Java Code Examples for org.apache.commons.math3.stat.descriptive.DescriptiveStatistics#getPercentile()

/**
 * Computes robustness weights using bisquare weight function.
 *
 * @param remainder
 *  The remainder, series - trend - seasonal.
 * @return
 *  A new array containing the robustness weights.
 */
private double[] robustnessWeights(double[] remainder) {
  // Compute "h" = 6 median(|R_v|)
  double[] absRemainder = new double[remainder.length];
  for (int i = 0; i < remainder.length; i++) {
    absRemainder[i] = Math.abs(remainder[i]);
  }
  DescriptiveStatistics stats = new DescriptiveStatistics(absRemainder);
  double outlierThreshold = 6 * stats.getPercentile(50);

  // Compute robustness weights
  double[] robustness = new double[remainder.length];
  for (int i = 0; i < remainder.length; i++) {
    robustness[i] = biSquareWeight(absRemainder[i] / outlierThreshold);
  }

  return robustness;
}
 

private List<OutlierCellChange> generateOutliers(List<Cell> originalDistribution, double[] distribution, double percentage, EGTask task) {
    List<OutlierCellChange> outliers = new ArrayList<OutlierCellChange>();
    DescriptiveStatistics stats = new DescriptiveStatistics(distribution);
    if (task.getConfiguration().isDebug()) System.out.println(printStat(stats));

    double q1 = stats.getPercentile(25);
    double q3 = stats.getPercentile(75);
    double iqr = q3 - q1;
    // using Tukey’s Outlier rule
    // outlier if value < q1 - 1.5 * iqr or value > q3 + 1.5 * iqr
    double minRange = q1 - 1.5 * iqr;
    double maxRange = q3 + 1.5 * iqr;
    for (Cell cell : originalDistribution) {
        if (BartUtility.pickRandom(percentage)) {
            OutlierCellChange cellChange = buildCellChange(cell, task, minRange, maxRange, q1, q3);
            outliers.add(cellChange);
        }
    }
    return outliers;
}
 

private String printStat(DescriptiveStatistics stats) {
    double mean = stats.getMean();
    double std = stats.getStandardDeviation();
    double median = stats.getPercentile(50);
    double q1 = stats.getPercentile(25);
    double q3 = stats.getPercentile(75);
    double iqr = q3 - q1;
    double trimmedMean = (q1 + q3 + 2 * median) / 4;
    double skewness = stats.getSkewness();

    StringBuilder sb = new StringBuilder();
    sb.append(" *** Distribution Analysis ***").append("\n")
            .append("\tMean= ").append(mean).append("\n")
            .append("\tStd= ").append(std).append("\n")
            .append("\tMedian= ").append(median).append("\n")
            .append("\tQ1= ").append(q1).append("\tQ3=").append(q3).append("\tIQR=").append(iqr).append("\n")
            .append("\tTrimmed Mean= ").append(trimmedMean).append("\n")
            .append("\tSkewness= ").append(skewness).append("\n");
    return sb.toString();
}
 

/**
 * If alpha = 1, than it must be Cauchy distribution
 */
@Test
public void testCauchyCase() {
    StableRandomGenerator generator = new StableRandomGenerator(rg, 1d, 0.0);
    DescriptiveStatistics summary = new DescriptiveStatistics();

    for (int i = 0; i < sampleSize; ++i) {
        double sample = generator.nextNormalizedDouble();
        summary.addValue(sample);
    }

    // Standard Cauchy distribution should have zero median and mode
    double median = summary.getPercentile(50);
    Assert.assertEquals(0.0, median, 0.2);
}
 

public static double getMedianHighOpenSpread(String symbol, int days) throws Exception {
    YahooQuoteFetcher yahooFetcher = new YahooQuoteFetcher();
    String quoteStr = yahooFetcher.fetchQuotes(symbol, days, 60*60*24);
    List<Quote> dailyQuoteList = yahooFetcher.parseQuotes(quoteStr, 60*60*24);
    
    DescriptiveStatistics stats = new DescriptiveStatistics();
    
    for(Quote quote : dailyQuoteList) {
        stats.addValue(quote.getHigh().subtract(quote.getOpen()).doubleValue());
    }
    
    return stats.getPercentile(50);
}
 

/**
 * If alpha = 1, than it must be Cauchy distribution
 */
@Test
public void testCauchyCase() {
    StableRandomGenerator generator = new StableRandomGenerator(rg, 1d, 0.0);
    DescriptiveStatistics summary = new DescriptiveStatistics();

    for (int i = 0; i < sampleSize; ++i) {
        double sample = generator.nextNormalizedDouble();
        summary.addValue(sample);
    }

    // Standard Cauchy distribution should have zero median and mode
    double median = summary.getPercentile(50);
    Assert.assertEquals(0.0, median, 0.2);
}
 

/**
 * If alpha = 1, than it must be Cauchy distribution
 */
@Test
public void testCauchyCase() {
    StableRandomGenerator generator = new StableRandomGenerator(rg, 1d, 0.0);
    DescriptiveStatistics summary = new DescriptiveStatistics();

    for (int i = 0; i < sampleSize; ++i) {
        double sample = generator.nextNormalizedDouble();
        summary.addValue(sample);
    }

    // Standard Cauchy distribution should have zero median and mode
    double median = summary.getPercentile(50);
    Assert.assertEquals(0.0, median, 0.2);
}
 

/**
 * If alpha = 1, than it must be Cauchy distribution
 */
@Test
public void testCauchyCase() {
    StableRandomGenerator generator = new StableRandomGenerator(rg, 1d, 0.0);
    DescriptiveStatistics summary = new DescriptiveStatistics();

    for (int i = 0; i < sampleSize; ++i) {
        double sample = generator.nextNormalizedDouble();
        summary.addValue(sample);
    }

    // Standard Cauchy distribution should have zero median and mode
    double median = summary.getPercentile(50);
    Assert.assertEquals(0.0, median, 0.2);
}
 

/**
 * If alpha = 1, than it must be Cauchy distribution
 */
@Test
public void testCauchyCase() {
    StableRandomGenerator generator = new StableRandomGenerator(rg, 1d, 0.0);
    DescriptiveStatistics summary = new DescriptiveStatistics();

    for (int i = 0; i < sampleSize; ++i) {
        double sample = generator.nextNormalizedDouble();
        summary.addValue(sample);
    }

    // Standard Cauchy distribution should have zero median and mode
    double median = summary.getPercentile(50);
    Assert.assertEquals(0.0, median, 0.2);
}
 

/**
 * If alpha = 1, than it must be Cauchy distribution
 */
@Test
public void testCauchyCase() {
    StableRandomGenerator generator = new StableRandomGenerator(rg, 1d, 0.0);
    DescriptiveStatistics summary = new DescriptiveStatistics();

    for (int i = 0; i < sampleSize; ++i) {
        double sample = generator.nextNormalizedDouble();
        summary.addValue(sample);
    }

    // Standard Cauchy distribution should have zero median and mode
    double median = summary.getPercentile(50);
    Assert.assertEquals(0.0, median, 0.2);
}
 

/**
 * If alpha = 1, than it must be Cauchy distribution
 */
@Test
public void testCauchyCase() {
    StableRandomGenerator generator = new StableRandomGenerator(rg, 1d, 0.0);
    DescriptiveStatistics summary = new DescriptiveStatistics();

    for (int i = 0; i < sampleSize; ++i) {
        double sample = generator.nextNormalizedDouble();
        summary.addValue(sample);
    }

    // Standard Cauchy distribution should have zero median and mode
    double median = summary.getPercentile(50);
    Assert.assertEquals(0.0, median, 0.2);
}
 

/**
 * If alpha = 1, than it must be Cauchy distribution
 */
@Test
public void testCauchyCase() {
    StableRandomGenerator generator = new StableRandomGenerator(rg, 1d, 0.0);
    DescriptiveStatistics summary = new DescriptiveStatistics();

    for (int i = 0; i < sampleSize; ++i) {
        double sample = generator.nextNormalizedDouble();
        summary.addValue(sample);
    }

    // Standard Cauchy distribution should have zero median and mode
    double median = summary.getPercentile(50);
    Assert.assertEquals(0.0, median, 0.2);
}
 

public double getIQR(ArrayList<Float> arrayList) {
    double[] vals = new double[arrayList.size()];
    for (int i = 0; i < arrayList.size(); i++) {
        vals[i] = (double)arrayList.get(i);
    }
    DescriptiveStatistics ds = new DescriptiveStatistics(vals);
    double fq = ds.getPercentile(25);
    double tq = ds.getPercentile(75);
    return (tq - fq);
}
 

@Override
public float getResult() {
    if (count == 0)
        return Float.NaN;
    
    double[] primitiveValues = new double[floatValues.size()];
    for (int i = 0; i < floatValues.size(); i++)
        primitiveValues[i] = floatValues.get(i);
    DescriptiveStatistics stats = new DescriptiveStatistics(primitiveValues);
    
    return (float) stats.getPercentile(50);
}
 

public void getDescStats(double[] values){
	DescriptiveStatistics stats = new DescriptiveStatistics();
	for( int i = 0; i < values.length; i++) {
	        stats.addValue(values[i]);
	}
	double mean = stats.getMean();
	double std = stats.getStandardDeviation();
	double median = stats.getPercentile(50);
	System.out.println(mean + "\t" + std + "\t" + median);
}
 

@SuppressWarnings("SameParameterValue")
public static double getPercentile(DescriptiveStatistics descriptiveStatistics, double percentile)
{
    return descriptiveStatistics.getPercentile(percentile);
}
 

@Override
protected int calculateNumericResult(DescriptiveStatistics ds) {
    return (int) ds.getPercentile(50);
}
 

@Override
protected int calculateNumericResult(DescriptiveStatistics ds) {
    ds.setPercentileImpl(new Percentile().withEstimationType(Percentile.EstimationType.R_3));
    return actualResult = (int) ds.getPercentile((double) percentile);
}
 

public static double median(double[] arr){
    DescriptiveStatistics stats = new DescriptiveStatistics(arr);
    return stats.getPercentile(50);
}
 

/**
 * Compute the interquartile range in a vector
 * @param a
 * @throws IllegalArgumentException if the input vector is empty
 * @return the interquartile range
 */
public static double iqr(final double[] a) {
	checkDims(a);
	DescriptiveStatistics d = new DescriptiveStatistics(a);
	return d.getPercentile(75) - d.getPercentile(25);
}