def gen_length_feats(self, e_set):
        """
        Generates length based features from an essay set
        Generally an internal function called by gen_feats
        Returns an array of length features
        e_set - EssaySet object
        """
        text = e_set._text
        lengths = [len(e) for e in text]
        word_counts = [max(len(t),1) for t in e_set._tokens]
        comma_count = [e.count(",") for e in text]
        ap_count = [e.count("'") for e in text]
        punc_count = [e.count(".") + e.count("?") + e.count("!") for e in text]
        chars_per_word = [lengths[m] / float(word_counts[m]) for m in xrange(0, len(text))]

        good_pos_tags,bad_pos_positions= self._get_grammar_errors(e_set._pos,e_set._text,e_set._tokens)
        good_pos_tag_prop = [good_pos_tags[m] / float(word_counts[m]) for m in xrange(0, len(text))]

        length_arr = numpy.array((
        lengths, word_counts, comma_count, ap_count, punc_count, chars_per_word, good_pos_tags,
        good_pos_tag_prop)).transpose()

        return length_arr.copy()
def gen_length_feats(self, e_set):
        """
        Generates length based features from an essay set
        Generally an internal function called by gen_feats
        Returns an array of length features
        e_set - EssaySet object
        """
        text = e_set._text
        lengths = [len(e) for e in text]
        word_counts = [max(len(t),1) for t in e_set._tokens]
        comma_count = [e.count(",") for e in text]
        ap_count = [e.count("'") for e in text]
        punc_count = [e.count(".") + e.count("?") + e.count("!") for e in text]
        chars_per_word = [lengths[m] / float(word_counts[m]) for m in xrange(0, len(text))]

        good_pos_tags,bad_pos_positions= self._get_grammar_errors(e_set._pos,e_set._text,e_set._tokens)
        good_pos_tag_prop = [good_pos_tags[m] / float(word_counts[m]) for m in xrange(0, len(text))]

        length_arr = numpy.array((
        lengths, word_counts, comma_count, ap_count, punc_count, chars_per_word, good_pos_tags,
        good_pos_tag_prop)).transpose()

        return length_arr.copy()
def gen_length_feats ( self , e_set ) : text = e_set . _text lengths = [ len ( e ) for e in text ] word_counts = [ max ( len ( t ) , 1 ) for t in e_set . _tokens ] comma_count = [ e . count ( "," ) for e in text ] ap_count = [ e . count ( "'" ) for e in text ] punc_count = [ e . count ( "." ) + e . count ( "?" ) + e . count ( "!" ) for e in text ] chars_per_word = [ lengths [ m ] / float ( word_counts [ m ] ) for m in xrange ( 0 , len ( text ) ) ] good_pos_tags , bad_pos_positions = self . _get_grammar_errors ( e_set . _pos , e_set . _text , e_set . _tokens ) good_pos_tag_prop = [ good_pos_tags [ m ] / float ( word_counts [ m ] ) for m in xrange ( 0 , len ( text ) ) ] length_arr = numpy . array ( ( lengths , word_counts , comma_count , ap_count , punc_count , chars_per_word , good_pos_tags , good_pos_tag_prop ) ) . transpose ( ) return length_arr . copy ( )
Generates length based features from an essay set
        Generally an internal function called by gen_feats
        Returns an array of length features
        e_set - EssaySet object
Generates length based features from an essay set Generally an internal function called by gen_feats Returns an array of length features e_set - EssaySet object