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

List of usage examples for org.apache.commons.math3.stat.descriptive SummaryStatistics getVariance

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Introduction

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

Prototype

public double getVariance()

Source Link

Document

Returns the (sample) variance of the available values.

Usage

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

/**
 *
 * Estimation of effect size based on the distribution of score differences
 * (from paired samples)./*from  ww w.  j  ava 2  s.c  o  m*/
 *
 * @param <V> type of the keys of each map.
 * @param baselineMetricPerDimension map for the baseline method, one value
 * for each user (dimension)
 * @param testMetricPerDimension map for the test method, one value for each
 * user (dimension)
 * @return the effect size.
 */
public static <V> double getEffectSizePairedT(final Map<V, Double> baselineMetricPerDimension,
        final Map<V, Double> testMetricPerDimension) {
    Set<V> overlap = new HashSet<V>(baselineMetricPerDimension.keySet());
    overlap.retainAll(testMetricPerDimension.keySet());

    SummaryStatistics differences = new SummaryStatistics();
    for (V key : overlap) {
        double diff = testMetricPerDimension.get(key) - baselineMetricPerDimension.get(key);
        differences.addValue(diff);
    }

    return getEffectSizePairedT(differences.getMean(), Math.sqrt(differences.getVariance()));
}

From source file:co.turnus.common.util.CommonDataUtil.java

public static StatisticalData createFrom(SummaryStatistics summary) {
    StatisticalData data = CommonFactory.eINSTANCE.createStatisticalData();
    if (summary.getN() != 0) {
        data.setMax(summary.getMax());/*from w  ww  .  jav  a 2s . c  o  m*/
        data.setMin(summary.getMin());
        data.setSamples(summary.getN());
        data.setSum(summary.getSum());
        data.setVariance(summary.getVariance());
        data.setMean(summary.getMean());
    }
    return data;
}

From source file:cn.edu.suda.core.stats.StatsUtils.java

public static DataMatrix addTTest(DataMatrix dm, int m, int n) {
    int col = dm.getDcol();
    dm.addCols(2);//from  ww  w.  j  a  v a2s .co  m
    dm.setColname(col, "ABS_t_value");
    dm.setColname(col + 1, "P_value");
    for (int i = 0; i < dm.getDrow(); i++) {
        double[] array = dm.getRow(i);
        SummaryStatistics stats1 = new SummaryStatistics();
        SummaryStatistics stats2 = new SummaryStatistics();
        for (int j = 0; j < m; j++) {
            stats1.addValue(array[j]);
        }
        for (int j = m; j < m + n; j++) {
            stats2.addValue(array[j]);
        }
        double var1 = stats1.getVariance();
        double var2 = stats2.getVariance();
        if (var1 == 0 && var2 == 0) {
            dm.setValue(i, col, 0);
            dm.setValue(i, col + 1, 1);
        } else {
            double t = Math.abs(TestUtils.t(stats1, stats2));
            double p = TestUtils.tTest(stats1, stats2);
            t = Utils.formatNumber(t, 4);
            p = Utils.formatNumber(p, 4);
            dm.setValue(i, col, t);
            dm.setValue(i, col + 1, p);
        }
    }
    return dm;
}

From source file:com.trickl.stats.GammaDistributionOutlier.java

@Override
public IntPredicate apply(int[] edgeFlows) {
    // Calculate the distribution of flow across edges
    SummaryStatistics flowSummaryStatistics = new SummaryStatistics();
    for (int flow : edgeFlows) {
        flowSummaryStatistics.addValue(flow);
    }//www .  j a va2s  . c om
    double flowVar = flowSummaryStatistics.getVariance();
    double flowMean = flowSummaryStatistics.getMean();
    double gammaShape = (flowMean * flowMean) / flowVar;
    double gammaScale = flowVar / flowMean;
    GammaDistribution gammaDistribution = new GammaDistribution(gammaShape, gammaScale);
    return new ValueAboveHasProbabilityBelow(gammaDistribution, probability);
}

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

public void recordMoments(Object statName, Map<Object, Number> variables, SummaryStatistics stats) {
    variables.put(createVariable(statName + ".mean"), stats.getMean());
    variables.put(createVariable(statName + ".variance"), stats.getVariance());
}

From source file:io.fabric8.example.variance.http.VarianceProcessor.java

@Override
public void process(Exchange exchange) throws Exception {
    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);
    }/* w  w w  .  j  a  va  2s .c  o  m*/
    String variance = Double.toString(summaryStatistics.getVariance());
    exchange.getOut().setBody(variance);
}

From source file:io.fabric8.example.variance.http.VarianceProcessorTest.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();//from ww w.j a v  a2s  .  c om
    }
    String body = objectMapper.writeValueAsString(array);
    String expectedBody = "0.0";
    SummaryStatistics summaryStatistics = new SummaryStatistics();
    List<Double> list = new ObjectMapper().readValue(body, List.class);
    for (Double value : list) {
        summaryStatistics.addValue(value);
    }
    String variance = Double.toString(summaryStatistics.getVariance());

    resultEndpoint.expectedBodiesReceived(variance);

    template.sendBody(body);

    resultEndpoint.assertIsSatisfied();
}

From source file:io.fabric8.example.variance.msg.VarianceProcessor.java

@Override
public void process(Exchange exchange) throws Exception {
    System.err.println("VARIANCE 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 .  java 2 s .c o m*/
    String variance = Double.toString(summaryStatistics.getVariance());
    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(variance);
        producerTemplate.send("jms:queue:" + replyTo.getPhysicalName(), copy);
        System.err.println("REPLIED TO " + "jms:queue:" + replyTo.getPhysicalName());
    } else {
        System.err.println("REPLOY NOT SET for exchange: " + exchange.getIn().getHeaders());
    }
}

From source file:gr.cti.android.experimentation.controller.api.HistoryController.java

/**
 * Parse a time instant and create a TempReading object.
 *
 * @param millis     the millis of the timestamp.
 * @param function   the function to aggregate.
 * @param statistics the data values/*w  w w  .j av  a  2  s .com*/
 * @return the aggregated TempReading for this time instant.
 */
private TempReading parse(final long millis, final String function, SummaryStatistics statistics) {
    final Double value;
    switch (function) {
    case "avg":
        value = statistics.getMean();
        break;
    case "max":
        value = statistics.getMax();
        break;
    case "min":
        value = statistics.getMin();
        break;
    case "var":
        value = statistics.getVariance();
        break;
    case "sum":
        value = statistics.getSum();
        break;
    default:
        value = statistics.getMean();
    }
    return new TempReading(millis, value);
}

From source file:net.lizalab.util.RdRandRandomTest.java

/**
 * Tests RdRandRandom by verifying the average of the distribution of digits 0-9
 * over 100 million values. Also runs the test for Random and SecureRandom for
 * reference.//www .  j  ava 2 s .c o  m
 * Based on Mean Test outlined in <i>Beautiful Testing</i> published by O'Reilly.
 * @throws GeneralSecurityException 
 * @throws SeedException 
 */
@Test
public final void testRdRandRandomMean() throws GeneralSecurityException, SeedException {
    final String methodName = "testRdRandRandom : ";

    SummaryStatistics stats = new SummaryStatistics();
    // Initialize the array
    ndigits = new int[10];
    for (int i = 0; i < 10; i++) {
        ndigits[i] = 0;
        stats.addValue(i);
    }

    // Calculate the confidence intervals to assert.
    mean = stats.getMean();
    stdDev = stats.getStandardDeviation();
    var = stats.getVariance();
    LOGGER.info("{} Normal mean: {}", methodName, mean);
    LOGGER.info("{} Normal std: {}", methodName, stdDev);
    LOGGER.info("{} Normal var: {}", methodName, var);
    // 99.7% CI is within 3 std.
    double expectedDev3SD = 3 * stdDev / Math.sqrt(values);
    smLowerRng3SD = mean - expectedDev3SD;
    smUpperRng3SD = mean + expectedDev3SD;
    // 95% CI is within 2 std.
    double expectedDev2SD = 2 * stdDev / Math.sqrt(values);
    smLowerRng2SD = mean - expectedDev2SD;
    smUpperRng2SD = mean + expectedDev2SD;
    LOGGER.info("{} Generating {} values.", methodName, values);
    LOGGER.info("{} Sample mean expected in range {} - {} 99.7% of the times.", methodName, smLowerRng3SD,
            smUpperRng3SD);
    LOGGER.info("{} Sample mean expected in range {} - {} 95% of the times.", methodName, smLowerRng2SD,
            smUpperRng2SD);

    LOGGER.info("{} Running for Random..", methodName);
    Random random = new Random();
    meanTest(random, false);

    LOGGER.info("{} Running for RdRand..", methodName);
    random = new RdRandRandom();
    meanTest(random, true);

    LOGGER.info("{} Running for SecureRandom..", methodName);
    random = new SecureRandom();
    meanTest(random, false);

    LOGGER.info("{} Running Uncommons Maths AESCounterRNG using RdRandSeedGenerator..", methodName);
    random = new AESCounterRNG(new RdRandSeedGenerator());
    meanTest(random, false);
}