Edit Distance Algorithm for String Matching

strings/edit_distance.py

@@ -0,0 +1,40 @@
+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, 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 nucleotides, this matching
+    becomes essential in understanding the mutation in successive genes.
+    Hence, this algorithm comes in handy when we are trying to quantify the
+    mutations in successive generations.
+    Args:
+    source (type __string__): This is the source string, the initial string with
+    respect to which we are calculating the edit_distance for the target
+    target (type __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
+    Given two integers, return the sum.
+    :param source: str
+    :param target: str
+    :return: int
+    >>> edit_distance("GATTIC", "GALTIC")
+    1
+    """
+    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