Examples of org.apache.commons.math3.stat.descriptive.DescriptiveStatistics

Package org.apache.commons.math3.stat.descriptive

Examples of org.apache.commons.math3.stat.descriptive.DescriptiveStatistics

org.apache.commons.math3.util.Incrementor
Maintains a dataset of values of a single variable and computes descriptive statistics based on stored data. The {@link #getWindowSize() windowSize}property sets a limit on the number of values that can be stored in the dataset. The default value, INFINITE_WINDOW, puts no limit on the size of the dataset. This value should be used with caution, as the backing store will grow without bound in this case. For very large datasets, {@link SummaryStatistics}, which does not store the dataset, should be used instead of this class. If windowSize is not INFINITE_WINDOW and more values are added than can be stored in the dataset, new values are added in a "rolling" manner, with new values replacing the "oldest" values in the dataset.
Note: this class is not threadsafe. Use {@link SynchronizedDescriptiveStatistics} if concurrent access from multiplethreads is required.


    @Test
    public void testTableSampleBernoulli()
            throws Exception
    {
        DescriptiveStatistics stats = new DescriptiveStatistics();


        int total = computeExpected("SELECT orderkey FROM orders", ImmutableList.of(SINGLE_LONG)).getMaterializedTuples().size();


        for (int i = 0; i < 100; i++) {
            List<MaterializedTuple> values = computeActual("SELECT orderkey FROM ORDERS TABLESAMPLE BERNOULLI (50)").getMaterializedTuples();


            assertEquals(values.size(), ImmutableSet.copyOf(values).size(), "TABLESAMPLE produced duplicate rows");
            stats.addValue(values.size() * 1.0 / total);
        }


        double mean = stats.getGeometricMean();
        assertTrue(mean > 0.45 && mean < 0.55, format("Expected mean sampling rate to be ~0.5, but was %s", mean));
    }

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    @Test
    public void testTableSamplePoissonized()
            throws Exception
    {
        DescriptiveStatistics stats = new DescriptiveStatistics();


        int total = computeExpected("SELECT orderkey FROM orders", ImmutableList.of(SINGLE_LONG)).getMaterializedTuples().size();


        for (int i = 0; i < 100; i++) {
            List<MaterializedTuple> values = computeActual("SELECT orderkey FROM ORDERS TABLESAMPLE POISSONIZED (50)").getMaterializedTuples();
            stats.addValue(values.size() * 1.0 / total);
        }


        double mean = stats.getGeometricMean();
        assertTrue(mean > 0.45 && mean < 0.55, format("Expected mean sampling rate to be ~0.5, but was %s", mean));
    }

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    @Test
    public void testTableSamplePoissonizedRescaled()
            throws Exception
    {
        DescriptiveStatistics stats = new DescriptiveStatistics();


        long total = (long) computeExpected("SELECT COUNT(*) FROM orders", ImmutableList.of(TupleInfo.SINGLE_LONG)).getMaterializedTuples().get(0).getField(0);


        for (int i = 0; i < 100; i++) {
            long value = (long) computeActual("SELECT COUNT(*) FROM orders TABLESAMPLE POISSONIZED (50) RESCALED").getMaterializedTuples().get(0).getField(0);
            stats.addValue(value * 1.0 / total);
        }


        double mean = stats.getGeometricMean();
        assertTrue(mean > 0.90 && mean < 1.1, format("Expected sample to be rescaled to ~1.0, but was %s", mean));
        assertTrue(stats.getVariance() > 0, "Samples all had the exact same size");
    }

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    @Test
    public void testTableSampleBernoulli()
            throws Exception
    {
        DescriptiveStatistics stats = new DescriptiveStatistics();


        int total = computeExpected("SELECT orderkey FROM orders", ImmutableList.of(SINGLE_LONG)).getMaterializedTuples().size();


        for (int i = 0; i < 100; i++) {
            List<MaterializedTuple> values = computeActual("SELECT orderkey FROM ORDERS TABLESAMPLE BERNOULLI (50)").getMaterializedTuples();


            assertEquals(values.size(), ImmutableSet.copyOf(values).size(), "TABLESAMPLE produced duplicate rows");
            stats.addValue(values.size() * 1.0 / total);
        }


        double mean = stats.getGeometricMean();
        assertTrue(mean > 0.45 && mean < 0.55, format("Expected mean sampling rate to be ~0.5, but was %s", mean));
    }

View Full Code Here


    @Test
    public void testTableSamplePoissonized()
            throws Exception
    {
        DescriptiveStatistics stats = new DescriptiveStatistics();


        int total = computeExpected("SELECT orderkey FROM orders", ImmutableList.of(SINGLE_LONG)).getMaterializedTuples().size();


        for (int i = 0; i < 100; i++) {
            List<MaterializedTuple> values = computeActual("SELECT orderkey FROM ORDERS TABLESAMPLE POISSONIZED (50)").getMaterializedTuples();
            stats.addValue(values.size() * 1.0 / total);
        }


        double mean = stats.getGeometricMean();
        assertTrue(mean > 0.45 && mean < 0.55, format("Expected mean sampling rate to be ~0.5, but was %s", mean));
    }

View Full Code Here


    @Test
    public void testTableSamplePoissonizedRescaled()
            throws Exception
    {
        DescriptiveStatistics stats = new DescriptiveStatistics();


        long total = (long) computeExpected("SELECT COUNT(*) FROM orders", ImmutableList.of(TupleInfo.SINGLE_LONG)).getMaterializedTuples().get(0).getField(0);


        for (int i = 0; i < 100; i++) {
            long value = (long) computeActual("SELECT COUNT(*) FROM orders TABLESAMPLE POISSONIZED (50) RESCALED").getMaterializedTuples().get(0).getField(0);
            stats.addValue(value * 1.0 / total);
        }


        double mean = stats.getGeometricMean();
        assertTrue(mean > 0.90 && mean < 1.1, format("Expected sample to be rescaled to ~1.0, but was %s", mean));
        assertTrue(stats.getVariance() > 0, "Samples all had the exact same size");
    }

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    @Test
    public void testMultiplePositions()
            throws Exception
    {
        DescriptiveStatistics stats = new DescriptiveStatistics();


        for (int i = 0; i < 500; ++i) {
            int uniques = ThreadLocalRandom.current().nextInt(20000) + 1;


            List<Object> values = createRandomSample(uniques, (int) (uniques * 1.5));


            long actual = estimateGroupByCount(values);
            double error = (actual - uniques) * 1.0 / uniques;


            stats.addValue(error);
        }


        assertLessThan(stats.getMean(), 1.0e-2);
        assertLessThan(Math.abs(stats.getStandardDeviation() - ApproximateCountDistinctAggregation.getStandardError()), 1.0e-2);
    }

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    } else {
      start = 0;
      end = (int) dataSeries.getMaxX();
    }
    
    DescriptiveStatistics stats = new DescriptiveStatistics();
    for (int i = start; i<=end; i++) {
      stats.addValue(dataSeries.getY(i).doubleValue());
    }
    return stats.getMean();
    
  }

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        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }


        covarianceImpl =

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     * @param checker Convergence checker.
     */
    protected BaseOptimizer(ConvergenceChecker<PAIR> checker) {
        this.checker = checker;


        evaluations = new Incrementor(0, new MaxEvalCallback());
        iterations = new Incrementor(0, new MaxIterCallback());
    }

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