import org.apache.maven.model.resolution.ModelResolver;
import org.apache.maven.model.resolution.WorkspaceModelResolver;

/**
 * A model building request that delegates all methods invocations to another request, meant for easy transformations by
 * subclassing.
 *
 * @deprecated use {@link org.apache.maven.api.services.ModelBuilder} instead
 */
@Deprecated(since = "4.0.0")

     * {@code <groupId>:<artifactId>:<version>} but some of these coordinates may still be unknown at the point the
     * exception is thrown so this information is merely meant to assist the user.
     *
     * @return The identifier of the POM or an empty string if not known, never {@code null}.
     */
    public String getModelId() {

        #  And ReplaceSchema task basically ignores this.
        #
        #  Normally this 'except' means no getting meta data for excepted tables.
        #  (so the tables are not existing in SchemaHTML and HistoryHTML and so on).
        #  But you can specify the '@gen' suffix that means generate-only except.
        #  A table with the mark can be treated as documents but no generating classes.
        #

        accumulatorByAddAllStats.addAll(Stats.of(value));
      }
      double mean = accumulator.mean();
      double meanByAddAllStats = accumulatorByAddAllStats.mean();
      if (values.hasAnyNaN()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();
        assertWithMessage("mean by addAll(Stats) of " + values).that(meanByAddAllStats).isNaN();

      }
    } else {
      count++;
      if (isFinite(value) && isFinite(mean)) {
        // Art of Computer Programming vol. 2, Knuth, 4.2.2, (15) and (16)
        double delta = value - mean;
        mean += delta / count;
        sumOfSquaresOfDeltas += delta * (value - mean);
      } else {
        mean = calculateNewMeanNonFinite(mean, value);
        sumOfSquaresOfDeltas = NaN;
      }
      min = Math.min(min, value);

        accumulatorByAddAllStats.addAll(Stats.of(value));
      }
      double mean = accumulator.mean();
      double meanByAddAllStats = accumulatorByAddAllStats.mean();
      if (values.hasAnyNaN()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();
        assertWithMessage("mean by addAll(Stats) of " + values).that(meanByAddAllStats).isNaN();

        twoValuesAccumulator.xStats().mean(),
        twoValuesAccumulator.yStats().mean(),
        twoValuesAccumulator.xStats().populationVariance(),
        twoValuesAccumulator.populationCovariance());
    assertDiagonalLinearTransformation(
        twoValuesAccumulatorByAddAllPartitionedPairedStats.leastSquaresFit(),
        twoValuesAccumulatorByAddAllPartitionedPairedStats.xStats().mean(),

  }

  @GwtIncompatible // DoubleMath.mean
  @SuppressWarnings("deprecation") // test of deprecated method
  public void testMean_intVarargs() {
    assertThat(DoubleMath.mean(11, -22, 44, -88)).isWithin(1.0e-10).of(-13.75);
    assertThat(DoubleMath.mean(11)).isWithin(1.0e-10).of(11.0);
  }

  @GwtIncompatible // DoubleMath.mean
  @SuppressWarnings("deprecation") // test of deprecated method

  optional bytes raw = 2;

  // ContentEncoding is encoding used to encode 'Raw' data.
  // Unspecified means no encoding.
  optional string contentEncoding = 3;

  // ContentType  is serialization method used to serialize 'Raw'.
  // Unspecified means ContentTypeJSON.
  optional string contentType = 4;

 */
public class LoadingCacheSingleThreadBenchmark {
  @Param({"1000", "2000"})
  int maximumSize;

  @Param("5000")
  int distinctKeys;

  @Param("4")
  int segments;

  // 1 means uniform likelihood of keys; higher means some keys are more popular
  // tweak this to control hit rate
  @Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;