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]
