![]() ![]() This matrix can then be fed into a neural network. This matrix of strings can be one-hot encoded to form a matrix of one-hot encodings. ![]() Based on basic functions of the python-chess function, one can convert a chess board into a matrix of strings. The chess_dict is a dictionary that can be used to change each board into a matrix of one-hot encoded arrays. Step 2| Prepare Data: chess_dict = def make_matrix(board): pgn = board.epd() foo = pieces = pgn.split(" ", 1) rows = pieces.split("/") for row in rows: foo2 = for thing in row: if thing.isdigit(): for i in range(0, int(thing)): foo2.append('.') else: foo2.append(thing) foo.append(foo2) return foo def translate(matrix,chess_dict): rows = for row in matrix: terms = for term in row: terms.append(chess_dict) rows.append(terms) return rows Download it onto your local drive and import it using the read_csv function from pandas. Code: Step 1| Access Data: import os from pandas import read_csv os.chdir('XXXXXX') csv = read_csv('chessData.csv')Īccess the first dataset from Kaggle. The computation would be relatively low as training the stockfish deep learning algorithm could be done beforehand, and due to only running random moves, it would take less computational power. This would be better than the deep learning algorithm as it would only make legal moves and better than the search algorithm as it had the evaluation abilities of the stockfish algorithm. The first move that has the highest score would therefore be the best move. The position would then be evaluated and recorded. For each legal move for the position given, for a given depth, a random move would be made. I would train a deep neural network based on the evaluation made by the Stockfish algorithm from a dataset. I found out that putting two of the algorithms together would yield great results. However, I found that the heuristic values that my algorithm calculated were largely inaccurate and had no foresight for more complex tactics or positional advantages. I then wrote a search algorithm that used Monte Carlo methods by picking random moves after a recorded first move. The second method I used was manually creating a heuristic algorithm from scratch that took numerical measures of the board and calculated a numerical value that was the score of the board. It would be far too easy for the computer to make illegal moves, that is, moves that are not possible on the board configuration, making training learning legal moves before best moves, which would significantly slow down convergence. The first method I used was to convert chess moves and chess boards into one-hot encoding, and then using these values to train a deep neural network to come up with the best move, when given a certain board. Over the past few weeks I have tried two distinct approaches to create a chess algorithm that can play with great strategic depth without looking too far ahead and blundering. ![]()
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