Enhancement of the knapsack algorithm with memorization and generalisation

[Jiang15]

Describe your change:

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

Checklist:

• [x] I have read CONTRIBUTING.md.
• [x] This pull request is all my own work -- I have not plagiarized.
• [x] I know that pull requests will not be merged if they fail the automated tests.
• [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• [x] All new Python files are placed inside an existing directory.
• [x] All filenames are in all lowercase characters with no spaces or dashes.
• [x] All functions and variable names follow Python naming conventions.
• [x] All function parameters and return values are annotated with Python type hints.
• [x] All functions have doctests that pass the automated testing.
• [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a Enchancement of Knapsack algorithm: "Fixes #9266".

[Jiang15]

I forgot to add the description of the PR:)

• In knapsack/knapsack.py, I added a flag - allow_repetition. It can be set to true to allow picking the same item multiple times, and vice versa, only once if it is false.
• I refactored the naive recursion function to be more readable and added @lru_cache to optimize the time complexity.
• And simple documentation changes in knapsack/README.MD from 0-1 to 0-N knapsack problem.

I added test cases with allow_repetition = true/false in tests and tests are all passed.