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CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+This is a C++ implementation of the MDLP (Minimum Description Length Principle) discretization algorithm based on Fayyad & Irani's paper. The library provides discretization methods for continuous-valued attributes in classification learning.
+
+## Build System
+
+The project uses CMake with a Makefile wrapper for common tasks:
+
+### Common Commands
+- `make build` - Build release version with sample program
+- `make test` - Run full test suite with coverage report
+- `make install` - Install the library
+
+### Build Configurations
+- **Release**: Built in `build_release/` directory
+- **Debug**: Built in `build_debug/` directory (for testing)
+
+### Dependencies
+- PyTorch (libtorch) - Required dependency
+- GoogleTest - Fetched automatically for testing
+- Coverage tools: lcov, genhtml
+
+## Code Architecture
+
+### Core Components
+
+1. **Discretizer** (`src/Discretizer.h/cpp`) - Abstract base class for all discretizers
+2. **CPPFImdlp** (`src/CPPFImdlp.h/cpp`) - Main MDLP algorithm implementation
+3. **BinDisc** (`src/BinDisc.h/cpp`) - K-bins discretization (quantile/uniform strategies)
+4. **Metrics** (`src/Metrics.h/cpp`) - Entropy and information gain calculations
+
+### Key Data Types
+- `samples_t` - Input data samples
+- `labels_t` - Classification labels
+- `indices_t` - Index arrays for sorting/processing
+- `precision_t` - Floating-point precision type
+
+### Algorithm Flow
+1. Data is sorted using labels as tie-breakers for identical values
+2. MDLP recursively finds optimal cut points using entropy-based criteria
+3. Cut points are validated to ensure meaningful splits
+4. Transform method maps continuous values to discrete bins
+
+## Testing
+
+Tests are built with GoogleTest and include:
+- `Metrics_unittest` - Entropy/information gain tests
+- `FImdlp_unittest` - Core MDLP algorithm tests
+- `BinDisc_unittest` - K-bins discretization tests
+- `Discretizer_unittest` - Base class functionality tests
+
+### Running Tests
+```bash
+make test  # Runs all tests and generates coverage report
+cd build_debug/tests && ctest  # Run tests directly
+```
+
+Coverage reports are generated at `build_debug/tests/coverage/index.html`.
+
+## Sample Usage
+
+The sample program demonstrates basic usage:
+```bash
+build_release/sample/sample -f iris -m 2
+```
+
+## Development Notes
+
+- The library uses PyTorch tensors for efficient numerical operations
+- Code follows C++17 standards
+- Coverage is maintained at 100%
+- The implementation handles edge cases like duplicate values and small intervals
+- Conan package manager support is available via `conanfile.py`