# Similarity
[](https://travis-ci.org/preciz/similarity)
Cosine similarity & Simhash implementation
Full documentation can be found at [https://hexdocs.pm/similarity](https://hexdocs.pm/similarity).
## Installation
Add `similarity` to your list of dependencies in `mix.exs`:
```elixir
def deps do
[
{:similarity, "~> 0.2"}
]
end
```
## Cosine Similarity
Cosine similarity is not sensitive to the scale of the vector!
```elixir
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)
```elixir
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 number of common attributes don't match between some vectros this gives a better value).
```elixir
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
```elixir
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
```
## Performance
Similarity.simhash is 2x faster than simhash-ex v1.1.0 package.
```
Benchmark suite executing with the following configuration:
warmup: 2 s
time: 5 s
memory time: 0 ns
parallel: 1
inputs: none specified
Estimated total run time: 14 s
Benchmarking simhash-ex...
Benchmarking similarity.simhash...
Name ips average deviation median 99th %
similarity.simhash 3.67 K 272.69 μs ±6.50% 267.84 μs 353.05 μs
simhash-ex 1.75 K 572.14 μs ±12.31% 552.22 μs 781.02 μs
Comparison:
similarity.simhash 3.67 K
simhash-ex 1.75 K - 2.10x slower +299.46 μs
```
## License
Similarity is [MIT licensed](LICENSE).
