def test_sin():
x = Rev_Var(2.0)
y = 2
z = Rev_Var.sin(x)
z.grad_value = 1
assert z.value == pytest.approx(0.9092974268256817)
assert x.grad() == pytest.approx(-0.4161468365471424)
assert Rev_Var.sin(y) == np.sin(y)
def test_rev_grad():
x = Rev_Var(0.5)
y = Rev_Var(4.2)
z = x * y + Rev_Var.sin(x)
z.grad_value = 1.0
assert z.value == pytest.approx(2.579425538604203)
assert x.grad() == pytest.approx(4.2 + np.cos(x.value))
assert y.grad() == pytest.approx(0.5)
from EasyDiff.ad import AD
from EasyDiff.var import Var
from EasyDiff.rev_var import Rev_Var
from EasyDiff.ad import AD_Mode
import numpy as np
# test forward mode.
# give it a function of your choice
func = lambda x, y: Var.log(x)**Var.sin(y)
# give the initial values to take the derivatives at
ad = AD(vals=np.array([2, 2]), ders=np.array([1, 1]), mode=AD_Mode.FORWARD)
# calculate and print the derivatives
print("Var.log(x) ** Var.sin(y): {}".format(vars(ad.auto_diff(func))))
# test reverse mode.
func = lambda x, y: Rev_Var.log(x)**Rev_Var.sin(y)
ad = AD(vals=np.array([2, 2]), ders=np.array([1, 1]), mode=AD_Mode.REVERSE)
print("Rev_Var.log(x) ** Rev_Var.sin(y): {}".format(vars(ad.auto_diff(func))))
def test_jac_matrix(): f1 = lambda x, y: Rev_Var.log(x) ** Rev_Var.sin(y) f2 = lambda x, y: Rev_Var.sqrt(x) / y ad = AD(np.array([4.12, 5.13]), np.array([1, 1]), AD_Mode.REVERSE) assert np.array_equal(ad.jac_matrix([f1, f2]), np.array([[pytest.approx(-0.11403015), pytest.approx(0.10263124)], [pytest.approx(0.048018), pytest.approx(-0.07712832)]]))
def test_two_variables(): ad = AD(np.array([2, 2]), np.array([1, 1]), AD_Mode.REVERSE) f1 = lambda x, y: Rev_Var.log(x) ** Rev_Var.sin(y) assert ad.vars[0].value == 2 assert ad.vars[1].value == 2 assert AD.auto_diff(self=ad,func=f1) == Var(pytest.approx(0.7165772257590739),np.array([pytest.approx(0.47001694),pytest.approx(0.10929465)]))