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)
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_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])
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])
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])
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)
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, 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 test_buffer_standalone(self): B = Buffer() val = B.execute(1.0) assert np.allclose(deque(np.atleast_1d(1.0)), val)