The source code and dockerfile for the GSW2024 AI Lab.
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/*Curve fitting problem by Least Squares Nigel_Galloway@operamail.com October 1st., 2007 */ set Sample; param Sx {z in Sample}; param Sy {z in Sample};
var X; var Y; var Ex{z in Sample}; var Ey{z in Sample};
/* sum of variances is zero for Sx*/ variencesX{z in Sample}: X + Ex[z] = Sx[z]; zumVariancesX: sum{z in Sample} Ex[z] = 0; /* sum of variances is zero for Sy*/ variencesY{z in Sample}: Y + Ey[z] = Sy[z]; zumVariancesY: sum{z in Sample} Ey[z] = 0;
solve;
param b1 := (sum{z in Sample} Ex[z]*Ey[z])/(sum{z in Sample} Ex[z]*Ex[z]); printf "\nbest linear fit is:\n\ty = %f %s %fx\n\n", Y-b1*X, if b1 < 0 then "-" else "+", abs(b1);
data;
param: Sample: Sx Sy := 1 0 1 2 0.5 0.9 3 1 0.7 4 1.5 1.5 5 1.9 2 6 2.5 2.4 7 3 3.2 8 3.5 2 9 4 2.7 10 4.5 3.5 11 5 1 12 5.5 4 13 6 3.6 14 6.6 2.7 15 7 5.7 16 7.6 4.6 17 8.5 6 18 9 6.8 19 10 7.3 ;
end;
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