Alrecenk

//Calculate the probability that the given input is in the positive class
public double probability(double in[]){
   double relativepositive=0,relativenegative=0;
   for(int j=0; j<in.length; j++){
      relativepositive += (in[j]-posmean[j])*(in[j]-posmean[j]) / posvariance[j] ;
      relativenegative += (in[j]-negmean[j])*(in[j]-negmean[j]) / negvariance[j] ;
   }
   relativepositive = positives*Math.exp(0.5*relativepositive) ;
   relativenegative = negatives*Math.exp(0.5*relativenegative) ;   
   return relativepositive / (relativepositive + relativenegative) ;

Alrecenk

public double ProbabilityOfInputIfPositive(double in[]){
   double prob = 1/Math.sqrt(2 * Math.PI) ;
   for(int j=0; j<in.length;j++){
      prob*= Math.exp(- (in[j]-posmean[j])*(in[j]-posmean[j]) / (2*posvariance[j]) ) ;
   }
   return prob ;
}

Alrecenk

//constructs a naive Bayes binary classifier
public NaiveBayes(double in[][], boolean out[]){
   int inputs = in[0].length ;
   //initialize sums and sums of squares for each class
   double[] poss = new double[inputs], poss2 = new double[inputs];
   double[] negs = new double[inputs], negs2 = new double[inputs];
   //calculate amount of each class, sums, and sums of squares
   for(int k=0;k<in.length;k++){//for each data point
      if(out[k]){
         positives++;//keep track of total positives