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 May 30 comment Mathematical function for weighting results Another update: if I know, taht maximum difference is D, how can I make this equal to 1, using the above function, and rescale all the other, smaller values respectively? May 30 comment Mathematical function for weighting results Thank you very much Ilya. I further noticied that the basic function quite quickly reaches "high" weight values. if x and y differ by only 1, the value would still be at around 0.6. My values would most likely differ by 10-20, or even more, so I would like to be able to see big difference between 20 and 200, if that is the case. Should I simply divide the difference by 100 or other constant of mine or is there a better way to achieve what I described? thanks May 30 asked Mathematical function for weighting results May 28 accepted generative vs discriminative machine learning May 13 awarded Tumbleweed May 11 accepted Discriminant vs. generatve functions May 11 accepted orthogonal eigenvectors May 11 accepted orthogonal projection - simple exalanation needed May 11 accepted Bayes theorem probability question May 11 accepted geometric series for fractional n May 10 comment geometric sum with probabilities pi are pretty much random... May 10 comment geometric series for fractional n @Patrick OK, what you defined is the formula for annuity. it has a solution in the form A/r(1 - 1/(1+r)^n). Could I simply substitute fractional n now? May 10 comment geometric series for fractional n How to do that? That is precisely my question May 10 asked geometric series for fractional n May 10 asked geometric sum with probabilities May 9 comment orthogonal eigenvectors What do you mean by "setting up"? Is there some common technique to achive a singular matrix? May 8 awarded Commentator May 8 comment orthogonal eigenvectors Thank you very much for your answers. Could anyone state whether they are orthogonal in PCA case? May 8 asked orthogonal eigenvectors May 8 asked orthogonal projection - simple exalanation needed