Example usage for org.apache.commons.math3.stat.descriptive.rank Min Min

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.descriptive.rank Min Min.

Prototype

public Min() 

Source Link

Document

Create a Min instance

Usage

From source file:br.unicamp.ic.recod.gpsi.measures.gpsiDualScore.java

@Override
public double score(double[][][] input) {

    int least;// ww w. ja v  a 2s. c  o  m

    Min min = new Min();
    Max max = new Max();

    double dist[][] = new double[2][];
    double limits[][] = new double[2][2];

    for (int i = 0; i <= 1; i++) {
        dist[i] = MatrixUtils.createRealMatrix(input[i]).getColumn(0);
        limits[i] = new double[] { min.evaluate(dist[i]), max.evaluate(dist[i]) };
    }

    least = limits[0][0] <= limits[1][0] ? 0 : 1;
    double sep = limits[1 - least][0] - limits[least][1];

    if (sep >= 0)
        return sep / (max.evaluate(new double[] { limits[0][1], limits[1][1] })
                - min.evaluate(new double[] { limits[0][0], limits[1][0] }));

    double n = 0;

    for (int j = 0; j <= 1; j++)
        for (int i = 0; i < dist[j].length; i++) {
            if (dist[j][i] >= limits[1 - least][0] && dist[j][i] <= limits[least][1])
                n++;
        }

    return -n / (dist[0].length + dist[1].length);

}

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From source file:br.unicamp.ic.recod.gpsi.measures.gpsiHellingerDistanceScore.java

@Override
public double score(double[][][] input) {

    double dist[][] = new double[2][];

    int bins = 1000;

    dist[0] = MatrixUtils.createRealMatrix(input[0]).getColumn(0);
    dist[1] = MatrixUtils.createRealMatrix(input[1]).getColumn(0);

    gpsiHistogram hist = new gpsiHistogram();
    double globalMin = (new Min()).evaluate(ArrayUtils.addAll(dist[0], dist[1]));
    double globalMax = (new Max()).evaluate(ArrayUtils.addAll(dist[0], dist[1]));

    double[] h0 = hist.distribution(dist[0], bins, globalMin, globalMax);
    double[] h1 = hist.distribution(dist[1], bins, globalMin, globalMax);

    double BC = 0.0;

    for (int i = 0; i < bins; i++)
        BC += Math.sqrt(h0[i] * h1[i]);

    return Math.sqrt(1 - BC);

}

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From source file:com.cloudera.oryx.common.stats.RunningStatistics.java

public RunningStatistics() {
    this.mean = new Mean();
    this.min = new Min();
    this.max = new Max();
}

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From source file:com.itemanalysis.psychometrics.histogram.AbstractBinCalculation.java

/**
 * Creates the object and instantiates the min and max objects.
 */
public AbstractBinCalculation() {
    min = new Min();
    max = new Max();
}

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From source file:com.itemanalysis.psychometrics.kernel.LeastSquaresCrossValidation.java

private void computeBounds() throws Exception {
    StandardDeviation stdev = new StandardDeviation();
    this.sd = stdev.evaluate(x);
    Min min = new Min();
    double from = min.evaluate(x);
    Max max = new Max();
    double to = max.evaluate(x);

}

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From source file:com.itemanalysis.jmetrik.stats.irt.linking.DbThetaDistribution.java

public DistributionApproximation getDistribution(Connection conn, DataTableName tableName,
        VariableName thetaName, VariableName weightName, boolean hasWeight) throws SQLException {

    points = new ArrayList<Double>();
    Min min = new Min();
    Max max = new Max();

    Table sqlTable = new Table(tableName.getNameForDatabase());
    SelectQuery query = new SelectQuery();
    query.addColumn(sqlTable, thetaName.nameForDatabase());
    if (hasWeight) {
        query.addColumn(sqlTable, weightName.nameForDatabase());
        weights = new ArrayList<Double>();
    }/*  w  ww.ja  v a 2  s.  c om*/

    Statement stmt = conn.createStatement(ResultSet.TYPE_SCROLL_INSENSITIVE, ResultSet.CONCUR_READ_ONLY);
    ResultSet rs = stmt.executeQuery(query.toString());
    double value = 0.0;
    double w = 1.0;

    while (rs.next()) {
        value = rs.getDouble(thetaName.nameForDatabase());
        if (!rs.wasNull()) {
            if (hasWeight) {
                w = rs.getDouble(weightName.nameForDatabase());
                if (rs.wasNull()) {
                    w = 0.0;
                }
                points.add(value);
                weights.add(w);
                min.increment(value);
                max.increment(value);
            } else {
                points.add(value);
                min.increment(value);
                max.increment(value);
            }
        }
    }
    rs.close();
    stmt.close();

    ContinuousDistributionApproximation dist = new ContinuousDistributionApproximation(points.size(),
            min.getResult(), max.getResult());

    if (hasWeight) {
        for (int i = 0; i < points.size(); i++) {
            dist.setPointAt(i, points.get(i));
            dist.setDensityAt(i, weights.get(i));
        }
    } else {
        for (int i = 0; i < points.size(); i++) {
            dist.setPointAt(i, points.get(i));
        }
    }

    return dist;

}

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From source file:com.itemanalysis.psychometrics.measurement.DefaultItemScoring.java

public DefaultItemScoring(boolean isContinuous) {
    this.isContinuous = isContinuous;
    categoryMap = new TreeMap<Object, Category>(new ItemResponseComparator());
    maximumPossibleScore = new Max();
    minimumPossibleScore = new Min();
    specialDataCodes = new SpecialDataCodes();
    scoreLevels = new TreeSet<Double>();
}

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From source file:com.sciaps.utils.Util.java

public static Spectrum createAverage(Collection<? extends Spectrum> shots, double sampleRate) {

    Min minWL = new Min();
    Max maxWL = new Max();
    for (Spectrum shot : shots) {
        minWL.increment(shot.getValidRange().getMinimumDouble());
        maxWL.increment(shot.getValidRange().getMaximumDouble());
    }/*  w  w w .  j ava2 s  .  c o m*/

    double range = maxWL.getResult() - minWL.getResult();
    int numSamples = (int) Math.floor(range * sampleRate);
    double[][] data = new double[2][numSamples];
    Mean avgy = new Mean();
    for (int i = 0; i < numSamples; i++) {
        double x = minWL.getResult() + i * (1 / sampleRate);
        avgy.clear();
        for (Spectrum shot : shots) {
            if (shot.getValidRange().containsDouble(x)) {
                UnivariateFunction iv = shot.getIntensityFunction();
                double y = iv.value(x);
                avgy.increment(y);
            }
        }

        data[0][i] = x;
        data[1][i] = avgy.getResult();
    }

    RawDataSpectrum newSpectrum = new RawDataSpectrum(data);

    return newSpectrum;
}

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From source file:gedi.util.math.stat.counting.RollingStatistics.java

public double getCovariateRange() {
    return cov.evaluate(new Max()) - cov.evaluate(new Min());
}

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From source file:com.itemanalysis.psychometrics.measurement.DefaultItemScoring.java

public void clearCategory() {
    this.categoryMap.clear();
    this.scoreLevels.clear();
    this.categoryMap.clear();
    maximumPossibleScore = new Max();
    minimumPossibleScore = new Min();
}

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