def load_dataset(args):
path = args.dataset
keep_groups = args.keep_groups
print('Loading data set "%s" ...' % path)
print(' features: %s' % args.features)
print(' type: %s' % args.motion_type)
X, y, target_names, groups, _ = load_manifest(
path,
args.motion_type,
feature_names=args.features,
use_cache=not args.disable_cache)
assert len(X) == len(y)
if not keep_groups:
groups = None
if not args.disable_shuffle:
if args.permutation is not None:
permutation = np.load(open(args.permutation))
X, y = [X[i] for i in permutation], y[permutation, :]
else:
X, y, groups, permutation = shuffle(X, y, groups=groups)
print('Shuffling all data with permutation (keep_groups=%s):\n%s' %
(keep_groups, permutation))
if args.output_dir is not None:
permutation_path = os.path.join(args.output_dir, 'permutation.npy')
np.save(permutation_path, permutation)
print('Saved permutation to file "%s"' % permutation_path)
return Dataset(X, y, target_names, groups)
def load_dataset(args):
path = args.dataset
keep_groups = args.keep_groups
print('Loading data set "%s" ...' % path)
print(' features: %s' % args.features)
print(' type: %s' % args.motion_type)
X, y, target_names, groups, _ = load_manifest(path, args.motion_type, feature_names=args.features, use_cache=not args.disable_cache)
assert len(X) == len(y)
if not keep_groups:
groups = None
if not args.disable_shuffle:
if args.permutation is not None:
permutation = np.load(open(args.permutation))
X, y = [X[i] for i in permutation], y[permutation, :]
else:
X, y, groups, permutation = shuffle(X, y, groups=groups)
print('Shuffling all data with permutation (keep_groups=%s):\n%s' % (keep_groups, permutation))
if args.output_dir is not None:
permutation_path = os.path.join(args.output_dir, 'permutation.npy')
np.save(permutation_path, permutation)
print('Saved permutation to file "%s"' % permutation_path)
return Dataset(X, y, target_names, groups)
def load_dataset(path, motion_type, feature_names, args):
print('Loading data set "%s" ...' % path)
X, y, target_names, groups, lengths = load_manifest(path, motion_type, feature_names=feature_names,
use_cache=not args.disable_cache, normalize=args.normalize)
assert len(X) == len(y)
return Dataset(X, y, target_names, groups, lengths)
