def test_quadratic_minimizer_exceptions(self):
with self.assertRaises(ValueError):
# Invalid value for parameter ``bound_size_ratio``
wolfe.quadratic_minimizer(x_a=0,
y_a=1,
y_prime_a=-1,
x_b=1,
y_b=2,
bound_size_ratio=2)
def test_quadratic_minimizer(self):
testcases = (
Case(
func_input=dict(
y_prime_a=-1,
x_a=0,
y_a=1,
x_b=1,
y_b=2,
),
func_expected=0.25,
),
Case(
func_input=dict(
y_prime_a=-1,
x_a=1,
y_a=1,
x_b=2,
y_b=2,
),
func_expected=1.25,
),
)
for testcase in testcases:
actual_output = wolfe.quadratic_minimizer(**testcase.func_input)
self.assertAlmostEqual(
self.eval(actual_output),
testcase.func_expected,
)
def test_quadratic_minimizer(self):
testcases = (
Case(func_input=dict(x_a=0, y_a=1, y_prime_a=-1, x_b=1, y_b=2),
func_expected=0.25),
Case(func_input=dict(x_a=1, y_a=1, y_prime_a=-1, x_b=2, y_b=2),
func_expected=1.25),
)
for testcase in testcases:
actual_output = wolfe.quadratic_minimizer(**testcase.func_input)
self.assertAlmostEqual(actual_output.eval(),
testcase.func_expected)
def test_quadratic_minimizer(self):
nan = np.array(np.nan)
testcases = (
Case(func_input=dict(x_a=0, y_a=1, y_prime_a=-1, x_b=1, y_b=2),
func_expected=0.25),
Case(func_input=dict(x_a=1, y_a=1, y_prime_a=-1, x_b=2, y_b=2),
func_expected=1.25),
)
for testcase in testcases:
actual_output = wolfe.quadratic_minimizer(**testcase.func_input)
self.assertAlmostEqual(actual_output.eval(),
testcase.func_expected)
def test_quadratic_minimizer_exceptions(self):
with self.assertRaises(ValueError):
# Invalid value for parameter ``bound_size_ratio``
wolfe.quadratic_minimizer(x_a=0, y_a=1, y_prime_a=-1,
x_b=1, y_b=2,
bound_size_ratio=2)
