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

Example 41 with MaxEval

use of org.apache.commons.math3.optim.MaxEval in project gatk by broadinstitute.

the class CNLOHCaller method optimizeIt.

private double optimizeIt(final Function<Double, Double> objectiveFxn, final SearchInterval searchInterval) {
    final MaxEval BRENT_MAX_EVAL = new MaxEval(1000);
    final double RELATIVE_TOLERANCE = 0.001;
    final double ABSOLUTE_TOLERANCE = 0.001;
    final BrentOptimizer OPTIMIZER = new BrentOptimizer(RELATIVE_TOLERANCE, ABSOLUTE_TOLERANCE);
    final UnivariateObjectiveFunction objective = new UnivariateObjectiveFunction(x -> objectiveFxn.apply(x));
    return OPTIMIZER.optimize(objective, GoalType.MAXIMIZE, searchInterval, BRENT_MAX_EVAL).getPoint();
}

Example 42 with MaxEval

use of org.apache.commons.math3.optim.MaxEval in project gatk-protected by broadinstitute.

the class OptimizationUtils method argmax.

public static double argmax(final Function<Double, Double> function, final double min, final double max, final double guess, final double relativeTolerance, final double absoluteTolerance, final int maxEvaluations) {
    final BrentOptimizer optimizer = new BrentOptimizer(relativeTolerance, absoluteTolerance);
    final SearchInterval interval = new SearchInterval(min, max, guess);
    return optimizer.optimize(new UnivariateObjectiveFunction(function::apply), GoalType.MAXIMIZE, interval, new MaxEval(maxEvaluations)).getPoint();
}

Example 43 with MaxEval

use of org.apache.commons.math3.optim.MaxEval in project vcell by virtualcell.

the class FitBleachSpotOp method fitToGaussian.

static GaussianFitResults fitToGaussian(double init_center_i, double init_center_j, double init_radius2, FloatImage image) {
    // 
    // do some optimization on the image (fitting to a Gaussian)
    // set initial guesses from ROI operation.
    // 
    ISize imageSize = image.getISize();
    final int num_i = imageSize.getX();
    final int num_j = imageSize.getY();
    final float[] floatPixels = image.getFloatPixels();
    // 
    // initial guess based on previous fit of ROI
    // do gaussian fit in index space for center and standard deviation (later to translate it back to world coordinates)
    // 
    final int window_size = (int) Math.sqrt(init_radius2) * 4;
    // final int window_min_i = 0;       // (int) Math.max(0, Math.floor(init_center_i - window_size/2));
    // final int window_max_i = num_i-1; // (int) Math.min(num_i-1, Math.ceil(init_center_i + window_size/2));
    // final int window_min_j = 0;       // (int) Math.max(0, Math.floor(init_center_j - window_size/2));
    // final int window_max_j = num_j-1; // (int) Math.min(num_j-1, Math.ceil(init_center_j + window_size/2));
    final int window_min_i = (int) Math.max(0, Math.floor(init_center_i - window_size / 2));
    final int window_max_i = (int) Math.min(num_i - 1, Math.ceil(init_center_i + window_size / 2));
    final int window_min_j = (int) Math.max(0, Math.floor(init_center_j - window_size / 2));
    final int window_max_j = (int) Math.min(num_j - 1, Math.ceil(init_center_j + window_size / 2));
    final int PARAM_INDEX_CENTER_I = 0;
    final int PARAM_INDEX_CENTER_J = 1;
    final int PARAM_INDEX_K = 2;
    final int PARAM_INDEX_HIGH = 3;
    final int PARAM_INDEX_RADIUS_SQUARED = 4;
    final int NUM_PARAMETERS = 5;
    double[] initParameters = new double[NUM_PARAMETERS];
    initParameters[PARAM_INDEX_CENTER_I] = init_center_i;
    initParameters[PARAM_INDEX_CENTER_J] = init_center_j;
    initParameters[PARAM_INDEX_HIGH] = 1.0;
    initParameters[PARAM_INDEX_K] = 10;
    initParameters[PARAM_INDEX_RADIUS_SQUARED] = init_radius2;
    PowellOptimizer optimizer = new PowellOptimizer(1e-4, 1e-1);
    MultivariateFunction func = new MultivariateFunction() {

        @Override
        public double value(double[] point) {
            double center_i = point[PARAM_INDEX_CENTER_I];
            double center_j = point[PARAM_INDEX_CENTER_J];
            double high = point[PARAM_INDEX_HIGH];
            double K = point[PARAM_INDEX_K];
            double radius2 = point[PARAM_INDEX_RADIUS_SQUARED];
            double error2 = 0;
            for (int j = window_min_j; j <= window_max_j; j++) {
                // double y = j - center_j;
                double y = j;
                for (int i = window_min_i; i <= window_max_i; i++) {
                    // double x = i - center_i;
                    double x = i;
                    double modelValue = high - FastMath.exp(-K * FastMath.exp(-2 * (x * x + y * y) / radius2));
                    double imageValue = floatPixels[j * num_i + i];
                    double error = modelValue - imageValue;
                    error2 += error * error;
                }
            }
            System.out.println(new GaussianFitResults(center_i, center_j, radius2, K, high, error2));
            return error2;
        }
    };
    PointValuePair pvp = optimizer.optimize(new ObjectiveFunction(func), new InitialGuess(initParameters), new MaxEval(100000), GoalType.MINIMIZE);
    double[] fittedParamValues = pvp.getPoint();
    double fitted_center_i = fittedParamValues[PARAM_INDEX_CENTER_I];
    double fitted_center_j = fittedParamValues[PARAM_INDEX_CENTER_J];
    double fitted_radius2 = fittedParamValues[PARAM_INDEX_RADIUS_SQUARED];
    double fitted_K = fittedParamValues[PARAM_INDEX_K];
    double fitted_high = fittedParamValues[PARAM_INDEX_HIGH];
    double objectiveFunctionValue = pvp.getValue();
    return new GaussianFitResults(fitted_center_i, fitted_center_j, fitted_radius2, fitted_K, fitted_high, objectiveFunctionValue);
}

Example 44 with MaxEval

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

the class LogisticRegression2 method regress.

// I am going to try to maximize the liklehood function directly using the Powell Estimator.
public void regress(int[] target, int numValues, double[][] regressors) {
    try {
        int numParams = regressors.length + 1;
        double[] coefficients = new double[(numValues - 1) * numParams];
        // Apparently this needs to be fairly loose.
        int tolerance = 250;
        MultivariateOptimizer search = new PowellOptimizer(tolerance, tolerance);
        PointValuePair pair = search.optimize(new InitialGuess(coefficients), new ObjectiveFunction(new FittingFunction(target, regressors)), GoalType.MAXIMIZE, new MaxEval(1000000));
        this.likelihood = pair.getValue();
    } catch (TooManyEvaluationsException e) {
        e.printStackTrace();
        this.likelihood = Double.NaN;
    }
}

Example 45 with MaxEval

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

the class Mimbuild2 method optimizeMeasureVariancesConditionally.

private void optimizeMeasureVariancesConditionally(TetradMatrix measurescov, TetradMatrix latentscov, double[][] loadings, int[][] indicatorIndices, double[] delta) {
    double[] values2 = new double[delta.length];
    int count = 0;
    for (int i = 0; i < delta.length; i++) {
        values2[count++] = delta[i];
    }
    Function2 function2 = new Function2(indicatorIndices, measurescov, loadings, latentscov, delta, count);
    MultivariateOptimizer search = new PowellOptimizer(1e-7, 1e-7);
    PointValuePair pair = search.optimize(new InitialGuess(values2), new ObjectiveFunction(function2), GoalType.MINIMIZE, new MaxEval(100000));
    minimum = pair.getValue();
}