MCMC inference algorithms for normalized random measure mixtures
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README.md

nrmm.cpp

MCMC inference algorithms for normalized random measure mixtures

Coded & tested with Microsoft Visual Studio 2013 on Windows machine

Requires Eigen library (http://eigen.tuxfamily.org)

Usage: type nrmm data output bm nrm init sampler params after building

  • data: data name (e.g., toy, 10k, nips)
  • output: output folder name
  • bm: base measure, 0 for NormalWishart and 1 for Multinomial-Dirichlet
  • nrm: NRM, 0 for DP and 1 for NGGP
  • init: initialization option, 0 for exact IBHC and 1 for noisy IBHC (see experimental section of the paper)
  • sampler: sampler, 0 for Gibbs, 1 for split-merge and 2 for TGMCMC
  • params: parameters for samplers
    • nrmm data output bm nrm init 0 subset et_thres
    • nrmm data output bm nrm init 1 subset et_thres
      • subset for subset size (see paper) and et_thres for total running time
    • nrmm data output bm nrm init 2 num_sm depth et_thres
      • num_sm for the parameter G and depth for the parameter D in the paper

Demo: after building in Relase mode, put sample_script.bat in Relase folder and run to produce results for toy dataset. To see log-likelihood traces, run display_results.m in results folder.

The nips data was accquired from https://archive.ics.uci.edu/ml/datasets/Bag+of+Words.