Description: * <li> definition of variables and parameters: input variables X, W, variable power offset factor B, y, D output practice expected output and learning rate parameter c</li>
* <li> initialization: N and weight w</li>
* <li> input practice sample, for each sample to exercise the expected output of </li>
* <li> accounting practice output y=sgn (w*x+b) </li>
* <li> update the weight vector w (n+1) =w (n) +c[d-y (n)]*x (n) </li> 0<c<1
* <li> discriminant, if meet the convergence condition, the algorithm is completed. Come back, 3</li>
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d.java
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