Proving our alorithm's effectiveness

[Magulak]

Description

At some point, we will need to evaluate the effectiveness of our algorithm. Choosing the path for evaluation earlier allows us to structure our code accordingly, enabling it to work with the chosen solution.

We can consider the following options:

1. Utilizing accuracy metrics based on top-tier chess engines (as chess.org does).
   2..Checking our algorithm's ELO rating on public chess websites, similar to the approach described in this GitHub repository: https://github.com/lichess-bot-devs/lichess-bot/blob/master/README.md.
2. Developing our own evaluation method based on our thesis."
3. Evaluating effectiveness of our algorithm is not needed.

Area

Other

[Magulak]

stub instead of an algorithm:

`

Load and resize image

image = Image.open("Piece.png")
image = image.resize((87, 87))
image.save("Piece1.png")
image = pygame.image.load('Piece1.png').convert()

w_pawn_img = Image.open("w_pawn.png")
w_pawn_img = w_pawn_img.resize((87, 87))
w_pawn_img.save("w_pawn1.png")
w_pawn_img = pygame.image.load('w_pawn1.png').convert()

Make image see-through

image.set_alpha(200)
w_pawn_img.set_alpha(200)

Generate Pawn

piece_position_list = []
b_pawn = image.get_rect()  # image.get rect method only decides size of rect based on image size
b_pawn.x, b_pawn.y = 0, 87
x = 0
while x <= 8:
    exec(f"b_pawn{x} = b_pawn.copy()\n"
         f"piece_position_list.append(NamedRect(b_pawn{x},'b_pawn'))\n"

         f"b_pawn{x}.x, b_pawn{x}.y = x*87,87")  # i added {x} to b_pawn

    x = x + 1

white_pawn = w_pawn_img.get_rect()
x = 0
while x <= 8:
    exec(f"w_pawn{x} = white_pawn.copy()\n"
         f"piece_position_list.append(NamedRect(w_pawn{x},'w_pawn'))\n"
         f"w_pawn{x}.x, w_pawn{x}.y = x*87,522")
    x = x + 1

`