numerix
A collection of mathematical functions for Elixir focused on statistics, linear algebra, and machine learning.
What is numerix?
Numerix is an Elixir library that provides a collection of mathematical functions tailored for statistics, linear algebra, and machine learning. It includes a Tensor API for numpy-like operations and leverages parallel processing via Flow to handle large datasets efficiently. The library addresses the need for performant numerical computing within the functional programming paradigm of Elixir.
Elixir developers working on data science, machine learning, or statistical analysis projects who need efficient numerical computations without leaving the Elixir ecosystem.
Developers choose Numerix for its comprehensive feature set covering essential math operations, its Tensor API for simplified complex calculations, and its built-in parallel processing capabilities that scale with multi-core systems.
Overview
A collection of useful mathematical functions in Elixir with a slant towards statistics, linear algebra and machine learning
Use Cases
Best For
- Performing statistical analysis on datasets in Elixir applications
- Implementing machine learning algorithms like linear regression in Elixir
- Handling linear algebra operations such as vector norms and dot products
- Running parallel numerical computations using Flow for performance
- Building data processing pipelines with tensor-like operations
- Developing scientific computing tools within the Elixir ecosystem
Not Ideal For
- Applications demanding peak numerical performance, such as high-frequency algorithmic trading where microsecond delays are critical
- Teams deeply integrated with Python's data science stack (e.g., NumPy, SciPy) who prioritize mature libraries and extensive community support
- Projects requiring advanced machine learning capabilities like deep neural networks, reinforcement learning, or extensive pre-trained models
- Environments with strict dependency constraints, as Numerix relies on :gen_stage and :flow for parallel processing, adding complexity
Pros & Cons
Pros
Uses Flow to run independent computations in parallel, speeding up calculations on multi-core systems, as highlighted in the Tensor API section for handling large datasets efficiently.
Covers a wide range of statistics, linear algebra, and ML utilities, including mean, variance, dot product, and activation functions like ReLU and sigmoid, reducing the need for external libraries.
Provides a numpy-like Tensor API that simplifies implementing complex math functions with minimal code, making it accessible for developers familiar with Python's numpy.
Seamlessly integrates with Elixir applications, allowing developers to perform numerical computations within the functional programming environment without switching languages.
Cons
Acknowledged in the README, pure Elixir solutions are slower than low-level libraries like BLAS, making it unsuitable for highly performance-critical tasks despite parallelization efforts.
At version 0.6, it may have instability, breaking changes, and a less mature ecosystem compared to established libraries, posing risks for production use.
The README directs users to tests for examples, which can be a barrier for newcomers seeking straightforward usage guides and quick starts.
Focuses on basic linear regression and activation functions, lacking support for more complex machine learning models, optimizers, or GPU acceleration.
Frequently Asked Questions
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