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:org.mskcc.cbio.cgds.scripts.ImportProteinArrayData.java

private double[] convertToZscores(String[] strs) {
    double[] data = new double[strs.length - 1];
    for (int i = 1; i < strs.length; i++) {
        data[i - 1] = Double.parseDouble(strs[i]);
    }//from   w  ww  . j a va2s. com

    DescriptiveStatistics ds = new DescriptiveStatistics(data);
    double mean = ds.getMean();
    double std = ds.getStandardDeviation();

    for (int i = 0; i < data.length; i++) {
        data[i] = (data[i] - mean) / std;
    }
    return data;
}

From source file:org.openehealth.ipf.commons.test.performance.processingtime.ProcessingTimeDescriptiveStatistics.java

/**
 * Returns statistical summary of all the data.
 * //from  w w w.  j a  v a 2  s .com
 * @return a <code>StatisticalSummary</code> object
 */
@Override
public StatisticalSummary getStatisticalSummaryByName(String name) {
    DescriptiveStatistics stats = statisticsByMeasurementName.get(name);

    return new StatisticalSummaryValues(stats.getMean(), stats.getVariance(), stats.getN(), stats.getMax(),
            stats.getMin(), stats.getSum());
}

From source file:org.processmining.analysis.performance.dottedchart.ui.MetricsPanel.java

/**
 * Displays the performance metrics of each pattern on the east side of the
 * plug-in window./*from  w w w. j  av a 2s.co  m*/
 * 
 * @param sortedArray
 *            int[]
 */

public void displayPerformanceMetrics() {
    String type = dcPanel.getTimeOption();
    ArrayList<DescriptiveStatistics> aList = dcModel.getTimeStatistics();
    ArrayList<String> aTitles = dcModel.getDescriptiveStatisticsTitles();

    ArrayList<String> sortedTitleList = dcModel.getSortedKeySetList();

    this.removeAll();
    this.setLayout(new BoxLayout(this, BoxLayout.Y_AXIS));
    // add time option menu
    this.add(Box.createRigidArea(new Dimension(5, 10)));
    JPanel menuPanel = new JPanel(new BorderLayout());
    menuPanel.setPreferredSize(new Dimension(160, 45));
    menuPanel.setMaximumSize(new Dimension(180, 45));
    timeSortLabel.setAlignmentX(LEFT_ALIGNMENT);
    menuPanel.add(timeSortLabel, BorderLayout.NORTH);
    timeBox.setMaximumSize(new Dimension(160, 20));
    timeBox.setAlignmentX(LEFT_ALIGNMENT);
    menuPanel.add(Box.createRigidArea(new Dimension(5, 0)));
    menuPanel.add(timeBox, BorderLayout.CENTER);
    this.add(menuPanel);
    this.add(Box.createRigidArea(new Dimension(5, 10)));

    // for each frequency get the set of patterns that have that frequency
    // (run from high frequency to low)
    int size = 0;
    for (int i = 0; i < aList.size(); i++) {
        try {
            String key;
            DescriptiveStatistics currentDS = null;
            if (i != 0)
                key = sortedTitleList.get(i - 1);
            else {
                key = aTitles.get(0);
                currentDS = aList.get(i);
            }

            if (i > 0 && dcModel.getTypeHashMap().equals(DottedChartPanel.ST_INST)
                    && !dcModel.getInstanceTypeToKeep().contains(key))
                continue;
            size++;

            if (i > 0) {
                for (int j = 1; j < aTitles.size(); j++) {
                    if (aTitles.get(j).equals(key))
                        currentDS = aList.get(j);
                }
            }
            AbstractTableModel otm;
            // create labels that contains information about the pattern
            if (i == 0)
                otm = new OverallMetricTableModel();
            else
                otm = new OneMetricTableModel();
            DefaultTableCellRenderer dtcr = new DefaultTableCellRenderer();
            dtcr.setBackground(new Color(235, 235, 235));
            JTable table = new JTable(otm);
            table.setPreferredSize(new Dimension(200, 55));
            table.setMaximumSize(new Dimension(200, 55));
            table.getColumnModel().getColumn(0).setPreferredWidth(70);
            table.getColumnModel().getColumn(0).setMaxWidth(100);
            table.getTableHeader().setFont(new Font("SansSerif", Font.PLAIN, 12));
            table.getColumnModel().getColumn(0).setCellRenderer(dtcr);
            table.setBorder(BorderFactory.createEtchedBorder());

            // place throughput times in table
            if (type.equals(DottedChartPanel.TIME_ACTUAL)) {
                if (i == 0) {
                    table.setValueAt(DateFormat.getInstance().format(dcModel.getLogBoundaryLeft()), 0, 1);
                    table.setValueAt(DateFormat.getInstance().format(dcModel.getLogBoundaryRight()), 1, 1);
                } else {
                    table.setValueAt(DateFormat.getInstance().format(dcModel.getStartDateofLogUniList(key)), 0,
                            1);
                    table.setValueAt(DateFormat.getInstance().format(dcModel.getEndDateofLogUniList(key)), 1,
                            1);
                }
                table.setValueAt(formatString(currentDS.getMean() / timeDivider, 5), 2, 1);
                table.setValueAt(formatString(currentDS.getMin() / timeDivider, 5), 3, 1);
                table.setValueAt(formatString(currentDS.getMax() / timeDivider, 5), 4, 1);
            } else if (type.equals(DottedChartPanel.TIME_RELATIVE_TIME)) {
                if (i == 0) {
                    table.setValueAt(formatDate(dcModel.getLogBoundaryLeft()), 0, 1);
                    table.setValueAt(formatDate(dcModel.getLogBoundaryRight()), 1, 1);
                } else {
                    table.setValueAt(formatDate(dcModel.getStartDateofLogUniList(key)), 0, 1);
                    table.setValueAt(formatDate(dcModel.getEndDateofLogUniList(key)), 1, 1);
                }
                table.setValueAt(formatString(currentDS.getMean() / timeDivider, 5), 2, 1);
                table.setValueAt(formatString(currentDS.getMin() / timeDivider, 5), 3, 1);
                table.setValueAt(formatString(currentDS.getMax() / timeDivider, 5), 4, 1);
            } else if (type.equals(DottedChartPanel.TIME_RELATIVE_RATIO)) {
                if (i == 0) {
                    table.setValueAt(formatRatio(dcModel.getLogBoundaryLeft()), 0, 1);
                    table.setValueAt(formatRatio(dcModel.getLogBoundaryRight()), 1, 1);
                } else {
                    table.setValueAt(formatRatio(dcModel.getStartDateofLogUniList(key)), 0, 1);
                    table.setValueAt(formatRatio(dcModel.getEndDateofLogUniList(key)), 1, 1);
                }
                table.setValueAt(formatString(currentDS.getMean() / 100, 5), 2, 1);
                table.setValueAt(formatString(currentDS.getMin() / 100, 5), 3, 1);
                table.setValueAt(formatString(currentDS.getMax() / 100, 5), 4, 1);
            } else if (type.equals(DottedChartPanel.TIME_LOGICAL)
                    || type.equals(DottedChartPanel.TIME_LOGICAL_RELATIVE)) {
                if (i == 0) {
                    table.setValueAt(formatString(dcModel.getLogBoundaryLeft().getTime(), 5), 0, 1);
                    table.setValueAt(formatString(dcModel.getLogBoundaryRight().getTime(), 5), 1, 1);
                } else {
                    table.setValueAt(formatString((dcModel.getStartDateofLogUniList(key)).getTime(), 5), 0, 1);
                    table.setValueAt(formatString((dcModel.getEndDateofLogUniList(key)).getTime(), 5), 1, 1);
                }
                table.setValueAt(formatString(currentDS.getMean(), 5), 2, 1);
                table.setValueAt(formatString(currentDS.getMin(), 5), 3, 1);
                table.setValueAt(formatString(currentDS.getMax(), 5), 4, 1);
            }

            JPanel tempPanel = new JPanel(new BorderLayout());
            table.setAlignmentX(CENTER_ALIGNMENT);
            tempPanel.setPreferredSize(new Dimension(160, 98));
            tempPanel.setMaximumSize(new Dimension(180, 98));
            tempPanel.add(table.getTableHeader(), BorderLayout.NORTH);
            tempPanel.add(table, BorderLayout.CENTER);
            JPanel tempPanel2 = new JPanel(new BorderLayout());
            JLabel patternLabel = new JLabel("Component " + key + ":");
            patternLabel.setAlignmentX(LEFT_ALIGNMENT);

            JLabel frequencyLabel = null;
            if (i == 0)
                frequencyLabel = new JLabel("# of components: " + currentDS.getN());
            else
                frequencyLabel = new JLabel("# of dots: " + dcModel.getNumberOfLogUnits(key));

            frequencyLabel.setAlignmentX(LEFT_ALIGNMENT);
            frequencyLabel.setFont(new Font("SansSerif", Font.PLAIN, 12));
            tempPanel2.add(patternLabel, BorderLayout.NORTH);
            tempPanel2.add(frequencyLabel, BorderLayout.CENTER);
            tempPanel2.add(tempPanel, BorderLayout.SOUTH);
            this.add(tempPanel2);
            this.add(Box.createRigidArea(new Dimension(5, 10)));
        } catch (NullPointerException ex) {
            // can occur when patternMap does not contain a pattern with
            // this frequency
            size--;
        }
    }
    // make sure the pattern performance information is displayed properly
    this.setPreferredSize(new Dimension(200, 140 * (size + 1)));
    this.revalidate();
    this.repaint();

}

From source file:org.processmining.analysis.performance.fsmanalysis.FSMPerformanceAnalysisUI.java

protected double getData(DescriptiveStatistics ds) {
    String sort = (String) measureSort.getValue();
    if (sort.equals("Minimum")) {
        return ds.getMin();
    } else if (sort.equals("Average")) {
        return (ds.getMean());
    } else if (sort.equals("Median")) {
        return (ds.getPercentile(50));
    } else if (sort.equals("Maximum")) {
        return (ds.getMax());
    } else if (sort.equals("Sum")) {
        return (ds.getSum());
    } else if (sort.equals("StandDev")) {
        return (ds.getStandardDeviation());
    } else if (sort.equals("Variance")) {
        return (ds.getStandardDeviation() * ds.getStandardDeviation());
    } else if (sort.equals("Frequency")) {
        return (ds.getN());
    }//from   w ww .  j  a  va  2s .c  o  m
    return 0.0;
}

From source file:org.prom5.analysis.performance.dottedchart.ui.MetricsPanel.java

/**
 * Displays the performance metrics of each pattern on the east side of the
 * plug-in window./*from w  ww  . j  a v a  2s  .c  om*/
 * @param sortedArray int[]
 */

public void displayPerformanceMetrics() {
    String type = dcPanel.getTimeOption();
    ArrayList<DescriptiveStatistics> aList = dcModel.getTimeStatistics();
    ArrayList<String> aTitles = dcModel.getDescriptiveStatisticsTitles();

    ArrayList<String> sortedTitleList = dcModel.getSortedKeySetList();

    this.removeAll();
    this.setLayout(new BoxLayout(this, BoxLayout.Y_AXIS));
    //add time option menu
    this.add(Box.createRigidArea(new Dimension(5, 10)));
    JPanel menuPanel = new JPanel(new BorderLayout());
    menuPanel.setPreferredSize(new Dimension(160, 45));
    menuPanel.setMaximumSize(new Dimension(180, 45));
    timeSortLabel.setAlignmentX(LEFT_ALIGNMENT);
    menuPanel.add(timeSortLabel, BorderLayout.NORTH);
    timeBox.setMaximumSize(new Dimension(160, 20));
    timeBox.setAlignmentX(LEFT_ALIGNMENT);
    menuPanel.add(Box.createRigidArea(new Dimension(5, 0)));
    menuPanel.add(timeBox, BorderLayout.CENTER);
    this.add(menuPanel);
    this.add(Box.createRigidArea(new Dimension(5, 10)));

    //for each frequency get the set of patterns that have that frequency
    //(run from high frequency to low)
    int size = 0;
    for (int i = 0; i < aList.size(); i++) {
        try {
            String key;
            DescriptiveStatistics currentDS = null;
            if (i != 0)
                key = sortedTitleList.get(i - 1);
            else {
                key = aTitles.get(0);
                currentDS = aList.get(i);
            }

            if (i > 0 && dcModel.getTypeHashMap().equals(DottedChartPanel.ST_INST)
                    && !dcModel.getInstanceTypeToKeep().contains(key))
                continue;
            size++;

            if (i > 0) {
                for (int j = 1; j < aTitles.size(); j++) {
                    if (aTitles.get(j).equals(key))
                        currentDS = aList.get(j);
                }
            }
            AbstractTableModel otm;
            //create labels that contains information about the pattern
            if (i == 0)
                otm = new OverallMetricTableModel();
            else
                otm = new OneMetricTableModel();
            DefaultTableCellRenderer dtcr = new DefaultTableCellRenderer();
            dtcr.setBackground(new Color(235, 235, 235));
            JTable table = new JTable(otm);
            table.setPreferredSize(new Dimension(200, 55));
            table.setMaximumSize(new Dimension(200, 55));
            table.getColumnModel().getColumn(0).setPreferredWidth(70);
            table.getColumnModel().getColumn(0).setMaxWidth(100);
            table.getTableHeader().setFont(new Font("SansSerif", Font.PLAIN, 12));
            table.getColumnModel().getColumn(0).setCellRenderer(dtcr);
            table.setBorder(BorderFactory.createEtchedBorder());

            //place throughput times in table
            if (type.equals(DottedChartPanel.TIME_ACTUAL)) {
                if (i == 0) {
                    table.setValueAt(DateFormat.getInstance().format(dcModel.getLogBoundaryLeft()), 0, 1);
                    table.setValueAt(DateFormat.getInstance().format(dcModel.getLogBoundaryRight()), 1, 1);
                } else {
                    table.setValueAt(DateFormat.getInstance().format(dcModel.getStartDateofLogUniList(key)), 0,
                            1);
                    table.setValueAt(DateFormat.getInstance().format(dcModel.getEndDateofLogUniList(key)), 1,
                            1);
                }
                table.setValueAt(formatString(currentDS.getMean() / timeDivider, 5), 2, 1);
                table.setValueAt(formatString(currentDS.getMin() / timeDivider, 5), 3, 1);
                table.setValueAt(formatString(currentDS.getMax() / timeDivider, 5), 4, 1);
            } else if (type.equals(DottedChartPanel.TIME_RELATIVE_TIME)) {
                if (i == 0) {
                    table.setValueAt(formatDate(dcModel.getLogBoundaryLeft()), 0, 1);
                    table.setValueAt(formatDate(dcModel.getLogBoundaryRight()), 1, 1);
                } else {
                    table.setValueAt(formatDate(dcModel.getStartDateofLogUniList(key)), 0, 1);
                    table.setValueAt(formatDate(dcModel.getEndDateofLogUniList(key)), 1, 1);
                }
                table.setValueAt(formatString(currentDS.getMean() / timeDivider, 5), 2, 1);
                table.setValueAt(formatString(currentDS.getMin() / timeDivider, 5), 3, 1);
                table.setValueAt(formatString(currentDS.getMax() / timeDivider, 5), 4, 1);
            } else if (type.equals(DottedChartPanel.TIME_RELATIVE_RATIO)) {
                if (i == 0) {
                    table.setValueAt(formatRatio(dcModel.getLogBoundaryLeft()), 0, 1);
                    table.setValueAt(formatRatio(dcModel.getLogBoundaryRight()), 1, 1);
                } else {
                    table.setValueAt(formatRatio(dcModel.getStartDateofLogUniList(key)), 0, 1);
                    table.setValueAt(formatRatio(dcModel.getEndDateofLogUniList(key)), 1, 1);
                }
                table.setValueAt(formatString(currentDS.getMean() / 100, 5), 2, 1);
                table.setValueAt(formatString(currentDS.getMin() / 100, 5), 3, 1);
                table.setValueAt(formatString(currentDS.getMax() / 100, 5), 4, 1);
            } else if (type.equals(DottedChartPanel.TIME_LOGICAL)
                    || type.equals(DottedChartPanel.TIME_LOGICAL_RELATIVE)) {
                if (i == 0) {
                    table.setValueAt(formatString(dcModel.getLogBoundaryLeft().getTime(), 5), 0, 1);
                    table.setValueAt(formatString(dcModel.getLogBoundaryRight().getTime(), 5), 1, 1);
                } else {
                    table.setValueAt(formatString((dcModel.getStartDateofLogUniList(key)).getTime(), 5), 0, 1);
                    table.setValueAt(formatString((dcModel.getEndDateofLogUniList(key)).getTime(), 5), 1, 1);
                }
                table.setValueAt(formatString(currentDS.getMean(), 5), 2, 1);
                table.setValueAt(formatString(currentDS.getMin(), 5), 3, 1);
                table.setValueAt(formatString(currentDS.getMax(), 5), 4, 1);
            }

            JPanel tempPanel = new JPanel(new BorderLayout());
            table.setAlignmentX(CENTER_ALIGNMENT);
            tempPanel.setPreferredSize(new Dimension(160, 98));
            tempPanel.setMaximumSize(new Dimension(180, 98));
            tempPanel.add(table.getTableHeader(), BorderLayout.NORTH);
            tempPanel.add(table, BorderLayout.CENTER);
            JPanel tempPanel2 = new JPanel(new BorderLayout());
            JLabel patternLabel = new JLabel("Component " + key + ":");
            patternLabel.setAlignmentX(LEFT_ALIGNMENT);

            JLabel frequencyLabel = null;
            if (i == 0)
                frequencyLabel = new JLabel("# of components: " + currentDS.getN());
            else
                frequencyLabel = new JLabel("# of dots: " + dcModel.getNumberOfLogUnits(key));

            frequencyLabel.setAlignmentX(LEFT_ALIGNMENT);
            frequencyLabel.setFont(new Font("SansSerif", Font.PLAIN, 12));
            tempPanel2.add(patternLabel, BorderLayout.NORTH);
            tempPanel2.add(frequencyLabel, BorderLayout.CENTER);
            tempPanel2.add(tempPanel, BorderLayout.SOUTH);
            this.add(tempPanel2);
            this.add(Box.createRigidArea(new Dimension(5, 10)));
        } catch (NullPointerException ex) {
            //can occur when patternMap does not contain a pattern with this frequency
            size--;
        }
    }
    //make sure the pattern performance information is displayed properly
    this.setPreferredSize(new Dimension(200, 140 * (size + 1)));
    this.revalidate();
    this.repaint();

}

From source file:org.tellervo.desktop.graph.SkeletonPlot.java

private Integer getSkeletonCategoryFromCropper1979(Integer value, DescriptiveStatistics windowStats,
        Double criticalLevel) {/*w  w w .  j av  a2  s  .  c  o  m*/
    Integer skeletonCategory = 0;

    if (criticalLevel == null)
        criticalLevel = 0.5;
    double mean = windowStats.getMean();
    double stdev = windowStats.getStandardDeviation();
    double smallRingThreshold = mean - (stdev * criticalLevel);
    int min = (int) windowStats.getMin();

    if (value == min) {
        skeletonCategory = 10;
    } else if (value > smallRingThreshold) {
        skeletonCategory = 0;
    } else {
        Integer range = (int) (smallRingThreshold - min);
        Integer categoryStepSize = range / 10;
        skeletonCategory = (int) (0 - ((value - smallRingThreshold) / categoryStepSize));
    }

    return skeletonCategory;
}

From source file:org.xenmaster.monitoring.data.Record.java

protected final void applyStatistics(Collection<Double> values) {
    // Let's get statistical
    DescriptiveStatistics ds = new DescriptiveStatistics();

    for (double util : values) {
        ds.addValue(util);/*from   ww w. ja  v  a2s  .c o m*/
    }

    double a = ds.getMean();
    double stdDev = ds.getStandardDeviation();

    // TODO: actually test this and generate warning
    // Check if all vCPUs have a fair load, e.g. [45, 60, 50] would be fair, [90, 4, 2] indicates you should learn threading
    if (stdDev > 0.8) {
        Logger.getLogger(getClass())
                .info((vm ? "VM" : "Host") + " " + reference + " has an unfair load distribution");
    }

    if (stdDev > 0) {
        try {
            NormalDistributionImpl ndi = new NormalDistributionImpl(ds.getMean(), stdDev);
            double cp = ndi.cumulativeProbability(90);
            if (cp > 0.8) {
                // 80% of the CPUs have a >90% load
                // TODO warning
                Logger.getLogger(getClass()).info((vm ? "VM" : "Host") + " " + reference
                        + " has a load >=90% on 80% of the available CPUs");
            }
        } catch (MathException ex) {
            Logger.getLogger(getClass()).error("Flawed maths", ex);
        }
    }
}

From source file:playground.artemc.pricing.SocialCostCalculator.java

private void calcStatistics() {

    // Get a DescriptiveStatistics instance
    DescriptiveStatistics tripStats = new DescriptiveStatistics();
    DescriptiveStatistics tripStatsNormalized = new DescriptiveStatistics();

    // Add the data from the array
    for (LegTrip legTrip : performedLegs) {
        double distance = 0.0;
        double cost = 0.0;
        for (LinkTrip linkTrip : legTrip.linkTrips) {
            double socialCosts = calcSocCosts(linkTrip.link_id, linkTrip.enterTime);
            if (socialCosts > 0.0)
                cost = cost + socialCosts;
            distance = legTrip.distance + network.getLinks().get(linkTrip.link_id).getLength();
        }/*from   w  w  w. j ava  2  s. co m*/

        legTrip.distance = distance;
        legTrip.cost = cost;

        tripStats.addValue(cost);

        /*
         * Normalize a legs social cost by dividing them by the leg travel time or leg distance.
         */
        //double legTravelTime = legTrip.arrivalTime - legTrip.departureTime;
        if (cost > 0.0 && legTrip.distance > 0.0)
            tripStatsNormalized.addValue(cost / legTrip.distance);
    }

    // Compute some statistics
    double sum = tripStats.getSum();
    double mean = tripStats.getMean();
    double std = tripStats.getStandardDeviation();
    double median = tripStats.getPercentile(50);
    double quantile25 = tripStats.getPercentile(25);
    double quantile75 = tripStats.getPercentile(75);

    double sumNormalized = tripStatsNormalized.getSum();
    double meanNormalized = tripStatsNormalized.getMean();
    double stdNormalized = tripStatsNormalized.getStandardDeviation();
    double medianNormalized = tripStatsNormalized.getPercentile(50);
    double quantile25Normalized = tripStatsNormalized.getPercentile(25);
    double quantile75Normalized = tripStatsNormalized.getPercentile(75);

    log.info("Sum of all leg costs: " + sum);
    log.info("Mean leg costs: " + mean);
    log.info("Standard deviation: " + std);
    log.info("Median leg costs: " + median);
    log.info("25% quantile leg costs: " + quantile25);
    log.info("75% quantile leg costs: " + quantile75);

    log.info("Normalized sum of all leg costs: " + sumNormalized);
    log.info("Normalized mean leg costs: " + meanNormalized);
    log.info("Normalized standard deviation: " + stdNormalized);
    log.info("Normalized median leg costs: " + medianNormalized);
    log.info("Normalized 25% quantile leg costs: " + quantile25Normalized);
    log.info("Normalized 75% quantile leg costs: " + quantile75Normalized);

    meanSocialCosts.add(mean);
    medianSocialCosts.add(median);
    quantil25PctSocialCosts.add(quantile25);
    quantil75PctSocialCosts.add(quantile75);

    meanNormalizedSocialCosts.add(meanNormalized);
    medianNormalizedSocialCosts.add(medianNormalized);
    quantil25PctNormalizedSocialCosts.add(quantile25Normalized);
    quantil75PctNormalizedSocialCosts.add(quantile75Normalized);
}

From source file:playground.christoph.socialcosts.SocialCostCalculator.java

private void calcStatistics() {

    // Get a DescriptiveStatistics instance
    DescriptiveStatistics stats = new DescriptiveStatistics();
    DescriptiveStatistics statsNormalized = new DescriptiveStatistics();

    // Add the data from the array
    for (LegTrip legTrip : performedLegs) {
        double costs = 0.0;
        for (LinkTrip linkTrip : legTrip.linkTrips) {
            double socialCosts = calcSocCosts(linkTrip.link_id, linkTrip.enterTime);
            if (socialCosts > 0.0)
                costs = costs + socialCosts;
        }/*  w w  w .j  ava  2s.c  o m*/
        stats.addValue(costs);

        /*
         * Normalize a legs social cost by dividing them by the leg travel time.
         * As a result we get something like social costs per traveled second.
         * Another option would be doing this on link level instead of leg level.
         */
        double legTravelTime = legTrip.arrivalTime - legTrip.departureTime;
        if (costs > 0.0 && legTravelTime > 0.0)
            statsNormalized.addValue(costs / legTravelTime);
    }

    // Compute some statistics
    double sum = stats.getSum();
    double mean = stats.getMean();
    double std = stats.getStandardDeviation();
    double median = stats.getPercentile(50);
    double quantile25 = stats.getPercentile(25);
    double quantile75 = stats.getPercentile(75);

    double sumNormalized = statsNormalized.getSum();
    double meanNormalized = statsNormalized.getMean();
    double stdNormalized = statsNormalized.getStandardDeviation();
    double medianNormalized = statsNormalized.getPercentile(50);
    double quantile25Normalized = statsNormalized.getPercentile(25);
    double quantile75Normalized = statsNormalized.getPercentile(75);

    log.info("Sum of all leg costs: " + sum);
    log.info("Mean leg costs: " + mean);
    log.info("Standard deviation: " + std);
    log.info("Median leg costs: " + median);
    log.info("25% quantile leg costs: " + quantile25);
    log.info("75% quantile leg costs: " + quantile75);

    log.info("Normalized sum of all leg costs: " + sumNormalized);
    log.info("Normalized mean leg costs: " + meanNormalized);
    log.info("Normalized standard deviation: " + stdNormalized);
    log.info("Normalized median leg costs: " + medianNormalized);
    log.info("Normalized 25% quantile leg costs: " + quantile25Normalized);
    log.info("Normalized 75% quantile leg costs: " + quantile75Normalized);

    meanSocialCosts.add(mean);
    medianSocialCosts.add(median);
    quantil25PctSocialCosts.add(quantile25);
    quantil75PctSocialCosts.add(quantile75);

    meanNormalizedSocialCosts.add(meanNormalized);
    medianNormalizedSocialCosts.add(medianNormalized);
    quantil25PctNormalizedSocialCosts.add(quantile25Normalized);
    quantil75PctNormalizedSocialCosts.add(quantile75Normalized);
}

From source file:playground.johannes.coopsim.analysis.ScoreTask.java

@Override
public void analyze(Set<Trajectory> trajectories, Map<String, DescriptiveStatistics> results) {
    DescriptiveStatistics allScores = new DescriptiveStatistics();
    for (Trajectory t : trajectories)
        allScores.addValue(t.getPerson().getSelectedPlan().getScore());
    results.put("score", allScores);

    DescriptiveStatistics actScores = ActivityEvaluator.stopLogging();
    results.put("score_act", actScores);

    DescriptiveStatistics legScores = LegEvaluator.stopLogging();
    results.put("score_leg", legScores);

    Map<String, DescriptiveStatistics> jointScore = JointActivityEvaluator2.stopLogging();
    //      Map<String, DescriptiveStatistics> jointScore = JointActivityEvaluator.stopLogging();
    for (Entry<String, DescriptiveStatistics> entry : jointScore.entrySet()) {
        results.put("score_join_" + entry.getKey(), entry.getValue());
    }/* ww  w . j  a v a  2 s  . c  om*/

    DescriptiveStatistics typeScore = ActivityTypeEvaluator.stopLogging();
    results.put("score_type", typeScore);

    try {
        writeHistograms(allScores, "score", 50, 50);
        writeHistograms(actScores, "score_act", 50, 50);
        writeHistograms(legScores, "score_leg", 50, 50);
        for (Entry<String, DescriptiveStatistics> entry : jointScore.entrySet()) {
            writeHistograms(entry.getValue(), new LinearDiscretizer(0.5), "score_join_" + entry.getKey(),
                    false);
            writeHistograms(entry.getValue(), "score_join_" + entry.getKey(), 50, 50);
        }

        writeHistograms(typeScore, new DummyDiscretizer(), "score_type", false);
    } catch (IOException e) {
        e.printStackTrace();
    }

    scores.add(allScores.getMean());

    if (scores.size() >= MIN_SAMPLES) {
        SimpleRegression reg = new SimpleRegression();

        for (int i = scores.size() - MIN_SAMPLES; i < scores.size(); i++) {
            reg.addData(i, scores.get(i));
        }

        if (reg.getSlope() < THRESHOLD)
            converged = true;
    }

}