def setUpClass(cls):
cls.lim1 = 8
cls.lim2 = 3
cls.lim3 = 4
cls.link_num = 4
cls.points1 = [(float(i) / cls.lim1, ) for i in range(cls.lim1)]
cls.links1 = {
p: [
cls.points1[l] for l in
[random.randint(0, cls.lim1 - 1) for x in range(cls.link_num)]
]
for p in cls.points1
}
cls.points2 = [(float(i) / cls.lim2, float(j) / cls.lim2)
for i in range(cls.lim2) for j in range(cls.lim2)]
cls.links2 = {
p: [
cls.points2[l] for l in [
random.randint(0, cls.lim2**2 - 1)
for x in range(cls.link_num)
]
]
for p in cls.points2
}
cls.points3 = [(float(i) / cls.lim3, float(j) / cls.lim3,
float(k) / cls.lim3) for i in range(cls.lim3)
for j in range(cls.lim3) for k in range(cls.lim3)]
cls.links3 = {
p: [
cls.points3[l] for l in [
random.randint(0, cls.lim3**3 - 1)
for x in range(cls.link_num)
]
]
for p in cls.points3
}
cls.grid1 = Grid(cls.points1, cls.links1)
cls.grid2 = Grid(cls.points2, cls.links2)
cls.grid3 = Grid(cls.points3, cls.links3)
cls.cluster1 = Cluster(cls.grid1)
cls.cluster2 = Cluster(cls.grid2)
cls.cluster3 = Cluster(cls.grid3)
cls.cub1 = Cuboid(numpy.array([0]), numpy.array([1]))
cls.cub2 = Cuboid(numpy.array([0, 0]), numpy.array([1, 1]))
cls.cub3 = Cuboid(numpy.array([0, 0, 0]), numpy.array([1, 1, 1]))
cls.rc1 = RegularCuboid(cls.cluster1)
cls.rc2 = RegularCuboid(cls.cluster2)
cls.rc3 = RegularCuboid(cls.cluster3)
cls.ct1 = build_cluster_tree(cls.rc1)
cls.ct2 = build_cluster_tree(cls.rc2)
cls.ct3 = build_cluster_tree(cls.rc3)
cls.bct1 = build_block_cluster_tree(cls.ct1,
right_cluster_tree=cls.ct1,
admissible_function=admissible)
cls.bct2 = build_block_cluster_tree(cls.ct2,
right_cluster_tree=cls.ct2,
admissible_function=admissible)
cls.bct3 = build_block_cluster_tree(cls.ct3,
right_cluster_tree=cls.ct3,
admissible_function=admissible)
def test_build_hmatrix(self):
full_func = lambda x: numpy.matrix(numpy.ones(x.shape()))
block_func = lambda x: RMat(numpy.matrix(numpy.ones((x.shape()[0], 1))),
numpy.matrix(numpy.ones((x.shape()[1], 1))),
max_rank=1)
lim1 = 2
link_num = 4
points1 = [numpy.array([float(i) / lim1]) for i in range(lim1)]
links1 = [[points1[l] for l in [random.randint(0, lim1 - 1) for x in range(link_num)]] for i in range(lim1)]
grid1 = Grid(points1, links1)
cluster1 = Cluster(grid1)
rc1 = RegularCuboid(cluster1)
ct1 = build_cluster_tree(rc1, max_leaf_size=4)
bct1 = build_block_cluster_tree(ct1, right_cluster_tree=ct1, admissible_function=admissible)
hmat = build_hmatrix(bct1, generate_rmat_function=block_func, generate_full_matrix_function=block_func)
check_rmat = RMat(numpy.matrix(numpy.ones((2, 1))), numpy.matrix(numpy.ones((2, 1))), max_rank=1)
check = HMat(content=check_rmat, shape=(2, 2), parent_index=(0, 0))
self.assertEqual(hmat, check)
lim1 = 8
link_num = 4
points1 = [numpy.array([float(i) / lim1]) for i in range(lim1)]
links1 = [[points1[l] for l in [random.randint(0, lim1 - 1) for x in range(link_num)]] for i in range(lim1)]
grid1 = Grid(points1, links1)
cluster1 = Cluster(grid1)
rc1 = RegularCuboid(cluster1)
ct1 = build_cluster_tree(rc1, max_leaf_size=4)
bct1 = build_block_cluster_tree(ct1, right_cluster_tree=ct1, admissible_function=admissible)
hmat = build_hmatrix(bct1, generate_rmat_function=block_func, generate_full_matrix_function=block_func)
check_rmat = RMat(numpy.matrix(numpy.ones((4, 1))), numpy.matrix(numpy.ones((4, 1))), max_rank=1)
check1 = HMat(content=check_rmat, shape=(4, 4), parent_index=(0, 0))
check2 = HMat(content=check_rmat, shape=(4, 4), parent_index=(0, 4))
check3 = HMat(content=check_rmat, shape=(4, 4), parent_index=(4, 0))
check4 = HMat(content=check_rmat, shape=(4, 4), parent_index=(4, 4))
check = HMat(blocks=[check1, check2, check3, check4], shape=(8, 8), parent_index=(0, 0))
self.assertEqual(hmat, check)
bct1 = build_block_cluster_tree(ct1, right_cluster_tree=ct1, admissible_function=lambda x, y: True)
hmat = build_hmatrix(bct1, generate_rmat_function=block_func, generate_full_matrix_function=block_func)
self.assertIsInstance(hmat, HMat)
points2 = [
np.array([
math.cos(2 * math.pi * (float(i) / lim2)),
math.sin(2 * math.pi * (float(i) / lim2))
]) for i in range(lim2)
]
links2 = [[points2[i + 1]] for i in range(len(points2) - 1)]
links2.append([points2[0]])
grid2 = Grid(points2, links2)
grid2.plot('GridPlot.png')
cluster2 = Cluster(grid2)
rc2 = RegularCuboid(cluster2, )
ct2 = build_cluster_tree(rc2)
ct2.export('dot', 'out2.dot')
bct2 = build_block_cluster_tree(ct2)
# bct2.export('dot', 'out2.dot')
bct2.plot('plotUnitCircle256.png', face_color='#ffffff')
# lim3 = 2 ** 2
# points3 = [np.array([float(i) / lim3, float(j) / lim3, float(k) / lim3]) for i in xrange(lim3) for j in
# xrange(lim3) for k in xrange(lim3)]
# links3 = [[points3[l] for l in [random.randint(0, (lim3 - 1) ** 3) for x in xrange(link_num)]]
# for k in xrange(lim3) for j in xrange(lim3) for i in xrange(lim3)]
# grid3 = Grid(points3, links3)
# cluster3 = Cluster(grid3)
# rc3 = RegularCuboid(cluster3)
# ct3 = build_cluster_tree(rc3)
# bct3 = build_block_cluster_tree(ct3)
def setUpClass(cls):
cls.lim1 = 16
cls.lim2 = 4
cls.lim3 = 4
cls.link_num = 4
cls.points1 = [(float(i) / cls.lim1, ) for i in range(cls.lim1)]
cls.links1 = {
p: [
cls.points1[l] for l in
[random.randint(0, cls.lim1 - 1) for x in range(cls.link_num)]
]
for p in cls.points1
}
cls.points2 = [(float(i) / cls.lim2, float(j) / cls.lim2)
for i in range(cls.lim2) for j in range(cls.lim2)]
cls.links2 = {
p: [
cls.points2[l] for l in [
random.randint(0, cls.lim2**2 - 1)
for x in range(cls.link_num)
]
]
for p in cls.points2
}
cls.points3 = [(float(i) / cls.lim3, float(j) / cls.lim3,
float(k) / cls.lim3) for i in range(cls.lim3)
for j in range(cls.lim3) for k in range(cls.lim3)]
cls.links3 = {
p: [
cls.points3[l] for l in [
random.randint(0, cls.lim3**3 - 1)
for x in range(cls.link_num)
]
]
for p in cls.points3
}
cls.grid1 = Grid(cls.points1, cls.links1)
cls.grid2 = Grid(cls.points2, cls.links2)
cls.grid3 = Grid(cls.points3, cls.links3)
cls.cluster1 = Cluster(cls.grid1)
cls.cluster2 = Cluster(cls.grid2)
cls.cluster3 = Cluster(cls.grid3)
cls.cub1 = Cuboid(numpy.array([0]), numpy.array([1]))
cls.cub2 = Cuboid(numpy.array([0, 0]), numpy.array([1, 1]))
cls.cub3 = Cuboid(numpy.array([0, 0, 0]), numpy.array([1, 1, 1]))
cls.rc1 = RegularCuboid(cls.cluster1)
cls.rc2 = RegularCuboid(cls.cluster2)
cls.rc3 = RegularCuboid(cls.cluster3)
cls.ct1 = build_cluster_tree(cls.rc1, 4)
cls.ct2 = build_cluster_tree(cls.rc2, 4)
cls.ct3 = build_cluster_tree(cls.rc3, 4)
cls.bct1 = build_block_cluster_tree(cls.ct1,
cls.ct1,
admissible_function=admissible)
cls.bct2 = build_block_cluster_tree(cls.ct2,
cls.ct2,
admissible_function=admissible)
cls.bct3 = build_block_cluster_tree(cls.ct3,
cls.ct3,
admissible_function=admissible)
def build_full(x):
return numpy.matrix(numpy.ones(x.shape()))
def build_rmat(x):
return RMat(numpy.matrix(numpy.ones((x.shape()[0], 1))),
numpy.matrix(numpy.ones((x.shape()[1], 1))),
max_rank=1)
cls.hmat1 = build_hmatrix(cls.bct1, build_rmat, build_full)
cls.hmat2 = build_hmatrix(cls.bct2, build_rmat, build_full)
cls.hmat3 = build_hmatrix(cls.bct3, build_rmat, build_full)