Java InitialGuess类代码示例

import org.apache.commons.math3.optim.InitialGuess; //导入依赖的package包/类
@Override
public double applyToNetwork(final MixedClearingNetwork network) {
   Preconditions.checkNotNull(network);
   final int dimension = network.getNumberOfEdges();
   
   final ResidualCostFunction aggregateCostFunction =
      super.getResidualScalarCostFunction(network);
   final RealVector
      start = new ArrayRealVector(network.getNumberOfEdges());
   start.set(1.0);                                       // Initial rate guess.
   
   final BOBYQAOptimizer optimizer = new BOBYQAOptimizer(2*dimension + 1, 1.2, 1.e-8);
   final PointValuePair result = optimizer.optimize(
      new MaxEval(maximumEvaluations),
      new ObjectiveFunction(aggregateCostFunction),
      GoalType.MINIMIZE,
      new SimpleBounds(new double[dimension], ArrayUtil.ones(dimension)),
      new InitialGuess(start.toArray())
      );
   
   final double residualCost = result.getValue();
   System.out.println("Network cleared: residual cost: " + residualCost + ".");
   
   return residualCost;
}
 

import org.apache.commons.math3.optim.InitialGuess; //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, 0.0);
  
  PowellOptimizer optimizer = new PowellOptimizer(1e-3, 5);
  
  // needed since one optimization found positive 
  // charges (paying customer to consume...)
  double[][] boundaries = createBoundaries(NUM_RATES);
 
  final PointValuePair optimum
      = optimizer.optimize(
          new MaxEval(numEval),
          //new ObjectiveFunction(new MultivariateFunctionMappingAdapter(tariffUtilityEstimate, boundaries[0], boundaries[1])),
          new ObjectiveFunction(new OptimizerWrapperApacheObjective(tariffUtilityEstimate)),
          GoalType.MAXIMIZE,
          new InitialGuess(startingVertex)
          );
          
  TreeMap<Double, TariffSpecification> eval2TOUTariff = new TreeMap<Double, TariffSpecification>();
  eval2TOUTariff.put(optimum.getValue(), tariffUtilityEstimate.getCorrespondingSpec(optimum.getKey()));
  return eval2TOUTariff;
}
 

import org.apache.commons.math3.optim.InitialGuess; //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;
}
 

import org.apache.commons.math3.optim.InitialGuess; //导入依赖的package包/类
/**
 * Optimizes the circuit to the given ys vector
 * 
 * @param ys
 *            complex scattering parameters to which the model is optimized
 */
public void optimize(Complex[] ys) {
	// shorten parameters to optimize
	shortParameters = MCUtil.shortenParam(circuitType, parameters);
	errorFunction = new MCErrorSum(ys, this);
	optimum = null;
	try {
		optimum = optimizer.optimize(new MaxEval(10000), new ObjectiveFunction(errorFunction), GoalType.MINIMIZE,
				new InitialGuess(shortParameters), new NelderMeadSimplex(optStep));
		parameters = MCUtil.topo2Param(circuitType, optimum.getPoint());
	} catch (TooManyEvaluationsException ex) {
		// This exception can be ignored. If max eval is reached, the recent
		// parameters are stored
		// and no null pointer can appear
		parameters = new double[] { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
	}
	// save new parameters
}
 

import org.apache.commons.math3.optim.InitialGuess; //导入依赖的package包/类
/**
 * Test demonstrating that the user exception is finally thrown if none
 * of the runs succeed.
 */
@Test(expected=TestException.class)
public void testNoOptimum() {
    JacobianMultivariateVectorOptimizer underlyingOptimizer
        = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
    JDKRandomGenerator g = new JDKRandomGenerator();
    g.setSeed(12373523445l);
    RandomVectorGenerator generator
        = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
    MultiStartMultivariateVectorOptimizer optimizer
        = new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
    optimizer.optimize(new MaxEval(100),
                       new Target(new double[] { 0 }),
                       new Weight(new double[] { 1 }),
                       new InitialGuess(new double[] { 0 }),
                       new ModelFunction(new MultivariateVectorFunction() {
                               public double[] value(double[] point) {
                                   throw new TestException();
                               }
                           }));
}
 

import org.apache.commons.math3.optim.InitialGuess; //导入依赖的package包/类
@Test
public void testGetChiSquare() throws IOException {
    final StatisticalReferenceDataset dataset
        = StatisticalReferenceDatasetFactory.createKirby2();
    final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
    final double[] a = dataset.getParameters();
    final double[] y = dataset.getData()[1];
    final double[] w = new double[y.length];
    Arrays.fill(w, 1.0);

    StatisticalReferenceDataset.LeastSquaresProblem problem
        = dataset.getLeastSquaresProblem();

    optimizer.optimize(new MaxEval(1),
                       problem.getModelFunction(),
                       problem.getModelFunctionJacobian(),
                       new Target(y),
                       new Weight(w),
                       new InitialGuess(a));
    final double expected = dataset.getResidualSumOfSquares();
    final double actual = optimizer.getChiSquare();
    Assert.assertEquals(dataset.getName(), expected, actual,
                        1E-11 * expected);
}
 

import org.apache.commons.math3.optim.InitialGuess; //导入依赖的package包/类
@Test(expected=TooManyEvaluationsException.class)
public void testMaxEvaluations() throws Exception {
    CircleVectorial circle = new CircleVectorial();
    circle.addPoint( 30.0,  68.0);
    circle.addPoint( 50.0,  -6.0);
    circle.addPoint(110.0, -20.0);
    circle.addPoint( 35.0,  15.0);
    circle.addPoint( 45.0,  97.0);

    GaussNewtonOptimizer optimizer
        = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1e-30, 1e-30));

    optimizer.optimize(new MaxEval(100),
                       circle.getModelFunction(),
                       circle.getModelFunctionJacobian(),
                       new Target(new double[] { 0, 0, 0, 0, 0 }),
                       new Weight(new double[] { 1, 1, 1, 1, 1 }),
                       new InitialGuess(new double[] { 98.680, 47.345 }));
}
 

import org.apache.commons.math3.optim.InitialGuess; //导入依赖的package包/类
@Override
@Test(expected=SingularMatrixException.class)
public void testNonInvertible() {
    /*
     * Overrides the method from parent class, since the default singularity
     * threshold (1e-14) does not trigger the expected exception.
     */
    LinearProblem problem = new LinearProblem(new double[][] {
            {  1, 2, -3 },
            {  2, 1,  3 },
            { -3, 0, -9 }
    }, new double[] { 1, 1, 1 });

    AbstractLeastSquaresOptimizer optimizer = createOptimizer();
    PointVectorValuePair optimum
        = optimizer.optimize(new MaxEval(100),
                             problem.getModelFunction(),
                             problem.getModelFunctionJacobian(),
                             problem.getTarget(),
                             new Weight(new double[] { 1, 1, 1 }),
                             new InitialGuess(new double[] { 0, 0, 0 }));
    Assert.assertTrue(FastMath.sqrt(optimizer.getTargetSize()) * optimizer.getRMS() > 0.6);

    optimizer.computeCovariances(optimum.getPoint(), 1.5e-14);
}
 

import org.apache.commons.math3.optim.InitialGuess; //导入依赖的package包/类
@Test
public void testTrivial() {
    LinearProblem problem
        = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
    AbstractLeastSquaresOptimizer optimizer = createOptimizer();
    PointVectorValuePair optimum =
        optimizer.optimize(new MaxEval(100),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           problem.getTarget(),
                           new Weight(new double[] { 1 }),
                           new InitialGuess(new double[] { 0 }));
    Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
    Assert.assertEquals(1.5, optimum.getPoint()[0], 1e-10);
    Assert.assertEquals(3.0, optimum.getValue()[0], 1e-10);
}
 

import org.apache.commons.math3.optim.InitialGuess; //导入依赖的package包/类
@Test
public void testQRColumnsPermutation() {

    LinearProblem problem
        = new LinearProblem(new double[][] { { 1, -1 }, { 0, 2 }, { 1, -2 } },
                            new double[] { 4, 6, 1 });

    AbstractLeastSquaresOptimizer optimizer = createOptimizer();
    PointVectorValuePair optimum =
        optimizer.optimize(new MaxEval(100),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           problem.getTarget(),
                           new Weight(new double[] { 1, 1, 1 }),
                           new InitialGuess(new double[] { 0, 0 }));
    Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
    Assert.assertEquals(7, optimum.getPoint()[0], 1e-10);
    Assert.assertEquals(3, optimum.getPoint()[1], 1e-10);
    Assert.assertEquals(4, optimum.getValue()[0], 1e-10);
    Assert.assertEquals(6, optimum.getValue()[1], 1e-10);
    Assert.assertEquals(1, optimum.getValue()[2], 1e-10);
}
 

import org.apache.commons.math3.optim.InitialGuess; //导入依赖的package包/类
@Test
public void testNoDependency() {
    LinearProblem problem = new LinearProblem(new double[][] {
            { 2, 0, 0, 0, 0, 0 },
            { 0, 2, 0, 0, 0, 0 },
            { 0, 0, 2, 0, 0, 0 },
            { 0, 0, 0, 2, 0, 0 },
            { 0, 0, 0, 0, 2, 0 },
            { 0, 0, 0, 0, 0, 2 }
    }, new double[] { 0, 1.1, 2.2, 3.3, 4.4, 5.5 });
    AbstractLeastSquaresOptimizer optimizer = createOptimizer();
    PointVectorValuePair optimum =
        optimizer.optimize(new MaxEval(100),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           problem.getTarget(),
                           new Weight(new double[] { 1, 1, 1, 1, 1, 1 }),
                           new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
    Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
    for (int i = 0; i < problem.target.length; ++i) {
        Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1e-10);
    }
}
 

import org.apache.commons.math3.optim.InitialGuess; //导入依赖的package包/类
@Test
public void testOneSet() {

    LinearProblem problem = new LinearProblem(new double[][] {
            {  1,  0, 0 },
            { -1,  1, 0 },
            {  0, -1, 1 }
    }, new double[] { 1, 1, 1});
    AbstractLeastSquaresOptimizer optimizer = createOptimizer();
    PointVectorValuePair optimum =
        optimizer.optimize(new MaxEval(100),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           problem.getTarget(),
                           new Weight(new double[] { 1, 1, 1 }),
                           new InitialGuess(new double[] { 0, 0, 0 }));
    Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
    Assert.assertEquals(1, optimum.getPoint()[0], 1e-10);
    Assert.assertEquals(2, optimum.getPoint()[1], 1e-10);
    Assert.assertEquals(3, optimum.getPoint()[2], 1e-10);
}
 

import org.apache.commons.math3.optim.InitialGuess; //导入依赖的package包/类
@Test(expected=ConvergenceException.class)
public void testNonInvertible() throws Exception {

    LinearProblem problem = new LinearProblem(new double[][] {
            {  1, 2, -3 },
            {  2, 1,  3 },
            { -3, 0, -9 }
    }, new double[] { 1, 1, 1 });

    AbstractLeastSquaresOptimizer optimizer = createOptimizer();

    optimizer.optimize(new MaxEval(100),
                       problem.getModelFunction(),
                       problem.getModelFunctionJacobian(),
                       problem.getTarget(),
                       new Weight(new double[] { 1, 1, 1 }),
                       new InitialGuess(new double[] { 0, 0, 0 }));
}
 

import org.apache.commons.math3.optim.InitialGuess; //导入依赖的package包/类
@Test
public void testMoreEstimatedParametersSimple() {

    LinearProblem problem = new LinearProblem(new double[][] {
            { 3, 2,  0, 0 },
            { 0, 1, -1, 1 },
            { 2, 0,  1, 0 }
    }, new double[] { 7, 3, 5 });

    AbstractLeastSquaresOptimizer optimizer = createOptimizer();
    optimizer.optimize(new MaxEval(100),
                       problem.getModelFunction(),
                       problem.getModelFunctionJacobian(),
                       problem.getTarget(),
                       new Weight(new double[] { 1, 1, 1 }),
                       new InitialGuess(new double[] { 7, 6, 5, 4 }));
    Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
}
 

import org.apache.commons.math3.optim.InitialGuess; //导入依赖的package包/类
@Test
public void testMoreEstimatedParametersUnsorted() {
    LinearProblem problem = new LinearProblem(new double[][] {
            { 1, 1,  0,  0, 0,  0 },
            { 0, 0,  1,  1, 1,  0 },
            { 0, 0,  0,  0, 1, -1 },
            { 0, 0, -1,  1, 0,  1 },
            { 0, 0,  0, -1, 1,  0 }
   }, new double[] { 3, 12, -1, 7, 1 });

    AbstractLeastSquaresOptimizer optimizer = createOptimizer();
    PointVectorValuePair optimum =
        optimizer.optimize(new MaxEval(100),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           problem.getTarget(),
                           new Weight(new double[] { 1, 1, 1, 1, 1 }),
                           new InitialGuess(new double[] { 2, 2, 2, 2, 2, 2 }));
    Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
    Assert.assertEquals(3, optimum.getPointRef()[2], 1e-10);
    Assert.assertEquals(4, optimum.getPointRef()[3], 1e-10);
    Assert.assertEquals(5, optimum.getPointRef()[4], 1e-10);
    Assert.assertEquals(6, optimum.getPointRef()[5], 1e-10);
}