Oct 23, 2019 Replies: 5 Project is attached. Attachments LinearRegression.zip LinearRegression.zip 5.1 KB · Views: 577 Click to expand...
E emexes Expert Licensed User Longtime User Oct 23, 2019 #2 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).
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).
Erel B4X founder Staff member Licensed User Longtime User Oct 23, 2019 #3 emexes said: I like that it goes to higher orders of polynomials than just a plain old boring straight line Click to expand... It can actually match almost any function you like. The brown plot is A * x + B * Sin(X)
emexes said: I like that it goes to higher orders of polynomials than just a plain old boring straight line Click to expand... It can actually match almost any function you like. The brown plot is A * x + B * Sin(X)
Jeffrey Cameron Well-Known Member Licensed User Longtime User Oct 23, 2019 #4 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?
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?
Erel B4X founder Staff member Licensed User Longtime User Oct 23, 2019 #5 Jeffrey Cameron said: That looks cool, but for those of us dinosaurs that grew up with COBOL and monochrome CRT displays, what does this demonstrate? Click to expand... 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. Jeffrey Cameron said: Or, more accurately, how can this be applied to apps? Click to expand... It is probably a bit too primitive and simplistic to actually be used for something real, however inspiration is also important...
Jeffrey Cameron said: That looks cool, but for those of us dinosaurs that grew up with COBOL and monochrome CRT displays, what does this demonstrate? Click to expand... 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. Jeffrey Cameron said: Or, more accurately, how can this be applied to apps? Click to expand... It is probably a bit too primitive and simplistic to actually be used for something real, however inspiration is also important...
Jeffrey Cameron Well-Known Member Licensed User Longtime User Oct 23, 2019 #6 Still, cool I was wondering if it could be applied to some type of view animation.
