def test_eq(self, low, high, res):
"""This also tests Set.array().
This test can massively slow down hypothesis with even
reasonably large/small values.
"""
assume(low < high)
# to avoid MemoryError and runs that take forever..
assume(high - low <= 10)
D1 = Domain("1", low, high, res=res)
D1.s1 = fun.bounded_linear(low, high)
D2 = Domain("2", low, high, res=res)
D2.s2 = Set(fun.bounded_linear(low, high))
assert D1.s1 == D2.s2
def test_rating():
"""Tom is surveying restaurants.
He doesn't need fancy logic but rather uses a simple approach
with weights.
He went into a small, dirty bar that served some
really good drink and food that wasn't nicely arranged but still
yummmy. He rates the different factors on a scale from 1 to 10,
uses a bounded_linear function to normalize over [0,1] and
passes both the weights (how much each aspect should weigh in total)
and the domain as parameters into weighted_sum.
However, he can't just use Domain(value) because that would return
a dict of memberships, instead he uses Domain.min(value) which
returns the minimum of all memberships no matter how many sets
there are. He creates a dict of membership values corresponding to
the weights and passes that into the parametrized weighted_sum func
as argument to get the final rating for this restaurant.
"""
R = Domain("rating", 1, 10, res=0.1)
R.norm = bounded_linear(1, 10)
weights = {"beverage": 0.3, "atmosphere": 0.2, "looks": 0.2, "taste": 0.3}
w_func = weighted_sum(weights=weights, target_d=R)
ratings = {
"beverage": R.min(9),
"atmosphere": R.min(5),
"looks": R.min(4),
"taste": R.min(8),
}
assert w_func(ratings) == 6.9
def test_complement(self):
D = Domain("d", 0, 10, res=0.1)
D.s1 = Set(fun.bounded_linear(3, 12))
D.s2 = ~~D.s1
assert all(np.isclose(D.s1.array(), D.s2.array()))
def test_sub_super_set(self):
D = Domain("d", 0, 10, res=0.1)
D.s = Set(fun.bounded_linear(3, 12))
D.x = D.s.normalized()
assert D.x >= D.s
assert D.s <= D.x
def test_normalized(self):
D = Domain("d", 0, 10, res=0.1)
D.s = Set(fun.bounded_linear(3, 12))
D.x = D.s.normalized()
D.y = D.x.normalized()
assert D.x == D.y
def test_bounded_linear(self, x, low, high, c_m, no_m):
assume(low < high)
assume(c_m > no_m)
f = fun.bounded_linear(low, high, c_m=c_m, no_m=no_m)
assert 0 <= f(x) <= 1