Water Bottles Solution
In this page, we will solve the Water Bottles problem using different approaches: iterative simulation and a more optimized approach. We will provide the implementation of the solution in Python, Java, C++, JavaScript, and more.
Problem Description
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.
Examples
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
Solution for Water Bottles Problem
Intuition and Approach
The problem can be solved by simulating the process of drinking water bottles and exchanging empty ones for full ones until no more exchanges can be made.
- Iterative Simulation
- Optimized Approach
Approach 1: Iterative Simulation
We iteratively drink the water bottles and exchange the empty ones until no more exchanges are possible.
Implementation
function maxBottles() { const numBottles = 9; const numExchange = 3; const maxWaterBottles = function(numBottles, numExchange) { let totalDrank = numBottles; let emptyBottles = numBottles; while (emptyBottles >= numExchange) { const newBottles = Math.floor(emptyBottles / numExchange); totalDrank += newBottles; emptyBottles = emptyBottles % numExchange + newBottles; } return totalDrank; }; const result = maxWaterBottles(numBottles, numExchange); return ( <div> <p> <b>Input:</b> numBottles = {numBottles}, numExchange = {numExchange} </p> <p> <b>Output:</b> {result} </p> </div> ); }
Code in Different Languages
- JavaScript
- TypeScript
- Python
- Java
- C++
function maxWaterBottles(numBottles, numExchange) {
let totalDrank = numBottles;
let emptyBottles = numBottles;
while (emptyBottles >= numExchange) {
const newBottles = Math.floor(emptyBottles / numExchange);
totalDrank += newBottles;
emptyBottles = emptyBottles % numExchange + newBottles;
}
return totalDrank;
}
function maxWaterBottles(numBottles: number, numExchange: number): number {
let totalDrank: number = numBottles;
let emptyBottles: number = numBottles;
while (emptyBottles >= numExchange) {
const newBottles: number = Math.floor(emptyBottles / numExchange);
totalDrank += newBottles;
emptyBottles = emptyBottles % numExchange + newBottles;
}
return totalDrank;
}
class Solution:
def numWaterBottles(self, numBottles: int, numExchange: int) -> int:
total_drank = numBottles
empty_bottles = numBottles
while empty_bottles >= numExchange:
new_bottles = empty_bottles // numExchange
total_drank += new_bottles
empty_bottles = empty_bottles % numExchange + new_bottles
return total_drank
class Solution {
public int numWaterBottles(int numBottles, int numExchange) {
int totalDrank = numBottles;
int emptyBottles = numBottles;
while (emptyBottles >= numExchange) {
int newBottles = emptyBottles / numExchange;
totalDrank += newBottles;
emptyBottles = emptyBottles % numExchange + newBottles;
}
return totalDrank;
}
}
class Solution {
public:
int numWaterBottles(int numBottles, int numExchange) {
int totalDrank = numBottles;
int emptyBottles = numBottles;
while (emptyBottles >= numExchange) {
int newBottles = emptyBottles / numExchange;
totalDrank += newBottles;
emptyBottles = emptyBottles % numExchange + newBottles;
}
return totalDrank;
}
};
Complexity Analysis
- Time Complexity: , where
n
is the initial number of bottles, due to the iterative division. - Space Complexity: , as we are using a constant amount of extra space.
Approach 2: Optimized Approach
We can derive a mathematical formula to calculate the total number of water bottles drank based on the initial number of bottles and the exchange rate.
Implementation
function maxBottles() { const numBottles = 9; const numExchange = 3; const maxWaterBottles = function(numBottles, numExchange) { return numBottles + Math.floor((numBottles - 1) / (numExchange - 1)); }; const result = maxWaterBottles(numBottles, numExchange); return ( <div> <p> <b>Input:</b> numBottles = {numBottles}, numExchange = {numExchange} </p> <p> <b>Output:</b> {result} </p> </div> ); }
Code in Different Languages
- JavaScript
- TypeScript
- Python
- Java
- C++
function maxWaterBottles(numBottles, numExchange) {
return numBottles + Math.floor((numBottles - 1) / (numExchange - 1));
}
function maxWaterBottles(numBottles: number, numExchange: number): number {
return numBottles + Math.floor((numBottles - 1) / (numExchange - 1));
}
class Solution:
def numWaterBottles(self, numBottles: int, numExchange: int) -> int:
return numBottles + (numBottles - 1) // (numExchange - 1)
class Solution {
public int numWaterBottles(int numBottles, int numExchange) {
return numBottles + (num
Bottles - 1) / (numExchange - 1);
}
}
class Solution {
public:
int numWaterBottles(int numBottles, int numExchange) {
return numBottles + (numBottles - 1) / (numExchange - 1);
}
};
Complexity Analysis
- Time Complexity: , as we directly calculate the result using a formula.
- Space Complexity: , as we are using a constant amount of extra space.
By using both iterative simulation and an optimized mathematical approach, we can efficiently solve the Water Bottles problem. The choice between the two approaches depends on the specific requirements and constraints of the problem.
References
- LeetCode Problem: Water Bottles
- Solution Link: Water Bottles Solution on LeetCode
- Authors LeetCode Profile: Manish Kumar Gupta