def test_DDM_Integrator():
stim = 10
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(initializer=20.0),
time_scale=TimeScale.TIME_STEP)
val = float(T.execute(stim)[0])
T.function_object.initializer = 30.0
val2 = float(T.execute(stim)[0])
def test_DDM_noise_2_0():
stim = 10
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=2.0,
rate=1.0,
time_step_size=1.0))
val = float(T.execute(stim)[0])
assert val == 12.641125838188323
def test_DDM_time():
D = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=0.2,
t0=0.5))
time_0 = D.function_object.previous_time # t_0 = 0.5
np.testing.assert_allclose(time_0, [0.5], atol=1e-08)
time_1 = D.execute(10)[1][0] # t_1 = 0.5 + 0.2 = 0.7
np.testing.assert_allclose(time_1, [0.7], atol=1e-08)
for i in range(10): # t_11 = 0.7 + 10*0.2 = 2.7
D.execute(10)
time_12 = D.execute(10)[1][0] # t_12 = 2.7 + 0.2 = 2.9
np.testing.assert_allclose(time_12, [2.9], atol=1e-08)
def test_DDM_size_int_inputs_():
T = DDM(name='DDM',
size=1.0,
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
val = T.execute([.4]).tolist()
assert val == [[-2.0], [1.0]]
def test_DDM_zero_noise():
stim = 10
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=1.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
val = float(T.execute(stim)[0])
assert val == 10
def test_DDM_noise_0_5():
stim = 10
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=0.5,
rate=1.0,
time_step_size=1.0))
val = float(T.execute(stim)[0])
assert val == 11.320562919094161
def test_DDM_input_list_len_1():
stim = [10]
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=1.0,
time_step_size=1.0),
)
val = float(T.execute(stim)[0])
assert val == 10
def test_DDM_rate_float():
stim = 10
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=5,
time_step_size=1.0),
)
val = float(T.execute(stim)[0])
assert val == 50
def test_DDM_input_rate_negative():
stim = [10]
T = DDM(name='DDM',
default_variable=[0],
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
val = float(T.execute(stim)[0])
assert val == -50
def test_DDM_input_fn():
with pytest.raises(TypeError) as error_text:
stim = NormalDist().function
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=1.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
float(T.execute(stim))
assert "not supported for the input types" in str(error_text.value)
def test_DDM_noise_fn():
with pytest.raises(FunctionError) as error_text:
stim = 10
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=NormalDist().function,
rate=1.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
float(T.execute(stim)[0])
assert "DriftDiffusionIntegrator requires noise parameter to be a float" in str(
error_text.value)
def test_DDM_rate_fn():
with pytest.raises(typecheck.framework.InputParameterError) as error_text:
stim = [10]
T = DDM(name='DDM',
default_variable=[0],
function=DriftDiffusionIntegrator(noise=0.0,
rate=NormalDist().function,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
float(T.execute(stim)[0])
assert "incompatible value" in str(error_text.value)
def test_DDM_input_list_len_2():
with pytest.raises(DDMError) as error_text:
stim = [10, 10]
T = DDM(name='DDM',
default_variable=[0, 0],
function=DriftDiffusionIntegrator(noise=0.0,
rate=1.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
float(T.execute(stim)[0])
assert "must have only a single numeric item" in str(error_text.value)
def test_DDM_noise_int():
with pytest.raises(FunctionError) as error_text:
stim = 10
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=2,
rate=1.0,
time_step_size=1.0),
)
float(T.execute(stim)[0])
assert "DriftDiffusionIntegrator requires noise parameter to be a float" in str(
error_text.value)
def test_DDM_size_int_inputs():
T = DDM(
name='DDM',
size=1,
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
)
val = T.execute([.4])
decision_variable = val[0][0]
time = val[1][0]
assert decision_variable == -2.0
assert time == 1.0
def test_threshold_stops_accumulation(self):
D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=5.0))
decision_variables = []
time_points = []
for i in range(5):
output = D.execute(2.0)
decision_variables.append(output[0][0][0])
time_points.append(output[1][0][0])
# decision variable accumulation stops
assert np.allclose(decision_variables, [2.0, 4.0, 5.0, 5.0, 5.0])
# time accumulation does not stop
assert np.allclose(time_points, [1.0, 2.0, 3.0, 4.0, 5.0])
def test_threshold_sets_is_finished(self):
D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=5.0))
D.execute(2.0) # 2.0 < 5.0
assert not D.is_finished
D.execute(2.0) # 4.0 < 5.0
assert not D.is_finished
D.execute(2.0) # 5.0 = threshold
assert D.is_finished
def test_DDM_Integrator_Bogacz():
stim = 10
T = DDM(name='DDM', function=BogaczEtAl())
val = float(T.execute(stim)[0])
assert val == 1.0
def test_valid(self):
D = DDM(
name='DDM',
function=DriftDiffusionIntegrator(),
)
# returns previous_value + rate * variable * time_step_size + noise
# 0.0 + 1.0 * 1.0 * 1.0 + 0.0
D.execute(1.0)
assert np.allclose(D.value, [[1.0], [1.0]])
assert np.allclose(D.output_states[0].value, 1.0)
assert np.allclose(D.output_states[1].value, 1.0)
# reinitialize function
D.function_object.reinitialize(2.0, 0.1)
assert np.allclose(D.function_object.value, 2.0)
assert np.allclose(D.function_object.previous_value, 2.0)
assert np.allclose(D.function_object.previous_time, 0.1)
assert np.allclose(D.value, [[1.0], [1.0]])
assert np.allclose(D.output_states[0].value, 1.0)
assert np.allclose(D.output_states[1].value, 1.0)
# reinitialize function without value spec
D.function_object.reinitialize()
assert np.allclose(D.function_object.value, 0.0)
assert np.allclose(D.function_object.previous_value, 0.0)
assert np.allclose(D.function_object.previous_time, 0.0)
assert np.allclose(D.value, [[1.0], [1.0]])
assert np.allclose(D.output_states[0].value, 1.0)
assert np.allclose(D.output_states[1].value, 1.0)
# reinitialize mechanism
D.reinitialize(2.0, 0.1)
assert np.allclose(D.function_object.value, 2.0)
assert np.allclose(D.function_object.previous_value, 2.0)
assert np.allclose(D.function_object.previous_time, 0.1)
assert np.allclose(D.value, [[2.0], [0.1]])
assert np.allclose(D.output_states[0].value, 2.0)
assert np.allclose(D.output_states[1].value, 0.1)
D.execute(1.0)
# 2.0 + 1.0 = 3.0 ; 0.1 + 1.0 = 1.1
assert np.allclose(D.value, [[[3.0]], [[1.1]]])
assert np.allclose(D.output_states[0].value, 3.0)
assert np.allclose(D.output_states[1].value, 1.1)
# reinitialize mechanism without value spec
D.reinitialize()
assert np.allclose(D.function_object.value, 0.0)
assert np.allclose(D.function_object.previous_value, 0.0)
assert np.allclose(D.function_object.previous_time, 0.0)
assert np.allclose(D.output_states[0].value[0], 0.0)
assert np.allclose(D.output_states[1].value[0], 0.0)
# reinitialize only decision variable
D.reinitialize(1.0)
assert np.allclose(D.function_object.value, 1.0)
assert np.allclose(D.function_object.previous_value, 1.0)
assert np.allclose(D.function_object.previous_time, 0.0)
assert np.allclose(D.output_states[0].value[0], 1.0)
assert np.allclose(D.output_states[1].value[0], 0.0)
def test_selected_input_array(self):
action_selection = DDM(input_format=ARRAY,
function=BogaczEtAl(),
output_states=[SELECTED_INPUT_ARRAY],
name='DDM')
action_selection.execute([1.0])