tufts-ml / any_parameter_encoder Goto Github PK
View Code? Open in Web Editor NEWPublic code release accompanying the AABI 2019 paper "Rapid Model Comparison by Amortizing Across Models" by Lily H. Zhang and Michael C. Hughes
License: MIT License
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Standardize an accessible interface for computing per-document representations
Would like to see TimedSVI, TimedMCMC, etc (all approaches) offer a standard interface to getting samples from the learned "q" distribution
def draw_samples_from_normal_approx_posterior(docs, topics, n_samples):
'''
Args
-----
docs : 2d array (n_docs, n_vocab)
topics : 2d array (n_topics, n_vocab)
n_samples : int
Returns
--------
h_SK : 2d array (n_samples, n_topics)
Each row is an unconstrained real vector
Represents draw from q(h_n | x_n, topics)
'''
def draw_samples_from_softmax_normal_approx_posterior(docs, topics, n_samples):
'''
Args
-----
docs : 2d array (n_docs, n_vocab)
topics : 2d array (n_topics, n_vocab)
n_samples : int
Returns
-------
pi_SK : 2d array (n_samples, n_topics)
Each row is a vector on the simplex (non-negative, sums to one)
Represents draw from q(h_n | x_n, topics) that is transformed via softmax
'''
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