Examples with Sum org.apache.commons.math3.stat.descriptive.summary.Sum used on opensource projects

Example 31 with Sum

use of org.apache.commons.math3.stat.descriptive.summary.Sum in project tetrad by cmu-phil.

the class SemBicScoreImages3 method logdet2.

private double logdet2(TetradMatrix m) {
    if (m.rows() == 0)
        return 0.0;
    RealMatrix M = m.getRealMatrix();
    final LUDecomposition luDecomposition = new LUDecomposition(M);
    RealMatrix L = luDecomposition.getL();
    RealMatrix U = luDecomposition.getU();
    RealMatrix P = luDecomposition.getP();
    // System.out.println(new TetradMatrix(L.multiply(U)));
    // System.out.println(new TetradMatrix(m));
    // System.out.println(new TetradMatrix(L));
    // System.out.println(new TetradMatrix(U));
    // System.out.println(new TetradMatrix(P));
    double sum = 0.0;
    for (int i = 0; i < L.getRowDimension(); i++) {
        sum += FastMath.log(L.getEntry(i, i));
    }
    for (int i = 0; i < U.getRowDimension(); i++) {
        sum += FastMath.log(U.getEntry(i, i));
    }
    return sum;
}

Example 32 with Sum

use of org.apache.commons.math3.stat.descriptive.summary.Sum in project tetrad by cmu-phil.

the class SemLikelihood2 method logdet.

private double logdet(TetradMatrix m) {
    if (m.rows() == 0)
        return 0;
    RealMatrix M = m.getRealMatrix();
    final double tol = 1e-9;
    RealMatrix LT = new org.apache.commons.math3.linear.CholeskyDecomposition(M, tol, tol).getLT();
    double sum = 0.0;
    for (int i = 0; i < LT.getRowDimension(); i++) {
        sum += FastMath.log(LT.getEntry(i, i));
    }
    return 2.0 * sum;
}

Example 33 with Sum

use of org.apache.commons.math3.stat.descriptive.summary.Sum in project chordatlas by twak.

the class Prof method findProfileLines.

/**
 * We find an initial base offset. Then we cluster the start point of all
 * (clean) profiles. If any are a good distance from the initial base, we
 * add those as their own profile lines.
 *
 * The original line is offset by the remaiing data.
 */
public static List<SuperLine> findProfileLines(Collection<Prof> profiles, Line3d line) {
    List<SuperLine> out = new ArrayList();
    // PaintThing.debug.clear();
    SuperLine superLine = new SuperLine(line.start.x, line.start.z, line.end.x, line.end.z);
    double outLen = superLine.length();
    double min = Double.MAX_VALUE, max = -Double.MAX_VALUE;
    Cache<Prof, Double> vLength = new Cache<Prof, Double>() {

        @Override
        public Double create(Prof i) {
            return i.verticalLength(0.5);
        }
    };
    double vLen = profiles.stream().mapToDouble(p -> vLength.get(p)).sum();
    boolean useVertical = vLen / profiles.size() > 1;
    class Wrapper implements Clusterable {

        double[] pt;

        public Wrapper(Point2d pt) {
            this.pt = new double[] { pt.x, pt.y };
        }

        @Override
        public double[] getPoint() {
            return pt;
        }
    }
    List<Wrapper> toCluster = new ArrayList();
    List<Double> baseLineOffset = new ArrayList();
    for (Prof p : profiles) {
        if (// vLen / (5*profiles.size()))
        useVertical && vLength.get(p) < 1)
            continue;
        Prof clean = p.parameterize();
        Point2d pt = clean.get(0);
        Point3d pt3 = clean.to3d(pt);
        double ppram = superLine.findPPram(new Point2d(pt3.x, pt3.z));
        baseLineOffset.add(pt.x);
        toCluster.add(new Wrapper(new Point2d(pt.x, ppram * outLen)));
        min = Math.min(min, ppram);
        max = Math.max(max, ppram);
    }
    if (min == max || toCluster.isEmpty())
        return out;
    if (true) {
        baseLineOffset.sort(Double::compareTo);
        double modeBaselineOffset = baseLineOffset.get(baseLineOffset.size() / 2);
        DBSCANClusterer<Wrapper> cr = new DBSCANClusterer<>(1.5, 0);
        List<Cluster<Wrapper>> results = cr.cluster(toCluster);
        Iterator<Cluster<Wrapper>> cit = results.iterator();
        while (cit.hasNext()) {
            Cluster<Wrapper> cw = cit.next();
            if (cw.getPoints().size() < 2 / TweedSettings.settings.profileHSampleDist) {
                cit.remove();
                double cMeanY = cw.getPoints().stream().mapToDouble(x -> x.pt[1]).average().getAsDouble();
                double bestDist = Double.MAX_VALUE;
                Cluster<Wrapper> bestWrapper = null;
                for (Cluster<Wrapper> near : results) {
                    double meanY = near.getPoints().stream().mapToDouble(x -> x.pt[1]).average().getAsDouble();
                    double dist = Math.abs(meanY - cMeanY);
                    if (dist < bestDist) {
                        bestDist = dist;
                        bestWrapper = near;
                    }
                }
                if (bestWrapper != null)
                    bestWrapper.getPoints().addAll(cw.getPoints());
            }
        }
        {
            baseLineOffset.clear();
            int c = 0;
            for (Cluster<Wrapper> cw : results) {
                double[] minMax = cw.getPoints().stream().map(p -> new double[] { p.pt[1] }).collect(new InAxDoubleArray());
                double[] offsetA = cw.getPoints().stream().mapToDouble(p -> p.pt[0]).sorted().toArray();
                double offset = offsetA[offsetA.length / 2];
                if (offset - modeBaselineOffset < 1) {
                    for (Wrapper w : cw.getPoints()) baseLineOffset.add(w.pt[0]);
                    continue;
                }
                SuperLine sl = new SuperLine(superLine.fromPPram(minMax[0] / outLen), superLine.fromPPram(minMax[1] / outLen));
                sl.moveLeft(offset);
                out.add(sl);
                List<Point2d> pts = cw.getPoints().stream().map(w -> new Point2d(w.pt[0], w.pt[1])).collect(Collectors.toList());
                PaintThing.debug(Rainbow.getColour(c++), 1, pts);
            }
        }
    }
    Point2d nStart = superLine.fromPPram(min), nEnd = superLine.fromPPram(max);
    superLine.start = nStart;
    superLine.end = nEnd;
    baseLineOffset.sort(Double::compare);
    if (!baseLineOffset.isEmpty())
        superLine.moveLeft(baseLineOffset.get(baseLineOffset.size() / 2));
    out.add(0, superLine);
    return out;
}

Example 34 with Sum

use of org.apache.commons.math3.stat.descriptive.summary.Sum in project druid by druid-io.

the class BenchmarkColumnValueGenerator method initDistribution.

private void initDistribution() {
    BenchmarkColumnSchema.ValueDistribution distributionType = schema.getDistributionType();
    ValueType type = schema.getType();
    List<Object> enumeratedValues = schema.getEnumeratedValues();
    List<Double> enumeratedProbabilities = schema.getEnumeratedProbabilities();
    List<Pair<Object, Double>> probabilities = new ArrayList<>();
    switch(distributionType) {
        case SEQUENTIAL:
            // not random, just cycle through numbers from start to end, or cycle through enumerated values if provided
            distribution = new SequentialDistribution(schema.getStartInt(), schema.getEndInt(), schema.getEnumeratedValues());
            break;
        case UNIFORM:
            distribution = new UniformRealDistribution(schema.getStartDouble(), schema.getEndDouble());
            break;
        case DISCRETE_UNIFORM:
            if (enumeratedValues == null) {
                enumeratedValues = new ArrayList<>();
                for (int i = schema.getStartInt(); i < schema.getEndInt(); i++) {
                    Object val = convertType(i, type);
                    enumeratedValues.add(val);
                }
            }
            // give them all equal probability, the library will normalize probabilities to sum to 1.0
            for (int i = 0; i < enumeratedValues.size(); i++) {
                probabilities.add(new Pair<>(enumeratedValues.get(i), 0.1));
            }
            distribution = new EnumeratedTreeDistribution<>(probabilities);
            break;
        case NORMAL:
            distribution = new NormalDistribution(schema.getMean(), schema.getStandardDeviation());
            break;
        case ROUNDED_NORMAL:
            NormalDistribution normalDist = new NormalDistribution(schema.getMean(), schema.getStandardDeviation());
            distribution = new RealRoundingDistribution(normalDist);
            break;
        case ZIPF:
            int cardinality;
            if (enumeratedValues == null) {
                Integer startInt = schema.getStartInt();
                cardinality = schema.getEndInt() - startInt;
                ZipfDistribution zipf = new ZipfDistribution(cardinality, schema.getZipfExponent());
                for (int i = 0; i < cardinality; i++) {
                    probabilities.add(new Pair<>((Object) (i + startInt), zipf.probability(i)));
                }
            } else {
                cardinality = enumeratedValues.size();
                ZipfDistribution zipf = new ZipfDistribution(enumeratedValues.size(), schema.getZipfExponent());
                for (int i = 0; i < cardinality; i++) {
                    probabilities.add(new Pair<>(enumeratedValues.get(i), zipf.probability(i)));
                }
            }
            distribution = new EnumeratedTreeDistribution<>(probabilities);
            break;
        case ENUMERATED:
            for (int i = 0; i < enumeratedValues.size(); i++) {
                probabilities.add(new Pair<>(enumeratedValues.get(i), enumeratedProbabilities.get(i)));
            }
            distribution = new EnumeratedTreeDistribution<>(probabilities);
            break;
        default:
            throw new UnsupportedOperationException("Unknown distribution type: " + distributionType);
    }
    if (distribution instanceof AbstractIntegerDistribution) {
        ((AbstractIntegerDistribution) distribution).reseedRandomGenerator(seed);
    } else if (distribution instanceof AbstractRealDistribution) {
        ((AbstractRealDistribution) distribution).reseedRandomGenerator(seed);
    } else if (distribution instanceof EnumeratedDistribution) {
        ((EnumeratedDistribution) distribution).reseedRandomGenerator(seed);
    }
}

Example 35 with Sum

use of org.apache.commons.math3.stat.descriptive.summary.Sum in project GDSC-SMLM by aherbert.

the class PeakResult method computeI1.

/**
	 * Compute the function I1 using numerical integration. See Mortensen, et al (2010) Nature Methods 7, 377-383), SI
	 * equation 43.
	 * 
	 * <pre>
	 * I1 = 1 + sum [ ln(t) / (1 + t/rho) ] dt
	 *    = - sum [ t * ln(t) / (t + rho) ] dt
	 * </pre>
	 * 
	 * Where sum is the integral between 0 and 1. In the case of rho=0 the function returns 1;
	 * 
	 * @param rho
	 * @param integrationPoints
	 *            the number of integration points for the LegendreGaussIntegrator
	 * @return the I1 value
	 */
private static double computeI1(final double rho, int integrationPoints) {
    if (rho == 0)
        return 1;
    final double relativeAccuracy = 1e-4;
    final double absoluteAccuracy = 1e-8;
    final int minimalIterationCount = 3;
    final int maximalIterationCount = 32;
    // Use an integrator that does not use the boundary since log(0) is undefined.
    UnivariateIntegrator i = new IterativeLegendreGaussIntegrator(integrationPoints, relativeAccuracy, absoluteAccuracy, minimalIterationCount, maximalIterationCount);
    // Specify the function to integrate
    UnivariateFunction f = new UnivariateFunction() {

        public double value(double x) {
            return x * Math.log(x) / (x + rho);
        }
    };
    final double i1 = -i.integrate(2000, f, 0, 1);
    return i1;
}