hasktorch/hasktorch
Jun 2025 – Jun 2025
1 Month active
Languages Used
Haskell
Technical Skills
Algorithm ImplementationData VisualizationFunctional ProgrammingMachine Learning
le.niu
PROFILE
Le.niu
Le Niu developed a complete K-Means Clustering demo with visualization for the hasktorch/hasktorch repository, focusing on practical machine learning workflows in Haskell. The project involved implementing data generation, distance calculations, and iterative centroid refinement using functional programming techniques. Le Niu added a new Cabal executable target to streamline building and running the demo, and integrated plotting functionality to visualize clustering results, enhancing interpretability for users. This work provided a concrete example of algorithm integration in Haskell, supporting onboarding for new users and demonstrating effective use of algorithm implementation, data visualization, and machine learning within the Haskell ecosystem.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
156
Activity Months1
Your Network
7 peopleShared Repositories
7
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025: Delivered an end-to-end K-Means Clustering Demo with Visualization for hasktorch/hasktorch. The implementation includes data generation, distance calculation, iterative centroid refinement, a new Cabal executable target, and plotting functionality to visualize clustering results. This work enhances practical ML workflow demonstrations, improves onboarding for users integrating clustering workflows with the library, and showcases a concrete example of algorithm integration in Haskell.
1 Commits • 1 Features
Jun 1, 2025June 2025: Delivered an end-to-end K-Means Clustering Demo with Visualization for hasktorch/hasktorch. The implementation includes data generation, distance calculation, iterative centroid refinement, a new Cabal executable target, and plotting functionality to visualize clustering results. This work enhances practical ML workflow demonstrations, improves onboarding for users integrating clustering workflows with the library, and showcases a concrete example of algorithm integration in Haskell.
June 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Haskell
Technical Skills
Algorithm ImplementationData VisualizationFunctional ProgrammingMachine Learning
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