def test_check_grad():
"""Tests the check_grad() function"""
def f_df_correct(x):
return x**2, 2 * x
def f_df_incorrect(x):
return x**3, 0.5 * x**2
output = StringIO()
check_grad(f_df_correct, 5, out=output, style='grid')
# helper functions
ansi_escape = re.compile(r'\x1b[^m]*m')
getvalues = lambda o: [
float(ansi_escape.sub('', s.strip()).split(' ')[0])
for s in o.getvalue().split('\n')[3].split('|')[1:-1]
]
# get the first row of data
values = getvalues(output)
print(values)
assert values[0] == values[1] == 10.0, "Correct gradient computation"
assert np.allclose(values[2], 0), "Correct error computation"
output = StringIO()
check_grad(f_df_incorrect, 5, out=output, style='grid')
values = getvalues(output)
printed_error = values[2]
correct_error = np.abs(values[0] - values[1]) / (np.abs(values[0]) +
np.abs(values[1]))
assert np.isclose(printed_error, correct_error,
atol=1e-4), "Correct relative error"
def test_check_grad():
"""Tests the check_grad() function"""
def f_df_correct(x):
return x**2, 2 * x
def f_df_incorrect(x):
return x**3, 0.5 * x**2
output = StringIO()
check_grad(f_df_correct, 5, out=output, style='grid')
# helper functions
ansi_escape = re.compile(r'\x1b[^m]*m')
getvalues = lambda o: [float(ansi_escape.sub('', s.strip()).split(' ')[0])
for s in o.getvalue().split('\n')[3].split('|')[1:-1]]
# get the first row of data
values = getvalues(output)
print(values)
assert values[0] == values[1] == 10.0, "Correct gradient computation"
assert np.allclose(values[2], 0), "Correct error computation"
output = StringIO()
check_grad(f_df_incorrect, 5, out=output, style='grid')
values = getvalues(output)
printed_error = values[2]
correct_error = np.abs(values[0] - values[1]) / (np.abs(values[0]) + np.abs(values[1]))
assert np.isclose(printed_error, correct_error, atol=1e-4), "Correct relative error"
def test_check_grad():
"""Tests the check_grad() function"""
def f_df_correct(x):
return x ** 2, 2 * x
def f_df_incorrect(x):
return x ** 3, 0.5 * x ** 2
output = StringIO()
check_grad(f_df_correct, 5, out=output)
# helper functions
getvalues = lambda o: [float(s.strip()) for s in o.getvalue().split("\n")[3].split("|")[:-1]]
# get the first row of data
values = getvalues(output)
assert values[0] == values[1] == 10.0, "Correct gradient computation"
assert values[2] == 0.0, "Correct error computation"
output = StringIO()
check_grad(f_df_incorrect, 5, out=output)
values = getvalues(output)
printed_error = values[2]
correct_error = np.abs(values[0] - values[1]) / (np.abs(values[0]) + np.abs(values[1]))
assert np.isclose(printed_error, correct_error), "Correct relative error"
def test_gradient(function, tol=1e-2):
x0 = function.param_init()
assert check_grad(function, x0) == 0
