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)