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

Package org.apache.commons.math.stat.descriptive

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

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

org.apache.commons.math.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 time : env.getExecutionLatenciesNanos()) {
          ds.addValue(time);
        }
        // Ensures that web ui circumvents the display of NaN values when there are zero samples.
        if (ds.getN() == 0) {
          ds.addValue(0);
        }
        results.put(env.getInstance().getClass().getSimpleName(), ds);
      }
    }
    return results;

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      super.testTakedown();
      if (opts.reportLatency) {
        Arrays.sort(times);
        DescriptiveStatistics ds = new DescriptiveStatistics();
        for (double t : times) {
          ds.addValue(t);
        }
        LOG.info("randomRead latency log (ms), on " + times.length + " measures");
        LOG.info("99.9999% = " + ds.getPercentile(99.9999d));
        LOG.info(" 99.999% = " + ds.getPercentile(99.999d));
        LOG.info("  99.99% = " + ds.getPercentile(99.99d));

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    Map<String, DescriptiveStatistics> results = new HashMap<String, DescriptiveStatistics>();
    for (RegionEnvironment env : coprocessors) {
      DescriptiveStatistics ds = new DescriptiveStatistics();
      if (env.getInstance() instanceof RegionObserver) {
        for (Long time : env.getExecutionLatenciesNanos()) {
          ds.addValue(time);
        }
        // Ensures that web ui circumvents the display of NaN values when there are zero samples.
        if (ds.getN() == 0) {
          ds.addValue(0);
        }

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        for (Long time : env.getExecutionLatenciesNanos()) {
          ds.addValue(time);
        }
        // Ensures that web ui circumvents the display of NaN values when there are zero samples.
        if (ds.getN() == 0) {
          ds.addValue(0);
        }
        results.put(env.getInstance().getClass().getSimpleName(), ds);
      }
    }
    return results;

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


            long actual = estimateCount(values, 0);
            double error = (actual - uniques) * 1.0 / uniques;


            stats.addValue(error);
        }


        assertLessThan(stats.getMean(), 1e-2);
        assertLessThan(Math.abs(stats.getStandardDeviation() - getAggregationFunction().getStandardError()), 1e-2);
    }

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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, String.format("Expected mean sampling rate to be ~0.5, but was %s", mean));
    }

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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, String.format("Expected mean sampling rate to be ~0.5, but was %s", mean));
    }

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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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        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, String.format("Expected mean sampling rate to be ~0.5, but was %s", mean));
    }

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