本文整理汇总了Java中org.apache.commons.math3.optimization.univariate.UnivariatePointValuePair类的典型用法代码示例。如果您正苦于以下问题：Java UnivariatePointValuePair类的具体用法？Java UnivariatePointValuePair怎么用？Java UnivariatePointValuePair使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
UnivariatePointValuePair类属于org.apache.commons.math3.optimization.univariate包，在下文中一共展示了UnivariatePointValuePair类的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: search
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());
}
示例2: search
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());
}
示例3: fitPoisson
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;
}