def test_buffer_standalone_noise_ndarray(self):
B = Buffer(history=3, rate = 1.0, noise=[10.0, 20.0, 30.0])
B.execute([1,2,3])
B.execute([4,5,6])
B.execute([7,8,9])
val = B.execute([10,11,12])
assert np.allclose(deque(np.atleast_1d([ 24., 45., 66.], [ 17., 28., 39.], [10, 11, 12])), val)
def test_buffer_initializer_len_3(self):
B = Buffer(default_variable=[[0.0], [1.0], [2.0]],
initializer=[[0.0], [1.0], [2.0]],
history=3)
assert np.allclose(B.execute(3.0), deque([[1.0], [2.0], np.array([3.])]))
assert np.allclose(B.execute(4.0), deque([[2.0], np.array([3.]), np.array([4.])]))
assert np.allclose(B.execute(5.0), deque([np.array([3.]), np.array([4.]), np.array([5.])]))
def test_buffer_standalone_noise_float(self):
B = Buffer(history=3, rate = 1.0, noise=10.0)
B.execute([1,2,3])
B.execute([4,5,6])
B.execute([7,8,9])
val = B.execute([10,11,12])
assert np.allclose(deque(np.atleast_1d([ 24., 25., 26.], [ 17., 18., 19.], [10, 11, 12])), val)
def test_buffer_standalone_rate_ndarray(self):
B = Buffer(history=3, rate = np.array([0.1, 0.5, 0.9]))
B.execute([1,2,3])
B.execute([4,5,6])
B.execute([7,8,9])
val = B.execute([10,11,12])
assert np.allclose(deque(np.atleast_1d([ 0.04, 1.25, 4.86], [ 0.7, 4. , 8.1], [10, 11, 12])), val)
def test_buffer_standalone_rate_float(self):
B = Buffer(history=3, rate = 0.1)
B.execute([1,2,3])
B.execute([4,5,6])
B.execute([7,8,9])
val = B.execute([10,11,12])
assert np.allclose(deque(np.atleast_1d([ 0.04, 0.05, 0.06], [ 0.7, 0.8, 0.9], [10, 11, 12])), val)
def test_buffer_standalone_rate_list(self, benchmark):
B = Buffer(history=3, rate = [0.1, 0.5, 0.9])
B.execute([1,2,3])
B.execute([4,5,6])
B.execute([7,8,9])
val = B.execute([10,11,12])
assert np.allclose(deque(np.atleast_1d([ 0.04, 1.25, 4.86], [ 0.7, 4. , 8.1], [10, 11, 12])), val)
if benchmark.enabled:
benchmark(B.execute, [1, 2, 3])
def test_buffer_standalone_noise_function(self):
np.random.seed(22)
B = Buffer(history=3, rate = 1.0, noise=NormalDist(standard_deviation=0.1))
B.execute([1,2,3])
B.execute([4,5,6])
B.execute([7,8,9])
val = B.execute([10,11,12])
assert np.allclose(deque(np.atleast_1d([[ 3.8925223, 5.03957263, 6.00262384],
[ 7.00288551, 7.97692328, 9.05877522],
[10, 11, 12]])), val)
def test_buffer_standalone_noise_function(self, benchmark):
B = Buffer(history=3, rate = 1.0, noise=NormalDist(standard_deviation=0.1))
B.execute([1, 2, 3])
B.execute([4, 5, 6])
B.execute([7, 8, 9])
val = B.execute([10,11,12])
assert np.allclose(deque(np.atleast_1d([[4.02430687, 4.91927251, 5.95087965],
[7.09586966, 7.91823773, 8.86077491],
[10, 11, 12]])), val)
if benchmark.enabled:
benchmark(B.execute, [1, 2, 3])
def test_buffer_standalone_noise_function_in_array(self):
B = Buffer(history=3, noise=[10, NormalDist(standard_deviation=0.1), 20])
np.random.seed(22)
B.execute([1,2,3])
B.execute([4,5,6])
B.execute([7,8,9])
val = B.execute([10,11,12])
expected_val = [[24, 5.0800314416734444, 46], [17, 8.040015720836722, 29], [10, 11, 12]]
for i in range(len(val)):
for j in range(len(val[i])):
assert np.allclose(expected_val[i][j], val[i][j])
def test_buffer_standalone_noise_function_in_array(self, benchmark):
B = Buffer(history=3)
# Set noise parameter ouside of a constructor to avoid problems
# with extra copying
B.parameters.noise.set([10, NormalDist(standard_deviation=0.1), 20])
B.execute([1, 2, 3])
B.execute([4, 5, 6])
B.execute([7, 8, 9])
val = B.execute([10, 11, 12])
expected_val = [[24, 4.693117564500052, 46], [17, 7.744647273059847, 29], [10, 11, 12]]
for v_v, v_e in zip(val, expected_val):
for v, e in zip(v_v, v_e):
assert np.allclose(v, e)
if benchmark.enabled:
benchmark(B.execute, [1, 2, 3])
def test_buffer_standalone_noise_function_invocation(self):
class CallCount:
def __init__(self):
self.count = 0
def __call__(self):
self.count += 1
return self.count
counter_f = CallCount()
# Set noise parameter ouside of a constructor to avoid problems
# with extra copying. This test fails if noise is passed to constructor
B = Buffer(history=3)
B.parameters.noise.set([10, counter_f, 20])
B.execute([1, 2, 3])
B.execute([4, 5, 6])
B.execute([7, 8, 9])
val = B.execute([10, 11, 12])
assert counter_f.count == 4
expected_val = [[24, 12.0, 46], [17, 12.0, 29], [10, 11, 12]]
for v_v, v_e in zip(val, expected_val):
for v, e in zip(v_v, v_e):
assert np.allclose(v, e)
def test_buffer_standalone(self):
B = Buffer()
val = B.execute(1.0)
assert np.allclose(deque(np.atleast_1d(1.0)), val)