Example usage for org.apache.commons.math3.stat.descriptive StatisticalSummary getMean

List of usage examples for org.apache.commons.math3.stat.descriptive StatisticalSummary getMean

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Introduction

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

Prototype

double getMean();

Source Link

Document

Returns the <a href="http://www.xycoon.com/arithmetic_mean.htm"> arithmetic mean </a> of the available values

Usage

From source file:com.github.christofluyten.experiment.MeasureGendreau.java

/**
 * @param args//from  w w w. j a v  a2s .  c o m
 * @throws IOException
 */
public static void main(String[] args) throws IOException {
    final List<Gendreau06Scenario> scns = new ArrayList<>(
            Gendreau06Parser.parser().addDirectory("files/gendreau2006/requests").parse());

    Collections.sort(scns, new Comparator<Gendreau06Scenario>() {
        @Override
        public int compare(Gendreau06Scenario o1, Gendreau06Scenario o2) {
            final int compare = o1.getProblemClass().getId().compareTo(o2.getProblemClass().getId());
            if (compare == 0) {
                return o1.getProblemInstanceId().compareTo(o2.getProblemInstanceId());
            }
            return compare;
        }
    });

    final List<Map<Property, Object>> propsList = new ArrayList<>();
    for (final Gendreau06Scenario scen : scns) {
        final StatisticalSummary urgency = Metrics.measureUrgency(scen);
        final Multiset<Class<?>> counts = Metrics.getEventTypeCounts(scen);

        final long scenarioLength = scen.getProblemClass().duration * 60000;
        final double dyn = Metrics.measureDynamism(scen, scenarioLength);

        final ImmutableMap<Property, Object> prop = ImmutableMap.<Property, Object>builder()
                .put(Property.PROBLEM_CLASS, scen.getProblemClass().getId())
                .put(Property.INSTANCE_ID, scen.getProblemInstanceId()).put(Property.DYNAMISM, dyn)
                .put(Property.URGENCY_MEAN, urgency.getMean() / 60000d)
                .put(Property.URGENCY_SD, urgency.getStandardDeviation() / 60000d)
                .put(Property.NUM_ORDERS, counts.count(AddParcelEvent.class))
                .put(Property.NUM_VEHICLES, counts.count(AddVehicleEvent.class))
                .putAll(MAP.get(scen.getProblemInstanceId() + scen.getProblemClass().getId())).build();

        propsList.add(prop);
    }

    final File targetFile = new File(PROPS_FILE);
    write(propsList, targetFile, asList(Property.values()));
    System.out.println("Results written to " + targetFile.getAbsolutePath());
}

From source file:joinery.impl.Aggregation.java

@SuppressWarnings("unchecked")
public static <V> DataFrame<V> describe(final DataFrame<V> df) {
    final DataFrame<V> desc = new DataFrame<>();
    for (final Object col : df.columns()) {
        for (final Object row : df.index()) {
            final V value = df.get(row, col);
            if (value instanceof StatisticalSummary) {
                if (!desc.columns().contains(col)) {
                    desc.add(col);//w  w w . j  a  v a2 s .c o m
                    if (desc.isEmpty()) {
                        for (final Object r : df.index()) {
                            for (final Object stat : Arrays.asList("count", "mean", "std", "var", "max",
                                    "min")) {
                                final Object name = name(df, r, stat);
                                desc.append(name, Collections.<V>emptyList());
                            }
                        }
                    }
                }

                final StatisticalSummary summary = StatisticalSummary.class.cast(value);
                desc.set(name(df, row, "count"), col, (V) new Double(summary.getN()));
                desc.set(name(df, row, "mean"), col, (V) new Double(summary.getMean()));
                desc.set(name(df, row, "std"), col, (V) new Double(summary.getStandardDeviation()));
                desc.set(name(df, row, "var"), col, (V) new Double(summary.getVariance()));
                desc.set(name(df, row, "max"), col, (V) new Double(summary.getMax()));
                desc.set(name(df, row, "min"), col, (V) new Double(summary.getMin()));
            }
        }
    }
    return desc;
}

From source file:com.github.rinde.dynurg.Generator.java

static void createScenarios(RandomGenerator rng, GeneratorSettings generatorSettings,
        ScenarioGenerator generator, double dynLb, double dynUb, int levels) {
    final List<Scenario> scenarios = newArrayList();

    final Multimap<Double, Scenario> dynamismScenariosMap = LinkedHashMultimap.create();
    while (scenarios.size() < levels * TARGET_NUM_INSTANCES) {
        final Scenario scen = generator.generate(rng, "temp");
        Metrics.checkTimeWindowStrictness(scen);
        final StatisticalSummary urgency = Metrics.measureUrgency(scen);

        final long expectedUrgency = generatorSettings.urgency * 60000L;
        if (Math.abs(urgency.getMean() - expectedUrgency) < 0.01 && urgency.getStandardDeviation() < 0.01) {

            final int numParcels = Metrics.getEventTypeCounts(scen).count(PDPScenarioEvent.ADD_PARCEL);
            if (numParcels == NUM_ORDERS) {

                final double dynamism = Metrics.measureDynamism(scen, generatorSettings.officeHours);
                System.out.print(String.format("%1.3f ", dynamism));
                if ((dynamism % DYN_STEP_SIZE < DYN_BANDWIDTH
                        || dynamism % DYN_STEP_SIZE > DYN_STEP_SIZE - DYN_BANDWIDTH) && dynamism <= dynUb
                        && dynamism >= dynLb) {

                    final double targetDyn = Math.round(dynamism / DYN_STEP_SIZE) * DYN_STEP_SIZE;

                    final int numInstances = dynamismScenariosMap.get(targetDyn).size();

                    if (numInstances < TARGET_NUM_INSTANCES) {

                        final String instanceId = "#" + Integer.toString(numInstances);
                        dynamismScenariosMap.put(targetDyn, scen);

                        final String problemClassId = String.format("%d-%1.2f",
                                (long) (urgency.getMean() / 60000), targetDyn);
                        System.out.println();
                        System.out.println(" > ACCEPT " + problemClassId);
                        final String fileName = DATASET_DIR + problemClassId + instanceId;
                        try {
                            Files.createParentDirs(new File(fileName));
                            writePropertiesFile(scen, urgency, dynamism, problemClassId, instanceId,
                                    generatorSettings, fileName);
                            MetricsIO.writeLocationList(Metrics.getServicePoints(scen),
                                    new File(fileName + ".points"));
                            MetricsIO.writeTimes(scen.getTimeWindow().end, Metrics.getArrivalTimes(scen),
                                    new File(fileName + ".times"));

                            final ProblemClass pc = new SimpleProblemClass(problemClassId);
                            final Scenario finalScenario = Scenario.builder(pc).copyProperties(scen)
                                    .problemClass(pc).instanceId(instanceId).build();

                            ScenarioIO.write(finalScenario, new File(fileName + ".scen").toPath());
                        } catch (final IOException e) {
                            throw new IllegalStateException(e);
                        }/*  www. j  a  v  a 2  s . c  om*/
                        scenarios.add(scen);
                    }
                }
            }
        }
    }
}

From source file:com.github.rinde.dynurg.Generator.java

static void writePropertiesFile(Scenario scen, StatisticalSummary urgency, double dynamism,
        String problemClassId, String instanceId, GeneratorSettings settings, String fileName) {
    final DateTimeFormatter formatter = ISODateTimeFormat.dateHourMinuteSecondMillis();

    final ImmutableMap.Builder<String, Object> properties = ImmutableMap.<String, Object>builder()
            .put("problem_class", problemClassId).put("id", instanceId).put("dynamism", dynamism)
            .put("urgency_mean", urgency.getMean()).put("urgency_sd", urgency.getStandardDeviation())
            .put("creation_date", formatter.print(System.currentTimeMillis()))
            .put("creator", System.getProperty("user.name")).put("day_length", settings.dayLength)
            .put("office_opening_hours", settings.officeHours);

    properties.putAll(settings.properties);

    final ImmutableMultiset<Enum<?>> eventTypes = Metrics.getEventTypeCounts(scen);
    for (final Multiset.Entry<Enum<?>> en : eventTypes.entrySet()) {
        properties.put(en.getElement().name(), en.getCount());
    }// ww w  .j ava  2 s . c om

    try {
        Files.write(Joiner.on("\n").withKeyValueSeparator(" = ").join(properties.build()),
                new File(fileName + ".properties"), Charsets.UTF_8);
    } catch (final IOException e) {
        throw new IllegalStateException(e);
    }
}

From source file:com.github.rinde.datgen.pdptw.ScenarioCreator.java

@Nullable
@Override/* w  w w.  j a  va 2s .  c o  m*/
public GeneratedScenario call() throws Exception {
    final RandomGenerator rng = new MersenneTwister(getSeed());
    final Scenario scen = getGenerator().generate(rng, Long.toString(getId()));

    Metrics.checkTimeWindowStrictness(scen);

    // check that urgency matches expected urgency
    final StatisticalSummary urgency = Metrics.measureUrgency(scen);
    final long expectedUrgency = getSettings().getUrgency();
    if (!(Math.abs(urgency.getMean() - expectedUrgency) < URGENCY_THRESHOLD
            && urgency.getStandardDeviation() < URGENCY_THRESHOLD)) {
        System.out.println("Urgency too strict?");
        return null;
    }

    // check num orders
    final int numParcels = Metrics.getEventTypeCounts(scen).count(AddParcelEvent.class);
    if (numParcels != getSettings().getNumOrders()) {
        System.out.println("Parcels wrong number!");
        return null;
    }

    // check if dynamism fits in a bin
    final double dynamism = Metrics.measureDynamism(scen, getSettings().getOfficeHours());
    @Nullable
    final Double dynamismBin = getSettings().getDynamismRangeCenters().get(dynamism);
    if (dynamismBin == null) {
        System.out.println("Dynamism " + dynamism + " is too strict");
        return null;
    }

    return GeneratedScenario.create(scen, getSettings(), getId(), getSeed(), dynamismBin, dynamism);
}

From source file:embedded2.ESecure.TTest.java

/**
 * Computes a <a href="http://www.itl.nist.gov/div898/handbook/prc/section2/prc22.htm#formula">
 * t statistic </a> to use in comparing the mean of the dataset described by
 * <code>sampleStats</code> to <code>mu</code>.
 * <p>/*from   ww w . j a  v  a2  s.  co  m*/
 * This statistic can be used to perform a one sample t-test for the mean.
 * </p><p>
 * <strong>Preconditions</strong>: <ul>
 * <li><code>observed.getN() &ge; 2</code>.
 * </li></ul></p>
 *
 * @param mu comparison constant
 * @param sampleStats DescriptiveStatistics holding sample summary statitstics
 * @return t statistic
 * @throws NullArgumentException if <code>sampleStats</code> is <code>null</code>
 * @throws NumberIsTooSmallException if the number of samples is &lt; 2
 */
public static double t(final double mu, final StatisticalSummary sampleStats)
        throws NullArgumentException, NumberIsTooSmallException {

    checkSampleData(sampleStats);
    return t(sampleStats.getMean(), mu, sampleStats.getVariance(), sampleStats.getN());

}

From source file:embedded2.ESecure.TTest.java

/**
 * Returns the <i>observed significance level</i>, or
 * <i>p-value</i>, associated with a one-sample, two-tailed t-test
 * comparing the mean of the dataset described by <code>sampleStats</code>
 * with the constant <code>mu</code>.
 * <p>/*from ww w.  ja  v a 2  s . c o  m*/
 * The number returned is the smallest significance level
 * at which one can reject the null hypothesis that the mean equals
 * <code>mu</code> in favor of the two-sided alternative that the mean
 * is different from <code>mu</code>. For a one-sided test, divide the
 * returned value by 2.</p>
 * <p>
 * <strong>Usage Note:</strong><br>
 * The validity of the test depends on the assumptions of the parametric
 * t-test procedure, as discussed
 * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
 * here</a></p>
 * <p>
 * <strong>Preconditions</strong>: <ul>
 * <li>The sample must contain at least 2 observations.
 * </li></ul></p>
 *
 * @param mu constant value to compare sample mean against
 * @param sampleStats StatisticalSummary describing sample data
 * @return p-value
 * @throws NullArgumentException if <code>sampleStats</code> is <code>null</code>
 * @throws NumberIsTooSmallException if the number of samples is &lt; 2
 * @throws MaxCountExceededException if an error occurs computing the p-value
 */
public static double tTest(final double mu, final StatisticalSummary sampleStats)
        throws NullArgumentException, NumberIsTooSmallException, MaxCountExceededException {

    checkSampleData(sampleStats);
    return tTest(sampleStats.getMean(), mu, sampleStats.getVariance(), sampleStats.getN());

}

From source file:embedded2.ESecure.TTest.java

/**
 * Computes a 2-sample t statistic </a>, comparing the means of the datasets
 * described by two {@link StatisticalSummary} instances, without the
 * assumption of equal subpopulation variances.  Use
 * {@link #homoscedasticT(StatisticalSummary, StatisticalSummary)} to
 * compute a t-statistic under the equal variances assumption.
 * <p>//from   w  ww  .j a v  a  2 s  . c  om
 * This statistic can be used to perform a two-sample t-test to compare
 * sample means.</p>
 * <p>
 * The returned  t-statistic is</p>
 * <p>
 * &nbsp;&nbsp; <code>  t = (m1 - m2) / sqrt(var1/n1 + var2/n2)</code>
 * </p><p>
 * where <strong><code>n1</code></strong> is the size of the first sample;
 * <strong><code> n2</code></strong> is the size of the second sample;
 * <strong><code> m1</code></strong> is the mean of the first sample;
 * <strong><code> m2</code></strong> is the mean of the second sample
 * <strong><code> var1</code></strong> is the variance of the first sample;
 * <strong><code> var2</code></strong> is the variance of the second sample
 * </p><p>
 * <strong>Preconditions</strong>: <ul>
 * <li>The datasets described by the two Univariates must each contain
 * at least 2 observations.
 * </li></ul></p>
 *
 * @param sampleStats1 StatisticalSummary describing data from the first sample
 * @param sampleStats2 StatisticalSummary describing data from the second sample
 * @return t statistic
 * @throws NullArgumentException if the sample statistics are <code>null</code>
 * @throws NumberIsTooSmallException if the number of samples is &lt; 2
 */
public static double t(final StatisticalSummary sampleStats1, final StatisticalSummary sampleStats2)
        throws NullArgumentException, NumberIsTooSmallException {

    checkSampleData(sampleStats1);
    checkSampleData(sampleStats2);
    return t(sampleStats1.getMean(), sampleStats2.getMean(), sampleStats1.getVariance(),
            sampleStats2.getVariance(), sampleStats1.getN(), sampleStats2.getN());

}

From source file:embedded2.ESecure.TTest.java

/**
 * Computes a 2-sample t statistic, comparing the means of the datasets
 * described by two {@link StatisticalSummary} instances, under the
 * assumption of equal subpopulation variances.  To compute a t-statistic
 * without the equal variances assumption, use
 * {@link #t(StatisticalSummary, StatisticalSummary)}.
 * <p>/*from w w  w  . j  a  v a2  s .  c  om*/
 * This statistic can be used to perform a (homoscedastic) two-sample
 * t-test to compare sample means.</p>
 * <p>
 * The t-statistic returned is</p>
 * <p>
 * &nbsp;&nbsp;<code>  t = (m1 - m2) / (sqrt(1/n1 +1/n2) sqrt(var))</code>
 * </p><p>
 * where <strong><code>n1</code></strong> is the size of first sample;
 * <strong><code> n2</code></strong> is the size of second sample;
 * <strong><code> m1</code></strong> is the mean of first sample;
 * <strong><code> m2</code></strong> is the mean of second sample
 * and <strong><code>var</code></strong> is the pooled variance estimate:
 * </p><p>
 * <code>var = sqrt(((n1 - 1)var1 + (n2 - 1)var2) / ((n1-1) + (n2-1)))</code>
 * </p><p>
 * with <strong><code>var1</code></strong> the variance of the first sample and
 * <strong><code>var2</code></strong> the variance of the second sample.
 * </p><p>
 * <strong>Preconditions</strong>: <ul>
 * <li>The datasets described by the two Univariates must each contain
 * at least 2 observations.
 * </li></ul></p>
 *
 * @param sampleStats1 StatisticalSummary describing data from the first sample
 * @param sampleStats2 StatisticalSummary describing data from the second sample
 * @return t statistic
 * @throws NullArgumentException if the sample statistics are <code>null</code>
 * @throws NumberIsTooSmallException if the number of samples is &lt; 2
 */
public static double homoscedasticT(final StatisticalSummary sampleStats1,
        final StatisticalSummary sampleStats2) throws NullArgumentException, NumberIsTooSmallException {

    checkSampleData(sampleStats1);
    checkSampleData(sampleStats2);
    return homoscedasticT(sampleStats1.getMean(), sampleStats2.getMean(), sampleStats1.getVariance(),
            sampleStats2.getVariance(), sampleStats1.getN(), sampleStats2.getN());

}

From source file:ijfx.core.stats.DefaultImageStatisticsService.java

@Override
public Map<String, Double> summaryStatisticsToMap(StatisticalSummary summaryStats) {

    Map<String, Double> statistics = new HashMap<>();
    statistics.put(LBL_MEAN, summaryStats.getMean());
    statistics.put(LBL_MIN, summaryStats.getMin());
    statistics.put(LBL_MAX, summaryStats.getMax());
    statistics.put(LBL_SD, summaryStats.getStandardDeviation());
    statistics.put(LBL_VARIANCE, summaryStats.getVariance());
    statistics.put(LBL_PIXEL_COUNT, (double) summaryStats.getN());
    return statistics;
}