Examples with MaxEval org.apache.commons.math3.optim.MaxEval used on opensource projects

Example 16 with MaxEval

use of org.apache.commons.math3.optim.MaxEval in project narchy by automenta.

the class Optimize method solve.

public void solve(int dim, ObjectiveFunction func, double[] mid, double[] min, double[] max, double[] inc, int maxIterations) {
    if (dim == 1) {
        // use a solver capable of 1 dim
        new SimplexOptimizer(1e-10, 1e-30).optimize(new MaxEval(maxIterations), func, GoalType.MAXIMIZE, new InitialGuess(mid), // new NelderMeadSimplex(inc)
        new MultiDirectionalSimplex(inc));
    } else {
        int popSize = // 4 + 3 ln(n)
        (int) Math.ceil(4 + 3 * Math.log(tweaks.size()));
        double[] sigma = MathArrays.scale(1f, inc);
        MyCMAESOptimizer m = new MyCMAESOptimizer(maxIterations, Double.NaN, true, 0, 1, new MersenneTwister(System.nanoTime()), true, null, popSize, sigma);
        m.optimize(func, GoalType.MAXIMIZE, new MaxEval(maxIterations), new SimpleBounds(min, max), new InitialGuess(mid));
        m.print(System.out);
    // final int numIterpolationPoints = 3 * dim; //2 * dim + 1 + 1;
    // new BOBYQAOptimizer(numIterpolationPoints,
    // dim * 2.0,
    // 1.0E-4D /* ? */).optimize(
    // MaxEval.unlimited(), //new MaxEval(maxIterations),
    // new MaxIter(maxIterations),
    // func,
    // GoalType.MAXIMIZE,
    // new SimpleBounds(min, max),
    // new InitialGuess(mid));
    }
}

Example 17 with MaxEval

use of org.apache.commons.math3.optim.MaxEval in project narchy by automenta.

the class MyCMAESOptimizerTest method testFitAccuracyDependsOnBoundary.

/**
 * Cf. MATH-867
 */
@Test
public void testFitAccuracyDependsOnBoundary() {
    double[] sigma1 = { 1e-1 };
    MyCMAESOptimizer optimizer = new MyCMAESOptimizer(30000, 0, true, 10, 0, rng(), false, null, 5, sigma1);
    final MultivariateFunction fitnessFunction = new MultivariateFunction() {

        @Override
        public double value(double[] parameters) {
            final double target = 11.1;
            final double error = target - parameters[0];
            return error * error;
        }
    };
    final double[] start = { 1 };
    // No bounds.
    PointValuePair result = optimizer.optimize(new MaxEval(100000), new ObjectiveFunction(fitnessFunction), GoalType.MINIMIZE, SimpleBounds.unbounded(1), new InitialGuess(start));
    final double resNoBound = result.getPoint()[0];
    // Optimum is near the lower bound.
    final double[] lower = { -20 };
    final double[] upper = { 5e16 };
    final double[] sigma2 = { 10 };
    optimizer = new MyCMAESOptimizer(30000, 0, true, 10, 0, rng(), false, null, 5, sigma2);
    result = optimizer.optimize(new MaxEval(100000), new ObjectiveFunction(fitnessFunction), GoalType.MINIMIZE, new InitialGuess(start), new SimpleBounds(lower, upper));
    final double resNearLo = result.getPoint()[0];
    // Optimum is near the upper bound.
    lower[0] = -5e16;
    upper[0] = 20;
    result = optimizer.optimize(new MaxEval(100000), new ObjectiveFunction(fitnessFunction), GoalType.MINIMIZE, new InitialGuess(start), new SimpleBounds(lower, upper));
    final double resNearHi = result.getPoint()[0];
    // System.out.println("resNoBound=" + resNoBound +
    // " resNearLo=" + resNearLo +
    // " resNearHi=" + resNearHi);
    // The two values currently differ by a substantial amount, indicating that
    // the bounds definition can prevent reaching the optimum.
    assertEquals(resNoBound, resNearLo, 1e-3);
    assertEquals(resNoBound, resNearHi, 1e-3);
}

Example 18 with MaxEval

use of org.apache.commons.math3.optim.MaxEval in project narchy by automenta.

the class MyCMAESOptimizerTest method doTest.

/**
 * @param func Function to optimize.
 * @param startPoint Starting point.
 * @param inSigma Individual input sigma.
 * @param boundaries Upper / lower point limit.
 * @param goal Minimization or maximization.
 * @param lambda Population size used for offspring.
 * @param isActive Covariance update mechanism.
 * @param diagonalOnly Simplified covariance update.
 * @param stopValue Termination criteria for optimization.
 * @param fTol Tolerance relative error on the objective function.
 * @param pointTol Tolerance for checking that the optimum is correct.
 * @param maxEvaluations Maximum number of evaluations.
 * @param expected Expected point / value.
 */
private void doTest(MultivariateFunction func, double[] startPoint, double[] inSigma, double[][] boundaries, GoalType goal, int lambda, boolean isActive, int diagonalOnly, double stopValue, double fTol, double pointTol, int maxEvaluations, PointValuePair expected) {
    int dim = startPoint.length;
    // test diagonalOnly = 0 - slow but normally fewer feval#
    MyCMAESOptimizer optim = new MyCMAESOptimizer(30000, stopValue, isActive, diagonalOnly, 0, rng(), false, null, lambda, inSigma);
    PointValuePair result = boundaries == null ? optim.optimize(new MaxEval(maxEvaluations), new ObjectiveFunction(func), goal, new InitialGuess(startPoint), SimpleBounds.unbounded(dim)) : optim.optimize(new MaxEval(maxEvaluations), new ObjectiveFunction(func), goal, new SimpleBounds(boundaries[0], boundaries[1]), new InitialGuess(startPoint));
    // System.out.println("sol=" + Arrays.toString(result.getPoint()));
    assertEquals(expected.getValue(), result.getValue(), fTol);
    for (int i = 0; i < dim; i++) {
        assertEquals(expected.getPoint()[i], result.getPoint()[i], pointTol);
    }
    assertTrue(optim.getIterations() > 0);
}

Example 19 with MaxEval

use of org.apache.commons.math3.optim.MaxEval in project narchy by automenta.

the class MyCMAESOptimizerTest method testMath864.

@Test
public void testMath864() {
    final double[] sigma = { 1e-1 };
    final MyCMAESOptimizer optimizer = new MyCMAESOptimizer(30000, 0, true, 10, 0, rng(), false, null, 5, sigma);
    final MultivariateFunction fitnessFunction = new MultivariateFunction() {

        @Override
        public double value(double[] parameters) {
            final double target = 1;
            final double error = target - parameters[0];
            return error * error;
        }
    };
    final double[] start = { 0 };
    final double[] lower = { -1e6 };
    final double[] upper = { 1.5 };
    final double[] result = optimizer.optimize(new MaxEval(10000), new ObjectiveFunction(fitnessFunction), GoalType.MINIMIZE, new InitialGuess(start), new SimpleBounds(lower, upper)).getPoint();
    assertTrue(result[0] <= upper[0], () -> "Out of bounds (" + result[0] + " > " + upper[0] + ")");
}

Example 20 with MaxEval

use of org.apache.commons.math3.optim.MaxEval in project tetrad by cmu-phil.

the class Ling method optimizeNonGaussianity.

private void optimizeNonGaussianity(final int rowIndex, final TetradMatrix dataSetTetradMatrix, final double[][] W, List<Mapping> allMappings) {
    final List<Mapping> mappings = mappingsForRow(rowIndex, allMappings);
    MultivariateFunction function = new MultivariateFunction() {

        public double value(double[] values) {
            for (int i = 0; i < values.length; i++) {
                Mapping mapping = mappings.get(i);
                W[mapping.getI()][mapping.getJ()] = values[i];
            }
            double v = ngFullData(rowIndex, dataSetTetradMatrix, W);
            if (Double.isNaN(v))
                return 10000;
            return -(v);
        }
    };
    {
        double[] values = new double[mappings.size()];
        for (int k = 0; k < mappings.size(); k++) {
            Mapping mapping = mappings.get(k);
            values[k] = W[mapping.getI()][mapping.getJ()];
        }
        MultivariateOptimizer search = new PowellOptimizer(1e-7, 1e-7);
        PointValuePair pair = search.optimize(new InitialGuess(values), new ObjectiveFunction(function), GoalType.MINIMIZE, new MaxEval(100000));
        values = pair.getPoint();
        for (int k = 0; k < mappings.size(); k++) {
            Mapping mapping = mappings.get(k);
            W[mapping.getI()][mapping.getJ()] = values[k];
        }
    }
}