Java SummaryStatistics.getMean方法代码示例

import org.apache.commons.math.stat.descriptive.SummaryStatistics; //导入方法依赖的package包/类
/**
 * Generates a random value from this distribution.
 * 
 * @return the random value.
 * @throws IllegalStateException if the distribution has not been loaded
 */
public double getNextValue() throws IllegalStateException {

    if (!loaded) {
        throw new IllegalStateException("distribution not loaded");
    }

    // Start with a uniformly distributed random number in (0,1)
    double x = Math.random();

    // Use this to select the bin and generate a Gaussian within the bin
    for (int i = 0; i < binCount; i++) {
       if (x <= upperBounds[i]) {
           SummaryStatistics stats = (SummaryStatistics)binStats.get(i);
           if (stats.getN() > 0) {
               if (stats.getStandardDeviation() > 0) {  // more than one obs
                    return randomData.nextGaussian
                        (stats.getMean(),stats.getStandardDeviation());
               } else {
                   return stats.getMean(); // only one obs in bin
               }
           }
       }
    }
    throw new RuntimeException("No bin selected");
}
 

import org.apache.commons.math.stat.descriptive.SummaryStatistics; //导入方法依赖的package包/类
/** test failure modes and distribution of nextGaussian() */  
public void testNextGaussian() { 
    try {
        randomData.nextGaussian(0,0);
        fail("zero sigma -- IllegalArgumentException expected");
    } catch (IllegalArgumentException ex) {
        ;
    }
    SummaryStatistics u = new SummaryStatistics();
    for (int i = 0; i<largeSampleSize; i++) {
        u.addValue(randomData.nextGaussian(0,1));
    }
    double xbar = u.getMean();
    double s = u.getStandardDeviation();
    double n = (double) u.getN(); 
    /* t-test at .001-level TODO: replace with externalized t-test, with
     * test statistic defined in TestStatistic
     */
    assertTrue(Math.abs(xbar)/(s/Math.sqrt(n))< 3.29);
}
 

import org.apache.commons.math.stat.descriptive.SummaryStatistics; //导入方法依赖的package包/类
private static double checkValues(List<Integer> values) {

		System.out.println("Values: " + values);
		SummaryStatistics ss = new SummaryStatistics();
		for (int i=0; i<values.size(); i++) {
			ss.addValue(values.get(i));
		}

		double mean = ss.getMean();
		double stddev = ss.getStandardDeviation();

		double p = ((stddev*100)/mean);
		System.out.println("Percentage diff: " + p);

		Assert.assertTrue("" + p + " " + values, p<0.1);
		return p;
	}
 

import org.apache.commons.math.stat.descriptive.SummaryStatistics; //导入方法依赖的package包/类
/**
 * Generates a random value from this distribution.
 *
 * @return the random value.
 * @throws IllegalStateException if the distribution has not been loaded
 */
public double getNextValue() throws IllegalStateException {

    if (!loaded) {
        throw MathRuntimeException.createIllegalStateException("distribution not loaded");
    }

    // Start with a uniformly distributed random number in (0,1)
    double x = Math.random();

    // Use this to select the bin and generate a Gaussian within the bin
    for (int i = 0; i < binCount; i++) {
       if (x <= upperBounds[i]) {
           SummaryStatistics stats = binStats.get(i);
           if (stats.getN() > 0) {
               if (stats.getStandardDeviation() > 0) {  // more than one obs
                    return randomData.nextGaussian
                        (stats.getMean(),stats.getStandardDeviation());
               } else {
                   return stats.getMean(); // only one obs in bin
               }
           }
       }
    }
    throw new MathRuntimeException("no bin selected");
}
 

import org.apache.commons.math.stat.descriptive.SummaryStatistics; //导入方法依赖的package包/类
/** test failure modes and distribution of nextGaussian() */
public void testNextGaussian() {
    try {
        randomData.nextGaussian(0, 0);
        fail("zero sigma -- IllegalArgumentException expected");
    } catch (IllegalArgumentException ex) {
        // ignored
    }
    SummaryStatistics u = new SummaryStatistics();
    for (int i = 0; i < largeSampleSize; i++) {
        u.addValue(randomData.nextGaussian(0, 1));
    }
    double xbar = u.getMean();
    double s = u.getStandardDeviation();
    double n = u.getN();
    /*
     * t-test at .001-level TODO: replace with externalized t-test, with
     * test statistic defined in TestStatistic
     */
    assertTrue(Math.abs(xbar) / (s / Math.sqrt(n)) < 3.29);
}
 

import org.apache.commons.math.stat.descriptive.SummaryStatistics; //导入方法依赖的package包/类
/** test failure modes and distribution of nextGaussian() */
public void testNextGaussian() {
    try {
        randomData.nextGaussian(0, 0);
        fail("zero sigma -- MathIllegalArgumentException expected");
    } catch (MathIllegalArgumentException ex) {
        // ignored
    }
    SummaryStatistics u = new SummaryStatistics();
    for (int i = 0; i < largeSampleSize; i++) {
        u.addValue(randomData.nextGaussian(0, 1));
    }
    double xbar = u.getMean();
    double s = u.getStandardDeviation();
    double n = u.getN();
    /*
     * t-test at .001-level TODO: replace with externalized t-test, with
     * test statistic defined in TestStatistic
     */
    assertTrue(FastMath.abs(xbar) / (s / FastMath.sqrt(n)) < 3.29);
}
 

import org.apache.commons.math.stat.descriptive.SummaryStatistics; //导入方法依赖的package包/类
/** test failure modes and distribution of nextGaussian() */
public void testNextGaussian() {
	try {
		randomData.nextGaussian(0, 0);
		fail("zero sigma -- IllegalArgumentException expected");
	} catch (IllegalArgumentException ex) {
		// ignored
	}
	SummaryStatistics u = new SummaryStatistics();
	for (int i = 0; i < largeSampleSize; i++) {
		u.addValue(randomData.nextGaussian(0, 1));
	}
	double xbar = u.getMean();
	double s = u.getStandardDeviation();
	double n = u.getN();
	/*
	 * t-test at .001-level TODO: replace with externalized t-test, with
	 * test statistic defined in TestStatistic
	 */
	assertTrue(Math.abs(xbar) / (s / Math.sqrt(n)) < 3.29);
}
 

import org.apache.commons.math.stat.descriptive.SummaryStatistics; //导入方法依赖的package包/类
/** test failure modes and distribution of nextGaussian() */
@Test
public void testNextGaussian() {
    try {
        randomData.nextGaussian(0, 0);
        Assert.fail("zero sigma -- MathIllegalArgumentException expected");
    } catch (MathIllegalArgumentException ex) {
        // ignored
    }
    SummaryStatistics u = new SummaryStatistics();
    for (int i = 0; i < largeSampleSize; i++) {
        u.addValue(randomData.nextGaussian(0, 1));
    }
    double xbar = u.getMean();
    double s = u.getStandardDeviation();
    double n = u.getN();
    /*
     * t-test at .001-level TODO: replace with externalized t-test, with
     * test statistic defined in TestStatistic
     */
    Assert.assertTrue(FastMath.abs(xbar) / (s / FastMath.sqrt(n)) < 3.29);
}
 

import org.apache.commons.math.stat.descriptive.SummaryStatistics; //导入方法依赖的package包/类
/** test failure modes and distribution of nextGaussian() */  
public void testNextGaussian() { 
    try {
        double x = randomData.nextGaussian(0,0);
        fail("zero sigma -- IllegalArgumentException expected");
    } catch (IllegalArgumentException ex) {
        ;
    }
    SummaryStatistics u = SummaryStatistics.newInstance();
    for (int i = 0; i<largeSampleSize; i++) {
        u.addValue(randomData.nextGaussian(0,1));
    }
    double xbar = u.getMean();
    double s = u.getStandardDeviation();
    double n = (double) u.getN(); 
    /* t-test at .001-level TODO: replace with externalized t-test, with
     * test statistic defined in TestStatistic
     */
    assertTrue(Math.abs(xbar)/(s/Math.sqrt(n))< 3.29);
}
 

import org.apache.commons.math.stat.descriptive.SummaryStatistics; //导入方法依赖的package包/类
/**
 * Generates a random value from this distribution.
 * 
 * @return the random value.
 * @throws IllegalStateException if the distribution has not been loaded
 */
public double getNextValue() throws IllegalStateException {

    if (!loaded) {
        throw MathRuntimeException.createIllegalStateException("distribution not loaded");
    }

    // Start with a uniformly distributed random number in (0,1)
    double x = Math.random();

    // Use this to select the bin and generate a Gaussian within the bin
    for (int i = 0; i < binCount; i++) {
       if (x <= upperBounds[i]) {
           SummaryStatistics stats = binStats.get(i);
           if (stats.getN() > 0) {
               if (stats.getStandardDeviation() > 0) {  // more than one obs
                    return randomData.nextGaussian
                        (stats.getMean(),stats.getStandardDeviation());
               } else {
                   return stats.getMean(); // only one obs in bin
               }
           }
       }
    }
    throw new MathRuntimeException("no bin selected");
}
 

import org.apache.commons.math.stat.descriptive.SummaryStatistics; //导入方法依赖的package包/类
@Override
	public boolean analyzeResults(PageRankWithPriors<Vertex, Edge> pr) {
		boolean disambiguationStop = true;
		Collection<Vertex> vertexCol = graph.getVertices();
		for (int i = 0; i < repList.size(); i++) {
			if (!disambiguatedSurfaceForms.get(i)) {
				int qryNr = repList.get(i).getQueryNr();
				double maxScore = 0;
				SummaryStatistics stats = new SummaryStatistics();
				String tempSolution = "";
				List<Candidate> scores = new ArrayList<Candidate>();
				for (Vertex v : vertexCol) {
					if (v.getEntityQuery() == qryNr && v.isCandidate()) {
						scores.add(new Candidate(pr.getVertexScore(v)));
						double score = Math.abs(pr.getVertexScore(v));
						stats.addValue(score);
						System.out.println("Score for: "+v.getUris().get(0)+"  :  "+score);
						if (score > maxScore) {
							tempSolution = v.getUris().get(0);
							maxScore = score;
						}
					}
				}
				SurfaceForm rep = repList.get(i);
				Collections.sort(scores, Collections.reverseOrder());
				double secondMax = scores.get(1).score;

				if (!Double.isInfinite(maxScore)) {
					double avg = stats.getMean();
					double threshold = computeThreshold(avg, maxScore);
//					if (secondMax < threshold) {
						updateGraph(rep.getCandidates(), tempSolution,
								rep.getQueryNr());
						rep.setDisambiguatedEntity(tempSolution);
						System.out.println("Ich disambiguiere: "+tempSolution);
						disambiguatedSurfaceForms.set(i);
						disambiguationStop = false;
						break;
					}
//				}
			}
		}
		return disambiguationStop;
	}
 

import org.apache.commons.math.stat.descriptive.SummaryStatistics; //导入方法依赖的package包/类
@Override
public boolean analyzeResults(PageRankWithPriors<Vertex, Edge> pr) {
	boolean disambiguationStop = true;
	Collection<Vertex> vertexCol = graph.getVertices();
	for (int i = 0; i < repList.size(); i++) {
		if (!disambiguatedSurfaceForms.get(i) && repList.get(i).isRelevant()) {
			int qryNr = repList.get(i).getQueryNr();
			double maxScore = 0;
			SummaryStatistics stats = new SummaryStatistics();
			String tempSolution = "";
			List<Candidate> scores = new ArrayList<Candidate>();
			for (Vertex v : vertexCol) {
				if (v.getEntityQuery() == qryNr && v.isCandidate()) {
					scores.add(new Candidate(v.getUris().get(0), pr
							.getVertexScore(v)));
					double score = Math.abs(pr.getVertexScore(v));
					stats.addValue(score);
					if (score > maxScore) {
						tempSolution = v.getUris().get(0);
						maxScore = score;
					}
				}
			}
			SurfaceForm rep = repList.get(i);
			SurfaceForm clone = origList.get(i);
			Collections.sort(scores, Collections.reverseOrder());
			double secondMax = scores.get(1).score;
			
			List<String> newCandidates = new ArrayList<String>();
			for(int j = 0; j < maximumcandidatespersf; j++) {
				if(scores.size() > j) {
					newCandidates.add(scores.get(j).can);
				} else {
					break;
				}
			}

			if (!Double.isInfinite(maxScore)) {
				double avg = stats.getMean();
				double threshold = computeThreshold(avg, maxScore);
				if (secondMax < threshold && disambiguate) {
					updateGraph(rep.getCandidates(), tempSolution,
							rep.getQueryNr());
					rep.setDisambiguatedEntity(tempSolution);
					clone.setDisambiguatedEntity(tempSolution);
					disambiguatedSurfaceForms.set(i);
					disambiguationStop = false;
					break;
				} else {
					clone.setCandidates(newCandidates);
				}
			}
		}
	}
	return disambiguationStop;
}
 

import org.apache.commons.math.stat.descriptive.SummaryStatistics; //导入方法依赖的package包/类
double getValueFromSummaryStatistics(SummaryStatistics summaryStatistics) {
	return summaryStatistics.getN()==0 ? 0 : summaryStatistics.getMean();
}