def test_run_optimization(self):
f, _ = newton_rhapson(
x=a([
[0.0, 1.0],
[1.0, 1.0],
[-1.0, 0.0],
[2.0, 2.0],
[1.5, -1.0]
]),
f0=a([
[0.0],
[0.0],
[0.0],
[0.0],
[0.0],
]),
comparisons=a([
[3, 1],
[0, 1],
[2, 1],
[4, 0],
[2, 4],
]),
kernelfunc=default_kernel,
Hfunc=compute_H,
gfunc=compute_g,
sigma=2,
maxiter=20,
)
self.assertTrue(f[3][0] > f[1][0])
self.assertTrue(f[0][0] > f[1][0])
self.assertTrue(f[2][0] > f[1][0])
self.assertTrue(f[4][0] > f[0][0])
self.assertTrue(f[2][0] > f[4][0])
def test_one_iteration_yields_expected_result(self):
'''
Taking our results from the computation of H in the last test,
H^-1:
[
-1.066045352 -0.661325899 -0.301834093
-0.661325899 -1.045461463 -0.027289758
-0.301834093 -0.027289758 -1.066045352
]
b (computed fresh):
[
[-.603424068]
[0.0]
[.603424068]
]
g:
[
[-9.616613948]
[4.388339650]
[1.558974488]
]
Then, we compute that H^-1 * g:
[
[6.879072286]
[1.729331837]
[1.120927715]
]
'''
f1, _ = newton_rhapson(
x=a([
[0.0, 1.0],
[1.0, 1.0],
[-1.0, 0.0],
]),
f0=a([
[6.0],
[1.0],
[2.0],
]),
comparisons=a([
[0, 2],
[2, 0],
]),
kernelfunc=default_kernel,
Hfunc=compute_H,
gfunc=compute_g,
sigma=2.0,
maxiter=1,
)
self.assertAlmostEqual(f1, a([
[-.879072286],
[-.729331837],
[.879072285],
]))
