package uk.ac.leeds.ccg.cluster;
// Bootstrap test class for cluster methods

import java.util.*;
//import com.sun.java.util.collections.*;
import uk.ac.leeds.ccg.geotools.*;

public class BootstrapTest extends SignificanceTest implements Constants {

    double stat = (double) 0.0;
    
    public BootstrapTest() {
        System.out.println("Creating a bootstrap test");
    }
    
    public final boolean isSignificant(Vector points) {
        MyPoint p2;
        double sigsum = (double) 0.0;
        double summean = (double) 0.0;
        double cases = (double) 0.0;
        double pop = (double) 0.0;
        double mean, sig2, sig, Z, prob, cutoff;
        int psize, k;
        int maxSam = parameters.getBootstrapSampleSize();
        double rs[];
        rs = new double[maxSam];
        double thresh = parameters.getSignificanceThreshold();
        // Get Excess (or deficit)
        boolean excess = parameters.getExcess();
        
        psize = points.size();
        for ( int i = 0; i < maxSam; i ++ ) {
            pop = 0.0;
            cases = 0.0;
            //  sampling with replacement
            // System.out.println("Points:" +"  "+ psize);
            for ( int j = 0; j < points.size(); j ++ ) {
                k = ( int ) ( Math.random() * psize );
                p2 = ( MyPoint ) points.elementAt( k );
                pop += p2.getPop();
                cases += p2.getCases();
            }
            // rs[i] = pop;
            // summean += pop;
            rs[i] = cases / pop;
            summean += cases / pop;
        }
        cases = 0.0;
        pop = 0.0;
        for ( int j = 0; j < points.size(); j ++ ) {
            p2 = ( MyPoint ) points.elementAt( j );
            pop += p2.getPop();
            cases += p2.getCases();
        }
        if ( !isWorthTesting( pop, cases ) ) return false;
        //  NB.  bit of reordering will speed things up!
        //  ie.  test this before all the sampling!
        
        // calculate mean and standard deviation
        mean = summean / maxSam;
        for ( int i = 0; i < maxSam; i ++ ) {
            sigsum += ( ( rs[i] - mean ) * ( rs[i] - mean ) );
        }
        sig2 = sigsum / (maxSam - 1);
        sig = Math.sqrt(sig2);
        
        // calculate Z statistic, and probability
        // Z = (sumC - mean) / sig;
        // Z = (cases - mean) / sig;
        mean = cases / pop;
        if ( excess ) {
            Z = ( mean - 1.0d ) / sig;
        } else {
            Z = ( 1.0d - mean ) / sig;
        }
        //System.out.println("Z " + Z + " , mean " + mean + ", stdev "+ sig);
        
        // significance level & corresponding cutoff on the Normal
        cutoff = 0.0; // prob = 0.50
        for ( int j = 0; j < 27; j ++ ){
            if ( ( thresh <= THRSH[ j ] ) && ( thresh > THRSH[ j + 1 ] ) )
                cutoff = VALS[ j ];
        }
        if ( thresh < THRSH[ 27 ] ) cutoff = 3.7;
        // System.out.println("thresh " + thresh +" "+ cutoff);
        
        prob = thresh;
        // if (Z > 2.32) // prob = 0.99
        // Change andyt 11 Nov 03
        if ( Math.abs( Z ) > cutoff ) {
        //if ( Z > cutoff ) {
            switch ( parameters.getStatType() ) {
                // case 1: stat = sumC - sumP; break;
                // case 2: stat = sumC/sumP;   break;
                case 1: stat = cases - pop; break;
                case 2: stat = cases / pop;   break;
                case 3: stat = ( double ) 1.0 - prob;  break;
            }
            return true;
        }
        else
            stat=0.0;
        return false;
    }
    
    public double getStatistic(){
        return (double) stat;
    }
    
    public String toString() {return "Bootstrap";}
    private int canMax;
    private double cons[];
}