README.md

Similarity

Cosine similarity, Simhash, and Sørensen–Dice implementations.

Full documentation can be found at https://hexdocs.pm/similarity.

Installation

Add similarity to your list of dependencies in mix.exs:

def deps do
  [
    {:similarity, "~> 0.5"}
  ]
end

Similarity requires Elixir 1.14 or later.

Cosine Similarity

Cosine similarity is not sensitive to the scale of the vector:

Similarity.cosine([1,2,3], [1,2,3])
1.0
Similarity.cosine([1,2,3], [2,4,6])
1.0

Module Similarity.Cosine takes care of building a struct and streaming similarities: (It handles non matching attributes, elements added don't have to have the exact attributes)

s = Similarity.Cosine.new()
s = s |> Similarity.Cosine.add("a", [{"bananas", 9}, {"hair_color_r", 124}, {"hair_color_g", 8}, {"hair_color_b", 122}])
s = s |> Similarity.Cosine.add("b", [{"bananas", 19}, {"hair_color_r", 124}, {"hair_color_g", 8}, {"hair_color_b", 122}])
s = s |> Similarity.Cosine.add("c", [{"bananas", 9}, {"hair_color_r", 124}])

s |> Similarity.Cosine.stream |> Enum.to_list
[
  {"a", "b", 1.9967471152702767},
  {"a", "c", 1.4142135623730951},
  {"b", "c", 1.409736747211141}
]

s |> Similarity.Cosine.between("a", "b")
1.9967471152702767

Similarity.cosine_srol/2 Cosine similarity between two vectors, multiplied by the square root of the length of the vectors. (In my experience, where the number of common attributes doesn't match between some vectors, this gives a better value.)

a = [1,2,3,4]
b = [1,2,3]
c = [1,2,3,4]

Similarity.cosine_srol(a |> Enum.take(3), b)
1.7320508075688772
Similarity.cosine_srol(a, c)
2.0

Above even though the first 3 elements of a match with b, just like a with c, the a & c cosine similarity returns higher value due to more elements matching. In real world scenario I suggest using this if compared vectors aren't the same length.

Simhash

left = "pork belly jerky brisket tenderloin shank kevin spare ribs"
right = "porchetta pork loin. Leberkas ball tip biltong, beef ribs"

Similarity.simhash(left, right, ngram_size: 3)
0.484375

Sørensen–Dice

Similarity.sorensen_dice("this that", "just that")
0.42857142857142855

Performance

Reproduce the Simhash benchmark:

$ elixir bench/simhash.exs

On Linux with an AMD Ryzen 7 8845HS, Elixir 1.20.3, and Erlang/OTP 29.0.5:

Implementation	Average time	Throughput	Memory
Similarity 0.5.1	55.80 μs	17.92 K ips	126.82 KB
simhash-ex e04aa01	143.60 μs	6.96 K ips	262.54 KB

For this input, Similarity was 2.57× faster and used 52% less memory. Results vary by hardware and runtime version.

License

Similarity is MIT licensed.