Package fork.lib.math.applied.stat

Examples of fork.lib.math.applied.stat.Distribution

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    }


   
public void init(LandscapeBuilder lb) throws Exception{
    Distribution d= lb.getDistributionNonZero();
    ArrayList<Double> qs= d.quantileBoundaries(100);
    double m=0 ,sd=0;
    for( int i=0; i<50; i++ ){
        Distribution df= d.subset(qs.get(i), qs.get(qs.size()-1-i), true, true);
        m= df.mean();
        double med= df.median();
        sd= df.standartDeviation();
        double z= sd/m;
        //System.out.println(i+"   mean: "+m+"   med: "+med+"   sd: "+ sd+"  z:"+z);
        if(z<0.2){
            break;
        }
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        init();
    }
   
   
protected void init() throws Exception{
    Distribution d= new Distribution();
    d.add(lbf.getDistribution());
    d.add(lbr.getDistribution());
    double med= Math.ceil( d.median() *1.7 );
    double sd= d.standartDeviation();
    System.out.println("med:  "+med +"    sd:  "+sd);
    //d.print(); System.exit(1);
    new LandscapeTransformer(lbf).subtract(med);
    new LandscapeTransformer(lbr).subtract(med);
   
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* @param bthr
* @param n
* @return
*/
public static ArrayList<Double> getThresholdLevels(Landscape2D data, double bthr, int n){
    Distribution dis= new Distribution(data.getValues());
    double range= dis.max()-bthr;
    if(range<=0){
        return null;
    }
    double v= bthr;
    double step= range/n;
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/**
* threshold for noise
* @return
*/
protected double threshold() throws EMException, DistributionException, FunctionException{
    Distribution d= new Distribution(data.getValues());
    DistributionFunction[] fn= new DistributionFunction[]{
        new NormalDistribution(0,1),
        new NormalDistribution(0,1)
    };
    EM em= new EM(d, fn, null);
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public static void main(String[] args) throws Exception { //debug
    Distribution d= new Distribution();
    d.add(0, 8); d.add(1, 10); d.add(2, 15);
    d.add(7, 5); d.add(8, 8); d.add(9, 10); d.add(10,5);
    d.add(20, 5); d.add(21, 10); d.add(22, 8);
    d.add(120, 5); d.add(121, 10); d.add(122, 8);
   
    DistributionFunction[] types= new DistributionFunction[]{
        new NormalDistribution(0,1),
        new NormalDistribution(0,1),
        new NormalDistribution(0,1),
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* @return
*/
public static ArrayList<Distribution> cluster(Distribution data, ArrayList<DistributionFunction> funcs, ArrayList<Double> coeffs){
    ArrayList<Distribution> ret= new ArrayList<>();
    for( int i=0; i<funcs.size(); i++ ){
        ret.add(new Distribution());
    }
    Iterator<Double> it = data.getValueToFrequencey().keySet().iterator();
    while(it.hasNext()){
        Double v= it.next();
        ret.get( optimalCluster(v, funcs, coeffs)).add( v.doubleValue(), data.getValueToFrequencey().get(v).intValue() );
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/**
*
* @return
*/
public Distribution toDistribution(){
    Distribution d= new Distribution();
    for( int i=0; i<vs.length ; i++ ){
        d.add(vs[i]);
    }
    return d;
}
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