Java Code Examples for org.apache.commons.math.stat.descriptive.DescriptiveStatistics#getStandardDeviation()

private void scale(double[][] peakList) {
  DescriptiveStatistics stdDevStats = new DescriptiveStatistics();

  for (int columns = 0; columns < peakList.length; columns++) {
    stdDevStats.clear();
    for (int row = 0; row < peakList[columns].length; row++) {
      if (!Double.isInfinite(peakList[columns][row]) && !Double.isNaN(peakList[columns][row])) {
        stdDevStats.addValue(peakList[columns][row]);
      }
    }

    double stdDev = stdDevStats.getStandardDeviation();

    for (int row = 0; row < peakList[columns].length; row++) {
      if (stdDev != 0) {
        peakList[columns][row] = peakList[columns][row] / stdDev;
      }
    }
  }
}
 

public APARegionStatistics(RealMatrix data, int regionWidth) {
    int max = data.getColumnDimension();
    int midPoint = max / 2;
    double centralVal = data.getEntry(midPoint, midPoint);

    /** NOTE - indices are inclusive in java, but in python the second index is not inclusive */

    peak2mean = centralVal / ((sum(data.getData()) - centralVal) / (data.getColumnDimension() - 1));

    double avgUL = mean(data.getSubMatrix(0, regionWidth - 1, 0, regionWidth - 1).getData());
    peak2UL = centralVal / avgUL;

    double avgUR = mean(data.getSubMatrix(0, regionWidth - 1, max - regionWidth, max - 1).getData());
    peak2UR = centralVal / avgUR;

    double avgLL = mean(data.getSubMatrix(max - regionWidth, max - 1, 0, regionWidth - 1).getData());
    peak2LL = centralVal / avgLL;

    double avgLR = mean(data.getSubMatrix(max - regionWidth, max - 1, max - regionWidth, max - 1).getData());
    peak2LR = centralVal / avgLR;

    DescriptiveStatistics yStats = statistics(data.getSubMatrix(max - regionWidth, max - 1, 0, regionWidth - 1).getData());
    ZscoreLL = (centralVal - yStats.getMean()) / yStats.getStandardDeviation();
}
 

private void scale(double[][] peakList) {
  DescriptiveStatistics stdDevStats = new DescriptiveStatistics();

  for (int columns = 0; columns < peakList.length; columns++) {
    stdDevStats.clear();
    for (int row = 0; row < peakList[columns].length; row++) {
      if (!Double.isInfinite(peakList[columns][row]) && !Double.isNaN(peakList[columns][row])) {
        stdDevStats.addValue(peakList[columns][row]);
      }
    }

    double stdDev = stdDevStats.getStandardDeviation();

    for (int row = 0; row < peakList[columns].length; row++) {
      if (stdDev != 0) {
        peakList[columns][row] = peakList[columns][row] / stdDev;
      }
    }
  }
}
 

public APARegionStatistics(RealMatrix data, int regionWidth) {
    int max = data.getColumnDimension();
    int midPoint = max / 2;
    double centralVal = data.getEntry(midPoint, midPoint);

    /** NOTE - indices are inclusive in java, but in python the second index is not inclusive */
    double mean = (MatrixTools.sum(data.getData()) - centralVal) / (data.getRowDimension() * data.getColumnDimension() - 1);
    peak2mean = centralVal / mean;

    double avgUL = mean(data.getSubMatrix(0, regionWidth - 1, 0, regionWidth - 1).getData());
    peak2UL = centralVal / avgUL;

    avgUR = mean(data.getSubMatrix(0, regionWidth - 1, max - regionWidth, max - 1).getData());
    peak2UR = centralVal / avgUR;

    double avgLL = mean(data.getSubMatrix(max - regionWidth, max - 1, 0, regionWidth - 1).getData());
    peak2LL = centralVal / avgLL;

    double avgLR = mean(data.getSubMatrix(max - regionWidth, max - 1, max - regionWidth, max - 1).getData());
    peak2LR = centralVal / avgLR;

    DescriptiveStatistics yStats = statistics(data.getSubMatrix(max - regionWidth, max - 1, 0, regionWidth - 1).getData());
    ZscoreLL = (centralVal - yStats.getMean()) / yStats.getStandardDeviation();
}
 

/**
 * Normalize (standardize) the series, so in the end it is having a mean of 0 and a standard deviation of 1.
 *
 * @param sample Sample to normalize.
 * @return normalized (standardized) sample.
 * @since 2.2
 */
public static double[] normalize(final double[] sample) {
    DescriptiveStatistics stats = new DescriptiveStatistics();

    // Add the data from the series to stats
    for (int i = 0; i < sample.length; i++) {
        stats.addValue(sample[i]);
    }

    // Compute mean and standard deviation
    double mean = stats.getMean();
    double standardDeviation = stats.getStandardDeviation();

    // initialize the standardizedSample, which has the same length as the sample
    double[] standardizedSample = new double[sample.length];

    for (int i = 0; i < sample.length; i++) {
        // z = (x- mean)/standardDeviation
        standardizedSample[i] = (sample[i] - mean) / standardDeviation;
    }
    return standardizedSample;
}
 

/**
 * Normalize (standardize) the series, so in the end it is having a mean of 0 and a standard deviation of 1.
 *
 * @param sample Sample to normalize.
 * @return normalized (standardized) sample.
 * @since 2.2
 */
public static double[] normalize(final double[] sample) {
    DescriptiveStatistics stats = new DescriptiveStatistics();

    // Add the data from the series to stats
    for (int i = 0; i < sample.length; i++) {
        stats.addValue(sample[i]);
    }

    // Compute mean and standard deviation
    double mean = stats.getMean();
    double standardDeviation = stats.getStandardDeviation();

    // initialize the standardizedSample, which has the same length as the sample
    double[] standardizedSample = new double[sample.length];

    for (int i = 0; i < sample.length; i++) {
        // z = (x- mean)/standardDeviation
        standardizedSample[i] = (sample[i] - mean) / standardDeviation;
    }
    return standardizedSample;
}
 

/**
 * Normalize (standardize) the series, so in the end it is having a mean of 0 and a standard deviation of 1.
 *
 * @param sample Sample to normalize.
 * @return normalized (standardized) sample.
 * @since 2.2
 */
public static double[] normalize(final double[] sample) {
    DescriptiveStatistics stats = new DescriptiveStatistics();

    // Add the data from the series to stats
    for (int i = 0; i < sample.length; i++) {
        stats.addValue(sample[i]);
    }

    // Compute mean and standard deviation
    double mean = stats.getMean();
    double standardDeviation = stats.getStandardDeviation();

    // initialize the standardizedSample, which has the same length as the sample
    double[] standardizedSample = new double[sample.length];

    for (int i = 0; i < sample.length; i++) {
        // z = (x- mean)/standardDeviation
        standardizedSample[i] = (sample[i] - mean) / standardDeviation;
    }
    return standardizedSample;
}
 

public static double getFeatureMatchDistribution(GraphDatabaseService db, Long patternId)
{
    Transaction tx = db.beginTx();
    Node startNode = db.getNodeById(patternId);

    // Feature match distribution
    List<Double> matches = IteratorUtil.asCollection(db.traversalDescription()
            .depthFirst()
            .relationships(withName("HAS_CLASS"), Direction.OUTGOING)
            .evaluator(Evaluators.fromDepth(1))
            .evaluator(Evaluators.toDepth(1))
            .traverse(startNode)
            .relationships())
            .stream()
            .map(p -> ((Integer)p.getProperty("matches")).doubleValue())
            .collect(Collectors.toList());

    tx.success();
    tx.close();

    double variance = 1.0;

    if(matches.size() > 1) {
        Double[] matchArr = matches.toArray(new Double[matches.size()]);
        // Get the standard deviation
        DescriptiveStatistics ds = new DescriptiveStatistics();
        matches.forEach(m -> ds.addValue(m.doubleValue() / StatUtils.sum(ArrayUtils.toPrimitive(matchArr))));
        variance = ds.getStandardDeviation();
    }

    return variance;
}