#!/usr/bin/env python
import pickle

import gym
import numpy as np
import pandas as pd
from scipy.stats import gaussian_kde as gkde
from scipy.stats import norm


def train(data):
    D = pd.DataFrame(data)
    sd = np.array([1.0, 0.5, 0.2, 0.5])
    D["qoi"] = D["obs"].apply(lambda o: np.sum(o, axis=0) / sd / np.sqrt(len(o)))
    D["i"] = D["lam"].apply(lambda l: norm.pdf(l).prod())
    D["o"] = D["qoi"].apply(lambda q: norm.pdf(q).prod())
    Q = np.array(D["qoi"].to_list()).reshape(-1, 4)
    K = [gkde(Q[:, i]) for i in range(4)]
    D["p"] = D["qoi"].apply(lambda q: np.prod([K[i].pdf(q[i]) for i in range(4)]))
    D["u"] = D["i"] * D["o"] / D["p"]
    mud_point_idx = D["u"].argmax()
    mud_point = D["lam"].iloc[mud_point_idx]
    print(f"MUD Point {mud_point_idx}: {mud_point}")
    return mud_point


def test(decision=np.array([-0.09, -0.71, -0.43, -0.74]), seed=1992):
    env = gym.make("CartPole-v1")
    observation, info = env.reset(seed=seed, return_info=True)
    score = 0
    for i in range(10000):
        action = 1 if decision.T @ observation < 0 else 0
        observation, reward, done, info = env.step(action)
        score += reward
        env.render()
        if done:
            if score == 500:
                print("WIN")
            else:
                print(f"LOSE: {int(score)}")
            score = 0  # reset score
            observation, info = env.reset(return_info=True)
    env.close()


if __name__ == "__main__":
    data = pickle.load(open("data.pkl", "rb"))
    mud_point = train(data)
    test(mud_point)