[Goal] LinearRegression: Near-Identical Python→C# Translation
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- Dominant language
- C#
- Stars
- 1.5k
- Forks
- 205
- Avg merge
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- Merged PRs (30d)
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Description
The README screenshot showcases a LinearRegression implementation - Python on the left, C# on the right.
This is what NumSharp was built for: enabling near-identical code translation from Python to C#.
What This Demonstrates
The screenshot proves NumSharp's core promise: you can take NumPy code and translate it to C# with minimal changes:
| Python/NumPy | C#/NumSharp |
|---|---|
np.hstack(...) |
np.hstack(...) |
np.mean(X, 0) |
np.mean(X, 0) |
np.std(X, 0) |
np.std(X, 0) |
np.expand_dims(y, -1) |
np.expand_dims(y, -1) |
np.zeros(...) |
np.zeros(...) |
X @ self.params |
np.matmul(X, @params) |
(y - y_pred)**2 |
np.power(y - y_pred, 2) |
The entire LinearRegression class - constructor, fit(), score(), and predict() methods - translates almost 1-to-1.
Achievement Unlocked 🏆
This example should be preserved, tested, and maintained as a canonical demonstration of NumSharp's capabilities.
Tasks
- Add
LinearRegressionexample to test suite to ensure it never breaks - Verify all APIs used still work correctly with NumPy 2.x behavior
- Consider adding to documentation as a "getting started" example
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the README screenshot and locate the existing LinearRegression implementation or example in the NumSharp repository. Review the NumSharp APIs listed in the Python/C# comparison, then add coverage to the test suite and verify the example against the stated NumPy 2.x behavior; done means the example is tested and documented as a canonical getting-started demonstration.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- csharp, numpy, python
- Domain
- documentation, machine-learning, testing
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 40/100