/*
   Copyright 2010 Aaron J. Radke

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
*/
package cc.drx

object Bloom{
  def apply(n:Int, confidence:Double = 3.sigma.toDouble.not) = {
    val p = confidence.not //probability of false positive
    val m = (-n*p.ln/2.ln.sq).ceil.toInt  //number of bits to support confidence
    val k = (2.log*m/n).ceil.toInt //optimal hashCount
    new Bloom(n,m,k)
  }
}

class Bloom(val n:Int, val m:Int, val k:Int){
    //TODO implement  bloom filter feature test with murm3 hash from scala
    //TODO add byte/long data pseudo immutable
    lazy val p = (1 - (1 - m.inv)**(k*n))**k // val probFalsePositiveApprox = (1 - math.exp(-1d*k*n/m))**k
    val toKson = s"Bloom bits:$m elements:$n hashPoints:$k falseRate:$p confidence:${p.not} bits/elem:${m/n}"
}