Example usage for org.apache.commons.math3.stat.descriptive.moment StandardDeviation evaluate

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.descriptive.moment StandardDeviation evaluate.

Prototype

public double evaluate(final double[] values, final double mean) throws MathIllegalArgumentException 

Source Link

Document

Returns the Standard Deviation of the entries in the input array, using the precomputed mean value.

Usage

From source file:jp.ac.tohoku.ecei.sb.metabolomeqc.basiccorrector.helper.CoefficientOfVariation.java

public static double evaluate(double[] values) {
    double mean = DoubleStream.of(values).sum() / values.length;
    StandardDeviation standardDeviation = new StandardDeviation();
    double sd = standardDeviation.evaluate(values, mean);
    return sd / mean;
}

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From source file:jp.ac.tohoku.ecei.sb.metabolomeqc.basiccorrector.CompoundCVFilter.java

@Override
public IntensityMatrix doCorrection(IntensityMatrix original) {
    List<Sample> globalQC = original.getGlobalQCSamples();
    if (globalQC.size() == 0)
        throw new IllegalArgumentException("No global QC");
    Sample selectedGlobalQC = globalQC.get(0);

    log.info("selected global QC {}", selectedGlobalQC);

    Map<Integer, Map<Plate, Double>> compound2cv = new HashMap<>();
    Map<Integer, Compound> id2compound = new HashMap<>();

    for (Compound oneCompound : original.getRowKeys()) {
        compound2cv.put(oneCompound.getId(), new HashMap<>());
        id2compound.put(oneCompound.getId(), oneCompound);
    }/*from ww w  .  ja  v a 2 s .  co  m*/

    for (Compound oneCompound : original.getRowKeys()) {
        for (Map.Entry<Plate, List<Injection>> onePlate : original.getInjectionsByPlate().entrySet()) {
            double[] globalInjectionValues = onePlate.getValue().stream()
                    .filter(i -> i.getSample().equals(selectedGlobalQC) && !i.isIgnored())
                    .mapToDouble(i -> original.get(oneCompound, i)).toArray();
            double mean = DoubleStream.of(globalInjectionValues).sum() / globalInjectionValues.length;
            StandardDeviation standardDeviation = new StandardDeviation();
            double sd = standardDeviation.evaluate(globalInjectionValues, mean);
            double cv = sd / mean;

            compound2cv.get(oneCompound.getId()).put(onePlate.getKey(), cv);
            oneCompound.setAttribute(String.format("Plate-%d-CV", onePlate.getKey().getId()),
                    String.valueOf(cv));
        }
    }

    Map<Integer, Double> maxcv = new HashMap<>();
    List<Compound> newCompounds = new ArrayList<>();
    for (Map.Entry<Integer, Map<Plate, Double>> one : compound2cv.entrySet()) {
        maxcv.put(one.getKey(), one.getValue().values().stream().max(Double::compare).get());
        id2compound.get(one.getKey()).setAttribute("MaxCV", maxcv.get(one.getKey()));
        if (maxcv.get(one.getKey()) < CVThreshold) {
            newCompounds.add(id2compound.get(one.getKey()));
        }
    }

    IntensityMatrixImpl newMatrix = new IntensityMatrixImpl(newCompounds.size(), original.getSize()[1]);
    newMatrix.setRowKeys(newCompounds);
    newMatrix.setColumnKeys(original.getColumnKeys());

    for (Compound oneCompound : newCompounds) {
        for (Injection oneInjection : original.getColumnKeys()) {
            newMatrix.put(oneCompound, oneInjection, original.get(oneCompound, oneInjection));
        }
    }

    return newMatrix;
}

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From source file:RequestStat.java

public void computeStat(double duration, int maxUsers) {
    double[] times = getDurationAsArray();
    min = (long) StatUtils.min(times);
    max = (long) StatUtils.max(times);
    double sum = 0;
    for (double d : times)
        sum += d;/* www. j  a v a2 s. co m*/
    avg = sum / times.length;
    p50 = (long) StatUtils.percentile(times, 50.0);
    p95 = (long) StatUtils.percentile(times, 95.0);
    p99 = (long) StatUtils.percentile(times, 99.0);
    StandardDeviation stdDev = new StandardDeviation();
    stddev = (long) stdDev.evaluate(times, avg);
    this.duration = duration;
    this.maxUsers = maxUsers;
    rps = (count - errorCount) / duration;
    startDate = getDateFromInstant(start);
    successCount = count - errorCount;
}

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From source file:net.sf.javaml.clustering.AQBC.java

/**
 * Normalizes the data to mean 0 and standard deviation 1. This method
 * discards all instances that cannot be normalized, i.e. they have the same
 * value for all attributes.//from   w ww.  j  av a 2 s.co  m
 * 
 * @param data
 * @return
 */
private Vector<TaggedInstance> normalize(Dataset data) {
    Vector<TaggedInstance> out = new Vector<TaggedInstance>();

    for (int i = 0; i < data.size(); i++) {
        Double[] old = data.instance(i).values().toArray(new Double[0]);
        double[] conv = new double[old.length];
        for (int j = 0; j < old.length; j++) {
            conv[j] = old[j];
        }

        Mean m = new Mean();

        double MU = m.evaluate(conv);
        // System.out.println("MU = "+MU);
        StandardDeviation std = new StandardDeviation();
        double SIGM = std.evaluate(conv, MU);
        // System.out.println("SIGM = "+SIGM);
        if (!MathUtils.eq(SIGM, 0)) {
            double[] val = new double[old.length];
            for (int j = 0; j < old.length; j++) {
                val[j] = (float) ((old[j] - MU) / SIGM);

            }
            // System.out.println("VAL "+i+" = "+Arrays.toString(val));
            out.add(new TaggedInstance(new DenseInstance(val, data.instance(i).classValue()), i));
        }
    }
    // System.out.println("FIRST = "+out.get(0));

    return out;
}

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