def test_pow():
"""Test of exponent special method (__pow__) of Rnode class."""
# Test for exponent with scalar Rnode object and float value
x = Rnode(0.11)
z = x**2
z.grad_value = 1.0
try:
assert z.value == x.value**2
assert x.grad() == x.value**2 * np.log(x.value)
# assert x.children == (x.value ** 2 * np.log(x.value), z)
except AssertionError as e:
print(e)
# Test for exponent with two scalar Rnode object
x = Rnode(0.11)
y = Rnode(0.2)
z = x**y
z.grad_value = 1.0
try:
assert z.value == x.value**y.value
assert x.grad() == x.value**y.value * np.log(x.value)
except AssertionError as e:
print(e)
def test_clear():
"""Test of clear special method (clear) of Rnode class."""
# Test for clear with scalar Rnode object and float value
x = Rnode(0.11)
z = x**2 + x
z.grad_value = 1.0
x.grad()
x.clear()
try:
# assert z.value == x.value **2 + x.value
assert x.grad() is None
except AssertionError as e:
print(e)
def test_tanh():
"""Test of tanh method."""
# Test for sin with Rnode objects
x = Rnode(1.0)
z = Elem.tanh(x)
z.grad_value = 1.0
try:
assert z.value == np.tanh(x.value)
assert x.grad() == 1 / np.cosh(x.value)**2
except AssertionError as e:
print(e)
# Test for tan with two Dual objects
val = Dual(3, [4, 1])
z = Elem.tanh(val)
der = val.der / (np.cosh(val.val))**2
try:
assert z.val == np.tanh(val.val)
assert np.all(z.der == der)
except AssertionError as e:
print(e)
raise AssertionError
# Test for tanh with int,
x = 3
fx = Elem.tanh(x)
try:
assert fx == np.tanh(x)
except AssertionError as e:
print(e)
raise AssertionError
def test_cosh():
"""Test of cosh method."""
# Test for sin with Rnode objects
x = Rnode(1.0)
z = Elem.cosh(x)
z.grad_value = 1.0
try:
assert z.value == np.cosh(x.value)
assert x.grad() == np.sinh(x.value)
except AssertionError as e:
print(e)
# Test for cosh with two Dual objects
val = Dual(3, [4, 1])
z = Elem.cosh(val)
try:
assert z.val == np.cosh(val.val)
assert z.der[0] == np.sinh(val.val) * val.der[0]
assert z.der[1] == np.sinh(val.val) * val.der[1]
except AssertionError as e:
print(e)
raise AssertionError
# Test for cosh with int,
x = 3
fx = Elem.cosh(x)
try:
assert fx == np.cosh(x)
except AssertionError as e:
print(e)
raise AssertionError
def test_add():
"""Test of addition special method (__add__) of Rnode class."""
# Test for addition with scalar Rnode object and float value
x = Rnode(0.11)
z = x**2 + x
z.grad_value = 1.0
try:
assert z.value == x.value**2 + x.value
assert x.grad() == sum(weight * var.grad()
for weight, var in x.children)
except AssertionError as e:
print(e)
def test_arccos():
"""Test of arccos method."""
# Test for arccos with Rnode objects
x = Rnode(0.11)
z = Elem.arccos(x)
z.grad_value = 1.0
temp = 1 - x.value**2
print(temp)
if temp <= 0:
raise ValueError('Domain of sqrt is {x >= 0}')
try:
assert z.value == np.arccos(x.value)
assert x.grad() == -1 / np.sqrt(temp)
except AssertionError as e:
print(e)
# Test for arccos with invalid Rnode objects
with pytest.raises(ValueError, match=r".* sqrt .*"):
x = Rnode(2.0)
z = Elem.arccos(x)
Elem.arcsin(x)
z.grad_value = 1.0
# Test for arccos with two Dual objects
# arccos() input (-1,1)
x = Dual(0.2, [0.4, 0.1])
z = Elem.arccos(x)
print(z)
der = -1 / np.sqrt(1 - x.val**2) * np.asarray(x.der)
try:
assert z.val == np.arccos(x.val)
assert np.all(z.der == der)
except AssertionError as e:
print(e)
raise AssertionError
# Test for arccos with int
x = 0.1
fx = Elem.arccos(x)
try:
assert fx == np.arccos(x)
except AssertionError as e:
print(e)
raise AssertionError
def test_relu6():
"""Test of relu6 method."""
# Test for sin with Rnode objects
x = Rnode(7.0)
z = Elem.relu6(x)
z.grad_value = 1.0
a = max(0, x.value)
b = np.where(0.0 < a < 6.0, 1, 0)
if a > 6.0: # clip output to a maximum of 6
a = 6.0
try:
assert z.value == a
assert x.grad() == b
except AssertionError as e:
print(e)
# Test for relu6 with two Dual objects
x = Dual(8, [4, 1])
z = Elem.relu6(x)
a = max(0, x.val)
b = np.where(0.0 < a < 6.0, 1, 0)
print(b)
if a > 6: # clip output to a maximum of 6
a = 6
result = Dual(a, b * x.der)
try:
assert z.val == result.val
assert np.all(z.der == result.der)
except AssertionError as e:
print(e)
raise AssertionError
# Test for tanh with int,
x = 3
fx = Elem.relu6(x)
try:
assert fx == min(max(0, x), 6)
except AssertionError as e:
print(e)
raise AssertionError
def test_relu():
"""Test of relu method."""
# Test for sin with Rnode objects
x = Rnode(1.0)
z = Elem.relu(x)
z.grad_value = 1.0
a = max(0, x.value)
b = np.where(a > 0, 1, 0)
try:
assert z.value == a
assert x.grad() == b
except AssertionError as e:
print(e)
# Test for relu with two Dual objects
x = Dual(3, [4, 1])
z = Elem.relu(x)
a = max(0, x.val)
b = np.where(a > 0, 1, 0)
result = Dual(a, b * x.der)
try:
assert z.val == result.val
assert np.all(z.der == result.der)
except AssertionError as e:
print(e)
raise AssertionError
# Test for tanh with int,
x = 3
fx = Elem.relu(x)
try:
assert fx == max(0, x)
except AssertionError as e:
print(e)
raise AssertionError
def test_log2():
"""Test of log2 method."""
# Test for sin with Rnode objects
x = Rnode(1.0)
z = Elem.log2(x)
z.grad_value = 1.0
try:
assert z.value == np.log2(x.value)
assert x.grad() == 1 / (x.value * np.log(2))
except AssertionError as e:
print(e)
# Test for log2 with two Dual objects
val1 = Dual(3, [4, 1])
val2 = Dual(2, [3, 1])
val = val1 * val2
z = Elem.log2(val)
try:
assert z.val == np.log2(val.val)
assert z.der[0] == 1 / (val.val * np.log(2)) * val.der[0]
assert z.der[1] == 1 / (val.val * np.log(2)) * val.der[1]
except AssertionError as e:
print(e)
raise AssertionError
# Test for log2 with invalid Dual objects
with pytest.raises(ValueError, match=r".* logarithm .*"):
Elem.log2(Dual(-1, [4, 1]))
# Test for log2 with int,
x = 3
fx = Elem.log2(x)
try:
assert fx == np.log2(x)
except AssertionError as e:
print(e)
raise AssertionError
def test_sin():
"""Test of sin method."""
# Test for sin with Rnode objects
x = Rnode(1.0)
z = Elem.sin(x)
z.grad_value = 1.0
try:
assert z.value == np.sin(x.value)
assert x.grad() == np.cos(x.value)
except AssertionError as e:
print(e)
raise AssertionError
# Test for sin with two Dual objects
val1 = Dual(3, [4, 1])
val2 = Dual(2, [3, 1])
val = val1 + val2
z = Elem.sin(val)
try:
assert z.val == np.sin(val.val)
assert z.der[0] == np.cos(val.val) * val.der[0]
assert z.der[1] == np.cos(val.val) * val.der[1]
except AssertionError as e:
print(e)
raise AssertionError
# Test for sin with int
x = 3
fx = Elem.sin(x)
try:
assert fx == np.sin(x)
except AssertionError as e:
print(e)
raise AssertionError
def test_sqrt():
"""Test of sqrt method."""
# Test for sqrt with Rnode objects
x = Rnode(1.0)
z = Elem.sqrt(x)
z.grad_value = 1.0
try:
assert z.value == x.value**0.5
assert x.grad() == 0.5 * x.value**(-0.5)
except AssertionError as e:
print(e)
# Test for sqrt with two Dual objects
x = Dual(3, [4, 1])
z = Elem.sqrt(x)
result = x**0.5
try:
assert z.val == np.sqrt(x.val)
assert z.der[0] == result.der[0]
assert z.der[1] == result.der[1]
except AssertionError as e:
print(e)
raise AssertionError
# Test for sqrt with double
x = 3.0
fx = Elem.sqrt(x)
try:
assert fx == np.sqrt(x)
except AssertionError as e:
print(e)
raise AssertionError
def test_logistic():
"""Test of logistic method."""
# Test for sin with Rnode objects
x = Rnode(1.0)
z = Elem.logistic(x)
z.grad_value = 1.0
nominator = np.exp(x.value)
denominator = (1 + np.exp(x.value))**2
try:
assert z.value == 1 / (1 + np.exp(-x.value))
assert x.grad() == nominator / denominator
except AssertionError as e:
print(e)
# Test for logistic with two Dual objects
x = Dual(3, [4, 1])
z = Elem.logistic(x)
result = (1 / (1 + np.exp(-x.val)),
np.exp(x.val) / ((1 + np.exp(x.val))**2))
try:
assert z == result
except AssertionError as e:
print(e)
raise AssertionError
# Test for logistic with int
x = 3
fx = Elem.logistic(x)
try:
assert fx == 1 / (1 + np.exp(-x))
except AssertionError as e:
print(e)
raise AssertionError
def test_arctan():
"""Test of arctan method."""
# Test for arctan with Rnode objects
x = Rnode(0.11)
z = Elem.arctan(x)
z.grad_value = 1.0
try:
assert z.value == np.arctan(x.value)
assert x.grad() == 1 / (1 + x.value**2)
except AssertionError as e:
print(e)
# Test for arctan with two Dual objects
# arctan() input (-1,1)
x = Dual(0.2, [0.4, 0.1])
z = Elem.arctan(x)
der = 1 / (1 + x.val**2) * np.asarray(x.der)
try:
assert z.val == np.arctan(x.val)
assert np.all(z.der == der)
except AssertionError as e:
print(e)
raise AssertionError
# Test for arctan with int
x = 0.1
fx = Elem.arctan(x)
try:
assert fx == np.arctan(x)
except AssertionError as e:
print(e)
raise AssertionError
def test_exp():
"""Test of exp method."""
# Test for sin with Rnode objects
x = Rnode(1.0)
z = Elem.exp(x)
z.grad_value = 1.0
try:
assert z.value == np.exp(x.value)
assert x.grad() == np.exp(x.value)
except AssertionError as e:
print(e)
# Test for exp with two Dual objects
x = Dual(3, [4, 1])
z = Elem.exp(x)
der = np.exp(x.val) * np.asarray(x.der)
try:
assert z.val == np.exp(x.val)
assert np.all(z.der == der)
except AssertionError as e:
print(e)
raise AssertionError
# Test for exp with int,
x = 3
fx = Elem.exp(x)
try:
assert fx == np.exp(x)
except AssertionError as e:
print(e)
raise AssertionError
class RAutoDiff:
def __init__(self, fn):
"""Constructor for RAutoDiff class.
Parameters
==========
fn: The specific AD method for calculating the derivative.
"""
self.fn = fn
self._roots = None
self._value = None
self._der = None
def forwardpass(self, x):
"""Constructor the tree structure with input X for specific AD method
fn. Update the value and derivative of the AD method.
Parameters
==========
x: array_like
Returns:
No returns.
"""
self._roots = None
self._value = None
self._der = None
x = np.asarray(x)
try:
nf = len(self.fn) # if fn is a list of functions
except TypeError:
nf = 1
if nf == 1: # only one function
self._value, self._der = self._forwardpass1f(x, self.fn)
else: # vector functions
#for now, all the input functions must contain exactly the same parameters
#with the same order. function with only a subset of total Parameters
# is not allowed
nparams_all = [len(signature(fi).parameters) for fi in self.fn]
if len(set(nparams_all)) > 1:
raise TypeError('all input functions must contain the same parameters')
self._value = []
self._der = []
for idxf in range(nf):
tmp = self._forwardpass1f(x, self.fn[idxf])
self._value.append(tmp[0])
self._der.append(tmp[1])
self._value = np.asarray(self._value)
self._der = np.asarray(self._der)
if len(self._value.shape) == 2 and len(self._der.shape) == 2:
self._value = np.transpose(self._value, (1, 0))
self._der = np.transpose(self._der, (1, 0))
elif len(self._value.shape) == 2 and len(self._der.shape) == 3:
self._value = np.transpose(self._value, (1, 0))
self._der = np.transpose(self._der, (1, 0, 2))
try: # if _value or _der is a scalar array (size = 1), convert array to scalar
if self._value.size ==1:
self._value = np.asscalar(self._value)
if self._der.size == 1:
self._der = np.asscalar(self._der)
except AttributeError:
pass
def _forwardpass1f(self, x, fi): # deal with only one function case
"""Constructor the tree structure with input X for one single AD method
fi. This _forwardpass1f method will be called when dealing with vector function
input fn = [f1, f2, f3, ...]
Parameters
==========
x: array_like
fi: One AD method
Returns:
tmpval: array_like, the value of fi(x)
tmpder : array_like, the derivative of fi(x)
"""
# shoule only be called from forwardpass
self._roots = None
nparams = len(signature(fi).parameters) # number of parameters of input function
if nparams == 1: # function has only one parameter
if x.size == 1: # scalar input
self._roots = Rnode(x)
f = fi(self._roots)
f.grad_value = 1.0
tmpval = f.value
tmpder = self._roots.grad()
else: # vector input
if len(x.shape) > 1:
raise TypeError('input dimension size not supported')
else:
tmpval = np.zeros(len(x))
tmpder = np.zeros(len(x))
tmpidx = 0
for xi in x:
self._roots = Rnode(xi)
f = fi(self._roots)
f.grad_value = 1.0
tmpval[tmpidx] = f.value
tmpder[tmpidx] = self._roots.grad()
tmpidx = tmpidx + 1
else: # multiple input parameters (vector input)
if x.size == 1:
raise TypeError('input has insufficient parameters')
if len(x.shape) == 1: # evaluate at one vector point
if len(x) != nparams:
raise TypeError('input dimension size mismatch')
self._roots = [Rnode(xi) for xi in x]
f = fi(*self._roots)
f.grad_value = 1.0
tmpval = f.value
tmpder = [root.grad() for root in self._roots]
else: # evaluate at multiple vector points
if x.shape[1] != nparams:
raise TypeError('input dimension size mismatch')
nm = x.shape[0] # number of vector points to be evaluated
tmpval = np.zeros(nm)
tmpder = np.zeros((nm, nparams))
self._roots = []
for im in range(nm):
self._roots.append([Rnode(xi) for xi in x[im, :]])
f = fi(*self._roots[im])
f.grad_value = 1.0
tmpval[im] = f.value
tmpder[im, :] = [root.grad() for root in self._roots[im]]
return tmpval, tmpder
def values(self): # return the value of the function
"""Get value of the input method fn for given X
Returns:
y : array_like
"""
return self._value
def reverse(self): # return the derivative with respect to varname variable
"""Get the derivatives (scalar, vector, or matrix depending on the
the dimension of input method fn and X)
Returns:
y : array_like
"""
return self._der
