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This approach involves manually iterating over each element of the array using a for loop. For each element, we apply the provided function fn with the current element and its index as arguments. The result is then pushed into a new result array, which is returned at the end.
Time Complexity: O(n), where n is the number of elements in the array.
Space Complexity: O(n) for the resultant array.
using System.Collections.Generic;
public class Program
{
public static List<int> TransformArray(int[] arr, Func<int, int, int> fn)
{
List<int> result = new List<int>();
for (int i = 0; i < arr.Length; i++)
{
result.Add(fn(arr[i], i));
}
return result;
}
public static void Main()
{
int[] arr = { 1, 2, 3 };
List<int> result = TransformArray(arr, (n, i) => n + 1);
Console.WriteLine(String.Join(", ", result));
}
}The C# implementation involves a method TransformArray that applies a lambda function to each element of the input array and stores results in a list.
Another approach is to use recursion to apply the function to each element in the array. We define a recursive function that processes elements by moving from the start to the end of the array, applying transformations and constructing the result array.
Time Complexity: O(n)
Space Complexity: O(n) for result array and O(n) for function call stack.
1function transformArrayRecursive(arr, fn, index = 0, result = []) {
2 if (index === arr.length) return result;
3 result.push(fn(arr[index], index));
4 return transformArrayRecursive(arr, fn, index + 1, result);
5}
6
7const arr = [1, 2, 3];
8const plusOne = (n, i) => n + 1;
9console.log(transformArrayRecursive(arr, plusOne));JavaScript's recursive function transformArrayRecursive builds the result array by processing each element recursively.