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163 lines
4.9 KiB
Python
163 lines
4.9 KiB
Python
"""
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Algorithm for calculating the most cost-efficient sequence for converting one string
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into another.
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The only allowed operations are
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--- Cost to copy a character is copy_cost
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--- Cost to replace a character is replace_cost
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--- Cost to delete a character is delete_cost
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--- Cost to insert a character is insert_cost
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"""
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def compute_transform_tables(
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source_string: str,
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destination_string: str,
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copy_cost: int,
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replace_cost: int,
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delete_cost: int,
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insert_cost: int,
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) -> tuple[list[list[int]], list[list[str]]]:
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"""
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Finds the most cost efficient sequence
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for converting one string into another.
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>>> costs, operations = compute_transform_tables("cat", "cut", 1, 2, 3, 3)
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>>> costs[0][:4]
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[0, 3, 6, 9]
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>>> costs[2][:4]
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[6, 4, 3, 6]
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>>> operations[0][:4]
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['0', 'Ic', 'Iu', 'It']
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>>> operations[3][:4]
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['Dt', 'Dt', 'Rtu', 'Ct']
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>>> compute_transform_tables("", "", 1, 2, 3, 3)
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([[0]], [['0']])
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"""
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source_seq = list(source_string)
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destination_seq = list(destination_string)
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len_source_seq = len(source_seq)
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len_destination_seq = len(destination_seq)
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costs = [
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[0 for _ in range(len_destination_seq + 1)] for _ in range(len_source_seq + 1)
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]
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ops = [
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["0" for _ in range(len_destination_seq + 1)] for _ in range(len_source_seq + 1)
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]
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for i in range(1, len_source_seq + 1):
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costs[i][0] = i * delete_cost
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ops[i][0] = f"D{source_seq[i - 1]}"
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for i in range(1, len_destination_seq + 1):
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costs[0][i] = i * insert_cost
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ops[0][i] = f"I{destination_seq[i - 1]}"
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for i in range(1, len_source_seq + 1):
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for j in range(1, len_destination_seq + 1):
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if source_seq[i - 1] == destination_seq[j - 1]:
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costs[i][j] = costs[i - 1][j - 1] + copy_cost
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ops[i][j] = f"C{source_seq[i - 1]}"
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else:
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costs[i][j] = costs[i - 1][j - 1] + replace_cost
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ops[i][j] = f"R{source_seq[i - 1]}" + str(destination_seq[j - 1])
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if costs[i - 1][j] + delete_cost < costs[i][j]:
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costs[i][j] = costs[i - 1][j] + delete_cost
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ops[i][j] = f"D{source_seq[i - 1]}"
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if costs[i][j - 1] + insert_cost < costs[i][j]:
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costs[i][j] = costs[i][j - 1] + insert_cost
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ops[i][j] = f"I{destination_seq[j - 1]}"
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return costs, ops
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def assemble_transformation(ops: list[list[str]], i: int, j: int) -> list[str]:
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"""
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Assembles the transformations based on the ops table.
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>>> ops = [['0', 'Ic', 'Iu', 'It'],
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... ['Dc', 'Cc', 'Iu', 'It'],
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... ['Da', 'Da', 'Rau', 'Rat'],
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... ['Dt', 'Dt', 'Rtu', 'Ct']]
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>>> x = len(ops) - 1
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>>> y = len(ops[0]) - 1
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>>> assemble_transformation(ops, x, y)
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['Cc', 'Rau', 'Ct']
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>>> ops1 = [['0']]
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>>> x1 = len(ops1) - 1
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>>> y1 = len(ops1[0]) - 1
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>>> assemble_transformation(ops1, x1, y1)
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[]
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"""
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if i == 0 and j == 0:
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return []
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elif ops[i][j][0] in {"C", "R"}:
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seq = assemble_transformation(ops, i - 1, j - 1)
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seq.append(ops[i][j])
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return seq
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elif ops[i][j][0] == "D":
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seq = assemble_transformation(ops, i - 1, j)
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seq.append(ops[i][j])
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return seq
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else:
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seq = assemble_transformation(ops, i, j - 1)
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seq.append(ops[i][j])
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return seq
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if __name__ == "__main__":
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_, operations = compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)
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m = len(operations)
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n = len(operations[0])
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sequence = assemble_transformation(operations, m - 1, n - 1)
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string = list("Python")
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i = 0
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cost = 0
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with open("min_cost.txt", "w") as file:
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for op in sequence:
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print("".join(string))
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if op[0] == "C":
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file.write("%-16s" % "Copy %c" % op[1]) # noqa: UP031
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file.write("\t\t\t" + "".join(string))
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file.write("\r\n")
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cost -= 1
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elif op[0] == "R":
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string[i] = op[2]
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file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2]))) # noqa: UP031
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file.write("\t\t" + "".join(string))
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file.write("\r\n")
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cost += 1
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elif op[0] == "D":
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string.pop(i)
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file.write("%-16s" % "Delete %c" % op[1]) # noqa: UP031
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file.write("\t\t\t" + "".join(string))
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file.write("\r\n")
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cost += 2
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else:
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string.insert(i, op[1])
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file.write("%-16s" % "Insert %c" % op[1]) # noqa: UP031
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file.write("\t\t\t" + "".join(string))
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file.write("\r\n")
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cost += 2
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i += 1
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print("".join(string))
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print("Cost: ", cost)
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file.write("\r\nMinimum cost: " + str(cost))
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