The function that the perceptron is learning will be a polynomial of *N*
variables, where *N* is the number of nodes in the perceptron. The coefficients
of the polynomial are generated randomly (see the function
init). We can use these coefficients to generate a
value with a single reduction operation, and then compare the resulting value
to 0. If the value is greater than or equal to 0, the desired output is 1;
otherwise, the desired output is 0.
You are welcome to generate different examples, if you wish.

Tue Jan 21 09:43:37 CST 1997