def compute_tf(text):
    tf_text = collections.Counter(text)
    for i in tf_text:
        tf_text[i] = tf_text[i]/float(len(tf_text))
    return tf_text

def compute_idf(word, corpus):
    return math.log10(len(corpus)/sum([1.0 for i in corpus if word in i]))

def compute_tfidf(corpus, num):
    documents_list = []
    for text in corpus:
        tf_idf_dictionary = {}
        computed_tf = compute_tf(text.split(' '))
        for word in computed_tf:
            tf_idf_dictionary[word] = computed_tf[word] * compute_idf(word, corpus)
        #tf_idf_dictionary = collections.Counter(tf_idf_dictionary).most_common(num)
        documents_list.append(tf_idf_dictionary)
    return documents_list