There are numBottles
water bottles that are initially full of water. You can exchange numExchange
empty water bottles from the market with one full water bottle.
The operation of drinking a full water bottle turns it into an empty bottle.
Given the two integers numBottles
and numExchange
, return the maximum number of water bottles you can drink.
Example 1:
Input: numBottles = 9, numExchange = 3 Output: 13 Explanation: You can exchange 3 empty bottles to get 1 full water bottle. Number of water bottles you can drink: 9 + 3 + 1 = 13.
Example 2:
Input: numBottles = 15, numExchange = 4 Output: 19 Explanation: You can exchange 4 empty bottles to get 1 full water bottle. Number of water bottles you can drink: 15 + 3 + 1 = 19.
Constraints:
1 <= numBottles <= 100
2 <= numExchange <= 100
The problem involves calculating the maximum number of water bottles you can drink given an initial number of bottles and the exchange rate for empty bottles. The solution utilizes a simple iterative approach.
Approach:
Initialization: The total number of bottles drunk (ans
) is initialized to the initial number of full bottles (numBottles
).
Iteration: The loop continues as long as the number of empty bottles is greater than or equal to the exchange rate (numExchange
). In each iteration:
numBottles / numExchange
.ans
) is increased by this number.Return Value: The function returns ans
, representing the total number of water bottles drunk.
Code Explanation (Python Example):
class Solution:
def numWaterBottles(self, numBottles: int, numExchange: int) -> int:
ans = numBottles
while numBottles >= numExchange:
empty_bottles = numBottles // numExchange # Integer division to find the number of new bottles
ans += empty_bottles # Add the number of new bottles to the total
numBottles = numBottles % numExchange + empty_bottles # Update the number of bottles.
return ans
Time and Space Complexity Analysis:
Time Complexity: O(log n), where n is numBottles
. The loop iterates until numBottles
becomes less than numExchange
. In each iteration, numBottles
is reduced significantly, resulting in a logarithmic time complexity. In the worst case, it will be O(n) if numExchange is 2.
Space Complexity: O(1). The algorithm uses only a constant amount of extra space to store variables, regardless of the input size.
Example Walkthrough (Python):
Let's say numBottles = 9
and numExchange = 3
.
ans
is initialized to 9.numBottles
(9) >= numExchange
(3). empty_bottles
= 9 // 3 = 3. ans
becomes 9 + 3 = 12. numBottles
becomes 9 % 3 + 3 = 3.numBottles
(3) >= numExchange
(3). empty_bottles
= 3 // 3 = 1. ans
becomes 12 + 1 = 13. numBottles
becomes 3 % 3 + 1 = 1.numBottles
(1) < numExchange
(3).ans
, which is 13.The provided code in other languages follows the same logic and complexity. The only differences are syntactic variations specific to each programming language.