single-algebra 🧮

The companion algebra library for single-rust, providing powerful matrix operations and machine learning utilities.

Features 🚀

• Efficient operations on sparse and dense matrices
• Dimensionality reduction techniques
• Clustering algorithms including Louvain community detection
• Batch processing utilities with masking support
• Statistical analysis and inference
• More features planned!

Matrix Operations 📊

• SVD decomposition with parallel and LAPACK implementations
• Matrix convenience functions for statistical operations
• Support for both CSR and CSC sparse matrix formats
• Masked operations for selective data processing
• Batch-wise statistics (mean, variance) with flexible batch identifiers

Clustering 🔍

• Louvain community detection
• Similarity network construction
• K-nearest neighbors graph building
• Local moving algorithm for community refinement
• Leiden clustering implementation (work in progress)

Dimensionality Reduction ⬇️

• Incremental PCA implementation
• Support for sparse matrices in dimensionality reduction
• SVD-based compression and analysis

Statistical Analysis 📈

• Multiple testing correction methods
• Parametric and non-parametric hypothesis testing
• Effect size calculations
• Batch-wise statistical comparisons

Installation

Add this to your Cargo.toml:

[dependencies]
single-algebra = "0.2.2-alpha.0"

Batch Processing

The library now includes flexible batch processing capabilities with the BatchIdentifier trait, which supports common identifier types:

• String and string slices
• Integer types (i32, u32, usize)
• Custom types (by implementing the trait)

// Example of batch-wise statistics
let batches = vec!["batch1", "batch2", "batch3"];
let batch_means = matrix.mean_batch_col(&batches)?;

Masked Operations

Selective processing is now available through masked operations:

// Only process selected columns
let mask = vec![true, false, true, true, false];
let masked_sums = matrix.sum_col_masked(&mask)?;

Acknowledgments 🙏

The Louvain clustering implementation was adapted from louvain-rs written by Juan Morales ([email protected]). The original implementation has been modified to better suit the needs of single-algebra.