本文整理汇总了Java中org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer类的典型用法代码示例。如果您正苦于以下问题：Java SimplexOptimizer类的具体用法？Java SimplexOptimizer怎么用？Java SimplexOptimizer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。
SimplexOptimizer类属于org.apache.commons.math3.optim.nonlinear.scalar.noderiv包，在下文中一共展示了SimplexOptimizer类的11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: optimize
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer; //导入依赖的package包/类
/**
* Optimizes parameters using the Nelder-Mead Method
* @param initialGuess initial guess / state required for Nelder-Mead-Method
* @param costFunction which defines that the Mean Squared Error has to be minimized
* @return the optimized values
*/
private static double[] optimize(double[] initialGuess, MultivariateFunctionMappingAdapter costFunction) {
double[] result;
SimplexOptimizer optimizer = new SimplexOptimizer(simplexRelativeThreshold, simplexAbsoluteThreshold);
PointValuePair unBoundedResult = optimizer.optimize(
GoalType.MINIMIZE,
new MaxIter(MAX_ALLOWED_NUMBER_OF_ITERATION),
new MaxEval(MAX_ALLOWED_NUMBER_OF_EVALUATION),
new InitialGuess(initialGuess),
new ObjectiveFunction(costFunction),
new NelderMeadSimplex(initialGuess.length));
result = costFunction.unboundedToBounded(unBoundedResult.getPoint());
return result;
}
示例2: testStartSimplexInsideRange
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer; //导入依赖的package包/类
@Test
public void testStartSimplexInsideRange() {
final BiQuadratic biQuadratic = new BiQuadratic(2.0, 2.5, 1.0, 3.0, 2.0, 3.0);
final MultivariateFunctionMappingAdapter wrapped
= new MultivariateFunctionMappingAdapter(biQuadratic,
biQuadratic.getLower(),
biQuadratic.getUpper());
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final AbstractSimplex simplex = new NelderMeadSimplex(new double[][] {
wrapped.boundedToUnbounded(new double[] { 1.5, 2.75 }),
wrapped.boundedToUnbounded(new double[] { 1.5, 2.95 }),
wrapped.boundedToUnbounded(new double[] { 1.7, 2.90 })
});
final PointValuePair optimum
= optimizer.optimize(new MaxEval(300),
new ObjectiveFunction(wrapped),
simplex,
GoalType.MINIMIZE,
new InitialGuess(wrapped.boundedToUnbounded(new double[] { 1.5, 2.25 })));
final double[] bounded = wrapped.unboundedToBounded(optimum.getPoint());
Assert.assertEquals(biQuadratic.getBoundedXOptimum(), bounded[0], 2e-7);
Assert.assertEquals(biQuadratic.getBoundedYOptimum(), bounded[1], 2e-7);
}
示例3: testOptimumOutsideRange
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer; //导入依赖的package包/类
@Test
public void testOptimumOutsideRange() {
final BiQuadratic biQuadratic = new BiQuadratic(4.0, 0.0, 1.0, 3.0, 2.0, 3.0);
final MultivariateFunctionMappingAdapter wrapped
= new MultivariateFunctionMappingAdapter(biQuadratic,
biQuadratic.getLower(),
biQuadratic.getUpper());
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final AbstractSimplex simplex = new NelderMeadSimplex(new double[][] {
wrapped.boundedToUnbounded(new double[] { 1.5, 2.75 }),
wrapped.boundedToUnbounded(new double[] { 1.5, 2.95 }),
wrapped.boundedToUnbounded(new double[] { 1.7, 2.90 })
});
final PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
new ObjectiveFunction(wrapped),
simplex,
GoalType.MINIMIZE,
new InitialGuess(wrapped.boundedToUnbounded(new double[] { 1.5, 2.25 })));
final double[] bounded = wrapped.unboundedToBounded(optimum.getPoint());
Assert.assertEquals(biQuadratic.getBoundedXOptimum(), bounded[0], 2e-7);
Assert.assertEquals(biQuadratic.getBoundedYOptimum(), bounded[1], 2e-7);
}
示例4: testStartSimplexInsideRange
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer; //导入依赖的package包/类
@Test
public void testStartSimplexInsideRange() {
final BiQuadratic biQuadratic = new BiQuadratic(2.0, 2.5, 1.0, 3.0, 2.0, 3.0);
final MultivariateFunctionPenaltyAdapter wrapped
= new MultivariateFunctionPenaltyAdapter(biQuadratic,
biQuadratic.getLower(),
biQuadratic.getUpper(),
1000.0, new double[] { 100.0, 100.0 });
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final AbstractSimplex simplex = new NelderMeadSimplex(new double[] { 1.0, 0.5 });
final PointValuePair optimum
= optimizer.optimize(new MaxEval(300),
new ObjectiveFunction(wrapped),
simplex,
GoalType.MINIMIZE,
new InitialGuess(new double[] { 1.5, 2.25 }));
Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7);
Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7);
}
示例5: testStartSimplexOutsideRange
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer; //导入依赖的package包/类
@Test
public void testStartSimplexOutsideRange() {
final BiQuadratic biQuadratic = new BiQuadratic(2.0, 2.5, 1.0, 3.0, 2.0, 3.0);
final MultivariateFunctionPenaltyAdapter wrapped
= new MultivariateFunctionPenaltyAdapter(biQuadratic,
biQuadratic.getLower(),
biQuadratic.getUpper(),
1000.0, new double[] { 100.0, 100.0 });
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final AbstractSimplex simplex = new NelderMeadSimplex(new double[] { 1.0, 0.5 });
final PointValuePair optimum
= optimizer.optimize(new MaxEval(300),
new ObjectiveFunction(wrapped),
simplex,
GoalType.MINIMIZE,
new InitialGuess(new double[] { -1.5, 4.0 }));
Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7);
Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7);
}
示例6: testOptimumOutsideRange
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer; //导入依赖的package包/类
@Test
public void testOptimumOutsideRange() {
final BiQuadratic biQuadratic = new BiQuadratic(4.0, 0.0, 1.0, 3.0, 2.0, 3.0);
final MultivariateFunctionPenaltyAdapter wrapped
= new MultivariateFunctionPenaltyAdapter(biQuadratic,
biQuadratic.getLower(),
biQuadratic.getUpper(),
1000.0, new double[] { 100.0, 100.0 });
SimplexOptimizer optimizer = new SimplexOptimizer(new SimplePointChecker<PointValuePair>(1.0e-11, 1.0e-20));
final AbstractSimplex simplex = new NelderMeadSimplex(new double[] { 1.0, 0.5 });
final PointValuePair optimum
= optimizer.optimize(new MaxEval(600),
new ObjectiveFunction(wrapped),
simplex,
GoalType.MINIMIZE,
new InitialGuess(new double[] { -1.5, 4.0 }));
Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7);
Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7);
}
示例7: testUnbounded
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer; //导入依赖的package包/类
@Test
public void testUnbounded() {
final BiQuadratic biQuadratic = new BiQuadratic(4.0, 0.0,
Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY,
Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY);
final MultivariateFunctionPenaltyAdapter wrapped
= new MultivariateFunctionPenaltyAdapter(biQuadratic,
biQuadratic.getLower(),
biQuadratic.getUpper(),
1000.0, new double[] { 100.0, 100.0 });
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final AbstractSimplex simplex = new NelderMeadSimplex(new double[] { 1.0, 0.5 });
final PointValuePair optimum
= optimizer.optimize(new MaxEval(300),
new ObjectiveFunction(wrapped),
simplex,
GoalType.MINIMIZE,
new InitialGuess(new double[] { -1.5, 4.0 }));
Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7);
Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7);
}
示例8: testHalfBounded
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer; //导入依赖的package包/类
@Test
public void testHalfBounded() {
final BiQuadratic biQuadratic = new BiQuadratic(4.0, 4.0,
1.0, Double.POSITIVE_INFINITY,
Double.NEGATIVE_INFINITY, 3.0);
final MultivariateFunctionPenaltyAdapter wrapped
= new MultivariateFunctionPenaltyAdapter(biQuadratic,
biQuadratic.getLower(),
biQuadratic.getUpper(),
1000.0, new double[] { 100.0, 100.0 });
SimplexOptimizer optimizer = new SimplexOptimizer(new SimplePointChecker<PointValuePair>(1.0e-10, 1.0e-20));
final AbstractSimplex simplex = new NelderMeadSimplex(new double[] { 1.0, 0.5 });
final PointValuePair optimum
= optimizer.optimize(new MaxEval(400),
new ObjectiveFunction(wrapped),
simplex,
GoalType.MINIMIZE,
new InitialGuess(new double[] { -1.5, 4.0 }));
Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7);
Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7);
}
示例9: applyToNetwork
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer; //导入依赖的package包/类
@Override
public double applyToNetwork(final MixedClearingNetwork network) {
Preconditions.checkNotNull(network);
final SimplexOptimizer optimizer = new SimplexOptimizer(relErrorTarget, absErrorTarget);
final ResidualCostFunction aggregateCostFunction =
super.getResidualScalarCostFunction(network);
final RealVector
start = new ArrayRealVector(network.getNumberOfEdges());
for(int i = 0; i< network.getNumberOfEdges(); ++i)
start.setEntry(i, network.getEdges().get(i).getMaximumRateAdmissibleByBothParties());
start.set(1.);
final PointValuePair result = optimizer.optimize(
new MaxEval(maximumEvaluations),
new ObjectiveFunction(aggregateCostFunction),
GoalType.MINIMIZE,
new InitialGuess(start.toArray()),
new NelderMeadSimplex(network.getNumberOfEdges())
);
final double residualCost = result.getValue();
System.out.println("Network cleared: residual cost: " + residualCost + ".");
return residualCost;
}
示例10: findMin
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer; //导入依赖的package包/类
private UnivariatePointValuePair findMin(UnivariatePointValuePair current, SimplexOptimizer o,
MultivariateFunction f, double qValue)
{
try
{
NelderMeadSimplex simplex = new NelderMeadSimplex(1);
double[] initialSolution = { qValue };
PointValuePair solution = o.optimize(new MaxEval(1000), new InitialGuess(initialSolution), simplex,
new ObjectiveFunction(f), GoalType.MINIMIZE);
UnivariatePointValuePair next = (solution == null) ? null
: new UnivariatePointValuePair(solution.getPointRef()[0], solution.getValue());
if (next == null)
return current;
//System.out.printf("Simplex [%f] %f = %f\n", qValue, next.getPoint(), next.getValue());
if (current != null)
return (next.getValue() < current.getValue()) ? next : current;
return next;
}
catch (Exception e)
{
return current;
}
}
示例11: findOptimum
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer; //导入依赖的package包/类
@Override
public TreeMap<Double, TariffSpecification> findOptimum(TariffUtilityEstimate tariffUtilityEstimate,
int NUM_RATES, int numEval) {
double[] startingVertex = new double[NUM_RATES]; // start from the fixed-rate tariff's offset
//Arrays.fill(startingVertex, -0.5 * SIMPLEX_EDGE);
//Arrays.fill(startingVertex, -1 * SIMPLEX_EDGE);
// TODO if there are convergence issues, change these guessed thresholds
//SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
//SimplexOptimizer optimizer = new SimplexOptimizer(1e-4, 1e-4);
//SimplexOptimizer optimizer = new SimplexOptimizer(1e-2, 10);
SimplexOptimizer optimizer = new SimplexOptimizer(1e-3, 5);
//SimplexOptimizer optimizer = new SimplexOptimizer(1e-2, 5);
final PointValuePair optimum
= optimizer.optimize(
new MaxEval(numEval),
new ObjectiveFunction(new OptimizerWrapperApacheObjective(tariffUtilityEstimate)),
GoalType.MAXIMIZE,
new InitialGuess(startingVertex),
//new NelderMeadSimplex(NUM_RATES, -1 * SIMPLEX_EDGE));
new NelderMeadSimplex(NUM_RATES, SIMPLEX_EDGE));// should be positive since this reflects decrease in (negative) charges
TreeMap<Double, TariffSpecification> eval2TOUTariff = new TreeMap<Double, TariffSpecification>();
eval2TOUTariff.put(optimum.getValue(), tariffUtilityEstimate.getCorrespondingSpec(optimum.getKey()));
return eval2TOUTariff;
}