Author: bugman
Date: Tue Aug 26 19:51:12 2014
New Revision: 25319
URL: http://svn.gna.org/viewcvs/relax?rev=25319&view=rev
Log:
Implemented two unit tests to check the Hessian of the
target_functions.relax_fit.d2func() function.
This compares the calculated Hessian to the numerically integrated values from
the
test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.py script,
showing that the d2func()
function is implemented correctly.
Modified:
trunk/test_suite/unit_tests/_target_functions/test_relax_fit.py
Modified: trunk/test_suite/unit_tests/_target_functions/test_relax_fit.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/test_suite/unit_tests/_target_functions/test_relax_fit.py?rev=25319&r1=25318&r2=25319&view=diff
==============================================================================
--- trunk/test_suite/unit_tests/_target_functions/test_relax_fit.py
(original)
+++ trunk/test_suite/unit_tests/_target_functions/test_relax_fit.py Tue Aug
26 19:51:12 2014
@@ -24,7 +24,7 @@
from unittest import TestCase
# relax module imports.
-from target_functions.relax_fit import setup, func, dfunc
+from target_functions.relax_fit import setup, func, dfunc, d2func
class Test_relax_fit(TestCase):
@@ -98,3 +98,46 @@
# Check that the gradient matches the numerically derived values.
self.assertAlmostEqual(grad[0],
456.36655522098829*self.scaling_list[0], 3)
self.assertAlmostEqual(grad[1],
-10.8613338920982*self.scaling_list[1], 3)
+
+
+ def test_d2func(self):
+ """Unit test for the Hessian returned by the d2func() 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(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 test_d2func_off_minimum(self):
+ """Unit test for the Hessian returned by the d2func() 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(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)
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