def test_init(self):
fp = FastPair()
assert fp.min_points == 10
assert isinstance(fp.dist, FunctionType)
assert fp.initialized is False
assert len(fp.points) == 0
assert len(fp.neighbors) == 0
def test_call_and_closest_pair(self, PointSet):
ps = PointSet
fp = FastPair().build(ps)
cp = fp.closest_pair()
bf = fp.closest_pair_brute_force()
assert fp() == cp
assert abs(cp[0] - bf[0]) < 1e-8
assert cp[1] == bf[1]
def test_update_point_less_points(self, PointSet):
ps = PointSet
fp = FastPair()
for p in ps[:9]:
fp += p
assert fp.initialized is False
old = ps[0] # Just grab the first point...
new = rand_tuple(len(ps[0]))
res = fp._update_point(old, new)
assert len(fp) == 1
def test_add(self, PointSet):
ps = PointSet
fp = FastPair()
for p in ps[:9]:
fp += p
assert fp.initialized is False
assert len(fp) == 9
for p in ps[9:]:
fp += p
assert fp.initialized is True
def test_call_and_closest_pair_min_points(self, image_array):
ps = image_array
fp = FastPair(dist=image_distance)
for p in ps:
fp += p
assert fp.initialized is False
assert len(fp) == 6
cp = fp.closest_pair()
bf = fp.closest_pair_brute_force()
assert fp() == cp
assert abs(cp[0] - bf[0]) < 1e-8
assert cp[1] == bf[1]
def test_sub(self, PointSet):
ps = PointSet
fp = FastPair().build(ps)
start = fp._find_neighbor(ps[-1])
fp -= ps[-1]
end = fp._find_neighbor(start["neigh"])
assert end["neigh"] != ps[-1]
# This is risky, because it might legitimately be the same...?
assert start["dist"] != end["dist"]
assert len(fp) == len(ps) - 1
with pytest.raises(ValueError):
fp -= rand_tuple(len(ps[0]))
def test_update_point(self, PointSet):
# Still failing sometimes...
ps = PointSet
fp = FastPair().build(ps)
assert len(fp) == len(ps)
old = ps[0] # Just grab the first point...
new = rand_tuple(len(ps[0]))
res = fp._update_point(old, new)
assert old not in fp
assert new in fp
assert len(fp) == len(ps) # Size shouldn't change
l = [(fp.dist(a, b), b) for a, b in zip(cycle([new]), ps)]
res = min(l, key=itemgetter(0))
neigh = fp.neighbors[new]
def test_all_closest_pairs(self, PointSet):
ps = PointSet
fp = FastPair().build(ps)
cp = fp.closest_pair()
bf = fp.closest_pair_brute_force() # Ordering should be the same
# dc = fp.closest_pair_divide_conquer() # Maybe different ordering
assert abs(cp[0] - bf[0]) < 1e-8
assert cp[1] == bf[1] # Tuple comparison
test = min(
[(fp.dist(a, b), (a, b)) for a, b in combinations(ps, r=2)],
key=itemgetter(0),
)
assert abs(cp[0] - test[0]) < 1e-8
assert sorted(cp[1]) == sorted(test[1]) # Tuple comparison
def test_find_neighbor_and_sdist(self, PointSet):
ps = PointSet
fp = FastPair().build(ps)
rando = rand_tuple(len(ps[0]))
neigh = fp._find_neighbor(rando) # Abusing find_neighbor!
dist = fp.dist(rando, neigh["neigh"])
assert abs(dist - neigh["dist"]) < 1e-8
assert len(fp) == len(ps) # Make sure we didn't add a point...
l = [(fp.dist(a, b), b) for a, b in zip(cycle([rando]), ps)]
res = min(l, key=itemgetter(0))
assert abs(res[0] - neigh["dist"]) < 1e-8
assert res[1] == neigh["neigh"]
res = min(fp.sdist(rando), key=itemgetter(0))
assert abs(neigh["dist"] - res[0]) < 1e-8
assert neigh["neigh"] == res[1]
def __init__(self,
kmax=100,
dist=kernel_dist(gaussian),
centroid_factory=KernelCentroid):
"""Initialize an empty FastPair data-structure.
Parameters
----------
kmax : int, default=100
The maximum number of cluster centroids to store (i.e., size of
memory). This parameter controls the 'scale' of the desired
solution, such that larger values of `kmax` will lead to a higher
resolution cluster solution.
"""
self.kmax = kmax
self.npoints = 0
self.centroid_factory = centroid_factory
self.fastpair = FastPair(10, dist=dist)
def test_iter(self, PointSet):
ps = PointSet
fp = FastPair().build(ps)
assert fp.min_points == 10
assert isinstance(fp.dist, FunctionType)
my_iter = iter(fp)
assert next(my_iter) in set(ps)
assert fp[ps[0]].neigh in set(ps)
try:
myitem = fp[(2, 3, 4)]
except KeyError as err:
print(err)
fp[ps[0]] = fp[ps[0]].neigh
try:
fp[(2, 3, 4)] = fp[ps[0]].neigh
except KeyError as err:
print(err)
def test_merge_closest(self):
# This needs to be 'fleshed' out more... lots of things to test here
random.seed(1234)
ps = [rand_tuple(4) for _ in range(50)]
fp = FastPair().build(ps)
# fp2 = FastPair().build(ps)
n = len(ps)
while n >= 2:
dist, (a, b) = fp.closest_pair()
new = interact(a, b)
fp -= b # Drop b
fp._update_point(a, new)
n -= 1
assert len(fp) == 1 == n
points = [(
0.69903599809571437,
0.52457534006594131,
0.7614753848101149,
0.37011695654655385,
)]
assert all_close(fp.points[0], points[0])
# Should have < 2 points now...
with pytest.raises(ValueError):
fp.closest_pair()
def test_cluster(self, PointSet):
ps = PointSet
fp = FastPair().build(ps)
for i in range(len(fp) - 1):
# Version one
dist, (a, b) = fp.closest_pair()
c = interact(a, b)
fp -= b # Drop b
fp -= a
fp += c
# Order gets reversed here...
d, (e, f) = min(
[(fp.dist(i, j), (i, j)) for i, j in combinations(ps, r=2)],
key=itemgetter(0),
)
g = interact(e, f)
assert abs(d - dist) < 1e-8
assert (a == e or b == e) and (b == f or a == f)
assert c == g
ps.remove(e)
ps.remove(f)
ps.append(g)
assert contains_same(fp.points, ps)
assert len(fp.points) == len(ps) == 1
def test_build(self, PointSet):
ps = PointSet
fp = FastPair().build(ps)
assert len(fp) == len(ps)
assert len(fp.neighbors) == len(ps)
assert fp.initialized is True
def test_contains(self, PointSet):
ps = PointSet
fp = FastPair()
assert ps[0] not in fp
fp.build(ps)
assert ps[0] in fp
def test_len(self, PointSet):
ps = PointSet
fp = FastPair()
assert len(fp) == 0
fp.build(ps)
assert len(fp) == len(ps)