Adds hinge loss function algorithm

[PoojanSmart]

Describe your change:

• Adding Hinge loss function which is useful loss function in Support Vector Machine (SVM) for calculating loss.

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".

[tianyizheng02]

Suggested change

	defhinge_loss(y_true: np.ndarray, pred: np.ndarray) ->float:
	defhinge_loss(y_true: np.ndarray, y_pred: np.ndarray) ->float:

Please rename for consistency

[cclauss]

#10637 (comment) also applies here.

[PoojanSmart]

#10637 (comment) also applies here.

I can combine all loss function into single file, then you can review. We will do further modularization if required based on loss function category.
But before that, Is it fine if you merge this one?
I will raise new issue and PR to compile all loss functions.

Let me know your thoughts..

[cclauss]

I will allow @tianyizheng02 to review this PR because he has already had several good suggestions. On this repo, if you raise a new issue during Hacktoberfest then there likely will be several PRs to try to solve it. Just create your own PR.

[tianyizheng02]

Suggested change

	ifnp.array_equal(np.sort(np.unique(y_true)), np.array([-1, 1])) isFalse:
	ifnp.any((y_true!=1) & (y_true!=-1)):

This should do the same thing (check if any entry is neither 1 or -1)