i have NaN error in my project EDITED

Question

1. full code

     the full code of the neuron. am trying to code a radial basis                    neuron network but i can figure out where and getting an NaN error
   

   import java.util.ArrayList; import java.util.List; import java.util.Random;

   public class Neurons {
   
       static Random rand =new Random();
       instance variables
   
   
       private float m_neuronOutput;
       private float alpha=0.4f;
       private  float  m_gradient;
       private int neuronIndex;
       public  float eta;
       public float sigma=22.5f;
       public List<Connection> neuronWeights= new ArrayList<Connection>();
       //public List<Center> neuronCenter= new ArrayList<Center>();
       NetWork net=new NetWork();
       public Neurons(){
           //  sigma=22.5f;
           eta=0.5f;
   
       }
       //neuron constructor
       public Neurons(int outputNum, int m_neuronIndex){
           neuronIndex=m_neuronIndex;
   
           add connection weights
           for (int connect = 0; connect < outputNum; connect++) {
               neuronWeights.add(new Connection());
   
   
           }
   
   
       }
   
       public  void SetOutputValue(float value){
           m_neuronOutput = value;
   
       }
       public float GetOutputValue(){
           return m_neuronOutput;
       }
       ## feed the layer with the input ##
   

2. feed the layers

       public void FeedForward(Layer previousLayer, int n){
           float sum = 0;
           //weightsum the inputs of the last layer, including bias
           //System.out.println(n);
           m_neuronOutput= previousLayer.neuron.get(n).GetOutputValue () ;
           //System.out.println(m_neuronOutput);
       }
   

3. set the value of the hidden layer

       public void FeedHidden(Layer previousLayer, int j ){
   
           //      for(int i=0;i<center.length;i++)
           //          System.out.println(center[i]);
           float distance= 0;
           //System.out.println(n+" "+hiddenLayer.neuron.get(n).neuronCenter.get(n).center);
           //weightsum the inputs of the last layer, including bias
           for (int n = 0; n <previousLayer.neuron.size(); n++) {
               distance += (float) Math.pow(previousLayer.neuron.get(n).GetOutputValue ()-previousLayer.neuronCenter.get(j).center,2);
               //System.out.println(previousLayer.neuronCenter.get(j).center);
           }
   
        ## where am calling phi(Gaussian function) ##                  m_neuronOutput =  phi (distance);
           //System.out.println(m_neuronOutput);
       }
           private float phi( float distance){
   
           //System.out.println(sigma);
           return (float) Math.exp(- distance/(2*Math.pow(sigma, 2)));
       }
   

4. calculate output

       public void setOutput(Layer hidden){
           float output=0;
           for(int i=0;i<hidden.neuron.size();i++){
               output+=hidden.neuron.get(i).GetOutputValue()*hidden.neuron.get(i).neuronWeights.get(neuronIndex).weight;
               // System.out.println(hidden.neuron.get(i).GetOutputValue());
               // System.out.println(hidden.neuron.get(i).neuronWeights.get(neuronIndex).weight);
           }
           m_neuronOutput=output;
           //System.out.println(hidden.neuron.get(i).neuronWeights.get(neuronIndex).weight);
   
       }
   

5. gradient at the output layer

       public void CalcOutputGradients( float targetValue,float output){
           // System.out.println(output);
           float delta = targetValue -output;
   
   
           m_gradient = delta;
           //System.out.println( output);
   
       }   
   
       public void UpdateInputWeights(Layer previousLayer,float eta){
           //System.out.println(phi);
           the weights to be updated are in the connection container
           in the neurons in the preceding layer
           int numNeuronas = previousLayer.neuron.size();
           for (int n = 0; n < numNeuronas; n++) {//for every previous layer neuron
               Neurons neu = previousLayer.neuron.get(n);//getting the previous layer neuron
               //System.out.println(neu.neuronCenter.get(neuronIndex).center );
               float phi=previousLayer.neuron.get(n).GetOutputValue ();
               //double oldDeltaW = neu.neuronWeights.get(neuronIndex).deltaweight;
               double deltaWeight=
                       //Individual input magnified by the gradient and trainrate
                       eta *phi*m_gradient;
               also add momentum = a fraction of the previos delta weight
               +oldDeltaW;
               neu.neuronWeights.get(neuronIndex).deltaweight = deltaWeight;
               neu.neuronWeights.get(neuronIndex).weight+= deltaWeight;
   
               //System.out.println(m_gradient +" delta");
   
           }
       }
   ## my method to update the center but yet to call it in my network  work class ##
       public void updateCenter( Layer previousLayer,float[] input, float eta){
           int numNeuronas = previousLayer.neuron.size();
   
           for (int n = 0; n < numNeuronas-1; n++) {//for every previous layer neuron
               Neurons neu = previousLayer.neuron.get(n);//getting the previous layer neuro
               Center neu1 = previousLayer.neuronCenter.get(n);
               float phi=previousLayer.neuron.get(n).GetOutputValue ();
               double oldcenter = neu1.center;
   
               double weight= neu.neuronWeights.get(neuronIndex).weight;
               double deltaC=
                       //Individual input magnified by the gradient and trainrate
                       eta *phi*((m_gradient*weight)/Math.pow(sigma, 2))*(input[n]-oldcenter);
               //also add momentum = a fraction of the previos delta weight
               //+oldcenter;
   
   
               neu1.center+= deltaC;
   
               //System.out.println(gredient+" delta");
   
           }
   
   
   
       }
   

   update sigma

       public void updateSigma(Layer previousLayer, float[] center, float[] input, float eta){
           float distance= 0;
           //weightsum the inputs of the last layer, including bias
           for (int n = 0; n < center.length; n++) {
               distance+= Math.pow(previousLayer.neuron.get(n).GetOutputValue () -center[n],2);
               //System.out.println(distance);
           }
           float phi=(float) phi(distance);
           float sigma1=0;
           for(int i=0;i<center.length;i++){
               double w =previousLayer.neuron.get(i).neuronWeights.get(neuronIndex).weight;
               sigma1=(float) (sigma+(eta*((m_gradient*w)/Math.pow(sigma, 3))*phi*Math.pow(input[i]-center[i],2)));
           }
           sigma=sigma1;
       }
       get the current learning rate
       public  float updateLearningRate(float epoch){  
           //search the converge algorithm
   
           return (float) (eta/((epoch)/500));
   
       }
   
       set the learning rate
       public  void  setLearningRate(float rate){  
   
           eta=rate;
   
       }
   
       update the learining rate
       public  float getLearningRate(){
           return eta;
   
       }
   
   
   
   
   }
   

   looking for word to your help. Thanks

Answer 1

return (float) Math.exp(- distance/(2*Math.pow(sigma, 2)));

in the above line, when sigma and distance are 0, you execute Math.exp(-0/0) which is NaN.

NaN stands for Not a Number which occurs e.g. when you divide by 0 or take a square root of a negative number.

NaN is defined by IEE 754 and stands for a double which isn't actually numeric value.

And as a side remark, as I believe you will read, please always add the stacktrace to the question. Always add all important information about the problem.