[Term Entry] Python - NumPy Linear-algebra: .cholesky()

[mamtawardhani]

Description

[Term Entry] Python - NumPy Linear-algebra: .cholesky(

Issue Solved

Closes #6417

Type of Change

• Adding a new entry

Checklist

• All writings are my own.
• My entry follows the Codecademy Docs style guide.
• My changes generate no new warnings.
• I have performed a self-review of my own writing and code.
• I have checked my entry and corrected any misspellings.
• I have made corresponding changes to the documentation if needed.
• I have confirmed my changes are not being pushed from my forked main branch.
• I have confirmed that I'm pushing from a new branch named after the changes I'm making.
• I have linked any issues that are relevant to this PR in the Issues Solved section.

[Radhika-okhade]

remove this line

[raditya1117]

Great content. Example 1 is sufficient to explain the syntax and working of the function. Example 2 and 3 will be more suitable for an article instead of the term entry. We can remove those in my opinion. @mamtawardhani

[Radhika-okhade]

We need 2-3 examples per entry. Do you suggest replacing the existing examples with simpler ones, or can we keep these?

[raditya1117]

In the second example, we can use np.allclose() function along with np.linalg.eigvals() function instead of the manual calculation to show if the input matrix is positive definite (All the eigenvalues of a positive definite matrix are greater than 0). We can give one example where the input matrix is positive definite and another example where the input matrix isn't positive definite to show that the function only processes positive-definite matrices. This will reduce extra information contained in the examples.
Update: As cholesky decomposition is only applied to square (symmetric) matrices, we can even give an example where the functions fails if the input matrix is of shapes like 3x5 or 2x3. @mamtawardhani @Radhika-okhade

[mamtawardhani]

Hey @raditya1117, I've changed examples 2 and 3 based on your suggestions. Could you please check? Thanks! 🙇🏻

[raditya1117]

Looks good @mamtawardhani.

[github-actions]

👋 @mamtawardhani
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