A gem to test what metric is best for certain kinds of datasets in machine
learning. Besides the
Array class, I also want to support
This is a fork of the gem Distance Measure, which has a similar objective, but isn't actively maintained and doesn't support NMatrix. Thank you, @reddavis. :)
gem install measurable
I test this gem (via Travis CI) on Ruby MRI 2.5, 2.6 and 2.7.
Available distance measures
I'm using the term "distance measure" without much concern for the strict mathematical definition of a metric. If the documentation for one of the methods isn't clear about it being or not a metric, please open an issue.
The following are the similarity measures supported at the moment:
- Euclidean distance
- Squared euclidean distance
- Cosine distance
- Max-min distance (from "K-Means clustering using max-min distance measure")
- Jaccard distance
- Tanimoto distance
- Haversine distance
- Minkowski (aka Cityblock or Manhattan) distance
- Chebyshev distance
- Hamming distance
- Levenshtein distance
- Kullback-Leibler divergence
How to use
The API I intend to support is something like this:
require 'measurable' # Calculate the distance between two points in space. Measurable.euclidean([1, 1], [0, 0]) # => 1.41421 # Calculate the norm of a vector, i.e. its distance from the origin. Measurable.euclidean([1, 1]) # => 1.4142135623730951 # Get the cosine distance between Measurable.cosine_distance([1, 2], [2, 3]) # => 0.007722123286332261 # Calculate sum of squares directly. Measurable.euclidean_squared([3, 4]) # => 25
Most of the methods accept arbitrary enumerable objects instead of Arrays. For example, it's possible to use NMatrix.
The documentation is hosted on rubydoc.
See LICENSE for details.
distance_measures gem is copyrighted by @reddavis.