def runTest(self):
n, m = 2, 2
npt = n + 1
x0 = np.array([-1.2, 1.0])
xl = -1e2 * np.ones((n, ))
xu = 1e2 * np.ones((n, ))
model = Model(npt, x0, rosenbrock(x0), xl, xu, 1)
x1 = np.array([1.0, 0.9])
model.change_point(1, x1 - model.xbase, rosenbrock(x1))
x2 = np.array([1.0, 1.0])
model.change_point(2, x2 - model.xbase, rosenbrock(x2))
self.assertEqual(model.kopt, 2, 'Wrong kopt before resampling')
# Originally, x2 is the ideal point
# Here, testing that kopt moves back to x1 after adding heaps of bad x2 samples
for i in range(10):
model.add_new_sample(2, 5.0)
self.assertEqual(model.kopt, 1, 'Wrong kopt after resampling')
def runTest(self):
n = 2
npt = n + 1
x0 = np.array([-1.2, 1.0])
delta = 0.5
xl = -1e2 * np.ones((n, ))
xu = 1e2 * np.ones((n, ))
model = Model(npt, x0, rosenbrock(x0), xl, xu, 1)
model.add_new_sample(0, rosenbrock(x0))
x1 = x0 + delta * np.array([1.0, 0.0])
model.change_point(1, x1 - model.xbase, rosenbrock(x1))
x2 = x0 + delta * np.array([0.0, 1.0])
model.change_point(2, x2 - model.xbase, rosenbrock(x2))
model.kopt = 0 # force this
# Here (use delta=1), Lagrange polynomials are (1-x-y), 1-x and 1-y
# Maximum value in ball is for (1-x-y) at (x,y)=(1/sqrt2, 1/sqrt2) --> max value = 1 + sqrt(2)
self.assertAlmostEqual(model.poisedness_constant(delta),
1.0 + sqrt(2.0),
places=6,
msg="Poisedness wrong")
def runTest(self):
n, m = 2, 2
npt = n + 1
x0 = np.array([-1.2, 1.0])
xl = -1e2 * np.ones((n, ))
xu = 1e2 * np.ones((n, ))
model = Model(npt, x0, rosenbrock(x0), xl, xu, 1)
self.assertTrue(array_compare(model.sl, xl - x0),
'Wrong sl after initialisation')
self.assertTrue(array_compare(model.su, xu - x0),
'Wrong su after initialisation')
x1 = np.array([1.0, 0.9])
model.change_point(1, x1 - model.xbase, rosenbrock(x1))
self.assertTrue(
array_compare(model.as_absolute_coordinates(x1 - x0), x1),
'Wrong abs coords')
self.assertTrue(
array_compare(
model.as_absolute_coordinates(np.array([-1e3, 1e3]) - x0),
np.array([-1e2, 1e2])), 'Bad abs coords with bounds')
x2 = np.array([2.0, 0.9])
model.change_point(2, x2 - model.xbase, rosenbrock(x2))
sqdists = model.distances_to_xopt()
self.assertAlmostEqual(sqdists[0],
sumsq(x0 - x1),
msg='Wrong distance 0')
self.assertAlmostEqual(sqdists[1],
sumsq(x1 - x1),
msg='Wrong distance 1')
self.assertAlmostEqual(sqdists[2],
sumsq(x2 - x1),
msg='Wrong distance 2')
model.add_new_sample(0, rosenbrock(x0))
self.assertEqual(model.nsamples[0], 2, 'Wrong number of samples 0')
self.assertEqual(model.nsamples[1], 1, 'Wrong number of samples 1')
self.assertEqual(model.nsamples[2], 1, 'Wrong number of samples 2')
for i in range(50):
model.add_new_sample(0, 0.0)
self.assertEqual(model.kopt, 0, 'Wrong kopt after bad resampling')
self.assertTrue(array_compare(model.fopt(), 2 * rosenbrock(x0) / 52),
'Wrong fopt after bad resampling')
d = np.array([10.0, 10.0])
dirns_old = model.xpt_directions(include_kopt=True)
model.shift_base(d)
dirns_new = model.xpt_directions(include_kopt=True)
self.assertTrue(array_compare(model.xbase, x0 + d), 'Wrong new base')
self.assertEqual(model.kopt, 0, 'Wrong kopt after shift base')
for i in range(3):
self.assertTrue(array_compare(dirns_old[i, :], dirns_new[i, :]),
'Wrong dirn %i after shift base' % i)
self.assertTrue(array_compare(model.sl, xl - x0 - d),
'Wrong sl after shift base')
self.assertTrue(array_compare(model.su, xu - x0 - d),
'Wrong su after shift base')
# save_point and get_final_results
model.change_point(0, x0 - model.xbase,
rosenbrock(x0)) # revert after resampling
model.change_point(1, x1 - model.xbase,
rosenbrock(x1)) # revert after resampling
x, f, gradmin, hessmin, nsamples = model.get_final_results()
self.assertTrue(array_compare(x, x1), 'Wrong final x')
self.assertAlmostEqual(rosenbrock(x1), f, msg='Wrong final f')
self.assertTrue(array_compare(np.zeros((2, )), gradmin),
'Wrong final gradmin')
self.assertTrue(array_compare(np.zeros((2, 2)), hessmin),
'Wrong final hessmin')
self.assertEqual(1, nsamples, 'Wrong final nsamples')
self.assertIsNone(model.xsave, 'xsave not none after initialisation')
self.assertIsNone(model.fsave, 'fsave not none after initialisation')
self.assertIsNone(model.nsamples_save,
'nsamples_save not none after initialisation')
model.save_point(x0, rosenbrock(x0), 1, x_in_abs_coords=True)
self.assertTrue(array_compare(model.xsave, x0),
'Wrong xsave after saving')
self.assertAlmostEqual(model.fsave,
rosenbrock(x0),
msg='Wrong fsave after saving')
self.assertEqual(model.nsamples_save, 1,
'Wrong nsamples_save after saving')
x, f, gradmin, hessmin, nsamples = model.get_final_results()
self.assertTrue(array_compare(x, x1), 'Wrong final x after saving')
self.assertAlmostEqual(rosenbrock(x1),
f,
msg='Wrong final f after saving')
self.assertEqual(1, nsamples, 'Wrong final nsamples after saving')
model.save_point(x2 - model.xbase, 0.0, 2, x_in_abs_coords=False)
self.assertTrue(array_compare(model.xsave, x2),
'Wrong xsave after saving 2')
self.assertAlmostEqual(model.fsave,
0.0,
msg='Wrong fsave after saving 2')
self.assertEqual(model.nsamples_save, 2,
'Wrong nsamples_save after saving 2')
x, f, gradmin, hessmin, nsamples = model.get_final_results()
self.assertTrue(array_compare(x, x2), 'Wrong final x after saving 2')
self.assertAlmostEqual(f, 0.0, msg='Wrong final f after saving 2')
self.assertEqual(2, nsamples, 'Wrong final nsamples after saving 2')
model.save_point(x0, rosenbrock(x0), 3,
x_in_abs_coords=True) # try to re-save a worse value
self.assertTrue(array_compare(model.xsave, x2),
'Wrong xsave after saving 3')
self.assertAlmostEqual(model.fsave,
0.0,
msg='Wrong fsave after saving 3')
self.assertEqual(model.nsamples_save, 2,
'Wrong nsamples_save after saving 3')