NearPy 0.1

Framework for fast approximated nearest neighbour search.

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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.

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