Java DescriptiveStatistics.getStandardDeviation方法代码示例

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
/**
 * Find local maxima in the spectrum and returns the indices of those maxima as integer
 * array.
 *
 * @param threshold Threshold for search. Values bellow that threshold won't be considered.
 * @return Array containing indices (zero-based) of local maxima.
 */
public List<Pair<Float, Double>> findLocalMaxima(double threshold, boolean significant) {
    List<Pair<Float,Double>> peaks = new ArrayList<>();
    for (int i=1;i<this.spectrum.length-1;i++) {
        if (this.spectrum[i] < threshold) {
          continue;
        }
        if (spectrum[i] > Math.max(spectrum[i+1], spectrum[i-1])) {
          peaks.add(this.get(i));
        }
    }

    if (significant) {
        DescriptiveStatistics statistics = new DescriptiveStatistics();
        for (Pair<Float, Double> peak : peaks) {
            statistics.addValue(peak.second);
        }
        final double mean = statistics.getMean();
        final double stddev = statistics.getStandardDeviation();
        peaks.removeIf(p -> p.second < (mean + stddev * 2));
    }

    return peaks;
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
/**
 * filter snapshots using statistically significant points only
 * @param selectedLists list of snapshots
 */
private void statBasedSelectMetricHistory(final LinkedList<InMemoryHistoryNode> selectedLists)
    throws ClassCastException {
  logger.debug("selecting snapshots which are far away from mean value");
  DescriptiveStatistics descStats = getDescriptiveStatistics(selectedLists);
  Double mean = descStats.getMean();
  Double std = descStats.getStandardDeviation();

  Iterator<InMemoryHistoryNode> ite = selectedLists.iterator();
  while (ite.hasNext()) {
    InMemoryHistoryNode currentNode = ite.next();
    double value = ((Number) currentNode.getValue()).doubleValue();
    // remove all elements which lies in 95% value band
    if (value < mean + standardDeviationFactor * std && value > mean - standardDeviationFactor * std) {
      ite.remove();
    }
  }
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
/**
 * Normalize (standardize) the sample, so it is has 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;
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
/**
 * Calculates the outlier score using value to be checked and time series data
 *
 * @param valueToCheck   the given values to check
 * @param timeSeriesData the time series data
 * @return an outlier score
 * @throws Exception
 */
public double getOutlierScore(double valueToCheck, double[] timeSeriesData) {
    // get mean and standard deviation
    DescriptiveStatistics statistics = new DescriptiveStatistics(timeSeriesData);
    double sampleMean = statistics.getMean();
    double sampleStdDev = statistics.getStandardDeviation();

    // get k (number of standard deviations away from the mean)
    double k = getK(probabilityThreshold);

    validateChebyshev(valueToCheck, sampleMean, sampleStdDev, k);

    double acceptableDeviation = k * sampleStdDev;
    double min = sampleMean - acceptableDeviation;
    double max = sampleMean + acceptableDeviation;
    double currentDeviation = Math.abs(valueToCheck - sampleMean);

    double outlierScore = currentDeviation / acceptableDeviation;
    UtilsRG.debug("outlierScore = " + outlierScore);
    UtilsRG.debug(Math.floor(acceptableDeviation) + " acceptableDeviation");
    UtilsRG.debug(valueToCheck + " observed");
    UtilsRG.debug(Math.floor(min) + " min");
    UtilsRG.debug(Math.floor(max) + " max");
    return outlierScore;
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
@Test
public void testOutlierUsing3SigmaRule(){

    // some observed data points
    double dataPoints[] = {0.464,0.443,0.424,0.386,0.367,0.382,0.455,0.410,0.411,0.424,0.338,0.355,0.342,0.324,
            0.354,0.322,0.364,0.375,1.085,0.575,0.597,0.464,0.414,0.408,1.156,0.819,1.156,1.024,1.152,1.103,
            0.431,0.378,0.358,0.382,0.354,0.435,0.386,0.361,0.397,0.362,0.334,0.357,0.344,0.362,0.317,0.331,
            0.199,0.351,0.284,0.343,0.354,0.336,0.280,0.312,0.778,0.723,0.755,0.774,0.759,0.762,0.490,0.400,
            0.364,0.439,0.441,0.673};

    DescriptiveStatistics maths = new DescriptiveStatistics(dataPoints);
    double sampleMean = maths.getMean();
    double sampleStdDev = maths.getStandardDeviation();
    double sampleSkev = (Math.abs(sampleMean - maths.getPercentile(50)) / sampleStdDev);

    // parameter estimation using method of moments ex-gaussian distribution
    double mean = sampleMean - sampleStdDev * Math.pow(sampleSkev/2., 1./3) ;
    double stdDev = Math.sqrt(sampleStdDev*(1 - Math.pow(sampleSkev/2., 2./3)));
    double tau = sampleStdDev * (Math.pow(sampleSkev/2., 1./3));
    double lambda = 1 / tau;

    ExponentiallyModifiedGaussianDistribution exGaussian = new ExponentiallyModifiedGaussianDistribution(mean, stdDev, lambda);
    isOutlier(exGaussian.getStddev(), 1.2);
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
public boolean isSignificant(double singletonKL[],double topValue,double p){
	DescriptiveStatistics ds=new DescriptiveStatistics((double[])singletonKL);
	double mean=ds.getMean();
	double std=ds.getStandardDeviation();
	double z=(Math.log(contextShiftingScore)-mean)/std;
	//double pValue1;
	
	// Compute z value of the scores based on the null distribution of scores of kmers computed from aptamers with small counts
	if (z>0.0)
		pvalue = computePValue(z);
	else 
		pvalue=1.0;

	if ((pvalue<=p)&&(Math.log(contextShiftingScore)>=topValue))
		return true;
	else
		return false;
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
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);
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
/**
 * Determines the significance for the given {@link Itemset} by modeling the background normal distribution.
 *
 * @param itemset The {@link Itemset} for which the significance should be calculated.
 */
private void determineSignificance(Itemset<LabelType> itemset) {

    double[] values = backgroundDistributions.get(itemset).getObservations().stream()
                                             .mapToDouble(Double::doubleValue).toArray();

    // model normal distribution
    DescriptiveStatistics descriptiveStatistics = new DescriptiveStatistics(values);
    double mean = descriptiveStatistics.getMean();
    double standardDeviation = descriptiveStatistics.getStandardDeviation();
    NormalDistribution normalDistribution = new NormalDistribution(mean, standardDeviation);

    // calculate KS p-value to estimate quality of fit
    double ks = TestUtils.kolmogorovSmirnovTest(normalDistribution, values, false);
    if (ks < ksCutoff) {
        logger.warn("itemset {} background distribution of type {} violates KS-cutoff, skipping", itemset, type);
        return;
    }

    double pValue = Double.NaN;
    if (type == SignificanceEstimatorType.COHESION) {
        pValue = normalDistribution.cumulativeProbability(itemset.getCohesion());
    } else if (type == SignificanceEstimatorType.CONSENSUS) {
        pValue = normalDistribution.cumulativeProbability(itemset.getConsensus());
    } else if (type == SignificanceEstimatorType.AFFINITY) {
        pValue = normalDistribution.cumulativeProbability(itemset.getAffinity());
    }

    logger.debug("p-value for itemset {} is {}", itemset.toSimpleString(), pValue);
    if (pValue < significanceCutoff) {
        Significance significance = new Significance(pValue, ks);
        significantItemsets.put(significance, itemset);
        logger.info("itemset {} is significant with {}", itemset.toSimpleString(), significance);
    }
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
private void appendResultLineWithStats(Map<Metric, DescriptiveStatistics> results, boolean formatMillis, StringBuilder sb, Metric metric)
{
    DescriptiveStatistics statistics = results.get(metric);
    double mean = statistics.getMean();
    double stdev = statistics.getStandardDeviation();
    double confidenceInterval = computeConfidenceInterval(statistics, 0.05);

    if (formatMillis && (metric == Metric.TRAIN_TIME || metric == Metric.TEST_TIME))
        sb.append(String.format("%s;%s;%s;", formatMillis(mean), formatMillis(stdev), formatMillis(confidenceInterval)));
    else
        sb.append(String.format("%.2f;%.2f;%.2f;", mean, stdev, confidenceInterval));
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
private double standardDeviation(double[][] x) {
	DescriptiveStatistics stats = new DescriptiveStatistics();
	for (int i = 0; i < x.length; i ++)
		for (int j = 0; j < x[i].length; j++)
			stats.addValue(x[i][j]);
	return stats.getStandardDeviation();
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
/**
 * Computes the maximum normalized auto correlation of raw with respect to time lags TAU_MIN and TAU_MAX
 */
private double maxNormAutoCorr() {
    List<Double> rawConcat = new ArrayList<>();
    double std, mean, stdTau, meanTau, sum;
    double max = Double.MIN_VALUE;
    //int tauOpt = 0;
    for (List<Double> l :
            raw) {
        rawConcat.addAll(l);
    }
    //Log.i("walk", "Raw size: " + rawConcat.size());
    for (int tau = (FastMath.toIntExact(TAU_MIN) / 100) - 1; tau < FastMath.toIntExact(TAU_MAX) / 100; tau += TAU_STEP / 100) {
        int tauIndex = indexOfTau(tau);
        for (int m = 0; m < rawConcat.size() - (tauIndex * 2) - 1; m++) {
            DescriptiveStatistics stats = getStatsForDataSegment(rawConcat.subList(m, m + tauIndex));
            //Log.i("walk", "stats size: " + stats.getValues().length);
            std = stats.getStandardDeviation();
            mean = stats.getMean();
            DescriptiveStatistics statsTau = getStatsForDataSegment(rawConcat.subList(m + tauIndex, m + tauIndex * 2));
            //Log.i("walk", "statsTau size: " + statsTau.getValues().length);
            stdTau = statsTau.getStandardDeviation();
            meanTau = statsTau.getMean();
            sum = 0;
            int k;
            for (k = 0; k < tauIndex - 1; k++) {
                sum += (rawConcat.get(m + k) - mean) * (rawConcat.get(m + k + tauIndex) - meanTau);
            }
            double temp = sum / (std * stdTau * tauIndex);
            //Log.i("walk", "Tau: " + tau + " - X(" + m + "," + tauIndex + ") = " + temp);
            max = max(max, temp);
            //if (temp == max) tauOpt = tau;
        }
    }
    return max;
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
private double[] normalize(double[] array) {
    DescriptiveStatistics stats = new DescriptiveStatistics(array);
    double mean = stats.getMean();
    double stdDev = stats.getStandardDeviation();
    for (int i = 0; i < array.length; i++) {
        array[i] = (array[i] - mean)/stdDev;
    }
    return array;
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
/**
 * Check if given value is an outlier in the time series data using z-score
 *
 * @param valueToCheck   the given values to check
 * @param timeSeriesData the time series data
 * @return true if value is an outlier. Otherwise false.
 */
public boolean isOutlier(double valueToCheck, double[] timeSeriesData) {

    // get mean and standard deviation
    DescriptiveStatistics statistics = new DescriptiveStatistics(timeSeriesData);
    double sampleMean = statistics.getMean();
    double sampleStdDev = statistics.getStandardDeviation();

    // get z-score
    double zScore = getZScore(valueToCheck, sampleMean, sampleStdDev);

    // check if z-score is anomalous
    return isOutlier(zScore);
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
private double standardDeviation(double[][] x) {
  DescriptiveStatistics stats = new DescriptiveStatistics();
  for (int i = 0; i < x.length; i ++)
    for (int j = 0; j < x[i].length; j++)
      stats.addValue(x[i][j]);
  return stats.getStandardDeviation();
}
 

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
public void computePValue(double singletonKL[]){
	DescriptiveStatistics ds=new DescriptiveStatistics((double[])singletonKL);
	double mean=ds.getMean();
	double std=ds.getStandardDeviation();
	double z=(Math.log(contextShiftingScore)-mean)/std;
	
	if (z>0.0)
		pvalue = computePValue(z);
	else
		pvalue = 1.0;
}