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Edit_Distance algorithm for genetic string matching #10336

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Closed
wants to merge 10 commits into from
34 changes: 34 additions & 0 deletions genetic_algorithm/edit_distance.py
Original file line number Diff line number Diff line change
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def edit_distance(source, target):

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Please provide return type hint for the function: edit_distance. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file genetic_algorithm/edit_distance.py, please provide doctest for the function edit_distance

Please provide type hint for the parameter: source

Please provide type hint for the parameter: target

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Please provide return type hint for the function: edit_distance. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file genetic_algorithm/edit_distance.py, please provide doctest for the function edit_distance

Please provide type hint for the parameter: source

Please provide type hint for the parameter: target

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Please provide return type hint for the function: edit_distance. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: source

Please provide type hint for the parameter: target

"""
Edit distance algorithm is a string metric, i.e., it is a way of quantifying
how dissimilar two strings are to one another, that is measured by
counting the minimum number of operations required to transform one string
into another.
In genetic algorithms consisting of A,T, G, and C ncleotides, this matching
becomes essential in understanding the mutation in succesive genes.
Hence, this algorithm comes in handy when we are trying to quantify the
mutations in successive generations.
Args:
source (string): This is the source string, the initial string with
respect to which we are calculating the edit_distance for the target
target (string): This is the target string, which is formed after n
number of operations performed on the source string.
Assumptions:
The cost of operations (insertion, deletion and subtraction) is all 1
"""
delta = {True: 0, False: 1} # Substitution

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An error occurred while parsing the file: genetic_algorithm/edit_distance.py

Traceback (most recent call last):
  File "/opt/render/project/src/algorithms_keeper/parser/python_parser.py", line 146, in parse
    reports = lint_file(
              ^^^^^^^^^^
libcst._exceptions.ParserSyntaxError: Syntax Error @ 19:4.
parser error: error at 18:3: expected INDENT

    delta = {True: 0, False: 1}  # Substitution
   ^


if len(source) == 0:
return len(target)
elif len(target) == 0:
return len(source)

return min(
edit_distance(source[:-1], target[:-1]) + delta[source[-1] == target[-1]],
edit_distance(source, target[:-1]) + 1,
edit_distance(source[:-1], target) + 1,
)


print(edit_distance("ATCGCTG", "TAGCTAA"))
# Answer is 4