/** * {@inheritDoc} */ @Override public GeometricMean copy() { GeometricMean result = new GeometricMean(); copy(this, result); return result; }
/** * {@inheritDoc} */ @Override public GeometricMean copy() { GeometricMean result = new GeometricMean(); copy(this, result); return result; }
/** * Construct a MultivariateSummaryStatistics instance * @param k dimension of the data * @param isCovarianceBiasCorrected if true, the unbiased sample * covariance is computed, otherwise the biased population covariance * is computed */ public MultivariateSummaryStatistics(int k, boolean isCovarianceBiasCorrected) { this.k = k; sumImpl = new StorelessUnivariateStatistic[k]; sumSqImpl = new StorelessUnivariateStatistic[k]; minImpl = new StorelessUnivariateStatistic[k]; maxImpl = new StorelessUnivariateStatistic[k]; sumLogImpl = new StorelessUnivariateStatistic[k]; geoMeanImpl = new StorelessUnivariateStatistic[k]; meanImpl = new StorelessUnivariateStatistic[k]; for (int i = 0; i < k; ++i) { sumImpl[i] = new Sum(); sumSqImpl[i] = new SumOfSquares(); minImpl[i] = new Min(); maxImpl[i] = new Max(); sumLogImpl[i] = new SumOfLogs(); geoMeanImpl[i] = new GeometricMean(); meanImpl[i] = new Mean(); } covarianceImpl = new VectorialCovariance(k, isCovarianceBiasCorrected); }
/** * Construct a MultivariateSummaryStatistics instance * @param k dimension of the data * @param isCovarianceBiasCorrected if true, the unbiased sample * covariance is computed, otherwise the biased population covariance * is computed */ public MultivariateSummaryStatistics(int k, boolean isCovarianceBiasCorrected) { this.k = k; sumImpl = new StorelessUnivariateStatistic[k]; sumSqImpl = new StorelessUnivariateStatistic[k]; minImpl = new StorelessUnivariateStatistic[k]; maxImpl = new StorelessUnivariateStatistic[k]; sumLogImpl = new StorelessUnivariateStatistic[k]; geoMeanImpl = new StorelessUnivariateStatistic[k]; meanImpl = new StorelessUnivariateStatistic[k]; for (int i = 0; i < k; ++i) { sumImpl[i] = new Sum(); sumSqImpl[i] = new SumOfSquares(); minImpl[i] = new Min(); maxImpl[i] = new Max(); sumLogImpl[i] = new SumOfLogs(); geoMeanImpl[i] = new GeometricMean(); meanImpl[i] = new Mean(); } covarianceImpl = new VectorialCovariance(k, isCovarianceBiasCorrected); }
/** * Construct a MultivariateSummaryStatistics instance * @param k dimension of the data * @param isCovarianceBiasCorrected if true, the unbiased sample * covariance is computed, otherwise the biased population covariance * is computed */ public MultivariateSummaryStatistics(int k, boolean isCovarianceBiasCorrected) { this.k = k; sumImpl = new StorelessUnivariateStatistic[k]; sumSqImpl = new StorelessUnivariateStatistic[k]; minImpl = new StorelessUnivariateStatistic[k]; maxImpl = new StorelessUnivariateStatistic[k]; sumLogImpl = new StorelessUnivariateStatistic[k]; geoMeanImpl = new StorelessUnivariateStatistic[k]; meanImpl = new StorelessUnivariateStatistic[k]; for (int i = 0; i < k; ++i) { sumImpl[i] = new Sum(); sumSqImpl[i] = new SumOfSquares(); minImpl[i] = new Min(); maxImpl[i] = new Max(); sumLogImpl[i] = new SumOfLogs(); geoMeanImpl[i] = new GeometricMean(); meanImpl[i] = new Mean(); } covarianceImpl = new VectorialCovariance(k, isCovarianceBiasCorrected); }
if (source.getGeoMeanImpl() instanceof GeometricMean) { dest.geoMeanImpl = new GeometricMean((SumOfLogs) dest.sumLogImpl); } else { dest.geoMeanImpl = source.geoMeanImpl.copy();
if (source.getGeoMeanImpl() instanceof GeometricMean) { dest.geoMeanImpl = new GeometricMean((SumOfLogs) dest.sumLogImpl); } else { dest.geoMeanImpl = source.geoMeanImpl.copy();