def gen_feats(self, p_set):
        """
        Generates features based on an iput p_set
        p_set - PredictorSet
        """
        if self._initialized!=True:
            error_message = "Dictionaries have not been initialized."
            log.exception(error_message)
            raise util_functions.InputError(p_set, error_message)

        textual_features = []
        for i in xrange(0,len(p_set._essay_sets)):
            textual_features.append(self._extractors[i].gen_feats(p_set._essay_sets[i]))

        textual_matrix = numpy.concatenate(textual_features, axis=1)
        predictor_matrix = numpy.array(p_set._numeric_features)

        print textual_matrix.shape
        print predictor_matrix.shape

        overall_matrix = numpy.concatenate((textual_matrix, predictor_matrix), axis=1)

        return overall_matrix.copy()
def gen_feats(self, p_set):
        """
        Generates features based on an iput p_set
        p_set - PredictorSet
        """
        if self._initialized!=True:
            error_message = "Dictionaries have not been initialized."
            log.exception(error_message)
            raise util_functions.InputError(p_set, error_message)

        textual_features = []
        for i in xrange(0,len(p_set._essay_sets)):
            textual_features.append(self._extractors[i].gen_feats(p_set._essay_sets[i]))

        textual_matrix = numpy.concatenate(textual_features, axis=1)
        predictor_matrix = numpy.array(p_set._numeric_features)

        print textual_matrix.shape
        print predictor_matrix.shape

        overall_matrix = numpy.concatenate((textual_matrix, predictor_matrix), axis=1)

        return overall_matrix.copy()
def gen_feats ( self , p_set ) : if self . _initialized != True : error_message = "Dictionaries have not been initialized." log . exception ( error_message ) raise util_functions . InputError ( p_set , error_message ) textual_features = [ ] for i in xrange ( 0 , len ( p_set . _essay_sets ) ) : textual_features . append ( self . _extractors [ i ] . gen_feats ( p_set . _essay_sets [ i ] ) ) textual_matrix = numpy . concatenate ( textual_features , axis = 1 ) predictor_matrix = numpy . array ( p_set . _numeric_features ) print textual_matrix . shape print predictor_matrix . shape overall_matrix = numpy . concatenate ( ( textual_matrix , predictor_matrix ) , axis = 1 ) return overall_matrix . copy ( )
Generates features based on an iput p_set
        p_set - PredictorSet
Generates features based on an iput p_set p_set - PredictorSet