Team:Paris/Modeling/More f3 Algo

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(→find_ƒ3 ( OmpR* ))
 
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== find_&#131;3 ( ''FliA'' ) ==
== find_&#131;3 ( ''FliA'' ) ==
 +
<html><pre class="codeinput">
 +
<span class="keyword">function</span> optimal_parameters = find_f3_FliA(X_data, Y_data, initial_parameters)
 +
<span class="comment">% gives the 'best parameters' involved in f3 with OmpR = 0 by least-square optimisation
 +
</span><span class="comment">% -> USE IT AFTER find_f3_OmpR
 +
</span>
 +
<span class="comment">% X_data = vector of given values of ( [FliA]i ) (experimentally
 +
</span><span class="comment">% controled)
 +
</span><span class="comment">% Y_data = vector of experimentally measured values f3 corresponding of
 +
</span><span class="comment">% the X_data
 +
</span><span class="comment">% initial_parameters = values of the parameters proposed by the literature
 +
</span><span class="comment">%                      or simply guessed
 +
</span><span class="comment">%                    = [beta22, K6 -> (K6)/(coefOmp), n6]
 +
</span>
 +
<span class="keyword">global</span> beta17;<span class="comment"> % parameter GIVEN BY find_f3_OmpR
 +
</span>
 +
    <span class="keyword">function</span> output = act_pFlhDC(parameters, X_data)
 +
        <span class="keyword">for</span> k = 1:length(X_data)
 +
                output(k) = beta17*(1 - hill( X_data(k), parameters(2), parameters(3))) ...
 +
                    + parameters(1)*hill(X_data(k), parameters(2), parameters(3));
 +
        <span class="keyword">end</span>
 +
    <span class="keyword">end</span>
 +
 +
options=optimset(<span class="string">'LevenbergMarquardt'</span>,<span class="string">'on'</span>,<span class="string">'TolX'</span>,1e-10,<span class="string">'MaxFunEvals'</span>,1e10,<span class="string">'TolFun'</span>,1e-10,<span class="string">'MaxIter'</span>,1e4);
 +
<span class="comment">% options for the function lsqcurvefit
 +
</span>
 +
optimal_parameters = lsqcurvefit( @(parameters, X_data) act_pFlhDC(parameters, X_data), ...
 +
    initial_parameters, X_data, Y_data, options );
 +
<span class="comment">% search for the fittest parameters, between 1/10 and 10 times the initial
 +
</span><span class="comment">% parameters
 +
</span>
 +
<span class="keyword">end</span>
 +
</pre></html>
 +
== find_&#131;3 ( ''OmpR<sup>*</sup>'' ) ==
-
== find_&#131;3 ( ''OmpR^{*}'' ) ==
+
<html><pre class="codeinput">
-
 
+
<span class="keyword">function</span> optimal_parameters = find_f3_OmpR(X_data, Y_data, initial_parameters)
-
 
+
<span class="comment">% gives the 'best parameters' involved in f3 with FliA = 0 by least-square optimisation
 +
</span><span class="comment">% -> USE IT BEFORE find_f3_FliA
 +
</span>
 +
<span class="comment">% X_data = vector of given values of ( [OmpR]i ) (experimentally
 +
</span><span class="comment">% controled)
 +
</span><span class="comment">% Y_data = vector of experimentally measured values f3 corresponding of
 +
</span><span class="comment">% the X_data
 +
</span><span class="comment">% initial_parameters = values of the parameters proposed by the literature
 +
</span><span class="comment">%                      or simply guessed
 +
</span><span class="comment">%                    = [beta17, K15 -> (K15)/(coefOmp), n15]
 +
</span>
 +
    <span class="keyword">function</span> output = act_pFlhDC(parameters, X_data)
 +
        <span class="keyword">for</span> k = 1:length(X_data)
 +
                output(k) =(1 - hill( X_data(k), parameters(2), parameters(3) )) * parameters(1);
 +
        <span class="keyword">end</span>
 +
    <span class="keyword">end</span>
 +
 +
options=optimset(<span class="string">'LevenbergMarquardt'</span>,<span class="string">'on'</span>,<span class="string">'TolX'</span>,1e-10,<span class="string">'MaxFunEvals'</span>,1e10,<span class="string">'TolFun'</span>,1e-10,<span class="string">'MaxIter'</span>,1e4);
 +
<span class="comment">% options for the function lsqcurvefit
 +
</span>
 +
optimal_parameters = lsqcurvefit( @(parameters, X_data) act_pFlhDC(parameters, X_data), ...
 +
    initial_parameters, X_data, Y_data, options );
 +
<span class="comment">% search for the fittest parameters, between 1/10 and 10 times the initial
 +
</span><span class="comment">% parameters
 +
</span>
 +
<span class="keyword">end</span>
 +
</pre></html>
</div>
</div>

Latest revision as of 03:45, 30 October 2008

find_ƒ3 ( FliA ) 

function optimal_parameters = find_f3_FliA(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 ( [FliA]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
%                    = [beta22, K6 -> (K6)/(coefOmp), n6]
 
global beta17; % parameter GIVEN BY find_f3_OmpR
 
     function output = act_pFlhDC(parameters, X_data)
         for k = 1:length(X_data)
                 output(k) = beta17*(1 - hill( X_data(k), parameters(2), parameters(3))) ...
                     + parameters(1)*hill(X_data(k), parameters(2), parameters(3));
         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

find_ƒ3 ( OmpR* ) 

function optimal_parameters = find_f3_OmpR(X_data, Y_data, initial_parameters)
% gives the 'best parameters' involved in f3 with FliA = 0 by least-square optimisation
% -> USE IT BEFORE find_f3_FliA
 
% X_data = vector of given values of ( [OmpR]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
%                    = [beta17, K15 -> (K15)/(coefOmp), n15]
 
     function output = act_pFlhDC(parameters, X_data)
         for k = 1:length(X_data)
                 output(k) =(1 - hill( X_data(k), parameters(2), parameters(3) )) * parameters(1);
         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

Retrieved from "http://2008.igem.org/Team:Paris/Modeling/More_f3_Algo"