Spaces:
Running
Running
KAI MAURIN-JONES
commited on
Commit
•
5203d64
1
Parent(s):
7f2f1bd
app updates and data added
Browse files- .DS_Store +0 -0
- README.md +4 -4
- app.py +55 -0
- data/official_words_processed.txt +2309 -0
- data/wordle_humans.csv +40 -0
- wordle_functions.py +1447 -0
.DS_Store
ADDED
Binary file (6.15 kB). View file
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README.md
CHANGED
@@ -1,8 +1,8 @@
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---
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-
title: Wordle Wizard
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-
emoji:
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-
colorFrom:
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colorTo:
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sdk: streamlit
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sdk_version: 1.17.0
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app_file: app.py
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---
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title: Wordle Wizard Collection
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+
emoji: 💻
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colorFrom: green
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colorTo: indigo
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sdk: streamlit
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sdk_version: 1.17.0
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app_file: app.py
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app.py
ADDED
@@ -0,0 +1,55 @@
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import streamlit as st
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# import pandas as pd
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from wordle_functions import *
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official_words = []
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with open("data/official_words_processed.txt", "r", encoding = "utf-8") as f:
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for word in f.read().split("\n"):
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official_words.append(word)
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f.close() # closes connection to file
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print(len(official_words))
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official_words[:5]
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### App stuff
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# to appear above the structured text lines
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st.write('Please enter a starting word and a target word, and click the "Abracadabra" button to solve the puzzle.')
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# st.write('If you would like to randomly generate a puzzle solution, instead, word, and click the "Abracadabra" button to solve the puzzle.\n')
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# user starting word
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starting_word = st.text_input("Enter starting word")
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if len(starting_word) != 5:
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st.write('Please double check and make sure there are only 5 letters in the starting word.\n')
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# user target word
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target_word = st.text_input("Enter target word")
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if len(target_word) != 5:
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st.write('Please double check and make sure there are only 5 letters in the target word.\n')
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# "Submit" button
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# if st.button('Abracadabra') and (len(starting_word) and len(target_word)) == 5:
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if st.button('Abracadabra'):
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if (len(starting_word) and len(target_word)) == 5:
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st.write(wordle_wizard(word_list = official_words, max_guesses = 6,
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guess = starting_word, target = target_word, bias = 'entropy',
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random_guess = False, random_target = False,
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verbose = True, drama = 0, return_stats = False, record = False))
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# if rand_start_word:
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# st.write(wordle_wizard(word_list = official_words, max_guesses = 6,
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# guess = "heart", target = "cloud", bias = 'entropy',
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# random_guess = False, random_target = False,
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# verbose = True, drama = 0, return_stats = False, record = True))
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# if rand_start_word:
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# st.write(wordle_wizard(word_list = official_words, max_guesses = 6,
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# guess = "heart", target = "cloud", bias = 'entropy',
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# random_guess = False, random_target = False,
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# verbose = True, drama = 0, return_stats = False, record = True))
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# st.write('Thank you for your submission!')
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data/official_words_processed.txt
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@@ -0,0 +1,2309 @@
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|
1 |
+
wince
|
2 |
+
thyme
|
3 |
+
mower
|
4 |
+
horde
|
5 |
+
heard
|
6 |
+
tenor
|
7 |
+
zonal
|
8 |
+
parry
|
9 |
+
shied
|
10 |
+
fizzy
|
11 |
+
flush
|
12 |
+
skunk
|
13 |
+
quilt
|
14 |
+
baton
|
15 |
+
cocoa
|
16 |
+
blend
|
17 |
+
haste
|
18 |
+
pansy
|
19 |
+
seize
|
20 |
+
thick
|
21 |
+
marsh
|
22 |
+
metal
|
23 |
+
eagle
|
24 |
+
droit
|
25 |
+
flock
|
26 |
+
legal
|
27 |
+
heart
|
28 |
+
posit
|
29 |
+
neigh
|
30 |
+
gamma
|
31 |
+
rabbi
|
32 |
+
wooer
|
33 |
+
ahead
|
34 |
+
horny
|
35 |
+
flaky
|
36 |
+
knave
|
37 |
+
probe
|
38 |
+
guilt
|
39 |
+
torus
|
40 |
+
berth
|
41 |
+
model
|
42 |
+
input
|
43 |
+
girly
|
44 |
+
tuber
|
45 |
+
booty
|
46 |
+
lurid
|
47 |
+
altar
|
48 |
+
scold
|
49 |
+
chock
|
50 |
+
crush
|
51 |
+
krill
|
52 |
+
toddy
|
53 |
+
rerun
|
54 |
+
poser
|
55 |
+
creme
|
56 |
+
bluer
|
57 |
+
slosh
|
58 |
+
sound
|
59 |
+
chess
|
60 |
+
lobby
|
61 |
+
viper
|
62 |
+
pesky
|
63 |
+
pleat
|
64 |
+
hunky
|
65 |
+
flick
|
66 |
+
lunge
|
67 |
+
cheek
|
68 |
+
forte
|
69 |
+
shady
|
70 |
+
purer
|
71 |
+
boozy
|
72 |
+
snack
|
73 |
+
debut
|
74 |
+
stump
|
75 |
+
enter
|
76 |
+
choir
|
77 |
+
broad
|
78 |
+
quash
|
79 |
+
exalt
|
80 |
+
smell
|
81 |
+
jewel
|
82 |
+
coupe
|
83 |
+
might
|
84 |
+
vinyl
|
85 |
+
sugar
|
86 |
+
gleam
|
87 |
+
deity
|
88 |
+
blame
|
89 |
+
finer
|
90 |
+
safer
|
91 |
+
cutie
|
92 |
+
thumb
|
93 |
+
tutor
|
94 |
+
grand
|
95 |
+
cache
|
96 |
+
camel
|
97 |
+
grace
|
98 |
+
pivot
|
99 |
+
apart
|
100 |
+
vegan
|
101 |
+
sense
|
102 |
+
queen
|
103 |
+
under
|
104 |
+
karma
|
105 |
+
duchy
|
106 |
+
smith
|
107 |
+
amend
|
108 |
+
drill
|
109 |
+
asset
|
110 |
+
awoke
|
111 |
+
aging
|
112 |
+
canon
|
113 |
+
wrong
|
114 |
+
glory
|
115 |
+
adorn
|
116 |
+
shelf
|
117 |
+
repay
|
118 |
+
rayon
|
119 |
+
briny
|
120 |
+
eying
|
121 |
+
unify
|
122 |
+
yield
|
123 |
+
leant
|
124 |
+
cabin
|
125 |
+
merry
|
126 |
+
strap
|
127 |
+
bunny
|
128 |
+
snare
|
129 |
+
jumpy
|
130 |
+
fable
|
131 |
+
beget
|
132 |
+
moody
|
133 |
+
craze
|
134 |
+
unset
|
135 |
+
furor
|
136 |
+
slant
|
137 |
+
sloth
|
138 |
+
brain
|
139 |
+
poppy
|
140 |
+
crump
|
141 |
+
decal
|
142 |
+
drool
|
143 |
+
thank
|
144 |
+
tiger
|
145 |
+
lover
|
146 |
+
crank
|
147 |
+
voter
|
148 |
+
mound
|
149 |
+
slick
|
150 |
+
birch
|
151 |
+
aping
|
152 |
+
impel
|
153 |
+
demur
|
154 |
+
close
|
155 |
+
fudge
|
156 |
+
sonar
|
157 |
+
aloof
|
158 |
+
baron
|
159 |
+
bevel
|
160 |
+
tread
|
161 |
+
iliac
|
162 |
+
scaly
|
163 |
+
rusty
|
164 |
+
frond
|
165 |
+
sedan
|
166 |
+
skulk
|
167 |
+
flail
|
168 |
+
tunic
|
169 |
+
snuff
|
170 |
+
dirty
|
171 |
+
hello
|
172 |
+
stare
|
173 |
+
sloop
|
174 |
+
madly
|
175 |
+
proxy
|
176 |
+
ebony
|
177 |
+
fully
|
178 |
+
omega
|
179 |
+
ghoul
|
180 |
+
woven
|
181 |
+
quite
|
182 |
+
stamp
|
183 |
+
brake
|
184 |
+
natal
|
185 |
+
valet
|
186 |
+
augur
|
187 |
+
shall
|
188 |
+
wrist
|
189 |
+
harpy
|
190 |
+
swoop
|
191 |
+
motel
|
192 |
+
foamy
|
193 |
+
coast
|
194 |
+
yacht
|
195 |
+
lusty
|
196 |
+
there
|
197 |
+
nasal
|
198 |
+
bleep
|
199 |
+
rearm
|
200 |
+
staid
|
201 |
+
tonic
|
202 |
+
cheap
|
203 |
+
attic
|
204 |
+
enemy
|
205 |
+
abode
|
206 |
+
being
|
207 |
+
sheep
|
208 |
+
canoe
|
209 |
+
derby
|
210 |
+
recap
|
211 |
+
merge
|
212 |
+
crypt
|
213 |
+
fatal
|
214 |
+
block
|
215 |
+
pubic
|
216 |
+
disco
|
217 |
+
kitty
|
218 |
+
heavy
|
219 |
+
naive
|
220 |
+
sight
|
221 |
+
threw
|
222 |
+
crawl
|
223 |
+
known
|
224 |
+
torso
|
225 |
+
thief
|
226 |
+
navel
|
227 |
+
slack
|
228 |
+
crowd
|
229 |
+
paint
|
230 |
+
champ
|
231 |
+
butch
|
232 |
+
nutty
|
233 |
+
thigh
|
234 |
+
intro
|
235 |
+
order
|
236 |
+
turbo
|
237 |
+
tacky
|
238 |
+
enema
|
239 |
+
frown
|
240 |
+
slime
|
241 |
+
shear
|
242 |
+
ester
|
243 |
+
organ
|
244 |
+
revel
|
245 |
+
bunch
|
246 |
+
spoof
|
247 |
+
color
|
248 |
+
mango
|
249 |
+
vouch
|
250 |
+
crate
|
251 |
+
scope
|
252 |
+
hinge
|
253 |
+
north
|
254 |
+
tapir
|
255 |
+
plumb
|
256 |
+
folly
|
257 |
+
ionic
|
258 |
+
spawn
|
259 |
+
affix
|
260 |
+
total
|
261 |
+
wedge
|
262 |
+
broom
|
263 |
+
satin
|
264 |
+
ozone
|
265 |
+
polyp
|
266 |
+
tilde
|
267 |
+
faith
|
268 |
+
blitz
|
269 |
+
whole
|
270 |
+
roast
|
271 |
+
anime
|
272 |
+
gusto
|
273 |
+
pluck
|
274 |
+
cavil
|
275 |
+
tipsy
|
276 |
+
newer
|
277 |
+
board
|
278 |
+
nudge
|
279 |
+
twirl
|
280 |
+
lever
|
281 |
+
honey
|
282 |
+
tempo
|
283 |
+
scare
|
284 |
+
semen
|
285 |
+
built
|
286 |
+
chime
|
287 |
+
crust
|
288 |
+
admin
|
289 |
+
diary
|
290 |
+
clear
|
291 |
+
guise
|
292 |
+
cough
|
293 |
+
baker
|
294 |
+
evict
|
295 |
+
moral
|
296 |
+
piper
|
297 |
+
libel
|
298 |
+
sweet
|
299 |
+
wrung
|
300 |
+
after
|
301 |
+
gazer
|
302 |
+
drawn
|
303 |
+
torch
|
304 |
+
lying
|
305 |
+
ethos
|
306 |
+
filth
|
307 |
+
siren
|
308 |
+
flake
|
309 |
+
still
|
310 |
+
knoll
|
311 |
+
udder
|
312 |
+
shard
|
313 |
+
drone
|
314 |
+
sally
|
315 |
+
style
|
316 |
+
hutch
|
317 |
+
stalk
|
318 |
+
loamy
|
319 |
+
relay
|
320 |
+
crude
|
321 |
+
atone
|
322 |
+
relax
|
323 |
+
cover
|
324 |
+
comic
|
325 |
+
blurb
|
326 |
+
point
|
327 |
+
brass
|
328 |
+
jerky
|
329 |
+
equip
|
330 |
+
rinse
|
331 |
+
trait
|
332 |
+
knife
|
333 |
+
since
|
334 |
+
swear
|
335 |
+
sword
|
336 |
+
trope
|
337 |
+
aider
|
338 |
+
vigor
|
339 |
+
youth
|
340 |
+
rebel
|
341 |
+
chart
|
342 |
+
rivet
|
343 |
+
islet
|
344 |
+
rival
|
345 |
+
dodge
|
346 |
+
human
|
347 |
+
gummy
|
348 |
+
preen
|
349 |
+
baste
|
350 |
+
awash
|
351 |
+
inert
|
352 |
+
reach
|
353 |
+
chili
|
354 |
+
carve
|
355 |
+
chant
|
356 |
+
wield
|
357 |
+
floor
|
358 |
+
agree
|
359 |
+
stomp
|
360 |
+
clump
|
361 |
+
suing
|
362 |
+
aptly
|
363 |
+
spare
|
364 |
+
conic
|
365 |
+
shape
|
366 |
+
gouge
|
367 |
+
adopt
|
368 |
+
flash
|
369 |
+
blood
|
370 |
+
grape
|
371 |
+
gross
|
372 |
+
peril
|
373 |
+
chase
|
374 |
+
snarl
|
375 |
+
burst
|
376 |
+
fetch
|
377 |
+
mucky
|
378 |
+
retry
|
379 |
+
shrub
|
380 |
+
shake
|
381 |
+
pesto
|
382 |
+
begun
|
383 |
+
toxic
|
384 |
+
sassy
|
385 |
+
sorry
|
386 |
+
quasi
|
387 |
+
cadet
|
388 |
+
mucus
|
389 |
+
scant
|
390 |
+
moose
|
391 |
+
admit
|
392 |
+
trout
|
393 |
+
upset
|
394 |
+
album
|
395 |
+
burnt
|
396 |
+
dwell
|
397 |
+
lumpy
|
398 |
+
tight
|
399 |
+
civic
|
400 |
+
sewer
|
401 |
+
flier
|
402 |
+
recut
|
403 |
+
inlet
|
404 |
+
syrup
|
405 |
+
scuba
|
406 |
+
abbot
|
407 |
+
brook
|
408 |
+
feign
|
409 |
+
cheat
|
410 |
+
embed
|
411 |
+
furry
|
412 |
+
scent
|
413 |
+
mirth
|
414 |
+
surly
|
415 |
+
bison
|
416 |
+
mambo
|
417 |
+
quell
|
418 |
+
tubal
|
419 |
+
husky
|
420 |
+
plane
|
421 |
+
prowl
|
422 |
+
abled
|
423 |
+
angle
|
424 |
+
later
|
425 |
+
baggy
|
426 |
+
tabby
|
427 |
+
amble
|
428 |
+
cloth
|
429 |
+
wispy
|
430 |
+
rarer
|
431 |
+
blink
|
432 |
+
amuse
|
433 |
+
flute
|
434 |
+
stony
|
435 |
+
plaid
|
436 |
+
epoch
|
437 |
+
truck
|
438 |
+
saute
|
439 |
+
groom
|
440 |
+
sixth
|
441 |
+
chair
|
442 |
+
fruit
|
443 |
+
tying
|
444 |
+
easel
|
445 |
+
humus
|
446 |
+
entry
|
447 |
+
butte
|
448 |
+
lapel
|
449 |
+
eject
|
450 |
+
cramp
|
451 |
+
cumin
|
452 |
+
eking
|
453 |
+
tooth
|
454 |
+
rebus
|
455 |
+
great
|
456 |
+
blind
|
457 |
+
broth
|
458 |
+
ocean
|
459 |
+
stern
|
460 |
+
inner
|
461 |
+
shell
|
462 |
+
timid
|
463 |
+
tonal
|
464 |
+
south
|
465 |
+
hymen
|
466 |
+
drake
|
467 |
+
smite
|
468 |
+
scrap
|
469 |
+
wheat
|
470 |
+
dense
|
471 |
+
labor
|
472 |
+
gipsy
|
473 |
+
sumac
|
474 |
+
issue
|
475 |
+
leggy
|
476 |
+
skiff
|
477 |
+
earth
|
478 |
+
spelt
|
479 |
+
miser
|
480 |
+
smile
|
481 |
+
sushi
|
482 |
+
minty
|
483 |
+
smack
|
484 |
+
maxim
|
485 |
+
taste
|
486 |
+
taffy
|
487 |
+
patio
|
488 |
+
steer
|
489 |
+
party
|
490 |
+
clued
|
491 |
+
genre
|
492 |
+
druid
|
493 |
+
widen
|
494 |
+
remit
|
495 |
+
agile
|
496 |
+
parka
|
497 |
+
dusky
|
498 |
+
savor
|
499 |
+
caper
|
500 |
+
ninny
|
501 |
+
motto
|
502 |
+
foist
|
503 |
+
prune
|
504 |
+
blimp
|
505 |
+
revue
|
506 |
+
ounce
|
507 |
+
grill
|
508 |
+
cruel
|
509 |
+
melee
|
510 |
+
ovoid
|
511 |
+
rifle
|
512 |
+
agent
|
513 |
+
mania
|
514 |
+
blond
|
515 |
+
fixer
|
516 |
+
lemon
|
517 |
+
cater
|
518 |
+
sprig
|
519 |
+
rupee
|
520 |
+
roger
|
521 |
+
weave
|
522 |
+
begin
|
523 |
+
steel
|
524 |
+
pudgy
|
525 |
+
lager
|
526 |
+
chick
|
527 |
+
paste
|
528 |
+
booby
|
529 |
+
ridge
|
530 |
+
prize
|
531 |
+
audit
|
532 |
+
swept
|
533 |
+
meaty
|
534 |
+
siege
|
535 |
+
humor
|
536 |
+
kneel
|
537 |
+
grave
|
538 |
+
lasso
|
539 |
+
comfy
|
540 |
+
below
|
541 |
+
chief
|
542 |
+
tough
|
543 |
+
mercy
|
544 |
+
found
|
545 |
+
dryer
|
546 |
+
valid
|
547 |
+
grail
|
548 |
+
wrest
|
549 |
+
humph
|
550 |
+
nadir
|
551 |
+
awful
|
552 |
+
fiber
|
553 |
+
short
|
554 |
+
exact
|
555 |
+
adage
|
556 |
+
swath
|
557 |
+
gorge
|
558 |
+
slang
|
559 |
+
notch
|
560 |
+
carol
|
561 |
+
alarm
|
562 |
+
marry
|
563 |
+
assay
|
564 |
+
jetty
|
565 |
+
roach
|
566 |
+
edify
|
567 |
+
juice
|
568 |
+
chain
|
569 |
+
gravy
|
570 |
+
shout
|
571 |
+
abate
|
572 |
+
scour
|
573 |
+
elfin
|
574 |
+
rhino
|
575 |
+
defer
|
576 |
+
pried
|
577 |
+
amaze
|
578 |
+
dingy
|
579 |
+
drown
|
580 |
+
arose
|
581 |
+
wager
|
582 |
+
snail
|
583 |
+
antic
|
584 |
+
hurry
|
585 |
+
guppy
|
586 |
+
dozen
|
587 |
+
louse
|
588 |
+
filer
|
589 |
+
swung
|
590 |
+
moldy
|
591 |
+
hyena
|
592 |
+
milky
|
593 |
+
sepia
|
594 |
+
forum
|
595 |
+
fiend
|
596 |
+
outer
|
597 |
+
anode
|
598 |
+
flown
|
599 |
+
stoop
|
600 |
+
cabal
|
601 |
+
cobra
|
602 |
+
dully
|
603 |
+
hardy
|
604 |
+
ensue
|
605 |
+
knelt
|
606 |
+
stool
|
607 |
+
worry
|
608 |
+
slash
|
609 |
+
digit
|
610 |
+
drawl
|
611 |
+
perky
|
612 |
+
glint
|
613 |
+
first
|
614 |
+
dandy
|
615 |
+
coyly
|
616 |
+
scowl
|
617 |
+
tidal
|
618 |
+
raspy
|
619 |
+
stilt
|
620 |
+
dwarf
|
621 |
+
whirl
|
622 |
+
stunt
|
623 |
+
diode
|
624 |
+
hasty
|
625 |
+
glaze
|
626 |
+
storm
|
627 |
+
scary
|
628 |
+
salve
|
629 |
+
banal
|
630 |
+
bloke
|
631 |
+
right
|
632 |
+
brine
|
633 |
+
biddy
|
634 |
+
comet
|
635 |
+
erode
|
636 |
+
quirk
|
637 |
+
shone
|
638 |
+
venue
|
639 |
+
fever
|
640 |
+
start
|
641 |
+
suave
|
642 |
+
stave
|
643 |
+
rodeo
|
644 |
+
solve
|
645 |
+
parer
|
646 |
+
juicy
|
647 |
+
occur
|
648 |
+
scene
|
649 |
+
above
|
650 |
+
puppy
|
651 |
+
surer
|
652 |
+
dimly
|
653 |
+
imply
|
654 |
+
elite
|
655 |
+
arise
|
656 |
+
drunk
|
657 |
+
react
|
658 |
+
hatch
|
659 |
+
leach
|
660 |
+
smelt
|
661 |
+
focal
|
662 |
+
dough
|
663 |
+
pinto
|
664 |
+
jazzy
|
665 |
+
otter
|
666 |
+
plait
|
667 |
+
reply
|
668 |
+
flunk
|
669 |
+
irony
|
670 |
+
manga
|
671 |
+
freer
|
672 |
+
spiel
|
673 |
+
wharf
|
674 |
+
these
|
675 |
+
retch
|
676 |
+
third
|
677 |
+
whine
|
678 |
+
eight
|
679 |
+
nerdy
|
680 |
+
hence
|
681 |
+
worst
|
682 |
+
uncle
|
683 |
+
motor
|
684 |
+
flyer
|
685 |
+
curse
|
686 |
+
askew
|
687 |
+
strut
|
688 |
+
wheel
|
689 |
+
lingo
|
690 |
+
sadly
|
691 |
+
goner
|
692 |
+
poise
|
693 |
+
shove
|
694 |
+
buddy
|
695 |
+
spell
|
696 |
+
awake
|
697 |
+
midge
|
698 |
+
crier
|
699 |
+
drier
|
700 |
+
tango
|
701 |
+
grout
|
702 |
+
stunk
|
703 |
+
purge
|
704 |
+
lymph
|
705 |
+
lucid
|
706 |
+
sheer
|
707 |
+
fjord
|
708 |
+
dowry
|
709 |
+
brink
|
710 |
+
liege
|
711 |
+
loose
|
712 |
+
posse
|
713 |
+
prank
|
714 |
+
movie
|
715 |
+
shorn
|
716 |
+
place
|
717 |
+
bless
|
718 |
+
unmet
|
719 |
+
elope
|
720 |
+
shade
|
721 |
+
bylaw
|
722 |
+
hedge
|
723 |
+
bicep
|
724 |
+
uncut
|
725 |
+
fluke
|
726 |
+
month
|
727 |
+
space
|
728 |
+
speck
|
729 |
+
denim
|
730 |
+
chaos
|
731 |
+
civil
|
732 |
+
avoid
|
733 |
+
dryly
|
734 |
+
brand
|
735 |
+
fence
|
736 |
+
chirp
|
737 |
+
daddy
|
738 |
+
exist
|
739 |
+
cluck
|
740 |
+
amass
|
741 |
+
perch
|
742 |
+
spiny
|
743 |
+
curvy
|
744 |
+
front
|
745 |
+
thong
|
746 |
+
fifty
|
747 |
+
borax
|
748 |
+
noble
|
749 |
+
curio
|
750 |
+
death
|
751 |
+
bough
|
752 |
+
limbo
|
753 |
+
bathe
|
754 |
+
timer
|
755 |
+
rugby
|
756 |
+
vault
|
757 |
+
panel
|
758 |
+
giddy
|
759 |
+
learn
|
760 |
+
spend
|
761 |
+
retro
|
762 |
+
leery
|
763 |
+
nasty
|
764 |
+
bitty
|
765 |
+
women
|
766 |
+
ranch
|
767 |
+
tamer
|
768 |
+
gauge
|
769 |
+
thump
|
770 |
+
booth
|
771 |
+
elder
|
772 |
+
devil
|
773 |
+
myrrh
|
774 |
+
geese
|
775 |
+
grass
|
776 |
+
trove
|
777 |
+
pause
|
778 |
+
molar
|
779 |
+
octal
|
780 |
+
brawl
|
781 |
+
email
|
782 |
+
build
|
783 |
+
early
|
784 |
+
guess
|
785 |
+
scone
|
786 |
+
weird
|
787 |
+
cress
|
788 |
+
chafe
|
789 |
+
noisy
|
790 |
+
guard
|
791 |
+
cleft
|
792 |
+
paler
|
793 |
+
brawn
|
794 |
+
inlay
|
795 |
+
robot
|
796 |
+
price
|
797 |
+
vying
|
798 |
+
climb
|
799 |
+
brute
|
800 |
+
liver
|
801 |
+
serve
|
802 |
+
emcee
|
803 |
+
topaz
|
804 |
+
cheer
|
805 |
+
alibi
|
806 |
+
quake
|
807 |
+
chard
|
808 |
+
motif
|
809 |
+
flour
|
810 |
+
joint
|
811 |
+
punch
|
812 |
+
facet
|
813 |
+
manor
|
814 |
+
pence
|
815 |
+
bleat
|
816 |
+
vowel
|
817 |
+
froze
|
818 |
+
cigar
|
819 |
+
trump
|
820 |
+
overt
|
821 |
+
least
|
822 |
+
final
|
823 |
+
sever
|
824 |
+
aloft
|
825 |
+
idler
|
826 |
+
roomy
|
827 |
+
rebar
|
828 |
+
inane
|
829 |
+
joist
|
830 |
+
elect
|
831 |
+
stung
|
832 |
+
surge
|
833 |
+
primo
|
834 |
+
nicer
|
835 |
+
river
|
836 |
+
feral
|
837 |
+
scrum
|
838 |
+
dizzy
|
839 |
+
crave
|
840 |
+
dross
|
841 |
+
video
|
842 |
+
belie
|
843 |
+
spoon
|
844 |
+
mayor
|
845 |
+
ramen
|
846 |
+
vocal
|
847 |
+
spool
|
848 |
+
curly
|
849 |
+
glove
|
850 |
+
equal
|
851 |
+
terra
|
852 |
+
murky
|
853 |
+
check
|
854 |
+
ripen
|
855 |
+
white
|
856 |
+
proof
|
857 |
+
belch
|
858 |
+
began
|
859 |
+
rural
|
860 |
+
query
|
861 |
+
vague
|
862 |
+
dance
|
863 |
+
chose
|
864 |
+
windy
|
865 |
+
gland
|
866 |
+
maize
|
867 |
+
viral
|
868 |
+
burly
|
869 |
+
crisp
|
870 |
+
pinky
|
871 |
+
slunk
|
872 |
+
wring
|
873 |
+
verge
|
874 |
+
pitch
|
875 |
+
graph
|
876 |
+
trace
|
877 |
+
pixel
|
878 |
+
cried
|
879 |
+
stove
|
880 |
+
slush
|
881 |
+
crimp
|
882 |
+
lumen
|
883 |
+
brisk
|
884 |
+
heave
|
885 |
+
tepid
|
886 |
+
reset
|
887 |
+
flank
|
888 |
+
moist
|
889 |
+
totem
|
890 |
+
fussy
|
891 |
+
blade
|
892 |
+
circa
|
893 |
+
apple
|
894 |
+
night
|
895 |
+
spoke
|
896 |
+
rover
|
897 |
+
oxide
|
898 |
+
scalp
|
899 |
+
rumba
|
900 |
+
whiff
|
901 |
+
aunty
|
902 |
+
sauce
|
903 |
+
often
|
904 |
+
rowdy
|
905 |
+
prime
|
906 |
+
union
|
907 |
+
downy
|
908 |
+
apron
|
909 |
+
wrath
|
910 |
+
ladle
|
911 |
+
beard
|
912 |
+
stead
|
913 |
+
tardy
|
914 |
+
sixty
|
915 |
+
slain
|
916 |
+
glare
|
917 |
+
yeast
|
918 |
+
terse
|
919 |
+
vicar
|
920 |
+
hoard
|
921 |
+
bride
|
922 |
+
vigil
|
923 |
+
tasty
|
924 |
+
utter
|
925 |
+
mossy
|
926 |
+
spiky
|
927 |
+
boast
|
928 |
+
swami
|
929 |
+
brace
|
930 |
+
until
|
931 |
+
woozy
|
932 |
+
shirk
|
933 |
+
pithy
|
934 |
+
depth
|
935 |
+
giver
|
936 |
+
sonic
|
937 |
+
snake
|
938 |
+
field
|
939 |
+
sheik
|
940 |
+
leafy
|
941 |
+
offer
|
942 |
+
slimy
|
943 |
+
stint
|
944 |
+
ruddy
|
945 |
+
taper
|
946 |
+
kinky
|
947 |
+
stoke
|
948 |
+
sappy
|
949 |
+
sweep
|
950 |
+
smock
|
951 |
+
humid
|
952 |
+
steak
|
953 |
+
leech
|
954 |
+
gonad
|
955 |
+
leaky
|
956 |
+
shuck
|
957 |
+
route
|
958 |
+
piano
|
959 |
+
umbra
|
960 |
+
penny
|
961 |
+
kebab
|
962 |
+
money
|
963 |
+
betel
|
964 |
+
unwed
|
965 |
+
beast
|
966 |
+
cinch
|
967 |
+
snuck
|
968 |
+
maker
|
969 |
+
sheet
|
970 |
+
fried
|
971 |
+
fiery
|
972 |
+
latte
|
973 |
+
bliss
|
974 |
+
debit
|
975 |
+
grasp
|
976 |
+
mulch
|
977 |
+
spied
|
978 |
+
tease
|
979 |
+
judge
|
980 |
+
corny
|
981 |
+
write
|
982 |
+
sooth
|
983 |
+
trick
|
984 |
+
clean
|
985 |
+
cubic
|
986 |
+
fetid
|
987 |
+
skimp
|
988 |
+
eerie
|
989 |
+
prick
|
990 |
+
piety
|
991 |
+
sport
|
992 |
+
vital
|
993 |
+
train
|
994 |
+
lefty
|
995 |
+
hotel
|
996 |
+
micro
|
997 |
+
graft
|
998 |
+
spear
|
999 |
+
opium
|
1000 |
+
tenth
|
1001 |
+
catch
|
1002 |
+
waver
|
1003 |
+
outdo
|
1004 |
+
bravo
|
1005 |
+
delve
|
1006 |
+
craft
|
1007 |
+
tumor
|
1008 |
+
carat
|
1009 |
+
slate
|
1010 |
+
again
|
1011 |
+
liner
|
1012 |
+
crime
|
1013 |
+
baler
|
1014 |
+
hound
|
1015 |
+
macaw
|
1016 |
+
drain
|
1017 |
+
rainy
|
1018 |
+
store
|
1019 |
+
could
|
1020 |
+
hotly
|
1021 |
+
morph
|
1022 |
+
snide
|
1023 |
+
meant
|
1024 |
+
gamut
|
1025 |
+
daisy
|
1026 |
+
teach
|
1027 |
+
boxer
|
1028 |
+
shoal
|
1029 |
+
crony
|
1030 |
+
ulcer
|
1031 |
+
sandy
|
1032 |
+
squat
|
1033 |
+
payee
|
1034 |
+
skull
|
1035 |
+
villa
|
1036 |
+
class
|
1037 |
+
nerve
|
1038 |
+
porch
|
1039 |
+
scram
|
1040 |
+
wacky
|
1041 |
+
undue
|
1042 |
+
jumbo
|
1043 |
+
satyr
|
1044 |
+
aloud
|
1045 |
+
raven
|
1046 |
+
detox
|
1047 |
+
mason
|
1048 |
+
taker
|
1049 |
+
boule
|
1050 |
+
croup
|
1051 |
+
junto
|
1052 |
+
pupil
|
1053 |
+
slurp
|
1054 |
+
belle
|
1055 |
+
scoff
|
1056 |
+
track
|
1057 |
+
trail
|
1058 |
+
polar
|
1059 |
+
lucky
|
1060 |
+
robin
|
1061 |
+
major
|
1062 |
+
snaky
|
1063 |
+
roost
|
1064 |
+
stoic
|
1065 |
+
corer
|
1066 |
+
blunt
|
1067 |
+
group
|
1068 |
+
gayly
|
1069 |
+
blaze
|
1070 |
+
truer
|
1071 |
+
winch
|
1072 |
+
wreck
|
1073 |
+
going
|
1074 |
+
pinch
|
1075 |
+
covey
|
1076 |
+
plier
|
1077 |
+
spicy
|
1078 |
+
bench
|
1079 |
+
caste
|
1080 |
+
cello
|
1081 |
+
spike
|
1082 |
+
polka
|
1083 |
+
ratty
|
1084 |
+
lunch
|
1085 |
+
serum
|
1086 |
+
piece
|
1087 |
+
royal
|
1088 |
+
world
|
1089 |
+
evoke
|
1090 |
+
algae
|
1091 |
+
adapt
|
1092 |
+
shine
|
1093 |
+
mural
|
1094 |
+
gooey
|
1095 |
+
unfit
|
1096 |
+
kiosk
|
1097 |
+
icing
|
1098 |
+
spill
|
1099 |
+
shore
|
1100 |
+
helix
|
1101 |
+
cross
|
1102 |
+
gaffe
|
1103 |
+
waltz
|
1104 |
+
throb
|
1105 |
+
would
|
1106 |
+
piney
|
1107 |
+
quote
|
1108 |
+
snipe
|
1109 |
+
annoy
|
1110 |
+
wiser
|
1111 |
+
whisk
|
1112 |
+
slept
|
1113 |
+
twist
|
1114 |
+
epoxy
|
1115 |
+
covet
|
1116 |
+
fuzzy
|
1117 |
+
frame
|
1118 |
+
harry
|
1119 |
+
pique
|
1120 |
+
skier
|
1121 |
+
ovine
|
1122 |
+
vomit
|
1123 |
+
octet
|
1124 |
+
rocky
|
1125 |
+
drank
|
1126 |
+
handy
|
1127 |
+
ideal
|
1128 |
+
bushy
|
1129 |
+
shame
|
1130 |
+
sling
|
1131 |
+
sneak
|
1132 |
+
peach
|
1133 |
+
flare
|
1134 |
+
swash
|
1135 |
+
outgo
|
1136 |
+
trunk
|
1137 |
+
stiff
|
1138 |
+
basic
|
1139 |
+
dumpy
|
1140 |
+
rebut
|
1141 |
+
shale
|
1142 |
+
joust
|
1143 |
+
binge
|
1144 |
+
plain
|
1145 |
+
shire
|
1146 |
+
sieve
|
1147 |
+
titan
|
1148 |
+
gavel
|
1149 |
+
wafer
|
1150 |
+
freed
|
1151 |
+
refit
|
1152 |
+
putty
|
1153 |
+
munch
|
1154 |
+
madam
|
1155 |
+
poker
|
1156 |
+
gloss
|
1157 |
+
blurt
|
1158 |
+
stack
|
1159 |
+
shank
|
1160 |
+
layer
|
1161 |
+
testy
|
1162 |
+
oaken
|
1163 |
+
verso
|
1164 |
+
fault
|
1165 |
+
holly
|
1166 |
+
risen
|
1167 |
+
juror
|
1168 |
+
dopey
|
1169 |
+
width
|
1170 |
+
wreak
|
1171 |
+
theme
|
1172 |
+
agony
|
1173 |
+
snout
|
1174 |
+
lathe
|
1175 |
+
trust
|
1176 |
+
pizza
|
1177 |
+
voila
|
1178 |
+
decay
|
1179 |
+
relic
|
1180 |
+
gourd
|
1181 |
+
ficus
|
1182 |
+
alone
|
1183 |
+
viola
|
1184 |
+
swill
|
1185 |
+
creek
|
1186 |
+
stood
|
1187 |
+
guest
|
1188 |
+
moron
|
1189 |
+
apply
|
1190 |
+
mushy
|
1191 |
+
ratio
|
1192 |
+
chuck
|
1193 |
+
fetus
|
1194 |
+
proud
|
1195 |
+
droop
|
1196 |
+
koala
|
1197 |
+
whose
|
1198 |
+
pulse
|
1199 |
+
teddy
|
1200 |
+
slice
|
1201 |
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beach
|
1202 |
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fluff
|
1203 |
+
brash
|
1204 |
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serif
|
1205 |
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batch
|
1206 |
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canal
|
1207 |
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plume
|
1208 |
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dilly
|
1209 |
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trial
|
1210 |
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braid
|
1211 |
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naval
|
1212 |
+
treat
|
1213 |
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shawl
|
1214 |
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claim
|
1215 |
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finch
|
1216 |
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imbue
|
1217 |
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arbor
|
1218 |
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randy
|
1219 |
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taken
|
1220 |
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clasp
|
1221 |
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macho
|
1222 |
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sniff
|
1223 |
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brown
|
1224 |
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ascot
|
1225 |
+
clout
|
1226 |
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lupus
|
1227 |
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bossy
|
1228 |
+
pulpy
|
1229 |
+
biome
|
1230 |
+
tripe
|
1231 |
+
chump
|
1232 |
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verse
|
1233 |
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swift
|
1234 |
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pedal
|
1235 |
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smear
|
1236 |
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abbey
|
1237 |
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azure
|
1238 |
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dally
|
1239 |
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ditto
|
1240 |
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haute
|
1241 |
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hefty
|
1242 |
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alert
|
1243 |
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fugue
|
1244 |
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curve
|
1245 |
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droll
|
1246 |
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while
|
1247 |
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spout
|
1248 |
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recur
|
1249 |
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basil
|
1250 |
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drive
|
1251 |
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flora
|
1252 |
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fungi
|
1253 |
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payer
|
1254 |
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bully
|
1255 |
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vogue
|
1256 |
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amber
|
1257 |
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drove
|
1258 |
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edict
|
1259 |
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zesty
|
1260 |
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dunce
|
1261 |
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stork
|
1262 |
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valor
|
1263 |
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filet
|
1264 |
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heron
|
1265 |
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grade
|
1266 |
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mimic
|
1267 |
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actor
|
1268 |
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cacao
|
1269 |
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fanny
|
1270 |
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hilly
|
1271 |
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quill
|
1272 |
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watch
|
1273 |
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plead
|
1274 |
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maybe
|
1275 |
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fauna
|
1276 |
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magic
|
1277 |
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couch
|
1278 |
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loyal
|
1279 |
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mecca
|
1280 |
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bawdy
|
1281 |
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swine
|
1282 |
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enjoy
|
1283 |
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hairy
|
1284 |
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grunt
|
1285 |
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brave
|
1286 |
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idyll
|
1287 |
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alpha
|
1288 |
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dowel
|
1289 |
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evade
|
1290 |
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rajah
|
1291 |
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exile
|
1292 |
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rogue
|
1293 |
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adobe
|
1294 |
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eater
|
1295 |
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wrote
|
1296 |
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other
|
1297 |
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foyer
|
1298 |
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amply
|
1299 |
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havoc
|
1300 |
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elate
|
1301 |
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artsy
|
1302 |
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study
|
1303 |
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privy
|
1304 |
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beret
|
1305 |
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throw
|
1306 |
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deter
|
1307 |
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allot
|
1308 |
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tract
|
1309 |
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which
|
1310 |
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bloat
|
1311 |
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plate
|
1312 |
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fleck
|
1313 |
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solid
|
1314 |
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frank
|
1315 |
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diver
|
1316 |
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fraud
|
1317 |
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dolly
|
1318 |
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truce
|
1319 |
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harsh
|
1320 |
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squib
|
1321 |
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tried
|
1322 |
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delay
|
1323 |
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bowel
|
1324 |
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freak
|
1325 |
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quoth
|
1326 |
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balmy
|
1327 |
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gumbo
|
1328 |
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waive
|
1329 |
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liken
|
1330 |
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frill
|
1331 |
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incur
|
1332 |
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jiffy
|
1333 |
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weigh
|
1334 |
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batty
|
1335 |
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bleak
|
1336 |
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welsh
|
1337 |
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their
|
1338 |
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merit
|
1339 |
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leper
|
1340 |
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rally
|
1341 |
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sworn
|
1342 |
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virus
|
1343 |
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opine
|
1344 |
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thorn
|
1345 |
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jelly
|
1346 |
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funny
|
1347 |
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happy
|
1348 |
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flint
|
1349 |
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vapor
|
1350 |
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array
|
1351 |
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lodge
|
1352 |
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brood
|
1353 |
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gulch
|
1354 |
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feast
|
1355 |
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talon
|
1356 |
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renal
|
1357 |
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frock
|
1358 |
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sulky
|
1359 |
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green
|
1360 |
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spice
|
1361 |
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tibia
|
1362 |
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idiom
|
1363 |
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flame
|
1364 |
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shock
|
1365 |
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crepe
|
1366 |
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phase
|
1367 |
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spark
|
1368 |
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grind
|
1369 |
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haven
|
1370 |
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forge
|
1371 |
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media
|
1372 |
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salad
|
1373 |
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bused
|
1374 |
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abuse
|
1375 |
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seven
|
1376 |
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large
|
1377 |
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mangy
|
1378 |
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round
|
1379 |
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swish
|
1380 |
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stole
|
1381 |
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gaudy
|
1382 |
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fleet
|
1383 |
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tryst
|
1384 |
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elegy
|
1385 |
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score
|
1386 |
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petal
|
1387 |
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tulip
|
1388 |
+
sigma
|
1389 |
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armor
|
1390 |
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segue
|
1391 |
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fancy
|
1392 |
+
dogma
|
1393 |
+
mince
|
1394 |
+
pagan
|
1395 |
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angst
|
1396 |
+
slump
|
1397 |
+
quest
|
1398 |
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state
|
1399 |
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hater
|
1400 |
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fight
|
1401 |
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china
|
1402 |
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olden
|
1403 |
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unite
|
1404 |
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saner
|
1405 |
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paper
|
1406 |
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heist
|
1407 |
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stark
|
1408 |
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clang
|
1409 |
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gamer
|
1410 |
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qualm
|
1411 |
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dying
|
1412 |
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eaten
|
1413 |
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gloom
|
1414 |
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smoke
|
1415 |
+
hitch
|
1416 |
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quark
|
1417 |
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aphid
|
1418 |
+
fairy
|
1419 |
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anvil
|
1420 |
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curry
|
1421 |
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decor
|
1422 |
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minus
|
1423 |
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quota
|
1424 |
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goose
|
1425 |
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radar
|
1426 |
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lance
|
1427 |
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reign
|
1428 |
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sleet
|
1429 |
+
suite
|
1430 |
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value
|
1431 |
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click
|
1432 |
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tower
|
1433 |
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stone
|
1434 |
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forth
|
1435 |
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gnome
|
1436 |
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boney
|
1437 |
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ninth
|
1438 |
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obese
|
1439 |
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rotor
|
1440 |
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mamma
|
1441 |
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along
|
1442 |
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knead
|
1443 |
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ought
|
1444 |
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unity
|
1445 |
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foray
|
1446 |
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phony
|
1447 |
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clank
|
1448 |
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gecko
|
1449 |
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quick
|
1450 |
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cynic
|
1451 |
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silly
|
1452 |
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riser
|
1453 |
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spire
|
1454 |
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irate
|
1455 |
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manic
|
1456 |
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groan
|
1457 |
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noose
|
1458 |
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badge
|
1459 |
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spite
|
1460 |
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plush
|
1461 |
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topic
|
1462 |
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chasm
|
1463 |
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slide
|
1464 |
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cabby
|
1465 |
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tweak
|
1466 |
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optic
|
1467 |
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genie
|
1468 |
+
grove
|
1469 |
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wryly
|
1470 |
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march
|
1471 |
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refer
|
1472 |
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caulk
|
1473 |
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seedy
|
1474 |
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bagel
|
1475 |
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scrub
|
1476 |
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ready
|
1477 |
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quack
|
1478 |
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catty
|
1479 |
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hussy
|
1480 |
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cower
|
1481 |
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wound
|
1482 |
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clink
|
1483 |
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child
|
1484 |
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crass
|
1485 |
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slink
|
1486 |
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float
|
1487 |
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ample
|
1488 |
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spray
|
1489 |
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geeky
|
1490 |
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banjo
|
1491 |
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loopy
|
1492 |
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glade
|
1493 |
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chill
|
1494 |
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lilac
|
1495 |
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stand
|
1496 |
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kayak
|
1497 |
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warty
|
1498 |
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shoot
|
1499 |
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print
|
1500 |
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bezel
|
1501 |
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cream
|
1502 |
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inter
|
1503 |
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goody
|
1504 |
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sooty
|
1505 |
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clack
|
1506 |
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saucy
|
1507 |
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matey
|
1508 |
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title
|
1509 |
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spoil
|
1510 |
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stout
|
1511 |
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canny
|
1512 |
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prude
|
1513 |
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crazy
|
1514 |
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widow
|
1515 |
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picky
|
1516 |
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truth
|
1517 |
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leapt
|
1518 |
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rouge
|
1519 |
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melon
|
1520 |
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vivid
|
1521 |
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every
|
1522 |
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acute
|
1523 |
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quiet
|
1524 |
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wider
|
1525 |
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spine
|
1526 |
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vodka
|
1527 |
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small
|
1528 |
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sully
|
1529 |
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bread
|
1530 |
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graze
|
1531 |
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mauve
|
1532 |
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larva
|
1533 |
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palsy
|
1534 |
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globe
|
1535 |
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bland
|
1536 |
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coral
|
1537 |
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super
|
1538 |
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befit
|
1539 |
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homer
|
1540 |
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flume
|
1541 |
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quail
|
1542 |
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novel
|
1543 |
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chore
|
1544 |
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crone
|
1545 |
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coach
|
1546 |
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ashen
|
1547 |
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tenet
|
1548 |
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grime
|
1549 |
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steep
|
1550 |
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truss
|
1551 |
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macro
|
1552 |
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snore
|
1553 |
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queue
|
1554 |
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lurch
|
1555 |
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twang
|
1556 |
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comma
|
1557 |
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focus
|
1558 |
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skate
|
1559 |
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black
|
1560 |
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token
|
1561 |
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zebra
|
1562 |
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drink
|
1563 |
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eager
|
1564 |
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gruff
|
1565 |
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cleat
|
1566 |
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apnea
|
1567 |
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dream
|
1568 |
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tepee
|
1569 |
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habit
|
1570 |
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fling
|
1571 |
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tweet
|
1572 |
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leash
|
1573 |
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snort
|
1574 |
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house
|
1575 |
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donor
|
1576 |
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hover
|
1577 |
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haunt
|
1578 |
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smash
|
1579 |
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infer
|
1580 |
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hydro
|
1581 |
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swing
|
1582 |
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fifth
|
1583 |
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eclat
|
1584 |
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reedy
|
1585 |
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onion
|
1586 |
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shush
|
1587 |
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giant
|
1588 |
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bongo
|
1589 |
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cling
|
1590 |
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cyber
|
1591 |
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flesh
|
1592 |
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dingo
|
1593 |
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stain
|
1594 |
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pearl
|
1595 |
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vixen
|
1596 |
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bilge
|
1597 |
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crown
|
1598 |
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prone
|
1599 |
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tribe
|
1600 |
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flood
|
1601 |
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taunt
|
1602 |
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dummy
|
1603 |
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weary
|
1604 |
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ethic
|
1605 |
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award
|
1606 |
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nomad
|
1607 |
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flair
|
1608 |
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ghost
|
1609 |
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pilot
|
1610 |
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beset
|
1611 |
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drift
|
1612 |
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crept
|
1613 |
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break
|
1614 |
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silky
|
1615 |
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breed
|
1616 |
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abort
|
1617 |
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twine
|
1618 |
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trash
|
1619 |
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towel
|
1620 |
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foggy
|
1621 |
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medic
|
1622 |
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blank
|
1623 |
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wrack
|
1624 |
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scoop
|
1625 |
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croak
|
1626 |
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farce
|
1627 |
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shrew
|
1628 |
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locus
|
1629 |
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lofty
|
1630 |
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smart
|
1631 |
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truly
|
1632 |
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tally
|
1633 |
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sharp
|
1634 |
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ennui
|
1635 |
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demon
|
1636 |
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growl
|
1637 |
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stank
|
1638 |
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clung
|
1639 |
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regal
|
1640 |
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alloy
|
1641 |
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femur
|
1642 |
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spank
|
1643 |
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doubt
|
1644 |
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sober
|
1645 |
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filmy
|
1646 |
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spent
|
1647 |
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daily
|
1648 |
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charm
|
1649 |
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guild
|
1650 |
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touch
|
1651 |
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nymph
|
1652 |
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vapid
|
1653 |
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inept
|
1654 |
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piggy
|
1655 |
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arson
|
1656 |
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llama
|
1657 |
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lemur
|
1658 |
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stair
|
1659 |
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photo
|
1660 |
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phone
|
1661 |
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young
|
1662 |
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deign
|
1663 |
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those
|
1664 |
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light
|
1665 |
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stock
|
1666 |
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chaff
|
1667 |
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usual
|
1668 |
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tense
|
1669 |
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hyper
|
1670 |
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gusty
|
1671 |
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slung
|
1672 |
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buxom
|
1673 |
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tawny
|
1674 |
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bacon
|
1675 |
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dried
|
1676 |
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worth
|
1677 |
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basis
|
1678 |
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using
|
1679 |
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speak
|
1680 |
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penne
|
1681 |
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scree
|
1682 |
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willy
|
1683 |
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visit
|
1684 |
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raise
|
1685 |
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hoist
|
1686 |
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clone
|
1687 |
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utile
|
1688 |
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nurse
|
1689 |
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usher
|
1690 |
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plied
|
1691 |
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ankle
|
1692 |
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radii
|
1693 |
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tulle
|
1694 |
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pasty
|
1695 |
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lithe
|
1696 |
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sheen
|
1697 |
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barge
|
1698 |
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crick
|
1699 |
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aside
|
1700 |
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gaily
|
1701 |
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kneed
|
1702 |
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copse
|
1703 |
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tonga
|
1704 |
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swoon
|
1705 |
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bleed
|
1706 |
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index
|
1707 |
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basal
|
1708 |
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shaft
|
1709 |
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dairy
|
1710 |
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aback
|
1711 |
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swirl
|
1712 |
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scale
|
1713 |
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inbox
|
1714 |
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allow
|
1715 |
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ombre
|
1716 |
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churn
|
1717 |
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choke
|
1718 |
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bulky
|
1719 |
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ultra
|
1720 |
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smoky
|
1721 |
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pygmy
|
1722 |
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bribe
|
1723 |
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afoul
|
1724 |
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fluid
|
1725 |
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bugle
|
1726 |
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avail
|
1727 |
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kappa
|
1728 |
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borne
|
1729 |
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orbit
|
1730 |
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sinew
|
1731 |
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acorn
|
1732 |
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envoy
|
1733 |
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glyph
|
1734 |
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cameo
|
1735 |
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three
|
1736 |
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tithe
|
1737 |
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cloak
|
1738 |
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fella
|
1739 |
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amiss
|
1740 |
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mount
|
1741 |
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blare
|
1742 |
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delta
|
1743 |
+
blush
|
1744 |
+
saint
|
1745 |
+
given
|
1746 |
+
whack
|
1747 |
+
deuce
|
1748 |
+
heath
|
1749 |
+
shift
|
1750 |
+
bayou
|
1751 |
+
stuff
|
1752 |
+
joker
|
1753 |
+
court
|
1754 |
+
pride
|
1755 |
+
plant
|
1756 |
+
pushy
|
1757 |
+
flack
|
1758 |
+
wagon
|
1759 |
+
split
|
1760 |
+
verve
|
1761 |
+
scion
|
1762 |
+
swarm
|
1763 |
+
bulge
|
1764 |
+
mammy
|
1765 |
+
prism
|
1766 |
+
plump
|
1767 |
+
ditty
|
1768 |
+
decry
|
1769 |
+
salon
|
1770 |
+
shack
|
1771 |
+
woken
|
1772 |
+
align
|
1773 |
+
error
|
1774 |
+
frost
|
1775 |
+
forty
|
1776 |
+
synod
|
1777 |
+
reuse
|
1778 |
+
hobby
|
1779 |
+
nobly
|
1780 |
+
fishy
|
1781 |
+
dusty
|
1782 |
+
clamp
|
1783 |
+
pound
|
1784 |
+
dutch
|
1785 |
+
colon
|
1786 |
+
niece
|
1787 |
+
whiny
|
1788 |
+
bluff
|
1789 |
+
prose
|
1790 |
+
grief
|
1791 |
+
ember
|
1792 |
+
chide
|
1793 |
+
tatty
|
1794 |
+
cider
|
1795 |
+
scald
|
1796 |
+
dread
|
1797 |
+
shave
|
1798 |
+
cagey
|
1799 |
+
petty
|
1800 |
+
egret
|
1801 |
+
witty
|
1802 |
+
golem
|
1803 |
+
anger
|
1804 |
+
idiot
|
1805 |
+
scarf
|
1806 |
+
audio
|
1807 |
+
boost
|
1808 |
+
extra
|
1809 |
+
force
|
1810 |
+
lanky
|
1811 |
+
strip
|
1812 |
+
owner
|
1813 |
+
brush
|
1814 |
+
belly
|
1815 |
+
budge
|
1816 |
+
cause
|
1817 |
+
upper
|
1818 |
+
radio
|
1819 |
+
spree
|
1820 |
+
botch
|
1821 |
+
ether
|
1822 |
+
fetal
|
1823 |
+
gnash
|
1824 |
+
ruder
|
1825 |
+
logic
|
1826 |
+
vaunt
|
1827 |
+
scamp
|
1828 |
+
ovary
|
1829 |
+
musky
|
1830 |
+
spurt
|
1831 |
+
godly
|
1832 |
+
unfed
|
1833 |
+
offal
|
1834 |
+
rhyme
|
1835 |
+
birth
|
1836 |
+
soapy
|
1837 |
+
debar
|
1838 |
+
greet
|
1839 |
+
mealy
|
1840 |
+
solar
|
1841 |
+
await
|
1842 |
+
crack
|
1843 |
+
nylon
|
1844 |
+
mourn
|
1845 |
+
horse
|
1846 |
+
exult
|
1847 |
+
ralph
|
1848 |
+
igloo
|
1849 |
+
twice
|
1850 |
+
ledge
|
1851 |
+
theta
|
1852 |
+
login
|
1853 |
+
shrug
|
1854 |
+
usurp
|
1855 |
+
grown
|
1856 |
+
condo
|
1857 |
+
riper
|
1858 |
+
scout
|
1859 |
+
brief
|
1860 |
+
nosey
|
1861 |
+
excel
|
1862 |
+
welch
|
1863 |
+
shaky
|
1864 |
+
visor
|
1865 |
+
patch
|
1866 |
+
swamp
|
1867 |
+
bible
|
1868 |
+
fresh
|
1869 |
+
beefy
|
1870 |
+
alike
|
1871 |
+
stale
|
1872 |
+
share
|
1873 |
+
acrid
|
1874 |
+
adept
|
1875 |
+
straw
|
1876 |
+
credo
|
1877 |
+
needy
|
1878 |
+
mover
|
1879 |
+
bobby
|
1880 |
+
witch
|
1881 |
+
showy
|
1882 |
+
broke
|
1883 |
+
whelp
|
1884 |
+
trade
|
1885 |
+
dowdy
|
1886 |
+
junta
|
1887 |
+
range
|
1888 |
+
staff
|
1889 |
+
spurn
|
1890 |
+
olive
|
1891 |
+
miner
|
1892 |
+
tiara
|
1893 |
+
axion
|
1894 |
+
caddy
|
1895 |
+
bonus
|
1896 |
+
valve
|
1897 |
+
grimy
|
1898 |
+
bigot
|
1899 |
+
broil
|
1900 |
+
honor
|
1901 |
+
drama
|
1902 |
+
donut
|
1903 |
+
stuck
|
1904 |
+
pasta
|
1905 |
+
loser
|
1906 |
+
mange
|
1907 |
+
annex
|
1908 |
+
lousy
|
1909 |
+
spook
|
1910 |
+
hippy
|
1911 |
+
alter
|
1912 |
+
usage
|
1913 |
+
daunt
|
1914 |
+
alien
|
1915 |
+
trite
|
1916 |
+
shunt
|
1917 |
+
aorta
|
1918 |
+
tramp
|
1919 |
+
psalm
|
1920 |
+
essay
|
1921 |
+
steal
|
1922 |
+
sneer
|
1923 |
+
spore
|
1924 |
+
elude
|
1925 |
+
avert
|
1926 |
+
frisk
|
1927 |
+
spade
|
1928 |
+
knock
|
1929 |
+
prove
|
1930 |
+
story
|
1931 |
+
singe
|
1932 |
+
speed
|
1933 |
+
gassy
|
1934 |
+
odder
|
1935 |
+
berry
|
1936 |
+
buggy
|
1937 |
+
ninja
|
1938 |
+
leave
|
1939 |
+
shook
|
1940 |
+
dicey
|
1941 |
+
harem
|
1942 |
+
gloat
|
1943 |
+
manly
|
1944 |
+
booze
|
1945 |
+
waste
|
1946 |
+
rough
|
1947 |
+
flout
|
1948 |
+
false
|
1949 |
+
mogul
|
1950 |
+
modem
|
1951 |
+
stick
|
1952 |
+
ingot
|
1953 |
+
extol
|
1954 |
+
guava
|
1955 |
+
blast
|
1956 |
+
hazel
|
1957 |
+
erupt
|
1958 |
+
shalt
|
1959 |
+
swell
|
1960 |
+
brunt
|
1961 |
+
alive
|
1962 |
+
brick
|
1963 |
+
stall
|
1964 |
+
aroma
|
1965 |
+
abide
|
1966 |
+
musty
|
1967 |
+
beech
|
1968 |
+
slyly
|
1969 |
+
theft
|
1970 |
+
latch
|
1971 |
+
aisle
|
1972 |
+
papal
|
1973 |
+
gully
|
1974 |
+
knack
|
1975 |
+
match
|
1976 |
+
trawl
|
1977 |
+
urine
|
1978 |
+
rouse
|
1979 |
+
endow
|
1980 |
+
ruler
|
1981 |
+
blown
|
1982 |
+
angry
|
1983 |
+
lorry
|
1984 |
+
heady
|
1985 |
+
toxin
|
1986 |
+
aglow
|
1987 |
+
friar
|
1988 |
+
begat
|
1989 |
+
khaki
|
1990 |
+
rumor
|
1991 |
+
dodgy
|
1992 |
+
clerk
|
1993 |
+
adore
|
1994 |
+
crock
|
1995 |
+
where
|
1996 |
+
teary
|
1997 |
+
toast
|
1998 |
+
modal
|
1999 |
+
queer
|
2000 |
+
flask
|
2001 |
+
abase
|
2002 |
+
depot
|
2003 |
+
sissy
|
2004 |
+
empty
|
2005 |
+
cloud
|
2006 |
+
dress
|
2007 |
+
owing
|
2008 |
+
avian
|
2009 |
+
enact
|
2010 |
+
razor
|
2011 |
+
today
|
2012 |
+
thing
|
2013 |
+
agate
|
2014 |
+
gripe
|
2015 |
+
creak
|
2016 |
+
waist
|
2017 |
+
crook
|
2018 |
+
annul
|
2019 |
+
etude
|
2020 |
+
opera
|
2021 |
+
savvy
|
2022 |
+
draft
|
2023 |
+
setup
|
2024 |
+
sower
|
2025 |
+
voice
|
2026 |
+
erase
|
2027 |
+
screw
|
2028 |
+
image
|
2029 |
+
smirk
|
2030 |
+
chest
|
2031 |
+
bingo
|
2032 |
+
conch
|
2033 |
+
tweed
|
2034 |
+
howdy
|
2035 |
+
metro
|
2036 |
+
minor
|
2037 |
+
amity
|
2038 |
+
label
|
2039 |
+
atoll
|
2040 |
+
fecal
|
2041 |
+
prior
|
2042 |
+
penal
|
2043 |
+
whoop
|
2044 |
+
duvet
|
2045 |
+
caput
|
2046 |
+
waxen
|
2047 |
+
medal
|
2048 |
+
plaza
|
2049 |
+
chunk
|
2050 |
+
snowy
|
2051 |
+
flung
|
2052 |
+
afoot
|
2053 |
+
mouth
|
2054 |
+
bound
|
2055 |
+
think
|
2056 |
+
gruel
|
2057 |
+
trice
|
2058 |
+
girth
|
2059 |
+
axiom
|
2060 |
+
forgo
|
2061 |
+
yearn
|
2062 |
+
fritz
|
2063 |
+
lapse
|
2064 |
+
basin
|
2065 |
+
rehab
|
2066 |
+
woman
|
2067 |
+
glide
|
2068 |
+
never
|
2069 |
+
about
|
2070 |
+
laugh
|
2071 |
+
grope
|
2072 |
+
grate
|
2073 |
+
beady
|
2074 |
+
unlit
|
2075 |
+
moult
|
2076 |
+
faint
|
2077 |
+
shiny
|
2078 |
+
tacit
|
2079 |
+
groin
|
2080 |
+
chute
|
2081 |
+
expel
|
2082 |
+
hovel
|
2083 |
+
hippo
|
2084 |
+
argue
|
2085 |
+
pecan
|
2086 |
+
folio
|
2087 |
+
ivory
|
2088 |
+
local
|
2089 |
+
sauna
|
2090 |
+
undid
|
2091 |
+
prong
|
2092 |
+
rigid
|
2093 |
+
floss
|
2094 |
+
parse
|
2095 |
+
untie
|
2096 |
+
billy
|
2097 |
+
lunar
|
2098 |
+
plank
|
2099 |
+
mafia
|
2100 |
+
paddy
|
2101 |
+
peace
|
2102 |
+
quart
|
2103 |
+
stray
|
2104 |
+
pixie
|
2105 |
+
sunny
|
2106 |
+
swore
|
2107 |
+
shirt
|
2108 |
+
chalk
|
2109 |
+
sweat
|
2110 |
+
wordy
|
2111 |
+
older
|
2112 |
+
lower
|
2113 |
+
abyss
|
2114 |
+
urban
|
2115 |
+
steed
|
2116 |
+
mummy
|
2117 |
+
bosom
|
2118 |
+
among
|
2119 |
+
onset
|
2120 |
+
goofy
|
2121 |
+
froth
|
2122 |
+
halve
|
2123 |
+
filly
|
2124 |
+
alley
|
2125 |
+
jaunt
|
2126 |
+
livid
|
2127 |
+
taboo
|
2128 |
+
puree
|
2129 |
+
clock
|
2130 |
+
coven
|
2131 |
+
risky
|
2132 |
+
greed
|
2133 |
+
resin
|
2134 |
+
salty
|
2135 |
+
drape
|
2136 |
+
abhor
|
2137 |
+
level
|
2138 |
+
weedy
|
2139 |
+
allay
|
2140 |
+
meter
|
2141 |
+
crest
|
2142 |
+
limit
|
2143 |
+
stage
|
2144 |
+
thrum
|
2145 |
+
troop
|
2146 |
+
salsa
|
2147 |
+
cacti
|
2148 |
+
sperm
|
2149 |
+
felon
|
2150 |
+
tarot
|
2151 |
+
fewer
|
2152 |
+
niche
|
2153 |
+
rider
|
2154 |
+
pouch
|
2155 |
+
ditch
|
2156 |
+
press
|
2157 |
+
cedar
|
2158 |
+
clash
|
2159 |
+
newly
|
2160 |
+
lowly
|
2161 |
+
buyer
|
2162 |
+
adult
|
2163 |
+
woody
|
2164 |
+
taint
|
2165 |
+
gauze
|
2166 |
+
sleek
|
2167 |
+
angel
|
2168 |
+
triad
|
2169 |
+
frail
|
2170 |
+
stake
|
2171 |
+
spunk
|
2172 |
+
repel
|
2173 |
+
crumb
|
2174 |
+
candy
|
2175 |
+
arrow
|
2176 |
+
troll
|
2177 |
+
worse
|
2178 |
+
patty
|
2179 |
+
nanny
|
2180 |
+
gypsy
|
2181 |
+
creep
|
2182 |
+
guide
|
2183 |
+
power
|
2184 |
+
salvo
|
2185 |
+
glean
|
2186 |
+
racer
|
2187 |
+
bloom
|
2188 |
+
diner
|
2189 |
+
lyric
|
2190 |
+
dwelt
|
2191 |
+
erect
|
2192 |
+
fatty
|
2193 |
+
datum
|
2194 |
+
stink
|
2195 |
+
smote
|
2196 |
+
icily
|
2197 |
+
axial
|
2198 |
+
shyly
|
2199 |
+
golly
|
2200 |
+
itchy
|
2201 |
+
snoop
|
2202 |
+
ovate
|
2203 |
+
missy
|
2204 |
+
muddy
|
2205 |
+
cairn
|
2206 |
+
pouty
|
2207 |
+
panic
|
2208 |
+
crash
|
2209 |
+
femme
|
2210 |
+
decoy
|
2211 |
+
masse
|
2212 |
+
spasm
|
2213 |
+
noise
|
2214 |
+
skirt
|
2215 |
+
oddly
|
2216 |
+
minim
|
2217 |
+
hunch
|
2218 |
+
gawky
|
2219 |
+
crane
|
2220 |
+
steam
|
2221 |
+
tangy
|
2222 |
+
whale
|
2223 |
+
laden
|
2224 |
+
midst
|
2225 |
+
linen
|
2226 |
+
savoy
|
2227 |
+
dealt
|
2228 |
+
soggy
|
2229 |
+
grain
|
2230 |
+
cable
|
2231 |
+
cycle
|
2232 |
+
venom
|
2233 |
+
doing
|
2234 |
+
shark
|
2235 |
+
flirt
|
2236 |
+
stash
|
2237 |
+
squad
|
2238 |
+
elide
|
2239 |
+
realm
|
2240 |
+
cliff
|
2241 |
+
sleep
|
2242 |
+
vista
|
2243 |
+
rapid
|
2244 |
+
badly
|
2245 |
+
shown
|
2246 |
+
pooch
|
2247 |
+
creed
|
2248 |
+
loath
|
2249 |
+
jolly
|
2250 |
+
mocha
|
2251 |
+
briar
|
2252 |
+
cease
|
2253 |
+
arena
|
2254 |
+
unzip
|
2255 |
+
elbow
|
2256 |
+
gaunt
|
2257 |
+
teeth
|
2258 |
+
poesy
|
2259 |
+
clown
|
2260 |
+
prawn
|
2261 |
+
rower
|
2262 |
+
rigor
|
2263 |
+
ferry
|
2264 |
+
wimpy
|
2265 |
+
splat
|
2266 |
+
skill
|
2267 |
+
patsy
|
2268 |
+
agape
|
2269 |
+
puffy
|
2270 |
+
exert
|
2271 |
+
sting
|
2272 |
+
lipid
|
2273 |
+
ardor
|
2274 |
+
afire
|
2275 |
+
slope
|
2276 |
+
clove
|
2277 |
+
aware
|
2278 |
+
gayer
|
2279 |
+
grant
|
2280 |
+
magma
|
2281 |
+
purse
|
2282 |
+
glass
|
2283 |
+
dirge
|
2284 |
+
water
|
2285 |
+
renew
|
2286 |
+
count
|
2287 |
+
funky
|
2288 |
+
bring
|
2289 |
+
scorn
|
2290 |
+
lease
|
2291 |
+
spilt
|
2292 |
+
stein
|
2293 |
+
wooly
|
2294 |
+
plunk
|
2295 |
+
cargo
|
2296 |
+
carry
|
2297 |
+
rabid
|
2298 |
+
mouse
|
2299 |
+
wight
|
2300 |
+
event
|
2301 |
+
favor
|
2302 |
+
maple
|
2303 |
+
debug
|
2304 |
+
trend
|
2305 |
+
twixt
|
2306 |
+
chord
|
2307 |
+
music
|
2308 |
+
guile
|
2309 |
+
table
|
data/wordle_humans.csv
ADDED
@@ -0,0 +1,40 @@
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|
1 |
+
player,target,first_guess,second_guess,third_guess,fourth_guess,fifth_guess,sixth_guess
|
2 |
+
human,vague,arose,plate,cache,mauve,vague,none
|
3 |
+
human,apple,douce,lairy,slave,algae,apple,none
|
4 |
+
human,ninth,douce,lairy,gimps,ninth,none,none
|
5 |
+
human,flail,douce,lairy,snail,flail,none,none
|
6 |
+
human,stage,douce,lairy,phase,stage,none,none
|
7 |
+
human,heady,douce,lairy,ready,beady,heady,none
|
8 |
+
human,none,douce,dairy,dunes,debug,none,none
|
9 |
+
human,usage,douce,lairy,aunts,swamp,usage,none
|
10 |
+
human,sound,douce,pound,found,bound,mound,sound
|
11 |
+
human,salsa,douce,lairy,palms,salts,ghost,salsa
|
12 |
+
human,magic,douce,lairy,cabin,manic,magic,none
|
13 |
+
human,none,douce,lairy,cache,none,none,none
|
14 |
+
human,avail,douce,lairy,snail,flail,avail,none
|
15 |
+
human,kiosk,douce,lairy,lions,moist,frosh,kiosk
|
16 |
+
human,sweat,douce,lairy,wheat,sweat,none,none
|
17 |
+
human,ruddy,douce,lairy,ruddy,none,none,none
|
18 |
+
human,riper,douce,lairy,tries,miner,viper,wiper
|
19 |
+
human,vague,douce,lairy,mauve,vague,none,none
|
20 |
+
human,syrup,arose,unity,rubby,syrup,none,none
|
21 |
+
human,above,adore,above,none,none,none,none
|
22 |
+
human,moose,arose,those,close,unity,moose,none
|
23 |
+
human,polka,arose,unity,polar,polka,none,none
|
24 |
+
human,syrup,audio,plume,usurp,syrup,none,none
|
25 |
+
human,worse,audio,store,horse,worse,none,none
|
26 |
+
human,worse,audio,stone,posse,worse,none,none
|
27 |
+
human,polka,audio,polar,polka,none,none,none
|
28 |
+
human,polka,audio,groan,poach,polka,none,none
|
29 |
+
human,moose,audio,store,chose,loose,goose,moose
|
30 |
+
human,moose,audio,store,close,moose,none,none
|
31 |
+
human,squat,homes,start,squat,none,none,none
|
32 |
+
human,above,audio,alone,above,none,none,none
|
33 |
+
human,squat,audio,usual,squat,none,none,none
|
34 |
+
human,squat,audio,taupe,squat,none,none,none
|
35 |
+
human,Trend,Audio,Nerdy,Trend,none,none,none
|
36 |
+
human,Trend,Audio,Depth,Trend,none,none,none
|
37 |
+
human,Trend,Later,Rents,Trend,none,none,none
|
38 |
+
human,toxic,Later,throw,toxic,none,none,none
|
39 |
+
human,toxic,AUDIO,CHOIR,toxic,none,none,none
|
40 |
+
human,toxic,audio,movie,colic,robin,toxic,none
|
wordle_functions.py
ADDED
@@ -0,0 +1,1447 @@
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|
1 |
+
import numpy as np # for stats
|
2 |
+
import random # for randomly generating target and start words
|
3 |
+
import operator # for sorting letter frequency distribution
|
4 |
+
import time # for #dramaticeffect
|
5 |
+
import pandas as pd
|
6 |
+
# from nltk.corpus import movie_reviews, treebank, brown, gutenberg, switchboard
|
7 |
+
|
8 |
+
english_alphabet = "abcdefghijklmnopqrstuvwxyz"
|
9 |
+
|
10 |
+
def get_letter_counts(letters: str, word_list: list, sort: str = "descending"):
|
11 |
+
"""
|
12 |
+
Given a passed str of letters and a list of words, produces a frequency distribution of all letters
|
13 |
+
|
14 |
+
Parameters:
|
15 |
+
------
|
16 |
+
`letters`: str
|
17 |
+
a string of letters to be counted. String must only be desired letters, with no spaces. Default is local variable containing all letters of the English alphabet
|
18 |
+
`word_list`: list
|
19 |
+
list of words (str) from which word frequencies will be counted
|
20 |
+
`sort`: str
|
21 |
+
if either "descending" or "ascending" are passed, returned list of tuples will be sorted accoringly, else returned dictionary will be unsorted
|
22 |
+
|
23 |
+
Returns:
|
24 |
+
------
|
25 |
+
`letters_counts_dict`: dict
|
26 |
+
dictionary of {letter : count} pairs for each letter in passed `letters` sequence
|
27 |
+
`sorted_counts_dicts`: list of tuples
|
28 |
+
list of tuples. Format is ("letter", frequency). Ordered according to `sort` values
|
29 |
+
"""
|
30 |
+
|
31 |
+
words_counts_dict = {}
|
32 |
+
|
33 |
+
for word in word_list: # real dataset
|
34 |
+
word_dict = {}
|
35 |
+
|
36 |
+
for letter in word:
|
37 |
+
if letter in word_dict:
|
38 |
+
word_dict[letter] += 1
|
39 |
+
else:
|
40 |
+
word_dict[letter] = 1
|
41 |
+
words_counts_dict[word] = word_dict
|
42 |
+
|
43 |
+
letters_counts_dict = {}
|
44 |
+
|
45 |
+
for letter in letters:
|
46 |
+
letters_counts_dict[letter] = 0
|
47 |
+
|
48 |
+
for word, count_dict in words_counts_dict.items():
|
49 |
+
# print (word, count_dict)
|
50 |
+
for letter, count in count_dict.items():
|
51 |
+
letters_counts_dict[letter] += count
|
52 |
+
|
53 |
+
if sort == "ascending":
|
54 |
+
sorted_counts_dict = (sorted(letters_counts_dict.items(), key = operator.itemgetter(1), reverse = False))
|
55 |
+
return sorted_counts_dicts
|
56 |
+
|
57 |
+
if sort == "descending":
|
58 |
+
sorted_counts_dict = sorted(letters_counts_dict.items(), key = operator.itemgetter(1), reverse = True)
|
59 |
+
return sorted_counts_dict
|
60 |
+
else:
|
61 |
+
return letters_counts_dict
|
62 |
+
|
63 |
+
### Best first guesses for a given Wordle list
|
64 |
+
|
65 |
+
def best_guess_words(word_list: list, show_letters: bool = False):
|
66 |
+
"""
|
67 |
+
Given a passed list of English words of a consistent length, calculates the most statistically optimal first guess words, alongside a rating for each word.
|
68 |
+
|
69 |
+
Rating = sum(frequency of each unique letter in that word) / sum (all unique letter frequencies in word_list) * 100, rounded to 2 decimals.
|
70 |
+
|
71 |
+
------
|
72 |
+
Parameters:
|
73 |
+
------
|
74 |
+
`word_list`: list
|
75 |
+
list of words (str) of consistent length
|
76 |
+
`show_letters`: bool
|
77 |
+
if True, also prints set of most optimal letters to guess
|
78 |
+
|
79 |
+
------
|
80 |
+
Returns:
|
81 |
+
------
|
82 |
+
`word_ratings`: list
|
83 |
+
list of tuples. Format is [(word, rating)], where rating is calculated according to above formula
|
84 |
+
`sorted_counts`: list of tuples
|
85 |
+
list of tuples. Format is ("letter", frequency). Sorted according to `sort` value; ["descending" or "ascending"] if passed
|
86 |
+
"""
|
87 |
+
|
88 |
+
english_alphabet = "abcdefghijklmnopqrstuvwxyz"
|
89 |
+
|
90 |
+
sorted_counts = get_letter_counts(english_alphabet, word_list, sort = "descending")
|
91 |
+
|
92 |
+
max_len_possible = len(word_list[0])
|
93 |
+
|
94 |
+
### Get words with the highest letter diversity
|
95 |
+
while max_len_possible:
|
96 |
+
|
97 |
+
best_letters = set()
|
98 |
+
best_words = []
|
99 |
+
|
100 |
+
for letter, freq in sorted_counts:
|
101 |
+
best_letters.add(letter)
|
102 |
+
if len(best_letters) == max_len_possible:
|
103 |
+
break
|
104 |
+
|
105 |
+
### Get all words that have one of each of the 5 top most frequent letters
|
106 |
+
for word in word_list:
|
107 |
+
word_set = set()
|
108 |
+
|
109 |
+
for letter in word:
|
110 |
+
word_set.add(letter)
|
111 |
+
|
112 |
+
if best_letters.issubset(word_set):
|
113 |
+
best_words.append(word)
|
114 |
+
|
115 |
+
if len(best_words) > 0:
|
116 |
+
break
|
117 |
+
else:
|
118 |
+
max_len_possible -= 1 # only try the top 4 letters, then 3, then 2, ...
|
119 |
+
|
120 |
+
if max_len_possible == 0:
|
121 |
+
break
|
122 |
+
|
123 |
+
all_letters_count = 0
|
124 |
+
for letter, freq in sorted_counts:
|
125 |
+
all_letters_count += freq
|
126 |
+
|
127 |
+
word_ratings = []
|
128 |
+
for word in best_words:
|
129 |
+
ratings_dict = {}
|
130 |
+
for letter in word:
|
131 |
+
for freq_letter, freq in sorted_counts:
|
132 |
+
if letter == freq_letter:
|
133 |
+
ratings_dict[letter] = freq
|
134 |
+
|
135 |
+
total_rating = 0
|
136 |
+
for letter, rating in ratings_dict.items():
|
137 |
+
total_rating += rating
|
138 |
+
|
139 |
+
word_ratings.append((word, round(total_rating / all_letters_count * 100, 2)))
|
140 |
+
|
141 |
+
word_ratings = sorted(word_ratings, key = operator.itemgetter(1), reverse = True)
|
142 |
+
|
143 |
+
if show_letters == True:
|
144 |
+
return word_ratings, sorted_counts
|
145 |
+
else:
|
146 |
+
return word_ratings
|
147 |
+
|
148 |
+
def count_vows_cons(word: str, y_vow = True):
|
149 |
+
"""
|
150 |
+
Given a passed word, calculate the number of non-unique vowels and consonants in the word (duplicates counted more than once).
|
151 |
+
|
152 |
+
------
|
153 |
+
Parameters:
|
154 |
+
------
|
155 |
+
`word`: str
|
156 |
+
a single passed word (str)
|
157 |
+
`y_vow`: bool
|
158 |
+
if True, "y" is considered a vowel. If False, "y" considered a consonant. Default is True
|
159 |
+
|
160 |
+
------
|
161 |
+
Returns:
|
162 |
+
------
|
163 |
+
`counts`: dict
|
164 |
+
dictionary, where format is {letter type : count}
|
165 |
+
"""
|
166 |
+
|
167 |
+
word = word.lower() # for consistency
|
168 |
+
|
169 |
+
if y_vow == True:
|
170 |
+
vows = "aeiouy"
|
171 |
+
cons = "bcdfghjklmnpqrstvwxz"
|
172 |
+
elif y_vow == False:
|
173 |
+
vows = "aeiou"
|
174 |
+
cons = "bcdfghjklmnpqrstvwxyz"
|
175 |
+
|
176 |
+
counts = {}
|
177 |
+
counts["vows"] = 0
|
178 |
+
counts["cons"] = 0
|
179 |
+
for letter in word:
|
180 |
+
if letter in vows:
|
181 |
+
counts["vows"] += 1
|
182 |
+
if letter in cons:
|
183 |
+
counts["cons"] += 1
|
184 |
+
|
185 |
+
return counts
|
186 |
+
|
187 |
+
def get_word_entropy(words_to_rate: list, word_list: list, normalized: bool = True, ascending: bool = False):
|
188 |
+
"""
|
189 |
+
Given a word and a word list, calculates entropy each word as a measure of its impact to the next possible guesses in Wordle, ordered according to `reverse` parameter.
|
190 |
+
|
191 |
+
------
|
192 |
+
Parameters:
|
193 |
+
------
|
194 |
+
`words_to_rate`: list
|
195 |
+
list of strings to be rated
|
196 |
+
`word_list`: list
|
197 |
+
list of all possible words (str) of consistent length, to which each word in `words_to_rate` will be compared
|
198 |
+
`normalized`: bool
|
199 |
+
if True, normalizes all ratings on a scale of 0-100, with 100 being the rating for the most optimal word, and 0 for the least optimal word
|
200 |
+
`ascending`: bool
|
201 |
+
if True, returns list ordered ascending. If False, returns list in descending order
|
202 |
+
|
203 |
+
------
|
204 |
+
Returns:
|
205 |
+
------
|
206 |
+
`word_ratings`: list
|
207 |
+
list of tuples. Format is [(word, rating)], where rating is calculated according to above formula
|
208 |
+
`sorted_counts`: list of tuples
|
209 |
+
list of tuples. Format is ("letter", frequency). Sorted according to `sort` value; ["descending" or "ascending"] if passed
|
210 |
+
"""
|
211 |
+
|
212 |
+
if ascending == True:
|
213 |
+
sorted_counts = get_letter_counts(english_alphabet, word_list, sort = "ascending")
|
214 |
+
else:
|
215 |
+
sorted_counts = get_letter_counts(english_alphabet, word_list, sort = "descending")
|
216 |
+
|
217 |
+
all_letters_count = 0
|
218 |
+
for letter, freq in sorted_counts:
|
219 |
+
all_letters_count += freq
|
220 |
+
|
221 |
+
unnormalized_ratings = []
|
222 |
+
for word in words_to_rate:
|
223 |
+
word = word.lower()
|
224 |
+
ratings_dict = {}
|
225 |
+
for letter in word:
|
226 |
+
for freq_letter, freq in sorted_counts:
|
227 |
+
if letter == freq_letter:
|
228 |
+
ratings_dict[letter] = freq
|
229 |
+
|
230 |
+
total_rating = 0
|
231 |
+
for letter, rating in ratings_dict.items():
|
232 |
+
total_rating += rating
|
233 |
+
|
234 |
+
unnormalized_ratings.append((word, round(total_rating / all_letters_count * 100, 2)))
|
235 |
+
|
236 |
+
word_ratings = sorted(unnormalized_ratings, key = operator.itemgetter(1), reverse = True)
|
237 |
+
# print (word_ratings)
|
238 |
+
|
239 |
+
if normalized == True:
|
240 |
+
if len(word_ratings) > 1:
|
241 |
+
new_tests = []
|
242 |
+
|
243 |
+
for tup in word_ratings:
|
244 |
+
try:
|
245 |
+
normd = round(((tup[1] - word_ratings[-1][1]) / (word_ratings[0][1] - word_ratings[-1][1])) * 100, 2)
|
246 |
+
new_tests.append((tup[0], normd))
|
247 |
+
except:
|
248 |
+
ZeroDivisionError
|
249 |
+
new_tests.append((tup[0], 0.0))
|
250 |
+
|
251 |
+
return new_tests
|
252 |
+
else:
|
253 |
+
return [(word_ratings[0][0], float(100))]
|
254 |
+
|
255 |
+
elif normalized == False:
|
256 |
+
|
257 |
+
return word_ratings
|
258 |
+
|
259 |
+
### Gets most common words of all words of the dataset
|
260 |
+
|
261 |
+
def get_word_distribution(word_list: list, sort: str = "descending"):
|
262 |
+
"""
|
263 |
+
Given a passed str of words and a list of words, produces a frequency distribution of all words
|
264 |
+
|
265 |
+
------
|
266 |
+
Parameters:
|
267 |
+
------
|
268 |
+
`word_list`: list
|
269 |
+
list of words (str) from which word frequencies will be counted
|
270 |
+
`sort`: str
|
271 |
+
if either "descending" or "ascending" are passed, returned list of tuples will be sorted accoringly, else returned dictionary will be unsorted
|
272 |
+
|
273 |
+
------
|
274 |
+
Returns:
|
275 |
+
------
|
276 |
+
`words_counts_dict`: dict
|
277 |
+
dictionary of {word : count} pairs for each word in passed `word_list`
|
278 |
+
`sorted_counts_dicts`: list of tuples
|
279 |
+
list of tuples. Format is ("word", frequency). Ordered according to `sort` values
|
280 |
+
"""
|
281 |
+
|
282 |
+
words_counts_dict = {}
|
283 |
+
|
284 |
+
for word in word_list:
|
285 |
+
if word in words_counts_dict:
|
286 |
+
words_counts_dict[word] += 1
|
287 |
+
else:
|
288 |
+
words_counts_dict[word] = 1
|
289 |
+
|
290 |
+
if sort == "ascending":
|
291 |
+
sorted_counts_dict = (sorted(words_counts_dict.items(), key = operator.itemgetter(1), reverse = False))
|
292 |
+
return sorted_counts_dict
|
293 |
+
|
294 |
+
if sort == "descending":
|
295 |
+
sorted_counts_dict = sorted(words_counts_dict.items(), key = operator.itemgetter(1), reverse = True)
|
296 |
+
return sorted_counts_dict
|
297 |
+
|
298 |
+
def wordle_wizard(word_list: list, max_guesses: int = None,
|
299 |
+
guess: str = None, target: str = None, bias: bool = True,
|
300 |
+
random_guess: bool = False, random_target: bool = False,
|
301 |
+
verbose: bool = False, drama: float = None,
|
302 |
+
return_stats: bool = False, record: bool = False):
|
303 |
+
"""
|
304 |
+
Mimicking the popular web game, this function matches a current word to a target word automatically, in the most statistically optimal way possible.
|
305 |
+
|
306 |
+
------
|
307 |
+
Parameters:
|
308 |
+
------
|
309 |
+
`word_list`: list
|
310 |
+
list of valid words to be considered
|
311 |
+
`guess`: str
|
312 |
+
a string -- must be the same length as `target_word`
|
313 |
+
`target`: str
|
314 |
+
a string -- must be the same length as `opening_word`
|
315 |
+
`bias`: str ['entropy', 'common', 'rare', None]
|
316 |
+
'entropy' biases next word guesses to be the ones with the highest impact on the range of next possible guesses. Entropy values associated with each word are normalized across the list.
|
317 |
+
|
318 |
+
'common' biases next word guesses to be words that are more commonly used
|
319 |
+
|
320 |
+
'rare' biases next word guesses to be words that are more rarely used
|
321 |
+
|
322 |
+
'no_bias' chooses a next guess at random of all available guesses
|
323 |
+
|
324 |
+
`max_guesses`: int
|
325 |
+
the maximum number of attempts allowed to solve the Wordle
|
326 |
+
`random_guess`: bool
|
327 |
+
if True, randomly chooses a starting word from all words within `word_list`. If False, passed starting word must be used instead
|
328 |
+
`random_target`: bool
|
329 |
+
if True, randomly chooses a target word from all words within `word_list`. If False, passed target word must be used instead
|
330 |
+
`verbose`: bool
|
331 |
+
if True, prints progress and explanation of how function solves the puzzle. If False, prints only the guessed word at each guess.
|
332 |
+
`drama`: float or int
|
333 |
+
if int provided, each guess' output is delayed by that number of seconds, else each output is shown as quickly as possible. For ~dRaMaTiC eFfEcT~
|
334 |
+
`return_stats`: bool
|
335 |
+
if True, prints nothing and returns a dictionary of various statistics about the function's performance trying to solve the puzzle
|
336 |
+
`record`: bool
|
337 |
+
if True, creates a .txt file with the same information printed according to the indicated verbosity
|
338 |
+
|
339 |
+
------
|
340 |
+
Returns:
|
341 |
+
------
|
342 |
+
`stats_dict`: dict
|
343 |
+
dictionary containing various statistics about the function's performance trying to solve the puzzle
|
344 |
+
"""
|
345 |
+
|
346 |
+
sugg_words = []
|
347 |
+
|
348 |
+
for i in range(0, 20):
|
349 |
+
ran_int = random.randint(0, len(word_list) - 1)
|
350 |
+
word = word_list[ran_int]
|
351 |
+
sugg_words.append(word)
|
352 |
+
|
353 |
+
if guess not in word_list:
|
354 |
+
print ("Guess word not in passed word list.\nOnly words within the given word list are valid.")
|
355 |
+
print (f"Here are some examples of valid words from the passed word list.\n\t{sugg_words[:10]}")
|
356 |
+
return None
|
357 |
+
|
358 |
+
if target not in word_list:
|
359 |
+
print ("Target word not in passed word list.\nOnly words within the given word list are valid.")
|
360 |
+
print (f"Here are some examples of valid words from the passed word list.\n\t{sugg_words[-10:]}")
|
361 |
+
return None
|
362 |
+
|
363 |
+
if random_guess == True:
|
364 |
+
randomint_guess = random.randint(0, len(word_list) - 1)
|
365 |
+
guess = word_list[randomint_guess]
|
366 |
+
|
367 |
+
if random_target == True:
|
368 |
+
randomint_target = random.randint(0, len(word_list) - 1)
|
369 |
+
target = word_list[randomint_target]
|
370 |
+
|
371 |
+
stats_dict = {}
|
372 |
+
stats_dict['first_guess'] = guess
|
373 |
+
stats_dict['target_word'] = target
|
374 |
+
stats_dict['first_guess_vowels'] = float(count_vows_cons(guess, y_vow = True)['vows'])
|
375 |
+
stats_dict['first_guess_consonants'] = float(count_vows_cons(guess, y_vow = True)['cons'])
|
376 |
+
stats_dict['target_vowels'] = float(count_vows_cons(target, y_vow = True)['vows'])
|
377 |
+
stats_dict['target_consonants'] = float(count_vows_cons(target, y_vow = True)['cons'])
|
378 |
+
|
379 |
+
# get entropy of the first guess word and target word in the entire word_list
|
380 |
+
for tup in get_word_entropy(word_list, word_list, normalized = True):
|
381 |
+
if tup[0] == guess:
|
382 |
+
stats_dict['first_guess_entropy'] = tup[1]
|
383 |
+
if tup[0] == target:
|
384 |
+
stats_dict['target_entropy'] = tup[1]
|
385 |
+
|
386 |
+
guess_entropies = []
|
387 |
+
guess_entropies.append(stats_dict['first_guess_entropy'])
|
388 |
+
|
389 |
+
# luck_guess_1 = round(1 - ((1 / len(word_list)) * guess_entropies[0] / 100), 2) * 100
|
390 |
+
|
391 |
+
english_alphabet = "abcdefghijklmnopqrstuvwxyz"
|
392 |
+
|
393 |
+
word_list_sorted_counts = get_letter_counts(english_alphabet, word_list, sort = "descending")
|
394 |
+
|
395 |
+
wordlen = len(guess)
|
396 |
+
letter_positions = set(i for i in range(0, wordlen))
|
397 |
+
|
398 |
+
guess_set = set()
|
399 |
+
perfect_dict = {}
|
400 |
+
wrong_pos_dict = {}
|
401 |
+
wrong_pos_set = set()
|
402 |
+
dont_guess_again = set()
|
403 |
+
|
404 |
+
guessed_words = [] # running set of guessed words
|
405 |
+
guess_num = 0 # baseline for variable
|
406 |
+
dont_guess_words = set()
|
407 |
+
incorrect_positions = []
|
408 |
+
reduction_per_guess = []
|
409 |
+
|
410 |
+
if max_guesses == None: # if no value is passed, default is len(guess)
|
411 |
+
max_guesses = wordlen
|
412 |
+
else: # else it is the value passed
|
413 |
+
max_guesses = max_guesses
|
414 |
+
|
415 |
+
perfect_letts_per_guess = []
|
416 |
+
wrong_pos_per_guess = []
|
417 |
+
wrong_letts_per_guess = []
|
418 |
+
|
419 |
+
record_list = []
|
420 |
+
|
421 |
+
while guess: # while there is any guess -- there are conditions to break it at the bottom
|
422 |
+
|
423 |
+
guess_num += 1
|
424 |
+
|
425 |
+
guessed_words.append(guess)
|
426 |
+
|
427 |
+
if drama:
|
428 |
+
time.sleep(drama)
|
429 |
+
|
430 |
+
# guess_num += 1 # each time the guess is processed
|
431 |
+
if return_stats == False:
|
432 |
+
if guess_num == 1:
|
433 |
+
print("-----------------------------\n")
|
434 |
+
record_list.append("-----------------------------\n")
|
435 |
+
|
436 |
+
if return_stats == False:
|
437 |
+
print(f"Guess {guess_num}: '{guess}'")
|
438 |
+
record_list.append(f"Guess {guess_num}: '{guess}'")
|
439 |
+
|
440 |
+
if guess == target:
|
441 |
+
stats_dict['target_guessed'] = True
|
442 |
+
if return_stats == False:
|
443 |
+
if guess_num == 1:
|
444 |
+
print(f"Congratulations! The Wordle has been solved in {guess_num} guess, that's amazingly lucky!")
|
445 |
+
print(f"The target word was {target}")
|
446 |
+
record_list.append(f"Congratulations! The Wordle has been solved in {guess_num} guess, that's amazingly lucky!")
|
447 |
+
record_list.append(f"The target word was {target}")
|
448 |
+
perfect_letts_per_guess.append(5)
|
449 |
+
wrong_pos_per_guess.append(0)
|
450 |
+
wrong_letts_per_guess.append(0)
|
451 |
+
break
|
452 |
+
|
453 |
+
guess_set = set()
|
454 |
+
wrong_pos_set = set()
|
455 |
+
|
456 |
+
#### Step 2 -- ALL PERFECT
|
457 |
+
for i in letter_positions: # number of letters in each word (current word and target word)
|
458 |
+
guess_set.add(guess[i])
|
459 |
+
|
460 |
+
if guess[i] not in perfect_dict:
|
461 |
+
perfect_dict[guess[i]] = set()
|
462 |
+
if guess[i] not in wrong_pos_dict:
|
463 |
+
wrong_pos_dict[guess[i]] = set()
|
464 |
+
|
465 |
+
### EVALUATE CURRENT GUESS
|
466 |
+
if guess[i] == target[i]: # letter == correct and position == correct
|
467 |
+
perfect_dict[guess[i]].add(i)
|
468 |
+
|
469 |
+
if (guess[i] != target[i] and guess[i] in target): # letter == correct and position != correct
|
470 |
+
wrong_pos_dict[guess[i]].add(i)
|
471 |
+
wrong_pos_set.add(guess[i])
|
472 |
+
|
473 |
+
if guess[i] not in target: # if letter is not relevant at all
|
474 |
+
dont_guess_again.add(guess[i])
|
475 |
+
|
476 |
+
#### Step 3 -- ALL PERFECT
|
477 |
+
next_letters = set()
|
478 |
+
for letter, positions in perfect_dict.items():
|
479 |
+
if len(positions) > 0:
|
480 |
+
next_letters.add(letter)
|
481 |
+
|
482 |
+
for letter, positions in wrong_pos_dict.items():
|
483 |
+
if len(positions) > 0:
|
484 |
+
next_letters.add(letter)
|
485 |
+
|
486 |
+
#### List of tuples of correct letter positions in new valid words. Eg: [('e', 2), ('a', 3)]
|
487 |
+
perfect_letters = []
|
488 |
+
for letter, positions in perfect_dict.items():
|
489 |
+
for pos in positions:
|
490 |
+
if len(positions) > 0:
|
491 |
+
perfect_letters.append((letter, pos))
|
492 |
+
|
493 |
+
#### all words that have correct letters in same spots
|
494 |
+
words_matching_correct_all = []
|
495 |
+
for word in word_list:
|
496 |
+
word_set = set()
|
497 |
+
for letter, pos in perfect_letters:
|
498 |
+
if word[pos] == letter:
|
499 |
+
words_matching_correct_all.append(word)
|
500 |
+
|
501 |
+
#### excluding words with letters in known incorrect positions
|
502 |
+
for letter, positions in wrong_pos_dict.items():
|
503 |
+
for pos in positions:
|
504 |
+
if len(positions) > 0:
|
505 |
+
if (letter, pos) not in incorrect_positions:
|
506 |
+
incorrect_positions.append((letter, pos))
|
507 |
+
|
508 |
+
# sorting lists of tuples just to make them look nice in the printout
|
509 |
+
incorrect_positions = sorted(incorrect_positions, key = operator.itemgetter(1), reverse = False)
|
510 |
+
perfect_letters = sorted(perfect_letters, key = operator.itemgetter(1), reverse = False)
|
511 |
+
|
512 |
+
#### all words that have correct letters in incorrect spots -- so they can be excluded efficiently
|
513 |
+
|
514 |
+
# print(incorrect_positions)
|
515 |
+
|
516 |
+
for word in word_list:
|
517 |
+
word_set = set()
|
518 |
+
for letter, pos in incorrect_positions:
|
519 |
+
if word[pos] == letter:
|
520 |
+
dont_guess_words.add(word)
|
521 |
+
for word in word_list:
|
522 |
+
word_set = set()
|
523 |
+
for letter, pos in incorrect_positions:
|
524 |
+
if word[pos] == letter:
|
525 |
+
dont_guess_words.add(word)
|
526 |
+
|
527 |
+
for bad_letter in dont_guess_again:
|
528 |
+
for word in word_list:
|
529 |
+
if (bad_letter in word and word not in dont_guess_words):
|
530 |
+
dont_guess_words.add(word)
|
531 |
+
|
532 |
+
if return_stats == False:
|
533 |
+
if verbose == True:
|
534 |
+
print(f"Letters in correct positions:\n\t{perfect_letters}\n")
|
535 |
+
print(f"Letters in incorrect positions:\n\t{incorrect_positions}\n")
|
536 |
+
print (f"Letters to guess again:\n\t{sorted(list(next_letters), reverse = False)}\n")
|
537 |
+
print(f"Letters to not guess again:\n\t{sorted(list(dont_guess_again), reverse = False)}\n") # works
|
538 |
+
record_list.append(f"Letters in correct positions:\n\t{perfect_letters}\n")
|
539 |
+
record_list.append(f"Letters in incorrect positions:\n\t{incorrect_positions}\n")
|
540 |
+
record_list.append(f"Letters to guess again:\n\t{sorted(list(next_letters), reverse = False)}\n")
|
541 |
+
record_list.append(f"Letters to not guess again:\n\t{sorted(list(dont_guess_again), reverse = False)}\n") # works
|
542 |
+
|
543 |
+
# Returns True
|
544 |
+
# print(A.issubset(B)) # "if everything in A is in B", returns Bool
|
545 |
+
|
546 |
+
perfect_letts_per_guess.append(len(perfect_letters))
|
547 |
+
wrong_pos_per_guess.append(len(incorrect_positions))
|
548 |
+
wrong_letts_per_guess.append(len(dont_guess_again))
|
549 |
+
|
550 |
+
potential_next_guesses = set()
|
551 |
+
middle_set = set()
|
552 |
+
|
553 |
+
if len(perfect_letters) == 0 and len(incorrect_positions) == 0: # if there are NEITHER perfect letters, NOR incorrect positions, ....
|
554 |
+
for word in word_list:
|
555 |
+
if word not in dont_guess_words:
|
556 |
+
if word not in guessed_words:
|
557 |
+
potential_next_guesses.add(word)
|
558 |
+
|
559 |
+
# print(f"GUESS {guess_num} : TEST 1-1")
|
560 |
+
|
561 |
+
if len(perfect_letters) == 0 and len(incorrect_positions) != 0: # if there are no perfect letters whatsoever, but there ARE incorrect positions ....
|
562 |
+
for word in word_list:
|
563 |
+
for incor_letter, incor_pos in incorrect_positions:
|
564 |
+
if word[incor_pos] != incor_letter:
|
565 |
+
if word not in dont_guess_words: # just in case
|
566 |
+
word_set = set()
|
567 |
+
for letter in word:
|
568 |
+
word_set.add(letter)
|
569 |
+
|
570 |
+
if next_letters.issubset(word_set):
|
571 |
+
if word not in guessed_words:
|
572 |
+
if len(dont_guess_again) > 0:
|
573 |
+
for bad_letter in dont_guess_again:
|
574 |
+
if bad_letter not in word:
|
575 |
+
# potential_next_guesses.append(word)
|
576 |
+
potential_next_guesses.add(word)
|
577 |
+
else:
|
578 |
+
potential_next_guesses.add(word)
|
579 |
+
|
580 |
+
# print(f"GUESS {guess_num} : TEST 2-1")
|
581 |
+
|
582 |
+
else:
|
583 |
+
for word in word_list:
|
584 |
+
if word not in dont_guess_words: # just in case
|
585 |
+
word_set = set()
|
586 |
+
for letter in word:
|
587 |
+
word_set.add(letter)
|
588 |
+
if next_letters.issubset(word_set):
|
589 |
+
if word not in guessed_words:
|
590 |
+
# print ("TEST 3-2")
|
591 |
+
|
592 |
+
if len(dont_guess_again) > 0:
|
593 |
+
for bad_letter in dont_guess_again:
|
594 |
+
if bad_letter not in word:
|
595 |
+
middle_set.add(word)
|
596 |
+
else:
|
597 |
+
middle_set.add(word)
|
598 |
+
for word in middle_set:
|
599 |
+
dummy_list = []
|
600 |
+
for good_lett, good_pos in perfect_letters:
|
601 |
+
if word[good_pos] == good_lett:
|
602 |
+
dummy_list.append(1)
|
603 |
+
if len(dummy_list) == len(perfect_letters):
|
604 |
+
potential_next_guesses.add(word)
|
605 |
+
for word in middle_set:
|
606 |
+
dummy_list = []
|
607 |
+
for bad_lett, bad_pos in incorrect_positions:
|
608 |
+
if word[bad_pos] == bad_lett:
|
609 |
+
dummy_list.append(1)
|
610 |
+
if len(dummy_list) > 0:
|
611 |
+
potential_next_guesses.remove(word)
|
612 |
+
|
613 |
+
# print(f"GUESS {guess_num} : TEST 3-1")
|
614 |
+
|
615 |
+
if return_stats == False:
|
616 |
+
if verbose == True:
|
617 |
+
print(f"At this point:")
|
618 |
+
print(f"\t{len(word_list) - len(potential_next_guesses)}, {round((len(word_list) - len(potential_next_guesses)) / len(word_list) * 100, 2)}% of total words have been eliminated, and")
|
619 |
+
print(f"\t{len(potential_next_guesses)}, {round(len(potential_next_guesses) / len(word_list) * 100, 2)}% of total words remain possible.\n")
|
620 |
+
record_list.append(f"At this point:")
|
621 |
+
record_list.append(f"\t{len(word_list) - len(potential_next_guesses)}, {round((len(word_list) - len(potential_next_guesses)) / len(word_list) * 100, 2)}% of total words have been eliminated, and")
|
622 |
+
record_list.append(f"\t{len(potential_next_guesses)}, {round(len(potential_next_guesses) / len(word_list) * 100, 2)}% of total words remain possible.\n")
|
623 |
+
|
624 |
+
reduction_per_guess.append(len(potential_next_guesses))
|
625 |
+
|
626 |
+
#### Guessing next word
|
627 |
+
if len(potential_next_guesses) == 1:
|
628 |
+
|
629 |
+
if return_stats == False:
|
630 |
+
if verbose == True:
|
631 |
+
print(f"The only remaining possible word is:\n\t'{list(potential_next_guesses)[0]}'\n")
|
632 |
+
record_list.append(f"The only remaining possible word is:\n\t'{list(potential_next_guesses)[0]}'\n")
|
633 |
+
|
634 |
+
guess = list(potential_next_guesses)[0]
|
635 |
+
guess_entropies.append(get_word_entropy([guess], word_list, normalized = True, ascending = False)[0][1])
|
636 |
+
|
637 |
+
else:
|
638 |
+
|
639 |
+
if bias == "entropy":
|
640 |
+
|
641 |
+
best_next_guesses = list(potential_next_guesses)
|
642 |
+
# print (best_next_guesses)
|
643 |
+
word_ratings = get_word_entropy(best_next_guesses, word_list, normalized = True, ascending = False) # "internal" ratings
|
644 |
+
|
645 |
+
# Get max rated word
|
646 |
+
max_rating = -np.inf
|
647 |
+
for word, rating in word_ratings:
|
648 |
+
if rating > max_rating:
|
649 |
+
max_rating = rating
|
650 |
+
|
651 |
+
for word, rating in word_ratings:
|
652 |
+
if rating == max_rating:
|
653 |
+
guess = word
|
654 |
+
|
655 |
+
guess_entropies.append(get_word_entropy([guess], word_list, normalized = True, ascending = False)[0][1])
|
656 |
+
|
657 |
+
if return_stats == False:
|
658 |
+
if verbose == True:
|
659 |
+
if len(word_ratings) <= 40:
|
660 |
+
print(f"All potential next guesses:\n\t{word_ratings}\n")
|
661 |
+
print(f"Words guessed so far:\n\t{guessed_words}.\n")
|
662 |
+
record_list.append(f"Potential next guesses:\n\t{word_ratings}\n")
|
663 |
+
record_list.append(f"Words guessed so far:\n\t{guessed_words}.\n")
|
664 |
+
else:
|
665 |
+
print(f"The top 40 potential next guesses are:\n\t{word_ratings[:40]}\n")
|
666 |
+
print(f"Words guessed so far:\n\t{guessed_words}.\n")
|
667 |
+
record_list.append(f"The top 40 potential next guesses are::\n\t{word_ratings[:40]}\n")
|
668 |
+
record_list.append(f"Words guessed so far:\n\t{guessed_words}.\n")
|
669 |
+
|
670 |
+
if bias == "no_bias":
|
671 |
+
best_next_guesses = set()
|
672 |
+
for word in potential_next_guesses:
|
673 |
+
for letter, freq in word_list_sorted_counts:
|
674 |
+
if letter not in dont_guess_again:
|
675 |
+
if len(next_letters) > 0:
|
676 |
+
if letter in next_letters:
|
677 |
+
if letter in word:
|
678 |
+
best_next_guesses.add(word)
|
679 |
+
break
|
680 |
+
else:
|
681 |
+
if letter in word:
|
682 |
+
best_next_guesses.add(word)
|
683 |
+
break
|
684 |
+
|
685 |
+
if return_stats == False:
|
686 |
+
if verbose == True:
|
687 |
+
if len(best_next_guesses) <= 40:
|
688 |
+
print(f"Potential next guesses:\n\t{best_next_guesses}\n")
|
689 |
+
print(f"Words guessed so far:\n\t{guessed_words}.\n")
|
690 |
+
record_list.append(f"Potential next guesses:\n\t{best_next_guesses}\n")
|
691 |
+
record_list.append(f"Words guessed so far:\n\t{guessed_words}.\n")
|
692 |
+
|
693 |
+
if bias == ("common" or "rare"):
|
694 |
+
found_words = []
|
695 |
+
for word in word_list:
|
696 |
+
if word in nltk_counts.keys():
|
697 |
+
found_words.append(word)
|
698 |
+
|
699 |
+
found_words_sorted = sorted(found_words, key = operator.itemgetter(1), reverse = True) # sorted descending
|
700 |
+
|
701 |
+
rated_words = []
|
702 |
+
for word in potential_next_guesses:
|
703 |
+
for tup in found_words_sorted:
|
704 |
+
if tup[0] == word:
|
705 |
+
rated_words.append(tup)
|
706 |
+
|
707 |
+
rated_words = sorted(rated_words, key = operator.itemgetter(1), reverse = True) # sorted descending
|
708 |
+
|
709 |
+
if bias == "common":
|
710 |
+
guess = rated_words[0][0] # word in first position // most frequent word
|
711 |
+
|
712 |
+
if return_stats == False:
|
713 |
+
if verbose == True:
|
714 |
+
if len(potential_next_guesses) <= 40:
|
715 |
+
print(f"Potential next guesses:\n\t{rated_words}\n")
|
716 |
+
print(f"Words guessed so far:\n\t{guessed_words}.\n")
|
717 |
+
record_list.append(f"Potential next guesses:\n\t{potential_next_guesses}\n")
|
718 |
+
record_list.append(f"Words guessed so far:\n\t{guessed_words}.\n")
|
719 |
+
|
720 |
+
if bias == "rare":
|
721 |
+
guess = rated_words[-1][0] # word in last position // least frequent word
|
722 |
+
|
723 |
+
if return_stats == False:
|
724 |
+
if verbose == True:
|
725 |
+
if len(potential_next_guesses) <= 40:
|
726 |
+
print(f"Potential next guesses:\n\t{rated_words}\n")
|
727 |
+
print(f"Words guessed so far:\n\t{guessed_words}.\n")
|
728 |
+
record_list.append(f"Potential next guesses:\n\t{potential_next_guesses}\n")
|
729 |
+
record_list.append(f"Words guessed so far:\n\t{guessed_words}.\n")
|
730 |
+
|
731 |
+
# guess = list(best_next_guesses)[0]
|
732 |
+
guess_entropies.append(get_word_entropy([guess], word_list, normalized = True, ascending = False)[0][1])
|
733 |
+
|
734 |
+
#### Guess has now been made -- what to do next
|
735 |
+
if guess_num == max_guesses: # if at max guesses allowed
|
736 |
+
guessed_words.append(guess)
|
737 |
+
stats_dict['target_guessed'] = False
|
738 |
+
if return_stats == False:
|
739 |
+
if verbose == True:
|
740 |
+
# print("-----------------------------\n")
|
741 |
+
print(f"Unfortunately, the Wordle could not be solved in {max_guesses} guesses.\n")
|
742 |
+
print(f"The target word was '{target}'. Better luck next time!\n")
|
743 |
+
print("-----------------------------\n")
|
744 |
+
record_list.append(f"Unfortunately, the Wordle could not be solved in {max_guesses} guesses.\n")
|
745 |
+
record_list.append(f"The target word was '{target}'. Better luck next time!\n")
|
746 |
+
record_list.append("-----------------------------\n")
|
747 |
+
else:
|
748 |
+
print(f"\nUnfortunately, the Wordle could not be solved in {max_guesses} guesses.")
|
749 |
+
print(f"The target word was '{target}'. Better luck next time!\n")
|
750 |
+
record_list.append(f"\nUnfortunately, the Wordle could not be solved in {max_guesses} guesses.")
|
751 |
+
record_list.append(f"The target word was '{target}'. Better luck next time!\n")
|
752 |
+
break
|
753 |
+
else: # if not at max guesses yet allowed
|
754 |
+
# stats_dict['target_guessed'] = False
|
755 |
+
if return_stats == False:
|
756 |
+
if verbose == True:
|
757 |
+
print(f"Next guess:\n\t'{guess}'")
|
758 |
+
print("\n-----------------------------\n")
|
759 |
+
record_list.append(f"Next guess:\n\t'{guess}'")
|
760 |
+
record_list.append("\n-----------------------------\n")
|
761 |
+
|
762 |
+
if guess == target:
|
763 |
+
guess_num += 1
|
764 |
+
guessed_words.append(guess)
|
765 |
+
stats_dict['target_guessed'] = True
|
766 |
+
|
767 |
+
if return_stats == False:
|
768 |
+
print(f"Guess {guess_num}: '{guess}'\n")
|
769 |
+
print(f"Congratulations! The Wordle has been solved in {guess_num} guesses!")
|
770 |
+
record_list.append(f"Guess {guess_num}: '{guess}'\n")
|
771 |
+
record_list.append(f"Congratulations! The Wordle has been solved in {guess_num} guesses!")
|
772 |
+
|
773 |
+
if max_guesses - guess_num == 0:
|
774 |
+
print(f"Lucky! It was the last guess.")
|
775 |
+
record_list.append(f"Lucky! It was the last guess.")
|
776 |
+
else:
|
777 |
+
print(f"There were still {max_guesses - guess_num} guesses remaining.")
|
778 |
+
record_list.append(f"There were still {max_guesses - guess_num} guesses remaining.")
|
779 |
+
|
780 |
+
if return_stats == False:
|
781 |
+
# stats_dict['target_guessed'] = True
|
782 |
+
print(f"\nThe target word was '{target}'.")
|
783 |
+
print("\n-----------------------------")
|
784 |
+
record_list.append(f"\nThe target word was '{target}'.")
|
785 |
+
record_list.append("\n-----------------------------")
|
786 |
+
break
|
787 |
+
|
788 |
+
#### STATS STUFF
|
789 |
+
mid_guesses_vows = 0
|
790 |
+
mid_guesses_cons = 0
|
791 |
+
avg_perf_letters = 0
|
792 |
+
avg_wrong_pos_letters = 0
|
793 |
+
avg_wrong_letters = 0
|
794 |
+
|
795 |
+
for i, word in enumerate(guessed_words):
|
796 |
+
mid_guesses_vows += count_vows_cons(word, y_vow = True)['vows']
|
797 |
+
mid_guesses_cons += count_vows_cons(word, y_vow = True)['cons']
|
798 |
+
|
799 |
+
for i in range(0, len(guessed_words) - 1):
|
800 |
+
avg_perf_letters += perfect_letts_per_guess[i]
|
801 |
+
avg_wrong_pos_letters += wrong_pos_per_guess[i]
|
802 |
+
avg_wrong_letters += wrong_letts_per_guess[i]
|
803 |
+
|
804 |
+
stats_dict['mid_guesses_avg_vows'] = float(round(mid_guesses_vows / len(guessed_words), 2))
|
805 |
+
stats_dict['mid_guesses_avg_cons'] = float(round(mid_guesses_cons / len(guessed_words), 2))
|
806 |
+
|
807 |
+
stats_dict['avg_perf_letters'] = float(round(np.mean(avg_perf_letters), 2))
|
808 |
+
stats_dict['avg_wrong_pos_letters'] = float(round(np.mean(avg_wrong_pos_letters), 2))
|
809 |
+
stats_dict['avg_wrong_letters'] = float(round(np.mean(avg_wrong_letters), 2))
|
810 |
+
|
811 |
+
# average number of words remaining after each guess -- the higher this is, the luckier the person got (the lower, the more guesses it took)
|
812 |
+
stats_dict['avg_remaining'] = float(round(np.mean(reduction_per_guess), 2))
|
813 |
+
|
814 |
+
# avg entropy of each guessed word relative to all other words possible at that moment -- this should consistently be 100 for the algorithm, but will be different for user
|
815 |
+
if len(guess_entropies) > 1: # in case of guessing it correctly on the first try
|
816 |
+
sum_entropies = 0
|
817 |
+
for entropy in guess_entropies:
|
818 |
+
sum_entropies += entropy
|
819 |
+
|
820 |
+
average_entropy = float(round(sum_entropies / len(guess_entropies), 2))
|
821 |
+
stats_dict['avg_intermediate_guess_entropy'] = average_entropy
|
822 |
+
else:
|
823 |
+
stats_dict['avg_intermediate_guess_entropy'] = float(100)
|
824 |
+
|
825 |
+
expected_guesses = 3.85
|
826 |
+
|
827 |
+
# guess_num = 3
|
828 |
+
# average_entropy = 95
|
829 |
+
luck = round(1 - ((((guess_num / expected_guesses) * (stats_dict['avg_intermediate_guess_entropy'] / 100)) / max_guesses) * 5), 2)
|
830 |
+
stats_dict['luck'] = luck
|
831 |
+
|
832 |
+
stats_dict['bias'] = bias
|
833 |
+
|
834 |
+
|
835 |
+
if record == True:
|
836 |
+
if verbose == True:
|
837 |
+
with open(f"solutions/{guessed_words[0]}_{target}_wizard_detailed.txt", "w") as fout:
|
838 |
+
for line in record_list:
|
839 |
+
fout.write(line + "\n") # write each line of list of printed text to .txt file
|
840 |
+
else:
|
841 |
+
with open(f"solutions/{guessed_words[0]}_{target}_wizard_summary.txt", "w") as fout:
|
842 |
+
for line in record_list:
|
843 |
+
fout.write(line + "\n") # write
|
844 |
+
|
845 |
+
|
846 |
+
# if guess_num <= len(guess):
|
847 |
+
if guess_num <= 6:
|
848 |
+
stats_dict['valid_success'] = True
|
849 |
+
else:
|
850 |
+
stats_dict['valid_success'] = False
|
851 |
+
|
852 |
+
stats_dict['num_guesses'] = float(guess_num)
|
853 |
+
|
854 |
+
if return_stats == True:
|
855 |
+
return stats_dict
|
856 |
+
|
857 |
+
def compare_wordle(word_list: list, max_guesses: int = None, guess_list: list = None,
|
858 |
+
player: str = None, target: str = None,
|
859 |
+
verbose: bool = False,
|
860 |
+
return_stats: bool = False, record: bool = False):
|
861 |
+
"""
|
862 |
+
Mimicking the popular web game, this function matches a current word to a target word automatically, in the most statistically optimal way possible.
|
863 |
+
|
864 |
+
------
|
865 |
+
Parameters:
|
866 |
+
------
|
867 |
+
`word_list`: list
|
868 |
+
list of valid words to be considered
|
869 |
+
`target`: str
|
870 |
+
a string -- must be the same length as `opening_word`
|
871 |
+
`max_guesses`: int
|
872 |
+
the maximum number of attempts allowed to solve the Wordle
|
873 |
+
`verbose`: bool
|
874 |
+
if True, prints progress and explanation of how function solves the puzzle. If False, prints only the guessed word at each guess.
|
875 |
+
`return_stats`: bool
|
876 |
+
if True, prints nothing and returns a dictionary of various statistics about the function's performance trying to solve the puzzle
|
877 |
+
`record`: bool
|
878 |
+
if True, creates a .txt file with the same information printed according to the indicated verbosity
|
879 |
+
|
880 |
+
------
|
881 |
+
Returns:
|
882 |
+
------
|
883 |
+
`stats_dict`: dict
|
884 |
+
dictionary containing various statistics about the function's performance trying to solve the puzzle
|
885 |
+
"""
|
886 |
+
|
887 |
+
stats_dict = {}
|
888 |
+
|
889 |
+
# official_words list seems to not be 100% the same as the real game, so this adds new words to it
|
890 |
+
for word in guess_list:
|
891 |
+
if word not in word_list:
|
892 |
+
word_list.append(word)
|
893 |
+
|
894 |
+
guess = guess_list[0]
|
895 |
+
first_guess = guess_list[0]
|
896 |
+
|
897 |
+
stats_dict['first_guess'] = guess
|
898 |
+
stats_dict['target_word'] = target
|
899 |
+
stats_dict['first_guess_vowels'] = float(count_vows_cons(guess, y_vow = True)['vows'])
|
900 |
+
stats_dict['first_guess_consonants'] = float(count_vows_cons(guess, y_vow = True)['cons'])
|
901 |
+
stats_dict['target_vowels'] = float(count_vows_cons(target, y_vow = True)['vows'])
|
902 |
+
stats_dict['target_consonants'] = float(count_vows_cons(target, y_vow = True)['cons'])
|
903 |
+
|
904 |
+
# get entropy of the first guess word and target word in the entire word_list
|
905 |
+
for tup in get_word_entropy(word_list, word_list, normalized = True):
|
906 |
+
if tup[0] == guess:
|
907 |
+
stats_dict['first_guess_entropy'] = tup[1]
|
908 |
+
if tup[0] == target:
|
909 |
+
stats_dict['target_entropy'] = tup[1]
|
910 |
+
|
911 |
+
guess_entropies = []
|
912 |
+
guess_entropies.append(stats_dict['first_guess_entropy'])
|
913 |
+
|
914 |
+
english_alphabet = "abcdefghijklmnopqrstuvwxyz"
|
915 |
+
|
916 |
+
word_list_sorted_counts = get_letter_counts(english_alphabet, word_list, sort = "descending")
|
917 |
+
|
918 |
+
wordlen = len(guess)
|
919 |
+
letter_positions = set(i for i in range(0, wordlen))
|
920 |
+
|
921 |
+
guess_set = set()
|
922 |
+
perfect_dict = {}
|
923 |
+
wrong_pos_dict = {}
|
924 |
+
wrong_pos_set = set()
|
925 |
+
dont_guess_again = set()
|
926 |
+
|
927 |
+
guessed_words = [] # running set of guessed words
|
928 |
+
guess_num = 0 # baseline for variable
|
929 |
+
dont_guess_words = set()
|
930 |
+
incorrect_positions = []
|
931 |
+
reduction_per_guess = []
|
932 |
+
|
933 |
+
if max_guesses == None: # if no value is passed, default is len(guess)
|
934 |
+
max_guesses = wordlen
|
935 |
+
else: # else it is the value passed
|
936 |
+
max_guesses = max_guesses
|
937 |
+
|
938 |
+
perfect_letts_per_guess = []
|
939 |
+
wrong_pos_per_guess = []
|
940 |
+
wrong_letts_per_guess = []
|
941 |
+
|
942 |
+
record_list = []
|
943 |
+
|
944 |
+
while guess: # while there is any guess -- there are conditions to break it at the bottom
|
945 |
+
|
946 |
+
guess_num += 1
|
947 |
+
|
948 |
+
guessed_words.append(guess)
|
949 |
+
|
950 |
+
# if drama:
|
951 |
+
# time.sleep(drama)
|
952 |
+
|
953 |
+
# guess_num += 1 # each time the guess is processed
|
954 |
+
if return_stats == False:
|
955 |
+
if guess_num == 1:
|
956 |
+
print("-----------------------------\n")
|
957 |
+
record_list.append("-----------------------------\n")
|
958 |
+
|
959 |
+
if return_stats == False:
|
960 |
+
print(f"Guess {guess_num}: '{guess}'")
|
961 |
+
record_list.append(f"Guess {guess_num}: '{guess}'")
|
962 |
+
|
963 |
+
if guess == target:
|
964 |
+
stats_dict['target_guessed'] = True
|
965 |
+
if return_stats == False:
|
966 |
+
if guess_num == 1:
|
967 |
+
print(f"Congratulations! The Wordle has been solved in {guess_num} guess, that's amazingly lucky!")
|
968 |
+
print(f"The target word was {target}")
|
969 |
+
record_list.append(f"Congratulations! The Wordle has been solved in {guess_num} guess, that's amazingly lucky!")
|
970 |
+
record_list.append(f"The target word was {target}")
|
971 |
+
perfect_letts_per_guess.append(5)
|
972 |
+
wrong_pos_per_guess.append(0)
|
973 |
+
wrong_letts_per_guess.append(0)
|
974 |
+
break
|
975 |
+
|
976 |
+
guess_set = set()
|
977 |
+
wrong_pos_set = set()
|
978 |
+
|
979 |
+
#### Step 2 -- ALL PERFECT
|
980 |
+
for i in letter_positions: # number of letters in each word (current word and target word)
|
981 |
+
guess_set.add(guess[i])
|
982 |
+
|
983 |
+
if guess[i] not in perfect_dict:
|
984 |
+
perfect_dict[guess[i]] = set()
|
985 |
+
if guess[i] not in wrong_pos_dict:
|
986 |
+
wrong_pos_dict[guess[i]] = set()
|
987 |
+
|
988 |
+
### EVALUATE CURRENT GUESS
|
989 |
+
if guess[i] == target[i]: # letter == correct and position == correct
|
990 |
+
perfect_dict[guess[i]].add(i)
|
991 |
+
|
992 |
+
if (guess[i] != target[i] and guess[i] in target): # letter == correct and position != correct
|
993 |
+
wrong_pos_dict[guess[i]].add(i)
|
994 |
+
wrong_pos_set.add(guess[i])
|
995 |
+
|
996 |
+
if guess[i] not in target: # if letter is not relevant at all
|
997 |
+
dont_guess_again.add(guess[i])
|
998 |
+
|
999 |
+
#### Step 3 -- ALL PERFECT
|
1000 |
+
next_letters = set()
|
1001 |
+
for letter, positions in perfect_dict.items():
|
1002 |
+
if len(positions) > 0:
|
1003 |
+
next_letters.add(letter)
|
1004 |
+
|
1005 |
+
for letter, positions in wrong_pos_dict.items():
|
1006 |
+
if len(positions) > 0:
|
1007 |
+
next_letters.add(letter)
|
1008 |
+
|
1009 |
+
#### List of tuples of correct letter positions in new valid words. Eg: [('e', 2), ('a', 3)]
|
1010 |
+
perfect_letters = []
|
1011 |
+
for letter, positions in perfect_dict.items():
|
1012 |
+
for pos in positions:
|
1013 |
+
if len(positions) > 0:
|
1014 |
+
perfect_letters.append((letter, pos))
|
1015 |
+
|
1016 |
+
#### all words that have correct letters in same spots
|
1017 |
+
words_matching_correct_all = []
|
1018 |
+
for word in word_list:
|
1019 |
+
word_set = set()
|
1020 |
+
for letter, pos in perfect_letters:
|
1021 |
+
if word[pos] == letter:
|
1022 |
+
words_matching_correct_all.append(word)
|
1023 |
+
|
1024 |
+
#### excluding words with letters in known incorrect positions
|
1025 |
+
for letter, positions in wrong_pos_dict.items():
|
1026 |
+
for pos in positions:
|
1027 |
+
if len(positions) > 0:
|
1028 |
+
if (letter, pos) not in incorrect_positions:
|
1029 |
+
incorrect_positions.append((letter, pos))
|
1030 |
+
|
1031 |
+
# sorting lists of tuples just to make them look nice in the printout
|
1032 |
+
incorrect_positions = sorted(incorrect_positions, key = operator.itemgetter(1), reverse = False)
|
1033 |
+
perfect_letters = sorted(perfect_letters, key = operator.itemgetter(1), reverse = False)
|
1034 |
+
|
1035 |
+
#### all words that have correct letters in incorrect spots -- so they can be excluded efficiently
|
1036 |
+
|
1037 |
+
# print(incorrect_positions)
|
1038 |
+
|
1039 |
+
for word in word_list:
|
1040 |
+
word_set = set()
|
1041 |
+
for letter, pos in incorrect_positions:
|
1042 |
+
if word[pos] == letter:
|
1043 |
+
dont_guess_words.add(word)
|
1044 |
+
for word in word_list:
|
1045 |
+
word_set = set()
|
1046 |
+
for letter, pos in incorrect_positions:
|
1047 |
+
if word[pos] == letter:
|
1048 |
+
dont_guess_words.add(word)
|
1049 |
+
|
1050 |
+
for bad_letter in dont_guess_again:
|
1051 |
+
for word in word_list:
|
1052 |
+
if (bad_letter in word and word not in dont_guess_words):
|
1053 |
+
dont_guess_words.add(word)
|
1054 |
+
|
1055 |
+
if return_stats == False:
|
1056 |
+
if verbose == True:
|
1057 |
+
print(f"Letters in correct positions:\n\t{perfect_letters}\n")
|
1058 |
+
print(f"Letters in incorrect positions:\n\t{incorrect_positions}\n")
|
1059 |
+
print (f"Letters to guess again:\n\t{sorted(list(next_letters), reverse = False)}\n")
|
1060 |
+
print(f"Letters to not guess again:\n\t{sorted(list(dont_guess_again), reverse = False)}\n") # works
|
1061 |
+
record_list.append(f"Letters in correct positions:\n\t{perfect_letters}\n")
|
1062 |
+
record_list.append(f"Letters in incorrect positions:\n\t{incorrect_positions}\n")
|
1063 |
+
record_list.append(f"Letters to guess again:\n\t{sorted(list(next_letters), reverse = False)}\n")
|
1064 |
+
record_list.append(f"Letters to not guess again:\n\t{sorted(list(dont_guess_again), reverse = False)}\n") # works
|
1065 |
+
|
1066 |
+
# Returns True
|
1067 |
+
# print(A.issubset(B)) # "if everything in A is in B", returns Bool
|
1068 |
+
|
1069 |
+
perfect_letts_per_guess.append(len(perfect_letters))
|
1070 |
+
wrong_pos_per_guess.append(len(incorrect_positions))
|
1071 |
+
wrong_letts_per_guess.append(len(dont_guess_again))
|
1072 |
+
|
1073 |
+
potential_next_guesses = set()
|
1074 |
+
middle_set = set()
|
1075 |
+
|
1076 |
+
if len(perfect_letters) == 0 and len(incorrect_positions) == 0: # if there are NEITHER perfect letters, NOR incorrect positions, ....
|
1077 |
+
for word in word_list:
|
1078 |
+
if word not in dont_guess_words:
|
1079 |
+
if word not in guessed_words:
|
1080 |
+
potential_next_guesses.add(word)
|
1081 |
+
|
1082 |
+
# print(f"GUESS {guess_num} : TEST 1-1")
|
1083 |
+
|
1084 |
+
if len(perfect_letters) == 0 and len(incorrect_positions) != 0: # if there are no perfect letters whatsoever, but there ARE incorrect positions ....
|
1085 |
+
for word in word_list:
|
1086 |
+
for incor_letter, incor_pos in incorrect_positions:
|
1087 |
+
if word[incor_pos] != incor_letter:
|
1088 |
+
if word not in dont_guess_words: # just in case
|
1089 |
+
word_set = set()
|
1090 |
+
for letter in word:
|
1091 |
+
word_set.add(letter)
|
1092 |
+
|
1093 |
+
if next_letters.issubset(word_set):
|
1094 |
+
if word not in guessed_words:
|
1095 |
+
if len(dont_guess_again) > 0:
|
1096 |
+
for bad_letter in dont_guess_again:
|
1097 |
+
if bad_letter not in word:
|
1098 |
+
# potential_next_guesses.append(word)
|
1099 |
+
potential_next_guesses.add(word)
|
1100 |
+
else:
|
1101 |
+
potential_next_guesses.add(word)
|
1102 |
+
|
1103 |
+
# print(f"GUESS {guess_num} : TEST 2-1")
|
1104 |
+
|
1105 |
+
else:
|
1106 |
+
for word in word_list:
|
1107 |
+
if word not in dont_guess_words: # just in case
|
1108 |
+
word_set = set()
|
1109 |
+
for letter in word:
|
1110 |
+
word_set.add(letter)
|
1111 |
+
if next_letters.issubset(word_set):
|
1112 |
+
if word not in guessed_words:
|
1113 |
+
# print ("TEST 3-2")
|
1114 |
+
|
1115 |
+
if len(dont_guess_again) > 0:
|
1116 |
+
for bad_letter in dont_guess_again:
|
1117 |
+
if bad_letter not in word:
|
1118 |
+
middle_set.add(word)
|
1119 |
+
else:
|
1120 |
+
middle_set.add(word)
|
1121 |
+
for word in middle_set:
|
1122 |
+
dummy_list = []
|
1123 |
+
for good_lett, good_pos in perfect_letters:
|
1124 |
+
if word[good_pos] == good_lett:
|
1125 |
+
dummy_list.append(1)
|
1126 |
+
if len(dummy_list) == len(perfect_letters):
|
1127 |
+
potential_next_guesses.add(word)
|
1128 |
+
for word in middle_set:
|
1129 |
+
dummy_list = []
|
1130 |
+
for bad_lett, bad_pos in incorrect_positions:
|
1131 |
+
if word[bad_pos] == bad_lett:
|
1132 |
+
dummy_list.append(1)
|
1133 |
+
if len(dummy_list) > 0:
|
1134 |
+
potential_next_guesses.remove(word)
|
1135 |
+
|
1136 |
+
# print(f"GUESS {guess_num} : TEST 3-1")
|
1137 |
+
|
1138 |
+
if return_stats == False:
|
1139 |
+
if verbose == True:
|
1140 |
+
print(f"At this point:")
|
1141 |
+
print(f"\t{len(word_list) - len(potential_next_guesses)}, {round((len(word_list) - len(potential_next_guesses)) / len(word_list) * 100, 2)}% of total words have been eliminated, and")
|
1142 |
+
print(f"\t{len(potential_next_guesses)}, {round(len(potential_next_guesses) / len(word_list) * 100, 2)}% of total words remain possible.\n")
|
1143 |
+
record_list.append(f"At this point:")
|
1144 |
+
record_list.append(f"\t{len(word_list) - len(potential_next_guesses)}, {round((len(word_list) - len(potential_next_guesses)) / len(word_list) * 100, 2)}% of total words have been eliminated, and")
|
1145 |
+
record_list.append(f"\t{len(potential_next_guesses)}, {round(len(potential_next_guesses) / len(word_list) * 100, 2)}% of total words remain possible.\n")
|
1146 |
+
|
1147 |
+
reduction_per_guess.append(len(potential_next_guesses))
|
1148 |
+
|
1149 |
+
#### Guessing next word
|
1150 |
+
if len(potential_next_guesses) == 1:
|
1151 |
+
|
1152 |
+
if return_stats == False:
|
1153 |
+
if verbose == True:
|
1154 |
+
print(f"The only remaining possible word is:\n\t'{list(potential_next_guesses)[0]}'\n")
|
1155 |
+
record_list.append(f"The only remaining possible word is:\n\t'{list(potential_next_guesses)[0]}'\n")
|
1156 |
+
|
1157 |
+
# guess = list(potential_next_guesses)[0]
|
1158 |
+
del guess_list[0]
|
1159 |
+
# print (guess_list)
|
1160 |
+
guess = guess_list[0]
|
1161 |
+
guess_entropies.append(get_word_entropy([guess], word_list, normalized = True, ascending = False)[0][1])
|
1162 |
+
|
1163 |
+
else:
|
1164 |
+
|
1165 |
+
best_next_guesses = list(potential_next_guesses)
|
1166 |
+
word_ratings = get_word_entropy(best_next_guesses, word_list, normalized = True, ascending = False) # "internal" ratings
|
1167 |
+
|
1168 |
+
del guess_list[0]
|
1169 |
+
# print (guess_list)
|
1170 |
+
guess = guess_list[0]
|
1171 |
+
|
1172 |
+
guess_entropies.append(get_word_entropy([guess], word_list, normalized = True, ascending = False)[0][1])
|
1173 |
+
|
1174 |
+
if return_stats == False:
|
1175 |
+
if verbose == True:
|
1176 |
+
if len(word_ratings) <= 40:
|
1177 |
+
print(f"All potential next guesses:\n\t{word_ratings}\n")
|
1178 |
+
print(f"Words guessed so far:\n\t{guessed_words}.\n")
|
1179 |
+
record_list.append(f"Potential next guesses:\n\t{word_ratings}\n")
|
1180 |
+
record_list.append(f"Words guessed so far:\n\t{guessed_words}.\n")
|
1181 |
+
else:
|
1182 |
+
print(f"The top 40 potential next guesses are:\n\t{word_ratings[:40]}\n")
|
1183 |
+
print(f"Words guessed so far:\n\t{guessed_words}.\n")
|
1184 |
+
record_list.append(f"The top 40 potential next guesses are::\n\t{word_ratings[:40]}\n")
|
1185 |
+
record_list.append(f"Words guessed so far:\n\t{guessed_words}.\n")
|
1186 |
+
|
1187 |
+
#### Guess has now been made -- what to do next
|
1188 |
+
if guess_num == max_guesses: # if at max guesses allowed
|
1189 |
+
guessed_words.append(guess)
|
1190 |
+
stats_dict['target_guessed'] = False
|
1191 |
+
if return_stats == False:
|
1192 |
+
if verbose == True:
|
1193 |
+
# print("-----------------------------\n")
|
1194 |
+
print(f"Unfortunately, the Wordle could not be solved in {max_guesses} guesses.\n")
|
1195 |
+
print(f"The target word was '{target}'. Better luck next time!\n")
|
1196 |
+
print("-----------------------------\n")
|
1197 |
+
record_list.append(f"Unfortunately, the Wordle could not be solved in {max_guesses} guesses.\n")
|
1198 |
+
record_list.append(f"The target word was '{target}'. Better luck next time!\n")
|
1199 |
+
record_list.append("-----------------------------\n")
|
1200 |
+
else:
|
1201 |
+
print(f"\nUnfortunately, the Wordle could not be solved in {max_guesses} guesses.")
|
1202 |
+
print(f"The target word was '{target}'. Better luck next time!\n")
|
1203 |
+
record_list.append(f"\nUnfortunately, the Wordle could not be solved in {max_guesses} guesses.")
|
1204 |
+
record_list.append(f"The target word was '{target}'. Better luck next time!\n")
|
1205 |
+
break
|
1206 |
+
else: # if not at max guesses yet allowed
|
1207 |
+
# stats_dict['target_guessed'] = False
|
1208 |
+
if return_stats == False:
|
1209 |
+
if verbose == True:
|
1210 |
+
print(f"Next guess:\n\t'{guess}'")
|
1211 |
+
print("\n-----------------------------\n")
|
1212 |
+
record_list.append(f"Next guess:\n\t'{guess}'")
|
1213 |
+
record_list.append("\n-----------------------------\n")
|
1214 |
+
|
1215 |
+
if guess == target:
|
1216 |
+
guess_num += 1
|
1217 |
+
guessed_words.append(guess)
|
1218 |
+
stats_dict['target_guessed'] = True
|
1219 |
+
|
1220 |
+
if return_stats == False:
|
1221 |
+
print(f"Guess {guess_num}: '{guess}'\n")
|
1222 |
+
print(f"Congratulations! The Wordle has been solved in {guess_num} guesses!")
|
1223 |
+
record_list.append(f"Guess {guess_num}: '{guess}'\n")
|
1224 |
+
record_list.append(f"Congratulations! The Wordle has been solved in {guess_num} guesses!")
|
1225 |
+
|
1226 |
+
if max_guesses - guess_num == 0:
|
1227 |
+
print(f"Lucky! It was the last guess.")
|
1228 |
+
record_list.append(f"Lucky! It was the last guess.")
|
1229 |
+
else:
|
1230 |
+
print(f"There were still {max_guesses - guess_num} guesses remaining.")
|
1231 |
+
record_list.append(f"There were still {max_guesses - guess_num} guesses remaining.")
|
1232 |
+
|
1233 |
+
if return_stats == False:
|
1234 |
+
# stats_dict['target_guessed'] = True
|
1235 |
+
print(f"\nThe target word was '{target}'.")
|
1236 |
+
print("\n-----------------------------")
|
1237 |
+
record_list.append(f"\nThe target word was '{target}'.")
|
1238 |
+
record_list.append("\n-----------------------------")
|
1239 |
+
break
|
1240 |
+
|
1241 |
+
#### STATS STUFF
|
1242 |
+
mid_guesses_vows = 0
|
1243 |
+
mid_guesses_cons = 0
|
1244 |
+
avg_perf_letters = 0
|
1245 |
+
avg_wrong_pos_letters = 0
|
1246 |
+
avg_wrong_letters = 0
|
1247 |
+
|
1248 |
+
for i, word in enumerate(guessed_words):
|
1249 |
+
mid_guesses_vows += count_vows_cons(word, y_vow = True)['vows']
|
1250 |
+
mid_guesses_cons += count_vows_cons(word, y_vow = True)['cons']
|
1251 |
+
|
1252 |
+
for i in range(0, len(guessed_words) - 1):
|
1253 |
+
avg_perf_letters += perfect_letts_per_guess[i]
|
1254 |
+
avg_wrong_pos_letters += wrong_pos_per_guess[i]
|
1255 |
+
avg_wrong_letters += wrong_letts_per_guess[i]
|
1256 |
+
|
1257 |
+
stats_dict['mid_guesses_avg_vows'] = float(round(mid_guesses_vows / len(guessed_words), 2))
|
1258 |
+
stats_dict['mid_guesses_avg_cons'] = float(round(mid_guesses_cons / len(guessed_words), 2))
|
1259 |
+
|
1260 |
+
stats_dict['avg_perf_letters'] = float(round(np.mean(avg_perf_letters), 2))
|
1261 |
+
stats_dict['avg_wrong_pos_letters'] = float(round(np.mean(avg_wrong_pos_letters), 2))
|
1262 |
+
stats_dict['avg_wrong_letters'] = float(round(np.mean(avg_wrong_letters), 2))
|
1263 |
+
|
1264 |
+
# average number of words remaining after each guess -- the higher this is, the luckier the person got (the lower, the more guesses it took)
|
1265 |
+
stats_dict['avg_remaining'] = float(round(np.mean(reduction_per_guess), 2))
|
1266 |
+
|
1267 |
+
# avg entropy of each guessed word relative to all other words possible at that moment -- this should consistently be 100 for the algorithm, but will be different for user
|
1268 |
+
if len(guess_entropies) > 1: # in case of guessing it correctly on the first try
|
1269 |
+
sum_entropies = 0
|
1270 |
+
for entropy in guess_entropies:
|
1271 |
+
sum_entropies += entropy
|
1272 |
+
|
1273 |
+
average_entropy = float(round(sum_entropies / len(guess_entropies), 2))
|
1274 |
+
stats_dict['avg_intermediate_guess_entropy'] = average_entropy
|
1275 |
+
else:
|
1276 |
+
stats_dict['avg_intermediate_guess_entropy'] = float(100)
|
1277 |
+
|
1278 |
+
# stats_dict['bias'] = bias
|
1279 |
+
|
1280 |
+
if record == True:
|
1281 |
+
if verbose == True:
|
1282 |
+
with open(f"solutions/{guessed_words[0]}_{target}_wizard_detailed.txt", "w") as fout:
|
1283 |
+
for line in record_list:
|
1284 |
+
fout.write(line + "\n") # write each line of list of printed text to .txt file
|
1285 |
+
else:
|
1286 |
+
with open(f"solutions/{guessed_words[0]}_{target}_wizard_summary.txt", "w") as fout:
|
1287 |
+
for line in record_list:
|
1288 |
+
fout.write(line + "\n") # write
|
1289 |
+
|
1290 |
+
# if guess_num <= len(guess):
|
1291 |
+
if guess_num <= 6:
|
1292 |
+
stats_dict['valid_success'] = True
|
1293 |
+
else:
|
1294 |
+
stats_dict['valid_success'] = False
|
1295 |
+
|
1296 |
+
stats_dict['player'] = player
|
1297 |
+
|
1298 |
+
stats_dict['num_guesses'] = float(guess_num)
|
1299 |
+
|
1300 |
+
wizard_dict = wordle_wizard(word_list = word_list, max_guesses = max_guesses,
|
1301 |
+
guess = first_guess, target = target, bias = 'entropy',
|
1302 |
+
random_guess = False, random_target = False,
|
1303 |
+
verbose = False, drama = 0, return_stats = return_stats, record = False)
|
1304 |
+
|
1305 |
+
wizard_dict['player'] = "wizard"
|
1306 |
+
del wizard_dict['bias'] # leftover from the wordle_wizard() output stats_dict, but isn't relevant anymore
|
1307 |
+
wizard_dict['luck'] = 0
|
1308 |
+
|
1309 |
+
wizard_dict['expected_guesses'] = wizard_dict['num_guesses']
|
1310 |
+
stats_dict['expected_guesses'] = wizard_dict['num_guesses']
|
1311 |
+
|
1312 |
+
expected_guesses = wizard_dict['num_guesses']
|
1313 |
+
# stats_dict['luck'] = round((1 - (guess_num / expected_guesses)) * (stats_dict['avg_intermediate_guess_entropy'] / 100), 2)
|
1314 |
+
stats_dict['luck'] = round((1 - ((guess_num / expected_guesses)) * (stats_dict['avg_intermediate_guess_entropy'] / 100)), 2)
|
1315 |
+
|
1316 |
+
stats_master = {}
|
1317 |
+
for metric, result in stats_dict.items():
|
1318 |
+
if metric in stats_master:
|
1319 |
+
stats_master[metric].append(result)
|
1320 |
+
else:
|
1321 |
+
stats_master[metric] = []
|
1322 |
+
stats_master[metric].append(result)
|
1323 |
+
|
1324 |
+
for metric, result in wizard_dict.items():
|
1325 |
+
stats_master[metric].append(result)
|
1326 |
+
|
1327 |
+
if return_stats == True:
|
1328 |
+
return stats_master
|
1329 |
+
|
1330 |
+
def convert_row(df, row):
|
1331 |
+
"""
|
1332 |
+
Converts row of passed pandas dataFrame object into usable inputs for `compare_wordle()` function
|
1333 |
+
|
1334 |
+
------
|
1335 |
+
Parameters:
|
1336 |
+
------
|
1337 |
+
`df`: pandas df object
|
1338 |
+
pandas dataFrame object
|
1339 |
+
`row`: int
|
1340 |
+
row number of pd df object
|
1341 |
+
|
1342 |
+
------
|
1343 |
+
Returns:
|
1344 |
+
------
|
1345 |
+
3-tuple containing:
|
1346 |
+
`guess_list`: list
|
1347 |
+
list of words guessed in this playthrough of passed iteration of the puzzle
|
1348 |
+
`target`: str
|
1349 |
+
target word of passed iteration of the puzzle
|
1350 |
+
`player`: str
|
1351 |
+
name of player of passed iteration of the puzzle
|
1352 |
+
"""
|
1353 |
+
|
1354 |
+
df.fillna("none", inplace = True)
|
1355 |
+
df.loc[row, :].str.lower()
|
1356 |
+
|
1357 |
+
list_1 = df.loc[row, :].tolist()
|
1358 |
+
# print(list_1)
|
1359 |
+
|
1360 |
+
player = list_1[0]
|
1361 |
+
del list_1[0]
|
1362 |
+
target = list_1[0]
|
1363 |
+
del list_1[0]
|
1364 |
+
|
1365 |
+
to_delete = []
|
1366 |
+
for i, word in enumerate(list_1):
|
1367 |
+
if word == "none":
|
1368 |
+
to_delete.append(i)
|
1369 |
+
|
1370 |
+
to_delete = sorted(to_delete, reverse = True) # this has to be done or else index will be eventually be out of range
|
1371 |
+
|
1372 |
+
for pos in to_delete:
|
1373 |
+
del list_1[pos]
|
1374 |
+
|
1375 |
+
guess_list = list_1
|
1376 |
+
|
1377 |
+
lower_player = player.lower()
|
1378 |
+
lower_target = target.lower()
|
1379 |
+
lower_guess_list = [word.lower() for word in guess_list]
|
1380 |
+
|
1381 |
+
return (lower_player, lower_target, lower_guess_list)
|
1382 |
+
|
1383 |
+
def create_compared_df(player_df, to_csv: bool = False, show_shapes: bool = False):
|
1384 |
+
"""
|
1385 |
+
Creates master df of player wordle scores compared to how wordle_wizard would perform on the same puzzles
|
1386 |
+
|
1387 |
+
Parameters:
|
1388 |
+
-----
|
1389 |
+
`player_df`: Pandas dataFrame object
|
1390 |
+
df of player scores of wordle puzzles
|
1391 |
+
`to_csv`: bool
|
1392 |
+
If True, writes returned df to csv
|
1393 |
+
`show_shapes`: bool
|
1394 |
+
If True, prints shape of new df before and after deleting duplicate rows (created by wordle_wizard running the same puzzles multiple times)
|
1395 |
+
|
1396 |
+
Returns:
|
1397 |
+
-----
|
1398 |
+
`df_master`: Pandas dataFrame object
|
1399 |
+
df of player scores and wordle_wizard scores of wordle puzzles
|
1400 |
+
"""
|
1401 |
+
|
1402 |
+
stats_master = {}
|
1403 |
+
excepts = []
|
1404 |
+
for row in player_df.index:
|
1405 |
+
player = convert_row(player_df, row)[0]
|
1406 |
+
target_word = convert_row(player_df, row)[1]
|
1407 |
+
guess_list = convert_row(player_df, row)[2]
|
1408 |
+
try:
|
1409 |
+
complete = compare_wordle(word_list = official_words, max_guesses = 6,
|
1410 |
+
guess_list = guess_list, player = player, target = target_word,
|
1411 |
+
verbose = True, return_stats = True, record = False)
|
1412 |
+
for metric, results in complete.items():
|
1413 |
+
if metric in stats_master:
|
1414 |
+
for result in results:
|
1415 |
+
stats_master[metric].append(result)
|
1416 |
+
else:
|
1417 |
+
stats_master[metric] = []
|
1418 |
+
for result in results:
|
1419 |
+
stats_master[metric].append(result)
|
1420 |
+
except:
|
1421 |
+
AttributeError
|
1422 |
+
excepts.append(guess_list)
|
1423 |
+
|
1424 |
+
df_master = pd.DataFrame(stats_master)
|
1425 |
+
# print(df_master.columns.tolist())
|
1426 |
+
|
1427 |
+
# Re-organizing columns to a more logical order (for viewing)
|
1428 |
+
df_master = df_master[['first_guess', 'target_word', 'player', 'num_guesses', 'expected_guesses', 'luck', 'first_guess_vowels', 'first_guess_consonants',
|
1429 |
+
'target_vowels', 'target_consonants', 'first_guess_entropy', 'target_entropy',
|
1430 |
+
'target_guessed', 'mid_guesses_avg_vows', 'mid_guesses_avg_cons', 'avg_perf_letters',
|
1431 |
+
'avg_wrong_pos_letters', 'avg_wrong_letters', 'avg_remaining', 'avg_intermediate_guess_entropy',
|
1432 |
+
'valid_success']]
|
1433 |
+
|
1434 |
+
# print(excepts)
|
1435 |
+
if show_shapes == True:
|
1436 |
+
print(df_master.shape) # check shape before deleting dups
|
1437 |
+
|
1438 |
+
# Delete duplicate rows (some created by process)
|
1439 |
+
df_master.drop_duplicates(inplace = True)
|
1440 |
+
|
1441 |
+
if to_csv == True:
|
1442 |
+
df_master.to_csv('compared_data/players_compared.csv') # write new data to csv
|
1443 |
+
|
1444 |
+
if show_shapes == True:
|
1445 |
+
print(df_master.shape) # check shape after deleting dups
|
1446 |
+
|
1447 |
+
return df_master
|