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140 lines
4.5 KiB
140 lines
4.5 KiB
# pylint: skip-file
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import time
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import unittest
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import numpy as np
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from fastcluster import linkage_vector
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from scipy.cluster import _hierarchy
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from scipy.spatial.distance import pdist
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from selfdrive.controls.lib.cluster.fastcluster_py import hclust, ffi
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from selfdrive.controls.lib.cluster.fastcluster_py import cluster_points_centroid
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def fcluster(Z, t, criterion='inconsistent', depth=2, R=None, monocrit=None):
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# supersimplified function to get fast clustering. Got it from scipy
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Z = np.asarray(Z, order='c')
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n = Z.shape[0] + 1
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T = np.zeros((n,), dtype='i')
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_hierarchy.cluster_dist(Z, T, float(t), int(n))
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return T
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TRACK_PTS = np.array([[59.26000137, -9.35999966, -5.42500019],
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[91.61999817, -0.31999999, -2.75],
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[31.38000031, 0.40000001, -0.2],
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[89.57999725, -8.07999992, -18.04999924],
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[53.42000122, 0.63999999, -0.175],
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[31.38000031, 0.47999999, -0.2],
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[36.33999939, 0.16, -0.2],
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[53.33999939, 0.95999998, -0.175],
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[59.26000137, -9.76000023, -5.44999981],
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[33.93999977, 0.40000001, -0.22499999],
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[106.74000092, -5.76000023, -18.04999924]])
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CORRECT_LINK = np.array([[2., 5., 0.07999998, 2.],
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[4., 7., 0.32984889, 2.],
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[0., 8., 0.40078104, 2.],
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[6., 9., 2.41209933, 2.],
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[11., 14., 3.76342275, 4.],
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[12., 13., 13.02297651, 4.],
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[1., 3., 17.27626057, 2.],
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[10., 17., 17.92918845, 3.],
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[15., 16., 23.68525366, 8.],
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[18., 19., 52.52351319, 11.]])
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CORRECT_LABELS = np.array([7, 1, 4, 2, 6, 4, 5, 6, 7, 5, 3], dtype=np.int32)
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def plot_cluster(pts, idx_old, idx_new):
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import matplotlib.pyplot as plt
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m = 'Set1'
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plt.figure()
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plt.subplot(1, 2, 1)
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plt.scatter(pts[:, 0], pts[:, 1], c=idx_old, cmap=m)
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plt.title("Old")
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plt.colorbar()
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plt.subplot(1, 2, 2)
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plt.scatter(pts[:, 0], pts[:, 1], c=idx_new, cmap=m)
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plt.title("New")
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plt.colorbar()
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plt.show()
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def same_clusters(correct, other):
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correct = np.asarray(correct)
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other = np.asarray(other)
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if len(correct) != len(other):
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return False
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for i in range(len(correct)):
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c = np.where(correct == correct[i])
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o = np.where(other == other[i])
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if not np.array_equal(c, o):
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return False
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return True
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class TestClustering(unittest.TestCase):
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def test_scipy_clustering(self):
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old_link = linkage_vector(TRACK_PTS, method='centroid')
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old_cluster_idxs = fcluster(old_link, 2.5, criterion='distance')
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np.testing.assert_allclose(old_link, CORRECT_LINK)
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np.testing.assert_allclose(old_cluster_idxs, CORRECT_LABELS)
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def test_pdist(self):
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pts = np.ascontiguousarray(TRACK_PTS, dtype=np.float64)
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pts_ptr = ffi.cast("double *", pts.ctypes.data)
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n, m = pts.shape
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out = np.zeros((n * (n - 1) // 2, ), dtype=np.float64)
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out_ptr = ffi.cast("double *", out.ctypes.data)
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hclust.hclust_pdist(n, m, pts_ptr, out_ptr)
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np.testing.assert_allclose(out, np.power(pdist(TRACK_PTS), 2))
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def test_cpp_clustering(self):
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pts = np.ascontiguousarray(TRACK_PTS, dtype=np.float64)
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pts_ptr = ffi.cast("double *", pts.ctypes.data)
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n, m = pts.shape
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labels = np.zeros((n, ), dtype=np.int32)
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labels_ptr = ffi.cast("int *", labels.ctypes.data)
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hclust.cluster_points_centroid(n, m, pts_ptr, 2.5**2, labels_ptr)
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self.assertTrue(same_clusters(CORRECT_LABELS, labels))
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def test_cpp_wrapper_clustering(self):
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labels = cluster_points_centroid(TRACK_PTS, 2.5)
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self.assertTrue(same_clusters(CORRECT_LABELS, labels))
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def test_random_cluster(self):
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np.random.seed(1337)
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N = 1000
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t_old = 0.
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t_new = 0.
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for _ in range(N):
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n = int(np.random.uniform(2, 32))
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x = np.random.uniform(-10, 50, (n, 1))
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y = np.random.uniform(-5, 5, (n, 1))
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vrel = np.random.uniform(-5, 5, (n, 1))
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pts = np.hstack([x, y, vrel])
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t = time.time()
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old_link = linkage_vector(pts, method='centroid')
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old_cluster_idx = fcluster(old_link, 2.5, criterion='distance')
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t_old += time.time() - t
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t = time.time()
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cluster_idx = cluster_points_centroid(pts, 2.5)
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t_new += time.time() - t
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self.assertTrue(same_clusters(old_cluster_idx, cluster_idx))
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if __name__ == "__main__":
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unittest.main()
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