-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathengin.py
More file actions
1088 lines (825 loc) · 36 KB
/
engin.py
File metadata and controls
1088 lines (825 loc) · 36 KB
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
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
"""
--------------------------------------------------
HELLO AND WELCOME TO 'AI CHESS' BY ITAMAR STOLLMAN
--------------------------------------------------
INSTRUCTIONS->
TO ACTIVATE THE PROGRAM, YOU WILL NEED TO DO SOME FEW THINGS:
1. MAKE SURE YOU ARE ABLE TO IMPORT :
chess.polyglot
chess.engine
2. DOWNLOAD computer_data file and save it in the project files
3. DOWNLOAD stockfish file and save it in the project files
4. GO TO theory_book FUNCTION AND PLACE THE COMPLETE PATH AT THE RELEVANT PLACE
5. GO TO game_loop FUNCTION AND PLACE THE COMPLETE PATH AT THE RELEVANT PLACE
THATS IT! YOU ARE READY TO ACTIVATE THE PROGRAM.
--------------------------------------------------
AFTER STARTING THE PROGRAM, YOU WILL HAVE A FEW OPTION FOR MESSING AROUND THE PROGRAM.
******************************************************************************
BY TYPING 1 :
YOU WILL GET OT PLAY WITH A RANDOM-DNA AI CHESS.
WHEN YOUR TURN COMES, TYPE A MOVE IN THE FOLLOWING FORMAT: a2a4 FOR EXAMPLE.
******************************************************************************
BY TYPING 2 :
YOU WILL NEED TO TYPE HOW MANY GAMES YOU WOULD LIKE TO WATCH
BETWEEN A RANDOM-DNA CHESS VS STOCKFISH-15
POPCORN IS HIGHLY RECOMMENDED
******************************************************************************
"""
import random
import time
import chess.polyglot
import chess.engine
import numpy
NUM_OF_GAMES = 1
PAWN = 1
BISHOP = 2
KNIGHT = 3
ROOK = 4
QUEEN = 5
KING = 6
UNICODE_PIECE_SYMBOLS = {
'R': '♖', "r": "♜",
'N': "♘", "n": "♞",
'B': "♗", "b": "♝",
'Q': "♕", "q": "♛",
'K': "♔", "k": "♚",
'P': "♙", "p": "♟",
}
pawntable = [
0, 0, 0, 0, 0, 0, 0, 0,
5, 10, 10, -20, -20, 10, 10, 5,
5, -5, -10, 0, 0, -10, -5, 5,
0, 0, 0, 20, 35, 0, 0, 0,
5, 5, 10, 25, 25, 10, 5, 5,
10, 10, 20, 30, 30, 20, 10, 10,
50, 50, 50, 50, 50, 50, 50, 50,
0, 0, 0, 0, 0, 0, 0, 0]
knightstable = [
-50, -40, -30, -30, -30, -30, -40, -50,
-40, -20, 0, 5, 5, 0, -20, -40,
-30, 5, 10, 15, 15, 10, 5, -30,
-30, 0, 15, 20, 20, 15, 0, -30,
-30, 5, 15, 20, 20, 15, 5, -30,
-30, 0, 10, 15, 15, 10, 0, -30,
-40, -20, 0, 0, 0, 0, -20, -40,
-50, -40, -30, -30, -30, -30, -40, -50]
bishopstable = [
-20, -10, -30, -10, -10, -30, -10, -20,
-10, 5, 0, 0, 0, 0, 5, -10,
-10, 10, 10, 10, 10, 10, 10, -10,
-10, 0, 10, 10, 10, 10, 0, -10,
-10, 5, 5, 10, 10, 5, 5, -10,
-10, 0, 5, 10, 10, 5, 0, -10,
-10, 0, 0, 0, 0, 0, 0, -10,
-20, -10, -10, -10, -10, -10, -10, -20]
rookstable = [
0, 0, 0, 5, 5, 0, 0, 0,
-5, 0, 0, 0, 0, 0, 0, -5,
-5, 0, 0, 0, 0, 0, 0, -5,
-5, 0, 0, 0, 0, 0, 0, -5,
-5, 0, 0, 0, 0, 0, 0, -5,
-5, 0, 0, 0, 0, 0, 0, -5,
5, 10, 10, 10, 10, 10, 10, 5,
0, 0, 0, 0, 0, 0, 0, 0]
queenstable = [
-20, -10, -10, -5, -5, -10, -10, -20,
-10, 0, 0, 0, 0, 0, 0, -10,
-10, 5, 5, 5, 5, 5, 0, -10,
0, 0, 5, 5, 5, 5, 0, -5,
-5, 0, 5, 5, 5, 5, 0, -5,
-10, 0, 5, 5, 5, 5, 0, -10,
-10, 0, 0, 0, 0, 0, 0, -10,
-20, -10, -10, -5, -5, -10, -10, -20]
kingstable = [
20, 200, 10, 0, 0, 10, 200, 20,
-20, -20, -20, -20, -20, -20, -20, -20,
-10, -20, -20, -20, -20, -20, -20, -10,
-20, -30, -30, -40, -40, -30, -30, -20,
-30, -40, -40, -50, -50, -40, -40, -30,
-30, -40, -40, -50, -50, -40, -40, -30,
-30, -40, -40, -50, -50, -40, -40, -30,
-30, -40, -40, -50, -50, -40, -40, -30
]
fixed_board = [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]
piece_value = {1: 1, 2: 3.15, 3: 3, 4: 5, 5: 9, 6: 1}
'''
a game between engine ai and stockfish-15
@:param DNA->space_M_BLACK, capture_M_BLACK, pawn_structure_M_BLACK, connected_rooks_M_BLACK,
enemy_king_magnet_M_BLACK, the_defending_bishop_M_BLACK, defending_vs_attacking_M_BLACK
@:return "WINNER": the winner, "num_of_moves": num_of_moves, "ave time per move" : ave time per move
'''
def game_loop(space_M_BLACK,
capture_M_BLACK, pawn_structure_M_BLACK, connected_rooks_M_BLACK,
enemy_king_magnet_M_BLACK, the_defending_bishop_M_BLACK, defending_vs_attacking_M_BLACK):
board = chess.Board()
num_of_moves = 0
print_board(board)
total_time = 0
while not board.is_game_over(claim_draw=True):
try:
engine = chess.engine.SimpleEngine.popen_uci("C:\\Users\\itama\\Downloads\\stockfish.exe")
move = engine.play(board, chess.engine.Limit(time=0.3))
board.push(move.move)
except:
if total_time == 0:
total_time = 1
if num_of_moves == 0:
num_of_moves = 1
return {"WINNER": chess.WHITE, "num_of_moves": num_of_moves,
"ave time per move": total_time / num_of_moves / 2}
depth = 3
print("WHITE STOCKFISH AI played:", move.move)
print_board(board)
if board.is_checkmate():
print("GAME OVER - WHITE WON THE GAME")
print({"WINNER": chess.BLACK, "num_of_moves": num_of_moves,
"ave time per move": total_time / num_of_moves / 2})
return {"WINNER": chess.WHITE, "num_of_moves": count_moves(num_of_moves),
"ave time per move": total_time / num_of_moves / 2}
start_time = time.time()
BLACK_AI_move = ai_move(board, chess.BLACK, depth, space_M_BLACK, capture_M_BLACK, pawn_structure_M_BLACK,
connected_rooks_M_BLACK,
enemy_king_magnet_M_BLACK, the_defending_bishop_M_BLACK, defending_vs_attacking_M_BLACK)
end_time = time.time()
total_time += end_time - start_time
print(total_time)
print("BLACK_AI_move", BLACK_AI_move)
board.push(BLACK_AI_move)
print("-------------------")
print("BLACK AI played", BLACK_AI_move)
print_board(board)
num_of_moves = num_of_moves + 1
print("---------count_moves----------", count_moves(num_of_moves))
if board.is_checkmate():
print("GAME OVER - BLACK WON THE GAME")
print(total_time)
print({"WINNER": chess.BLACK, "num_of_moves": num_of_moves,
"ave time per move": total_time / num_of_moves / 2})
return {"WINNER": chess.BLACK, "num_of_moves": num_of_moves,
"ave time per move": total_time / num_of_moves / 2}
'''
a game between manual human and stockfish-15
@:param DNA->space_M_BLACK, capture_M_BLACK, pawn_structure_M_BLACK, connected_rooks_M_BLACK,
enemy_king_magnet_M_BLACK, the_defending_bishop_M_BLACK, defending_vs_attacking_M_BLACK
@:return "WINNER": the winner, "num_of_moves": num_of_moves, "ave time per move" : ave time per move
'''
def game_loop_human(space_M_BLACK,
capture_M_BLACK, pawn_structure_M_BLACK, connected_rooks_M_BLACK,
enemy_king_magnet_M_BLACK, the_defending_bishop_M_BLACK, defending_vs_attacking_M_BLACK):
board = chess.Board()
num_of_moves = 0
print_board(board)
total_time = 0
while not board.is_game_over(claim_draw=True):
input_move = input("HUMAN, Enter your move: ")
move = chess.Move.from_uci(input_move)
if move in board.legal_moves:
board.push(move)
else:
# TODO - to add a perpetual condition
# to add an exception for an invalid typo
print("enter your move again, a2a4 FOR EXAMPLE, notice that the board is opposite. ")
input_move = input("HUMAN, Enter your move: ")
move = chess.Move.from_uci(input_move)
board.push(move)
if 1 == 1:
if board.is_checkmate():
print("GAME OVER - WHITE WON THE GAME")
print({"WINNER": chess.BLACK, "num_of_moves": num_of_moves,
"ave time per move": total_time / num_of_moves / 2})
return {"WINNER": chess.WHITE, "num_of_moves": count_moves(num_of_moves),
"ave time per move": total_time / num_of_moves / 2}
start_time = time.time()
# TODO - wrap with a function due to repeated code in other methods
BLACK_AI_move = ai_move(board, chess.BLACK, 3, space_M_BLACK, capture_M_BLACK, pawn_structure_M_BLACK,
connected_rooks_M_BLACK,
enemy_king_magnet_M_BLACK, the_defending_bishop_M_BLACK,
defending_vs_attacking_M_BLACK)
end_time = time.time()
total_time += end_time - start_time
print(total_time)
print("BLACK_AI_move", BLACK_AI_move)
board.push(BLACK_AI_move)
print("-------------------")
print("BLACK AI played", BLACK_AI_move)
print_board(board)
num_of_moves = num_of_moves + 1
print("---------count_moves----------", count_moves(num_of_moves))
if board.is_checkmate():
print("GAME OVER - BLACK WON THE GAME")
print(total_time)
print({"WINNER": chess.BLACK, "num_of_moves": num_of_moves,
"ave time per move": total_time / num_of_moves / 2})
return {"WINNER": chess.BLACK, "num_of_moves": num_of_moves,
"ave time per move": total_time / num_of_moves / 2}
'''
generates NUM_OF_GAMES_ games against stockfish15, returns the score.
@:param DNA:
num_space_M_BLACK,
num_capture_M_BLACK,
num_pawn_structure_M_BLACK,
num_connected_rooks_M_BLACK,
num_enemy_king_magnet_M_BLACK,
num_the_defending_bishop_M_BLACK,
num_defending_vs_attacking_M_BLACK
@:param NUM_OF_GAMES_ -> how many games to play
@:return dna_score-> the DNA score against stockfish15, and the DNA
'''
def game_in_genetic_algo(
num_space_M_BLACK,
num_capture_M_BLACK,
num_pawn_structure_M_BLACK,
num_connected_rooks_M_BLACK,
num_enemy_king_magnet_M_BLACK,
num_the_defending_bishop_M_BLACK,
num_defending_vs_attacking_M_BLACK):
NUM_OF_GAMES_ = 3
game_data_dict = {"WINNER": 0, "num_of_moves": 0, "ave time per move": 0}
games_list = []
for i in range(NUM_OF_GAMES_):
game_data = game_loop(
space_M_BLACK=num_space_M_BLACK,
capture_M_BLACK=num_capture_M_BLACK,
pawn_structure_M_BLACK=num_pawn_structure_M_BLACK,
connected_rooks_M_BLACK=num_connected_rooks_M_BLACK,
enemy_king_magnet_M_BLACK=num_enemy_king_magnet_M_BLACK,
the_defending_bishop_M_BLACK=num_the_defending_bishop_M_BLACK,
defending_vs_attacking_M_BLACK=num_defending_vs_attacking_M_BLACK)
games_list.append({"num_of_moves": game_data["num_of_moves"],
"ave time per move": game_data["ave time per move"]})
game_data_dict["WINNER"] += game_data["WINNER"]
game_data_dict["num_of_moves"] += game_data["num_of_moves"]
game_data_dict["ave time per move"] += game_data["ave time per move"]
game_data_dict["num_of_moves"] = game_data_dict["num_of_moves"] / NUM_OF_GAMES_
game_data_dict["ave time per move"] = game_data_dict["ave time per move"] / NUM_OF_GAMES_
print(game_data_dict)
print(games_list)
dna_score = [game_data_dict, [num_space_M_BLACK,
num_capture_M_BLACK,
num_pawn_structure_M_BLACK,
num_connected_rooks_M_BLACK,
num_enemy_king_magnet_M_BLACK,
num_the_defending_bishop_M_BLACK,
num_defending_vs_attacking_M_BLACK]]
return dna_score
'''
generates a random DNA
@:param max -> the max for the random method.
@:return random DNA
'''
def get_random_DNA(max):
return {"num_space_M": random.choice(range(1, max)),
"num_capture_M": random.choice(range(1, max)),
"num_pawn_structure_M": random.choice(range(1, max)),
"num_connected_rooks_M": random.choice(range(1, max)),
"num_enemy_king_magnet_M": random.choice(range(1, max)),
"num_the_defending_bishop_M": random.choice(range(1, max)),
"num_defending_vs_attacking_M": random.choice(range(1, max))}
'''
generates a score of a random DNA against stockfish15
@:param max -> the max for the random method.
@:return dna_score-> the DNA score against stockfish15, and the DNA
'''
def generating_random_DNA_winner(max):
random_DNA = get_random_DNA(max)
print("GENERATED RANDOMLY -> ", random_DNA)
return game_in_genetic_algo(random_DNA["num_space_M"],
random_DNA["num_capture_M"],
random_DNA["num_pawn_structure_M"],
random_DNA["num_connected_rooks_M"],
random_DNA["num_enemy_king_magnet_M"],
random_DNA["num_the_defending_bishop_M"],
random_DNA["num_defending_vs_attacking_M"])
'''
main genetic algorythm.
generates the best DNA against stockfish15.
@:return the best DNA that the genetic algorythm has found
'''
def genetic_algo():
max_range = random.choice(range(0, 1000))
_DNA_1 = generating_random_DNA_winner(max_range)
_DNA_1_sharper = sharper_dna(_DNA_1)
print("FINAL _DNA_1 IS", _DNA_1_sharper)
max_range = random.choice(range(0, 1000))
_DNA_2 = generating_random_DNA_winner(max_range)
_DNA_2_sharper = sharper_dna(_DNA_2)
print("FINAL _DNA_2 IS", _DNA_2_sharper)
max_range = random.choice(range(0, 1000))
_DNA_3 = generating_random_DNA_winner(max_range)
_DNA_3_sharper = sharper_dna(_DNA_3)
print("FINAL _DNA_3 IS", _DNA_3_sharper)
max_range = random.choice(range(0, 1000))
_DNA_4 = generating_random_DNA_winner(max_range)
_DNA_4_sharper = sharper_dna(_DNA_4)
print("FINAL _DNA_4 IS", _DNA_4_sharper)
max_range = random.choice(range(0, 1000))
_DNA_5 = generating_random_DNA_winner(max_range)
_DNA_5_sharper = sharper_dna(_DNA_5)
print("FINAL _DNA_5 IS", _DNA_5_sharper)
max_range = random.choice(range(0, 1000))
_DNA_6 = generating_random_DNA_winner(max_range)
_DNA_6_sharper = sharper_dna(_DNA_6)
print("FINAL _DNA_6 IS", _DNA_6_sharper)
max_range = random.choice(range(0, 1000))
_DNA_7 = generating_random_DNA_winner(max_range)
_DNA_7_sharper = sharper_dna(_DNA_7)
print("FINAL _DNA_7 IS", _DNA_7_sharper)
DNA_list = [_DNA_1_sharper, _DNA_2_sharper, _DNA_3_sharper, _DNA_4_sharper, _DNA_5_sharper, _DNA_6_sharper,
_DNA_7_sharper]
print("DNA_list", DNA_list)
best_DNA = _DNA_1_sharper
for i in range(7):
print("DNA_list[i][0][num_of_moves]", int(DNA_list[i][0]["num_of_moves"]))
print("best_DNA[0][num_of_moves]", int(best_DNA[0]["num_of_moves"]))
print("DNA_list[i][0][num_of_moves]", DNA_list[i][0]["num_of_moves"])
print("best_DNA[0][num_of_moves]", best_DNA[0]["num_of_moves"])
if DNA_list[i][0]["num_of_moves"] >= best_DNA[0]["num_of_moves"] and \
DNA_list[i][0]["num_of_moves"] >= best_DNA[1]["num_of_moves"] and \
DNA_list[i][0]["num_of_moves"] >= best_DNA[2]["num_of_moves"] and \
DNA_list[i][0]["num_of_moves"] >= best_DNA[3]["num_of_moves"] and \
DNA_list[i][0]["num_of_moves"] >= best_DNA[4]["num_of_moves"] and \
DNA_list[i][0]["num_of_moves"] >= best_DNA[5]["num_of_moves"] and \
DNA_list[i][0]["num_of_moves"] >= best_DNA[6]["num_of_moves"]:
return DNA_list[i]
'''
takes a DNA, and makes it 'sharper' - finds near DNA that is better.
@:param _DNA_
@:return current_best-> near DNA, that is better than the current DNA.
'''
def sharper_dna(_DNA_):
print("MAKING THE DNA SHARPER", _DNA_)
best_num_of_moves = _DNA_[0]["num_of_moves"]
current_best = _DNA_
for i in range(int(_DNA_[0]["num_of_moves"])):
max_range0 = random.choice(range(0, 60 - int(_DNA_[0]["num_of_moves"])))
max_range1 = random.choice(range(0, 60 - int(_DNA_[0]["num_of_moves"])))
max_range2 = random.choice(range(0, 60 - int(_DNA_[0]["num_of_moves"])))
max_range3 = random.choice(range(0, 60 - int(_DNA_[0]["num_of_moves"])))
max_range4 = random.choice(range(0, 60 - int(_DNA_[0]["num_of_moves"])))
max_range5 = random.choice(range(0, 60 - int(_DNA_[0]["num_of_moves"])))
max_range6 = random.choice(range(0, 60 - int(_DNA_[0]["num_of_moves"])))
results = game_loop(_DNA_[1][0] + max_range0, _DNA_[1][1] + max_range1, _DNA_[1][2] + max_range2,
_DNA_[1][3] + max_range3, _DNA_[1][4] + max_range4, _DNA_[1][5] + max_range5,
_DNA_[1][6] + max_range6)
print("best_num_of_moves", best_num_of_moves)
if results["num_of_moves"] > best_num_of_moves:
current_best = [_DNA_[1][0] + max_range0, _DNA_[1][1] + max_range1, _DNA_[1][2] + max_range2,
_DNA_[1][3] + max_range3, _DNA_[1][4] + max_range4, _DNA_[1][5] + max_range5,
_DNA_[1][6] + max_range6]
best_num_of_moves = results["num_of_moves"]
print(_DNA_, "GOT SHARPED INTO ->", current_best)
return current_best
'''
generates a move from a theory book.
@:param board
@:return move-> move if exists, 0 otherwise
'''
def theory_book(board):
try:
"""
PLACE YOUR FILE LOCATION HERE->
"""
move = chess.polyglot.MemoryMappedReader \
("computer_data.bin").weighted_choice(board).move
print("move taken from computer book")
return move
except:
return 0
'''
generates an ai move.
@:param board
@:param turn
@:param depth
@:param DNA-> space_M, capture_M, pawn_structure_M,connected_rooks_M,
enemy_king_magnet_M, the_defending_bishop_M, defending_vs_attacking_M
@:return move-> or via minimax algorythm, or via theory book
'''
def ai_move(board, turn, depth, space_M, capture_M, pawn_structure_M,
connected_rooks_M,
enemy_king_magnet_M, the_defending_bishop_M, defending_vs_attacking_M):
move = theory_book(board)
# print(move)
if move == 0: # didn't find a play in the playbook
return minimax(board, turn, depth, space_M, capture_M, pawn_structure_M, connected_rooks_M,
enemy_king_magnet_M, the_defending_bishop_M, defending_vs_attacking_M)
# #
else:
return move
'''
evaluation functions bach.
computes the overall evaluation.
@:param board -> given position
@:param turn -> who's turn is it
@:param evaluation functions-> space_M, capture_M, enemy_king_magnet_M, pawn_structure_M, connected_rooks_M,
defending_vs_attacking_M, the_defending_bishop_M
@:return total_ev-> overall evaluation of given position
'''
def evaluation_functions(board, turn, space_M, capture_M, enemy_king_magnet_M, pawn_structure_M, connected_rooks_M,
defending_vs_attacking_M, the_defending_bishop_M):
# print("--------------------------------")
total_ev = evaluation_by_pieces_location(board)
# print(total_ev)
total_ev = total_ev + (space_M * space(board))
# print(total_ev)
total_ev = total_ev + (capture_M * capture(turn, board))
# print(total_ev)
total_ev = total_ev + (enemy_king_magnet_M * enemy_king_magnet(turn, board))
# print(total_ev)
total_ev = total_ev + (pawn_structure_M * pawn_structure(turn, board))
total_ev = total_ev + (connected_rooks_M * connected_rooks(turn, board))
# print ("--------------")
# print("before", total_ev)
total_ev = total_ev + (defending_vs_attacking_M * defending_vs_attacking(turn, board))
total_ev = total_ev + (the_defending_bishop_M * the_defending_bishop(turn, board))
return total_ev
#####################################################
def minimax(board, turn, depth, space_M, capture_M, pawn_structure_M, connected_rooks_M,
enemy_king_magnet_M, the_defending_bishop_M, defending_vs_attacking_M):
bestMove = chess.Move.null()
bestValue = numpy.NINF - 10
alpha = numpy.NINF
beta = numpy.PINF
for move in board.legal_moves:
board.push(move)
boardValue = -alphabeta(board, turn, -beta, -alpha, depth - 1, space_M, capture_M, pawn_structure_M,
connected_rooks_M,
enemy_king_magnet_M, the_defending_bishop_M, defending_vs_attacking_M)
if boardValue > bestValue:
bestValue = boardValue
bestMove = move
if boardValue > alpha:
alpha = boardValue
board.pop()
return bestMove
#####################################################
#####################################################
def alphabeta(board, turn, alpha, beta, depthleft, space_M, capture_M, pawn_structure_M, connected_rooks_M,
enemy_king_magnet_M, the_defending_bishop_M, defending_vs_attacking_M):
bestscore = numpy.NINF
if depthleft == 0:
return quiesce(board, turn, alpha, beta, space_M, capture_M, pawn_structure_M, connected_rooks_M,
enemy_king_magnet_M, the_defending_bishop_M, defending_vs_attacking_M)
for move in board.legal_moves:
board.push(move)
score = -alphabeta(board, turn, -beta, -alpha, depthleft - 1, space_M, capture_M, pawn_structure_M,
connected_rooks_M, enemy_king_magnet_M, the_defending_bishop_M,
defending_vs_attacking_M)
board.pop()
if score >= beta:
return score
if score > bestscore:
bestscore = score
if score > alpha:
alpha = score
return bestscore
#####################################################
#####################################################
def quiesce(board, turn, alpha, beta, space_M, capture_M, pawn_structure_M, connected_rooks_M,
enemy_king_magnet_M, the_defending_bishop_M, defending_vs_attacking_M):
score = 0
stand_pat = evaluation_functions(board, turn, space_M, capture_M, pawn_structure_M, connected_rooks_M,
enemy_king_magnet_M, the_defending_bishop_M, defending_vs_attacking_M)
if stand_pat >= beta:
return beta
if alpha < stand_pat:
alpha = stand_pat
for move in board.legal_moves:
if board.is_capture(move):
board.push(move)
score = -quiesce(board, turn, -beta, -alpha, space_M, capture_M, pawn_structure_M, connected_rooks_M,
enemy_king_magnet_M, the_defending_bishop_M, defending_vs_attacking_M)
board.pop()
if score >= beta:
return beta
if score > alpha:
alpha = score
return alpha
#####################################################
#####################################################
'''
checks how many pieces alive are there. pieces represented with self value.
the more the better
@:param board
@:param true_or_false -> who's turn is it
@:return total_sum-> total pieces, piece value included
'''
def left_pieces_side(board, true_or_false):
total_sum = 0
total_sum = total_sum + len(board.pieces(1, true_or_false))
total_sum = total_sum + len(board.pieces(2, true_or_false)) * piece_value[2]
total_sum = total_sum + len(board.pieces(3, true_or_false)) * piece_value[3]
total_sum = total_sum + len(board.pieces(4, true_or_false)) * piece_value[4]
total_sum = total_sum + len(board.pieces(5, true_or_false)) * piece_value[5]
return total_sum
'''
checks how many pieces alive are there.
the more the better
@:param board
'''
def left_pieces_total(board):
return left_pieces_side(board, True) \
+ left_pieces_side(board, False)
'''
determines current game phase with respect to number of moves been played
@:param num_of_moves -> number of moves played till current status
@:return 0 , 1 , 2 (phases)
'''
# TODO - to use in more functions
def phase_generator(num_of_moves):
if count_moves(num_of_moves) < 10:
# opening
return 0
if count_moves(num_of_moves) < 25:
# middle_game
return 1
else:
# end_game
return 2
def count_moves(num_of_moves):
return num_of_moves
'''
evaluation function.
checks if the rooks are on the same rank without other pieces in between
connected rooks are valuable
@:param board -> current board
@:param turn -> who's turn is it
@:return count
'''
def connected_rooks(turn, board):
count = 0
for i in board.pieces(chess.ROOK, turn):
for j in board.pieces(chess.ROOK, turn):
if fixed_board[i] % 8 == fixed_board[j] % 8:
count = count + 10
if fixed_board[i] // 8 == fixed_board[j] // 8:
count = count + 10
return count
'''
evaluation function.
checks if a capture has occur
capturing enemy's pieces is valuable
@:param board -> current board
@:param turn -> who's turn is it
@:return valuation
'''
def capture(turn, board):
move = board.pop()
if turn == chess.BLACK:
before = left_pieces_side(board, True)
board.push(move)
after = left_pieces_side(board, True)
else:
before = left_pieces_side(board, False)
board.push(move)
after = left_pieces_side(board, False)
valuation = before - after
return valuation
'''
evaluation function.
checks castle status relate to number of played moves.
at the beginning castle status is valuable
@:param num_of_moves -> moves been played
@:param board -> current board
@:param turn -> who's turn is it
@:return valuation
'''
def keep_castle(num_of_moves, turn, board):
count = 0
# if middle game:
if phase_generator(num_of_moves, board) == 1:
if turn == chess.WHITE:
if board.piece_at(7).piece_type == KING:
if board.piece_at(15).piece_type == PAWN:
count = count + 1
if board.piece_at(16).piece_type == PAWN:
count = count + 1
if board.piece_at(17).piece_type == PAWN:
count = count + 1
if board.piece_at(3).piece_type == KING:
if board.piece_at(10).piece_type == PAWN:
count = count + 1.5
if board.piece_at(11).piece_type == PAWN:
count = count + 1.5
if board.piece_at(9).piece_type == PAWN:
count = count + 1
return count
'''
evaluation function.
haw many sqrs you are attacking. how many own sqrs are defending.
the more the better in both categories.
@:param board -> current board
@:param turn -> who's turn is it
@:return valuation
'''
def defending_vs_attacking(turn, board):
attacking_1 = 0
defending_1 = 0
attacking_2 = 0
defending_2 = 0
for i in range(63):
if board.piece_at(i):
if board.piece_at(i).color == turn: # black
for square in board.attacks(i):
if board.piece_at(square):
if board.piece_at(square).color != turn: # white:
attacking_1 = piece_value[board.piece_at(i).piece_type] - piece_value[
board.piece_at(square).piece_type]
if board.piece_at(square).color == turn:
defending_1 = piece_value[board.piece_at(i).piece_type] - piece_value[
board.piece_at(square).piece_type]
if board.piece_at(i).color != turn: # white
for square in board.attacks(i):
if board.piece_at(square):
if board.piece_at(square).color == turn: # black:
attacking_2 = piece_value[board.piece_at(i).piece_type] - piece_value[
board.piece_at(square).piece_type]
if board.piece_at(square).color != turn:
defending_2 = piece_value[board.piece_at(i).piece_type] - piece_value[
board.piece_at(square).piece_type]
valuation = (attacking_1 - defending_2) + (attacking_2 - defending_1)
return valuation
'''
evaluation function.
the bishop can function as an important protector of the king in some defensive setups.
keeping the bishop in its place is valuable.
@:param board -> current board
@:param turn -> who's turn is it
@:return count-> 100 "points" upon keeping the bishop in its place.
'''
def the_defending_bishop(turn, board):
count = 0
if turn == chess.WHITE:
if board.piece_at(7):
if board.piece_at(7).piece_type == KING:
if board.piece_at(15):
if board.piece_at(15).piece_type == BISHOP and board.piece_at(15).color == turn: # black
count = 100
if turn == chess.BLACK:
if board.piece_at(55):
if board.piece_at(55).piece_type == BISHOP and board.piece_at(55).color != turn:
if board.piece_at(63):
if board.piece_at(63).piece_type == KING: # black
count = -100
return count
'''
evaluation function.
evaluates position by proximity to the enemy's king
the closer the better
@:param board -> current board
@:param turn -> who's turn is it
@:return magnet_rank-> the sum of sqrs between all pieces to the enemy's king
'''
def enemy_king_magnet(turn, board):
king_location = board.king(not turn) # white
letters_king = fixed_board[king_location] % 8
numbers_king = fixed_board[king_location] // 8 + 1
magnet_rank = 0
for i in range(63):
if board.piece_at(i):
if board.piece_at(i).color == turn: # black
letters_piece = fixed_board[i] % 8
numbers_piece = fixed_board[i] // 8 + 1
magnet_rank = magnet_rank + (abs(letters_king - letters_piece) + abs(numbers_king - numbers_piece))
return magnet_rank
'''
evaluation function.
evaluates position by space occupied by pieces
the more space the better.
@:param board -> current board
@:return count-> the attacked sqrs
'''
def space(board):
i = 0
count = 0
while i != 64:
count = count + len(board.attackers(False, i))
i = i + 1
return count
'''
evaluation function.
evaluates position by pawn structure
pawn chains are valuable.
@:param board -> current board
@:param turn -> who's turn is it
@:return eval-> the valuation. positive/negative determined by board.turn
'''
def pawn_structure(turn, board):
count = 0
for i in board.pieces(chess.PAWN, turn):
for j in board.pieces(chess.PAWN, turn):
if abs(fixed_board[j] - fixed_board[i]) == 8:
count = count - 4
if fixed_board[i] % 8 == 0:
if fixed_board[j] == fixed_board[i] + 7 or fixed_board[j] + 1 == fixed_board[i]:
count = count + 2
elif fixed_board[i] % 8 == 1:
if fixed_board[j] == fixed_board[i] + 9 or fixed_board[j] == fixed_board[i] + 1:
count = count + 2
else:
if fixed_board[j] == fixed_board[i] + 1:
count = count + 1
if fixed_board[j] + 1 == fixed_board[i]:
count = count + 1
if fixed_board[j] == fixed_board[i] + 9:
count = count + 2
if fixed_board[j] == fixed_board[i] + 7:
count = count + 2
return count
'''
evaluation function.
evaluates position by pieces location
@:param board -> current board
@:return eval-> the valuation. positive/negative determined by board.turn
'''
def evaluation_by_pieces_location(board):
if board.is_checkmate():
if board.turn:
return numpy.NINF
else:
return numpy.PINF
if board.is_stalemate():
return 0
if board.is_insufficient_material():
return 0
wp = len(board.pieces(chess.PAWN, chess.WHITE))
bp = len(board.pieces(chess.PAWN, chess.BLACK))
wn = len(board.pieces(chess.KNIGHT, chess.WHITE))
bn = len(board.pieces(chess.KNIGHT, chess.BLACK))