empirical_covariance implementation (sklearn)

[Anubhav-Goyal01]

Closes #22904

[github-actions]

Thanks for contributing to Ivy! 😊👏
Here are some of the important points from our Contributing Guidelines 📝:
1. Feel free to ignore the run_tests (1), run_tests (2), … jobs, and only look at the display_test_results job. 👀 It contains the following two sections:
- Combined Test Results: This shows the results of all the ivy tests that ran on the PR. ✔️
- New Failures Introduced: This lists the tests that are passing on main, but fail on the PR Fork. Please try to make sure that there are no such tests. 💪
2. The lint / Check formatting / check-formatting tests check for the formatting of your code. 📜 If it fails, please check the exact error message in the logs and fix the same. ⚠️🔧
3. Finally, the test-docstrings / run-docstring-tests check for the changes made in docstrings of the functions. This may be skipped, as well. 📚
Happy coding! 🎉👨‍💻

[Ishticode]

Almost there. I've fixed the "0 not equal 1" issue. which was due to too many or too less dimensions. The sklearn docs specify 2 dimensional input as you might have seen.

There are dtype mismatches still. This mean our test detect the output to have a different dtype when function is called from our sklearn frontend compard against that of the native sklearn front. Ive fixed somewhat. but best maybe you give it a go. Run the test and look at the input to out put dtypes.
An error message like
AssertionError: returned dtype = float16, ground-truth returned dtype = float64
means that our frontend sklearn is returning float16 while native sklearn is return float64 for the same input.
The input data can be seen under Falsifying example ......
you can also experiment by playing around with native sklearn (from sklearn.convariance import ...) or our frontend sklearn ("from ivy.functional.frontends.sklearn.covariance import ..."

Don't hesitate to reach out with questions ☺️

[Anubhav-Goyal01]

Hi @Ishticode, thanks for the feedback, I have made a new commit trying to fix those issues

[Ishticode]

Hi @Anubhav-Goyal01
It seems like the test_sklearn_empirical_convariance has failed due to int8 not being supported by dot function. check the logs for test_sklearn_empirical_convariance here. Thanks 🙂

[ivy-seed]

This PR has been labelled as stale because it has been inactive for more than 7 days. If you would like to continue working on this PR, then please add another comment or this PR will be closed in 7 days.

[github-actions]

Thank you for this PR, here is the CI results:

---

This pull request does not result in any additional test failures. Congratulations!