Googled it out lately:
class Calc {
public static double sigmoid(double x) {
return (1/( 1 + Math.pow(Math.E,(-1*x))))
}
}
For example, let’s say you have 18 and 8. You subtract 8 from 18 and get ten and pass it to the Sigmoid function. You’re returned with 0.9999546021312976. If you had a difference of 0 though, you would get .50 and if you had a negative difference, like -18, get a really tiny number that’s bigger than 0. So in short, the Sigmoid function is easy and quite interesting.
I just passed my E = X1W2 + X2W2 + … + X3W3 + W0 there.
Here is the original source, Thanks to the author.
Disclaimer: I really dont know, will it work or not. I made a class, which – yes, produces something. The question is – will it work in a neural network.
