本文整理汇总了Java中org.apache.commons.math.optimization.direct.NelderMead类的典型用法代码示例。如果您正苦于以下问题：Java NelderMead类的具体用法？Java NelderMead怎么用？Java NelderMead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。
NelderMead类属于org.apache.commons.math.optimization.direct包，在下文中一共展示了NelderMead类的2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: testRosenbrock
import org.apache.commons.math.optimization.direct.NelderMead; //导入依赖的package包/类
@Test
public void testRosenbrock()
throws FunctionEvaluationException, ConvergenceException {
Rosenbrock rosenbrock = new Rosenbrock();
NelderMead underlying = new NelderMead();
underlying.setStartConfiguration(new double[][] {
{ -1.2, 1.0 }, { 0.9, 1.2 } , { 3.5, -2.3 }
});
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(16069223052l);
RandomVectorGenerator generator =
new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g));
MultiStartMultivariateRealOptimizer optimizer =
new MultiStartMultivariateRealOptimizer(underlying, 10, generator);
optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1, 1.0e-3));
optimizer.setMaxIterations(100);
RealPointValuePair optimum =
optimizer.optimize(rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1.0 });
assertEquals(rosenbrock.getCount(), optimizer.getEvaluations());
assertTrue(optimizer.getEvaluations() > 20);
assertTrue(optimizer.getEvaluations() < 250);
assertTrue(optimum.getValue() < 8.0e-4);
}
示例2: detect
import org.apache.commons.math.optimization.direct.NelderMead; //导入依赖的package包/类
@Override
public double detect(final double[] vec) {
double[] t = {this.mean.detect(vec)};
MultivariateRealFunction func = new MultivariateRealFunction() {
@Override
public double value(double[] x) throws FunctionEvaluationException {
ObjectDoublePair<double[]> ind = indicator(vec,x[0]);
double sumd = MatlibMatrixUtils.sum(D);
double k = ind.second / sumd;
double b = k / (1-k);
double[][] y = new double[1][vec.length];
for (int i = 0; i < vec.length; i++) {
y[0][i] = ind.first[i] + 1 - b * (1 - ind.first[i]);
}
SparseMatrix dmw = MatlibMatrixUtils.minusInplace(D, W);
Vector yv = Vector.wrap(y[0]);
double nom = new DenseMatrix(y).mult(dmw.transposeMultiply(yv)).get(0); // y' * ( (D-W) * y)
double denom = new DenseMatrix(y).mult(D.transposeMultiply(yv)).get(0);
return nom/denom;
}
};
//
RealPointValuePair ret;
try {
NelderMead nelderMead = new NelderMead();
nelderMead.setConvergenceChecker(new SimpleRealPointChecker(0.0001, -1));
ret = nelderMead.optimize(func, GoalType.MINIMIZE, t);
return ret.getPoint()[0];
} catch (Exception e) {
e.printStackTrace();
System.err.println("Reverting to mean");
}
return t[0];
}