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
|
from enum import Enum
from collections import Counter
import math
import sys
path = "./data/words.txt"
if len(sys.argv) == 1:
print(f"using path = {
path}\nif u want to use another path please pass by argument")
else:
path = sys.argv[1]
class LetterState(Enum):
NOT_USED = "NOT_USED"
RIGHT_POSTION = "RIGHT_POSTION"
WRONG_POSITION = "WRONG_POSITION"
NONE = "NONE"
class PatternState(Enum):
GREEN = "GREEN"
YELLOW = "YELLOW"
GRAY = "GRAY"
class Pattern:
def __init__(self, states):
assert (len(states) == 5)
self.states = states
def __repr__(self):
return f"{self.states[0]} {self.states[1]} {self.states[2]} {self.states[3]} {self.states[4]}"
class Letter:
def __init__(self, char, position=-1, state=LetterState.NONE):
self.char = char.upper()
self.position = position
self.state = state
self.possible_positions = [0, 1, 2, 3, 4]
def __repr__(self):
return f"Letter(char='{self.char}', position={self.position}, state={self.state.name})"
class Word:
def __init__(self, word, self_info):
self.word = word
self.self_info = self_info
def __repr__(self):
return f"Word(word={self.word}, info={self.self_info})"
letters = []
for i in range(0, 26):
letters.append(Letter(chr(ord('a') + i)))
# print(letters[i])
words = []
with open(path, 'r') as file:
for word in file:
words.append(Word(word.strip(), 0))
patterns = []
patterns_size = 3**5
for i in range(0, patterns_size):
tmp = i
p = [PatternState.GREEN] * 5
for j in range(0, 5):
if tmp % 3 == 2:
p[j] = PatternState.GREEN
if tmp % 3 == 1:
p[j] = PatternState.YELLOW
if tmp % 3 == 0:
p[j] = PatternState.GRAY
tmp = tmp // 3
patterns.append(Pattern(p))
# print(patterns[i])
############## TUDO INICIALIZADO #################################
# calcular a entropia, e pegar a palavra que da mais informacao
# pi = probabilidade do padrao i dado a palavra x
# -sum(pi * log2(pi))
def get_pattern_given_answer(guess, answer):
pattern = [PatternState.GRAY] * 5
used = [False] * 5
for i in range(5):
if guess[i] == answer[i]:
pattern[i] = PatternState.GREEN
used[i] = True
for i in range(5):
if pattern[i] == PatternState.GREEN:
continue
for j in range(5):
if not used[j] and guess[i] == answer[j]:
pattern[i] = PatternState.YELLOW
used[j] = True
break
return Pattern(pattern)
# p(word | pat) = numero de palavras que fazem o padrao pat se eu colocasse word
# palavras restantes
def calculate_entropy(word):
pattern_freq = Counter()
for answer in words:
pat = get_pattern_given_answer(word.word, answer.word)
pattern_freq[tuple(pat.states)] += 1
entropy = 0
for pat in patterns:
pi = pattern_freq[tuple(pat.states)] / len(words)
if pi == 0:
continue
entropy += pi * math.log2(pi)
return -entropy
def parse_pattern(pattern_input):
ret_pat = [PatternState.GRAY] * 5
for i in range(5):
if (pattern_input[i].upper() == 'G'):
ret_pat[i] = PatternState.GREEN
if (pattern_input[i].upper() == 'Y'):
ret_pat[i] = PatternState.YELLOW
if (pattern_input[i].upper() == 'B'):
ret_pat[i] = PatternState.GRAY
return Pattern(ret_pat)
def filter_words(words, guess, pattern):
ret = []
for w in words:
if (get_pattern_given_answer(guess.word, w.word).states == pattern.states):
ret.append(w)
return ret
all_words = words.copy()
skip_first = True
while (len(words) > 1):
print(f"size: {len(words)}")
# update words
if skip_first:
guess_input = input("Enter your guess word: ").strip().lower()
pattern_input = input("Enter the resulting pattern (G/Y/B): ").strip()
assert (len(pattern_input) == 5)
pattern = parse_pattern(pattern_input)
words = filter_words(words, Word(guess_input, 0), pattern)
assert (len(words) > 0)
for word in words:
entropy = calculate_entropy(word)
word.self_info = entropy
# print(f"word {word.word}, entropy: {entropy}")
for word in all_words:
entropy = calculate_entropy(word)
word.self_info = entropy
words.sort(key=lambda w: w.self_info, reverse=True)
print("Possible answers")
for w in words[:5]:
print(w)
all_words.sort(key=lambda w: w.self_info, reverse=True)
print("All words")
for w in all_words[:5]:
print(w)
skip_first = True
print(words[0])
|
