def test_d2func_exp_off_minimum(self):
"""Unit test for the Hessian returned by the d2func_exp() function at a position away from the minimum.
This uses the data from test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.log.
"""
# The off-minimum parameter values.
I0 = 500.0
R = 2.0
params = [R / self.scaling_list[0], I0 / self.scaling_list[1]]
# Get the chi-squared Hessian.
hess = d2func_exp(params)
# Printout.
print("The Hessian at %s is:\n%s" % (params, hess))
# Check that the Hessian matches the numerically derived values.
self.assertAlmostEqual(hess[0][0],
-4.11964848e+02 * self.scaling_list[0]**2, 3)
self.assertAlmostEqual(
hess[0][1],
7.22678641e-01 * self.scaling_list[0] * self.scaling_list[1], 3)
self.assertAlmostEqual(
hess[1][0],
7.22678641e-01 * self.scaling_list[0] * self.scaling_list[1], 3)
self.assertAlmostEqual(hess[1][1],
2.03731472e-02 * self.scaling_list[1]**2, 3)
def test_d2func_exp(self):
"""Unit test for the Hessian returned by the d2func_exp() function at the minimum.
This uses the data from test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.log.
"""
# Get the chi-squared Hessian.
hess = d2func_exp(self.params)
# Printout.
print("The Hessian at the minimum is:\n%s" % hess)
# Check that the Hessian matches the numerically derived values.
self.assertAlmostEqual(hess[0][0], 4.72548021e+03*self.scaling_list[0]**2, 3)
self.assertAlmostEqual(hess[0][1], -3.61489336e+00*self.scaling_list[0]*self.scaling_list[1], 3)
self.assertAlmostEqual(hess[1][0], -3.61489336e+00*self.scaling_list[0]*self.scaling_list[1], 3)
self.assertAlmostEqual(hess[1][1], 2.31293027e-02*self.scaling_list[1]**2, 3)
def d2func_exp(self, params):
"""Wrapper function for the C module, for converting numpy arrays.
@param params: The parameter array from the minimisation code.
@type params: numpy array
@return: The Hessian generated by the C module converted to numpy format.
@rtype: numpy float64 rank-2 array
"""
# Convert if necessary.
if isinstance(params, ndarray):
params = params.tolist()
# Call the C code.
d2chi2 = d2func_exp(params)
# Return the chi2 Hessian as a numpy array.
return array(d2chi2, float64)
def d2func_exp(self, params):
"""Wrapper function for the C module, for converting numpy arrays.
@param params: The parameter array from the minimisation code.
@type params: numpy array
@return: The Hessian generated by the C module converted to numpy format.
@rtype: numpy float64 rank-2 array
"""
# Convert if necessary.
if isinstance(params, ndarray):
params = params.tolist()
# Call the C code.
d2chi2 = d2func_exp(params)
# Return the chi2 Hessian as a numpy array.
return array(d2chi2, float64)
def test_d2func_exp_off_minimum(self):
"""Unit test for the Hessian returned by the d2func_exp() function at a position away from the minimum.
This uses the data from test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.log.
"""
# The off-minimum parameter values.
I0 = 500.0
R = 2.0
params = [R/self.scaling_list[0], I0/self.scaling_list[1]]
# Get the chi-squared Hessian.
hess = d2func_exp(params)
# Printout.
print("The Hessian at %s is:\n%s" % (params, hess))
# Check that the Hessian matches the numerically derived values.
self.assertAlmostEqual(hess[0][0], -4.11964848e+02*self.scaling_list[0]**2, 3)
self.assertAlmostEqual(hess[0][1], 7.22678641e-01*self.scaling_list[0]*self.scaling_list[1], 3)
self.assertAlmostEqual(hess[1][0], 7.22678641e-01*self.scaling_list[0]*self.scaling_list[1], 3)
self.assertAlmostEqual(hess[1][1], 2.03731472e-02*self.scaling_list[1]**2, 3)
def test_d2func_exp(self):
"""Unit test for the Hessian returned by the d2func_exp() function at the minimum.
This uses the data from test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.log.
"""
# Get the chi-squared Hessian.
hess = d2func_exp(self.params)
# Printout.
print("The Hessian at the minimum is:\n%s" % hess)
# Check that the Hessian matches the numerically derived values.
self.assertAlmostEqual(hess[0][0],
4.72548021e+03 * self.scaling_list[0]**2, 3)
self.assertAlmostEqual(
hess[0][1],
-3.61489336e+00 * self.scaling_list[0] * self.scaling_list[1], 3)
self.assertAlmostEqual(
hess[1][0],
-3.61489336e+00 * self.scaling_list[0] * self.scaling_list[1], 3)
self.assertAlmostEqual(hess[1][1],
2.31293027e-02 * self.scaling_list[1]**2, 3)
