Java SummaryStatistics.getMean方法代碼示例

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
private static Stats getStats(FloatColumn values, SummaryStatistics summaryStatistics) {
    Stats stats = new Stats("Column: " + values.name());
    stats.min = (float) summaryStatistics.getMin();
    stats.max = (float) summaryStatistics.getMax();
    stats.n = summaryStatistics.getN();
    stats.sum = summaryStatistics.getSum();
    stats.variance = summaryStatistics.getVariance();
    stats.populationVariance = summaryStatistics.getPopulationVariance();
    stats.quadraticMean = summaryStatistics.getQuadraticMean();
    stats.geometricMean = summaryStatistics.getGeometricMean();
    stats.mean = summaryStatistics.getMean();
    stats.standardDeviation = summaryStatistics.getStandardDeviation();
    stats.sumOfLogs = summaryStatistics.getSumOfLogs();
    stats.sumOfSquares = summaryStatistics.getSumsq();
    stats.secondMoment = summaryStatistics.getSecondMoment();
    return stats;
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
private void computeStats() {
  SummaryStatistics centroidStats = new SummaryStatistics();
  SummaryStatistics edgeStats = new SummaryStatistics();
  for (IFeature poi : pois) {
    centroidStats.addValue(getDistToCentroid().get(poi));
    edgeStats.addValue(getDistToEdge().get(poi));
  }
  meanDistanceCentroid = centroidStats.getMean();
  stdDistanceCentroid = centroidStats.getStandardDeviation();
  minDistanceCentroid = centroidStats.getMin();
  maxDistanceCentroid = centroidStats.getMax();
  meanDistanceEdge = edgeStats.getMean();
  stdDistanceEdge = edgeStats.getStandardDeviation();
  minDistanceEdge = edgeStats.getMin();
  maxDistanceEdge = edgeStats.getMax();
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
private double calcClusterMeanInfo(Set<Integer> members) throws AdeInternalException {
    if (members.size() == 1){
        return 0;
    }
    final SummaryStatistics sumS = new SummaryStatistics();
    for (int i : members){
        for (int j : members) {
            if (i <= j){
                continue;
            }
            double v;
            v = m_informationMat.get(i, j);
            if (!Double.isNaN(v)){
                sumS.addValue(v);
            }
        }
    }
    final double res = sumS.getMean();
    if (Double.isNaN(res)){
        return 0;
    }
    return res;

}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
/**
 * This function will take an aggregated collection of Summary Statistics
 * and will generate a derived {@link SummaryStatistic} based on a flag for the  
 * desired summation.  This is particularly helpful for finding out the
 * means of the individual statistics of the collection.
 * For example, if you wanted to find out the mean of means of the collection
 * you would call this function like <p>
 * getSummaryStatisticsForCollection(aggregate,1).getMean(); <p>
 * Or if you wanted to determine the max number of annotations per
 * individual, you could call: <p>
 * getSummaryStatisticsForCollection(aggregate,5).getMax(); <p>
 * The stat flag should be set to the particular individual statistic that should
 * be summarized over.
 *
 * @param aggregate The aggregated collection of summary statistics
 * @param stat  Integer flag for the statistic (1:mean ; 2:sum; 3:min; 4:max; 5:N)
 * @return {@link SummaryStatistics} of the selected statistic
 */
public SummaryStatistics getSummaryStatisticsForCollection(Collection<SummaryStatistics> aggregate, Stat stat) {
	//LOG.info("Computing stats over collection of "+aggregate.size()+" elements ("+stat+"):");
	//TODO: turn stat into enum
	int x = 0;
	//To save memory, I am using SummaryStatistics, which does not store the values,
	//but this could be changed to DescriptiveStatistics to see values
	//as well as other statistical functions like distributions
	SummaryStatistics stats = new SummaryStatistics();
	Double v = 0.0;
	ArrayList<String> vals = new ArrayList();
	for (SummaryStatistics s : aggregate) {
		switch (stat) {
		case MEAN : v= s.getMean(); stats.addValue(s.getMean()); break;
		case SUM : v=s.getSum(); stats.addValue(s.getSum()); break;
		case MIN : v=s.getMin(); stats.addValue(s.getMin()); break;
		case MAX : v=s.getMax(); stats.addValue(s.getMax()); break;
		case N : v= ((int)s.getN())*1.0; stats.addValue(s.getN()); break;
		};
		//vals.add(v.toString());
	};
	//LOG.info("vals: "+vals.toString());
	return stats;
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
public double calculateOverallAnnotationSufficiencyForAttributeSet(Set<OWLClass> atts) throws UnknownOWLClassException {
	SummaryStatistics stats = computeAttributeSetSimilarityStats(atts);
	if ((this.getSummaryStatistics() == null) || Double.isNaN(this.getSummaryStatistics().mean.getMean())) {
		LOG.info("Stats have not been computed yet - doing this now");
		this.computeSystemStats();
	}
	// score = mean(atts)/mean(overall) + max(atts)/max(overall) + sum(atts)/mean(sum(overall))
	double overall_score = 0.0;
	Double mean_score = stats.getMean();
	Double max_score = stats.getMax();
	Double sum_score = stats.getSum();
	if (!(mean_score.isNaN() || max_score.isNaN() || sum_score.isNaN())) {
		mean_score = StatUtils.min(new double[]{(mean_score / this.overallSummaryStatsPerIndividual.mean.getMean()),1.0});
		max_score = StatUtils.min(new double[]{(max_score / this.overallSummaryStatsPerIndividual.max.getMax()),1.0});
		sum_score = StatUtils.min(new double[]{(sum_score / this.overallSummaryStatsPerIndividual.sum.getMean()),1.0});
		overall_score = (mean_score + max_score + sum_score) / 3;		
	}
	LOG.info("Overall mean: "+mean_score + " max: "+max_score + " sum:"+sum_score + " combined:"+overall_score);
	return overall_score;
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
/**
 * Computes the TaskTimeCompletion average for all finished Cloudlets on
 * this experiment.
 *
 * @return the TaskTimeCompletion average
 */
double getCloudletsTaskTimeCompletionAverage() {
    SummaryStatistics cloudletTaskTimeCompletion = new SummaryStatistics();
    DatacenterBroker broker = getBrokerList().stream()
            .findFirst()
            .orElse(DatacenterBroker.NULL);
    broker.getCloudletFinishedList().stream()
            .map(c -> c.getFinishTime() - c.getLastDatacenterArrivalTime())
            .forEach(cloudletTaskTimeCompletion::addValue);

    taskCompletionTimeSlaContract = getTaskTimeCompletionFromContract(broker);

   /* Log.printFormattedLine(
            "\t\t\n TaskTimeCompletion simulation: %.2f \n TaskTimeCompletion contrato SLA: %.2f \n",
            cloudletTaskTimeCompletion.getMean(), taskCompletionTimeSlaContract);*/
    return cloudletTaskTimeCompletion.getMean();
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
private Map<Attribute, Map<Double, NormalDistribution>> initialiseGaussianDistributionForNumericAttribute(Instance instanceInfo, ArrayList<Instance> instancesList){
	
	Map<Attribute, Map<Double, NormalDistribution>> numericAttributeClassGaussDistributions = new HashMap<>();
	
	// go through each numeric attibute
	for (Attribute attribute : Collections.list(instanceInfo.enumerateAttributes())) {
		
		// check whether the attribute is numeric
		if(attribute.isNumeric()){
			
			// for each class label
			HashMap<Double, NormalDistribution> classLabelDistribution = new HashMap<>();
			for (int classLabelNo = 0; classLabelNo < instanceInfo.numClasses(); classLabelNo++) {
				
				// go through all instance in the dataset to create normal distribution
				SummaryStatistics summaryStatistics = new SummaryStatistics();
				for (Instance instance : instancesList) {
					
					summaryStatistics.addValue(instance.value(attribute));
				}
				
				// create normal distribution for this attribute with corresponding
				// class label
				NormalDistribution normalDistribution = new NormalDistribution(
						summaryStatistics.getMean(), 
						summaryStatistics.getStandardDeviation());
				
				// map to hold classLabel and distribution
				classLabelDistribution.put((double) classLabelNo, normalDistribution);
				
			}
			
			// put it into the map
			numericAttributeClassGaussDistributions.put(attribute, classLabelDistribution);
		}
		
	}
				
	return numericAttributeClassGaussDistributions;
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
public void getSummaryStats(double[] values){
	SummaryStatistics stats = new SummaryStatistics();
	for( int i = 0; i < values.length; i++) {
	        stats.addValue(values[i]);
	}
	double mean = stats.getMean();
	double std = stats.getStandardDeviation();
	System.out.println(mean + "\t" + std);
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
/**
 * @param spectrogram
 * @return double[]{mean, sd}
 */
public static double[] spectrogramMeanSd(List<float[]> spectrogram)
{
	SummaryStatistics stat=new SummaryStatistics();
	for(float[] spec: spectrogram) for(float v: spec) stat.addValue(v);
	return new double[]{stat.getMean(), stat.getStandardDeviation()};
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
public SubGraph(int subGraphId, Collection<Integer> itemIndices, SummaryStatistics summaryStatisticsQ, SummaryStatistics summaryStatisticsArea) {
  minInit(subGraphId, itemIndices, (float) summaryStatisticsQ.getMean());
  standardDeviationQ = (float) summaryStatisticsQ.getStandardDeviation();
  meanArea = (float) summaryStatisticsArea.getMean();
  standardDeviationArea = (float) summaryStatisticsArea.getStandardDeviation();
  debug();
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
private void computeStats() {
  SummaryStatistics centroidStats = new SummaryStatistics();
  SummaryStatistics edgeStats = new SummaryStatistics();
  for (IFeature atm : atms) {
    centroidStats.addValue(getDistToCentroid().get(atm));
    edgeStats.addValue(getDistToEdge().get(atm));
  }
  meanDistanceCentroid = centroidStats.getMean();
  stdDistanceCentroid = centroidStats.getStandardDeviation();
  meanDistanceEdge = edgeStats.getMean();
  stdDistanceEdge = edgeStats.getStandardDeviation();
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
/**
 * Applies a global filter on pitches in S1 and moves all pitches whose salience is bellow a certain
 * threshold from S1 to S0. This filter is described in [1], section II-C.
 */
private void applyGlobalFilter() {
    SummaryStatistics statistics = new SummaryStatistics();

    /* Iteration #1: Gather data to obtain salience statistics. */
    for (int t=0; t<this.s1.length; t++) {
        for (int i=0; i<this.s1[t].length; i++) {
            if (this.s1[t][i] == null) {
              continue;
            }
            statistics.addValue(this.s1[t][i].getSalience());
        }
    }

    /* Iteration #2: Move pitches that are bellow the threshold. */
    final double threshold = statistics.getMean() - this.t2 * statistics.getStandardDeviation();
    for (int t=0; t<this.s1.length; t++) {
        for (int i=0; i<this.s1[t].length; i++) {
            if (this.s1[t][i] == null) {
              continue;
            }
            if (this.s1[t][i].getSalience() < threshold) {
                this.moveToS0(t,i);
            }
        }
    }
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
/**
 * Sets min, max, mean, and deviation for a week
 *
 * @param week Statistics for the week
 */
public void setWeek(SummaryStatistics week)
{
    this.weekMin = week.getMin();
    this.weekMax = week.getMax();
    this.weekAvg = week.getMean();
    this.weekDeviation = week.getStandardDeviation();
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
/**
 * Sets min, max, mean, and deviation for a month.
 *
 * @param month Statistics for the month.
 */
public void setMonth(SummaryStatistics month)
{
    this.monthMin = month.getMin();
    this.monthMax = month.getMax();
    this.monthAvg = month.getMean();
    this.monthDeviation = month.getStandardDeviation();
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
/**
 * Sets min, max, mean, and deviation for a year.
 *
 * @param year Statistics for the year.
 */
public void setYear(SummaryStatistics year)
{
    this.yearMin = year.getMin();
    this.yearMax = year.getMax();
    this.yearAvg = year.getMean();
    this.yearDeviation = year.getStandardDeviation();
}