Example usage for org.apache.commons.math.stat.descriptive.moment Variance Variance

Introduction

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

Prototype

public Variance(Variance original) 

Source Link

Document

Copy constructor, creates a new Variance identical to the original

Usage

From source file:com.clican.pluto.dataprocess.dpl.function.impl.SharpeRatio.java

public Object calculate(List<Map<String, Object>> rowSet)
        throws CalculationException, PrefixAndSuffixException {
    if (rowSet.size() == 0) {
        throw new CalculationException("SharpeRatio??");
    }//from  w ww.ja  va2  s .c  o  m

    double[] values = new double[rowSet.size()];
    double sum = 0;
    for (int i = 0; i < rowSet.size(); i++) {
        Map<String, Object> row = rowSet.get(i);
        Double value = valuePas.getValue(row);
        values[i] = value;
        sum += value;
    }
    double avg = sum / (rowSet.size());
    Variance var = new Variance(false);
    Double result = Math.sqrt(var.evaluate(values, avg));
    if (log.isDebugEnabled()) {
        log.debug("FRR[" + avg + "],STDEV[" + result + "]");
    }
    return avg / result;
}

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From source file:com.clican.pluto.dataprocess.dpl.function.impl.StandardDeviation.java

public Object calculate(List<Map<String, Object>> rowSet)
        throws CalculationException, PrefixAndSuffixException {
    if (rowSet.size() == 0) {
        throw new CalculationException("SharpeRatio??");
    }/* w w w.j  a  v a 2 s  . com*/
    double[] values = new double[rowSet.size()];
    double sum = 0;
    for (int i = 0; i < rowSet.size(); i++) {
        Map<String, Object> row = rowSet.get(i);
        Double value = valuePas.getValue(row);
        values[i] = value;
        sum += value;
    }
    double avg = sum / rowSet.size();
    Variance var = new Variance(false);
    return Math.sqrt(var.evaluate(values, avg));
}

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From source file:com.clican.pluto.dataprocess.dpl.function.impl.TrackError.java

/**
 * @param rowSet//from w  w w  . ja va  2s  .  co  m
 *            List
 * @see com.clican.pluto.dataprocess.dpl.function.MultiRowFunction#calculate(java.util.List)
 */

public Object calculate(List<Map<String, Object>> rowSet)
        throws CalculationException, PrefixAndSuffixException {
    if (rowSet == null || rowSet.size() == 0) {
        throw new CalculationException("??");
    }

    double[] values = new double[rowSet.size()];
    double sum = 0;
    for (int i = 0; i < rowSet.size(); i++) {
        Map<String, Object> row = rowSet.get(i);
        Double fundValue = fundNavList.getValue(row);
        Double indexValue = indexList.getValue(row);
        double value = fundValue.doubleValue() - indexValue.doubleValue();
        values[i] = value;
        sum += value;
    }
    double avg = sum / (rowSet.size());

    Variance var = new Variance(false);
    Double result = Math.sqrt(var.evaluate(values, avg));

    return result;
}

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From source file:com.clican.pluto.dataprocess.dpl.function.impl.Beta.java

private double getBeta(double[] referValueList, double[] estimateValueList) {
    Variance var = new Variance(false);
    Covariance cov = new Covariance();
    double varValue = var.evaluate(referValueList);
    double covValue = cov.covariance(referValueList, estimateValueList, false);
    if (log.isDebugEnabled()) {
        log.debug("Covariance=[" + covValue + "],Variance=[" + varValue + "]");
    }/*from  w  w  w.j av a  2 s  .  c  o  m*/
    Double beta = covValue / varValue;
    return beta;
}

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From source file:com.clican.pluto.dataprocess.dpl.function.impl.InformationRatio.java

public Object calculate(List<Map<String, Object>> rowSet)
        throws CalculationException, PrefixAndSuffixException {
    if (rowSet.size() == 0) {
        throw new CalculationException("InformationRatio??");
    }//from  w ww.java 2  s .co  m
    List<Double> estimateValueList = new ArrayList<Double>();
    List<Double> referValueList = new ArrayList<Double>();
    Double estimateSum = 0d;
    Double referSum = 0d;
    Double differencingSum = 0d;
    double[] differencingList = new double[rowSet.size()];
    for (int i = 0; i < rowSet.size(); i++) {
        Map<String, Object> row = rowSet.get(i);
        Double estimateValue = estimateVectorPas.getValue(row);
        Double referValue = referVectorPas.getValue(row);
        Double differencing = estimateValue - referValue;
        estimateSum += estimateValue;
        referSum += referValue;
        differencingSum += differencing;
        estimateValueList.add(estimateSum);
        referValueList.add(referValue);
        differencingList[i] = differencing;
    }
    Double estimateAvg = estimateSum / estimateValueList.size();
    Double referAvg = referSum / referValueList.size();

    Variance var = new Variance(false);
    Double standard = Math.sqrt(var.evaluate(differencingList, differencingSum / differencingList.length));
    Double result = (estimateAvg - referAvg) / standard;
    if (log.isDebugEnabled()) {
        log.debug("FR=[" + estimateAvg + "],BR=[" + referAvg + "],=[" + standard + "],information=[" + result
                + "]");
    }
    return result;
}

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From source file:Covariance.java

/**
 * Compute a covariance matrix from a matrix whose columns represent 
 * covariates. /*from w w  w  . j  ava  2s  .co m*/
 * @param matrix input matrix (must have at least two columns and two rows) 
 * @param biasCorrected determines whether or not covariance estimates are bias-corrected 
 * @return covariance matrix 
 */
protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected) {
    int dimension = matrix.getColumnDimension();
    Variance variance = new Variance(biasCorrected);
    RealMatrix outMatrix = new BlockRealMatrix(dimension, dimension);
    for (int i = 0; i < dimension; i++) {
        for (int j = 0; j < i; j++) {
            double cov = covariance(matrix.getColumn(i), i, matrix.getColumn(j), j, biasCorrected);
            outMatrix.setEntry(i, j, cov);
            outMatrix.setEntry(j, i, cov);
        }
        outMatrix.setEntry(i, i, variance.evaluate(matrix.getColumn(i)));
    }
    return outMatrix;
}

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From source file:org.beedraz.semantics_II.expression.number.real.double64.stat.DoublePopulationVarianceBeed.java

/**
 * @post  getSource() == null;//  w ww .ja v a  2s . com
 * @post  getDouble() == null;
 * @post  owner != null ? owner.registerAggregateElement(this);
 */
public DoublePopulationVarianceBeed(AggregateBeed owner) {
    super(new Variance(false), owner);
}

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From source file:org.beedraz.semantics_II.expression.number.real.double64.stat.DoubleSampleVarianceBeed.java

/**
 * @post  getSource() == null;//from  w  w  w .j  a v  a 2  s  . c  o  m
 * @post  getDouble() == null;
 * @post  owner != null ? owner.registerAggregateElement(this);
 */
public DoubleSampleVarianceBeed(AggregateBeed owner) {
    super(new Variance(true), owner);
}

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From source file:uk.ac.diamond.scisoft.analysis.dataset.AbstractDataset.java

/**
 * The sample variance can be calculated in two ways: if the dataset is considered as the entire population then the
 * sample variance is simply the second central moment:
 * /*  w  w w  .  ja  v a  2 s  .co m*/
 * <pre>
 *    sum((x_i - m)^2)/N
 * where {x_i} are set of N population values and m is the mean
 *    m = sum(x_i)/N
 * </pre>
 * 
 * Otherwise, if the dataset is a set of samples (with replacement) from the population then
 * 
 * <pre>
 *    sum((x_i - m)^2)/(N-1)
 * where {x_i} are set of N sample values and m is the unbiased estimate of the mean
 *    m = sum(x_i)/N
 * </pre>
 * 
 * Note that the second definition is also the unbiased estimator of population variance.
 * 
 * @param isDatasetWholePopulation
 * @return sample variance
 */
public Number variance(boolean isDatasetWholePopulation) {
    SummaryStatistics stats = getStatistics();

    if (isDatasetWholePopulation) {
        StorelessUnivariateStatistic oldVar = stats.getVarianceImpl();
        stats.setVarianceImpl(new Variance(false));
        Number var = stats.getVariance();
        stats.setVarianceImpl(oldVar);
        return var;
    }
    return stats.getVariance();
}

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