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test_buffer.py
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test_buffer.py
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import numpy as np
import pytest
from collections import deque
from psyneulink.core.compositions.composition import Composition
from psyneulink.core.components.functions.nonstateful.distributionfunctions import NormalDist
from psyneulink.core.components.functions.stateful.memoryfunctions import Buffer
from psyneulink.core.components.mechanisms.processing.processingmechanism import ProcessingMechanism
from psyneulink.core.scheduling.condition import Never
class TestBuffer():
def test_buffer_standalone(self):
B = Buffer()
val = B.execute(1.0)
assert np.allclose(deque(np.atleast_1d(1.0)), val)
@pytest.mark.benchmark(group="BufferFunction")
def test_buffer_standalone_rate_float(self, benchmark):
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)
if benchmark.enabled:
benchmark(B.execute, [1, 2, 3])
@pytest.mark.benchmark(group="BufferFunction")
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])
@pytest.mark.benchmark(group="BufferFunction")
def test_buffer_standalone_rate_ndarray(self, benchmark):
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)
if benchmark.enabled:
benchmark(B.execute, [1, 2, 3])
@pytest.mark.benchmark(group="BufferFunction")
def test_buffer_standalone_noise_float(self, benchmark):
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)
if benchmark.enabled:
benchmark(B.execute, [1, 2, 3])
@pytest.mark.benchmark(group="BufferFunction")
def test_buffer_standalone_noise_list(self, benchmark):
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)
if benchmark.enabled:
benchmark(B.execute, [1, 2, 3])
@pytest.mark.benchmark(group="BufferFunction")
def test_buffer_standalone_noise_ndarray(self, benchmark):
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)
if benchmark.enabled:
benchmark(B.execute, [1, 2, 3])
@pytest.mark.benchmark(group="BufferFunction")
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])
@pytest.mark.benchmark(group="BufferFunction")
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)
@pytest.mark.benchmark(group="BufferFunction")
def test_buffer_initializer_len_3(self, benchmark):
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.])]))
if benchmark.enabled:
benchmark(B.execute, 5.0)
@pytest.mark.benchmark(group="BufferFunction")
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)
# NOTE: actual output is [0, [[1]]]
assert np.allclose(np.asfarray(val), [[0., 1.]])
if benchmark.enabled:
benchmark(P.execute, 5.0)
# fails due to value and variable problems when Buffer is the function of a mechanism
# P = ProcessingMechanism(function=Buffer(default_variable=[[0.0], [1.0], [2.0]],
# initializer=[[0.0], [1.0], [2.0]],
# history=3))
# P.execute(1.0)
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]))