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Code Implementation

Turbulence Detector Implementation

Below is the implementation of the turbulence detector:

solution.js

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/**
 * Find the length of the longest turbulent subarray.
 * Dynamic Programming approach.
 *
 * @param {number[]} arr - The input array
 * @return {number} - The length of the longest turbulent subarray
 */
function maxTurbulenceSize(arr) {
  const n = arr.length;
  if (n === 1) {
    return 1;
  }
 
  // Initialize state variables
  let inc = 1;  // Length of turbulent subarray ending with increasing comparison
  let dec = 1;  // Length of turbulent subarray ending with decreasing comparison
  let maxLength = 1;
 
  for (let i = 1; i < n; i++) {
    if (arr[i - 1] < arr[i]) {
      // Current comparison is increasing
      inc = dec + 1;
      dec = 1;
    } else if (arr[i - 1] > arr[i]) {
      // Current comparison is decreasing
      dec = inc + 1;
      inc = 1;
    } else {
      // Elements are equal, reset both
      inc = 1;
      dec = 1;
    }
 
    maxLength = Math.max(maxLength, Math.max(inc, dec));
  }
 
  return maxLength;
}
 
/**
 * Find the length of the longest turbulent subarray.
 * Sliding Window approach.
 *
 * @param {number[]} arr - The input array
 * @return {number} - The length of the longest turbulent subarray
 */
function maxTurbulenceSizeWindow(arr) {
  const n = arr.length;
  if (n === 1) {
    return 1;
  }
 
  let anchor = 0;
  let maxLength = 1;
  let prevComp = 0;  // Previous comparison result
 
  for (let i = 1; i < n; i++) {
    // Calculate current comparison result
    let currComp = 0;
    if (arr[i - 1] < arr[i]) {
      currComp = 1;
    } else if (arr[i - 1] > arr[i]) {
      currComp = -1;
    }
 
    // Adjust the window
    if (currComp === 0) {
      // Elements are equal, reset the window
      anchor = i;
    } else if (i === 1 || currComp * prevComp !== -1) {
      // First comparison or comparison sign doesn't flip, reset the window
      anchor = i - 1;
    }
 
    maxLength = Math.max(maxLength, i - anchor + 1);
    prevComp = currComp;
  }
 
  return maxLength;
}
 
/**
 * Find the length of the longest turbulent subarray.
 * Count Consecutive Flips approach.
 *
 * @param {number[]} arr - The input array
 * @return {number} - The length of the longest turbulent subarray
 */
function maxTurbulenceSizeFlips(arr) {
  const n = arr.length;
  if (n === 1) {
    return 1;
  }
 
  let currentLength = 1;
  let maxLength = 1;
  let prevComp = 0;  // Previous comparison result
 
  for (let i = 1; i < n; i++) {
    // Calculate current comparison result
    let currComp = 0;
    if (arr[i - 1] < arr[i]) {
      currComp = 1;
    } else if (arr[i - 1] > arr[i]) {
      currComp = -1;
    }
 
    if (currComp === 0) {
      // Elements are equal, reset the counter
      currentLength = 1;
    } else if (prevComp * currComp >= 0) {
      // Comparison sign doesn't flip, reset the counter but count the current pair
      currentLength = 2;
    } else {
      // Comparison sign flips, extend the current turbulent subarray
      currentLength++;
    }
 
    maxLength = Math.max(maxLength, currentLength);
    prevComp = currComp;
  }
 
  return maxLength;
}
 
// Test cases
console.log(maxTurbulenceSize([9, 4, 2, 10, 7, 8, 8, 1, 9]));  // 5
console.log(maxTurbulenceSize([4, 8, 12, 16]));  // 2
console.log(maxTurbulenceSize([100]));  // 1
 
console.log(maxTurbulenceSizeWindow([9, 4, 2, 10, 7, 8, 8, 1, 9]));  // 5
console.log(maxTurbulenceSizeWindow([4, 8, 12, 16]));  // 2
console.log(maxTurbulenceSizeWindow([100]));  // 1
 
console.log(maxTurbulenceSizeFlips([9, 4, 2, 10, 7, 8, 8, 1, 9]));  // 5
console.log(maxTurbulenceSizeFlips([4, 8, 12, 16]));  // 2
console.log(maxTurbulenceSizeFlips([100]));  // 1

Step-by-Step Explanation

Let's break down the implementation:

Understand the Problem: First, understand that we need to find the length of the longest subarray where the comparison sign (< or >) alternates between adjacent elements.
Define State Variables: Define state variables to track the length of turbulent subarrays ending with increasing and decreasing comparisons.
Iterate Through the Array: Iterate through the array, updating the state variables based on the comparison between adjacent elements.
Handle Edge Cases: Handle edge cases such as equal elements and single-element arrays.
Track Maximum Length: Keep track of the maximum length of a turbulent subarray encountered during the iteration.

Previous Next

String Problems

Palindrome Checker

Determine if a string reads the same forward and backward.

EasyString Manipulation

Message Encryption

Reverse a string to create a simple encryption system.

EasyString Manipulation

Word Puzzle Challenge

Check if two strings are anagrams of each other.

EasyString Manipulation

Learning Objective

Implement the turbulence detector solution in different programming languages.

Turbulence Detector Implementation

Below is the implementation of the turbulence detector in different programming languages. Select a language tab to view the corresponding code.

solution.js

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/**
 * Find the length of the longest turbulent subarray.
 * Dynamic Programming approach.
 *
 * @param {number[]} arr - The input array
 * @return {number} - The length of the longest turbulent subarray
 */
function maxTurbulenceSize(arr) {
  const n = arr.length;
  if (n === 1) {
    return 1;
  }
 
  // Initialize state variables
  let inc = 1;  // Length of turbulent subarray ending with increasing comparison
  let dec = 1;  // Length of turbulent subarray ending with decreasing comparison
  let maxLength = 1;
 
  for (let i = 1; i < n; i++) {
    if (arr[i - 1] < arr[i]) {
      // Current comparison is increasing
      inc = dec + 1;
      dec = 1;
    } else if (arr[i - 1] > arr[i]) {
      // Current comparison is decreasing
      dec = inc + 1;
      inc = 1;
    } else {
      // Elements are equal, reset both
      inc = 1;
      dec = 1;
    }
 
    maxLength = Math.max(maxLength, Math.max(inc, dec));
  }
 
  return maxLength;
}
 
/**
 * Find the length of the longest turbulent subarray.
 * Sliding Window approach.
 *
 * @param {number[]} arr - The input array
 * @return {number} - The length of the longest turbulent subarray
 */
function maxTurbulenceSizeWindow(arr) {
  const n = arr.length;
  if (n === 1) {
    return 1;
  }
 
  let anchor = 0;
  let maxLength = 1;
  let prevComp = 0;  // Previous comparison result
 
  for (let i = 1; i < n; i++) {
    // Calculate current comparison result
    let currComp = 0;
    if (arr[i - 1] < arr[i]) {
      currComp = 1;
    } else if (arr[i - 1] > arr[i]) {
      currComp = -1;
    }
 
    // Adjust the window
    if (currComp === 0) {
      // Elements are equal, reset the window
      anchor = i;
    } else if (i === 1 || currComp * prevComp !== -1) {
      // First comparison or comparison sign doesn't flip, reset the window
      anchor = i - 1;
    }
 
    maxLength = Math.max(maxLength, i - anchor + 1);
    prevComp = currComp;
  }
 
  return maxLength;
}
 
/**
 * Find the length of the longest turbulent subarray.
 * Count Consecutive Flips approach.
 *
 * @param {number[]} arr - The input array
 * @return {number} - The length of the longest turbulent subarray
 */
function maxTurbulenceSizeFlips(arr) {
  const n = arr.length;
  if (n === 1) {
    return 1;
  }
 
  let currentLength = 1;
  let maxLength = 1;
  let prevComp = 0;  // Previous comparison result
 
  for (let i = 1; i < n; i++) {
    // Calculate current comparison result
    let currComp = 0;
    if (arr[i - 1] < arr[i]) {
      currComp = 1;
    } else if (arr[i - 1] > arr[i]) {
      currComp = -1;
    }
 
    if (currComp === 0) {
      // Elements are equal, reset the counter
      currentLength = 1;
    } else if (prevComp * currComp >= 0) {
      // Comparison sign doesn't flip, reset the counter but count the current pair
      currentLength = 2;
    } else {
      // Comparison sign flips, extend the current turbulent subarray
      currentLength++;
    }
 
    maxLength = Math.max(maxLength, currentLength);
    prevComp = currComp;
  }
 
  return maxLength;
}
 
// Test cases
console.log(maxTurbulenceSize([9, 4, 2, 10, 7, 8, 8, 1, 9]));  // 5
console.log(maxTurbulenceSize([4, 8, 12, 16]));  // 2
console.log(maxTurbulenceSize([100]));  // 1
 
console.log(maxTurbulenceSizeWindow([9, 4, 2, 10, 7, 8, 8, 1, 9]));  // 5
console.log(maxTurbulenceSizeWindow([4, 8, 12, 16]));  // 2
console.log(maxTurbulenceSizeWindow([100]));  // 1
 
console.log(maxTurbulenceSizeFlips([9, 4, 2, 10, 7, 8, 8, 1, 9]));  // 5
console.log(maxTurbulenceSizeFlips([4, 8, 12, 16]));  // 2
console.log(maxTurbulenceSizeFlips([100]));  // 1

Step-by-Step Explanation

1Understand the Problem

First, understand that we need to find the length of the longest subarray where the comparison sign (< or >) alternates between adjacent elements.

2Define State Variables

Define state variables to track the length of turbulent subarrays ending with increasing and decreasing comparisons.

3Iterate Through the Array

Iterate through the array, updating the state variables based on the comparison between adjacent elements.

4Handle Edge Cases

Handle edge cases such as equal elements and single-element arrays.

5Track Maximum Length

Keep track of the maximum length of a turbulent subarray encountered during the iteration.

Language-Specific Notes

javascript

JavaScript uses === for strict equality comparison.
Math.max() is used to find the maximum of multiple values.
The conditional (ternary) operator can be used for concise comparison logic.
Early return for single-element arrays improves efficiency.

python

Python uses == for equality comparison.
The max() function is used to find the maximum of multiple values.
Multiple variable assignment (inc, dec = dec + 1, 1) allows for concise state updates.
The range() function is used for iterating through the array.
Python's if-elif-else structure provides clear conditional logic.

java

Java uses == for primitive type equality comparison.
Math.max() is used to find the maximum of values, but it only accepts two arguments at a time.
For multiple values, nested calls like Math.max(a, Math.max(b, c)) are used.
Java requires explicit type declarations for all variables.
The main() method allows for easy testing of the implementation.

cpp

C++ uses std::vector for dynamic arrays.
std::max() is used to find the maximum of values, similar to Java's Math.max().
The reference parameter (std::vector& arr) avoids copying the input array.
C++ uses the size() method to get the length of a vector.
The main() function demonstrates how to create and use vectors for testing.

Key Takeaways

The dynamic programming approach efficiently tracks the length of turbulent subarrays ending at each position.
The sliding window approach provides an alternative way to solve the problem by adjusting the window based on the turbulence property.
The count consecutive flips approach is more intuitive and directly counts the number of comparison sign flips.
All three approaches have O(n) time complexity and O(1) space complexity, making them efficient solutions for this problem.

Try It Yourself

Modify the code to implement an alternative approach and test it with the same examples.

Challenge:

Implement a function that solves the turbulence detector problem using a different approach than shown above.

Common Edge Cases

Single Element

Handle the case where the array has only one element (return 1).

arr = [100]

All Equal Elements

Handle the case where all elements in the array are equal (return 1).

arr = [5, 5, 5, 5]

Monotonically Increasing/Decreasing

Handle the case where the array is monotonically increasing or decreasing (return 2).

arr = [1, 2, 3, 4] or arr = [4, 3, 2, 1]

Solution Approach All Problems

Problem Solution Code

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Turbulence Detector - Implementation

Code Implementation

Turbulence Detector Implementation

Below is the implementation of the turbulence detector:

solution.js

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/**
 * Find the length of the longest turbulent subarray.
 * Dynamic Programming approach.
 *
 * @param {number[]} arr - The input array
 * @return {number} - The length of the longest turbulent subarray
 */
function maxTurbulenceSize(arr) {
  const n = arr.length;
  if (n === 1) {
    return 1;
  }
 
  // Initialize state variables
  let inc = 1;  // Length of turbulent subarray ending with increasing comparison
  let dec = 1;  // Length of turbulent subarray ending with decreasing comparison
  let maxLength = 1;
 
  for (let i = 1; i < n; i++) {
    if (arr[i - 1] < arr[i]) {
      // Current comparison is increasing
      inc = dec + 1;
      dec = 1;
    } else if (arr[i - 1] > arr[i]) {
      // Current comparison is decreasing
      dec = inc + 1;
      inc = 1;
    } else {
      // Elements are equal, reset both
      inc = 1;
      dec = 1;
    }
 
    maxLength = Math.max(maxLength, Math.max(inc, dec));
  }
 
  return maxLength;
}
 
/**
 * Find the length of the longest turbulent subarray.
 * Sliding Window approach.
 *
 * @param {number[]} arr - The input array
 * @return {number} - The length of the longest turbulent subarray
 */
function maxTurbulenceSizeWindow(arr) {
  const n = arr.length;
  if (n === 1) {
    return 1;
  }
 
  let anchor = 0;
  let maxLength = 1;
  let prevComp = 0;  // Previous comparison result
 
  for (let i = 1; i < n; i++) {
    // Calculate current comparison result
    let currComp = 0;
    if (arr[i - 1] < arr[i]) {
      currComp = 1;
    } else if (arr[i - 1] > arr[i]) {
      currComp = -1;
    }
 
    // Adjust the window
    if (currComp === 0) {
      // Elements are equal, reset the window
      anchor = i;
    } else if (i === 1 || currComp * prevComp !== -1) {
      // First comparison or comparison sign doesn't flip, reset the window
      anchor = i - 1;
    }
 
    maxLength = Math.max(maxLength, i - anchor + 1);
    prevComp = currComp;
  }
 
  return maxLength;
}
 
/**
 * Find the length of the longest turbulent subarray.
 * Count Consecutive Flips approach.
 *
 * @param {number[]} arr - The input array
 * @return {number} - The length of the longest turbulent subarray
 */
function maxTurbulenceSizeFlips(arr) {
  const n = arr.length;
  if (n === 1) {
    return 1;
  }
 
  let currentLength = 1;
  let maxLength = 1;
  let prevComp = 0;  // Previous comparison result
 
  for (let i = 1; i < n; i++) {
    // Calculate current comparison result
    let currComp = 0;
    if (arr[i - 1] < arr[i]) {
      currComp = 1;
    } else if (arr[i - 1] > arr[i]) {
      currComp = -1;
    }
 
    if (currComp === 0) {
      // Elements are equal, reset the counter
      currentLength = 1;
    } else if (prevComp * currComp >= 0) {
      // Comparison sign doesn't flip, reset the counter but count the current pair
      currentLength = 2;
    } else {
      // Comparison sign flips, extend the current turbulent subarray
      currentLength++;
    }
 
    maxLength = Math.max(maxLength, currentLength);
    prevComp = currComp;
  }
 
  return maxLength;
}
 
// Test cases
console.log(maxTurbulenceSize([9, 4, 2, 10, 7, 8, 8, 1, 9]));  // 5
console.log(maxTurbulenceSize([4, 8, 12, 16]));  // 2
console.log(maxTurbulenceSize([100]));  // 1
 
console.log(maxTurbulenceSizeWindow([9, 4, 2, 10, 7, 8, 8, 1, 9]));  // 5
console.log(maxTurbulenceSizeWindow([4, 8, 12, 16]));  // 2
console.log(maxTurbulenceSizeWindow([100]));  // 1
 
console.log(maxTurbulenceSizeFlips([9, 4, 2, 10, 7, 8, 8, 1, 9]));  // 5
console.log(maxTurbulenceSizeFlips([4, 8, 12, 16]));  // 2
console.log(maxTurbulenceSizeFlips([100]));  // 1

Step-by-Step Explanation

Let's break down the implementation:

Understand the Problem: First, understand that we need to find the length of the longest subarray where the comparison sign (< or >) alternates between adjacent elements.
Define State Variables: Define state variables to track the length of turbulent subarrays ending with increasing and decreasing comparisons.
Iterate Through the Array: Iterate through the array, updating the state variables based on the comparison between adjacent elements.
Handle Edge Cases: Handle edge cases such as equal elements and single-element arrays.
Track Maximum Length: Keep track of the maximum length of a turbulent subarray encountered during the iteration.

Previous Next

String Problems

Palindrome Checker

Determine if a string reads the same forward and backward.

EasyString Manipulation

Message Encryption

Reverse a string to create a simple encryption system.

EasyString Manipulation

Word Puzzle Challenge

Check if two strings are anagrams of each other.

EasyString Manipulation

Learning Objective

Implement the turbulence detector solution in different programming languages.

Turbulence Detector Implementation

Below is the implementation of the turbulence detector in different programming languages. Select a language tab to view the corresponding code.

solution.js

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/**
 * Find the length of the longest turbulent subarray.
 * Dynamic Programming approach.
 *
 * @param {number[]} arr - The input array
 * @return {number} - The length of the longest turbulent subarray
 */
function maxTurbulenceSize(arr) {
  const n = arr.length;
  if (n === 1) {
    return 1;
  }
 
  // Initialize state variables
  let inc = 1;  // Length of turbulent subarray ending with increasing comparison
  let dec = 1;  // Length of turbulent subarray ending with decreasing comparison
  let maxLength = 1;
 
  for (let i = 1; i < n; i++) {
    if (arr[i - 1] < arr[i]) {
      // Current comparison is increasing
      inc = dec + 1;
      dec = 1;
    } else if (arr[i - 1] > arr[i]) {
      // Current comparison is decreasing
      dec = inc + 1;
      inc = 1;
    } else {
      // Elements are equal, reset both
      inc = 1;
      dec = 1;
    }
 
    maxLength = Math.max(maxLength, Math.max(inc, dec));
  }
 
  return maxLength;
}
 
/**
 * Find the length of the longest turbulent subarray.
 * Sliding Window approach.
 *
 * @param {number[]} arr - The input array
 * @return {number} - The length of the longest turbulent subarray
 */
function maxTurbulenceSizeWindow(arr) {
  const n = arr.length;
  if (n === 1) {
    return 1;
  }
 
  let anchor = 0;
  let maxLength = 1;
  let prevComp = 0;  // Previous comparison result
 
  for (let i = 1; i < n; i++) {
    // Calculate current comparison result
    let currComp = 0;
    if (arr[i - 1] < arr[i]) {
      currComp = 1;
    } else if (arr[i - 1] > arr[i]) {
      currComp = -1;
    }
 
    // Adjust the window
    if (currComp === 0) {
      // Elements are equal, reset the window
      anchor = i;
    } else if (i === 1 || currComp * prevComp !== -1) {
      // First comparison or comparison sign doesn't flip, reset the window
      anchor = i - 1;
    }
 
    maxLength = Math.max(maxLength, i - anchor + 1);
    prevComp = currComp;
  }
 
  return maxLength;
}
 
/**
 * Find the length of the longest turbulent subarray.
 * Count Consecutive Flips approach.
 *
 * @param {number[]} arr - The input array
 * @return {number} - The length of the longest turbulent subarray
 */
function maxTurbulenceSizeFlips(arr) {
  const n = arr.length;
  if (n === 1) {
    return 1;
  }
 
  let currentLength = 1;
  let maxLength = 1;
  let prevComp = 0;  // Previous comparison result
 
  for (let i = 1; i < n; i++) {
    // Calculate current comparison result
    let currComp = 0;
    if (arr[i - 1] < arr[i]) {
      currComp = 1;
    } else if (arr[i - 1] > arr[i]) {
      currComp = -1;
    }
 
    if (currComp === 0) {
      // Elements are equal, reset the counter
      currentLength = 1;
    } else if (prevComp * currComp >= 0) {
      // Comparison sign doesn't flip, reset the counter but count the current pair
      currentLength = 2;
    } else {
      // Comparison sign flips, extend the current turbulent subarray
      currentLength++;
    }
 
    maxLength = Math.max(maxLength, currentLength);
    prevComp = currComp;
  }
 
  return maxLength;
}
 
// Test cases
console.log(maxTurbulenceSize([9, 4, 2, 10, 7, 8, 8, 1, 9]));  // 5
console.log(maxTurbulenceSize([4, 8, 12, 16]));  // 2
console.log(maxTurbulenceSize([100]));  // 1
 
console.log(maxTurbulenceSizeWindow([9, 4, 2, 10, 7, 8, 8, 1, 9]));  // 5
console.log(maxTurbulenceSizeWindow([4, 8, 12, 16]));  // 2
console.log(maxTurbulenceSizeWindow([100]));  // 1
 
console.log(maxTurbulenceSizeFlips([9, 4, 2, 10, 7, 8, 8, 1, 9]));  // 5
console.log(maxTurbulenceSizeFlips([4, 8, 12, 16]));  // 2
console.log(maxTurbulenceSizeFlips([100]));  // 1

Step-by-Step Explanation

1Understand the Problem

First, understand that we need to find the length of the longest subarray where the comparison sign (< or >) alternates between adjacent elements.

2Define State Variables

Define state variables to track the length of turbulent subarrays ending with increasing and decreasing comparisons.

3Iterate Through the Array

Iterate through the array, updating the state variables based on the comparison between adjacent elements.

4Handle Edge Cases

Handle edge cases such as equal elements and single-element arrays.

5Track Maximum Length

Keep track of the maximum length of a turbulent subarray encountered during the iteration.

Language-Specific Notes

javascript

JavaScript uses === for strict equality comparison.
Math.max() is used to find the maximum of multiple values.
The conditional (ternary) operator can be used for concise comparison logic.
Early return for single-element arrays improves efficiency.

python

Python uses == for equality comparison.
The max() function is used to find the maximum of multiple values.
Multiple variable assignment (inc, dec = dec + 1, 1) allows for concise state updates.
The range() function is used for iterating through the array.
Python's if-elif-else structure provides clear conditional logic.

java

Java uses == for primitive type equality comparison.
Math.max() is used to find the maximum of values, but it only accepts two arguments at a time.
For multiple values, nested calls like Math.max(a, Math.max(b, c)) are used.
Java requires explicit type declarations for all variables.
The main() method allows for easy testing of the implementation.

cpp

C++ uses std::vector for dynamic arrays.
std::max() is used to find the maximum of values, similar to Java's Math.max().
The reference parameter (std::vector& arr) avoids copying the input array.
C++ uses the size() method to get the length of a vector.
The main() function demonstrates how to create and use vectors for testing.

Key Takeaways

The dynamic programming approach efficiently tracks the length of turbulent subarrays ending at each position.
The sliding window approach provides an alternative way to solve the problem by adjusting the window based on the turbulence property.
The count consecutive flips approach is more intuitive and directly counts the number of comparison sign flips.
All three approaches have O(n) time complexity and O(1) space complexity, making them efficient solutions for this problem.

Try It Yourself

Modify the code to implement an alternative approach and test it with the same examples.

Challenge:

Implement a function that solves the turbulence detector problem using a different approach than shown above.

Common Edge Cases

Single Element

Handle the case where the array has only one element (return 1).

arr = [100]

All Equal Elements

Handle the case where all elements in the array are equal (return 1).

arr = [5, 5, 5, 5]

Monotonically Increasing/Decreasing

Handle the case where the array is monotonically increasing or decreasing (return 2).

arr = [1, 2, 3, 4] or arr = [4, 3, 2, 1]

Solution Approach All Problems

Code Implementation

Turbulence Detector Implementation

Step-by-Step Explanation

String ProblemsShow All

Palindrome Checker

Message Encryption

Word Puzzle Challenge

Learning Objective

Turbulence Detector Implementation

Step-by-Step Explanation

1Understand the Problem

2Define State Variables

3Iterate Through the Array

4Handle Edge Cases

5Track Maximum Length

Language-Specific Notes

javascript

python

java

cpp

Key Takeaways

Try It Yourself

Challenge:

Common Edge Cases

Single Element

All Equal Elements

Monotonically Increasing/Decreasing

Turbulence Detector - Implementation

Code Implementation

Turbulence Detector Implementation

Step-by-Step Explanation

String ProblemsShow All

Palindrome Checker

Message Encryption

Word Puzzle Challenge

Learning Objective

Turbulence Detector Implementation

Step-by-Step Explanation

1Understand the Problem

2Define State Variables

3Iterate Through the Array

4Handle Edge Cases

5Track Maximum Length

Language-Specific Notes

javascript

python

java

cpp

Key Takeaways

Try It Yourself

Challenge:

Common Edge Cases

Single Element

All Equal Elements

Monotonically Increasing/Decreasing

String Problems

String Problems