def test_difficult_derivative_case():
x1 = CMG(1.0)
# ## the following is a test case for: sin(tan(x)) + 2^(cos(x)) + sin(x)^tan(x)^exp(x) - (cos(x))^2, seeded at x = 1. Try it in autograd, it works.
test_func1 = CMfunc.sin(CMfunc.tan(x1)) + 2**(CMfunc.cos(x1)) + CMfunc.sin(
x1)**CMfunc.tan(x1)**CMfunc.exp(x1) - CMfunc.cos(x1)**2
print("test_func1 val, der: {}, {}".format(test_func1.val,
test_func1.grad))
assert (np.round(test_func1.val,
8), np.round(test_func1.grad,
8)) == (np.round(2.7246781638986564, 8),
np.round(-1.0139897786023675, 8))
print("Difficult derivative test passed.")
def test_CMV_add_sub():
x1 = CMG(1, np.array([1, 0, 0, 0]))
x2 = CMG(2, np.array([0, 1, 0, 0]))
x3 = CMG(3, np.array([0, 0, 1, 0]))
x4 = CMG(4, np.array([0, 0, 0, 1]))
x5 = CMG(1, np.array([1, 0]))
x6 = CMG(2, np.array([0, 1]))
F1_list = [
CMfunc.cos(x1 - 2 * x2),
CMfunc.log(x3) - 3 * x4 * x3, x2**2, (x1 + x2) / (x3 - x4)
]
F2_list = [
2 * x3 + CMfunc.cos(x1 - 2 * x2), 3 * x4 - x3, x2**x4, 1 / (x3 - x4)
]
F1 = CMV(F1_list)
F2 = CMV(F2_list)
F3 = F1 + F2
F4 = F2 + F1
F5 = F2 - F1 - F1
F6 = CMV([x5 * x6, x6])
F7 = F6 + x5
F8 = F6 - x6
assert np.array_equal(
F3.val, np.array([4.02001500679911, -25.901387711331893, 20., -4.]))
assert np.array_equal(
F3.jac,
np.array([[0.2822400161197344, -0.5644800322394689, 2., 0.],
[0., 0., -12.666666666666666, -6.],
[0., 36., 0., 11.090354888959125], [-1., -1., -4., 4.]]))
assert np.array_equal(
F4.val, np.array([4.02001500679911, -25.901387711331893, 20., -4.]))
assert np.array_equal(
F4.jac,
np.array([[0.2822400161197344, -0.5644800322394689, 2., 0.],
[0., 0., -12.666666666666666, -6.],
[0., 36., 0., 11.090354888959125], [-1., -1., -4., 4.]]))
assert np.array_equal(
F5.val, np.array([6.989992496600445, 78.80277542266379, 8., 5.]))
assert np.array_equal(
F5.jac,
np.array([[-0.1411200080598672, 0.2822400161197344, 2., 0.],
[0., 0., 22.333333333333332, 21.],
[0., 24., 0., 11.090354888959125], [2., 2., 5., -5.]]))
assert np.array_equal(F7.val, np.array([3., 3.]))
assert np.array_equal(F7.jac, np.array([[3., 1.], [1., 1.]]))
assert np.array_equal(F8.val, np.array([4., 4.]))
assert np.array_equal(F8.jac, np.array([[2., 0.], [0., 0.]]))
def compute_flow(self): ## assumes, for now, unitary b
for pos in self._points:
r = cmg(pos[0], np.array([1., 0.]))
theta = cmg(pos[1], np.array([0., 1.]))
self.CMGs.append(self._strength*cm.cos(theta)/r)
return Flow_it(self.CMGs)
def test_CMfunc_constant():
assert CMfunc.sin(2) == np.sin(2)
assert CMfunc.cos(5) == np.cos(5)
assert CMfunc.tan(9) == np.tan(9)
assert CMfunc.arcsin(.5) == np.arcsin(.5)
assert CMfunc.arccos(.4) == np.arccos(.4)
assert CMfunc.arctan(.1) == np.arctan(.1)
assert CMfunc.exp(3) == np.exp(3)
assert CMfunc.log(
74088, 42) == np.log(74088) / np.log(42) #using alternative base
print('passed constants test')
def test_CMV_init():
x1 = CMG(1, np.array([1, 0, 0, 0]))
x2 = CMG(2, np.array([0, 1, 0, 0]))
x3 = CMG(3, np.array([0, 0, 1, 0]))
x4 = CMG(4, np.array([0, 0, 0, 1]))
F1_list = [
CMfunc.cos(x1 - 2 * x2),
CMfunc.log(x3) - 3 * x4 * x3, x2**2, (x1 + x2) / (x3 - x4)
]
F1 = CMV(F1_list)
assert F1.__repr__(
) == 'CMvector(val = [ -0.9899925 -34.90138771 4. -3. ], \n jacobian = [[ 0.14112001 -0.28224002 0. 0. ]\n [ 0. 0. -11.66666667 -9. ]\n [ 0. 4. 0. 0. ]\n [ -1. -1. -3. 3. ]])'
assert np.array_equal(
F1.val, np.array([-0.9899924966004454, -34.90138771133189, 4., -3.]))
def compute_flow(self):
for pos in self._points:
r = cmg(pos[0], np.array([1., 0.]))
theta = cmg(pos[1], np.array([0., 1.]))
self.CMGs.append(self._strength*r*cm.cos(theta))
return Flow_it(self.CMGs)