BloomFilterImplementation.js

class BloomFilter {
  static DEFAULT_HASH_FN(size) {
    return (hex) => {
      const hex32 = XXH.h32(hex);
      return (string) => {
        return Math.abs(hex32.update(string).digest().toNumber() % size);
      };
    };
  }
  
  static DEFAULT_HEXES = [0xABCD, 0x1234, 0x6789];

  constructor(
    size = 100,
    hashingConstructor = BloomFilter.DEFAULT_HASH_FN,
    hexes = BloomFilter.DEFAULT_HEXES
  ){
    const sizedHashGenerator = hashingConstructor(size);
    this.bitz = Array(size).fill(0);
    this.hashingFns =
     hexes.map(sizedHashGenerator)
  }

  add = (string) => {
     this.hash(string)
     .forEach((index) => {
       this.bitz[index] = 1;
     })
  }

  contains = (string) => {
    return this.hash(string).reduce((ans, index) => this.bitz[index] == 1 && ans, true);
  }

  hash = (string) => {
    return this.hashingFns
     .map((fn) => fn(string))
  }
};

/* UNIT TESTS
describe('BloomFilter', function() {
  let bf;
  beforeEach(() => {
    bf = new BloomFilter();
  })
  it('returns false when empty', () => {
    expect(bf.contains("Brian")).toBe(false);
    expect(bf.contains("Sarah")).toBe(false);
    expect(bf.contains("Simona")).toBe(false);
  });
  it('handles one item', () => {
    expect(bf.contains("Brian")).toBe(false);
    bf.add("Brian");
    expect(bf.contains("Brian")).toBe(true);
    expect(bf.contains("Sarah")).toBe(false);
    expect(bf.contains("Simona")).toBe(false);
  });
  it('handles many items', () => {
    const names = ["Brian", "Simona", "Sarah", "Asim", "John", "Sean", "Jessie", "Paige", "Ashley"];
    names.forEach((item) => bf.add(item));
    names.forEach((item) => expect(bf.contains(item)).toBe(true));
    ["Sam", "Chris", "Taylor", "Florence"].forEach((item) => expect(bf.contains(item)).toBe(false));
  });
});
*/

For my future reference:
Bloom filter probability calculator