def test_sin():
# Scalar
f = rop.sin(x)
assert f._val == np.sin(x._val)
assert f.compute_gradient() == np.cos(x._val) * x.compute_gradient()
#Constant
assert rop.sin(c) == np.sin(c)
@author: weiruchen
"""
from VorDiff.reverse_operator import ReverseOperator as rop
from VorDiff.reverse_autodiff import ReverseAutoDiff as rad
def create_reverse_vector(array):
x, y = rad.reverse_vector(array)
return x, y
x, y = create_reverse_vector([[1, 2, 3], [1, 3, 6]])
# for scalar
f = 1 / (x[1]) + rop.sin(1 / x[1])
assert rad.partial_scalar(f) == -0.4693956404725932
# for vector
x, y = create_reverse_vector([[1, 2, 3], [1, 3, 6]])
a = x + 1
assert (rad.partial_vector(a, x) == [1.0, 1.0, 1.0]).all()
x, y = create_reverse_vector([[1, 2, 3], [1, 3, 6]])
h = rop.sin(x)
partial_derivative = rad.partial_vector(h, x)
answer = [0.54030231, -0.41614684, -0.9899925]
for i in range(len(partial_derivative)):
partial_derivative[i] = round(partial_derivative[i], 3)
def F2(array):
x, y = create_reverse_vector(array)
return rop.sin(x) + 2 * rop.sin(y), x, y