<<< Fibonacci series using recursion | Logical Flow of Code (LFoC)

The given code is an implementation of the Fibonacci series using recursion. The code consists of two parts: the main logic and the additional recursive function.

1. Main Logic:

The main logic uses a for loop to iterate from 0 to n-1 and calls the `feb` function for each value of i. The `feb` function is responsible for calculating the Fibonacci number for a given input. The result of each call is printed using `printf` in the loop.

2. Additional Logic (Recursive Function):

The recursive function `feb` calculates the Fibonacci number for a given input `n`. It uses a simple recursive approach to compute the Fibonacci numbers. The base cases are when n is 0 or 1, in which case the function returns n (since the Fibonacci series starts with 0 and 1). For any other value of n, the function recursively calls itself for n-1 and n-2, adding the results of these two recursive calls.

Example:

Let's walk through the example of input 5:

- The loop runs for i = 0, 1, 2, 3, and 4.

- For i = 0, the `feb` function is called with n = 0, which returns 0.

- For i = 1, the `feb` function is called with n = 1, which returns 1.

- For i = 2, the `feb` function is called with n = 2. Now, the recursive calls will be made:

    - `feb(1) + feb(0)`, which is 1 + 0 = 1.

- For i = 3, the `feb` function is called with n = 3. Now, the recursive calls will be made:

    - `feb(2) + feb(1)`. The first part is already calculated as 1, and the second part is 1 (from the earlier call to `feb(1)`), so the result is 1 + 1 = 2.

- For i = 4, the `feb` function is called with n = 4. Now, the recursive calls will be made:

    - `feb(3) + feb(2)`. The first part is already calculated as 2, and the second part is 1 (from the earlier call to `feb(2)`), so the result is 2 + 1 = 3.

Thus, the output for input 5 is "0 1 1 2 3" as indicated in the example comment.

The Fibonacci series is a classic mathematical sequence, and this implementation uses recursion to calculate each term. However, it's important to note that recursion can be less efficient than iterative methods for larger values of n, as it can lead to redundant calculations. Memoization or iterative approaches can be used to improve efficiency in such cases.

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