Examples of org.apache.commons.math3.stat.descriptive.DescriptiveStatistics.addValue()

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

Class org.apache.commons.math3.stat.descriptive.DescriptiveStatistics

Examples of org.apache.commons.math3.stat.descriptive.DescriptiveStatistics.addValue()

org.apache.commons.math3.stat.descriptive.DescriptiveStatistics.addValue()
Adds the value to the dataset. If the dataset is at the maximum size (i.e., the number of stored elements equals the currently configured windowSize), the first (oldest) element in the dataset is discarded to make room for the new value. @param v the value to be added


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


        for (int i = 0; i < 100; i++) {
            List<MaterializedRow> values = computeActual("SELECT orderkey FROM ORDERS TABLESAMPLE POISSONIZED (50)").getMaterializedRows();
            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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        long total = (long) computeExpected("SELECT COUNT(*) FROM orders", ImmutableList.of(BIGINT)).getMaterializedRows().get(0).getField(0);


        for (int i = 0; i < 100; i++) {
            long value = (long) computeActual("SELECT COUNT(*) FROM orders TABLESAMPLE POISSONIZED (50) RESCALED").getMaterializedRows().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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    public static double[] normalize(final double[] sample) {
        DescriptiveStatistics stats = new DescriptiveStatistics();


        // Add the data from the series to stats
        for (int i = 0; i < sample.length; i++) {
            stats.addValue(sample[i]);
        }


        // Compute mean and standard deviation
        double mean = stats.getMean();
        double standardDeviation = stats.getStandardDeviation();

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        StableRandomGenerator generator = new StableRandomGenerator(rg, 1d, 0.0);
        DescriptiveStatistics summary = new DescriptiveStatistics();


        for (int i = 0; i < sampleSize; ++i) {
            double sample = generator.nextNormalizedDouble();
            summary.addValue(sample);
        }


        // Standard Cauchy distribution should have zero median and mode
        double median = summary.getPercentile(50);
        Assert.assertEquals(0.0, median, 0.2);

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        line = in.readLine();


        while (line != null) {
            if (d != null) {
                d.addValue(Double.parseDouble(line.trim()));
            }  else {
                s.addValue(Double.parseDouble(line.trim()));
            }
            line = in.readLine();
        }

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            // Record deviations from "true" beta
            double dist = olsBeta.getDistance(b);
            olsBetaStats.addValue(dist * dist);
            dist = glsBeta.getDistance(b);
            glsBetaStats.addValue(dist * dist);
            
        }
        
        // Verify that GLS is on average more efficient, lower variance
        assert(olsBetaStats.getMean() > 1.5 * glsBetaStats.getMean());

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        double standardizedSample[] = StatUtils.normalize(sample);


        DescriptiveStatistics stats = new DescriptiveStatistics();
        // Add the data from the array
        for (int i = 0; i < length; i++) {
            stats.addValue(standardizedSample[i]);
        }
        // the calculations do have a limited precision    
        double distance = 1E-10;
        // check the mean an standard deviation
        Assert.assertEquals(0.0, stats.getMean(), distance);

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        {
            DescriptiveStatistics statistics = new DescriptiveStatistics();
            for (int i = 0; i < accumulators.size(); i++) {
                BlockCursor cursor = accumulators.get(i).evaluateFinal().cursor();
                checkArgument(cursor.advanceNextPosition(), "accumulator returned no results");
                statistics.addValue(getNumeric(cursor));
            }


            BlockBuilder builder = VARCHAR.createBlockBuilder(new BlockBuilderStatus());
            String result = formatApproximateOutput(statistics, confidence);
            builder.appendSlice(Slices.utf8Slice(result));

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            for (int i = 0; i < accumulators.size(); i++) {
                BlockBuilder builder = accumulators.get(i).getFinalType().createBlockBuilder(new BlockBuilderStatus());
                accumulators.get(i).evaluateFinal(groupId, builder);
                BlockCursor cursor = builder.build().cursor();
                checkArgument(cursor.advanceNextPosition(), "accumulator returned no results");
                statistics.addValue(getNumeric(cursor));
            }


            String result = formatApproximateOutput(statistics, confidence);
            output.appendSlice(Slices.utf8Slice(result));
        }

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            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() - ApproximateCountDistinctAggregations.getStandardError()), 1.0e-2);
    }

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