package nb;import shared.MLJ;/** NBNorm is a helper class to hold the 3rd dimension of  * an Array2 containing the parameters of the Normal  * Density for each (continuous attribute,label) combination.  * The hd (hasData) argument defaults to FALSE.  */public class NBNorm {  /** stores the mean value of the data.    */  public double mean;
  /** stores the variance of the data.    */  public double var;
  /** If true the data in this NBNorm is valid, if false there is no valid data     * stored in this object.    */   public boolean hasData;
  /** This is the default constructor. All fields set to default.    */  public NBNorm() {
    mean = 0.0;
    var = 0.0;
    hasData = false;
 }  /** This constructor sets fields to parameter values.    * @param m - the mean value.    * @param v - the variance.    * @param hd - the has data boolean.    */  public NBNorm(double m, double v, boolean hd) {
    mean = m;
    var = v;
    hasData = hd;
  }  /** The copy constructor for NBNorm.    * @param rhs - the NBNorm to copy.    */  public NBNorm(NBNorm rhs) {
    this.mean = rhs.mean;
    this.var = rhs.var;
    this.hasData = rhs.hasData;
  }  /** Sets the mean and variance.    * @param m - the new mean.    * @param v - the new variance.    */  public void set_mean_and_var(double m, double v) {
    mean = m;
    var = v;
    hasData = true;
  }
  /** Equivalent to operator==. Provided for use by NaiveBayesCat.equals.    * @param rhs - the NBNorm to test equalitly.    * @return true if the this is equal to rhs; false else.    */  public boolean equals(NBNorm rhs) {    return (hasData == rhs.hasData && MLJ.approx_equal(mean, rhs.mean)                && MLJ.approx_equal(var, rhs.var));  }}