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1.  final def !=(arg0: Any): Boolean

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2.  final def ##: Int

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3.  final def ==(arg0: Any): Boolean

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4.  def apply[A](numEntries: Int, fpProb: Double)(implicit hash: Hash128[A]): BloomFilterMonoid[A]
5.  final def asInstanceOf[T0]: T0

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6.  def clone(): AnyRef

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7.  final def eq(arg0: AnyRef): Boolean

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8.  def equals(arg0: AnyRef): Boolean

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9.  def finalize(): Unit

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10.  final def getClass(): Class[_ <: AnyRef]

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11.  def hashCode(): Int

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12.  final def isInstanceOf[T0]: Boolean

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13.  final def ne(arg0: AnyRef): Boolean

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14.  final def notify(): Unit

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15.  final def notifyAll(): Unit

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16.  def optimalNumHashes(numEntries: Int, width: Int): Int
17.  def optimalWidth(numEntries: Int, fpProb: Double): Option[Int]
18.  def sizeEstimate(numBits: Int, numHashes: Int, width: Int, approximationWidth: Double = 0.05): Approximate[Long]

    Cardinality estimates are taken from Theorem 1 on page 15 of "Cardinality estimation and dynamic length adaptation for Bloom filters" by Papapetrou, Siberski, and Nejdl: http://www.softnet.tuc.gr/~papapetrou/publications/Bloomfilters-DAPD.pdf

    Cardinality estimates are taken from Theorem 1 on page 15 of "Cardinality estimation and dynamic length adaptation for Bloom filters" by Papapetrou, Siberski, and Nejdl: http://www.softnet.tuc.gr/~papapetrou/publications/Bloomfilters-DAPD.pdf

    Roughly, by using bounds on the expected number of true bits after n elements have been inserted into the Bloom filter, we can go from the actual number of true bits (which is known) to an estimate of the cardinality.

    approximationWidth defines an interval around the maximum-likelihood cardinality estimate. Namely, the approximation returned is of the form (min, estimate, max) = ((1 - approxWidth) * estimate, estimate, (1 + approxWidth) * estimate)

19.  final def synchronized[T0](arg0: => T0): T0

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20.  def toString(): String

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21.  final def wait(): Unit

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22.  final def wait(arg0: Long, arg1: Int): Unit

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23.  final def wait(arg0: Long): Unit

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