def test_aggregate_average(self):
a = np.array([1, 6, 3, 4, 4])
b = np.array([5, 7, 2, 5, 1])
c = np.array([6, 8, 4, 6, 1])
expect = np.array([4, 7, 3, 5, 2])
actual = aggregate([a, b, c], method=AVERAGE)
assert(np.allclose(actual, expect))
def test_aggregate_or(self):
a = np.array([1, 6, 3, 4, 4])
b = np.array([5, 7, 2, 5, 1])
c = np.array([6, 8, 4, 6, 1])
expect = np.array([6, 8, 4, 6, 4])
actual = aggregate([a, b, c], method=OR)
assert(np.allclose(actual, expect))
def aggregate(self):
"""Aggregates all implicated membership functions into one resulting mf.
"""
variables = self.get_output_variables()
for varname, variable in variables.items():
imfs = []
for rule in self.rules:
imf = rule.consequent.result[varname]
if imf is not None:
imfs.append(rule.consequent.result[varname])
variable.aggrmf = helper.aggregate(imfs, self.aggregation)
def test_aggregate_or_single(self):
a = np.array([1, 6, 3, 4, 4])
expect = np.array([1, 6, 3, 4, 4])
actual = aggregate([a], method=OR)
assert(np.allclose(actual, expect))
def test_aggregate_average_single(self):
a = np.array([1, 6, 3, 4, 4])
expect = np.array([1, 6, 3, 4, 4])
actual = aggregate([a], method=AVERAGE)
assert(np.allclose(actual, expect))