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@popey456963
Created April 2, 2021 17:20
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Codenames bot
import math
import random
import numpy as np
import time
from fastapi import FastAPI
from pydantic import BaseModel
from typing import List
beginInit = time.time()
gloves = {row[0]: (np.array([float(val) for val in row[1:]])) for row in (
[line.split() for line in open('glove.6B.300d.txt').read().strip().split("\n")])}
mags = {word: np.sqrt(np.dot(vec, vec)) for (word, vec) in gloves.items()}
print("took", str(time.time() - beginInit), "seconds to start up")
expected = []
findWordsTime = 0
app = FastAPI()
def cosSim(word1, word2):
# safely returns cosine similarity
if word1 not in gloves or word2 not in gloves:
return 0
vec1 = gloves[word1]
vec2 = gloves[word2]
num = np.dot(vec1, vec2)
denom = mags[word1]*mags[word2]
return num/denom
def cut(simScore):
# reduces noise of unassociated words
thres = 0.2
if simScore < thres and simScore > -1*thres:
return 0
else:
return simScore
def scoreWord(word1, roles):
# returns a list of associated words to word1 from the `good side`
wordscores = [(role, cut(cosSim(word1, word2)), word2)
for word2, (role, revealed) in roles.items()
if not revealed]
sorted_scores = sorted(wordscores, key=lambda x: x[1])
sorted_scores.reverse()
for x in range(len(wordscores)):
if (sorted_scores[x][0] < 0 or sorted_scores[x][1] == 0) and x > 0:
return sorted_scores[:x]
elif sorted_scores[x][0] < 0:
return []
return []
def findWords(roles):
# for each number, returns the best list of words of that length
start = time.time()
bests = {}
bestScores = [0] * 25
for word1 in gloves.keys():
exp = scoreWord(word1, roles)
count = len(exp)
score = sum([x[1] for x in exp])
if score > bestScores[count] and word1 not in roles.keys():
bestScores[count] = score
bests[count] = (word1, bestScores[count], exp)
print("took", str(time.time() - start), "seconds to find words")
print(bests)
return bests
def pickBest(bests):
# picks the best length of list
# bests cannot be empty
global expected
bestScore = 0
bestList = []
for wordList in bests.items():
if wordList[1][1] > bestScore:
bestScore = wordList[1][1]
bestList = wordList
print(bestList[1][0], bestList[0])
expected = bestList[1][2]
return {
'word': bestList[1][0],
'count': bestList[0],
'expected': bestList[1][2]
}
class Entry(BaseModel):
word: str
team: str
picked: bool
class Board(BaseModel):
board: List[Entry]
team: str
def other_team(team):
if team == 'red':
return 'blue'
if team == 'blue':
return 'red'
@app.post('/api/v1/word')
async def pickWord(board: Board):
roles = {}
for entry in board.board:
score = 0
if board.team == entry.team:
score = 1
if entry.team == 'civilian':
score = -8
if entry.team == 'assassin':
score = -20
if other_team(board.team) == entry.team:
score = -8
roles[entry.word.lower()] = (score, entry.picked)
print(roles)
print(len(roles.keys()))
res = pickBest(findWords(roles))
return res
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