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_as_function_of_origin_mech_in_composition(self):
P = ProcessingMechanism(function=Buffer(
default_variable=[[0.0]], initializer=[[0.0]], history=3))
C = Composition(pathways=[P])
P.reset_stateful_function_when = Never()
full_result = []
def assemble_full_result():
full_result.append(P.parameters.value.get(C))
C.run(inputs={P: [[1.0], [2.0], [3.0], [4.0], [5.0]]},
call_after_trial=assemble_full_result)
# only returns index 0 item of the deque on each trial (OutputPort value)
assert np.allclose(np.asfarray(C.results),
[[[0.0]], [[0.0]], [[1.0]], [[2.0]], [[3.0]]])
# stores full mechanism value (full deque) on each trial
expected_full_result = [
np.array([[0.], [1.]]),
np.array([[0.], [1.], [2.]]),
np.array([[1.], [2.], [3.]]), # Shape change
np.array([[2.], [3.], [4.]]),
np.array([[3.], [4.], [5.]])
]
for i in range(5):
assert np.allclose(expected_full_result[i],
np.asfarray(full_result[i]))
def test_buffer_as_function_of_origin_mech_in_system(self):
P = ProcessingMechanism(function=Buffer(default_variable=[[0.0]],
initializer=[[0.0]],
history=3))
process = Process(pathway=[P])
system = System(processes=[process])
P.reinitialize_when = Never()
full_result = []
def assemble_full_result():
full_result.append(P.parameters.value.get(system))
result = system.run(inputs={P: [[1.0], [2.0], [3.0], [4.0], [5.0]]},
call_after_trial=assemble_full_result)
# only returns index 0 item of the deque on each trial (output state value)
assert np.allclose(result, [[[0.0]], [[0.0]], [[1.0]], [[2.0]], [[3.0]]])
# stores full mechanism value (full deque) on each trial
expected_full_result = [np.array([[0.], [1.]]),
np.array([[0.], [1.], [2.]]),
np.array([[1.], [2.], [3.]]), # Shape change
np.array([[2.], [3.], [4.]]),
np.array([[3.], [4.], [5.]])]
for i in range(5):
assert np.allclose(expected_full_result[i], full_result[i])
def test_buffer_as_function_of_processing_mech(self):
P = ProcessingMechanism(function=Buffer(default_variable=[[0.0]],
initializer=[0.0],
history=3))
val = P.execute(1.0)
assert np.allclose(val, [[0., 1.]])
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_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_as_function_of_processing_mech(self, benchmark):
P = ProcessingMechanism(function=Buffer(
default_variable=[[0.0]], initializer=[0.0], history=3))
val = P.execute(1.0)
assert np.allclose(np.asfarray(val), [[0., 1.]])
if benchmark.enabled:
benchmark(P.execute, 5.0)
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 __init__(
self,
default_variable=None,
size=None,
input_ports: tc.optional(tc.any(list, dict)) = None,
function: tc.optional(
tc.enum(INPUT, TIME_AVERAGE_INPUT, AVERAGE_INPUTS,
INPUT_SEQUENCE)) = TIME_AVERAGE_INPUT,
initial_value=None,
# rate:tc.optional(tc.any(int, float))=1.0,
# noise:tc.optional(tc.any(int, float, callable))=0.0,
rate: tc.any(int, float) = 1.0,
noise: tc.any(int, float, callable) = 0.0,
window_size=1,
filter_function: tc.optional(callable) = None,
params=None,
name=None,
prefs: is_pref_set = None,
context=None):
if not context.source in {
ContextFlags.COMPONENT, ContextFlags.COMPOSITION,
ContextFlags.COMMAND_LINE
}:
warnings.warn(
"PredictionMechanism should not be constructed on its own. If you insist,"
"set context=Context(source=ContextFlags.COMMAND_LINE), but proceed at your peril!"
)
return
if params is None:
params = {}
if params and FUNCTION in params:
function = params[FUNCTION]
input_type = None
if function in input_types:
input_type = function
if function in input_types:
if function is INPUT:
function = Linear(slope=rate, intercept=noise)
elif function is TIME_AVERAGE_INPUT:
# Use default for IntegratorMechanism: AdaptiveIntegrator
function = self.class_defaults.function
elif function in {AVERAGE_INPUTS, INPUT_SEQUENCE}:
# Maintain the preceding sequence of inputs (of length window_size), and use those for each simulation
function = Buffer(default_variable=[[0]],
initializer=initial_value,
rate=rate,
noise=noise,
history=window_size)
params.update({FUNCTION_PARAMS: {RATE: rate, NOISE: noise}})
super().__init__(default_variable=default_variable,
size=size,
input_ports=input_ports,
function=function,
params=params,
name=name,
prefs=prefs)
def test_buffer_standalone(self):
B = Buffer()
val = B.execute(1.0)
assert np.allclose(deque(np.atleast_1d(1.0)), val)
