def test_distance_matrix():
metric = dipymetric.SumPointwiseEuclideanMetric()
for dtype in [np.int32, np.int64, np.float32, np.float64]:
# Compute distances of all tuples spawn by the Cartesian product
# of `data` with itself.
data = (np.random.rand(4, 10, 3) * 10).astype(dtype)
D = dipymetric.distance_matrix(metric, data)
assert_equal(D.shape, (len(data), len(data)))
assert_array_equal(np.diag(D), np.zeros(len(data)))
if metric.is_order_invariant:
# Distance matrix should be symmetric
assert_array_equal(D, D.T)
for i in range(len(data)):
for j in range(len(data)):
assert_equal(D[i, j], dipymetric.dist(metric, data[i],
data[j]))
# Compute distances of all tuples spawn by the Cartesian product
# of `data` with `data2`.
data2 = (np.random.rand(3, 10, 3) * 10).astype(dtype)
D = dipymetric.distance_matrix(metric, data, data2)
assert_equal(D.shape, (len(data), len(data2)))
for i in range(len(data)):
for j in range(len(data2)):
assert_equal(D[i, j], dipymetric.dist(metric, data[i],
data2[j]))
def test_quickbundles_empty_data():
threshold = 10
metric = dipymetric.SumPointwiseEuclideanMetric()
clusters = quickbundles([], metric, threshold)
assert_equal(len(clusters), 0)
assert_equal(len(clusters.centroids), 0)
clusters = quickbundles([], metric, threshold, ordering=[])
assert_equal(len(clusters), 0)
assert_equal(len(clusters.centroids), 0)
def test_quickbundles_2D():
# Test quickbundles clustering using 2D points and the Eulidean metric.
rng = np.random.RandomState(42)
data = []
data += [rng.randn(1, 2) + np.array([0, 0]) for i in range(1)]
data += [rng.randn(1, 2) + np.array([10, 10]) for i in range(2)]
data += [rng.randn(1, 2) + np.array([-10, 10]) for i in range(3)]
data += [rng.randn(1, 2) + np.array([10, -10]) for i in range(4)]
data += [rng.randn(1, 2) + np.array([-10, -10]) for i in range(5)]
data = np.array(data, dtype=dtype)
clusters_truth = [[0], [1, 2], [3, 4, 5], [6, 7, 8, 9],
[10, 11, 12, 13, 14]]
# # Uncomment the following to visualize this test
# import pylab as plt
# plt.plot(*zip(*data[0:1, 0]), linestyle='None', marker='s')
# plt.plot(*zip(*data[1:3, 0]), linestyle='None', marker='o')
# plt.plot(*zip(*data[3:6, 0]), linestyle='None', marker='+')
# plt.plot(*zip(*data[6:10, 0]), linestyle='None', marker='.')
# plt.plot(*zip(*data[10:, 0]), linestyle='None', marker='*')
# plt.show()
# Theorically using a threshold above the following value will not
# produce expected results.
threshold = np.sqrt(2 * (10**2)) - np.sqrt(2)
metric = dipymetric.SumPointwiseEuclideanMetric()
ordering = np.arange(len(data))
for i in range(100):
rng.shuffle(ordering)
clusters = quickbundles(data, metric, threshold, ordering=ordering)
# Check if clusters are the same as 'clusters_truth'
for cluster in clusters:
# Find the corresponding cluster in 'clusters_truth'
for cluster_truth in clusters_truth:
if cluster_truth[0] in cluster.indices:
assert_equal(sorted(cluster.indices),
sorted(cluster_truth))
# Cluster each cluster again using a small threshold
for cluster in clusters:
subclusters = quickbundles(data,
metric,
threshold=0,
ordering=cluster.indices)
assert_equal(len(subclusters), len(cluster))
assert_equal(sorted(itertools.chain(*subclusters)),
sorted(cluster.indices))
# A very large threshold should produce only 1 cluster
clusters = quickbundles(data, metric, threshold=np.inf)
assert_equal(len(clusters), 1)
assert_equal(len(clusters[0]), len(data))
assert_array_equal(clusters[0].indices, range(len(data)))
# A very small threshold should produce only N clusters where N=len(data)
clusters = quickbundles(data, metric, threshold=0)
assert_equal(len(clusters), len(data))
assert_array_equal(list(map(len, clusters)), np.ones(len(data)))
assert_array_equal(
[idx for cluster in clusters for idx in cluster.indices],
range(len(data)))