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

        {
            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 = new BlockBuilder(SINGLE_VARBINARY);
            builder.append(formatApproximateOutput(statistics, confidence));
            return builder.build();

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


            output.append(formatApproximateOutput(statistics, confidence));
        }
    }

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        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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        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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        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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        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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        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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        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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            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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      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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