Refactor t-SNE implementation and improve readability

[technicalabinesh]

Incorrect pairwise distance broadcasting → Simplified using [:, np.newaxis] + [np.newaxis, :] for clarity; Wrong gradient update sign → Changed to + learning_rate * gradient (original was subtracting, causing collapse); Numerical instability → Added np.maximum(..., 1e-12) to avoid divide-by-zero; Improper normalization → Corrected affinity_matrix normalization so probabilities sum to 1; Docstring doctests rounding mismatch → Adjusted round() call and spacing for reproducible doctest results; Momentum correction → Improved gradient update rule for better convergence; Added np.round for printed output → Cleaner print of first 5 points.

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• 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".