static_assert(
-        is_detected_exact<bool, string_function_t, SAX, string_t>::value,
-        "Missing/invalid function: bool string(string_t&)");
-    static_assert(
-        is_detected_exact<bool, binary_function_t, SAX, binary_t>::value,
-        "Missing/invalid function: bool binary(binary_t&)");
-    static_assert(is_detected_exact<bool, start_object_function_t, SAX>::value,
-                  "Missing/invalid function: bool start_object(std::size_t)");
-    static_assert(is_detected_exact<bool, key_function_t, SAX, string_t>::value,
-                  "Missing/invalid function: bool key(string_t&)");
-    static_assert(is_detected_exact<bool, end_object_function_t, SAX>::value,
-                  "Missing/invalid function: bool end_object()");
-    static_assert(is_detected_exact<bool, start_array_function_t, SAX>::value,
-                  "Missing/invalid function: bool start_array(std::size_t)");
-    static_assert(is_detected_exact<bool, end_array_function_t, SAX>::value,
-                  "Missing/invalid function: bool end_array()");
-    static_assert(
-        is_detected_exact<bool, parse_error_function_t, SAX, exception_t>::value,
-        "Missing/invalid function: bool parse_error(std::size_t, const "
-        "std::string&, const exception&)");
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END

sample/lib/json/include/nlohmann/detail/meta/std_fs.hpp

@@ -1,29 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <nlohmann/detail/macro_scope.hpp>
-
-#if JSON_HAS_EXPERIMENTAL_FILESYSTEM
-#include <experimental/filesystem>
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-namespace std_fs = std::experimental::filesystem;
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-#elif JSON_HAS_FILESYSTEM
-#include <filesystem>
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-namespace std_fs = std::filesystem;
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-#endif

sample/lib/json/include/nlohmann/detail/meta/type_traits.hpp

@@ -1,795 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.3
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#pragma once
-
-#include <limits> // numeric_limits
-#include <type_traits> // false_type, is_constructible, is_integral, is_same, true_type
-#include <utility> // declval
-#include <tuple> // tuple
-#include <string> // char_traits
-
-#include <nlohmann/detail/iterators/iterator_traits.hpp>
-#include <nlohmann/detail/macro_scope.hpp>
-#include <nlohmann/detail/meta/call_std/begin.hpp>
-#include <nlohmann/detail/meta/call_std/end.hpp>
-#include <nlohmann/detail/meta/cpp_future.hpp>
-#include <nlohmann/detail/meta/detected.hpp>
-#include <nlohmann/json_fwd.hpp>
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-/*!
-@brief detail namespace with internal helper functions
-
-This namespace collects functions that should not be exposed,
-implementations of some @ref basic_json methods, and meta-programming helpers.
-
-@since version 2.1.0
-*/
-namespace detail
-{
-
-/////////////
-// helpers //
-/////////////
-
-// Note to maintainers:
-//
-// Every trait in this file expects a non CV-qualified type.
-// The only exceptions are in the 'aliases for detected' section
-// (i.e. those of the form: decltype(T::member_function(std::declval<T>())))
-//
-// In this case, T has to be properly CV-qualified to constraint the function arguments
-// (e.g. to_json(BasicJsonType&, const T&))
-
-template<typename> struct is_basic_json : std::false_type {};
-
-NLOHMANN_BASIC_JSON_TPL_DECLARATION
-struct is_basic_json<NLOHMANN_BASIC_JSON_TPL> : std::true_type {};
-
-// used by exceptions create() member functions
-// true_type for pointer to possibly cv-qualified basic_json or std::nullptr_t
-// false_type otherwise
-template<typename BasicJsonContext>
-struct is_basic_json_context :
-    std::integral_constant < bool,
-    is_basic_json<typename std::remove_cv<typename std::remove_pointer<BasicJsonContext>::type>::type>::value
-    || std::is_same<BasicJsonContext, std::nullptr_t>::value >
-{};
-
-//////////////////////
-// json_ref helpers //
-//////////////////////
-
-template<typename>
-class json_ref;
-
-template<typename>
-struct is_json_ref : std::false_type {};
-
-template<typename T>
-struct is_json_ref<json_ref<T>> : std::true_type {};
-
-//////////////////////////
-// aliases for detected //
-//////////////////////////
-
-template<typename T>
-using mapped_type_t = typename T::mapped_type;
-
-template<typename T>
-using key_type_t = typename T::key_type;
-
-template<typename T>
-using value_type_t = typename T::value_type;
-
-template<typename T>
-using difference_type_t = typename T::difference_type;
-
-template<typename T>
-using pointer_t = typename T::pointer;
-
-template<typename T>
-using reference_t = typename T::reference;
-
-template<typename T>
-using iterator_category_t = typename T::iterator_category;
-
-template<typename T, typename... Args>
-using to_json_function = decltype(T::to_json(std::declval<Args>()...));
-
-template<typename T, typename... Args>
-using from_json_function = decltype(T::from_json(std::declval<Args>()...));
-
-template<typename T, typename U>
-using get_template_function = decltype(std::declval<T>().template get<U>());
-
-// trait checking if JSONSerializer<T>::from_json(json const&, udt&) exists
-template<typename BasicJsonType, typename T, typename = void>
-struct has_from_json : std::false_type {};
-
-// trait checking if j.get<T> is valid
-// use this trait instead of std::is_constructible or std::is_convertible,
-// both rely on, or make use of implicit conversions, and thus fail when T
-// has several constructors/operator= (see https://github.com/nlohmann/json/issues/958)
-template <typename BasicJsonType, typename T>
-struct is_getable
-{
-    static constexpr bool value = is_detected<get_template_function, const BasicJsonType&, T>::value;
-};
-
-template<typename BasicJsonType, typename T>
-struct has_from_json < BasicJsonType, T, enable_if_t < !is_basic_json<T>::value >>
-{
-    using serializer = typename BasicJsonType::template json_serializer<T, void>;
-
-    static constexpr bool value =
-        is_detected_exact<void, from_json_function, serializer,
-        const BasicJsonType&, T&>::value;
-};
-
-// This trait checks if JSONSerializer<T>::from_json(json const&) exists
-// this overload is used for non-default-constructible user-defined-types
-template<typename BasicJsonType, typename T, typename = void>
-struct has_non_default_from_json : std::false_type {};
-
-template<typename BasicJsonType, typename T>
-struct has_non_default_from_json < BasicJsonType, T, enable_if_t < !is_basic_json<T>::value >>
-{
-    using serializer = typename BasicJsonType::template json_serializer<T, void>;
-
-    static constexpr bool value =
-        is_detected_exact<T, from_json_function, serializer,
-        const BasicJsonType&>::value;
-};
-
-// This trait checks if BasicJsonType::json_serializer<T>::to_json exists
-// Do not evaluate the trait when T is a basic_json type, to avoid template instantiation infinite recursion.
-template<typename BasicJsonType, typename T, typename = void>
-struct has_to_json : std::false_type {};
-
-template<typename BasicJsonType, typename T>
-struct has_to_json < BasicJsonType, T, enable_if_t < !is_basic_json<T>::value >>
-{
-    using serializer = typename BasicJsonType::template json_serializer<T, void>;
-
-    static constexpr bool value =
-        is_detected_exact<void, to_json_function, serializer, BasicJsonType&,
-        T>::value;
-};
-
-template<typename T>
-using detect_key_compare = typename T::key_compare;
-
-template<typename T>
-struct has_key_compare : std::integral_constant<bool, is_detected<detect_key_compare, T>::value> {};
-
-// obtains the actual object key comparator
-template<typename BasicJsonType>
-struct actual_object_comparator
-{
-    using object_t = typename BasicJsonType::object_t;
-    using object_comparator_t = typename BasicJsonType::default_object_comparator_t;
-    using type = typename std::conditional < has_key_compare<object_t>::value,
-          typename object_t::key_compare, object_comparator_t>::type;
-};
-
-template<typename BasicJsonType>
-using actual_object_comparator_t = typename actual_object_comparator<BasicJsonType>::type;
-
-/////////////////
-// char_traits //
-/////////////////
-
-// Primary template of char_traits calls std char_traits
-template<typename T>
-struct char_traits : std::char_traits<T>
-{};
-
-// Explicitly define char traits for unsigned char since it is not standard
-template<>
-struct char_traits<unsigned char> : std::char_traits<char>
-{
-    using char_type = unsigned char;
-    using int_type = uint64_t;
-
-    // Redefine to_int_type function
-    static int_type to_int_type(char_type c) noexcept
-    {
-        return static_cast<int_type>(c);
-    }
-
-    static char_type to_char_type(int_type i) noexcept
-    {
-        return static_cast<char_type>(i);
-    }
-
-    static constexpr int_type eof() noexcept
-    {
-        return static_cast<int_type>(EOF);
-    }
-};
-
-// Explicitly define char traits for signed char since it is not standard
-template<>
-struct char_traits<signed char> : std::char_traits<char>
-{
-    using char_type = signed char;
-    using int_type = uint64_t;
-
-    // Redefine to_int_type function
-    static int_type to_int_type(char_type c) noexcept
-    {
-        return static_cast<int_type>(c);
-    }
-
-    static char_type to_char_type(int_type i) noexcept
-    {
-        return static_cast<char_type>(i);
-    }
-
-    static constexpr int_type eof() noexcept
-    {
-        return static_cast<int_type>(EOF);
-    }
-};
-
-///////////////////
-// is_ functions //
-///////////////////
-
-// https://en.cppreference.com/w/cpp/types/conjunction
-template<class...> struct conjunction : std::true_type { };
-template<class B> struct conjunction<B> : B { };
-template<class B, class... Bn>
-struct conjunction<B, Bn...>
-: std::conditional<static_cast<bool>(B::value), conjunction<Bn...>, B>::type {};
-
-// https://en.cppreference.com/w/cpp/types/negation
-template<class B> struct negation : std::integral_constant < bool, !B::value > { };
-
-// Reimplementation of is_constructible and is_default_constructible, due to them being broken for
-// std::pair and std::tuple until LWG 2367 fix (see https://cplusplus.github.io/LWG/lwg-defects.html#2367).
-// This causes compile errors in e.g. clang 3.5 or gcc 4.9.
-template <typename T>
-struct is_default_constructible : std::is_default_constructible<T> {};
-
-template <typename T1, typename T2>
-struct is_default_constructible<std::pair<T1, T2>>
-            : conjunction<is_default_constructible<T1>, is_default_constructible<T2>> {};
-
-template <typename T1, typename T2>
-struct is_default_constructible<const std::pair<T1, T2>>
-            : conjunction<is_default_constructible<T1>, is_default_constructible<T2>> {};
-
-template <typename... Ts>
-struct is_default_constructible<std::tuple<Ts...>>
-            : conjunction<is_default_constructible<Ts>...> {};
-
-template <typename... Ts>
-struct is_default_constructible<const std::tuple<Ts...>>
-            : conjunction<is_default_constructible<Ts>...> {};
-
-template <typename T, typename... Args>
-struct is_constructible : std::is_constructible<T, Args...> {};
-
-template <typename T1, typename T2>
-struct is_constructible<std::pair<T1, T2>> : is_default_constructible<std::pair<T1, T2>> {};
-
-template <typename T1, typename T2>
-struct is_constructible<const std::pair<T1, T2>> : is_default_constructible<const std::pair<T1, T2>> {};
-
-template <typename... Ts>
-struct is_constructible<std::tuple<Ts...>> : is_default_constructible<std::tuple<Ts...>> {};
-
-template <typename... Ts>
-struct is_constructible<const std::tuple<Ts...>> : is_default_constructible<const std::tuple<Ts...>> {};
-
-template<typename T, typename = void>
-struct is_iterator_traits : std::false_type {};
-
-template<typename T>
-struct is_iterator_traits<iterator_traits<T>>
-{
-  private:
-    using traits = iterator_traits<T>;
-
-  public:
-    static constexpr auto value =
-        is_detected<value_type_t, traits>::value &&
-        is_detected<difference_type_t, traits>::value &&
-        is_detected<pointer_t, traits>::value &&
-        is_detected<iterator_category_t, traits>::value &&
-        is_detected<reference_t, traits>::value;
-};
-
-template<typename T>
-struct is_range
-{
-  private:
-    using t_ref = typename std::add_lvalue_reference<T>::type;
-
-    using iterator = detected_t<result_of_begin, t_ref>;
-    using sentinel = detected_t<result_of_end, t_ref>;
-
-    // to be 100% correct, it should use https://en.cppreference.com/w/cpp/iterator/input_or_output_iterator
-    // and https://en.cppreference.com/w/cpp/iterator/sentinel_for
-    // but reimplementing these would be too much work, as a lot of other concepts are used underneath
-    static constexpr auto is_iterator_begin =
-        is_iterator_traits<iterator_traits<iterator>>::value;
-
-  public:
-    static constexpr bool value = !std::is_same<iterator, nonesuch>::value && !std::is_same<sentinel, nonesuch>::value && is_iterator_begin;
-};
-
-template<typename R>
-using iterator_t = enable_if_t<is_range<R>::value, result_of_begin<decltype(std::declval<R&>())>>;
-
-template<typename T>
-using range_value_t = value_type_t<iterator_traits<iterator_t<T>>>;
-
-// The following implementation of is_complete_type is taken from
-// https://blogs.msdn.microsoft.com/vcblog/2015/12/02/partial-support-for-expression-sfinae-in-vs-2015-update-1/
-// and is written by Xiang Fan who agreed to using it in this library.
-
-template<typename T, typename = void>
-struct is_complete_type : std::false_type {};
-
-template<typename T>
-struct is_complete_type<T, decltype(void(sizeof(T)))> : std::true_type {};
-
-template<typename BasicJsonType, typename CompatibleObjectType,
-         typename = void>
-struct is_compatible_object_type_impl : std::false_type {};
-
-template<typename BasicJsonType, typename CompatibleObjectType>
-struct is_compatible_object_type_impl <
-    BasicJsonType, CompatibleObjectType,
-    enable_if_t < is_detected<mapped_type_t, CompatibleObjectType>::value&&
-    is_detected<key_type_t, CompatibleObjectType>::value >>
-{
-    using object_t = typename BasicJsonType::object_t;
-
-    // macOS's is_constructible does not play well with nonesuch...
-    static constexpr bool value =
-        is_constructible<typename object_t::key_type,
-        typename CompatibleObjectType::key_type>::value &&
-        is_constructible<typename object_t::mapped_type,
-        typename CompatibleObjectType::mapped_type>::value;
-};
-
-template<typename BasicJsonType, typename CompatibleObjectType>
-struct is_compatible_object_type
-    : is_compatible_object_type_impl<BasicJsonType, CompatibleObjectType> {};
-
-template<typename BasicJsonType, typename ConstructibleObjectType,
-         typename = void>
-struct is_constructible_object_type_impl : std::false_type {};
-
-template<typename BasicJsonType, typename ConstructibleObjectType>
-struct is_constructible_object_type_impl <
-    BasicJsonType, ConstructibleObjectType,
-    enable_if_t < is_detected<mapped_type_t, ConstructibleObjectType>::value&&
-    is_detected<key_type_t, ConstructibleObjectType>::value >>
-{
-    using object_t = typename BasicJsonType::object_t;
-
-    static constexpr bool value =
-        (is_default_constructible<ConstructibleObjectType>::value &&
-         (std::is_move_assignable<ConstructibleObjectType>::value ||
-          std::is_copy_assignable<ConstructibleObjectType>::value) &&
-         (is_constructible<typename ConstructibleObjectType::key_type,
-          typename object_t::key_type>::value &&
-          std::is_same <
-          typename object_t::mapped_type,
-          typename ConstructibleObjectType::mapped_type >::value)) ||
-        (has_from_json<BasicJsonType,
-         typename ConstructibleObjectType::mapped_type>::value ||
-         has_non_default_from_json <
-         BasicJsonType,
-         typename ConstructibleObjectType::mapped_type >::value);
-};
-
-template<typename BasicJsonType, typename ConstructibleObjectType>
-struct is_constructible_object_type
-    : is_constructible_object_type_impl<BasicJsonType,
-      ConstructibleObjectType> {};
-
-template<typename BasicJsonType, typename CompatibleStringType>
-struct is_compatible_string_type
-{
-    static constexpr auto value =
-        is_constructible<typename BasicJsonType::string_t, CompatibleStringType>::value;
-};
-
-template<typename BasicJsonType, typename ConstructibleStringType>
-struct is_constructible_string_type
-{
-    // launder type through decltype() to fix compilation failure on ICPC
-#ifdef __INTEL_COMPILER
-    using laundered_type = decltype(std::declval<ConstructibleStringType>());
-#else
-    using laundered_type = ConstructibleStringType;
-#endif
-
-    static constexpr auto value =
-        conjunction <
-        is_constructible<laundered_type, typename BasicJsonType::string_t>,
-        is_detected_exact<typename BasicJsonType::string_t::value_type,
-        value_type_t, laundered_type >>::value;
-};
-
-template<typename BasicJsonType, typename CompatibleArrayType, typename = void>
-struct is_compatible_array_type_impl : std::false_type {};
-
-template<typename BasicJsonType, typename CompatibleArrayType>
-struct is_compatible_array_type_impl <
-    BasicJsonType, CompatibleArrayType,
-    enable_if_t <
-    is_detected<iterator_t, CompatibleArrayType>::value&&
-    is_iterator_traits<iterator_traits<detected_t<iterator_t, CompatibleArrayType>>>::value&&
-// special case for types like std::filesystem::path whose iterator's value_type are themselves
-// c.f. https://github.com/nlohmann/json/pull/3073
-    !std::is_same<CompatibleArrayType, detected_t<range_value_t, CompatibleArrayType>>::value >>
-{
-    static constexpr bool value =
-        is_constructible<BasicJsonType,
-        range_value_t<CompatibleArrayType>>::value;
-};
-
-template<typename BasicJsonType, typename CompatibleArrayType>
-struct is_compatible_array_type
-    : is_compatible_array_type_impl<BasicJsonType, CompatibleArrayType> {};
-
-template<typename BasicJsonType, typename ConstructibleArrayType, typename = void>
-struct is_constructible_array_type_impl : std::false_type {};
-
-template<typename BasicJsonType, typename ConstructibleArrayType>
-struct is_constructible_array_type_impl <
-    BasicJsonType, ConstructibleArrayType,
-    enable_if_t<std::is_same<ConstructibleArrayType,
-    typename BasicJsonType::value_type>::value >>
-            : std::true_type {};
-
-template<typename BasicJsonType, typename ConstructibleArrayType>
-struct is_constructible_array_type_impl <
-    BasicJsonType, ConstructibleArrayType,
-    enable_if_t < !std::is_same<ConstructibleArrayType,
-    typename BasicJsonType::value_type>::value&&
-    !is_compatible_string_type<BasicJsonType, ConstructibleArrayType>::value&&
-    is_default_constructible<ConstructibleArrayType>::value&&
-(std::is_move_assignable<ConstructibleArrayType>::value ||
- std::is_copy_assignable<ConstructibleArrayType>::value)&&
-is_detected<iterator_t, ConstructibleArrayType>::value&&
-is_iterator_traits<iterator_traits<detected_t<iterator_t, ConstructibleArrayType>>>::value&&
-is_detected<range_value_t, ConstructibleArrayType>::value&&
-// special case for types like std::filesystem::path whose iterator's value_type are themselves
-// c.f. https://github.com/nlohmann/json/pull/3073
-!std::is_same<ConstructibleArrayType, detected_t<range_value_t, ConstructibleArrayType>>::value&&
-        is_complete_type <
-        detected_t<range_value_t, ConstructibleArrayType >>::value >>
-{
-    using value_type = range_value_t<ConstructibleArrayType>;
-
-    static constexpr bool value =
-        std::is_same<value_type,
-        typename BasicJsonType::array_t::value_type>::value ||
-        has_from_json<BasicJsonType,
-        value_type>::value ||
-        has_non_default_from_json <
-        BasicJsonType,
-        value_type >::value;
-};
-
-template<typename BasicJsonType, typename ConstructibleArrayType>
-struct is_constructible_array_type
-    : is_constructible_array_type_impl<BasicJsonType, ConstructibleArrayType> {};
-
-template<typename RealIntegerType, typename CompatibleNumberIntegerType,
-         typename = void>
-struct is_compatible_integer_type_impl : std::false_type {};
-
-template<typename RealIntegerType, typename CompatibleNumberIntegerType>
-struct is_compatible_integer_type_impl <
-    RealIntegerType, CompatibleNumberIntegerType,
-    enable_if_t < std::is_integral<RealIntegerType>::value&&
-    std::is_integral<CompatibleNumberIntegerType>::value&&
-    !std::is_same<bool, CompatibleNumberIntegerType>::value >>
-{
-    // is there an assert somewhere on overflows?
-    using RealLimits = std::numeric_limits<RealIntegerType>;
-    using CompatibleLimits = std::numeric_limits<CompatibleNumberIntegerType>;
-
-    static constexpr auto value =
-        is_constructible<RealIntegerType,
-        CompatibleNumberIntegerType>::value &&
-        CompatibleLimits::is_integer &&
-        RealLimits::is_signed == CompatibleLimits::is_signed;
-};
-
-template<typename RealIntegerType, typename CompatibleNumberIntegerType>
-struct is_compatible_integer_type
-    : is_compatible_integer_type_impl<RealIntegerType,
-      CompatibleNumberIntegerType> {};
-
-template<typename BasicJsonType, typename CompatibleType, typename = void>
-struct is_compatible_type_impl: std::false_type {};
-
-template<typename BasicJsonType, typename CompatibleType>
-struct is_compatible_type_impl <
-    BasicJsonType, CompatibleType,
-    enable_if_t<is_complete_type<CompatibleType>::value >>
-{
-    static constexpr bool value =
-        has_to_json<BasicJsonType, CompatibleType>::value;
-};
-
-template<typename BasicJsonType, typename CompatibleType>
-struct is_compatible_type
-    : is_compatible_type_impl<BasicJsonType, CompatibleType> {};
-
-template<typename T1, typename T2>
-struct is_constructible_tuple : std::false_type {};
-
-template<typename T1, typename... Args>
-struct is_constructible_tuple<T1, std::tuple<Args...>> : conjunction<is_constructible<T1, Args>...> {};
-
-template<typename BasicJsonType, typename T>
-struct is_json_iterator_of : std::false_type {};
-
-template<typename BasicJsonType>
-struct is_json_iterator_of<BasicJsonType, typename BasicJsonType::iterator> : std::true_type {};
-
-template<typename BasicJsonType>
-struct is_json_iterator_of<BasicJsonType, typename BasicJsonType::const_iterator> : std::true_type
-{};
-
-// checks if a given type T is a template specialization of Primary
-template<template <typename...> class Primary, typename T>
-struct is_specialization_of : std::false_type {};
-
-template<template <typename...> class Primary, typename... Args>
-struct is_specialization_of<Primary, Primary<Args...>> : std::true_type {};
-
-template<typename T>
-using is_json_pointer = is_specialization_of<::nlohmann::json_pointer, uncvref_t<T>>;
-
-// checks if A and B are comparable using Compare functor
-template<typename Compare, typename A, typename B, typename = void>
-struct is_comparable : std::false_type {};
-
-template<typename Compare, typename A, typename B>
-struct is_comparable<Compare, A, B, void_t<
-decltype(std::declval<Compare>()(std::declval<A>(), std::declval<B>())),
-decltype(std::declval<Compare>()(std::declval<B>(), std::declval<A>()))
->> : std::true_type {};
-
-template<typename T>
-using detect_is_transparent = typename T::is_transparent;
-
-// type trait to check if KeyType can be used as object key (without a BasicJsonType)
-// see is_usable_as_basic_json_key_type below
-template<typename Comparator, typename ObjectKeyType, typename KeyTypeCVRef, bool RequireTransparentComparator = true,
-         bool ExcludeObjectKeyType = RequireTransparentComparator, typename KeyType = uncvref_t<KeyTypeCVRef>>
-using is_usable_as_key_type = typename std::conditional <
-                              is_comparable<Comparator, ObjectKeyType, KeyTypeCVRef>::value
-                              && !(ExcludeObjectKeyType && std::is_same<KeyType,
-                                   ObjectKeyType>::value)
-                              && (!RequireTransparentComparator
-                                  || is_detected <detect_is_transparent, Comparator>::value)
-                              && !is_json_pointer<KeyType>::value,
-                              std::true_type,
-                              std::false_type >::type;
-
-// type trait to check if KeyType can be used as object key
-// true if:
-//   - KeyType is comparable with BasicJsonType::object_t::key_type
-//   - if ExcludeObjectKeyType is true, KeyType is not BasicJsonType::object_t::key_type
-//   - the comparator is transparent or RequireTransparentComparator is false
-//   - KeyType is not a JSON iterator or json_pointer
-template<typename BasicJsonType, typename KeyTypeCVRef, bool RequireTransparentComparator = true,
-         bool ExcludeObjectKeyType = RequireTransparentComparator, typename KeyType = uncvref_t<KeyTypeCVRef>>
-using is_usable_as_basic_json_key_type = typename std::conditional <
-        is_usable_as_key_type<typename BasicJsonType::object_comparator_t,
-        typename BasicJsonType::object_t::key_type, KeyTypeCVRef,
-        RequireTransparentComparator, ExcludeObjectKeyType>::value
-        && !is_json_iterator_of<BasicJsonType, KeyType>::value,
-        std::true_type,
-        std::false_type >::type;
-
-template<typename ObjectType, typename KeyType>
-using detect_erase_with_key_type = decltype(std::declval<ObjectType&>().erase(std::declval<KeyType>()));
-
-// type trait to check if object_t has an erase() member functions accepting KeyType
-template<typename BasicJsonType, typename KeyType>
-using has_erase_with_key_type = typename std::conditional <
-                                is_detected <
-                                detect_erase_with_key_type,
-                                typename BasicJsonType::object_t, KeyType >::value,
-                                std::true_type,
-                                std::false_type >::type;
-
-// a naive helper to check if a type is an ordered_map (exploits the fact that
-// ordered_map inherits capacity() from std::vector)
-template <typename T>
-struct is_ordered_map
-{
-    using one = char;
-
-    struct two
-    {
-        char x[2]; // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
-    };
-
-    template <typename C> static one test( decltype(&C::capacity) ) ;
-    template <typename C> static two test(...);
-
-    enum { value = sizeof(test<T>(nullptr)) == sizeof(char) }; // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
-};
-
-// to avoid useless casts (see https://github.com/nlohmann/json/issues/2893#issuecomment-889152324)
-template < typename T, typename U, enable_if_t < !std::is_same<T, U>::value, int > = 0 >
-T conditional_static_cast(U value)
-{
-    return static_cast<T>(value);
-}
-
-template<typename T, typename U, enable_if_t<std::is_same<T, U>::value, int> = 0>
-T conditional_static_cast(U value)
-{
-    return value;
-}
-
-template<typename... Types>
-using all_integral = conjunction<std::is_integral<Types>...>;
-
-template<typename... Types>
-using all_signed = conjunction<std::is_signed<Types>...>;
-
-template<typename... Types>
-using all_unsigned = conjunction<std::is_unsigned<Types>...>;
-
-// there's a disjunction trait in another PR; replace when merged
-template<typename... Types>
-using same_sign = std::integral_constant < bool,
-      all_signed<Types...>::value || all_unsigned<Types...>::value >;
-
-template<typename OfType, typename T>
-using never_out_of_range = std::integral_constant < bool,
-      (std::is_signed<OfType>::value && (sizeof(T) < sizeof(OfType)))
-      || (same_sign<OfType, T>::value && sizeof(OfType) == sizeof(T)) >;
-
-template<typename OfType, typename T,
-         bool OfTypeSigned = std::is_signed<OfType>::value,
-         bool TSigned = std::is_signed<T>::value>
-struct value_in_range_of_impl2;
-
-template<typename OfType, typename T>
-struct value_in_range_of_impl2<OfType, T, false, false>
-{
-    static constexpr bool test(T val)
-    {
-        using CommonType = typename std::common_type<OfType, T>::type;
-        return static_cast<CommonType>(val) <= static_cast<CommonType>((std::numeric_limits<OfType>::max)());
-    }
-};
-
-template<typename OfType, typename T>
-struct value_in_range_of_impl2<OfType, T, true, false>
-{
-    static constexpr bool test(T val)
-    {
-        using CommonType = typename std::common_type<OfType, T>::type;
-        return static_cast<CommonType>(val) <= static_cast<CommonType>((std::numeric_limits<OfType>::max)());
-    }
-};
-
-template<typename OfType, typename T>
-struct value_in_range_of_impl2<OfType, T, false, true>
-{
-    static constexpr bool test(T val)
-    {
-        using CommonType = typename std::common_type<OfType, T>::type;
-        return val >= 0 && static_cast<CommonType>(val) <= static_cast<CommonType>((std::numeric_limits<OfType>::max)());
-    }
-};
-
-template<typename OfType, typename T>
-struct value_in_range_of_impl2<OfType, T, true, true>
-{
-    static constexpr bool test(T val)
-    {
-        using CommonType = typename std::common_type<OfType, T>::type;
-        return static_cast<CommonType>(val) >= static_cast<CommonType>((std::numeric_limits<OfType>::min)())
-               && static_cast<CommonType>(val) <= static_cast<CommonType>((std::numeric_limits<OfType>::max)());
-    }
-};
-
-template<typename OfType, typename T,
-         bool NeverOutOfRange = never_out_of_range<OfType, T>::value,
-         typename = detail::enable_if_t<all_integral<OfType, T>::value>>
-struct value_in_range_of_impl1;
-
-template<typename OfType, typename T>
-struct value_in_range_of_impl1<OfType, T, false>
-{
-    static constexpr bool test(T val)
-    {
-        return value_in_range_of_impl2<OfType, T>::test(val);
-    }
-};
-
-template<typename OfType, typename T>
-struct value_in_range_of_impl1<OfType, T, true>
-{
-    static constexpr bool test(T /*val*/)
-    {
-        return true;
-    }
-};
-
-template<typename OfType, typename T>
-inline constexpr bool value_in_range_of(T val)
-{
-    return value_in_range_of_impl1<OfType, T>::test(val);
-}
-
-template<bool Value>
-using bool_constant = std::integral_constant<bool, Value>;
-
-///////////////////////////////////////////////////////////////////////////////
-// is_c_string
-///////////////////////////////////////////////////////////////////////////////
-
-namespace impl
-{
-
-template<typename T>
-inline constexpr bool is_c_string()
-{
-    using TUnExt = typename std::remove_extent<T>::type;
-    using TUnCVExt = typename std::remove_cv<TUnExt>::type;
-    using TUnPtr = typename std::remove_pointer<T>::type;
-    using TUnCVPtr = typename std::remove_cv<TUnPtr>::type;
-    return
-        (std::is_array<T>::value && std::is_same<TUnCVExt, char>::value)
-        || (std::is_pointer<T>::value && std::is_same<TUnCVPtr, char>::value);
-}
-
-}  // namespace impl
-
-// checks whether T is a [cv] char */[cv] char[] C string
-template<typename T>
-struct is_c_string : bool_constant<impl::is_c_string<T>()> {};
-
-template<typename T>
-using is_c_string_uncvref = is_c_string<uncvref_t<T>>;
-
-///////////////////////////////////////////////////////////////////////////////
-// is_transparent
-///////////////////////////////////////////////////////////////////////////////
-
-namespace impl
-{
-
-template<typename T>
-inline constexpr bool is_transparent()
-{
-    return is_detected<detect_is_transparent, T>::value;
-}
-
-}  // namespace impl
-
-// checks whether T has a member named is_transparent
-template<typename T>
-struct is_transparent : bool_constant<impl::is_transparent<T>()> {};
-
-///////////////////////////////////////////////////////////////////////////////
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END