Alternating Conditional Expectation Algorithm
=============================================
`Build Status <https://travis-ci.org/zhaofeng-shu33/ace_cream>`__
`PyPI <https://pypi.org/project/ace_cream>`__
`Windows <https://ci.appveyor.com/project/zhaofeng-shu33/ace_cream>`__
This project provides a wrapper program of Python for ACE algorithm
implementation of Fortran.
Install Binary Distribution
---------------------------
Currently, only 64-bit binary distribution is provided. Run
``pip install ace_cream`` to install the binary distribution.
+----------+-------+-------+-------+
| Platform | py3.6 | py3.7 | py2.7 |
+==========+=======+=======+=======+
| Windows | T | T | T |
+----------+-------+-------+-------+
| MacOS | | T | T |
+----------+-------+-------+-------+
| Linux | T | T | T |
+----------+-------+-------+-------+
How to build
------------
You need ``numpy`` and fortran compiler to build from source.
Windows
~~~~~~~
- Install `Visual
C++ <https://blogs.msdn.microsoft.com/vcblog/2017/03/07/msvc-the-best-choice-for-windows/>`__
toolchain.
- Download MinGW-w64 from
`sourceforge <https://sourceforge.net/projects/mingw-w64/files/latest/download?source=typ_redirect>`__,
which provides the necessary fortran compiler
- Install MinGW-w64 and add ``{install_dir}\mingw64\bin`` path to
environment variable (make ``gfortran`` accessible from command
line).
- (for conda environment) Add ``{install_dir}\Anaconda3\Scripts`` to
environment variable (make ``f2py`` accessible from command line).
Mac
~~~
You can use package manager to install ``gfortran`` (included within gnu
compiler collection). For example, with ``Homebrew`` you can use
.. code:: shell
brew install gcc
Ubuntu
~~~~~~
To install ``gfortran``, use the default package manager:
.. code:: shell
sudo apt-get install gfortran
Run ``python setup.py install`` from command line at the project root
directory.
How to use
----------
.. code:: python
import numpy as np
from ace_cream import ace_cream
# discrete case, binary symmetric channel with crossover probability 0.1
x = np.random.choice([0,1], size=N_SIZE)
n = np.random.choice([0,1], size=N_SIZE, p=[0.9, 0.1])
y = np.mod(x + n, 2)
# set both x(cat=0) and y(cat=-1) as categorical type
tx, ty = ace_cream(x, y, cat=[-1,0])
# continuous case
x = np.random.uniform(0, np.pi, 200)
y = np.exp(np.sin(x)+np.random.normal(size=200)/2)
tx, ty = ace_cream(x, y)
Result
------
.. figure:: ./example/continuous.svg
:alt: image
image
change log
----------
1. v0.1 initial commit
2. v0.2 modify to relative import in ``__init__.py``
3. v0.3 add support for multiple columns of x and other directions of
transformation
4. v0.4 add ``f_mapping`` function and unittests for this function
License
-------
Apache License Version 2.0
Reference
---------
1. https://en.wikipedia.org/wiki/Alternating_conditional_expectations
