Author: tlinnet Date: Wed Aug 27 17:16:07 2014 New Revision: 25339 URL: http://svn.gna.org/viewcvs/relax?rev=25339&view=rev Log: Implemented the Jacobian of exponential function in Python Code.
This now also gets the same error as leastsq and C code. task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting. Modified: trunk/specific_analyses/relax_disp/estimate_r2eff.py Modified: trunk/specific_analyses/relax_disp/estimate_r2eff.py URL: http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_disp/estimate_r2eff.py?rev=25339&r1=25338&r2=25339&view=diff ============================================================================== --- trunk/specific_analyses/relax_disp/estimate_r2eff.py (original) +++ trunk/specific_analyses/relax_disp/estimate_r2eff.py Wed Aug 27 17:16:07 2014 @@ -210,6 +210,39 @@ return i0 * exp( -times * r2eff) + def func_exp_grad(self, params): + """Target function for the gradient (Jacobian matrix) of func_exp to minfx, for exponential fit . + + @param params: The vector of parameter values. + @type params: numpy rank-1 float array + @return: The Jacobian matrix with 'm' rows of function derivatives per 'n' columns of parameters. + @rtype: numpy array + """ + + # Unpack the parameter values. + r2eff = params[0] + i0 = params[1] + + # Make partial derivative, with respect to r2eff. + d_exp_d_r2eff = -i0 * self.times * exp(-r2eff * self.times) + + # Make partial derivative, with respect to i0. + d_exp_d_i0 = exp(-r2eff * self.times) + + # Define Jacobian as m rows with function derivatives and n columns of parameters. + self.jacobian_matrix_exp = transpose(array( [d_exp_d_r2eff , d_exp_d_i0] ) ) + + # Take the sum, to send to minfx. + sum_d_exp_d_r2eff = sum( d_exp_d_r2eff ) + sum_d_exp_d_i0 = sum( d_exp_d_i0 ) + + # Define Jacobian as m rows with function derivatives and n columns of parameters. + sum_jacobian_matrix_exp = transpose(array( [sum_d_exp_d_r2eff , sum_d_exp_d_i0] ) ) + + # Return Jacobian matrix. + return sum_jacobian_matrix_exp + + def func_exp_chi2(self, params): """Target function for minimising chi2 in minfx, for exponential fit. @@ -733,7 +766,7 @@ E.set_settings_minfx(min_algor=min_algor) # Do C code - do_C = True + do_C = False if do_C: # Initialise the function to minimise. @@ -770,13 +803,13 @@ jacobian_matrix_exp = transpose(asarray( jacobian(param_vector) ) ) # Compare with python code. - #E.func_exp_chi2_grad(params=param_vector) - #jacobian_matrix2 = deepcopy(E.jacobian_matrix) - #print jacobian_matrix - #print " " - #print jacobian_matrix2 + #E.func_exp_grad(param_vector) + #jacobian_matrix_exp2 = E.jacobian_matrix_exp + #print jacobian_matrix_exp - jacobian_matrix_exp2 else: - jacobian_matrix_exp_chi2 = deepcopy(E.jacobian_matrix_exp_chi2) + # Call class, to store value. + E.func_exp_grad(param_vector) + jacobian_matrix_exp = E.jacobian_matrix_exp # Get the co-variance pcov = E.multifit_covar(J=jacobian_matrix_exp) _______________________________________________ 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
