Team:Paris/Modeling/More f3bis Algo

function optimal_parameters = find_f3_EnvZ(X_data, Y_data, initial_parameters) % gives the 'best parameters' involved in f3 with OmpR = 0 by least-square optimisation % -> USE IT AFTER find_f3_OmpR % X_data = vector of given values of ( [EnvZ]i ) (experimentally % controled) % Y_data = vector of experimentally measured values f3 corresponding of % the X_data % initial_parameters = values of the parameters proposed by the literature %                      or simply guessed %                   = [EnvZ_b, OmpR_b, K14, n14] global beta17 K15 n15; % parameters GIVEN BY find_f3_OmpR function output = act_pFlhDC(parameters, X_data) for k = 1:length(X_data) OmpR_P = complexes((parameters(1) + X_data(k)),parameters(2),parameters(3),parameters(4)); % complexes is a function that solve the "basical               % complexation equation" output(k) = beta17*(1 - hill( OmpR_P, K15, n15 )); end end options=optimset( 'LevenbergMarquardt', 'on' , 'TolX' ,1e-10, 'MaxFunEvals' ,1e10, 'TolFun' ,1e-10, 'MaxIter' ,1e4); % options for the function lsqcurvefit optimal_parameters = lsqcurvefit( @(parameters, X_data) act_pFlhDC(parameters, X_data), ...    initial_parameters, X_data, Y_data, options ); % search for the fittest parameters, between 1/10 and 10 times the initial % parameters end