import pickle
import sys
import torch
from tokenizator import tokenize


def get_x(line, weights, mapping):
    terms = tokenize(line)
    x = len(weights) * [0.]
    x[len(weights) - 1] = 1
    for word in terms:
        if word in mapping:
            x[mapping[word]] += 1
    return torch.tensor(x, dtype=torch.float)


def main():
    w, word_to_index_mapping = pickle.load(open('model.pkl', 'rb'))
    for line in sys.stdin:
        line = line.strip()
        x = get_x(line, w, word_to_index_mapping)
        y = torch.sigmoid(x @ w)
        if y > 0.85:
            y = torch.tensor([0.85])
        elif y < 0.15:
            y = torch.tensor([0.15])

        print(y.item())


main()