/**
* Modified from JavaSim RandomStream class to incorporate xorshift generators from Marsaglia as 
* basic generators. See the article 
* Marsaglia, George (July 2003). "Xorshift RNGs". Journal of Statistical Software 8 (14).
* http://www.jstatsoft.org/v08/i14/paper
*/
import java.io.IOException;


public abstract class RandomStream
{
    /**
     * In derived classes this method returns the value obtained by the stream.
     * It must be redefined by the deriving class.
     */

    public abstract double getNumber () throws IOException, ArithmeticException;

    protected RandomStream() 
    {
    	suffled=false;
    }
    
    protected RandomStream(boolean suffle)
    {
       	suffled=suffle;

        if(suffled)
        {
        for (int i = 0; i < RandomStream.sizeOfSeries; i++)
            series[i] = xor64();
        }

    }
    protected RandomStream(long Seed, boolean suffle)
    {
    	x=Seed;
        if(suffled)
        {
        for (int i = 0; i < RandomStream.sizeOfSeries; i++)
            series[i] = xor64();
        }
   	
    }

    protected final double uniform ()
    {
    	if(!suffled) { return 0.5d*(1.d + xor64()/(double) 0x7FFFFFFFFFFFFFFFL);}
    	else {
            choose = (xor()%128 +127)%128 ;
            result = 0.5d*(1.d +series[choose]/(double) 0x7FFFFFFFFFFFFFFFL);
            series[choose] = xor64() ;
            return result;
    	}
    }

    private long xor64(){
		x ^=(x<<13); x ^=(x>>7); return (x ^=(x<<17));
	}
    
	private int xor(){
		y ^= (y<<13); y ^= (y>>17); y ^= (y<<5);
		return y;
	}

    private long x=88172645463325252L;
    private int y=100000;
    private double result;
    private boolean suffled;
    private static int sizeOfSeries=128;
    private long[] series = new long[128];
    private int choose;

}