luccabb

(() => {
    "use strict";
    var t = {
            79: (t, e) => {
                const n = ["#docs-editor-container"],
                    o = ["#docs-editor", ...n],
                    r = ["iframe.docs-texteventtarget-iframe", ".docs-texteventtarget-iframe"],
                    i = [".kix-page", ".docs-page"],
                    s = [".kix-lineview", ".kix-paragraphrenderer"],
                    c = [".kix-lineview-text-block"],

luccabb

def negamax(
	board: chess.Board, 
	depth: int, 
	player: int, 
	null_move: bool,
	alpha: float = float("-inf"), 
	beta: float = float("inf")) -> Tuple[Union[int, chess.Move]]:
	"""
	This functions receives a board, depth and a player; and it returns
	the best move for the current board based on how many depths we're looking ahead

luccabb

def organize_moves(board: chess.Board):
	"""
	This function receives a board and it returns a list of all the
	possible moves for the current player, sorted by importance.
	Right now we are only sending the moves that are capturing pieces
	at the starting positions in our array (so we can prune more and earlier).

	Arguments:
		- board: chess board state

luccabb

def negamax(
	board: chess.Board, 
	depth: int, 
	player: int, 
	alpha: float = float("-inf"), 
	beta: float = float("inf")) -> Tuple[Union[int, chess.Move]]:
	"""
	This functions receives a board, depth and a player; and it returns
	the best move for the current board based on how many depths we're looking ahead
	and which player is playing. Alpha and beta are used to prune the search tree.

luccabb

def board_value(board: chess.Board) -> float:
	"""
	This functions receives a board and assigns a value to it, it acts as
	an evaluation function of the current state for this game. It returns


	Arguments:
		- board: current board state.

	Returns:

luccabb

def minimax(
	board: chess.Board, 
	depth: int, 
	player: int) -> Tuple[Union[int, chess.Move]]:
	"""
	This functions receives a board, depth and a player; and it returns
	the best move for the current board based on how many depths we're looking ahead
	and which player is playing. For every move we will do the recursion until
	leaf nodes and evaluate all leaf lodes.

luccabb

def negamax(
	board: chess.Board, 
	depth: int, 
	player: int) -> Tuple[Union[int, chess.Move]]:
	"""
	This functions receives a board, depth and a player; and it returns
	the best move for the current board based on how many depths we're looking ahead
	and which player is playing. For every move we will do the recursion until
	leaf nodes and evaluate all leaf lodes.
	OBS:

luccabb

luccabb

from kafka.admin import KafkaAdminClient, NewTopic

admin_client = KafkaAdminClient(
    bootstrap_servers="localhost:9093", 
    client_id='test'
)

admin_client.delete_topics(['topic_name'])

luccabb

from kafka.admin import KafkaAdminClient, NewTopic

admin_client = KafkaAdminClient(
    bootstrap_servers="localhost:9093", 
    client_id='test'
)

topic_list = []
topic_list.append(NewTopic(name="il181-topic", num_partitions=3, replication_factor=1))
admin_client.create_topics(new_topics=topic_list, validate_only=False)