eph2795

from fastText import load_model

model = load_model('official_fasttext_wiki_200_model')

def find_ngrams(string, n):
    ngrams = zip(*[string[i:] for i in range(n)])
    ngrams = [''.join(_) for _ in ngrams]
    return ngrams

string = 'грёзоблаженствующий'

eph2795

import gym
from tqdm import tqdm_notebook
import numpy as np
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')

def get_random_policy():
    return np.random.choice(n_actions, tuple(bins))

eph2795

import gym
import numpy as np
import matplotlib.pyplot as plt

def get_random_policy():
    """
    Build a numpy array representing agent policy.
    This array must have one element per each of 16 environment states.
    Element must be an integer from 0 to 3, representing action
    to take from that state.