Java SummaryStatistics.getMax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
/**
 * 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
/**
 * 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();
}
 

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 all.
 *
 * @param all Statistics for the all.
 */
public void setAll(SummaryStatistics all)
{
    this.allMin = all.getMin();
    this.allMax = all.getMax();
    this.allAvg = all.getMean();
    this.allDeviation = all.getStandardDeviation();
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
public double calculateSubgraphAnnotationSufficiencyForAttributeSet(Set<OWLClass> atts, OWLClass c) throws UnknownOWLClassException {
	SummaryStatistics stats = computeAttributeSetSimilarityStatsForSubgraph(atts,c);
	//TODO: compute statsPerIndividual for this subgraph
	if ((this.overallSummaryStatsPerIndividual == null ) || (Double.isNaN(this.overallSummaryStatsPerIndividual.max.getMean()))) {
		LOG.info("Stats have not been computed yet - doing this now");
		this.computeSystemStats();
	}

	if (!(this.subgraphSummaryStatsPerIndividual.containsKey(c))) {
		//only do this once for the whole system, per class requested
		this.computeSystemStatsForSubgraph(c);
	}
	// score = mean(atts)/mean(overall) + max(atts)/max(overall) + sum(atts)/mean(sum(overall))
	//TODO: need to normalize this based on the whole corpus
	double 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.subgraphSummaryStatsPerIndividual.get(c).mean.getMean()),1.0});
		max_score = StatUtils.min(new double[]{(max_score / this.subgraphSummaryStatsPerIndividual.get(c).max.getMax()),1.0});
		sum_score = StatUtils.min(new double[]{(sum_score / this.subgraphSummaryStatsPerIndividual.get(c).sum.getMean()),1.0});
		score = (mean_score + max_score + sum_score) / 3;		
	}
	LOG.info(getShortId(c)+" n: "+stats.getN()+" mean: "+mean_score + " max: "+max_score + " sum:"+sum_score + " combined:"+score);
	return score;
}
 

import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
@Override
protected RealDistribution getKernel(SummaryStatistics bStats) {
    return new UniformRealDistribution(randomData.getRandomGenerator(), bStats.getMin(), bStats.getMax(),
            UniformRealDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
}