def test_clustering():
"""Test learning of clusters for joint types."""
first_means = np.asarray([(10, 70), (58, 94), (66, 58), (95, 62)])
second_means = np.asarray([(88, 12), (56, 15), (25, 21), (24, 89)])
fake_locations = np.concatenate([
generate_fake_locations(100, first_means),
generate_fake_locations(100, second_means),
],
axis=0)
np.random.shuffle(fake_locations)
fake_pairs = [(0, 1), (1, 2), (2, 3)]
fake_joints = Joints(fake_locations, fake_pairs)
# Make two clusters for each relationship type. Yes, passing in zeros as
# your scale is stupid, and poor testing practice.
centers = from_dataset(fake_joints, 2, np.zeros(len(fake_locations)), 1)
assert centers.ndim == 3
# Three joints, two clusters per joint, two coordinates (i.e. x, y) per
# cluster
assert centers.shape == (3, 2, 2)
for idx, pair in enumerate(fake_pairs):
first_idx, second_idx = pair
first_mean = first_means[second_idx] - first_means[first_idx]
second_mean = second_means[second_idx] - second_means[first_idx]
found_means = centers[idx]
first_dists = np.linalg.norm(found_means - first_mean, axis=1)
second_dists = np.linalg.norm(found_means - second_mean, axis=1)
# Make sure that each of our specified means are within Euclidean
# distance 1 of at least one found cluster
first_within = first_dists < 1
assert first_within.any()
second_within = second_dists < 1
assert second_within.any()
def test_clustering():
"""Test learning of clusters for joint types."""
first_means = np.asarray([
(10, 70),
(58, 94),
(66, 58),
(95, 62)
])
second_means = np.asarray([
(88, 12),
(56, 15),
(25, 21),
(24, 89)
])
fake_locations = np.concatenate([
generate_fake_locations(100, first_means),
generate_fake_locations(100, second_means),
], axis=0)
np.random.shuffle(fake_locations)
fake_pairs = [
(0, 1),
(1, 2),
(2, 3)
]
fake_joints = Joints(fake_locations, fake_pairs)
# Make two clusters for each relationship type. Yes, passing in zeros as
# your scale is stupid, and poor testing practice.
centers = from_dataset(fake_joints, 2, np.zeros(len(fake_locations)), 1)
assert centers.ndim == 3
# Three joints, two clusters per joint, two coordinates (i.e. x, y) per
# cluster
assert centers.shape == (3, 2, 2)
for idx, pair in enumerate(fake_pairs):
first_idx, second_idx = pair
first_mean = first_means[second_idx] - first_means[first_idx]
second_mean = second_means[second_idx] - second_means[first_idx]
found_means = centers[idx]
first_dists = np.linalg.norm(found_means - first_mean, axis=1)
second_dists = np.linalg.norm(found_means - second_mean, axis=1)
# Make sure that each of our specified means are within Euclidean
# distance 1 of at least one found cluster
first_within = first_dists < 1
assert first_within.any()
second_within = second_dists < 1
assert second_within.any()
def do_pairwise_clustering(train_set, validate_set, test_set, cache_dir,
check_cache=True):
centroids_path = path.join(cache_dir, 'centroids.npy')
if check_cache and path.exists(centroids_path):
logging.info("Loading centroids from cache")
centroids = np.load(centroids_path)
else:
if check_cache:
msg = "Saved centroids not found"
else:
msg = "Ignoring saved centroids, if any"
logging.info(msg + '; deriving centroids')
centroids = from_dataset(
train_set.joints, K, train_set.scales, train_set.template_size
)
logging.info('Caching centroids')
np.save(centroids_path, centroids)
check_cache = False
labels_path = path.join(cache_dir, 'labels.pickle')
if check_cache and path.exists(labels_path):
with open(labels_path) as fp:
train_labels, validate_labels, test_labels = pickle.load(fp)
else:
if check_cache:
msg = "Cached labels not found"
else:
msg = "Ignoring cached labels, if any"
logging.info(msg + '; calculating labels')
logging.info("Labelling training set")
train_labels = TrainingLabels(train_set, centroids)
logging.info("Labelling validation set")
validate_labels = TrainingLabels(validate_set, centroids)
logging.info("Labelling test set")
test_labels = TrainingLabels(test_set, centroids)
logging.info("Pickling labels")
with open(labels_path, 'w') as fp:
pickle.dump((train_labels, validate_labels, test_labels), fp)
check_cache = False
return check_cache, centroids, train_labels, validate_labels, test_labels
