require "bloom_filter"

# Create filter with bitmap size of 32 bytes and 3 hash functions.
filter = BloomFilter.new(bytesize = 32, hash_num = 3)

# Insert elements
filter.insert("Esperanto")
filter.insert("Toki Pona")

# Check elements presence
filter.has?("Esperanto")  # => true
filter.has?("Toki Pona")  # => true
filter.has?("Englsh")     # => false

# Create a filter, that with one million inserted items, gives 2% of false positives for #has? method
filter = BloomFilter.new_optimal(1_000_000, 0.02)
filter.bytesize # => 1017796 (993Kb)
filter.hash_num # => 6

filter = BloomFilter.new_optimal(2, 0.01)
filter.insert("Esperanto")
filter.dump_file("/tmp/bloom_languages")

loaded_filter = BloomFilter.load_file("/tmp/bloom_languages")
loaded_filter.has?("Esperanto") # => true
loaded_filter.has?("English")   # => false

f1 = BloomFilter.new(32, 3)
f1.insert("Esperanto")
f1.insert("Spanish")

f2 = BloomFilter.new(32, 3)
f2.insert("Esperanto")
f2.insert("English")

# Union
f3 = f1 | f2
f3.has?("Esperanto") # => true
f3.has?("Spanish")   # => true
f3.has?("English")   # => true

# Intersection
f4 = f1 & f2
f4.has?("Esperanto") # => true
f4.has?("Spanish")   # => false
f4.has?("English")   # => false

f1 = BloomFilter.new(16, 2)
f1.insert("Esperanto")
puts "f1 = (Esperanto)"
puts f1.visualize

f2 = BloomFilter.new(16, 2)
f2.insert("Spanish")
puts "f2 = (Spanish)"
puts f2.visualize

f3 = f1 | f2
puts "f3 = f1 | f2 = (Esperanto, Spanish)"
puts f3.visualize