How do I install CLML with Quicklisp on SBCL?

Clone the repository into ~/quicklisp/local-projects and run (ql:quickload :clml). For SBCL, adjust the dynamic space size with 'sbcl --dynamic-space-size 2560' to avoid performance issues, as specified in the README.

CLML vs scikit-learn for machine learning?

CLML is designed for Common Lisp environments, offering similar statistical algorithms but lacks Python's vast ecosystem and deep learning support. Choose based on your language stack and need for interoperability with other tools.

How to perform k-means clustering using CLML?

Load the clml.clustering system and use functions from clml.clustering.k-means2. Prepare your data with clml.hjs.read-data, as demonstrated in the usage examples with sample datasets.

Does CLML support neural networks or GPU acceleration?

No, CLML focuses on statistical methods like SVM and clustering, with no mention of neural networks or GPU support in the README. For such features, you'd need to explore other libraries or bindings in Lisp.

What are the system requirements for running CLML efficiently?

Requires a Common Lisp implementation like SBCL with ASDF3 and Quicklisp, on Posix-compatible platforms. Ensure sufficient heap size (e.g., 2560K for SBCL) and set *read-default-float-format* to double-float before loading.

How can I contribute to or extend CLML?

Open issues or pull requests on GitHub; for core features, use the main repository, and for extras like integrations, use clml.extras. The README encourages contributions and provides contact details for maintainers.

clml — Common Lisp Machine Learning Library

What is clml?

CL Machine-Learning (CLML) is a comprehensive statistical machine learning library written in Common Lisp, designed for high performance and large-scale data processing. It provides a wide array of algorithms and utilities for data analysis, including classification, clustering, time series analysis, and dimensionality reduction, making it a robust tool within the Common Lisp ecosystem.

Target Audience

Common Lisp developers and researchers who need a native, high-performance machine learning library for statistical analysis and large-scale data processing tasks.

Value Proposition

Developers choose CLML for its extensive algorithm coverage, modular architecture organized into independent systems, and optimization for Common Lisp environments, offering a specialized alternative to general-purpose ML libraries in other languages.

Common Lisp Machine Learning Library

Use Cases

Best For

Implementing statistical machine learning algorithms in Common Lisp applications.
Large-scale data processing and analysis within the Common Lisp ecosystem.
Time series analysis tasks such as anomaly detection, autoregression, and state-space modeling.
Clustering and classification projects requiring algorithms like k-means, SVM, or decision trees.
Dimensionality reduction and feature extraction using Principal Component Analysis (PCA).
Graph analysis and algorithm implementation, including anomaly detection and centrality measures.

Not Ideal For

Projects requiring modern deep learning frameworks like TensorFlow or PyTorch
Teams integrated into Python or R data science ecosystems seeking extensive library interoperability
Applications needing rapid prototyping with minimal setup and configuration
Environments where Common Lisp is not already the primary development language

Pros & Cons

Pros

Extensive Algorithm Coverage

Provides a wide array of statistical ML algorithms including SVM, decision trees, clustering, and time series analysis, as listed in the README's key features.

Modular Architecture

Code is organized into independent systems based on functional categories, facilitating maintenance and extensibility, as emphasized in the philosophy section.

Multi-Platform Support

Supports SBCL, CCL, LispWorks, and Allegro Common Lisp, ensuring compatibility across different Lisp implementations, noted in the requirements.

Documentation and Resources

Offers online user and API documentation, tutorials, and sample datasets via fetch functions, with active community contributions encouraged.

Cons

Complex Initial Setup

Requires manual configurations like setting *read-default-float-format* to double-float and adjusting heap size for SBCL, adding overhead for new users.

Limited to Statistical ML

Focuses on traditional machine learning without support for deep learning or neural networks, missing features common in modern ML libraries.

Niche Documentation Build

Building documentation relies on Emacs and Org-mode tools, which may be unfamiliar and cumbersome for developers not using that workflow.

What is clml?

Target Audience

Common Lisp developers and researchers who need a native, high-performance machine learning library for statistical analysis and large-scale data processing tasks.

Value Proposition

Use Cases

Best For

Implementing statistical machine learning algorithms in Common Lisp applications.
Large-scale data processing and analysis within the Common Lisp ecosystem.
Time series analysis tasks such as anomaly detection, autoregression, and state-space modeling.
Clustering and classification projects requiring algorithms like k-means, SVM, or decision trees.
Dimensionality reduction and feature extraction using Principal Component Analysis (PCA).
Graph analysis and algorithm implementation, including anomaly detection and centrality measures.

Not Ideal For

Projects requiring modern deep learning frameworks like TensorFlow or PyTorch
Teams integrated into Python or R data science ecosystems seeking extensive library interoperability
Applications needing rapid prototyping with minimal setup and configuration
Environments where Common Lisp is not already the primary development language

Pros & Cons

Pros

Extensive Algorithm Coverage

Provides a wide array of statistical ML algorithms including SVM, decision trees, clustering, and time series analysis, as listed in the README's key features.

Modular Architecture

Code is organized into independent systems based on functional categories, facilitating maintenance and extensibility, as emphasized in the philosophy section.

Multi-Platform Support

Supports SBCL, CCL, LispWorks, and Allegro Common Lisp, ensuring compatibility across different Lisp implementations, noted in the requirements.

Documentation and Resources

Offers online user and API documentation, tutorials, and sample datasets via fetch functions, with active community contributions encouraged.

Cons

Complex Initial Setup

Requires manual configurations like setting *read-default-float-format* to double-float and adjusting heap size for SBCL, adding overhead for new users.

Limited to Statistical ML

Focuses on traditional machine learning without support for deep learning or neural networks, missing features common in modern ML libraries.

Niche Documentation Build

Building documentation relies on Emacs and Org-mode tools, which may be unfamiliar and cumbersome for developers not using that workflow.

clml

What is clml?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

clml

What is clml?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?