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NearPy
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NearPy is a Python framework for fast (approximated) nearest neighbour search in high dimensional vector spaces using different locality-sensitive hashing methods.
It allows to experiment and to evaluate new methods but is also production-ready. It comes with a redis storage adapter.
To install simply do *pip install NearPy*. It will also install the packages scipy, numpy and redis.
Example usage:
from nearpy import Engine
from nearpy.hashes import RandomBinaryProjections
# Dimension of our vector space
dimension = 500
# Create a random binary hash with 10 bits
rbp = RandomBinaryProjections('rbp', 10)
# Create engine with pipeline configuration
engine = Engine(dimension, lshashes=[rbp])
# Index 1000000 random vectors (set their data to a unique string)
for index in range(100000):
v = numpy.random.randn(dimension)
engine.store_vector(v, 'data_%d' % index)
# Create random query vector
query = numpy.random.randn(dimension)
# Get nearest neighbours
N = engine.neighbours(query)
Read more here: http://nearpy.io