@Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;

  static AtomicLong requests = new AtomicLong(0);
  static AtomicLong misses = new AtomicLong(0);

  @BeforeExperiment
  void setUp() {
    // random integers will be generated in this range, then raised to the
    // power of (1/concentration) and floor()ed

  @Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;

  static AtomicLong requests = new AtomicLong(0);
  static AtomicLong misses = new AtomicLong(0);

  @BeforeExperiment
  void setUp() {
    // random integers will be generated in this range, then raised to the
    // power of (1/concentration) and floor()ed

   * value can result in multiple misses, all returning the results of a single cache load
   * operation.
   */
  public long missCount() {
    return missCount;
  }

  /**
   * Returns the ratio of cache requests which were misses. This is defined as {@code missCount /

    }

    val bit = 1 shl id
    set = set or bit
    values[id] = value
    return this
  }

  /** Returns true if a value has been assigned for the setting `id`. */
  fun isSet(id: Int): Boolean {
    val bit = 1 shl id
    return set and bit != 0
  }

  /** Returns the value for the setting `id`, or 0 if unset. */
  operator fun get(id: Int): Int = values[id]

    assertEquals(4, binarySearch(0, 6) { index -> 9.compareTo(table[index]) })
    assertEquals(5, binarySearch(0, 6) { index -> 11.compareTo(table[index]) })

    // Search for misses.
    assertEquals(-1, binarySearch(0, 6) { index -> 0.compareTo(table[index]) })
    assertEquals(-2, binarySearch(0, 6) { index -> 2.compareTo(table[index]) })

    private final AtomicLong changeNotifications = new AtomicLong();
    
    public double getCacheHitRatio() {
        long hits = cacheHits.get();
        long misses = cacheMisses.get();
        long total = hits + misses;
        
        if (total == 0) return 0.0;
        return (double) hits / total;
    }
    
    public void recordCacheHit() { cacheHits.incrementAndGet(); }

import assertk.assertions.isFalse
import assertk.assertions.isTrue
import org.junit.jupiter.api.Test

class SettingsTest {
  @Test
  fun unsetField() {
    val settings = Settings()
    assertThat(settings.isSet(Settings.MAX_CONCURRENT_STREAMS)).isFalse()
    assertThat(settings.getMaxConcurrentStreams()).isEqualTo(Int.MAX_VALUE)
  }

  @Test
  fun setFields() {
    val settings = Settings()

vusercontent.net
now.sh

// VeryPositive SIA : http://very.lv
// Submitted by Danko Aleksejevs <******@****.***>
2038.io

// Viprinet Europe GmbH : http://www.viprinet.com
// Submitted by Simon Kissel <******@****.***>
router.management

// Virtual-Info : https://www.virtual-info.info/
// Submitted by Adnan RIHAN <******@****.***>
v-info.info

  /**
   * Closed addressing tends to perform well even with high load factors. Being conservative here
   * ensures that the table is still likely to be relatively sparse (hence it misses fast) while
   * saving space.
   */
  @VisibleForTesting static final double MAX_LOAD_FACTOR = 1.2;

  /**
   * Maximum allowed false positive probability of detecting a hash flooding attack given random
   * input.

      hits.add(allConstants[allConstants.length - 1].name());
    }

    List<String> misses = new ArrayList<>();
    for (int i = 0; i < 256 - hits.size(); ++i) {
      misses.add("INVALID");
    }

    List<String> sampleDataList = new ArrayList<>();
    sampleDataList.addAll(hits);
    sampleDataList.addAll(misses);
    Collections.shuffle(sampleDataList);