This code solves the problem of maximizing the number of...

This code solves the problem of maximizing the number of satisfied customers in a store, where the owner can use a "non-grumpy" technique for a fixed number of consecutive minutes (minutes). Here's what it does step-by-step:

Input:

• customers: An array where customers[i] represents the number of customers entering the store during the i-th minute.
• grumpy: An array where grumpy[i] is 1 if the owner is grumpy during the i-th minute (making customers leave dissatisfied) and 0 if not (customers are satisfied).
• minutes: The maximum duration for which the owner can stop being grumpy continuously.

Goal:

To maximize the number of satisfied customers by strategically using the "non-grumpy" period of length minutes.

Code Explanation:

1. Variables Initialization:

   • n: Length of the grumpy array (or customers array).
   • curWin: Tracks the extra number of customers that can be satisfied during the current sliding window of minutes where the owner is "non-grumpy".
   • maxWin: The maximum extra customers that can be saved by applying the "non-grumpy" technique.

2. Sliding Window to Compute Maximum Impact (maxWin):

   • We iterate through each minute (j from 0 to n-1) in the grumpy array.
   • If grumpy[j] is 1, then those otherwise dissatisfied customers (customers[j]) are added to the current window (curWin).
   • Once the window exceeds the allowed minutes size, the oldest value from the window (customers[i] * grumpy[i]) is subtracted, and i is incremented to slide the window forward.
   • Update maxWin with the maximum curWin observed.

3. Calculate Total Satisfied Customers:

   • Initiate res with maxWin, which represents the maximum extra satisfaction gained during the optimal "non-grumpy" period.
   • Iterate through all the minutes (grumpy.length), adding up customers who are already satisfied when the owner is not grumpy.

4. Return the Result:

   • The sum in res combines the already satisfied customers and the maximum additional satisfaction from using the "non-grumpy" technique.

Example Walkthrough:

Input:

customers = [1, 0, 1, 2, 1, 1, 7, 5];
grumpy = [0, 1, 0, 1, 1, 0, 1, 0];
minutes = 3;

Steps:

1. Base Satisfaction (without grumpy):

   • Only take satisfied customers where grumpy[i] == 0: customers[0], customers[2], customers[5], and customers[7].
   • Base sum = 1 + 1 + 1 + 5 = 8.

2. Sliding Window to Maximize Additional Satisfaction:

   • Window size is minutes = 3. Simulate the window:
     • 0-2: Extra = 0 + 0 + 2 = 2.
     • 1-3: Extra = 0 + 2 + 1 = 3.
     • 2-4: Extra = 0 + 1 + 1 = 2.
     • 3-5: Extra = 2 + 1 + 0 = 3.
     • 4-6: Extra = 1 + 0 + 7 = 8. (Max)
     • 5-7: Extra = 0 + 7 + 0 = 7.
   • Max additional satisfaction (maxWin) = 8.

3. Final Result:

   • Total satisfied customers = Base (8) + Max Additional Satisfaction (8) = 16.

Output:

16

Complexity:

• Time Complexity: O(n) - A single pass for the sliding window and another pass over the array.
• Space Complexity: O(1) - In-place computations with no additional data structures.

This code optimally calculates the maximum number of satisfied customers.