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))
def test_generate_random_choice():
numpy.random.seed(0xDEADBEEF)
eq_(list(generate_random_choices(5, range(10))), [(8, 1), (2, 1), (5, 1), (7, 4), (4, 0)])
def test_generate_random_choice():
numpy.random.seed(0xDEADBEEF)
eq_(list(generate_random_choices(5, range(10))),
[(8, 1), (2, 1), (5, 1), (7, 4), (4, 0)])