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

      for (Long value : randomSet) {
        estimator.add(value);
      }


      double error = (estimator.estimate() - randomSet.size()) * 1.0 / randomSet.size();
      stats.addValue(error);
    }


    assertTrue(stats.getMean() < 1e-2);
    assertTrue(stats.getStandardDeviation() < 1.04 / Math.sqrt(buckets));
  }

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                break;
            }


            double millis = 1E-6 * (System.nanoTime() - start);
            timeStats.addValue(millis);
            trysStats.addValue(trys);


            if (!good)
                throw new RuntimeException();
        }
        System.out.println(timeStats);

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                    timSort.addValue(stop - start);


                    start = System.currentTimeMillis();
                    ArraysUtils.insertionSort(t2, coSort);
                    stop = System.currentTimeMillis();
                    insertionSort.addValue(stop - start);
                }
                timMeanOut.write(arrayLength + "\t" + timSort.getMean() + "\n");
                insertionMeanOut.write(arrayLength + "\t" + insertionSort.getMean() + "\n");


                timMaxOut.write(arrayLength + "\t" + timSort.getMax() + "\n");

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                    for (int i = 0; i < 10; ++i) { //Each random sum repeatedly tested 1000 times
                        long start = System.nanoTime();


                        Tensor res = Expand.expandPairOfSumsConcurrent(sums[0], sums[1], threads);
//                        System.out.println(res.size() + " " + res);
                        ds.addValue(System.nanoTime() - start);
                    }


                    stats[threads - 1] = ds; //Saving statistics for one thread
                }

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


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


            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(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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