genetics/players/playerVeryGood.py

import numpy as np
import playerConfig
from itertools import product


class Player():

    def __init__(self):
        return

    def Step(self,step=0,own_state=[],known_state_opponent=[]):
        chaos = 5-step
        own_state_array = np.array(own_state)

        our_first_card = range((known_state_opponent[0]-chaos) * int(known_state_opponent[0]-chaos >= 0), known_state_opponent[0]+1)
        our_second_card = range((known_state_opponent[1]-chaos) * int(known_state_opponent[1]-chaos >= 0), known_state_opponent[1]+1)
        our_third_card = range((known_state_opponent[2]-chaos) * int(known_state_opponent[2]-chaos >= 0), known_state_opponent[2]+1)
        our_fourth_card = range((known_state_opponent[3]-chaos) * int(known_state_opponent[3]-chaos >= 0), known_state_opponent[3]+1)

        result = int(((own_state_array-np.array(known_state_opponent))*playerConfig.Weights).sum() > 0)
        itts = 1
        for combination in product(our_first_card, our_second_card, our_third_card, our_fourth_card):
            result += int(((own_state_array-np.array(combination))*playerConfig.Weights).sum() > 0)
            itts += 1

        our_prob = (result / itts) * 100

        if our_prob > 70:
            return playerConfig.ACT
        if our_prob < 31:
            return playerConfig.PASS
        return playerConfig.LOOK