def test_reset_state_integrator_mechanism(self):
A = IntegratorMechanism(name='A', function=DriftDiffusionIntegrator())
# Execute A twice
# [0] saves decision variable only (not time)
original_output = [A.execute(1.0)[0], A.execute(1.0)[0]]
# SAVING STATE - - - - - - - - - - - - - - - - - - - - - - - - -
reinitialize_values = []
for attr in A.function_object.stateful_attributes:
reinitialize_values.append(getattr(A.function_object, attr))
# Execute A twice AFTER saving the state so that it continues accumulating.
# We expect the next two outputs to repeat once we reset the state b/c we will return it to the current state
output_after_saving_state = [A.execute(1.0)[0], A.execute(1.0)[0]]
# RESETTING STATE - - - - - - - - - - - - - - - - - - - - - - - -
A.reinitialize(*reinitialize_values)
# We expect these results to match the results from immediately after saving the state
output_after_reinitialization = [A.execute(1.0)[0], A.execute(1.0)[0]]
assert np.allclose(output_after_saving_state,
output_after_reinitialization)
assert np.allclose(
original_output,
[np.array([[1.0]]), np.array([[2.0]])])
assert np.allclose(
output_after_reinitialization,
[np.array([[3.0]]), np.array([[4.0]])])
def test_utility_integrator_short_minus_long(self):
# default params:
# initial_short_term_utility = 0.0
# initial_long_term_utility = 0.0
# short_term_rate = 1.0
# long_term_rate = 1.0
U = IntegratorMechanism(
name = "AGTUtilityIntegrator",
function=AGTUtilityIntegrator(
operation="s-l"
)
)
engagement = []
short_term_util = []
long_term_util = []
for i in range(50):
engagement.append(U.execute([1])[0][0])
short_term_util.append(U.function_object.short_term_utility_logistic[0])
long_term_util.append(U.function_object.long_term_utility_logistic[0])
print("engagement = ", engagement)
print("short_term_util = ", short_term_util)
print("long_term_util = ", long_term_util)
def test_integrator_type_simple_rate_list(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
default_variable=[0, 0, 0],
function=SimpleIntegrator(rate=[5.0, 5.0, 5.0]))
# P = Process(pathway=[I])
val = list(I.execute([10.0, 10.0, 10.0])[0])
assert val == [50.0, 50.0, 50.0]
def test_integrator_drift_diffusion_noise_val(self):
I = IntegratorMechanism(name='IntegratorMechanism',
function=DriftDiffusionIntegrator(noise=5.0, ),
time_scale=TimeScale.TIME_STEP)
val = float(I.execute(10))
np.testing.assert_allclose(val, 15.010789523731438)
def test_integrator_type_diffusion_rate_list(self):
I = IntegratorMechanism(
default_variable=[0, 0, 0],
name='IntegratorMechanism',
function=DriftDiffusionIntegrator(rate=[5.0, 5.0, 5.0]))
# P = Process(pathway=[I])
val = list(I.execute([10.0, 10.0, 10.0])[0])
assert val == [50.0, 50.0, 50.0]
def test_integrator_input_array_less_than_default(self):
with pytest.raises(MechanismError) as error_text:
I = IntegratorMechanism(name='IntegratorMechanism',
default_variable=[0, 0, 0, 0, 0])
# P = Process(pathway=[I])
I.execute([10.0, 5.0, 2.0])
assert "does not match required length" in str(error_text)
def test_integrator_type_adaptive_rate_list(self):
I = IntegratorMechanism(
default_variable=[0, 0, 0],
name='IntegratorMechanism',
function=AdaptiveIntegrator(rate=[0.5, 0.5, 0.5]))
# P = Process(pathway=[I])
val = list(I.execute([10.0, 10.0, 10.0])[0])
assert val == [5.0, 5.0, 5.0]
def test_integrator_type_constant_rate_list_input_float(self):
with pytest.raises(ComponentError) as error_text:
I = IntegratorMechanism(
name='IntegratorMechanism',
function=ConstantIntegrator(rate=[5.0, 5.0, 5.0]))
# P = Process(pathway=[I])
float(I.execute(10.0))
assert ("is not compatible with the variable format" in str(error_text)
and "to which it is being assigned" in str(error_text))
def test_simple_integrator(self):
I = IntegratorMechanism(function=SimpleIntegrator(
initializer=10.0,
rate=5.0,
offset=10,
))
# P = Process(pathway=[I])
val = I.execute(1)
assert val == 25
def test_integrator_input_list(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
function=SimpleIntegrator(
)
)
# P = Process(pathway=[I])
val = float(I.execute([10.0]))
assert val == 10.0
def test_integrator_adaptive_noise_fn(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
function=AdaptiveIntegrator(noise=NormalDist().function),
time_scale=TimeScale.TIME_STEP)
val = float(I.execute(10))
np.testing.assert_allclose(val, 11.86755799)
def test_integrator_adaptive_noise_fn(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
function=AdaptiveIntegrator(noise=NormalDist()),
)
val = float(I.execute(10))
np.testing.assert_allclose(val, 12.240893199201459)
def test_integrator_drift_diffusion_noise_val(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
function=DriftDiffusionIntegrator(noise=5.0, ),
)
val = float(I.execute(10))
np.testing.assert_allclose(val, 12.188524664621541)
def test_integrator_constant_noise_fn(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
function=ConstantIntegrator(noise=NormalDist()),
)
val = float(I.execute(10))
np.testing.assert_allclose(val, 1.8675579901499675)
def test_adaptive_integrator(self):
I = IntegratorMechanism(function=AdaptiveIntegrator(
initializer=10.0,
rate=0.5,
offset=10,
))
# P = Process(pathway=[I])
# 10*0.5 + 1*0.5 + 10
val = I.execute(1)
assert val == 15.5
def test_integrator_accumulator_noise_fn_var_list(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
default_variable=[0, 0, 0, 0],
function=AccumulatorIntegrator(noise=NormalDist(), ),
)
val = I.execute([10, 10, 10, 10])[0]
np.testing.assert_allclose(
val, [0.95008842, -0.15135721, -0.10321885, 0.4105985])
def test_integrator_type_diffusion_rate_float(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
function=DriftDiffusionIntegrator(
rate=5.0
)
)
# P = Process(pathway=[I])
val = I.execute(10.0)
assert np.allclose([[[50.0]], [[1.0]]], val)
def test_integrator_drift_diffusion_noise_val(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
function=DriftDiffusionIntegrator(
noise=5.0,
),
)
val = I.execute(10.0)
assert np.allclose(val, [[[15.01078952]], [[1.]]])
def test_integrator_type_adaptive_rate_float(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
function=AdaptiveIntegrator(
rate=0.5
)
)
# P = Process(pathway=[I])
val = list(I.execute(10.0))
assert val == [5.0]
def test_integrator_type_diffusion_rate_list_input_float(self):
with pytest.raises(FunctionError) as error_text:
I = IntegratorMechanism(
name='IntegratorMechanism',
function=DriftDiffusionIntegrator(rate=[5.0, 5.0, 5.0]))
# P = Process(pathway=[I])
float(I.execute(10.0))
assert ("array specified for the rate parameter" in str(error_text)
and "must match the length" in str(error_text)
and "of the default input" in str(error_text))
def test_integrator_type_constant_rate_float(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
function=ConstantIntegrator(
rate=5.0
)
)
# P = Process(pathway=[I])
val = float(I.execute(10.0))
assert val == 5.0
def test_fhn_gilzenrat_figure_2(self):
# Isolate the FHN mechanism for testing and recreate figure 2 from the gilzenrat paper
initial_v = 0.2
initial_w = 0.0
F = IntegratorMechanism(name='IntegratorMech-FHNFunction',
function=FHNIntegrator(
initial_v=initial_v,
initial_w=initial_w,
time_step_size=0.01,
time_constant_w=1.0,
time_constant_v=0.01,
a_v=-1.0,
b_v=1.0,
c_v=1.0,
d_v=0.0,
e_v=-1.0,
f_v=1.0,
threshold=0.5,
mode=1.0,
uncorrelated_activity=0.0,
a_w=1.0,
b_w=-1.0,
c_w=0.0))
plot_v_list = [initial_v]
plot_w_list = [initial_w]
# found this stimulus by guess and check b/c one was not provided with Figure 2 params
stimulus = 0.073
# increase range to 200 to match Figure 2 in Gilzenrat
for i in range(10):
results = F.execute(stimulus)
plot_v_list.append(results[0][0][0])
plot_w_list.append(results[1][0][0])
# ** uncomment the lines below if you want to view the plot:
# from matplotlib import pyplot as plt
# plt.plot(plot_v_list)
# plt.plot(plot_w_list)
# plt.show()
np.testing.assert_allclose(plot_v_list, [
0.2, 0.22493312915681499, 0.24840327807265583, 0.27101619694032797,
0.29325863380332173, 0.31556552465130933, 0.33836727470568129,
0.36212868305470697, 0.38738542852040492, 0.41478016676749552,
0.44509530539552955
])
print(plot_w_list)
np.testing.assert_allclose(plot_w_list, [
0.0, 0.0019900332500000003, 0.0042083541185625045,
0.0066381342093118408, 0.009268739886338381, 0.012094486544132229,
0.015114073825358726, 0.018330496914962583, 0.021751346023501487,
0.025389465931011893, 0.029263968140538919
])
def test_integrator_adaptive_noise_fn_var_list(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
default_variable=[0, 0, 0, 0],
function=AdaptiveIntegrator(noise=NormalDist(), ),
)
val = I.execute([10, 10, 10, 10])[0]
np.testing.assert_allclose(
val, [10.95008842, 9.84864279, 9.89678115, 10.4105985])
def test_integrator_accumulator_noise_fn_var_list(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
default_variable=[0, 0, 0, 0],
function=AccumulatorIntegrator(
noise=NormalDist(),
),
)
val = I.execute([10, 10, 10, 10])[0]
np.testing.assert_allclose(val, [0.14404357, 1.45427351, 0.76103773, 0.12167502])
def test_integrator_constant_noise_fn_var_list(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
default_variable=[0, 0, 0, 0],
function=ConstantIntegrator(noise=NormalDist(), ),
)
val = I.execute([10, 10, 10, 10])[0]
np.testing.assert_allclose(
val, [-0.15135721, -0.10321885, 0.4105985, 0.14404357])
def test_fhn_gilzenrat_figure_2(self):
# Isolate the FHN mechanism for testing and recreate figure 2 from the gilzenrat paper
initial_v = 0.2
initial_w = 0.0
F = IntegratorMechanism(name='IntegratorMech-FHNFunction',
function=FHNIntegrator(
initial_v=initial_v,
initial_w=initial_w,
time_step_size=0.01,
time_constant_w=1.0,
time_constant_v=0.01,
a_v=-1.0,
b_v=1.0,
c_v=1.0,
d_v=0.0,
e_v=-1.0,
f_v=1.0,
threshold=0.5,
mode=1.0,
uncorrelated_activity=0.0,
a_w=1.0,
b_w=-1.0,
c_w=0.0))
plot_v_list = [initial_v]
plot_w_list = [initial_w]
# found this stimulus by guess and check b/c one was not provided with Figure 2 params
stimulus = 0.073
# increase range to 200 to match Figure 2 in Gilzenrat
for i in range(10):
results = F.execute(stimulus)
plot_v_list.append(results[0][0][0])
plot_w_list.append(results[1][0][0])
# ** uncomment the lines below if you want to view the plot:
# from matplotlib import pyplot as plt
# plt.plot(plot_v_list)
# plt.plot(plot_w_list)
# plt.show()
np.testing.assert_allclose(plot_v_list, [
0.2, 0.22493312915681499, 0.24844236992931412, 0.27113468959297515,
0.29350254152625221, 0.31599112332052792, 0.33904651470437225,
0.36315614063656521, 0.38888742632665502, 0.41692645840176923,
0.44811281741549686
])
np.testing.assert_allclose(plot_w_list, [
0.0, 0.0019518690642000148, 0.0041351416812363193,
0.0065323063637677276, 0.0091322677555586273, 0.011929028036111457,
0.014921084302726394, 0.018111324713170868, 0.021507331976846619,
0.025122069034563425, 0.028974949616469712
])
def test_integrator_accumulator_noise_fn_var_list(self):
I = IntegratorMechanism(name='IntegratorMechanism',
default_variable=[0, 0, 0, 0],
function=AccumulatorIntegrator(
noise=NormalDist().function,
default_variable=[0, 0, 0, 0]),
time_scale=TimeScale.TIME_STEP)
val = I.execute([10, 10, 10, 10])[0]
np.testing.assert_allclose(
val, [0.12167502, 0.44386323, 0.33367433, 1.49407907])
def test_drift_diffusion_integrator(self):
I = IntegratorMechanism(function=DriftDiffusionIntegrator(
initializer=10.0,
rate=10,
time_step_size=0.5,
offset=10,
))
# P = Process(pathway=[I])
# 10 + 10*0.5 + 0 + 10 = 25
val = I.execute(1)
assert val == 25
def test_integrator_ornstein_uhlenbeck_noise_val(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
function=OrnsteinUhlenbeckIntegrator(noise=2.0,
decay=0.5,
initializer=1.0,
rate=0.25),
)
# val = 1.0 + 0.5 * (1.0 - 0.25 * 2.5) * 1.0 + np.sqrt(1.0 * 2.0) * np.random.normal()
val = float(I.execute(2.5))
def test_integrator_constant_noise_fn_var_list(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
default_variable=[0, 0, 0, 0],
function=ConstantIntegrator(
noise=NormalDist(),
),
)
val = I.execute([10, 10, 10, 10])[0]
np.testing.assert_allclose(val, [1.45427351, 0.76103773, 0.12167502, 0.44386323])
