Java GaussianRandomGenerator.nextNormalizedDouble方法代码示例

import org.apache.commons.math3.random.GaussianRandomGenerator; //导入方法依赖的package包/类
@Test
public void testNormallyDistributedRandomData() {
  List<Double> values = new ArrayList<>();
  GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(0L));
  for(int i = 0;i < 1000000;++i) {
    double d = gaussian.nextNormalizedDouble();
    values.add(d);
  }
  validateEquality(values);
}
 

import org.apache.commons.math3.random.GaussianRandomGenerator; //导入方法依赖的package包/类
@Test
public void testNormallyDistributedRandomDataShifted() {
  List<Double> values = new ArrayList<>();
  GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(0L));
  for(int i = 0;i < 1000000;++i) {
    double d = gaussian.nextNormalizedDouble() + 10;
    values.add(d);
  }
  validateEquality(values);
}
 

import org.apache.commons.math3.random.GaussianRandomGenerator; //导入方法依赖的package包/类
@Test
public void testNormallyDistributedRandomDataShiftedBackwards() {
  List<Double> values = new ArrayList<>();
  GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(0L));
  for(int i = 0;i < 1000000;++i) {
    double d = gaussian.nextNormalizedDouble() - 10;
    values.add(d);
  }
  validateEquality(values);
}
 

import org.apache.commons.math3.random.GaussianRandomGenerator; //导入方法依赖的package包/类
@Test
public void testNormallyDistributedRandomDataSkewed() {
  List<Double> values = new ArrayList<>();
  GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(0L));
  for(int i = 0;i < 1000000;++i) {
    double d = (gaussian.nextNormalizedDouble()+ 10000) /1000;
    values.add(d);
  }
  validateEquality(values);
}
 

import org.apache.commons.math3.random.GaussianRandomGenerator; //导入方法依赖的package包/类
@Test
public void testNormallyDistributedRandomDataAllNegative() {
  List<Double> values = new ArrayList<>();
  GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(0L));
  for(int i = 0;i < 1000000;++i) {
    double d = -1*gaussian.nextNormalizedDouble();
    values.add(d);
  }
  validateEquality(values);
}
 

import org.apache.commons.math3.random.GaussianRandomGenerator; //导入方法依赖的package包/类
public static void main(String... argv) {
  DescriptiveStatistics perfStats = new DescriptiveStatistics();
  OnlineStatisticsProvider statsProvider = new OnlineStatisticsProvider();
  List<Double> values = new ArrayList<>();
  GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(0L));
  for(int i = 0;i < NUM_DATA_POINTS;++i) {
    //get the data point out of the [0,1] range
    double d = 1000*gaussian.nextNormalizedDouble();
    values.add(d);
    statsProvider.addValue(d);
  }

  for(int perfRun = 0;perfRun < NUM_RUNS;++perfRun) {
    StellarStatisticsFunctions.StatsBin bin = new StellarStatisticsFunctions.StatsBin();
    long start = System.currentTimeMillis();
    Random r = new Random(0);
    for (int i = 0; i < TRIALS_PER_RUN; ++i) {
      //grab a random value and fuzz it a bit so we make sure there's no cheating via caching in t-digest.
      bin.apply(ImmutableList.of(statsProvider, values.get(r.nextInt(values.size())) - 3.5, PERCENTILES));
    }
    perfStats.addValue(System.currentTimeMillis() - start);
  }
  System.out.println( "Min/25th/50th/75th/Max Milliseconds: "
                    + perfStats.getMin()
                    + " / " + perfStats.getPercentile(25)
                    + " / " + perfStats.getPercentile(50)
                    + " / " + perfStats.getPercentile(75)
                    + " / " + perfStats.getMax()
                    );
}
 

import org.apache.commons.math3.random.GaussianRandomGenerator; //导入方法依赖的package包/类
@BeforeClass
public static void beforeClass() {
  Random rng = new Random(0);
  GaussianRandomGenerator gen = new GaussianRandomGenerator(new MersenneTwister(0));
  for(int i = 0;i < SAMPLE_SIZE;++i) {
    double us= 10*rng.nextDouble();
    uniformSample.add(us);
    uniformStats.addValue(us);
    double gs= 10*gen.nextNormalizedDouble();
    gaussianSample.add(gs);
    gaussianStats.addValue(gs);
  }
}
 

import org.apache.commons.math3.random.GaussianRandomGenerator; //导入方法依赖的package包/类
@Test
public void testMergeProviders() throws Exception {
  List<StatisticsProvider> providers = new ArrayList<>();
  /*
  Create 10 providers, each with a sample drawn from a gaussian distribution.
  Update the reference stats from commons math to ensure we are
   */
  GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(1L));
  SummaryStatistics sStatistics= new SummaryStatistics();
  DescriptiveStatistics dStatistics = new DescriptiveStatistics();
  for(int i = 0;i < 10;++i) {
    List<Double> sample = new ArrayList<>();
    for(int j = 0;j < 100;++j) {
      double s = gaussian.nextNormalizedDouble();
      sample.add(s);
      sStatistics.addValue(s);
      dStatistics.addValue(s);
    }
    StatisticsProvider provider = (StatisticsProvider)run("STATS_ADD(STATS_INIT(), " + Joiner.on(",").join(sample) + ")"
                                                         , new HashMap<>()
                                                         );
    providers.add(provider);
  }

  /*
  Merge the providers and validate
   */
  Map<String, Object> providerVariables = new HashMap<>();
  for(int i = 0;i < providers.size();++i) {
    providerVariables.put("provider_" + i, providers.get(i));
  }
  StatisticsProvider mergedProvider =
          (StatisticsProvider)run("STATS_MERGE([" + Joiner.on(",").join(providerVariables.keySet()) + "])"
                                 , providerVariables
                                 );
  OnlineStatisticsProviderTest.validateStatisticsProvider(mergedProvider, sStatistics , dStatistics);

}