Author: bugman Date: Fri Aug 29 18:05:20 2014 New Revision: 25446 URL: http://svn.gna.org/viewcvs/relax?rev=25446&view=rev Log: Added a script and log file for calculating the numerical Jacobian for an exponential curve.
This uses the data at http://thread.gmane.org/gmane.science.nmr.relax.devel/6807/focus=6840 and calculates the Jacobian using the numdifftools.Jacobian object construct and obtain the matrix, both at the minimum and at a point away from the minimum. The values will be used to construct a unit test to check the C module implementation. Added: trunk/test_suite/shared_data/curve_fitting/numeric_gradient/jacobian.py - copied, changed from r25445, trunk/test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.py Copied: trunk/test_suite/shared_data/curve_fitting/numeric_gradient/jacobian.py (from r25445, trunk/test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.py) URL: http://svn.gna.org/viewcvs/relax/trunk/test_suite/shared_data/curve_fitting/numeric_gradient/jacobian.py?p2=trunk/test_suite/shared_data/curve_fitting/numeric_gradient/jacobian.py&p1=trunk/test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.py&r1=25445&r2=25446&rev=25446&view=diff ============================================================================== --- trunk/test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.py (original) +++ trunk/test_suite/shared_data/curve_fitting/numeric_gradient/jacobian.py Fri Aug 29 18:05:20 2014 @@ -3,11 +3,11 @@ # Python module imports. from math import exp from numpy import array, float64 -from numdifftools import Hessian +from numdifftools import Jacobian def func(params): - """Calculate the chi-squared value.""" + """Back-calculate the intensities.""" global times, I, errors @@ -19,13 +19,8 @@ for i in range(len(times)): back_calc.append(I0 * exp(-R*times[i])) - # The chi2. - chi2 = 0.0 - for i in range(len(times)): - chi2 += (I[i] - back_calc[i])**2 / errors[i]**2 - - # Return the value. - return chi2 + # Return the back-calculated intensities. + return array(back_calc) # The real parameters. @@ -41,17 +36,17 @@ # The intensity errors. errors = [10.0, 10.0, 10.0, 10.0, 10.0] -# Set up the Hessian function. -d2func = Hessian(func) +# Set up the Jacobian function. +jacobian = Jacobian(func) -# The numeric Hessian at the minimum. +# The numeric Jacobian at the minimum. print("\n\nOn-minimum:\n") -hess = d2func([R, I0]) -print("The Hessian at %s is:\n%s" % ([R, I0], hess)) +matrix = jacobian([R, I0]) +print("The Jacobian at %s is:\n%s" % ([R, I0], matrix)) -# The numeric Hessian off the minimum. +# The numeric Jacobian off the minimum. print("\n\nOff-minimum:\n") R = 2.0 I0 = 500.0 -hess = d2func([R, I0]) -print("The Hessian at %s is:\n%s" % ([R, I0], hess)) +matrix = jacobian([R, I0]) +print("The Jacobian at %s is:\n%s" % ([R, I0], matrix)) _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-commits mailing list [email protected] To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-commits
