Example usage for org.apache.commons.math3.stat StatUtils geometricMean

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat StatUtils geometricMean.

Prototype

public static double geometricMean(final double[] values) throws MathIllegalArgumentException 

Source Link

Document

Returns the geometric mean of the entries in the input array, or Double.NaN if the array is empty.

Usage

From source file:kieker.tools.opad.timeseries.AggregationMethod.java

/**
 * This method returns the result of the aggregation under one of the defined aggregation methods.
 *
 * @param aggregationValues/*w w w.j  a  va2  s  .co m*/
 *            Values to be aggregated
 * @return
 *         Result of the aggregation
 */
public double getAggregationValue(final double[] aggregationValues) {
    switch (this) {
    case GEOMETRIC_MEAN:
        return StatUtils.geometricMean(aggregationValues);
    case MAX:
        return StatUtils.max(aggregationValues);
    case MEAN:
        return StatUtils.mean(aggregationValues);
    case MIN:
        return StatUtils.min(aggregationValues);
    case PERCENTILE90:
        return StatUtils.percentile(aggregationValues, 90);
    case PERCENTILE95:
        return StatUtils.percentile(aggregationValues, 95);
    case PRODUCT:
        return StatUtils.product(aggregationValues);
    case SUM:
        return StatUtils.sum(aggregationValues);
    case SUMSQ:
        return StatUtils.sumSq(aggregationValues);
    case SUMLOG:
        return StatUtils.sumLog(aggregationValues);
    case VARIANCE:
        return StatUtils.variance(aggregationValues);
    default:
        return StatUtils.mean(aggregationValues);
    }
}

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From source file:de.hshannover.f4.trust.irondetectprocedures.Median.java

@Override
public ProcedureResult calculate(List<Feature> featureSet, List<Context> contextSet) {
    logger.trace("calculate");
    // get values from features
    double[] values = new double[featureSet.size()];
    for (int i = 0; i < featureSet.size(); i++) {
        if (featureSet.get(i).getType().getTypeId() == FeatureType.QUANTITIVE) {
            values[i] = Double.valueOf(featureSet.get(i).getValue());
        }//from  www .j  a v  a  2  s .  c  o  m
    }
    // calculate median
    double median = StatUtils.geometricMean(values);
    // dispatch between policy defined and trained mean, prefer trained mean
    double expected = this.trainingDone ? trained : expectedByPolicy;

    return ProcedureResultMapper.map(median, expected, DistanceType.percent, Boundary.high, 25, 50);
}

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From source file:com.mycompany.supersimplestockmarket.StockMarket.java

public double calculateGBCEAllShareIndex() throws Exception {
    double allShareIndex = 0.0;

    // build list of all stock prices for stocks that had trades in last 15 minutes (i.e. non zero ones)
    ArrayList<Double> stockPrices = new ArrayList<>();
    for (AbstractStock stock : stocks.values()) {
        double stockPrice = calculateVolumeWeightedStockPrice(stock.getStockSymbol());
        if (stockPrice > 0.0)
            stockPrices.add(stockPrice);
    }//  w ww  . j  av  a2s.c o m

    // convert to array of double, and calculate geometric mean
    if (stockPrices.size() > 0) {
        double[] prices = new double[stockPrices.size()];
        int i = 0;
        for (Double price : stockPrices)
            prices[i++] = price;

        allShareIndex = StatUtils.geometricMean(prices);
    }

    return allShareIndex;
}

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From source file:de.hshannover.f4.trust.irondetectprocedures.Median.java

@Override
public void train(List<Feature> featureSet, List<Context> contextSet, Calendar start, Calendar end) {
    logger.trace("start training ...");
    // get values from features
    // FIXME what if different types if features are used?
    double[] values = new double[featureSet.size()];
    for (int i = 0; i < featureSet.size(); i++) {
        if (featureSet.get(i).getType().getTypeId() == FeatureType.QUANTITIVE) {
            values[i] = Double.valueOf(featureSet.get(i).getValue());
        }/*w  w  w .j  a  va2  s.  com*/
    }

    // store as trained value
    this.trained = StatUtils.geometricMean(values);
    this.trainingDone = true;
    logger.trace("training was done. value is " + this.trained);
}

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From source file:com.jpmorgan.assgn.sss02mvn.service.StockTradeServiceImpl.java

@Override
public double calculateGBCEAllShareIndex() throws Exception {
    double allShareIndex = 0.0;

    // Calculate stock price for all stock in the system
    Map<String, Stock> stocks = stockTradeRepository.getStocks();
    ArrayList<Double> stockPrices = new ArrayList<Double>();

    for (String stockSymbol : stocks.keySet()) {
        double stockPrice = calculateStockPrice(stockSymbol, 0);

        if (stockPrice > 0) {
            stockPrices.add(stockPrice);
        }/*from w w w .j  a  v a 2 s .co m*/
    }

    if (stockPrices.size() >= 1) {
        double[] stockPricesArray = new double[stockPrices.size()];

        for (int i = 0; i <= (stockPrices.size() - 1); i++) {
            // stockPricesArray[i] = stockPrices.get(i).doubleValue();
            stockPricesArray[i] = stockPrices.get(i);
        }

        // Calculates the GBCE All Share Index
        allShareIndex = StatUtils.geometricMean(stockPricesArray);
    }

    return allShareIndex;
}

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From source file:tech.tablesaw.api.NumberColumnTest.java

@Test
public void testSummarize() {
    IntColumn c = IntColumn.indexColumn("t", 99, 1);
    IntColumn c2 = c.copy();/*from  w w w .  j av  a  2 s.com*/
    c2.appendCell("");
    double c2Variance = c2.variance();
    double cVariance = StatUtils.variance(c.asDoubleArray());
    assertEquals(cVariance, c2Variance, 0.00001);
    assertEquals(StatUtils.sumLog(c.asDoubleArray()), c2.sumOfLogs(), 0.00001);
    assertEquals(StatUtils.sumSq(c.asDoubleArray()), c2.sumOfSquares(), 0.00001);
    assertEquals(StatUtils.geometricMean(c.asDoubleArray()), c2.geometricMean(), 0.00001);
    assertEquals(StatUtils.product(c.asDoubleArray()), c2.product(), 0.00001);
    assertEquals(StatUtils.populationVariance(c.asDoubleArray()), c2.populationVariance(), 0.00001);
    assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getQuadraticMean(), c2.quadraticMean(), 0.00001);
    assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getStandardDeviation(), c2.standardDeviation(),
            0.00001);
    assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getKurtosis(), c2.kurtosis(), 0.00001);
    assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getSkewness(), c2.skewness(), 0.00001);

    assertEquals(StatUtils.variance(c.asDoubleArray()), c.variance(), 0.00001);
    assertEquals(StatUtils.sumLog(c.asDoubleArray()), c.sumOfLogs(), 0.00001);
    assertEquals(StatUtils.sumSq(c.asDoubleArray()), c.sumOfSquares(), 0.00001);
    assertEquals(StatUtils.geometricMean(c.asDoubleArray()), c.geometricMean(), 0.00001);
    assertEquals(StatUtils.product(c.asDoubleArray()), c.product(), 0.00001);
    assertEquals(StatUtils.populationVariance(c.asDoubleArray()), c.populationVariance(), 0.00001);
    assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getQuadraticMean(), c.quadraticMean(), 0.00001);
    assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getStandardDeviation(), c.standardDeviation(),
            0.00001);
    assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getKurtosis(), c.kurtosis(), 0.00001);
    assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getSkewness(), c.skewness(), 0.00001);
}

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