)
parser.add_argument('--node-classifier', metavar='FILE',
help='Train and output a node split classifier.'
)
args = parser.parse_args()
feature_map_function = eval(args.feature_map_function)
if args.load_classifier is not None:
mpf = classifier_probability(eval(args.feature_map_function),
args.load_classifier)
else:
mpf = eval(args.objective_function)
wsg = Rag(args.ws, args.probs, mpf)
features, labels, weights, history, ave_sizes = \
wsg.learn_agglomerate(args.gt, feature_map_function)
print 'shapes: ', features.shape, labels.shape
if args.load_classifier is not None:
try:
f = h5py.File(args.save_training_data)
old_features = array(f['samples'])
old_labels = array(f['labels'])
features = concatenate((features, old_features), 0)
labels = concatenate((labels, old_labels), 0)
except:
pass
print "fitting classifier of size, pos: ", labels.size, (labels==1).sum()
if args.balance_classes:
cw = 'auto'
help='Save node features and labels to FILE.')
parser.add_argument('--node-classifier',
metavar='FILE',
help='Train and output a node split classifier.')
args = parser.parse_args()
feature_map_function = eval(args.feature_map_function)
if args.load_classifier is not None:
mpf = classifier_probability(eval(args.feature_map_function),
args.load_classifier)
else:
mpf = eval(args.objective_function)
wsg = Rag(args.ws, args.probs, mpf)
features, labels, weights, history, ave_sizes = \
wsg.learn_agglomerate(args.gt, feature_map_function)
print 'shapes: ', features.shape, labels.shape
if args.load_classifier is not None:
try:
f = h5py.File(args.save_training_data)
old_features = array(f['samples'])
old_labels = array(f['labels'])
features = concatenate((features, old_features), 0)
labels = concatenate((labels, old_labels), 0)
except:
pass
print "fitting classifier of size, pos: ", labels.size, (labels == 1).sum()
if args.balance_classes:
cw = 'auto'
