-
Notifications
You must be signed in to change notification settings - Fork 0
/
ndar_to_board.py
214 lines (167 loc) · 8.41 KB
/
ndar_to_board.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
import chess
import numpy as np
piece_values = {'P': 10, 'N': 30, 'B': 30, 'R': 50, 'Q': 90, 'K': 100, 'p': -10, 'n': -30, 'b': -30, 'r': -50, 'q': -90, 'k': -100}
# These are all flipped
position_values = {
'P' : np.array([ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0],
[1.0, 1.0, 2.0, 3.0, 3.0, 2.0, 1.0, 1.0],
[0.5, 0.5, 1.0, 2.5, 2.5, 1.0, 0.5, 0.5],
[0.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 0.0],
[0.5, -0.5, -1.0, 0.0, 0.0, -1.0, -0.5, 0.5],
[0.5, 1.0, 1.0, -2.0, -2.0, 1.0, 1.0, 0.5],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] ]),
'N' : np.array([[-5.0, -4.0, -3.0, -3.0, -3.0, -3.0, -4.0, -5.0],
[-4.0, -2.0, 0.0, 0.0, 0.0, 0.0, -2.0, -4.0],
[-3.0, 0.0, 1.0, 1.5, 1.5, 1.0, 0.0, -3.0],
[-3.0, 0.5, 1.5, 2.0, 2.0, 1.5, 0.5, -3.0],
[-3.0, 0.0, 1.5, 2.0, 2.0, 1.5, 0.0, -3.0],
[-3.0, 0.5, 1.0, 1.5, 1.5, 1.0, 0.5, -3.0],
[-4.0, -2.0, 0.0, 0.5, 0.5, 0.0, -2.0, -4.0],
[-5.0, -4.0, -3.0, -3.0, -3.0, -3.0, -4.0, -5.0] ]),
'B' : np.array([[-2.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -2.0],
[-1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0],
[-1.0, 0.0, 0.5, 1.0, 1.0, 0.5, 0.0, -1.0],
[-1.0, 0.5, 0.5, 1.0, 1.0, 0.5, 0.5, -1.0],
[-1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, -1.0],
[-1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -1.0],
[-1.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.5, -1.0],
[-2.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -2.0] ]),
'R' : np.array([[ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[ 0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.5],
[-0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5],
[-0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5],
[-0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5],
[-0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5],
[-0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5],
[ 0.0, 0.0, 0.0, 0.5, 0.5, 0.0, 0.0, 0.0]]),
'Q' : np.array([[-2.0, -1.0, -1.0, -0.5, -0.5, -1.0, -1.0, -2.0],
[-1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0],
[-1.0, 0.0, 0.5, 0.5, 0.5, 0.5, 0.0, -1.0],
[-0.5, 0.0, 0.5, 0.5, 0.5, 0.5, 0.0, -0.5],
[-0.5, 0.0, 0.5, 0.5, 0.5, 0.5, 0.0, -0.5],
[-1.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.0, -1.0],
[-1.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, -1.0],
[-2.0, -1.0, -1.0, -0.5, -0.5, -1.0, -1.0, -2.0]]),
'K' : np.array([[ -3.0, -4.0, -4.0, -5.0, -5.0, -4.0, -4.0, -3.0],
[ -3.0, -4.0, -4.0, -5.0, -5.0, -4.0, -4.0, -3.0],
[ -3.0, -4.0, -4.0, -5.0, -5.0, -4.0, -4.0, -3.0],
[ -3.0, -4.0, -4.0, -5.0, -5.0, -4.0, -4.0, -3.0],
[ -2.0, -3.0, -3.0, -4.0, -4.0, -3.0, -3.0, -2.0],
[ -1.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -1.0],
[ 2.0, 2.0, 0.0, 0.0, 0.0, 0.0, 2.0, 2.0 ],
[ 2.0, 3.0, 1.0, 0.0, 0.0, 1.0, 3.0, 2.0 ]])}
def ndarr_to_board(RealPieces, ndarr):
boardStr = ''
for x in ndarr:
i=0
while i < len(x):
if x[i] in RealPieces.keys():
boardStr+=RealPieces[x[i]]
else:
j=1
while i+1<len(x) and x[i+1] not in RealPieces:
j+=1
i+=1
boardStr+=str(j)
i+=1
boardStr+='/'
boardStr=boardStr[:-1]
board = chess.Board()
board.set_board_fen(boardStr)
# board=chess.BaseBoard(boardStr)
return board
def positionEvaluation(position, piece_values=piece_values, position_values=position_values):
# Position of pieces is not taken into account for their strength
if position_values == 'None':
total_eval = 0
pieces = list(position.piece_map().values())
for piece in pieces:
total_eval += piece_values[str(piece)]
return total_eval
else:
positionTotalEval = 0
pieces = position.piece_map()
for j in pieces:
file = chess.square_file(j)
rank = chess.square_rank(j)
piece_type = str(pieces[j])
positionArray = position_values[piece_type.upper()]
if piece_type.isupper():
flippedPositionArray = np.flip(positionArray, axis=0)
positionTotalEval += piece_values[piece_type] + flippedPositionArray[rank, file]
else:
positionTotalEval += piece_values[piece_type] - positionArray[rank, file]
return positionTotalEval
def minimax(position, depth, alpha, beta, maximizingPlayer, bestMove = 'h1h3'):
if depth == 0 or position.is_game_over():
return positionEvaluation(position, piece_values, position_values), bestMove
if maximizingPlayer:
maxEval = -np.inf
for child in [str(i).replace("Move.from_uci(\'", '').replace('\')', '') for i in list(position.legal_moves)]:
position.push(chess.Move.from_uci(child))
eval_position = minimax(position, depth-1, alpha, beta, False)[0]
position.pop()
maxEval = np.maximum(maxEval, eval_position)
alpha = np.maximum(alpha, eval_position)
if beta <= alpha:
break
return maxEval
else:
minEval = np.inf
minMove = np.inf
for child in [str(i).replace("Move.from_uci(\'", '').replace('\')', '') for i in list(position.legal_moves)]:
position.push(chess.Move.from_uci(child))
eval_position = minimax(position, depth-1, alpha, beta, True)
position.pop()
minEval = np.minimum(minEval, eval_position)
if minEval < minMove:
minMove = minEval
bestMin = child
beta = np.minimum(beta, eval_position)
if beta <= alpha:
break
return minEval, bestMin
def moveToIndex(move):
d = {'a':0, 'b':1, 'c':2,'d':3,'e':4, 'f':5, 'g':6, 'h':7 }
start = (d[move[0]], int(move[1])-1)
finish = (d[move[2]], int(move[3])-1)
return start, finish
RealPieces = {"whitePawn":'P',
'whiteBishop':'B',
'whiteKnight':'N',
'whiteRook':'R',
'whiteQueen':'Q',
'whiteKing':'K',
'blackPawn':'p',
'blackBishop':'b',
'blackKnight':'n',
'blackRook':'r',
'blackQueen':'q',
'blackKing':'k'}
ExampleArr=np.array([ ['blackRook', 'blackKnight', 'blackBishop', 'blackQueen', 'blackKing', 'blackBishop', 'blackKnight', 'blackRook'],
['blackPawn', 'blackPawn', 5.0, 'blackPawn', 'blackPawn', 'blackPawn', 'blackPawn', 'blackPawn'],
[1.0, 1.0, 'blackPawn', 3.0, 3.0, 2.0, 1.0, 1.0],
[0.5, 0.5, 1.0, 2.5, 2.5, 1.0, 0.5, 0.5],
[0.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 0.0],
[0.5, -0.5, "whitePawn", "whitePawn", 0.0, -1.0, -0.5, 0.5],
["whitePawn", "whitePawn", 0, 0, "whitePawn", "whitePawn", "whitePawn", "whitePawn"],
['whiteRook', 'whiteKnight', 'whiteBishop', 'whiteQueen', 'whiteKing', 'whiteBishop', 'whiteKnight', 'whiteRook'] ])
def main():
# board = chess.Board()
board = ndarr_to_board(RealPieces,ExampleArr)
print(board)
print(type(np.array(board)))
# move = minimaxRoot(4,board,True)
# move = chess.Move.from_uci(str(move))
# board.push(move)
minimaxEval, bestMove = minimax(board, 4, -np.inf, np.inf, False)
print("AI Evaluation: {}\nAI Best Move: {}".format(minimaxEval, bestMove))
print(type(bestMove))
move = chess.Move.from_uci(bestMove)
print(type(move))
# board.push(move)
print("{}\n=========================".format(board))
# print(board)
if __name__ == "__main__":
main()