本文整理汇总了Java中org.apache.commons.math3.optimization.GoalType.MINIMIZE属性的典型用法代码示例。如果您正苦于以下问题：Java GoalType.MINIMIZE属性的具体用法？Java GoalType.MINIMIZE怎么用？Java GoalType.MINIMIZE使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类org.apache.commons.math3.optimization.GoalType的用法示例。
在下文中一共展示了GoalType.MINIMIZE属性的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: doOptimize
/** {@inheritDoc} */
@Override
protected PointValuePair doOptimize() {
final double[] lowerBound = getLowerBound();
final double[] upperBound = getUpperBound();
// Validity checks.
setup(lowerBound, upperBound);
isMinimize = (getGoalType() == GoalType.MINIMIZE);
currentBest = new ArrayRealVector(getStartPoint());
final double value = bobyqa(lowerBound, upperBound);
return new PointValuePair(currentBest.getDataRef(),
isMinimize ? value : -value);
}
示例2: doOptimize
/** {@inheritDoc} */
@Override
protected PointValuePair doOptimize() {
if (simplex == null) {
throw new NullArgumentException();
}
// Indirect call to "computeObjectiveValue" in order to update the
// evaluations counter.
final MultivariateFunction evalFunc
= new MultivariateFunction() {
/** {@inheritDoc} */
public double value(double[] point) {
return computeObjectiveValue(point);
}
};
final boolean isMinim = getGoalType() == GoalType.MINIMIZE;
final Comparator<PointValuePair> comparator
= new Comparator<PointValuePair>() {
/** {@inheritDoc} */
public int compare(final PointValuePair o1,
final PointValuePair o2) {
final double v1 = o1.getValue();
final double v2 = o2.getValue();
return isMinim ? Double.compare(v1, v2) : Double.compare(v2, v1);
}
};
// Initialize search.
simplex.build(getStartPoint());
simplex.evaluate(evalFunc, comparator);
PointValuePair[] previous = null;
int iteration = 0;
final ConvergenceChecker<PointValuePair> checker = getConvergenceChecker();
while (true) {
if (iteration > 0) {
boolean converged = true;
for (int i = 0; i < simplex.getSize(); i++) {
PointValuePair prev = previous[i];
converged = converged &&
checker.converged(iteration, prev, simplex.getPoint(i));
}
if (converged) {
// We have found an optimum.
return simplex.getPoint(0);
}
}
// We still need to search.
previous = simplex.getPoints();
simplex.iterate(evalFunc, comparator);
++iteration;
}
}
示例3: doOptimize
/** {@inheritDoc} */
@Override
protected PointValuePair doOptimize() {
if (simplex == null) {
throw new NullArgumentException();
}
// Indirect call to "computeObjectiveValue" in order to update the
// evaluations counter.
final MultivariateFunction evalFunc
= new MultivariateFunction() {
public double value(double[] point) {
return computeObjectiveValue(point);
}
};
final boolean isMinim = getGoalType() == GoalType.MINIMIZE;
final Comparator<PointValuePair> comparator
= new Comparator<PointValuePair>() {
public int compare(final PointValuePair o1,
final PointValuePair o2) {
final double v1 = o1.getValue();
final double v2 = o2.getValue();
return isMinim ? Double.compare(v1, v2) : Double.compare(v2, v1);
}
};
// Initialize search.
simplex.build(getStartPoint());
simplex.evaluate(evalFunc, comparator);
PointValuePair[] previous = null;
int iteration = 0;
final ConvergenceChecker<PointValuePair> checker = getConvergenceChecker();
while (true) {
if (iteration > 0) {
boolean converged = true;
for (int i = 0; i < simplex.getSize(); i++) {
PointValuePair prev = previous[i];
converged = converged &&
checker.converged(iteration, prev, simplex.getPoint(i));
}
if (converged) {
// We have found an optimum.
return simplex.getPoint(0);
}
}
// We still need to search.
previous = simplex.getPoints();
simplex.iterate(evalFunc, comparator);
++iteration;
}
}