Logistic Regression from Scratch - C++

Current Status

This project implements Logistic Regression from scratch in C++ with CUDA acceleration, featuring modular libraries for Matrix operations, Statistical computations, and GPU-optimized implementations.

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Phase 0: Foundation & Core Libraries ✅ COMPLETE

• Matrix Library: Full CPU implementation with constructors, operations, and vector operations
• Statistics Library: Descriptive statistics, correlation, normalization, and quartiles
• Project Structure: Organized modular architecture with cpp_lib and cuda_lib separation
• Build System: Makefile-based compilation for all modules
• Dataset Integration: Training and test CSV datasets prepared

Phase 1: Algorithm Implementation ⏳ IN PROGRESS (README)

• Logistic Regression (CPU): Core implementation with multiple optimization methods
• Gradient Descent Variants: Batch, Stochastic, and Mini-Batch implementations
• Cost Functions: Cross-entropy loss and regularization support
• Prediction Pipeline: Inference module for trained models
• Python Bindings: Seamless integration with Python for data science workflows

Phase 2: GPU Acceleration ⏳ IN PROGRESS

• CUDA Matrix Operations: GPU-accelerated matrix computations
• CUDA Memory Management: Efficient GPU memory allocation and transfer
• GPU Kernels: Optimized CUDA kernels for core operations
• CPU-GPU Interface: Seamless data transfer between CPU and GPU
• Training Scripts: train_logreg.py for model training
• Prediction Module: predict_logistic.py for inference

Phase 3: Optimization & Performance ⏳ IN PROGRESS

• Benchmark CPU vs CUDA implementations
• Optimize memory allocation patterns in CUDA kernels
• Implement advanced gradient descent variants (Adam, RMSprop)
• Profile and optimize bottlenecks

Phase 4: Testing & Validation 📋 PENDING

• Comprehensive unit tests for all modules
• Integration tests for CPU-GPU consistency
• Regression tests on standard datasets
• Numerical accuracy validation

Phase 5: Documentation & Examples 📋 PENDING

• Detailed API documentation for each module
• Performance comparison benchmarks
• Real-world usage examples
• Troubleshooting guide

Project Structure

dslr/
├── cpp_lib/              # CPU implementations
│   ├── Matrix/          # Matrix operations library
│   ├── Stats/           # Statistics library
│   └── LogisticRegression/  # Main ML model
├── cuda_lib/            # GPU implementations
│   ├── MatrixCuda/      # CUDA matrix operations
│   └── Stats/           # CUDA statistics
├── datasets/            # Training and test data
├── train_logreg.py      # Training script
└── predict_logistic.py  # Prediction script

Next Immediate Steps

1. Benchmark Suite - Compare CPU vs GPU performance metrics
2. Unit Testing Framework - Implement comprehensive tests for all modules
3. Documentation - Generate API docs and usage examples
4. Performance Profiling - Identify and optimize bottlenecks

Resources

• Linear Regression Using Normal Equation: https://www.deep-ml.com/problems/14
• Linear Regression Using Gradient Descent: https://www.deep-ml.com/problems/15