Author: bugman
Date: Fri Aug 29 18:18:40 2014
New Revision: 25448
URL: http://svn.gna.org/viewcvs/relax?rev=25448&view=rev
Log:
Created two unit tests showing the target_functions.relax_fit.jacobian()
function is correct.
This compares the calculated Jacobian to the numerically integrated values from
the
test_suite/shared_data/curve_fitting/numeric_gradient/jacobian.py script.
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=25448&r1=25447&r2=25448&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 Fri Aug
29 18:18:40 2014
@@ -20,11 +20,11 @@
###############################################################################
# Python module imports.
-from numpy import array, float64, zeros
+from numpy import array, float64, transpose, zeros
from unittest import TestCase
# relax module imports.
-from target_functions.relax_fit import setup, func, dfunc, d2func
+from target_functions.relax_fit import setup, func, dfunc, d2func, jacobian
class Test_relax_fit(TestCase):
@@ -141,3 +141,68 @@
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_jacobian(self):
+ """Unit test for the Jacobian returned by the jacobian() function at
the minimum.
+
+ This uses the data from
test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.log.
+ """
+
+ # Get the exponential curve Jacobian.
+ matrix = jacobian(self.params)
+
+ # The real Jacobian.
+ real = [[ 0.00000000e+00, 1.00000000e+00],
+ [ -3.67879441e+02, 3.67879441e-01],
+ [ -2.70670566e+02, 1.35335283e-01],
+ [ -1.49361205e+02, 4.97870684e-02],
+ [ -7.32625556e+01, 1.83156389e-02]]
+
+ # Numpy conversion.
+ matrix = array(matrix)
+ real = transpose(array(real))
+
+ # Printouts.
+ print("The Jacobian at the minimum is:\n%s" % matrix)
+ print("The real Jacobian at the minimum is:\n%s" % real)
+
+ # Check that the Jacobian matches the numerically derived values.
+ for i in range(len(matrix)):
+ for j in range(len(matrix[i])):
+ self.assertAlmostEqual(matrix[i, j], real[i, j], 3)
+
+
+ def test_jacobian_off_minimum(self):
+ """Unit test for the Jacobian returned by the jacobian() 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 exponential curve Jacobian.
+ matrix = jacobian(params)
+
+ # The real Jacobian.
+ real = [[ 0.00000000e+00, 1.00000000e+00],
+ [ -6.76676416e+01, 1.35335283e-01],
+ [ -1.83156389e+01, 1.83156389e-02],
+ [ -3.71812826e+00, 2.47875218e-03],
+ [ -6.70925256e-01, 3.35462628e-04]]
+
+ # Numpy conversion.
+ matrix = array(matrix)
+ real = transpose(array(real))
+
+ # Printout.
+ print("The Jacobian at %s is:\n%s" % (params, matrix))
+ print("The real Jacobian at the minimum is:\n%s" % real)
+
+ # Check that the Jacobian matches the numerically derived values.
+ for i in range(len(matrix)):
+ for j in range(len(matrix[i])):
+ self.assertAlmostEqual(matrix[i, j], real[i, j], 3)
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