def test_query_dual(self):
data = np.array([[1, 1]])
pts = np.array([[1, 1]])
tree = KDTree(data, leafsize=2)
treep = KDTree(pts, leafsize=2)
dens = KDTree.query_dual_tree(tree, treep, 1)
kfit = KernelDensity(bandwidth=1.0,
kernel="gaussian",
breadth_first=True,
leaf_size=2,
metric_params=None).fit(data)
result = kfit.score_samples(pts)
self.assertTrue((dens - np.exp(result)) < 0.001)
def test_query_dual_rand(self):
d = 5
data = np.random.rand(51, d)
pts = np.random.rand(3, d)
tree = KDTree(data, leafsize=4)
treep = KDTree(pts, leafsize=4)
dens = KDTree.query_dual_tree(tree, treep, 1)
kfit = KernelDensity(bandwidth=1.0,
kernel="gaussian",
breadth_first=True,
leaf_size=2,
metric_params=None).fit(data)
result = kfit.score_samples(pts)
self.assertTrue(np.sum(dens - np.exp(result)) < 0.001)