dest="queries", required=False) parser.add_argument("-c", dest="C", required=False) return parser model = None if __name__ == "__main__": parser = cmdline_parser() args = parser.parse_args() gta = list(SeqIO.parse(args.gta, "fasta")) viral = list(SeqIO.parse(args.viral, "fasta")) model = Classifier(gta, viral) queries = args.queries.split(',') for query in queries: query_seqs = list(SeqIO.parse(query, "fasta")) gene_num = int(query[query.find('orfg')+4]) if not model: # dist_matrix = parse_dists.get_dist_matrix(gene_num) model = Classifier(gta, viral) model.get_training_set() # model.get_weights() SVs = model.learn_SVM_model(float(args.C)) print model.classify(query_seqs)[1]