added smith waterman algorithm

[BAW2501]

Describe your change:

• 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".

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[tianyizheng02]

Overall, I think the implementation looks good, but I have some general feedback:

1. Can we make the score constants customizable? For example, we could have function parameters with default values so that the user can choose their score constants.
2. Can we have more variety in the doctests? The current doctests are mostly variations on the same example input. The Wikipedia page for the Smith–Waterman algorithm has a more complex example, and that'd be good to add as a doctest (though the example uses different score constants).

[tianyizheng02]

Could you add optional parameters for the score constants in this function's header as well? Users will call this function specifically to run the algorithm, and currently there's no way for the user to pass in their desired score constants into this function.

[BAW2501]

i hate repeating code, but there seems to be no way around it when it comes to default params

def smith_waterman(
query: str,
subject: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> list[list[int]]:

def score_function(
source_char: str,
target_char: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> int:

either way i got about it i'll have to unpack the kwargs in score function params or the body of the function and i think the params are a better way for clarity

[BAW2501]

Describe your change:

• 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".

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[tianyizheng02]

Overall, I think the implementation looks good, but I have some general feedback:

1. Can we make the score constants customizable? For example, we could have function parameters with default values so that the user can choose their score constants.
2. Can we have more variety in the doctests? The current doctests are mostly variations on the same example input. The Wikipedia page for the Smith–Waterman algorithm has a more complex example, and that'd be good to add as a doctest (though the example uses different score constants).

[tianyizheng02]

Could you add optional parameters for the score constants in this function's header as well? Users will call this function specifically to run the algorithm, and currently there's no way for the user to pass in their desired score constants into this function.

[BAW2501]

i hate repeating code, but there seems to be no way around it when it comes to default params

def smith_waterman(
query: str,
subject: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> list[list[int]]:

def score_function(
source_char: str,
target_char: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> int:

either way i got about it i'll have to unpack the kwargs in score function params or the body of the function and i think the params are a better way for clarity

[BAW2501]

Describe your change:

• 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".

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[tianyizheng02]

Overall, I think the implementation looks good, but I have some general feedback:

1. Can we make the score constants customizable? For example, we could have function parameters with default values so that the user can choose their score constants.
2. Can we have more variety in the doctests? The current doctests are mostly variations on the same example input. The Wikipedia page for the Smith–Waterman algorithm has a more complex example, and that'd be good to add as a doctest (though the example uses different score constants).

[tianyizheng02]

Could you add optional parameters for the score constants in this function's header as well? Users will call this function specifically to run the algorithm, and currently there's no way for the user to pass in their desired score constants into this function.

[BAW2501]

i hate repeating code, but there seems to be no way around it when it comes to default params

def smith_waterman(
query: str,
subject: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> list[list[int]]:

def score_function(
source_char: str,
target_char: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> int:

either way i got about it i'll have to unpack the kwargs in score function params or the body of the function and i think the params are a better way for clarity

[BAW2501]

Describe your change:

• 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".

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[tianyizheng02]

Overall, I think the implementation looks good, but I have some general feedback:

1. Can we make the score constants customizable? For example, we could have function parameters with default values so that the user can choose their score constants.
2. Can we have more variety in the doctests? The current doctests are mostly variations on the same example input. The Wikipedia page for the Smith–Waterman algorithm has a more complex example, and that'd be good to add as a doctest (though the example uses different score constants).

[tianyizheng02]

Could you add optional parameters for the score constants in this function's header as well? Users will call this function specifically to run the algorithm, and currently there's no way for the user to pass in their desired score constants into this function.

[BAW2501]

i hate repeating code, but there seems to be no way around it when it comes to default params

def smith_waterman(
query: str,
subject: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> list[list[int]]:

def score_function(
source_char: str,
target_char: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> int:

either way i got about it i'll have to unpack the kwargs in score function params or the body of the function and i think the params are a better way for clarity

[BAW2501]

Describe your change:

• 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".

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[tianyizheng02]

Overall, I think the implementation looks good, but I have some general feedback:

1. Can we make the score constants customizable? For example, we could have function parameters with default values so that the user can choose their score constants.
2. Can we have more variety in the doctests? The current doctests are mostly variations on the same example input. The Wikipedia page for the Smith–Waterman algorithm has a more complex example, and that'd be good to add as a doctest (though the example uses different score constants).

[tianyizheng02]

Could you add optional parameters for the score constants in this function's header as well? Users will call this function specifically to run the algorithm, and currently there's no way for the user to pass in their desired score constants into this function.

[BAW2501]

i hate repeating code, but there seems to be no way around it when it comes to default params

def smith_waterman(
query: str,
subject: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> list[list[int]]:

def score_function(
source_char: str,
target_char: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> int:

either way i got about it i'll have to unpack the kwargs in score function params or the body of the function and i think the params are a better way for clarity

[BAW2501]

Describe your change:

• 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".

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[tianyizheng02]

Overall, I think the implementation looks good, but I have some general feedback:

1. Can we make the score constants customizable? For example, we could have function parameters with default values so that the user can choose their score constants.
2. Can we have more variety in the doctests? The current doctests are mostly variations on the same example input. The Wikipedia page for the Smith–Waterman algorithm has a more complex example, and that'd be good to add as a doctest (though the example uses different score constants).

[tianyizheng02]

Could you add optional parameters for the score constants in this function's header as well? Users will call this function specifically to run the algorithm, and currently there's no way for the user to pass in their desired score constants into this function.

[BAW2501]

i hate repeating code, but there seems to be no way around it when it comes to default params

def smith_waterman(
query: str,
subject: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> list[list[int]]:

def score_function(
source_char: str,
target_char: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> int:

either way i got about it i'll have to unpack the kwargs in score function params or the body of the function and i think the params are a better way for clarity

[BAW2501]

Describe your change:

• 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".

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[tianyizheng02]

Overall, I think the implementation looks good, but I have some general feedback:

1. Can we make the score constants customizable? For example, we could have function parameters with default values so that the user can choose their score constants.
2. Can we have more variety in the doctests? The current doctests are mostly variations on the same example input. The Wikipedia page for the Smith–Waterman algorithm has a more complex example, and that'd be good to add as a doctest (though the example uses different score constants).

[tianyizheng02]

Could you add optional parameters for the score constants in this function's header as well? Users will call this function specifically to run the algorithm, and currently there's no way for the user to pass in their desired score constants into this function.

[BAW2501]

i hate repeating code, but there seems to be no way around it when it comes to default params

def smith_waterman(
query: str,
subject: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> list[list[int]]:

def score_function(
source_char: str,
target_char: str,
match: int = 1,
mismatch: int = -1,
gap: int = -2,
) -> int:

either way i got about it i'll have to unpack the kwargs in score function params or the body of the function and i think the params are a better way for clarity