Typescript Coding Chronicles: Can Place Flowers
Tags: webdev, javascript, programming, typescript
Published: 2024-07-10 20:02:03

## Problem Statement:
You have a long flowerbed in which some of the plots are planted, and some are not. However, flowers cannot be planted in adjacent plots.

Given an integer array `flowerbed` containing 0's and 1's, where 0 means empty and 1 means not empty, and an integer `n`, return `true` if `n` new flowers can be planted in the flowerbed without violating the no-adjacent-flowers rule and `false` otherwise.

### Example 1:
- Input: `flowerbed = [1,0,0,0,1]`, `n = 1`
- Output: `true`

### Example 2:
- Input: `flowerbed = [1,0,0,0,1]`, `n = 2`
- Output: `false`

### Constraints:
- `1 <= flowerbed.length <= 2 * 10^4`
- `flowerbed[i]` is 0 or 1.
- There are no two adjacent flowers in flowerbed.
- `0 <= n <= flowerbed.length`

## Initial Thought Process:
To solve this problem, we need to iterate through the flowerbed and check each position to determine if a flower can be planted. If a position is empty (0) and both adjacent positions are either empty or out of bounds, we can plant a flower there.

## Basic Solution:
### Code:
```typescript
function canPlaceFlowersBruteForce(flowerbed: number[], n: number): boolean {
    let count = 0;

    for (let i = 0; i < flowerbed.length; i++) {
        if (flowerbed[i] === 0) {
            let prevEmpty = (i === 0) || (flowerbed[i - 1] === 0);
            let nextEmpty = (i === flowerbed.length - 1) || (flowerbed[i + 1] === 0);

            if (prevEmpty && nextEmpty) {
                flowerbed[i] = 1;
                count++;
                if (count >= n) {
                    return true;
                }
            }
        }
    }

    return count >= n;
}
```

### Time Complexity Analysis:
- **Time Complexity:** O(n^2), where n is the length of the flowerbed array. The inner loop effectively makes this approach less efficient.
- **Space Complexity:** O(1), as we are modifying the flowerbed array in place and using only a constant amount of extra space.

### Limitations:
The brute force solution is not optimal for larger input sizes due to its higher time complexity.

## Optimized Solution:
The optimized solution will still iterate through the flowerbed array but will skip unnecessary checks by moving to the position after the next one once a flower is planted, ensuring we do not plant adjacent flowers.

### Code:
```typescript
function canPlaceFlowersOptimized(flowerbed: number[], n: number): boolean {
    let count = 0;
    let i = 0;

    while (i < flowerbed.length) {
        if (flowerbed[i] === 0 && 
            (i === 0 || flowerbed[i - 1] === 0) && 
            (i === flowerbed.length - 1 || flowerbed[i + 1] === 0)) {
            flowerbed[i] = 1;  // Plant a flower here
            count++;
            i += 2;  // Move to the position after the next one
        } else {
            i++;
        }

        if (count >= n) {
            return true;
        }
    }

    return count >= n;
}
```

### Time Complexity Analysis:
- **Time Complexity:** O(n), where n is the length of the flowerbed array. We iterate through the flowerbed array once.
- **Space Complexity:** O(1), as we are modifying the flowerbed array in place and using only a constant amount of extra space.

### Improvements Over Basic Solution:
- The optimized solution skips unnecessary checks by moving to the position after the next one once a flower is planted, ensuring we do not plant adjacent flowers.

## Edge Cases and Testing:
### Edge Cases:
1. The flowerbed is entirely empty.
2. The flowerbed has alternating empty and non-empty plots.
3. `n` is 0, meaning no new flowers need to be planted.
4. `n` is larger than the possible number of plantable positions in the flowerbed.

### Test Cases:
```typescript
console.log(canPlaceFlowersBruteForce([1,0,0,0,1], 1)); // true
console.log(canPlaceFlowersBruteForce([1,0,0,0,1], 2)); // false
console.log(canPlaceFlowersBruteForce([0,0,1,0,0], 1)); // true
console.log(canPlaceFlowersBruteForce([0,0,1,0,0], 2)); // true
console.log(canPlaceFlowersBruteForce([0,0,1,0,1], 1)); // false
console.log(canPlaceFlowersBruteForce([1,0,0,0,0,1], 1)); // true
console.log(canPlaceFlowersBruteForce([1,0,0,0,0,1], 2)); // false
console.log(canPlaceFlowersBruteForce([0,0,0,0,0,0], 3)); // true
console.log(canPlaceFlowersBruteForce([0,0,0,0,0,0], 4)); // false

console.log(canPlaceFlowersOptimized([1,0,0,0,1], 1)); // true
console.log(canPlaceFlowersOptimized([1,0,0,0,1], 2)); // false
console.log(canPlaceFlowersOptimized([0,0,1,0,0], 1)); // true
console.log(canPlaceFlowersOptimized([0,0,1,0,0], 2)); // true
console.log(canPlaceFlowersOptimized([0,0,1,0,1], 1)); // false
console.log(canPlaceFlowersOptimized([1,0,0,0,0,1], 1)); // true
console.log(canPlaceFlowersOptimized([1,0,0,0,0,1], 2)); // false
console.log(canPlaceFlowersOptimized([0,0,0,0,0,0], 3)); // true
console.log(canPlaceFlowersOptimized([0,0,0,0,0,0], 4)); // false
```

## General Problem-Solving Strategies:
1. **Understand the Problem:** Carefully read the problem statement to understand the requirements and constraints.
2. **Identify Key Operations:** Determine the key operations needed, such as checking adjacent plots and planting flowers.
3. **Optimize for Readability:** Use clear and concise logic to ensure the code is easy to follow.
4. **Test Thoroughly:** Test the solution with various cases, including edge cases, to ensure correctness.

## Identifying Similar Problems:
1. **Array Manipulation:**
   - Problems where you need to modify elements of an array based on specific conditions.
   - Example: Moving zeros to the end of an array.

2. **Greedy Algorithms:**
   - Problems where a greedy approach can be used to find an optimal solution by making the best choice at each step.
   - Example: Interval scheduling to find the maximum number of non-overlapping intervals.

3. **Simulation Problems:**
   - Problems where you need to simulate a process step-by-step based on given rules.
   - Example: Simulating the spread of a virus in a population represented by an array.

## Conclusion:
- The problem of determining if new flowers can be planted in a flowerbed without violating the no-adjacent-flowers rule can be efficiently solved using both a brute force approach and an optimized approach.
- Understanding the problem and breaking it down into manageable parts is crucial.
- Using clear logic and optimizing for readability ensures the solution is easy to follow.
- Testing with various edge cases ensures robustness.
- Recognizing patterns in problems can help apply similar solutions to other challenges.

By practicing such problems and strategies, you can improve your problem-solving skills and be better prepared for various coding challenges.