Mercurial > hg > ltpda
view m-toolbox/classes/+utils/@math/getjacobian.m @ 3:960fe1aa1c10 database-connection-manager
Add LTPDADatabaseConnectionManager implementation. Java code
author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Mon, 05 Dec 2011 16:20:06 +0100 |
parents | f0afece42f48 |
children |
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% GETJACOBIAN Calculate Jacobian of a given model function. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % DESCRIPTION % % Calculate Jacobian of a given model function for the given set of % coefficients. Jacobian is approximated with finite difference method. % % CALL: % % J = getjacobian(coeff,model,X) % % INPUT: % % J - fit coefficients % model - model function % X - x vactor (abscissa) % % % OUTPUT: % % J - Jacobian % % Note: Look at nlinfit.m of the stats toolbox. Model should be a matlab % function calculating Model values as a function of X and of coefficients % NOTE: The function prefer to work with column objects. Therefore it is % good practise to directly input coeff and X as column objects % % Examples: % % Use @ to specify MODELFUN: % load reaction; % J = getjacobian(coeff,@mymodel,X); % % where MYMODEL is a MATLAB function such as: % function yhat = mymodel(beta, X) % yhat = (beta(1)*X(:,2) - X(:,3)/beta(5)) ./ ... % (1+beta(2)*X(:,1)+beta(3)*X(:,2)+beta(4)*X(:,3)); % or % % mymodel = @(beta,X)(beta(1).*X(:,1)+beta(2).*X(:,2)+...) % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % VERSION: $Id: getjacobian.m,v 1.1 2009/03/26 16:26:05 luigi Exp $ % % HISTORY: 26-03-2009 L Ferraioli % Creation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function J = getjacobian(coeff,model,X) % checking size, willing to work with columns [a,b] = size(coeff); if a<b coeff = coeff.'; end [a,b] = size(X); if a<b X = X.'; end % finite difference relative step fdiffstep = eps^(1/3); % evaluate model on the input coefficients yfit = model(coeff,X); % check for NaNs nans = isnan(yfit(:)); % initialization p = numel(coeff); delta = zeros(size(coeff)); J = zeros(numel(yfit),p); for k = 1:p if (coeff(k) == 0) nb = sqrt(norm(coeff)); delta(k) = fdiffstep * (nb + (nb==0)); else delta(k) = fdiffstep*coeff(k); end yplus = model(coeff+delta,X); dy = yplus(:) - yfit(:); dy(nans) = []; J(:,k) = dy/delta(k); delta(k) = 0; end end