(board = mark and board = mark and board = mark) or # diagonal (board = mark and board = mark and board = mark) or # down the right side (board = mark and board = mark and board = mark) or # down the middle (board = mark and board = mark and board = mark) or # across the bottom (board = mark and board = mark and board = mark) or # across the middle
Got the content from Udemy Course.ĭef win_check(board,mark): return ((board = mark and board = mark and board = mark) or # across the top def find_winner():īut this is most likely what you searched for. In this function i'm trying to find the winner. Turn2 = int(input("Player 2 \nPlease play your move, between values 0-8: ")) While Turn2 not in acceptables_positions: Turn2 = int(input("Player 2 \nPlease play your move: ")) #Change the index value and replace it with Player 1 sign Turn1 = int(input("Player 1 \nPlease play your move, between values 0-8: ")) While Turn1 not in acceptables_positions: #Check if the input values is in the range of 0-8 Turn1 = int(input("Player 1 \nPlease play your move: ")) Print("Select position for your sign between 0 - 8\nYou can check the position board to be sure that your choice is in the place you want")Īcceptables_positions = Player1 = input("Please Choose Only X or O \n").upper() Player1 = input("Please Choose, X or O \n").upper()
Print("Position Board \n |\n".format(board,board,board,board,board,board,board,board,board)) Sorry for this, but I couldn't upload the question because it has too much code. When the indexes change with the letter x or o I want to check if they are equal. I check the indexes of my list if are equal, but it seems that is doesn't work. I'm trying to find the winner in tic-tac-toe game.
Github perian daata full#
Our academic paper which describes the process of building our dataset in detail and provides full results can be found here. You can read more about this licence here.
Github perian daata license#
Our Persian stance classification dataset is being provided to you under license CC BY-NC. The file named GuidLine_FA.pdf contains a Persian guideline and the file named GuideLine_EN.pdf contains an English guideline. We prepared a guideline in both English and Persian language, which consists of notes, suggestions, and examples about stance labels. The matrix embedding is then loaded whenever it is needed. With respect to text embedding, we created matrix embeddings by using fastText and the create_embedding_matrix function in the LSTMPersianStance_HeadToClaim.ipynb file and saved this dictionary (matrix embedding) as w2v_persian.pkl. In addition, we have released FullDataset.txt, this dataset can be used in order to stance detection and fake or rumor detection in Persian. We release here article-claim stance as ArticleToClaim.txt file and headline-claim stance as HeadlineToClaim.txt file.
In order to collect this dataset, after collecting articles, for each claim we allocate three labels the first label is article (body text) stance according to the claim (article-claim stance), the second label is the article’s headline stance according to the claim (headline-claim stance) and the third one is article (body text) stance according to its headline (article-headline stance). Although this dataset can be used for fact-checking and summarization, the focus of this work is on stance classification as a stepping stone for fake news detection in the Persian language. We released here a Persian dataset that can be used for a number of NLP tasks in the context of fact-checking.
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