I like that it goes to higher orders of polynomials than just a plain old boring straight line
One method of measuring internal losses of a rotating system is to spin it up, measure how the rate at which it slows down, multiply by the rotating inertia, and fit to a quadratic (because friction/resistance is usually proportional to the square of the speed).
That looks cool, but for those of us dinosaurs that grew up with COBOL and monochrome CRT displays, what does this demonstrate? Or, more accurately, how can this be applied to apps?
This is a primitive example of machine learning.
The input is made of the X values and the desired output is the Y values.
We have some known samples: the mapping between the X coordinates and the Y coordinates of the clicked points.
We want to find a formula that will allow us to find the Y values based on other X values.
If you have two points and you know that there is a linear relation between X and Y then it is trivial to find such formula. This example uses a more sophisticated algorithm to try to match more complex relations.
This site uses cookies to help personalise content, tailor your experience and to keep you logged in if you register.
By continuing to use this site, you are consenting to our use of cookies.