import org.apache.commons.math3.optimization.univariate.UnivariatePointValuePair; //导入依赖的package包/类
/**
 * Find the minimum of the function {@code f(p + alpha * d)}.
 *
 * @param p Starting point.
 * @param d Search direction.
 * @return the optimum.
 * @throws org.apache.commons.math3.exception.TooManyEvaluationsException
 * if the number of evaluations is exceeded.
 */
public UnivariatePointValuePair search(final double[] p, final double[] d) {
    final int n = p.length;
    final UnivariateFunction f = new UnivariateFunction() {
            /** {@inheritDoc} */
            public double value(double alpha) {
                final double[] x = new double[n];
                for (int i = 0; i < n; i++) {
                    x[i] = p[i] + alpha * d[i];
                }
                final double obj = PowellOptimizer.this.computeObjectiveValue(x);
                return obj;
            }
        };

    final GoalType goal = PowellOptimizer.this.getGoalType();
    bracket.search(f, goal, 0, 1);
    // Passing "MAX_VALUE" as a dummy value because it is the enclosing
    // class that counts the number of evaluations (and will eventually
    // generate the exception).
    return optimize(Integer.MAX_VALUE, f, goal,
                    bracket.getLo(), bracket.getHi(), bracket.getMid());
}
 

import org.apache.commons.math3.optimization.univariate.UnivariatePointValuePair; //导入依赖的package包/类
/**
 * Find the minimum of the function {@code f(p + alpha * d)}.
 *
 * @param p Starting point.
 * @param d Search direction.
 * @return the optimum.
 * @throws org.apache.commons.math3.exception.TooManyEvaluationsException
 * if the number of evaluations is exceeded.
 */
public UnivariatePointValuePair search(final double[] p, final double[] d) {
    final int n = p.length;
    final UnivariateFunction f = new UnivariateFunction() {
            public double value(double alpha) {
                final double[] x = new double[n];
                for (int i = 0; i < n; i++) {
                    x[i] = p[i] + alpha * d[i];
                }
                final double obj = PowellOptimizer.this.computeObjectiveValue(x);
                return obj;
            }
        };

    final GoalType goal = PowellOptimizer.this.getGoalType();
    bracket.search(f, goal, 0, 1);
    // Passing "MAX_VALUE" as a dummy value because it is the enclosing
    // class that counts the number of evaluations (and will eventually
    // generate the exception).
    return optimize(Integer.MAX_VALUE, f, goal,
                    bracket.getLo(), bracket.getHi(), bracket.getMid());
}
 

import org.apache.commons.math3.optimization.univariate.UnivariatePointValuePair; //导入依赖的package包/类
/**
 * Fit a truncated Poisson to the contents of a histogram.
 * Returns the background proportion in the sample
 *
 */
public double fitPoisson(RealValuedHistogram h, Sample samp){
	DRand re = new DRand();
	//Heuristic to find the upper bound for the truncated Poisson
	int pUpper = poissUpperBound;
	double uniformMean = samp.getHitCount()/(gen.getGenomeLength()/binWidth);
	if(cdfPercOfUniform>0 && cdfPercOfUniform<=1){
		Poisson uniPoiss = new Poisson(uniformMean, re);
		double tmpProp=0;
		int i=0;
		while(tmpProp<cdfPercOfUniform){
			tmpProp = uniPoiss.cdf(i);
			i++;
		}
		pUpper=Math.max(i,poissUpperBoundMin);
	}
	System.out.println("Truncated Poisson Upper Bound:\t"+pUpper);
	
	//Fit the Poisson
	int left=0, right=pUpper;
	double xsum=0, xcount=0;
	for(double i=left; i<=right; i++){
		xsum += i*h.getBin( h.getBinContainingVal(i));
		xcount += h.getBin( h.getBinContainingVal(i));
	}
	double xavg = xsum/xcount;
	UnivariateFunction func = new truncPoisson(xavg, left, right);
	double relativeAccuracy = 1.0e-6;
	double absoluteAccuracy = 1.0e-4;
	UnivariateOptimizer solver = new BrentOptimizer(relativeAccuracy, absoluteAccuracy);
	UnivariatePointValuePair pvp = solver.optimize(100, func, GoalType.MINIMIZE, 0.001, 50.0, xavg);
	double lambda = pvp.getPoint();
	System.out.println("xavg: "+ xavg+"\tlambda: "+lambda);
	
	//Calculate the background proportion
	Poisson poiss = new Poisson(lambda, re);
	double backsize = xsum / (poiss.cdf(right) - poiss.cdf(left - 1));
	double backprop = Math.min(backsize / sampleTotals[samp.getIndex()], 1.0);
	System.out.println("Background= "+ backsize+" / "+sampleTotals[samp.getIndex()]+" =\t"+backprop);
	
	return backprop;
}