* href="http://llimllib.github.io/bloomfilter-tutorial/">tutorial</a> may help you understand how
 * they work.
 *
 * <p>The false positive probability ({@code FPP}) of a Bloom filter is defined as the probability
 * that {@linkplain #mightContain(Object)} will erroneously return {@code true} for an object that
 * has not actually been put in the {@code BloomFilter}.
 *

   * saving space.
   */
  @VisibleForTesting static final double MAX_LOAD_FACTOR = 1.0;

  /**
   * Maximum allowed false positive probability of detecting a hash flooding attack given random
   * input.
   */
  @VisibleForTesting static final double HASH_FLOODING_FPP = 0.001;

  /**

        .addEqualityGroup(ImmutableSet.of(1, 2, 1), ImmutableSet.of(2, 1, 1))
        .testEquals();
  }

  /**
   * The maximum allowed probability of falsely detecting a hash flooding attack if the input is
   * randomly generated.
   */
  private static final double HASH_FLOODING_FPP = 0.001;

   * 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.
   */
  @VisibleForTesting static final double HASH_FLOODING_FPP = 0.001;

  /**

              @SuppressWarnings("ThreadPriorityCheck") // doing our best to test for races
              protected Schedule getNextSchedule() throws Exception {
                // Explicitly yield to increase the probability of a pathological scheduling.
                Thread.yield();
                return new Schedule(0, SECONDS);
              }
            };
          }
        };
    service.useBarriers = false;

              @SuppressWarnings("ThreadPriorityCheck") // doing our best to test for races
              protected Schedule getNextSchedule() throws Exception {
                // Explicitly yield to increase the probability of a pathological scheduling.
                Thread.yield();
                return new Schedule(0, SECONDS);
              }
            };
          }
        };
    service.useBarriers = false;

            // number of expected insertions or don't run the test for so long.
            // Don't forget, the bloom filter slowly saturates over time and the
            // expected false positive probability goes up!
            assertThat(bloomFilter.expectedFpp()).isLessThan(safetyFalsePositiveRate);
          } while (stopwatch.elapsed(SECONDS) < 1);
        };

 * <p>The average time complexity of the computation is O(N) in the size of the dataset. There is a
 * worst case time complexity of O(N^2). You are extremely unlikely to hit this quadratic case on
 * randomly ordered data (the probability decreases faster than exponentially in N), but if you are
 * passing in unsanitized user data then a malicious user could force it. A light shuffle of the

    you will have to set custom session_options in your `RunConfig`.
*   Moved Distributions and Bijectors from `tf.contrib.distributions` to
    [Tensorflow Probability (TFP)](https://github.com/tensorflow/probability).
    `tf.contrib.distributions` is now deprecated and will be removed by the end
    of 2018.
*   Adding new endpoints for existing tensorflow symbols. These endpoints are

prime by retrying the whole process, since 65537 is // probably only a factor of one of p-1 or q-1, but the probability // of this check failing is only 1/65537, so it doesn't matter. continue } if e.ExpandFor(λ).Mul(d, λ).IsOne() == 0 { return nil, errors.New("rsa: internal error: e*d != 1 mod λ(N)") } // FIPS 186-5, A.1.1(3) requires checking that d > 2^(nlen / 2). // // The probability of this check failing when d is derived from // (e, p, q) is roughly // // 2^(nlen/2) / 2^nlen = 2^(-nlen/2)...