Example usage for org.apache.commons.math.stat.descriptive DescriptiveStatistics getMean

List of usage examples for org.apache.commons.math.stat.descriptive DescriptiveStatistics getMean

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Introduction

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

Prototype

public 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:gov.nih.nci.caintegrator.analysis.messaging.DataPointVector.java

/**
 * Compute the mean of the values in the list
 * @param values/*w ww  .  j  a  v a2s .co m*/
 * @return
 */
private Double computeMean(List<Double> values) {

    DescriptiveStatistics stats = DescriptiveStatistics.newInstance();

    //       Add the data from the array
    for (Double value : values) {
        stats.addValue(value);
    }
    double mean = stats.getMean();
    return new Double(mean);
}

From source file:com.userweave.module.methoden.iconunderstandability.service.ComputeIconTestStatisticsImpl.java

private void setMeanReactionTime(final OverallStatistics overallStatistics, int iconCount,
        IconTestReactionTimeStatistics statisticsEntity) {
    final DescriptiveStatistics statisticsForIconCount = overallStatistics.getIconCount2Statistics()
            .get(new Integer(iconCount));
    if (statisticsForIconCount != null) {
        statisticsEntity.setMeanReactionTime(statisticsForIconCount.getMean());
    } else {//  ww  w  . j av a  2 s  . c o  m
        statisticsEntity.setMeanReactionTime(-1);
    }
}

From source file:de.unidue.langtech.teaching.rp.uimatools.Stopwatch.java

@Override
public void collectionProcessComplete() throws AnalysisEngineProcessException {
    super.collectionProcessComplete();

    if (isDownstreamTimer()) {
        getLogger().info("Results from Timer '" + timerName + "' after processing all documents.");

        DescriptiveStatistics statTimes = new DescriptiveStatistics();
        for (Long timeValue : times) {
            statTimes.addValue((double) timeValue / 1000);
        }//from  w w w.  j a v  a2 s  .com
        double sum = statTimes.getSum();
        double mean = statTimes.getMean();
        double stddev = statTimes.getStandardDeviation();

        StringBuilder sb = new StringBuilder();
        sb.append("Estimate after processing " + times.size() + " documents.");
        sb.append("\n");

        Formatter formatter = new Formatter(sb, Locale.US);

        formatter.format("Aggregated time: %,.1fs\n", sum);
        formatter.format("Time / Document: %,.3fs (%,.3fs)\n", mean, stddev);

        formatter.close();

        getLogger().info(sb.toString());

        if (outputFile != null) {
            try {
                Properties props = new Properties();
                props.setProperty(KEY_SUM, "" + sum);
                props.setProperty(KEY_MEAN, "" + mean);
                props.setProperty(KEY_STDDEV, "" + stddev);
                OutputStream out = new FileOutputStream(outputFile);
                props.store(out, "timer " + timerName + " result file");
            } catch (FileNotFoundException e) {
                throw new AnalysisEngineProcessException(e);
            } catch (IOException e) {
                throw new AnalysisEngineProcessException(e);
            }
        }
    }
}

From source file:info.raack.appliancelabeler.machinelearning.appliancedetection.algorithms.HighConfidenceFSMPowerSpikeDetectionAlgorithm.java

private Map<Integer, Integer[]> computeTrainingInstanceSpikeLimits(
        List<double[]> trainingInstancesWithClassLabels) {
    Map<Integer, List<Double>> trainingSpikes = new HashMap<Integer, List<Double>>();

    // collect all spikes for each class
    for (double[] instance : trainingInstancesWithClassLabels) {
        double clazz = instance[instance.length - 1];
        if (clazz != missingValue) {
            double trainingSpike = instance[0];

            if (!trainingSpikes.containsKey((int) clazz)) {
                trainingSpikes.put((int) clazz, new ArrayList<Double>());
            }// ww w .  ja va2s  .  c  o  m
            trainingSpikes.get((int) clazz).add(trainingSpike);
        }
    }

    Map<Integer, Integer[]> trainingInstanceLimits = new HashMap<Integer, Integer[]>();

    // calculate interval one standard deviation away from mean of labeled power spikes for each class
    for (Integer clazz : trainingSpikes.keySet()) {
        DescriptiveStatistics stats = new DescriptiveStatistics();
        for (Double spikeValue : trainingSpikes.get(clazz)) {
            stats.addValue(spikeValue);
        }
        trainingInstanceLimits.put(clazz,
                new Integer[] { (int) (stats.getMean() - stats.getStandardDeviation()),
                        (int) (stats.getMean() + stats.getStandardDeviation()) });
    }

    return trainingInstanceLimits;
}

From source file:com.google.caliper.runner.ConsoleResultProcessor.java

@Override
public void processTrial(Trial trial) {
    ImmutableListMultimap<String, Measurement> measurementsIndex = new ImmutableListMultimap.Builder<String, Measurement>()
            .orderKeysBy(Ordering.natural())
            .putAll(Multimaps.index(trial.measurements(), new Function<Measurement, String>() {
                @Override//from  w  ww .  ja  v  a2  s  .  c om
                public String apply(Measurement input) {
                    return input.description();
                }
            })).build();
    for (Entry<String, Collection<Measurement>> entry : measurementsIndex.asMap().entrySet()) {
        Collection<Measurement> measurements = entry.getValue();
        ImmutableSet<String> units = FluentIterable.from(measurements)
                .transform(new Function<Measurement, String>() {
                    @Override
                    public String apply(Measurement input) {
                        return input.value().unit();
                    }
                }).toSet();
        double[] weightedValues = new double[measurements.size()];
        int i = 0;
        for (Measurement measurement : measurements) {
            weightedValues[i] = measurement.value().magnitude() / measurement.weight();
            i++;
        }
        Percentile percentile = new Percentile();
        percentile.setData(weightedValues);
        DescriptiveStatistics descriptiveStatistics = new DescriptiveStatistics(weightedValues);
        String unit = Iterables.getOnlyElement(units);
        stdout.printf("  %s%s: min=%.2f, 1st qu.=%.2f, median=%.2f, mean=%.2f, 3rd qu.=%.2f, max=%.2f%n",
                entry.getKey(), unit.isEmpty() ? "" : "(" + unit + ")", descriptiveStatistics.getMin(),
                percentile.evaluate(25), percentile.evaluate(50), descriptiveStatistics.getMean(),
                percentile.evaluate(75), descriptiveStatistics.getMax());
    }

    instrumentSpecs.add(trial.instrumentSpec());
    Scenario scenario = trial.scenario();
    vmSpecs.add(scenario.vmSpec());
    benchmarkSpecs.add(scenario.benchmarkSpec());
    numMeasurements += trial.measurements().size();
}

From source file:com.google.caliper.runner.ConsoleOutput.java

/**
 * Prints a summary of a successful trial result.
 *///from w w  w  .j av  a 2s. c o m
void processTrial(TrialResult result) {
    trialsCompleted++;
    stdout.printf("Trial Report (%d of %d):%n  Experiment %s%n", trialsCompleted, numberOfTrials,
            result.getExperiment());
    if (!result.getTrialMessages().isEmpty()) {
        stdout.println("  Messages:");
        for (String message : result.getTrialMessages()) {
            stdout.print("    ");
            stdout.println(message);
        }
    }
    Trial trial = result.getTrial();
    ImmutableListMultimap<String, Measurement> measurementsIndex = new ImmutableListMultimap.Builder<String, Measurement>()
            .orderKeysBy(Ordering.natural())
            .putAll(Multimaps.index(trial.measurements(), new Function<Measurement, String>() {
                @Override
                public String apply(Measurement input) {
                    return input.description();
                }
            })).build();
    stdout.println("  Results:");
    for (Entry<String, Collection<Measurement>> entry : measurementsIndex.asMap().entrySet()) {
        Collection<Measurement> measurements = entry.getValue();
        ImmutableSet<String> units = FluentIterable.from(measurements)
                .transform(new Function<Measurement, String>() {
                    @Override
                    public String apply(Measurement input) {
                        return input.value().unit();
                    }
                }).toSet();
        double[] weightedValues = new double[measurements.size()];
        int i = 0;
        for (Measurement measurement : measurements) {
            weightedValues[i] = measurement.value().magnitude() / measurement.weight();
            i++;
        }
        Percentile percentile = new Percentile();
        percentile.setData(weightedValues);
        DescriptiveStatistics descriptiveStatistics = new DescriptiveStatistics(weightedValues);
        String unit = Iterables.getOnlyElement(units);
        stdout.printf("    %s%s: min=%.2f, 1st qu.=%.2f, median=%.2f, mean=%.2f, 3rd qu.=%.2f, max=%.2f%n",
                entry.getKey(), unit.isEmpty() ? "" : "(" + unit + ")", descriptiveStatistics.getMin(),
                percentile.evaluate(25), percentile.evaluate(50), descriptiveStatistics.getMean(),
                percentile.evaluate(75), descriptiveStatistics.getMax());
    }

    instrumentSpecs.add(trial.instrumentSpec());
    Scenario scenario = trial.scenario();
    vmSpecs.add(scenario.vmSpec());
    benchmarkSpecs.add(scenario.benchmarkSpec());
    numMeasurements += trial.measurements().size();
}

From source file:dk.ilios.spanner.internal.ConsoleOutput.java

/**
 * Prints a summary of a successful trial result.
 *//*from ww w  .  j  a v  a2 s .c  om*/
void processTrial(Trial.Result result) {
    Trial baseline = result.getExperiment().getBaseline();

    trialsCompleted++;

    stdout.printf("Trial Report (%d of %d):%n  Experiment %s%n", trialsCompleted, numberOfTrials,
            result.getExperiment());
    if (!result.getTrialMessages().isEmpty()) {
        stdout.println("  Messages:");
        for (String message : result.getTrialMessages()) {
            stdout.print("    ");
            stdout.println(message);
        }
    }
    Trial trial = result.getTrial();

    // Group measurements by their description
    // TODO Why? All measurements for a single trial should have the same description
    ImmutableListMultimap<String, Measurement> measurementsIndex = new ImmutableListMultimap.Builder<String, Measurement>()
            .orderKeysBy(Ordering.natural())
            .putAll(Multimaps.index(trial.measurements(), new Function<Measurement, String>() {
                @Override
                public String apply(Measurement input) {
                    return input.description();
                }
            })).build();

    stdout.println("  Results:");
    for (Map.Entry<String, Collection<Measurement>> entry : measurementsIndex.asMap().entrySet()) {

        Collection<Measurement> measurements = entry.getValue();
        String unit = measurements.iterator().next().value().unit();

        double[] weightedValues = new double[measurements.size()];
        int i = 0;
        for (Measurement measurement : measurements) {
            weightedValues[i] = measurement.value().magnitude() / measurement.weight();
            i++;
        }
        Percentile percentile = new Percentile();
        percentile.setData(weightedValues);
        DescriptiveStatistics descriptiveStatistics = new DescriptiveStatistics(weightedValues);
        stdout.printf("    %s%s: min=%.2f, 1st qu.=%.2f, median=%.2f (%s), mean=%.2f, 3rd qu.=%.2f, max=%.2f%n",
                entry.getKey(), unit.isEmpty() ? "" : "(" + unit + ")", descriptiveStatistics.getMin(),
                percentile.evaluate(25), percentile.evaluate(50),
                calculateDiff(percentile.evaluate(50), baseline), descriptiveStatistics.getMean(),
                percentile.evaluate(75), descriptiveStatistics.getMax());
    }

    instrumentSpecs.add(trial.instrumentSpec());
    Scenario scenario = trial.scenario();
    benchmarkSpecs.add(scenario.benchmarkSpec());
    numMeasurements += trial.measurements().size();
}

From source file:com.gtwm.pb.model.manageData.WordCloud.java

/**
 * @param textLowerCase//from  w w w.  j  a v  a 2  s  .com
 *            Input text, must be lower case
 * @param minWeight
 *            Minimum tag weight, e.g. a font size
 * @param maxWeight
 *            Max. tag weight
 * @param maxTags
 *            Maximum number of tags to return, -1 for all tags
 * @param additionalStopWords
 *            Set of words to specifically exclude, in addition to the
 *            standard set [and, not, after, yes, no, ...]
 */
public WordCloud(String textLowerCase, int minWeight, int maxWeight, int maxTags,
        Set<String> additionalStopWords) {
    String[] wordArray = textLowerCase.split("\\W");
    Set<String> stopWords = new HashSet<String>(Arrays.asList(stopWordsArray));
    for (String additionalStopWord : additionalStopWords) {
        stopWords.add(additionalStopWord.toLowerCase().trim());
    }
    LancasterStemmer stemmer = new LancasterStemmer();
    String wordStem;
    Frequency frequencies = new Frequency();
    for (String wordString : wordArray) {
        if ((!stopWords.contains(wordString)) && (wordString.length() >= minWordLength)) {
            wordStem = stemmer.stripSuffixes(wordString);
            // Record the mapping of the stem to its origin so the most
            // common origin can be re-introduced when the cloud is
            // generated
            this.recordStemOrigin(wordString, wordStem);
            frequencies.addValue(wordStem);
        }
    }
    // Compute std. dev of frequencies so we can remove outliers
    DescriptiveStatistics stats = new DescriptiveStatistics();
    Iterator freqIt = frequencies.valuesIterator();
    long stemFreq;
    while (freqIt.hasNext()) {
        stemFreq = frequencies.getCount(freqIt.next());
        stats.addValue(stemFreq);
    }
    double mean = stats.getMean();
    double stdDev = stats.getStandardDeviation();
    long minFreq = Long.MAX_VALUE;
    long maxFreq = 0;
    // Remove outliers
    freqIt = frequencies.valuesIterator();
    int upperLimit = (int) (mean + (stdDev * 10));
    int lowerLimit = (int) (mean - stdDev);
    if (lowerLimit < 2) {
        lowerLimit = 2;
    }
    int numWords = 0;
    int numRawWords = wordArray.length;
    boolean removeLowOutliers = (numRawWords > (maxTags * 10));
    while (freqIt.hasNext()) {
        wordStem = (String) freqIt.next();
        stemFreq = frequencies.getCount(wordStem);
        // For a large input set, remove high and low outliers.
        // For a smaller set, just high freq. outliers
        if ((stemFreq > upperLimit) || ((stemFreq < lowerLimit) && removeLowOutliers)) {
            freqIt.remove();
        } else {
            numWords++;
            if (stemFreq > maxFreq) {
                maxFreq = stemFreq;
            } else if (stemFreq < minFreq) {
                minFreq = stemFreq;
            }
        }
    }
    // Cut down to exact required number of tags by removing smallest
    if (lowerLimit < minFreq) {
        lowerLimit = (int) minFreq;
    }
    if (numWords > maxTags) {
        while (numWords > maxTags) {
            freqIt = frequencies.valuesIterator();
            SMALLREMOVAL: while (freqIt.hasNext()) {
                stemFreq = frequencies.getCount(freqIt.next());
                if (stemFreq < lowerLimit) {
                    freqIt.remove();
                    numWords--;
                    if (numWords == maxTags) {
                        break SMALLREMOVAL;
                    }
                }
            }
            int step = (int) ((mean - lowerLimit) / 3);
            if (step < 1) {
                step = 1;
            }
            lowerLimit += step;
        }
        // The new min. freq. may have changed
        minFreq = Long.MAX_VALUE;
        freqIt = frequencies.valuesIterator();
        while (freqIt.hasNext()) {
            stemFreq = frequencies.getCount(freqIt.next());
            if (stemFreq < minFreq) {
                minFreq = stemFreq;
            }
        }
    }
    // Scale and create tag objects
    double scaleFactor;
    if (maxFreq == minFreq) {
        scaleFactor = (double) (maxWeight - minWeight) / 4; // TODO: a realistic
        // scale factor in this
        // case
    } else {
        scaleFactor = (double) (maxWeight - minWeight) / (maxFreq - minFreq);
    }
    freqIt = frequencies.valuesIterator();
    int weight;
    while (freqIt.hasNext()) {
        wordStem = (String) freqIt.next();
        stemFreq = frequencies.getCount(wordStem);
        // Might still be some left less than the min. threshold
        if (stemFreq <= minFreq) {
            weight = minWeight;
        } else {
            weight = (int) (Math.ceil((double) (stemFreq - minFreq) * scaleFactor) + minWeight);
        }
        SortedSet<WordInfo> origins = this.stemOriginMap.get(wordStem);
        String mostCommonOrigin = origins.last().getName();
        Set<String> synonyms = new TreeSet<String>();
        for (WordInfo origin : origins) {
            synonyms.add(origin.getName());
        }
        WordInfo word = new Word(mostCommonOrigin, weight, synonyms);
        this.words.add(word);
    }
}

From source file:guineu.modules.filter.Alignment.centering.mean.MeanCenteringTask.java

private void normalize(Dataset data) {
    DescriptiveStatistics stats = new DescriptiveStatistics();
    for (String nameExperiment : data.getAllColumnNames()) {
        for (PeakListRow row : data.getRows()) {
            Object value = row.getPeak(nameExperiment);
            if (value != null && value instanceof Double) {
                stats.addValue((Double) value);
            }//  w w w . j  av  a  2s  .  com
        }
        for (PeakListRow row : data.getRows()) {
            Object value = row.getPeak(nameExperiment);
            if (value != null && value instanceof Double) {
                row.setPeak(nameExperiment, Math.abs((Double) value - stats.getMean()));
            }
        }
        stats.clear();
    }
}

From source file:guineu.modules.dataanalysis.variationCoefficient.VariationCoefficientTask.java

private double getvariationCoefficient(Dataset dataset) {
    DescriptiveStatistics superStats = new DescriptiveStatistics();
    DescriptiveStatistics stats = new DescriptiveStatistics();
    for (PeakListRow row : dataset.getRows()) {
        stats.clear();/*from   w  w w .j a  va2 s . c o  m*/
        for (String experimentName : dataset.getAllColumnNames()) {
            Object value = row.getPeak(experimentName);
            if (value != null && value instanceof Double) {
                stats.addValue((Double) value);
            } else {

                try {
                    stats.addValue(Double.valueOf((String) value));
                } catch (Exception e) {
                }
            }
        }
        if (stats.getMean() > 0) {
            double value = stats.getStandardDeviation() / stats.getMean();
            superStats.addValue(value);
        }
    }
    return superStats.getMean();
}