Applying logic to Curve Fitter Output
I am trying to use Curve Fitter and generating the code to help teach myself how to process my data without Curve Fitter.
Is there a way to apply a logic that a certain data point should be lower/higher than another certain data point, and have the coefficients adjust accordingly?
%CREATEFIT(TWOSOUSANDTQSPK,TWOSOUSANDTQAPC,TWOSOUSANDTQNM)
% Create a fit.
%
% Data for ‘2000 copy 1’ fit:
% X Input: twosousandTQSPK
% Y Input: twosousandTQAPC
% Z Output: twosousandTQnm
% Output:
% fitresult : a fit object representing the fit.
% gof : structure with goodness-of fit info.
%
% See also FIT, CFIT, SFIT.
% Auto-generated by MATLAB on 18-Jul-2024 20:43:50
%% Fit: ‘2000 copy 1’.
[xData, yData, zData] = prepareSurfaceData( twosousandTQSPK, twosousandTQAPC, twosousandTQnm );
% Set up fittype and options.
ft = fittype( ‘A+(x*B)+(C*x*x)+(y*x*D)+(y*x*x*E)+(y*F)’, ‘independent’, {‘x’, ‘y’}, ‘dependent’, ‘z’ );
opts = fitoptions( ‘Method’, ‘NonlinearLeastSquares’ );
opts.Display = ‘Off’;
opts.MaxFunEvals = 60000;
opts.MaxIter = 40000;
opts.StartPoint = [0.0526769976807926 0.737858095516997 0.269119426398556 0.422835615008808 0.547870901214845 0.942736984276934];
opts.TolFun = 0.1;
opts.TolX = 0.1;
% Fit model to data.
[fitresult, gof] = fit( [xData, yData], zData, ft, opts );
% Plot fit with data.
figure( ‘Name’, ‘2000 copy 1’ );
h = plot( fitresult, [xData, yData], zData );
legend( h, ‘2000 copy 1’, ‘twosousandTQnm vs. twosousandTQSPK, twosousandTQAPC’, ‘Location’, ‘NorthEast’, ‘Interpreter’, ‘none’ );
% Label axes
xlabel( ‘twosousandTQSPK’, ‘Interpreter’, ‘none’ );
ylabel( ‘twosousandTQAPC’, ‘Interpreter’, ‘none’ );
zlabel( ‘twosousandTQnm’, ‘Interpreter’, ‘none’ );
grid off
view( -2.9, 15.3 );
I am looking to apply that the output data should follow the ‘twosousandTQSPK’ axis relative. I reserached the weighting function, but I I don’t know how I could accurately match the data inputs I have to weight it.
This is an example of what I am expecting with a different set of variables, but the results above in question should resemble this outcome to some degree.
%CREATEFIT(SIXTEENTQSPK,SIXTEENTQAPC,SIXTEENTQNM)
% Create a fit.
%
% Data for ‘1600 copy 2’ fit:
% X Input: sixteenTQSPK
% Y Input: sixteenTQAPC
% Z Output: sixteenTQnm
% Output:
% fitresult : a fit object representing the fit.
% gof : structure with goodness-of fit info.
%
% See also FIT, CFIT, SFIT.
% Auto-generated by MATLAB on 18-Jul-2024 20:55:15
%% Fit: ‘1600 copy 2’.
[xData, yData, zData] = prepareSurfaceData( sixteenTQSPK, sixteenTQAPC, sixteenTQnm );
% Set up fittype and options.
ft = fittype( ‘(y*A)+B+(x*C)+(D*x*x)+(y*x*E)+(y*x*x*F)’, ‘independent’, {‘x’, ‘y’}, ‘dependent’, ‘z’ );
opts = fitoptions( ‘Method’, ‘NonlinearLeastSquares’ );
opts.Display = ‘Off’;
opts.MaxFunEvals = 60000;
opts.MaxIter = 40000;
opts.StartPoint = [0.0430238016578078 0.168990029462704 0.649115474956452 0.73172238565867 0.647745963136307 0.450923706430945];
opts.TolFun = 0.1;
opts.TolX = 0.1;
% Fit model to data.
[fitresult, gof] = fit( [xData, yData], zData, ft, opts );
% Plot fit with data.
figure( ‘Name’, ‘1600 copy 2’ );
h = plot( fitresult, [xData, yData], zData );
legend( h, ‘1600 copy 2’, ‘sixteenTQnm vs. sixteenTQSPK, sixteenTQAPC’, ‘Location’, ‘NorthEast’, ‘Interpreter’, ‘none’ );
% Label axes
xlabel( ‘sixteenTQSPK’, ‘Interpreter’, ‘none’ );
ylabel( ‘sixteenTQAPC’, ‘Interpreter’, ‘none’ );
zlabel( ‘sixteenTQnm’, ‘Interpreter’, ‘none’ );
grid off
view( -0.5, -13.0 );I am trying to use Curve Fitter and generating the code to help teach myself how to process my data without Curve Fitter.
Is there a way to apply a logic that a certain data point should be lower/higher than another certain data point, and have the coefficients adjust accordingly?
%CREATEFIT(TWOSOUSANDTQSPK,TWOSOUSANDTQAPC,TWOSOUSANDTQNM)
% Create a fit.
%
% Data for ‘2000 copy 1’ fit:
% X Input: twosousandTQSPK
% Y Input: twosousandTQAPC
% Z Output: twosousandTQnm
% Output:
% fitresult : a fit object representing the fit.
% gof : structure with goodness-of fit info.
%
% See also FIT, CFIT, SFIT.
% Auto-generated by MATLAB on 18-Jul-2024 20:43:50
%% Fit: ‘2000 copy 1’.
[xData, yData, zData] = prepareSurfaceData( twosousandTQSPK, twosousandTQAPC, twosousandTQnm );
% Set up fittype and options.
ft = fittype( ‘A+(x*B)+(C*x*x)+(y*x*D)+(y*x*x*E)+(y*F)’, ‘independent’, {‘x’, ‘y’}, ‘dependent’, ‘z’ );
opts = fitoptions( ‘Method’, ‘NonlinearLeastSquares’ );
opts.Display = ‘Off’;
opts.MaxFunEvals = 60000;
opts.MaxIter = 40000;
opts.StartPoint = [0.0526769976807926 0.737858095516997 0.269119426398556 0.422835615008808 0.547870901214845 0.942736984276934];
opts.TolFun = 0.1;
opts.TolX = 0.1;
% Fit model to data.
[fitresult, gof] = fit( [xData, yData], zData, ft, opts );
% Plot fit with data.
figure( ‘Name’, ‘2000 copy 1’ );
h = plot( fitresult, [xData, yData], zData );
legend( h, ‘2000 copy 1’, ‘twosousandTQnm vs. twosousandTQSPK, twosousandTQAPC’, ‘Location’, ‘NorthEast’, ‘Interpreter’, ‘none’ );
% Label axes
xlabel( ‘twosousandTQSPK’, ‘Interpreter’, ‘none’ );
ylabel( ‘twosousandTQAPC’, ‘Interpreter’, ‘none’ );
zlabel( ‘twosousandTQnm’, ‘Interpreter’, ‘none’ );
grid off
view( -2.9, 15.3 );
I am looking to apply that the output data should follow the ‘twosousandTQSPK’ axis relative. I reserached the weighting function, but I I don’t know how I could accurately match the data inputs I have to weight it.
This is an example of what I am expecting with a different set of variables, but the results above in question should resemble this outcome to some degree.
%CREATEFIT(SIXTEENTQSPK,SIXTEENTQAPC,SIXTEENTQNM)
% Create a fit.
%
% Data for ‘1600 copy 2’ fit:
% X Input: sixteenTQSPK
% Y Input: sixteenTQAPC
% Z Output: sixteenTQnm
% Output:
% fitresult : a fit object representing the fit.
% gof : structure with goodness-of fit info.
%
% See also FIT, CFIT, SFIT.
% Auto-generated by MATLAB on 18-Jul-2024 20:55:15
%% Fit: ‘1600 copy 2’.
[xData, yData, zData] = prepareSurfaceData( sixteenTQSPK, sixteenTQAPC, sixteenTQnm );
% Set up fittype and options.
ft = fittype( ‘(y*A)+B+(x*C)+(D*x*x)+(y*x*E)+(y*x*x*F)’, ‘independent’, {‘x’, ‘y’}, ‘dependent’, ‘z’ );
opts = fitoptions( ‘Method’, ‘NonlinearLeastSquares’ );
opts.Display = ‘Off’;
opts.MaxFunEvals = 60000;
opts.MaxIter = 40000;
opts.StartPoint = [0.0430238016578078 0.168990029462704 0.649115474956452 0.73172238565867 0.647745963136307 0.450923706430945];
opts.TolFun = 0.1;
opts.TolX = 0.1;
% Fit model to data.
[fitresult, gof] = fit( [xData, yData], zData, ft, opts );
% Plot fit with data.
figure( ‘Name’, ‘1600 copy 2’ );
h = plot( fitresult, [xData, yData], zData );
legend( h, ‘1600 copy 2’, ‘sixteenTQnm vs. sixteenTQSPK, sixteenTQAPC’, ‘Location’, ‘NorthEast’, ‘Interpreter’, ‘none’ );
% Label axes
xlabel( ‘sixteenTQSPK’, ‘Interpreter’, ‘none’ );
ylabel( ‘sixteenTQAPC’, ‘Interpreter’, ‘none’ );
zlabel( ‘sixteenTQnm’, ‘Interpreter’, ‘none’ );
grid off
view( -0.5, -13.0 ); I am trying to use Curve Fitter and generating the code to help teach myself how to process my data without Curve Fitter.
Is there a way to apply a logic that a certain data point should be lower/higher than another certain data point, and have the coefficients adjust accordingly?
%CREATEFIT(TWOSOUSANDTQSPK,TWOSOUSANDTQAPC,TWOSOUSANDTQNM)
% Create a fit.
%
% Data for ‘2000 copy 1’ fit:
% X Input: twosousandTQSPK
% Y Input: twosousandTQAPC
% Z Output: twosousandTQnm
% Output:
% fitresult : a fit object representing the fit.
% gof : structure with goodness-of fit info.
%
% See also FIT, CFIT, SFIT.
% Auto-generated by MATLAB on 18-Jul-2024 20:43:50
%% Fit: ‘2000 copy 1’.
[xData, yData, zData] = prepareSurfaceData( twosousandTQSPK, twosousandTQAPC, twosousandTQnm );
% Set up fittype and options.
ft = fittype( ‘A+(x*B)+(C*x*x)+(y*x*D)+(y*x*x*E)+(y*F)’, ‘independent’, {‘x’, ‘y’}, ‘dependent’, ‘z’ );
opts = fitoptions( ‘Method’, ‘NonlinearLeastSquares’ );
opts.Display = ‘Off’;
opts.MaxFunEvals = 60000;
opts.MaxIter = 40000;
opts.StartPoint = [0.0526769976807926 0.737858095516997 0.269119426398556 0.422835615008808 0.547870901214845 0.942736984276934];
opts.TolFun = 0.1;
opts.TolX = 0.1;
% Fit model to data.
[fitresult, gof] = fit( [xData, yData], zData, ft, opts );
% Plot fit with data.
figure( ‘Name’, ‘2000 copy 1’ );
h = plot( fitresult, [xData, yData], zData );
legend( h, ‘2000 copy 1’, ‘twosousandTQnm vs. twosousandTQSPK, twosousandTQAPC’, ‘Location’, ‘NorthEast’, ‘Interpreter’, ‘none’ );
% Label axes
xlabel( ‘twosousandTQSPK’, ‘Interpreter’, ‘none’ );
ylabel( ‘twosousandTQAPC’, ‘Interpreter’, ‘none’ );
zlabel( ‘twosousandTQnm’, ‘Interpreter’, ‘none’ );
grid off
view( -2.9, 15.3 );
I am looking to apply that the output data should follow the ‘twosousandTQSPK’ axis relative. I reserached the weighting function, but I I don’t know how I could accurately match the data inputs I have to weight it.
This is an example of what I am expecting with a different set of variables, but the results above in question should resemble this outcome to some degree.
%CREATEFIT(SIXTEENTQSPK,SIXTEENTQAPC,SIXTEENTQNM)
% Create a fit.
%
% Data for ‘1600 copy 2’ fit:
% X Input: sixteenTQSPK
% Y Input: sixteenTQAPC
% Z Output: sixteenTQnm
% Output:
% fitresult : a fit object representing the fit.
% gof : structure with goodness-of fit info.
%
% See also FIT, CFIT, SFIT.
% Auto-generated by MATLAB on 18-Jul-2024 20:55:15
%% Fit: ‘1600 copy 2’.
[xData, yData, zData] = prepareSurfaceData( sixteenTQSPK, sixteenTQAPC, sixteenTQnm );
% Set up fittype and options.
ft = fittype( ‘(y*A)+B+(x*C)+(D*x*x)+(y*x*E)+(y*x*x*F)’, ‘independent’, {‘x’, ‘y’}, ‘dependent’, ‘z’ );
opts = fitoptions( ‘Method’, ‘NonlinearLeastSquares’ );
opts.Display = ‘Off’;
opts.MaxFunEvals = 60000;
opts.MaxIter = 40000;
opts.StartPoint = [0.0430238016578078 0.168990029462704 0.649115474956452 0.73172238565867 0.647745963136307 0.450923706430945];
opts.TolFun = 0.1;
opts.TolX = 0.1;
% Fit model to data.
[fitresult, gof] = fit( [xData, yData], zData, ft, opts );
% Plot fit with data.
figure( ‘Name’, ‘1600 copy 2’ );
h = plot( fitresult, [xData, yData], zData );
legend( h, ‘1600 copy 2’, ‘sixteenTQnm vs. sixteenTQSPK, sixteenTQAPC’, ‘Location’, ‘NorthEast’, ‘Interpreter’, ‘none’ );
% Label axes
xlabel( ‘sixteenTQSPK’, ‘Interpreter’, ‘none’ );
ylabel( ‘sixteenTQAPC’, ‘Interpreter’, ‘none’ );
zlabel( ‘sixteenTQnm’, ‘Interpreter’, ‘none’ );
grid off
view( -0.5, -13.0 ); curve fitting, curve fitter, boudaries, weighting MATLAB Answers — New Questions
