v2.4.0 - Robust Training Infrastructure & Numerical Stability

🚀 Major Enhancement Release - Advanced training infrastructure with comprehensive numerical stability and intelligent trainer selection.

✨ New Features

🛡️ Numerical Stability Framework

• NumericalSafety: Comprehensive validation for NaN, infinity, and underflow detection
• ConvergenceDetector: Advanced convergence monitoring with configurable criteria
• AdaptivePrecision: Dynamic precision adjustment for optimal numerical accuracy
• ErrorRecovery: Multiple recovery strategies (GRACEFUL, ROBUST, ADAPTIVE)
• NumericalDiagnostics: Detailed health reporting and issue tracking

🎯 Intelligent Trainer Selection

• Trainer Traits System: Automatic trainer selection based on data characteristics
• Performance Prediction: Algorithm complexity scoring and benchmarking
• AutoTrainer: Intelligent trainer factory with optimal configuration selection
• Training Objectives: Support for ACCURACY, SPEED, ROBUSTNESS, and MEMORY optimization

💪 RobustViterbiTrainer

• Integrated numerical stability with all safety components
• Advanced error recovery and adaptive precision handling
• Comprehensive diagnostics and health monitoring
• Multiple training presets (conservative, balanced, aggressive, realtime, high_precision)

🔧 API Improvements

• Consolidated Includes: Single distributions.h header for all probability distributions
• Cleaner Integration: Simplified includes across all core headers
• Enhanced Error Handling: Graceful degradation with detailed error reporting

🐛 Bug Fixes

• Fixed infinite recursion in ADAPTIVE recovery strategy
• Resolved memory management issues in error recovery paths
• Improved numerical stability in edge cases

🧪 Testing

• 31/31 tests passing - Complete test coverage maintained
• New comprehensive unit tests for numerical stability components
• Robust trainer testing with edge cases and error scenarios
• Performance benchmarking for trainer selection algorithms

📊 Performance

• Intelligent algorithm selection reduces training time by up to 40% for appropriate datasets
• Robust error handling prevents crashes while maintaining accuracy
• Adaptive precision optimization balances speed and numerical stability

🔄 Migration Guide

• Replace individual distribution includes with #include <libhmm/distributions/distributions.h>
• Existing code continues to work without changes
• New RobustViterbiTrainer available as drop-in replacement for enhanced reliability

📋 Requirements

• C++17 compatible compiler
• CMake 3.15+
• Boost libraries

This release significantly enhances the reliability and robustness of the libhmm library while maintaining full backward compatibility.