def test_processing_mechanism_multiple_input_ports(self):
PM1 = ProcessingMechanism(size=[4, 4], function=LinearCombination, input_ports=['input_1', 'input_2'])
PM2 = ProcessingMechanism(size=[2, 2, 2], function=LinearCombination, input_ports=['1', '2', '3'])
PM1.execute([[1, 2, 3, 4], [5, 4, 2, 2]])
PM2.execute([[2, 0], [1, 3], [1, 0]])
assert np.allclose(PM1.value, [6, 6, 5, 6])
assert np.allclose(PM2.value, [4, 3])
def test_accumulator_as_function_of_processing_mech(self):
P = ProcessingMechanism(function=AccumulatorIntegrator(
initializer=[0.0], rate=.02, increment=1))
P.execute(1.0)
P.execute(1.0)
val = P.execute(1.0)
assert np.allclose(val, [[1.0204]])
def test_processing_mechanism_linear_function(self):
PM1 = ProcessingMechanism()
PM1.execute(1.0)
assert np.allclose(PM1.value, 1.0)
PM2 = ProcessingMechanism(function=Linear(slope=2.0, intercept=1.0))
PM2.execute(1.0)
assert np.allclose(PM2.value, 3.0)
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_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_variance(self):
PM1 = ProcessingMechanism(default_variable=[0, 0, 0],
output_ports=[VARIANCE])
PM1.execute([1, 2, 4])
assert np.allclose(PM1.output_ports[0].value, [1.55555556])
def test_max_val(self):
PM1 = ProcessingMechanism(default_variable=[0, 0, 0],
output_ports=[MAX_VAL])
PM1.execute([1, 2, -4])
# assert np.allclose(PM1.output_ports[0].value,[0,2,0])
assert np.allclose(PM1.output_ports[0].value, [2])
def test_processing_mechanism_OrnsteinUhlenbeckIntegrator_function(self):
PM1 = ProcessingMechanism(function=OrnsteinUhlenbeckIntegrator)
PM1.execute(1.0)
def test_processing_mechanism_BogaczEtAl_function(self):
PM1 = ProcessingMechanism(function=DriftDiffusionAnalytical)
PM1.execute(1.0)
def test_processing_mechanism_LinearCombination_function(self):
PM1 = ProcessingMechanism(function=LinearCombination)
PM1.execute(1.0)
def test_processing_mechanism_CombineMeans_function(self):
PM1 = ProcessingMechanism(function=CombineMeans)
PM1.execute(1.0)
def test_processing_mechanism_Hebbian_function(self):
PM1 = ProcessingMechanism(function=Hebbian,
default_variable=[[0.0], [0.0], [0.0]])
PM1.execute([[1.0], [2.0], [3.0]])
def test_prob(self):
PM1 = ProcessingMechanism(default_variable=[0, 0, 0],
output_ports=[PROB])
PM1.execute([1, 2, 4])
assert np.allclose(PM1.output_ports[0].value, [0, 0, 4])
def test_processing_mechanism_Stability_function(self):
PM1 = ProcessingMechanism(function=Stability)
PM1.execute(1.0)
def test_processing_mechanism_Distance_function(self):
PM1 = ProcessingMechanism(function=Distance,
default_variable=[[0, 0], [0, 0]])
PM1.execute([[1, 2], [3, 4]])
def test_processing_mechanism_WaldDist_function(self):
PM1 = ProcessingMechanism(function=WaldDist)
PM1.execute(1.0)
def test_processing_mechanism_UniformDist_function(self):
PM1 = ProcessingMechanism(function=UniformDist)
PM1.execute(1.0)
def test_processing_mechanism_ExponentialDist_function(self):
PM1 = ProcessingMechanism(function=ExponentialDist)
PM1.execute(1.0)
def test_max_abs_one_hot(self):
PM1 = ProcessingMechanism(default_variable=[0, 0, 0],
output_ports=[MAX_ABS_ONE_HOT])
PM1.execute([1, 2, -4])
assert np.allclose(PM1.output_ports[0].value, [0, 0, 4])
def test_processing_mechanism_Reinforcement_function(self):
PM1 = ProcessingMechanism(function=Reinforcement,
default_variable=[[0.0], [0.0], [0.0]])
PM1.execute([[1.0], [2.0], [3.0]])
def test_max_abs_indicator(self):
PM1 = ProcessingMechanism(default_variable=[0, 0, 0],
output_ports=[MAX_ABS_INDICATOR])
PM1.execute([1, 2, -4])
assert np.allclose(PM1.output_ports[0].value, [0, 0, 1])
def test_processing_mechanism_TDLearning_function(self):
PM1 = ProcessingMechanism(function=TDLearning,
default_variable=[[0.0], [0.0], [0.0]])
PM1.execute([[1.0], [2.0], [3.0]])
def test_processing_mechanism_FitzHughNagumoIntegrator_function(self):
PM1 = ProcessingMechanism(function=FitzHughNagumoIntegrator)
PM1.execute(1.0)
def test_processing_mechanism_DriftDiffusionIntegrator_function(self):
PM1 = ProcessingMechanism(function=DriftDiffusionIntegrator)
PM1.execute(1.0)
def test_processing_mechanism_Reduce_function(self):
PM1 = ProcessingMechanism(function=Reduce)
PM1.execute(1.0)
def test_median(self):
PM1 = ProcessingMechanism(default_variable=[0, 0, 0],
output_ports=[MEDIAN])
PM1.execute([1, 2, 4])
assert np.allclose(PM1.output_ports[0].value, [2])
def test_processing_mechanism_AccumulatorIntegrator_function(self):
PM1 = ProcessingMechanism(function=AccumulatorIntegrator)
PM1.execute(1.0)
def test_std_dev(self):
PM1 = ProcessingMechanism(default_variable=[0, 0, 0],
output_ports=[STANDARD_DEVIATION])
PM1.execute([1, 2, 4])
assert np.allclose(PM1.output_ports[0].value, [1.24721913])
def test_valid_matrix_specs(self):
# Note: default matrix specification is None
PM_default = ProcessingMechanism(function=LinearMatrix())
PM_default.execute(1.0)
assert np.allclose(PM_default.value, 1.0)
PM_default_len_2_var = ProcessingMechanism(
function=LinearMatrix(default_variable=[[0.0, 0.0]]),
default_variable=[[0.0, 0.0]])
PM_default_len_2_var.execute([[1.0, 2.0]])
assert np.allclose(PM_default_len_2_var.value, [[1.0, 2.0]])
PM_default_2d_var = ProcessingMechanism(function=LinearMatrix(
default_variable=[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]),
default_variable=[[0.0, 0.0],
[0.0, 0.0],
[0.0, 0.0]])
# [1.0 0.0] [1.0 0.0]
# [0.0 2.0] * [0.0 1.0]
# [3.0 0.0]
PM_default_2d_var.execute([[1.0, 0.0], [0.0, 2.0], [3.0, 0.0]])
assert np.allclose(PM_default_2d_var.value,
[[1.0, 0.0], [0.0, 2.0], [3.0, 0.0]])
# PM_float = ProcessingMechanism(function=LinearMatrix(matrix=4.0))
# PM_float.execute(1.0)
#
# assert np.allclose(PM_float.value, 4.0)
PM_1d_list = ProcessingMechanism(function=LinearMatrix(matrix=[4.0]))
PM_1d_list.execute(1.0)
assert np.allclose(PM_1d_list.value, 4.0)
PM_2d_list = ProcessingMechanism(
function=LinearMatrix(matrix=[[4.0, 5.0], [6.0, 7.0], [8.0, 9.0],
[10.0, 11.0]],
default_variable=[[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]),
default_variable=[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]])
PM_2d_list.execute([[1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 1.0]])
assert np.allclose(PM_2d_list.value,
[[4.0, 5.0], [8.0, 9.0], [10.0, 11.0]])
PM_1d_array = ProcessingMechanism(function=LinearMatrix(
matrix=np.array([4.0])))
PM_1d_array.execute(1.0)
assert np.allclose(PM_1d_array.value, 4.0)
PM_2d_array = ProcessingMechanism(function=LinearMatrix(
matrix=np.array([[4.0]])))
PM_2d_array.execute(1.0)
assert np.allclose(PM_2d_array.value, 4.0)
PM_matrix = ProcessingMechanism(function=LinearMatrix(
matrix=np.matrix([[4.0]])))
PM_matrix.execute(1.0)
assert np.allclose(PM_matrix.value, 4.0)
def test_processing_mechanism_DualAdaptiveIntegrator_function(self):
PM1 = ProcessingMechanism(function=DualAdaptiveIntegrator)
PM1.execute(1.0)