"""Functions to evaluate analytically derived equations related to the SPA."""
from nengo.dists import CosineSimilarity
[docs]def prob_cleanup(similarity, dimensions, vocab_size):
"""Estimate the chance of successful cleanup.
This returns the chance that, out of *vocab_size* randomly chosen
vectors, none of them will be closer to a particular
vector than the value given by *similarity*. To use this, compare
your noisy vector with the ideal vector, pass that value in as
the similarity parameter, and set *vocab_size* to be the number of
competing vectors.
Requires SciPy.
"""
p = CosineSimilarity(dimensions).cdf(similarity)
if similarity < 1.0 and p == 1.0:
raise ArithmeticError("Insufficient floating point precision to compute value.")
return p ** vocab_size
