def calculate_weights(self, measure='tfidf'):
        """
        Counts word frequency and calculates tf-idf values for words in every document.

            :param measure: example weights approach (can be one of ``tfidf, binary, tf``).
        """
        from math import log

        # TODO replace with spipy matrices (and calculate with scikit)

        if measure == 'tfidf':
            self.calculate_idf()

        for doc_idx, document in enumerate(self.resulting_documents):
            train_word_count = defaultdict(int)
            self.tf_idfs[doc_idx] = {}
            for word in document:
                train_word_count[word] += 1

            for word in document:
                if measure == "binary":
                    tf = 1
                    idf = 1
                else:
                    tf = train_word_count[word]
                    idf = 1 if measure == "tf" else (self.idf[word] if word in self.idf else None)

                if idf != None:
                    self.tf_idfs[doc_idx][word] = tf * idf
Counts word frequency and calculates tf-idf values for words in every document.

            :param measure: example weights approach (can be one of ``tfidf, binary, tf``).