Package org.apache.commons.math3.distribution

Examples of org.apache.commons.math3.distribution.BinomialDistribution

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            if (Math.abs(approx - actual) <= error) {
                inRange++;
            }
        }

        BinomialDistribution binomial = new BinomialDistribution(numberOfRuns, getConfidence());
        int lowerBound = binomial.inverseCumulativeProbability(0.01);
        int upperBound = binomial.inverseCumulativeProbability(0.99);
        assertTrue(lowerBound < inRange && inRange < upperBound, String.format("%d out of %d passed. Expected [%d, %d]", inRange, numberOfRuns, lowerBound, upperBound));
    }
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    @Test
    public void testErrorBound()
            throws Exception
    {
        int trials = 20;
        BinomialDistribution binomial = new BinomialDistribution(trials, 0.5);

        int successes = 0;
        Random rand = new Random(0);
        for (int i = 0; i < trials; i++) {
            int sum = 1_000;
            PageBuilder builder = new PageBuilder(ImmutableList.of(BIGINT, BIGINT));
            for (int j = 0; j < sum; j++) {
                if (rand.nextDouble() < 0.5) {
                    builder.getBlockBuilder(0).appendLong(1);
                    builder.getBlockBuilder(1).appendLong(2);
                }
            }

            AggregationFunction function = new DeterministicBootstrappedAggregation(createTestingBlockEncodingManager(), LONG_SUM);

            successes += approximateAggregationWithinErrorBound(function, 1, 0.5, (double) sum, builder.build()) ? 1 : 0;
        }

        // Since we used a confidence of 0.5, successes should have a binomial distribution B(n=20, p=0.5)
        assertTrue(binomial.inverseCumulativeProbability(0.01) < successes && successes < binomial.inverseCumulativeProbability(0.99));
    }
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    @Test
    public void testErrorBound()
            throws Exception
    {
        int trials = 20;
        BinomialDistribution binomial = new BinomialDistribution(trials, 0.5);

        int successes = 0;
        Random rand = new Random(0);
        for (int i = 0; i < trials; i++) {
            int sum = 1_000;
            List<TupleInfo> tupleInfos = ImmutableList.of(TupleInfo.SINGLE_LONG, TupleInfo.SINGLE_LONG);
            PageBuilder builder = new PageBuilder(tupleInfos);
            for (int j = 0; j < sum; j++) {
                if (rand.nextDouble() < 0.5) {
                    builder.getBlockBuilder(0).append(1);
                    builder.getBlockBuilder(1).append(2);
                }
            }

            BootstrappedAggregation function = new BootstrappedAggregation(LONG_SUM);

            successes += approximateAggregationWithinErrorBound(function, 1, 0.5, sum, 0, builder.build()) ? 1 : 0;
        }

        // Since we used a confidence of 0.5, successes should have a binomial distribution B(n=20, p=0.5)
        assertTrue(binomial.inverseCumulativeProbability(0.01) < successes && successes < binomial.inverseCumulativeProbability(0.99));
    }
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            }
            if (alternativeHypothesis == null) {
                throw new NullArgumentException();
            }

            final BinomialDistribution distribution = new BinomialDistribution(numberOfTrials, probability);
            switch (alternativeHypothesis) {
                case GREATER_THAN:
                    return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
                case LESS_THAN:
                    return distribution.cumulativeProbability(numberOfSuccesses);
                case TWO_SIDED:
                    int criticalValueLow = 0;
                    int criticalValueHigh = numberOfTrials;
                    double pTotal = 0;

                    while (true) {
                        double pLow = distribution.probability(criticalValueLow);
                        double pHigh = distribution.probability(criticalValueHigh);

                        if (pLow == pHigh) {
                            pTotal += 2 * pLow;
                            criticalValueLow++;
                            criticalValueHigh--;
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            if (Math.abs(approx - actual) <= error) {
                inRange++;
            }
        }

        BinomialDistribution binomial = new BinomialDistribution(numberOfRuns, getConfidence());
        int lowerBound = binomial.inverseCumulativeProbability(0.01);
        int upperBound = binomial.inverseCumulativeProbability(0.99);
        assertTrue(lowerBound < inRange && inRange < upperBound, String.format("%d out of %d passed. Expected [%d, %d]", inRange, numberOfRuns, lowerBound, upperBound));
    }
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      }
      lastTotalTrainCount = totalTrainCount;
      lastTestCount = testCount;
      int totalNew = testCount + newTrainInGen;

      IntegerDistribution dist = new BinomialDistribution(random, totalNew, TEST_FRACTION);
      double probability;
      if (testCount < dist.getNumericalMean()) {
        probability = dist.cumulativeProbability(testCount);
      } else {
        probability = 1.0 - dist.cumulativeProbability(testCount);
      }
      log.info("Probability of observing {} as {} sample of {}: {}",
               testCount, TEST_FRACTION, totalNew, probability);
      assertTrue(probability >= 0.001);
    }
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            }
            if (alternativeHypothesis == null) {
                throw new NullArgumentException();
            }

            final BinomialDistribution distribution = new BinomialDistribution(numberOfTrials, probability);
            switch (alternativeHypothesis) {
                case GREATER_THAN:
                    return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
                case LESS_THAN:
                    return distribution.cumulativeProbability(numberOfSuccesses);
                case TWO_SIDED:
                    int criticalValueLow = 0;
                    int criticalValueHigh = numberOfTrials;
                    double pTotal = 0;

                    while (true) {
                        double pLow = distribution.probability(criticalValueLow);
                        double pHigh = distribution.probability(criticalValueHigh);

                        if (pLow == pHigh) {
                            pTotal += 2 * pLow;
                            criticalValueLow++;
                            criticalValueHigh--;
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        BinomialDistributionTest testInstance = new BinomialDistributionTest();
        int[] densityPoints = testInstance.makeDensityTestPoints();
        double[] densityValues = testInstance.makeDensityTestValues();
        int sampleSize = 1000;
        int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
        BinomialDistribution distribution = (BinomialDistribution) testInstance.makeDistribution();
        double[] expectedCounts = new double[length];
        long[] observedCounts = new long[length];
        for (int i = 0; i < length; i++) {
            expectedCounts[i] = sampleSize * densityValues[i];
        }
        randomData.reSeed(1000);
        for (int i = 0; i < sampleSize; i++) {
          int value = randomData.nextBinomial(distribution.getNumberOfTrials(),
                  distribution.getProbabilityOfSuccess());
          for (int j = 0; j < length; j++) {
              if (value == densityPoints[j]) {
                  observedCounts[j]++;
              }
          }
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            if (Math.abs(approx - actual) <= error) {
                inRange++;
            }
        }

        BinomialDistribution binomial = new BinomialDistribution(numberOfRuns, getConfidence());
        int lowerBound = binomial.inverseCumulativeProbability(0.01);
        int upperBound = binomial.inverseCumulativeProbability(0.99);
        assertTrue(lowerBound < inRange && inRange < upperBound, String.format("%d out of %d passed. Expected [%d, %d]", inRange, numberOfRuns, lowerBound, upperBound));
    }
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    @Test
    public void testErrorBound()
            throws Exception
    {
        int trials = 20;
        BinomialDistribution binomial = new BinomialDistribution(trials, 0.5);

        int successes = 0;
        Random rand = new Random(0);
        for (int i = 0; i < trials; i++) {
            int sum = 1_000;
            List<TupleInfo> tupleInfos = ImmutableList.of(TupleInfo.SINGLE_LONG, TupleInfo.SINGLE_LONG);
            PageBuilder builder = new PageBuilder(tupleInfos);
            for (int j = 0; j < sum; j++) {
                if (rand.nextDouble() < 0.5) {
                    builder.getBlockBuilder(0).append(1);
                    builder.getBlockBuilder(1).append(2);
                }
            }

            AggregationFunction function = new DeterministicBootstrappedAggregation(LONG_SUM);

            successes += approximateAggregationWithinErrorBound(function, 1, 0.5, (double) sum, builder.build()) ? 1 : 0;
        }

        // Since we used a confidence of 0.5, successes should have a binomial distribution B(n=20, p=0.5)
        assertTrue(binomial.inverseCumulativeProbability(0.01) < successes && successes < binomial.inverseCumulativeProbability(0.99));
    }
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