WIP Two Layer Neural Netowork Java Code

public class twoLayerNeuralNetwork
{
   public static void main(String[] args)
   {
   double inputs[][] =
   {
   {0,0,1},
   {0,1,1},
   {1,0,1},
   {0,1,0},
   {1,0,0},
   {1,1,1},
   {0,0,0}
   };
   //patter: XOR for first two inputs- third input is irrelevant

   double neurons[] = {0.1,0.9,0.1,0.9};

   double outputs[] = {0,1,1,1,1,0,0};

   double weights1[][] =
   {
   {0.3,4.5,-6},
   {-8.75,0.2,-8.75},
   {6.2,4.4,0},
   {-0.5,0.08,-0.4}
   };

   double weights2[] =
   {0,0,0,0};

   for(int set=0; set<inputs.length; set++)
   {
   for(int neuron=0; neuron<weights1.length; neuron++)
   {System.out.println("set: "+set+" neuron: "+neuron+" "+sum(weights1[neuron],inputs[set]));}
   System.out.print("\n");}
   }



   public static double[] train(double inputs[][], double weights1[][], double neurons[], double weights2[], double outputs[])
   {

   }


   public static double sum(double w[], double i[])
   {
   double sum = 0;
   for(int p=0; p<w.length; p++)
   {sum+=w[p]*i[p];}
   return sum;
   }

   public static double sigmoid(double s)
   {
   return (1/(1+Math.exp(-1*s)));
   }

   public static double[] adjust(double inputs[], double outputs[], double weights[])
   {
   double testOutputs[] = new double[outputs.length];
   for(int n=0; n<outputs.length; n++)
   {testOutputs[n]=sigmoid(sum(weights,inputs));}

   for(int n=0; n<weights.length; n++)
   {
   double error = outputs[n]-testOutputs[n];
   weights[n]+=(error*inputs[n]*testOutputs[n]*(1-testOutputs[n]));
   }
   return weights;
   }
}