N

p

```
def edit_distance(source: str, target: str) -> int:
"""
Edit distance algorithm is a string metric, i.e., it is a way of quantifying how
dissimilar two strings are to one another. It is measured by counting the minimum
number of operations required to transform one string into another.
This implementation assumes that the cost of operations (insertion, deletion and
substitution) is always 1
Args:
source: the initial string with respect to which we are calculating the edit
distance for the target
target: the target string, formed after performing n operations on the source string
>>> edit_distance("GATTIC", "GALTIC")
1
"""
if len(source) == 0:
return len(target)
elif len(target) == 0:
return len(source)
delta = int(source[-1] != target[-1]) # Substitution
return min(
edit_distance(source[:-1], target[:-1]) + delta,
edit_distance(source, target[:-1]) + 1,
edit_distance(source[:-1], target) + 1,
)
if __name__ == "__main__":
print(edit_distance("ATCGCTG", "TAGCTAA")) # Answer is 4
```