genetics/players/playerVeryGood.py

import numpy as np
import playerConfig


class Player():
    def __init__(self):
        return

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

        if chaos <= 0:
            score=((np.array(own_state)-np.array(known_state_opponent))*playerConfig.Weights).sum()
            if score>0:
                return playerConfig.ACT
            return playerConfig.PASS


        our_first_card = range(known_state_opponent[0]-chaos if known_state_opponent[0]-chaos > 0 else 0, known_state_opponent[0]+1)
        our_second_card = range(known_state_opponent[1]-chaos if known_state_opponent[1]-chaos > 0 else 0, known_state_opponent[1]+1)
        our_third_card = range(known_state_opponent[2]-chaos if known_state_opponent[2]-chaos > 0 else 0, known_state_opponent[2]+1)
        our_fourth_card = range(known_state_opponent[3]-chaos if known_state_opponent[3]-chaos > 0 else 0, known_state_opponent[3]+1)
        print(our_first_card, our_second_card, our_fourth_card, our_third_card)
        our_results = []
        for first in our_first_card:
            for second in our_second_card:
                for third in our_third_card:
                    for fourth in our_fourth_card:
                        our_results.append(((np.array(own_state)-np.array([first, second, third, fourth]))*playerConfig.Weights).sum() > 0)
        our_prob = (our_results.count(True) / len(our_results))*100

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