def test_select_point_to_explore_in_two_dimensions(self):
'''
This test case just runs code on a multi-dimensional input dataset.
It doesn't test any conditions, but could presumably do so in the future.
'''
acquire(
x=a([
[-0.5, -0.5],
[-0.5, 0.5],
]),
# Fake fmap and Cmap values from another set of x's
fmap=a([
[0.03254087],
[-0.03254087],
]),
Cmap=a([
[0.07894662, -0.07894662],
[-0.07894662, 0.07894662],
]),
bounds=a([
[-1.0, 1.0],
[-1.0, 1.0],
]),
kernelfunc=default_kernel
)
def test_select_point_to_exploit(self):
# We attempt to force exploitation by covering most of the input
# space and expecting that the maximization algorithm will choose
# the point between the highest outputs, given a symmetric output function.
next_point = acquire(
x=a([
[-0.75],
[-0.25],
[0.25],
[0.75],
]),
# I got these fmap and Cmap values from running our optimizer
# on the input data with comparisons [1, 0], [1, 3], [2, 0], [2, 3].
fmap=a([
[0.08950024],
[0.21423927],
[0.21423927],
[0.08950024],
]),
Cmap=a([
[0.15672336, -0.07836168, -0.07836168, 0.0],
[-0.07836168, 0.15672336, 0.0, -0.07836168],
[-0.07836168, 0.0, 0.15672336, -0.07836168],
[0.0, -0.07836168, -0.07836168, 0.15672336],
]),
bounds=a([
[-1.0, 1.0],
]),
kernelfunc=default_kernel
)
self.assertTrue(next_point[0] > -.25)
self.assertTrue(next_point[0] < .25)
def test_select_point_to_explore(self):
next_point = acquire(
x=a([
[-0.75],
[-0.4],
]),
# I got these fmap and Cmap values from running our optimizer
# on the input data with comparisons [0, 1]
fmap=a([
[0.03254087],
[-0.03254087],
]),
Cmap=a([
[0.07894662, -0.07894662],
[-0.07894662, 0.07894662],
]),
bounds=a([
[-1.0, 1.0],
]),
kernelfunc=default_kernel
)
self.assertTrue(next_point[0] > -.4)