You are given a code snippet that represents a recursive algorithm for selecting the best move in a game. The algorithm simulates the effect of each legal move on a game board, and then recursively evaluates the move by calling the `best_move` function with an increased depth. The goal is to find the best move for the current player, considering the potential moves and their subsequent evaluation.

Your task is to implement the missing parts of the `best_move` function and complete the algorithm to make it work correctly.

The `best_move` function takes two parameters: `depth`, which represents the current depth in the game tree, and `board_cpy`, which is a copy of the game board. The function returns the evaluation value of the best move for the current player.

To implement the `best_move` function, you need to complete the following steps:

1. Check if the `depth` is equal to a defined constant `MAX_DEPTH`. If it is, return the evaluation value of the current game state using a predefined `eval` function. 

2. Get a list of legal moves from the `board_cpy` using a predefined `get_legal_moves` function.

3. Initialize two variables: `opt`, which represents the optimal evaluation value for the current player, and `index`, which represents the index of the best move.

4. Iterate over each legal move using a for loop.

5. Inside the loop, simulate the move by invoking the `move` method of `board_cpy` with the current legal move as an argument.

6. After simulating the move, recursively call the `best_move` function with an increased `depth` and modified `board_cpy` object. Assign the returned value to a temporary variable `temp`.

7. If `depth % 2` is equal to 0 and `temp` is greater than `opt`, update `opt` with `temp` and set `index` to the current loop index.

8. If `depth % 2` is equal to 1 and `temp` is less than `opt`, update `opt` with `temp` and set `index` to the current loop index.

9. After the loop, return `opt` if `depth` is even (0-based index) and `index` otherwise.

You are required to implement the missing parts of the `best_move` function using the provided instructions.
user
Sure, I can help you implement the missing parts of the `best_move` function as per the given instructions. Here's a Python function that does that:

```python
import random

# Predefined function to get legal moves
def get_legal_moves(board_cp):
    # This function should return a list of indices representing legal moves
    # Here's a simple example function for a Connect Four game
    legal_moves = []
    for row in range(6):
        if board_cp[row].count(-1) == 1:  # -1 is a placeholder for empty cells
            legal_moves.append(row)
    if not legal_moves:
        legal_moves = [-1]
    return legal_moves

# Predefined function to evaluate game state
def eval(board_cp):
    # This function should return an evaluation value based on the current state of the game
    # A simple example function can return the difference between the number of stones of the current player and the opponent
    return sum(board_cp)

# Predefined function to move a stone
class Board:
    def __init__(self, size):
        self.size = size
        self.board = [[-1] * self.size for _ in range(self.size)]

    def move(self, row, col):
        if row < 0 or row >= self.size or col < 0 or col >= self.size:
            print("Invalid cell")
            return None
        if self.board[row][col] != -1:
            print("That cell is already occupied")
            return None
        self.board[row][col] = 1

# Predefined constants
MAX_DEPTH = 4

def best_move(depth, board_cp):
    # Check if depth is at maximum level, return eval function if it is
    if depth == MAX_DEPTH:
        return eval(board_cp)

    # Get List of Legal Moves
    legal_moves = get_legal_moves(board_cp)

    # Initialize Opt and Index
    opt = float('-inf')
    index = random.choice(legal_moves) if random.choice([True, False]) else legal_moves[0]

    # Iterate Over Each Legal Move
    for move in legal_moves:
        board_cp.move(move, col)
        temp = best_move(depth + 1, board_cp)
        board_cp.move(-1, -1)  # Undo the move

        # Update Opt According to Depth and Temp
        if depth % 2 == 0:
            opt = max(opt, temp)
        else:
            opt = min(opt, temp)

        # Update Index
        if depth % 2 == 1 and temp > opt:
            index = move

    return opt, index
```

This function implements the recursive algorithm, where `best_move` function is called recursively to evaluate the game state at increasing depth levels. At each level, it gets the list of legal moves, finds the optimal move by evaluating the game state with each legal move, updates an evaluation value `opt` and choice the next move. The function chooses an optimal move randomly if it is hard to choose the optimal one.
assistant
You are given a code snippet that represents a recursive algorithm for selecting the best move in a game. The algorithm simulates the effect of each legal move on a game board, and then recursively evaluates the move by calling the `best_move` function with an increased depth. The goal is to find the best move for the current player, considering the potential moves and their subsequent evaluation.

Your task is to implement the missing parts of the `best_move` function and complete the algorithm to make it work correctly.

The `best_move` function takes two parameters: `depth`, which represents the current depth in the game tree, and `board_cpy`, which is a copy of the game board. The function returns the evaluation value of the best move for the current player.

To implement the `best_move` function, you need to complete the following steps:

1. Check if the `depth` is equal to a defined constant `MAX_DEPTH`. If it is, return the evaluation value of the current game state using a predefined `eval` function. 

2. Get a list of legal moves from the `board_cpy` using a predefined `get_legal_moves` function.

3. Initialize two variables: `opt`, which represents the optimal evaluation value for the current player, and `index`, which represents the index of the best move.

4. Iterate over each legal move using a for loop.

5. Inside the loop, simulate the move by invoking the `move` method of `board_cpy` with the current legal move as an argument.

6. After simulating the move, recursively call the `best_move` function with an increased `depth` and modified `board_cpy` object. Assign the returned value to a temporary variable `temp`.

7. If `depth % 2` is equal to 0 and `temp` is greater than `opt`, update `opt` with `temp` and set `index` to the current loop index.

8. If `depth % 2` is equal to 1 and `temp` is less than `opt`, update `opt` with `temp` and set `index` to the current loop index.

9. After the loop, return `opt` if `depth` is even (0-based index) and `index` otherwise.

You are required to implement the missing parts of the `best_move` function using the provided instructions.
user
Sure, here's how you can implement the `best_move` function based on the instructions you provided:

```python
def best_move(depth, board_cpy):
    # Step 1: Check if the depth is equal to MAX_DEPTH
    if depth == MAX_DEPTH:
        return eval(board_cpy)

    # Step 2: Get a list of legal moves
    legal_moves = get_legal_moves(board_cpy)

    # Step 3: Initialize opt and index
    opt = float('-inf') if depth % 2 == 0 else float('inf')
    index = -1

    # Step 4: Iterate over each legal move
    for i, move in enumerate(legal_moves):
        # Step 5: Simulate the move
        board_cpy.move(move)

        # Step 6: Recursively call the best_move function
        temp = best_move(depth + 1, board_cpy)

        # Step 7 and 8: Update opt and index based on depth and temp
        if (depth % 2 == 0 and temp > opt) or (depth % 2 == 1 and temp < opt):
            opt = temp
            index = i

        # Undo the move for the next iteration
        board_cpy.undo_move()

    # Step 9: Return opt if depth is even, index otherwise
    return opt if depth % 2 == 0 else index
```
assistant