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twinpeaks2018's Introduction

TwinPeaks2018

Build Status License: GPL v3

A new and (hopefully) correct implementation of TwinPeaks using the SwitchSampling idea.

(c) 2018 Brendon J. Brewer.

LICENSE: GNU General Public License version 3. See the LICENSE file for details.

Dependencies

For the C++ to compile, you'll need:

You can probably get these from your operating system's package manager. You'll also need the C++ header files, which are sometimes put into a separate package with the suffix -dev or -devel. For example, on Ubuntu, this will do the trick:

sudo apt-get install libboost-all-dev libyaml-cpp-dev

On the Python side, you'll need:

  • Python 3 and some packages (numpy, matploblib, and pandas). Anaconda is a good Python distribution that includes these. Otherwise they might come with (or be installable through) Python 3 as provided by your OS.
  • LaTeX and dvipng so that LaTeX can be used with matplotlib.

Compilation

First, compile the C++:

make

Then install the python package:

cd python
python3 setup.py install

Execution

To run the demo example:

./main

Then wait a long time. At any time during the run (or after it's completed), you can execute

python3 showresults.py

to see the results.

Acknowledgements

The authors of this paper.

twinpeaks2018's People

Contributors

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twinpeaks2018's Issues

Infer which temperatures are reliable

There ought to be a way to infer which canonical distributions are likely to have reliable properties and which ones are not. This could be based on something like an effective sample size but for reps instead of samples (effective number of reps).

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