Written by by Justin B. Kinney, Cold Spring Harbor Laboratory

Last updated on 23 March 2015

Reference: Kinney JB (2014) Unification of Field Theory and Maximum Entropy Methods for Learning Probability Densities. arXiv:1411.5371 [physics.data-an] http://arxiv.org/abs/1411.5371

Code: https://github.com/jbkinney/14_maxent

=== Instructions ===

All computations were performed using the Canopy Python Environment by Enthought, available at https://www.enthought.com/products/canopy/

Dependencies: scipy

The file deft_nobc.py and deft_utils.py contain all the density estmation routines used in this paper. Just copy this file to any directory in which you want to peform density estimation, and do execute "from deft_nobc.py import deft_nobc_1d" in your Python script

To see a simple demsontration of the function "deft_nobc_1d" in 1 dimension, run demo.py

To recreate Fig. 2, run fig_2.py.

To recreate Fig. 3, run fig_3_calculate.py, then fig_3_draw.py.

To recreate Fig. 4, run fig_4.py.

=== FILES ===

demo.py: Contains a quick example of DEFT without boundary conditions in 1D.

deft_nobc.py: Contains primary functions for field theory density estimation in 1D without boundary conditions.

deft_utils.py Contains helper functions for field theory density estimation in 1D without boundary conditions.

fig_2.py: Performs computations for and plots Fig. 2. Saves as fig_2.pdf

fig_3_calculate.py: Performs computations for Fig. 3. Saves results in fig_3_20.pickle (for 20 simulations, not 100).

fig_3_draw.py: Draws Fig. 3, saves as fig_3.pdf

fig_3.pickle: Contains results of simulations performed for fig_3.pdf

fig_4.py: Draws Fig. 4, saves as fig_4.pdf