Mercurial > hg > ltpda
view m-toolbox/classes/+utils/@math/stopfit.m @ 21:8be9deffe989 database-connection-manager
Update ltpda_uo.update
author | Daniele Nicolodi <nicolodi@science.unitn.it> |
---|---|
date | Mon, 05 Dec 2011 16:20:06 +0100 |
parents | f0afece42f48 |
children |
line wrap: on
line source
% STOPFIT verify fit accuracy checking for specified condition. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % DESCRIPTION: % % Verify fit accuracy checking for a specified condition. Available % conditions are: % % Log Residuals difference % Check if the minimum of the logarithmic difference between data and % residuals is larger than a specified value. ie. if the conditioning % value is 2, the function ensures that the difference between data and % residuals is at lest 2 order of magnitude lower than data itsleves. % Checking algorithm is: % lsr = log10(abs(y))-log10(abs(rdl)); % min(lsr) > lrscond; % % Log Residuals difference and Root Mean Squared Error % Check if the log difference between data and residuals is in % larger than the value indicated in lsrcond and that the variation of % the root mean squared error is lower than 10^(-1*msevar). % Checking algorithm is: % lsr = log10(abs(y))-log10(abs(rdl)); % (lsr > lrscond) && (mse < 10^(-1*lrsvarcond)); % % CALL: % % [ext,msg] = stopfit(y,rdl,mse,ctp,lrscond,msevar) % % INPUTS: % % - y are the fitting data (in case of 'lrs' and 'lrsmse') or the % fitted model (in case of 'rft' and 'rftmse') % - rdl are the fit residuals % - mse is a vector storing the values of root mean squared errors % difference for the present and previuos iterations % - order is the model order % - ctp defines the conditioning type. Admitted values are: % 1) 'chival' check if the value of the Mean Squared Error is lower % than 10^(-1*lsrcond). % 2) 'chivar' check if the value of the Mean Squared Error is lower % than 10^(-1*lsrcond) and if the relative variation of mean squared error is % lower than 10^(-1*msevar). % 3) 'lrs' check if the log difference between data and residuals is % point by point larger than the value indicated in lsrcond. This % mean that residuals are lsrcond order of magnitudes lower than % data. % 4) 'lrsmse' check if the log difference between data and % residuals is larger than the value indicated in lsrcond and if the % relative variation of root mean squared error is lower than % 10^(-1*msevar). % 5) 'rft' check if the spectral flatness coefficient for the % rersiduals is larger than the value passed in lrscond. In this case % only values 0 < lrscond < 1 are allowed. % 6) 'rftmse' check if the spectral flatness coefficient for the % rersiduals is larger than the value passed in lrscond and if the % relative variation of root mean squared error is lower than 10^(-1*msevar). % In this case only values 0 < lrscond < 1 are allowed. % % OUTPUT: % % - ext is 1 if the specified condition is satisfied or 0 if not % - msg is a string containing a messagge % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % VERSION: $Id: stopfit.m,v 1.8 2009/04/20 14:25:50 luigi Exp $ % % HISTORY: 09-10-2008 L Ferraioli % Creation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [ext,msg] = stopfit(y,rdl,mse,ctp,lrscond,msevar) % switching between conditions switch ctp case 'chival' if isempty(lrscond) lrscond = 2; % Default value end hh = length(mse); lsr = -1*log10(mse(hh)); if lsr > lrscond msg = 'Reached tolerance for Mean Squared Error Value'; ext = true; else ext = false; msg = ''; end case 'chivar' if isempty(lrscond) a = 2; % Default value else a = lrscond; end if isempty(msevar) b = 2; % Default value else b = msevar; end hh = length(mse); tlsr = -1*log10(mse(hh)); if hh == 1 stc = 1; else stc = diff(mse(hh-1:hh))/mse(hh-1); end if all(tlsr > a) && (abs(stc) < 10^(-1*b)) msg = 'Reached tolerance for Mean Squared Error Value and Variation'; ext = true; else ext = false; msg = ''; end case 'lrs' if isempty(lrscond) lrscond = 2; % Default value end lsr = log10(abs(y))-log10(abs(rdl)); if min(lsr) > lrscond msg = 'Reached tolerance for Log residuals'; ext = true; else ext = false; msg = ''; end case 'lrsmse' if isempty(lrscond) a = 2; % Default value else a = lrscond; end if isempty(msevar) b = 2; % Default value else b = msevar; end tlsr = log10(abs(y))-log10(abs(rdl)); hh = length(mse); if hh == 1 stc = 1; else stc = diff(mse(hh-1:hh))/mse(hh-1); end if all(tlsr > a) && (abs(stc) < 10^(-1*b)) msg = 'Reached tolerance for Log Residuals and Mean Squared Error variation'; ext = true; else ext = false; msg = ''; end case 'rft' % Check that residuals flatness is larger than a certain value % Calculate residual flatness rf = utils.math.spflat(abs(rdl)); % Checking that lrscond has the correct value a = lrscond; if (a >= 1) || (a < 0) a = 0.5; end if rf > a msg = 'Reached tolerance for residuals spectral flatness'; ext = true; else ext = false; msg = ''; end case 'rftmse' % Calculate residual flatness rf = utils.math.spflat(abs(rdl)); % Checking that lrscond has the correct value a = lrscond; if (a >= 1) || (a < 0) a = 0.5; end if isempty(msevar) b = 2; % Default value else b = msevar; end hh = length(mse); if hh == 1 stc = 1; else stc = diff(mse(hh-1:hh))/mse(hh-1); end if (rf > a) && (abs(stc) < 10^(-1*b)) msg = 'Reached tolerance for residuals spectral flatness and Mean Squared Error variation'; ext = true; else ext = false; msg = ''; end end