CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

BayesNet is a C++ library implementing Bayesian Network Classifiers. It provides various algorithms for machine learning classification including TAN, KDB, SPODE, SPnDE, AODE, A2DE, and their ensemble variants (Boost, XB). The library also includes local discretization variants (Ld) and feature selection algorithms.

Build System & Dependencies

Dependency Management

Uses vcpkg for package management with private registry at https://github.com/rmontanana/vcpkg-stash
Core dependencies: libtorch, nlohmann-json, folding, fimdlp, arff-files, catch2
All dependencies defined in vcpkg.json with version overrides

Build Commands

# Initialize dependencies
make init

# Build debug version (with tests and coverage)
make debug
make buildd

# Build release version  
make release
make buildr

# Run tests
make test

# Generate coverage report
make coverage
make viewcoverage

# Clean project
make clean

CMake Configuration

Uses CMake 3.27+ with C++17 standard
Debug builds automatically enable testing and coverage
Release builds optimize with -Ofast
Supports both static library and vcpkg package installation

Testing Framework

Catch2 testing framework (version 3.8.1)
Test executable: TestBayesNet in build_Debug/tests/
Individual test categories can be run: ./TestBayesNet "[CategoryName]"
Coverage reporting with lcov/genhtml

Test Categories

A2DE, BoostA2DE, BoostAODE, XSPODE, XSPnDE, XBAODE, XBA2DE
Classifier, Ensemble, FeatureSelection, Metrics, Models
Network, Node, MST, Modules

Code Architecture

Core Structure

bayesnet/
├── BaseClassifier.h          # Abstract base for all classifiers
├── classifiers/             # Basic Bayesian classifiers (TAN, KDB, SPODE, etc.)
├── ensembles/              # Ensemble methods (AODE, A2DE, Boost variants)
├── feature_selection/      # Feature selection algorithms (CFS, FCBF, IWSS, L1FS)
├── network/               # Bayesian network structure (Network, Node)
└── utils/                 # Utilities (metrics, MST, tensor operations)

Key Design Patterns

BaseClassifier abstract interface for all algorithms
Template-based design with both std::vector and torch::Tensor support
Network/Node abstraction for Bayesian network representation
Feature selection as separate, composable modules

Data Handling

Supports both discrete integer data and continuous data with discretization
ARFF file format support through arff-files library
Tensor operations via PyTorch C++ (libtorch)
Local discretization variants use fimdlp library

Documentation & Tools

Doxygen for API documentation: make doc
lcov for coverage reports: make coverage
plantuml + clang-uml for UML diagrams: make diagrams
Man pages available in docs/man3/

Sample Applications

Sample code in sample/ directory demonstrates library usage:

make sample fname=tests/data/iris.arff model=TANLd

Common Development Tasks

Add new classifier: Extend BaseClassifier, implement in appropriate subdirectory
Add new test: Update tests/CMakeLists.txt and create test in tests/
Modify build: Edit main CMakeLists.txt or use Makefile targets
Update dependencies: Modify vcpkg.json and run make init

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