Example usage for org.apache.commons.math3.stat.descriptive SummaryStatistics getN

Introduction

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

Prototype

public long getN() 

Source Link

Document

Returns the number of available values

Usage

From source file:org.lightjason.agentspeak.action.buildin.math.statistic.EStatisticValue.java

/**
 * returns a statistic value//from   w ww.  ja v a  2 s.  com
 *
 * @param p_statistic statistic object
 * @return statistic value
 */
public final double value(final SummaryStatistics p_statistic) {
    switch (this) {
    case GEOMETRICMEAN:
        return p_statistic.getGeometricMean();

    case MAX:
        return p_statistic.getMax();

    case MIN:
        return p_statistic.getMin();

    case COUNT:
        return p_statistic.getN();

    case POPULATIONVARIANCE:
        return p_statistic.getPopulationVariance();

    case QUADRATICMEAN:
        return p_statistic.getQuadraticMean();

    case SECONDMOMENT:
        return p_statistic.getSecondMoment();

    case STANDARDDEVIATION:
        return p_statistic.getStandardDeviation();

    case SUM:
        return p_statistic.getSum();

    case SUMLOG:
        return p_statistic.getSumOfLogs();

    case SUMSQUARE:
        return p_statistic.getSumsq();

    case VARIANCE:
        return p_statistic.getVariance();

    case MEAN:
        return p_statistic.getMean();

    default:
        throw new CIllegalStateException(
                org.lightjason.agentspeak.common.CCommon.languagestring(this, "unknown", this));
    }
}

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From source file:org.lightjason.agentspeak.action.builtin.math.statistic.EStatisticValue.java

/**
 * returns a statistic value//w ww  .  ja v  a2 s .c  o m
 *
 * @param p_statistic statistic object
 * @return statistic value
 */
public final double value(@Nonnull final SummaryStatistics p_statistic) {
    switch (this) {
    case GEOMETRICMEAN:
        return p_statistic.getGeometricMean();

    case MAX:
        return p_statistic.getMax();

    case MIN:
        return p_statistic.getMin();

    case COUNT:
        return p_statistic.getN();

    case POPULATIONVARIANCE:
        return p_statistic.getPopulationVariance();

    case QUADRATICMEAN:
        return p_statistic.getQuadraticMean();

    case SECONDMOMENT:
        return p_statistic.getSecondMoment();

    case STANDARDDEVIATION:
        return p_statistic.getStandardDeviation();

    case SUM:
        return p_statistic.getSum();

    case SUMLOG:
        return p_statistic.getSumOfLogs();

    case SUMSQUARE:
        return p_statistic.getSumsq();

    case VARIANCE:
        return p_statistic.getVariance();

    case MEAN:
        return p_statistic.getMean();

    default:
        throw new CIllegalStateException(
                org.lightjason.agentspeak.common.CCommon.languagestring(this, "unknown", this));
    }
}

---

From source file:org.lpe.common.util.LpeNumericUtils.java

/**
 * Calculates confidence interval width for the given SummaryStatistics and
 * the significance level.//  ww  w  .j  a v a2 s .  co  m
 * 
 * @param summaryStatistics
 *            the data
 * @param significance
 *            desired significance level
 * @return the width of the confidence interval around the mean with the
 *         given significance level
 */
public static double getConfidenceIntervalWidth(SummaryStatistics summaryStatistics, double significance) {
    return getConfidenceIntervalWidth(summaryStatistics.getN(), summaryStatistics.getStandardDeviation(),
            significance);
}

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From source file:org.orbisgis.corejdbc.ReadTable.java

/**
 * Compute numeric stats of the specified table column using a limited input rows. Stats are not done in the sql side.
 * @param connection Available connection
 * @param tableName Table name/*from   w  w w  .j a  v a2 s. c  om*/
 * @param columnName Column name
 * @param rowNum Row id
 * @param pm Progress monitor
 * @return An array of attributes {@link STATS}
 * @throws SQLException
 */
public static String[] computeStatsLocal(Connection connection, String tableName, String columnName,
        SortedSet<Integer> rowNum, ProgressMonitor pm) throws SQLException {
    String[] res = new String[STATS.values().length];
    SummaryStatistics stats = new SummaryStatistics();
    try (Statement st = connection.createStatement()) {
        // Cancel select
        PropertyChangeListener listener = EventHandler.create(PropertyChangeListener.class, st, "cancel");
        pm.addPropertyChangeListener(ProgressMonitor.PROP_CANCEL, listener);
        try (ResultSet rs = st.executeQuery(String.format("SELECT %s FROM %s", columnName, tableName))) {
            ProgressMonitor fetchProgress = pm.startTask(rowNum.size());
            while (rs.next() && !pm.isCancelled()) {
                if (rowNum.contains(rs.getRow())) {
                    stats.addValue(rs.getDouble(columnName));
                    fetchProgress.endTask();
                }
            }
        } finally {
            pm.removePropertyChangeListener(listener);
        }
    }
    res[STATS.SUM.ordinal()] = Double.toString(stats.getSum());
    res[STATS.AVG.ordinal()] = Double.toString(stats.getMean());
    res[STATS.COUNT.ordinal()] = Long.toString(stats.getN());
    res[STATS.MIN.ordinal()] = Double.toString(stats.getMin());
    res[STATS.MAX.ordinal()] = Double.toString(stats.getMax());
    res[STATS.STDDEV_SAMP.ordinal()] = Double.toString(stats.getStandardDeviation());
    return res;
}

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From source file:org.wso2.carbon.ml.core.impl.SummaryStatsGenerator.java

/**
 * Calculate the frequencies of each interval of a column.
 *
 * @param column Column of which the frequencies are to be calculated.
 * @param intervals Number of intervals to be split.
 *///  w ww .j a  va  2  s  .c  o  m
protected List<SortedMap<?, Integer>> calculateIntervalFreqs(int column, int intervals) {
    SortedMap<Integer, Integer> frequencies = new TreeMap<Integer, Integer>();
    double[] data = new double[this.columnData.get(column).size()];
    // Create an array from the column data.
    for (int row = 0; row < columnData.get(column).size(); row++) {
        if (this.columnData.get(column).get(row) != null
                && !MLConstants.MISSING_VALUES.contains(this.columnData.get(column).get(row))) {
            data[row] = Double.parseDouble(this.columnData.get(column).get(row));
        }
    }
    // Create equal partitions.
    this.histogram[column] = new EmpiricalDistribution(intervals);
    this.histogram[column].load(data);

    // Get the frequency of each partition.
    int bin = 0;
    for (SummaryStatistics stats : this.histogram[column].getBinStats()) {
        frequencies.put(bin++, (int) stats.getN());
    }
    this.graphFrequencies.set(column, frequencies);

    return graphFrequencies;
}

---

From source file:org.wso2.carbon.ml.dataset.internal.DatasetSummary.java

/**
 * Calculate the frequencies of each interval of a column.
 *
 * @param column        Column of which the frequencies are to be calculated.
 * @param intervals     Number of intervals to be split.
 *//*from   ww w .j a  v a 2  s. c om*/
private void claculateIntervalFreqs(int column, int intervals) {
    SortedMap<Integer, Integer> frequencies = new TreeMap<Integer, Integer>();
    double[] data = new double[this.columnData.get(column).size()];
    // Create an array from the column data.
    for (int row = 0; row < columnData.get(column).size(); row++) {
        if (!this.columnData.get(column).get(row).isEmpty()) {
            data[row] = Double.parseDouble(this.columnData.get(column).get(row));
        }
    }
    // Create equal partitions.
    this.histogram[column] = new EmpiricalDistribution(intervals);
    this.histogram[column].load(data);

    // Get the frequency of each partition.
    int bin = 0;
    for (SummaryStatistics stats : this.histogram[column].getBinStats()) {
        frequencies.put(bin++, (int) stats.getN());
    }
    this.graphFrequencies.set(column, frequencies);
}

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From source file:Server.ReadTXTFile.java

private static double calcMeanCI(SummaryStatistics stats, double level) {
    try {/*from  w  w w . ja v  a  2 s  .  c  o m*/
        // Create T Distribution with N-1 degrees of freedom
        TDistribution tDist = new TDistribution(stats.getN() - 1);
        // Calculate critical value
        double critVal = tDist.inverseCumulativeProbability(1.0 - (1 - level) / 2);
        // Calculate confidence interval
        return critVal * stats.getStandardDeviation() / Math.sqrt(stats.getN());
    } catch (MathIllegalArgumentException e) {
        return Double.NaN;
    }
}

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From source file:tech.tablesaw.columns.numbers.NumberMapFunctions.java

default DoubleColumn bin(int binCount) {
    double[] histogram = new double[binCount];
    EmpiricalDistribution distribution = new EmpiricalDistribution(binCount);
    distribution.load(asDoubleArray());/*from ww w  . jav a2 s  .c o m*/
    int k = 0;
    for (SummaryStatistics stats : distribution.getBinStats()) {
        histogram[k++] = stats.getN();
    }
    return DoubleColumn.create(name() + "[binned]", histogram);
}

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From source file:tech.tablesaw.columns.numbers.Stats.java

private static Stats getStats(NumericColumn<?> values, SummaryStatistics summaryStatistics) {
    Stats stats = new Stats("Column: " + values.name());
    stats.min = summaryStatistics.getMin();
    stats.max = summaryStatistics.getMax();
    stats.n = summaryStatistics.getN();
    stats.sum = summaryStatistics.getSum();
    stats.variance = summaryStatistics.getVariance();
    stats.populationVariance = summaryStatistics.getPopulationVariance();
    stats.quadraticMean = summaryStatistics.getQuadraticMean();
    stats.geometricMean = summaryStatistics.getGeometricMean();
    stats.mean = summaryStatistics.getMean();
    stats.standardDeviation = summaryStatistics.getStandardDeviation();
    stats.sumOfLogs = summaryStatistics.getSumOfLogs();
    stats.sumOfSquares = summaryStatistics.getSumsq();
    stats.secondMoment = summaryStatistics.getSecondMoment();
    return stats;
}

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From source file:tools.descartes.bungee.evaluation.ScalabilityReproducibilityEvaluation.java

private void printStatistics(SummaryStatistics summaryStats, double lowerConfidence, double upperConfidence,
        double wantedLower, double wantedUpper, boolean finished) {
    System.out.println("total runs: " + summaryStats.getN());
    System.out.println("mean: " + summaryStats.getMean());
    System.out.println("stdDev: " + summaryStats.getStandardDeviation());
    System.out.println("confidence interval [" + lowerConfidence + "," + upperConfidence + "]");
    System.out.println("wanted interval [" + wantedLower + "," + wantedUpper + "]");
    System.out.println("success: " + finished);
}

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