hasktorch/hasktorch
Jun 2025 – Jun 2025
1 Month active
Languages Used
Haskell
Technical Skills
Algorithm ImplementationData VisualizationFunctional ProgrammingMachine Learning
le.niu
PROFILE
Le.niu
Developed a complete K-Means clustering demonstration with visualization for the hasktorch/hasktorch repository, focusing on practical machine learning workflows in Haskell. The work encompassed algorithm implementation, including data generation, distance calculation, and iterative centroid refinement, all structured using functional programming principles. A new Cabal executable target was introduced to streamline building and running the demo, while integrated plotting functionality enabled clear visualization of clustering results. This contribution provided a concrete example for users seeking to integrate clustering algorithms with hasktorch, enhancing onboarding and interpretability. The project showcased skills in Haskell, data visualization, and machine learning within a real-world codebase.
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 peopleWork 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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