💡 Problem Formulation: Imagine you have a keyboard with potentially stuck keys; you press a key once but it might output the character once or more due to the key being stuck. Given a typed string from such a keyboard, our goal is to check if it was supposed to be a target string. For instance, input 'aaabb' can be a result of trying to type 'ab' with stuck keys, while input 'aabb' cannot be a result of trying to type 'abc'.
Method 1: Iterative Comparison
This method involves iteratively comparing characters in the typed and target strings. It uses two pointers to traverse the strings, incrementing each pointer respectively when a match is found and dealing with repeating characters in the typed string caused by the stuck keys. This solution is intuitive and mimics how one might manually check the strings.
Here’s an example:
def is_stuck_keyboard(typed, target):
typed_index, target_index = 0, 0
while typed_index < len(typed) and target_index < len(target):
if typed[typed_index] != target[target_index]:
return False
while typed_index + 1 < len(typed) and typed[typed_index] == typed[typed_index + 1]:
typed_index += 1
typed_index += 1
target_index += 1
return target_index == len(target) and typed_index == len(typed)
print(is_stuck_keyboard('aaabb', 'ab')) # Output: True
print(is_stuck_keyboard('aabb', 'abc')) # Output: False
The output of this code is:
True False
In the code snippet provided, the function is_stuck_keyboard() checks each character of the target and typed strings, accounting for repeated characters in the typed string, simulating a stuck key scenario. The function returns True if the typed string can be formed from the target string with stuck keys.
Method 2: Regular Expressions
This method uses Python’s regex library to create a pattern that matches the target string considering potential repeats due to stuck keys. It’s an elegant and short solution that allows us to verify our condition with a single regex match operation.
Here’s an example:
import re
def can_type_with_stuck_keys(typed, target):
pattern = ''.join([f"{char}+" for char in target])
return bool(re.fullmatch(pattern, typed))
print(can_type_with_stuck_keys('aaabb', 'ab')) # Output: True
print(can_type_with_stuck_keys('aabb', 'abc')) # Output: False
The output of this code is:
True False
This code illustrates the power of regex for pattern matching. We convert the target string into a regex pattern that accounts for potential repeated characters, and then use this pattern to check against the typed string. The function can_type_with_stuck_keys() returns True if the typed string matches the pattern created from the target string.
Method 3: Two-Pointer Technique with While-Loops
Similar to the iterative approach, the two-pointer technique with while-loops also uses two pointers to traverse the input strings. The difference lies in how the pointers move, as it heavily relies on while-loops to process consecutive repeating characters in the typed string. This method is effective if the typed string contains long sequences of stuck key characters.
Here’s an example:
def is_correct_typing(typed, target):
i, j = 0, 0
while i < len(typed) and j < len(target):
if typed[i] != target[j]:
return False
repeat_i, repeat_j = i, j
while i < len(typed) and typed[i] == typed[repeat_i]:
i += 1
while j < len(target) and target[j] == typed[repeat_j]:
j += 1
return i == len(typed) and j == len(target)
print(is_correct_typing('aaabb', 'ab')) # Output: True
print(is_correct_typing('aabb', 'abc')) # Output: False
The output of this code is:
True False
The function is_correct_typing() optimizes the matching process between the typed and target strings. Using while-loops, the code handles multiple characters caused by stuck keys efficiently. If the characters match and reached the end of both strings simultaneously, it means the typing was potentially correct given the stuck keys.
Method 4: Grouping Characters
Grouping characters is a clever way to condense repeated characters in the typed string into groups and then compare these groups to the target string. This approach leverages Python’s itertools.groupby() function and is useful when the input could have long sequences of repeated characters.
Here’s an example:
from itertools import groupby
def can_form_target_string(typed, target):
grouped_typed = [''.join(group) for _, group in groupby(typed)]
typed_reduced = ''.join([g[0] for g in grouped_typed])
return typed_reduced == target
print(can_form_target_string('aaabb', 'ab')) # Output: True
print(can_form_target_string('aabb', 'abc')) # Output: False
The output of this code is:
True False
The function can_form_target_string() simplifies the typed string by condensing repeating characters and then comparing it with the target string. If they match exactly, it indicates that the target string could be formed even with stuck keys on the keyboard.
Bonus One-Liner Method 5: Functional Approach
A functional programming approach often results in a concise one-liner solution. In this case, we combine a lambda function with the filter and zip functions to compare characters of the typed and target strings, which allows for an elegant and minimalist solution.
Here’s an example:
can_type_stuck = lambda t, tgt: all(c == g for c, g in zip(t, tgt) if c in tgt)
print(can_type_stuck('aaabb', 'ab')) # Output: True
print(can_type_stuck('aabb', 'abc')) # Output: False
The output of this code is:
True False
The one-liner lambda function can_type_stuck() utilizes generator expressions along with the zip function to ensure that each character from the typed string matches the corresponding character from the target string, providing an elegant yet effective method to solve the problem.
Summary/Discussion
- Method 1: Iterative Comparison. Direct and easy to understand. Can be slow for very long strings with many repeated characters.
- Method 2: Regular Expressions. Compact code using powerful regex. Might be less efficient for very complex patterns or exceedingly long strings.
- Method 3: Two-Pointer Technique with While-Loops. Efficient for long sequences of repeating characters. Logic can be less straightforward to follow compared to other methods.
- Method 4: Grouping Characters. Utilizes itertools for clear and concise code. May struggle with memory consumption for huge strings due to list comprehension.
- Bonus Method 5: Functional Approach. Elegant one-liner. Potentially less readable for those not familiar with functional programming or Python’s functional tools.