def test_at_least_N(self):
# With a window_length of K, AtLeastN should return 1
# if N or more 1's exist in the lookback window
# This smoothing filter gives customizable "stickiness"
data = array(
[[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0]],
dtype=bool)
expected_1 = array([[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 0, 0]],
dtype=bool)
expected_2 = array([[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 0, 0]],
dtype=bool)
expected_3 = array([[1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 0, 0],
[1, 1, 1, 0, 0, 0], [1, 1, 0, 0, 0, 0]],
dtype=bool)
expected_4 = array([[1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 0, 0],
[1, 1, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0]],
dtype=bool)
class Input(Filter):
inputs = ()
window_length = 0
all_but_one = AtLeastN(inputs=[Input()], window_length=4, N=3)
all_but_two = AtLeastN(inputs=[Input()], window_length=4, N=2)
any_equiv = AtLeastN(inputs=[Input()], window_length=4, N=1)
all_equiv = AtLeastN(inputs=[Input()], window_length=4, N=4)
results = self.run_graph(
TermGraph({
'AllButOne': all_but_one,
'AllButTwo': all_but_two,
'AnyEquiv': any_equiv,
'AllEquiv': all_equiv,
'Any': Any(inputs=[Input()], window_length=4),
'All': All(inputs=[Input()], window_length=4)
}),
initial_workspace={Input(): data},
mask=self.build_mask(ones(shape=data.shape)),
)
check_arrays(results['Any'], expected_1)
check_arrays(results['AnyEquiv'], expected_1)
check_arrays(results['AllButTwo'], expected_2)
check_arrays(results['AllButOne'], expected_3)
check_arrays(results['All'], expected_4)
check_arrays(results['AllEquiv'], expected_4)
def test_at_least_N(self):
# With a window_length of K, AtLeastN should return 1
# if N or more 1's exist in the lookback window
# This smoothing filter gives customizable "stickiness"
data = np.array(
[
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0],
],
dtype=bool,
)
expected_1 = np.array(
[
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0],
],
dtype=bool,
)
expected_2 = np.array(
[
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0],
[1, 1, 1, 0, 0, 0],
],
dtype=bool,
)
expected_3 = np.array(
[
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
],
dtype=bool,
)
expected_4 = np.array(
[
[1, 1, 1, 1, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0],
],
dtype=bool,
)
class Input(Filter):
inputs = ()
window_length = 0
all_but_one = AtLeastN(inputs=[Input()], window_length=4, N=3)
all_but_two = AtLeastN(inputs=[Input()], window_length=4, N=2)
any_equiv = AtLeastN(inputs=[Input()], window_length=4, N=1)
all_equiv = AtLeastN(inputs=[Input()], window_length=4, N=4)
self.check_terms(
terms={
"AllButOne": all_but_one,
"AllButTwo": all_but_two,
"AnyEquiv": any_equiv,
"AllEquiv": all_equiv,
"Any": Any(inputs=[Input()], window_length=4),
"All": All(inputs=[Input()], window_length=4),
},
expected={
"Any": expected_1,
"AnyEquiv": expected_1,
"AllButTwo": expected_2,
"AllButOne": expected_3,
"All": expected_4,
"AllEquiv": expected_4,
},
initial_workspace={Input(): data},
mask=self.build_mask(np.ones(shape=data.shape)),
)