import nltk
import numpy as np
#nltk.download('all')
from nltk.stem.porter import PorterStemmer
stemmer = PorterStemmer()
def tokenize(sentence):
    """
        split sentence into array of words/tokens
        a token can be a word or punctuation character, or number
    """
    return nltk.word_tokenize(sentence)

def stem(word):
    
    return stemmer.stem(word.lower())

def bag_of_words(tokenized_sentence, all_words):

    tokenized_sentence = [stem(w) for w in tokenized_sentence]

    bag = np.zeros(len(all_words), dtype=np.float32)
    for idx, w in enumerate(all_words):
        if w in tokenized_sentence:
            bag[idx] = 1.0
    return bag