Crate single_algebra 

Single Algebra

A high-performance linear algebra library optimized for sparse matrices and dimensionality reduction algorithms. Designed for machine learning, data analysis, and scientific computing applications where efficiency with sparse data is crucial.

Core Modules

Matrix Operations

• sparse: Sparse matrix implementations (CSR, CSC) with efficient operations
• dense: Dense matrix utilities and operations

Dimensionality Reduction

• dimred: Principal Component Analysis (PCA) and planned manifold learning algorithms

Utilities

• Normalize: Data normalization transformations
• Log1P: Logarithmic transformations for numerical stability

Key Features

• Sparse matrix efficiency: Optimized CSR/CSC formats for memory and computational efficiency
• Dimensionality reduction: PCA with both Lanczos and randomized SVD algorithms
• Feature masking: Selective analysis of feature subsets
• Parallel processing: Multi-threaded operations for large datasets
• Type flexibility: Generic implementations supporting f32 and f64

Typical Workflow

1. Load or create sparse matrices using the sparse module
2. Apply preprocessing with Normalize or Log1P utilities
3. Perform dimensionality reduction using dimred::pca algorithms
4. Analyze results with variance explanations and feature importance

Performance Focus

This library is optimized for scenarios involving:

• Large, sparse datasets (e.g., text analysis, genomics, recommendation systems)
• Memory-constrained environments
• High-dimensional data requiring dimensionality reduction
• Scientific computing workflows requiring numerical precision

Modules

dense

dimred

Dimensionality Reduction

sparse

Sparse Matrix Module

Traits

Log1P

Normalize