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

Introduction

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

Prototype

public double getStandardDeviation() 

Source Link

Document

Returns the standard deviation of the values that have been added.

Usage

From source file:net.recommenders.rival.evaluation.statistics.StandardError.java

/**
 * Implements equation (8.13) from "Elementary Statistics: A Problem Solving
 * Approach 4th Edition", Andrew L. Comrey, Howard B. Lee
 *
 * @return the standard error as the ratio of the standard deviation divided
 * by the sqrt(number of users) of the distribution of difference scores.
 */// ww  w .  j  a va  2  s. c  o m
public double getStandardError() {
    Set<V> overlap = new HashSet<V>(baselineMetricPerDimension.keySet());
    overlap.retainAll(testMetricPerDimension.keySet());

    // paired or matched samples --> analyse distribution of difference scores
    SummaryStatistics differences = new SummaryStatistics();
    for (V key : overlap) {
        double diff = baselineMetricPerDimension.get(key) - testMetricPerDimension.get(key);
        differences.addValue(diff);
    }

    double e = differences.getStandardDeviation() / Math.sqrt(differences.getN());
    return e;
}

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From source file:io.fabric8.example.stddev.http.StdDevProcessorTest.java

@Test
public void testProcess() throws Exception {
    RandomGenerator rg = new JDKRandomGenerator();
    double[] array = new double[10];
    ObjectMapper objectMapper = new ObjectMapper();
    for (int i = 0; i < array.length; i++) {
        array[i] = rg.nextDouble();/*www . j a va2 s .  c o  m*/
    }
    String body = objectMapper.writeValueAsString(array);
    SummaryStatistics summaryStatistics = new SummaryStatistics();
    List<Double> list = new ObjectMapper().readValue(body, List.class);
    for (Double value : list) {
        summaryStatistics.addValue(value);
    }
    String stdDev = Double.toString(summaryStatistics.getStandardDeviation());

    resultEndpoint.expectedBodiesReceived(stdDev);

    template.sendBody(body);

    resultEndpoint.assertIsSatisfied();
}

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From source file:io.fabric8.example.stddev.msg.StdDevProcessor.java

@Override
public void process(Exchange exchange) throws Exception {
    System.err.println("STD DEV GOT EXCHANGE " + exchange);
    String message = exchange.getIn().getBody(String.class);
    ObjectMapper objectMapper = new ObjectMapper();
    TypeFactory typeFactory = objectMapper.getTypeFactory();
    List<Double> values = objectMapper.readValue(message,
            typeFactory.constructCollectionType(List.class, Double.class));
    SummaryStatistics summaryStatistics = new SummaryStatistics();
    List<Double> list = new ObjectMapper().readValue(message, List.class);
    for (Double value : list) {
        summaryStatistics.addValue(value);
    }/*from  w w  w  . ja  v  a2  s  .c om*/
    String stdDev = Double.toString(summaryStatistics.getStandardDeviation());

    ActiveMQDestination replyTo = exchange.getIn().getHeader("JMSReplyTo", ActiveMQDestination.class);
    final String messageId = exchange.getIn().getHeader("JMSMessageID", String.class);

    if (replyTo != null) {
        Exchange copy = new DefaultExchange(exchange);
        copy.setPattern(ExchangePattern.InOnly);
        copy.getIn().setHeader(Variables.CORRELATION_HEADER, messageId);
        copy.getIn().setBody(stdDev);
        producerTemplate.send("jms:queue:" + replyTo.getPhysicalName(), copy);
    }
}

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From source file:cl.usach.managedbeans.CreditosManagedBean.java

public double buscarDesviacionStandarSrpintGrupo(SprintGrupos sprintG) {
    List<Equipo> eqs = buscarEquipos(sprintG);
    SummaryStatistics stats = new SummaryStatistics();
    int s;/* w ww  . ja va  2 s.  c  o m*/
    for (Equipo equipo : eqs) {
        s = buscarTiempoTareas(equipo);
        stats.addValue(s);
    }
    double dv = stats.getStandardDeviation();
    dv = (double) Math.round(dv * 10) / 10;
    return dv;
}

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From source file:gdsc.utils.HSB_Picker.java

private String summary(SummaryStatistics stats) {
    return String.format("%.3f +/- %.4f", stats.getMean(), stats.getStandardDeviation());
}

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From source file:net.recommenders.rival.evaluation.statistics.ConfidenceInterval.java

/**
 * Method that takes only one metric as parameter. It is useful when
 * comparing more than two metrics (so that a confidence interval is
 * computed for each of them), as suggested in [Sakai, 2014]
 *
 * @param alpha probability of incorrectly rejecting the null hypothesis (1
 * - confidence_level)/*from ww  w  .j av  a2s  .  com*/
 * @param metricValuesPerDimension one value of the metric for each
 * dimension
 * @return array with the confidence interval: [mean - margin of error, mean
 * + margin of error]
 */
public double[] getConfidenceInterval(final double alpha, final Map<?, Double> metricValuesPerDimension) {
    SummaryStatistics differences = new SummaryStatistics();
    for (Double d : metricValuesPerDimension.values()) {
        differences.addValue(d);
    }
    return getConfidenceInterval(alpha, (int) differences.getN() - 1, (int) differences.getN(),
            differences.getStandardDeviation(), differences.getMean());
}

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From source file:net.sourceforge.jabm.report.AbstractReportVariables.java

public void recordSummaryStatistics(Object statName, Map<Object, Number> variables, SummaryStatistics stats) {
    variables.put(createVariable(statName + ".mean"), stats.getMean());
    variables.put(createVariable(statName + ".min"), stats.getMin());
    variables.put(createVariable(statName + ".max"), stats.getMax());
    variables.put(createVariable(statName + ".n"), stats.getN());
    variables.put(createVariable(statName + ".stdev"), stats.getStandardDeviation());
}

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From source file:cl.usach.managedbeans.CreditosManagedBean.java

public double buscarDesviacionEstandarSprintAsignatura(SprintAsignatura sprintA) {
    List<SprintGrupos> spgs = buscarSprintGrupos(sprintA);
    SummaryStatistics stats = new SummaryStatistics();
    int a;/*from   w w  w . j a  v  a 2s . c  o m*/
    for (SprintGrupos sprintGrupos : spgs) {
        List<Equipo> eqs = buscarEquipos(sprintGrupos);
        for (Equipo equipo : eqs) {
            a = buscarTiempoTareas(equipo);
            stats.addValue(a);
        }
    }

    double dv = stats.getStandardDeviation();
    dv = (double) Math.round(dv * 10) / 10;
    return dv;
}

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From source file:fr.gael.drb.cortex.topic.sentinel3.jai.operator.QuicklookSlstrRIF.java

private BufferedImage toGrayScale(Raster in, PixelCorrection c, boolean invertColors, boolean ignoreBadStats) {
    int width = in.getWidth();
    int height = in.getHeight();
    // compute stats
    SummaryStatistics stats = new SummaryStatistics();
    for (int j = 0; j < height; j++) {
        for (int i = 0; i < width; i++) {
            int pixel = checkAndApplyCorrection(in.getSample(i, j, 0), c);
            if (pixel != c.nodata)
                stats.addValue(pixel);//from  w  w w  .j a va  2 s.  com
        }
    }
    double lowerBound = Math.max(stats.getMin(), stats.getMean() - 3 * stats.getStandardDeviation());
    double upperBound = Math.min(stats.getMax(), stats.getMean() + 3 * stats.getStandardDeviation());

    if (!ignoreBadStats)
        if (Double.isNaN(stats.getMean()) || Double.isNaN(stats.getStandardDeviation())
                || stats.getStandardDeviation() < 1)
            throw new IllegalStateException("Ugly band stats. Acquired during night?");

    return toGrayScale(in, c, invertColors, lowerBound, upperBound);
}

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From source file:net.recommenders.rival.evaluation.statistics.ConfidenceInterval.java

/**
 * Method that takes two metrics as parameters. It will compute the
 * differences between both (only considering the keys in the overlap)
 *
 * @param <V> type of keys for metrics
 * @param alpha probability of incorrectly rejecting the null hypothesis (1
 * - confidence_level)//from   w  ww  .j a v  a  2  s .  c om
 * @param baselineMetricPerDimension baseline metric, one value for each
 * dimension
 * @param testMetricPerDimension test metric, one value for each dimension
 * @param pairedSamples flag to indicate if the comparison should be made
 * for the distribution of difference scores (when true) or for the
 * distribution of differences between means
 * @return array with the confidence interval: [mean - margin of error, mean
 * + margin of error]
 */
public <V> double[] getConfidenceInterval(final double alpha, final Map<V, Double> baselineMetricPerDimension,
        final Map<V, Double> testMetricPerDimension, final boolean pairedSamples) {
    if (pairedSamples) {
        Set<V> overlap = new HashSet<V>(baselineMetricPerDimension.keySet());
        overlap.retainAll(testMetricPerDimension.keySet());

        // paired or matched samples --> analyse distribution of difference scores
        SummaryStatistics differences = new SummaryStatistics();
        for (V key : overlap) {
            double diff = Math.abs(testMetricPerDimension.get(key) - baselineMetricPerDimension.get(key));
            differences.addValue(diff);
        }
        return getConfidenceInterval(alpha / 2, (int) differences.getN() - 1, (int) differences.getN(),
                differences.getStandardDeviation(), differences.getMean());
    } else {
        // independent samples --> analyse distribution of differences between means
        SummaryStatistics statsBaseline = new SummaryStatistics();
        for (double d : baselineMetricPerDimension.values()) {
            statsBaseline.addValue(d);
        }
        SummaryStatistics statsTest = new SummaryStatistics();
        for (double d : testMetricPerDimension.values()) {
            statsTest.addValue(d);
        }
        long dfT = statsBaseline.getN() + statsTest.getN() - 2;
        double sDif = Math.sqrt((1.0 / statsBaseline.getN() + 1.0 / statsTest.getN())
                * (statsBaseline.getVariance() * (statsBaseline.getN() - 1)
                        + statsTest.getVariance() * (statsTest.getN() - 1)));
        double mDif = Math.abs(statsTest.getMean() - statsBaseline.getMean());
        return getConfidenceInterval(alpha, (int) dfT, (int) dfT, sDif, mDif);
    }
}

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