def test_generate_random_choice_weighted():
numpy.random.seed(0xDEADBEEF)
input_array = numpy.array([(8, 1), (2, 1), (5, 1), (7, 4), (4, 0)], dtype=int)
def distance(a, b):
return numpy.hypot(*(a - b))
eq_(distance(input_array[2], input_array[1]), 3)
pairs = zip(input_array[:-1], input_array[1:])
max_distance = max(map(lambda x: distance(x[0], x[1]), pairs))
eq_(max_distance, distance(input_array[0], input_array[1]))
def mc_selector(a, b):
throw = numpy.random.random_sample()
normalized_distance = distance(a, b) * 1.0 / max_distance
normalized_distance = normalized_distance ** 2
if throw < math.exp(-normalized_distance):
return True
return False
result = list(generate_random_choices(5, input_array, mc_selector))
eq_(
[(list(x[0]), list(x[1])) for x in result],
[([8, 1], [2, 1]), ([5, 1], [2, 1]), ([7, 4], [8, 1]), ([8, 1], [7, 4]), ([7, 4], [8, 1])],
)
# Make sure we have the same implementation
numpy.random.seed(0xDEADBEEF)
standard_impl = list(generate_random_choices_exponential(5, input_array))
assert numpy.array_equal(result, standard_impl)
def test_generate_random_choice_weighted():
numpy.random.seed(0xDEADBEEF)
input_array = numpy.array(
[(8, 1), (2, 1), (5, 1), (7, 4), (4, 0)],
dtype=int)
def distance(a, b):
return numpy.hypot(*(a - b))
eq_(distance(input_array[2], input_array[1]), 3)
pairs = zip(input_array[:-1], input_array[1:])
max_distance = max(map(lambda x: distance(x[0], x[1]), pairs))
eq_(max_distance, distance(input_array[0], input_array[1]))
def mc_selector(a, b):
throw = numpy.random.random_sample()
normalized_distance = distance(a, b) * 1./max_distance
normalized_distance = (normalized_distance**2)
if throw < math.exp(-normalized_distance):
return True
return False
result = list(generate_random_choices(5, input_array, mc_selector))
eq_([(list(x[0]), list(x[1])) for x in result],
[([8, 1], [2, 1]), ([5, 1], [2, 1]), ([7, 4], [8, 1]),
([8, 1], [7, 4]), ([7, 4], [8, 1])])
# Make sure we have the same implementation
numpy.random.seed(0xDEADBEEF)
standard_impl = list(generate_random_choices_exponential(5, input_array))
assert(numpy.array_equal(result, standard_impl))
host=args.host,
port=args.port)
# execute some jobs
route_count = 0
# Do the future mapping in chunks, to prevent memory
# blowup. I don't understand why the executor keeps
# so much crap around.
chunk_size = 1000
nchunks = max(int(math.ceil(args.N / chunk_size)), 1)
for ichunk in range(nchunks):
log.info("Processing %i route block %i/%i", chunk_size, ichunk + 1,
nchunks)
# We run each route forward and backwards to better
# describe the use-case for that region.
routes_to_run = rr.generate_forward_backward_pairs(
rr.generate_random_choices_exponential(chunk_size, nodes))
commit_every = 20
for route in executor.map(route_runner, routes_to_run):
coords, query_url, steps = route
if not len(steps):
log.error("No steps returned for route: %s", coords)
continue
route_hash = models.OSRMRoute.hash_route(
tuple(coords[0]),
tuple(coords[1]),
)
ormified_route = models.OSRMRoute(
route_hash=route_hash,
route_runner = functools.partial(
rr.run_route, host=args.host, port=args.port)
# execute some jobs
route_count = 0
# Do the future mapping in chunks, to prevent memory
# blowup. I don't understand why the executor keeps
# so much crap around.
chunk_size = 1000
nchunks = max(int(math.ceil(args.N / chunk_size)), 1)
for ichunk in range(nchunks):
log.info("Processing %i route block %i/%i",
chunk_size, ichunk + 1, nchunks)
# We run each route forward and backwards to better
# describe the use-case for that region.
routes_to_run = rr.generate_forward_backward_pairs(
rr.generate_random_choices_exponential(
chunk_size, nodes))
commit_every = 20
for route in executor.map(route_runner, routes_to_run):
coords, query_url, steps = route
if not len(steps):
log.error("No steps returned for route: %s", coords)
continue
route_hash = models.OSRMRoute.hash_route(
tuple(coords[0]),
tuple(coords[1]),
)
ormified_route = models.OSRMRoute(
route_hash=route_hash,
