5 Best Ways to Count Minimum K-Length Sublist Flips to Zeroize a List in Python

Rate this post

πŸ’‘ Problem Formulation: You are given a list of binary digits, i.e., list1 = [1, 0, 0, 1, 0], and an integer k which indicates the length of the sublist. The challenge is to find the minimum number of k-length sublists that need to be flipped (turning all 0s to 1s and all 1s to 0s) such that the entire list becomes composed of 0s. The desired output for list1 with k=2 would be one flip.

Method 1: Brute Force

The first method involves iterating over the list and trying every possible k-length sublist to see if flipping it will lead to a list of all zeros. It’s straightforward but can be inefficient for large lists, as it checks all possible combinations.

Here’s an example:

def count_flips_brute_force(lst, k):
    count = 0
    for i in range(len(lst) - k + 1):
        if lst[i] == 1:
            count += 1
            for j in range(i, i+k):
                lst[j] = 1 - lst[j] # Flip the bits
    return count

# Example usage
result = count_flips_brute_force([1, 0, 0, 1, 0], 2)
print(result)

Output: 1

This function loops through each index of the list and checks if the current element is a 1. If it is, it increments the flip count and flips each bit in the current k-length sublist. Since it checks every possible start position, its running time can be quite high in the worst-case scenario.

Method 2: Sliding Window

The sliding window method improves on the inefficiency of the brute force method by moving a k-length window across the list to flip only the segments necessary in one pass. This method is more efficient for larger lists.

Here’s an example:

def count_flips_sliding_window(lst, k):
    count = flip = 0
    for i in range(len(lst)):
        if i >= k:
            flip ^= lst[i - k]
        if lst[i] == flip:
            if i + k > len(lst):
                return -1
            count += 1
            flip ^= 1
    return count

# Example usage
result = count_flips_sliding_window([1, 0, 0, 1, 0], 2)
print(result)

Output: 1

This function maintains the flip state of the window start and uses XOR to keep track of whether an element should be flipped. When a loop is found that should be flipped, it increments the count and changes the flip state. It’s more efficient because it doesn’t flip each bit individually and avoids redundant work.

Method 3: Prefix Sums

Prefix sums can be used to calculate the number of flips required in a more efficient way by keeping a running total of flips made so far and using it to determine the state of each bit.

Here’s an example:

def count_flips_prefix_sums(lst, k):
    n = len(lst)
    flips = 0
    flipped = 0
    for i in range(n):
        if i >= k:
            flipped ^= lst[i - k]
        if lst[i] == flipped:
            if i+k > n:
                return -1
            lst[i] = 1 - lst[i]
            flipped ^= 1
            flips += 1
    return flips

# Example usage
result = count_flips_prefix_sums([1, 0, 0, 1, 0], 2)
print(result)

Output: 1

Here, a prefix sum technique is used to track the flip status at each index in the loop. When a flip is needed, the flip counter is incremented and the flip status is updated. It avoids recalculating flips for the whole list every time and is thus more efficient than previous methods.

Method 4: Optimize with Bit Manipulation

Bit manipulation can be used to optimize the flipping mechanism even further, making this method faster while reducing the memory footprint by working directly with bits and bit masks.

Here’s an example:

def count_flips_bit_manipulation(lst, k):
    n = len(lst)
    mask = flips = 0
    for i in range(n):
        if i >= k:
            mask ^= 1 <> i) & 1) % 2 == 0:
            if i+k > n:
                return -1
            mask ^= 1 << i
            flips += 1
    return flips

# Example usage
result = count_flips_bit_manipulation([1, 0, 0, 1, 0], 2)
print(result)

Output: 1

This algorithm uses a bitmask to represent the flip status across the list, incrementing the flips count and updating the bit mask when flips are needed. Since it solely operates on integer bits, it’s typically faster than using list operations.

Bonus One-Liner Method 5: Pythonic Approach

For Python enthusiasts, here’s a compact one-liner that uses list comprehension and Python’s slicing features to calculate the flips in a somewhat brute force way.

Here’s an example:

count_flips_pythonic = lambda lst, k: sum(1 for i in range(len(lst) - k + 1) if lst[i:i+k] == [1]*k)

# Example usage
result = count_flips_pythonic([1, 0, 0, 1, 0], 2)
print(result)

Output: 1

This one-liner checks for k-length sublists of ones and counts how many flips would be required to turn them all to zeros. It’s short and easy to understand, but it might not be the most efficient for long lists or long sublists.

Summary/Discussion

  • Method 1: Brute Force. Simple to implement. Inefficient for large datasets with higher time complexity.
  • Method 2: Sliding Window. More efficient than brute force. Leverages a single pass through the list but still not as fast as bit manipulation.
  • Method 3: Prefix Sums. Helps in optimizing space by not flipping actual list values. Offers a middle ground in terms of performance.
  • Method 4: Bit Manipulation. Highly efficient with reduced space usage. May be less readable for those not familiar with bit operations.
  • Bonus Method 5: Pythonic Approach. Elegant and concise. Not efficient for large inputs and can be considered more of an educational or quick-test approach.