PYADOLC, a wrapper for ADOL-C
- Short Description:
- This PYADOLC, a Python module to differentiate complex algorithms written in Python. It wraps the functionality of the library ADOL-C (C++).
- Sebastian F. Walter
- Licence (new BSD):
Copyright (c) 2008, Sebastian F. Walter All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the HU Berlin nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY Sebastian F. Walter ''AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL Sebastian F. Walter BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy from adolc import * N = M = 10 A = numpy.zeros((M,N)) A[:] = [[ 1./N +(n==m) for n in range(N)] for m in range(M)] def f(x): return numpy.dot(A,x) # tape a function evaluation ax = numpy.array([adouble(0) for n in range(N)]) trace_on(1) independent(ax) ay = f(ax) dependent(ay) trace_off() x = numpy.array([n+1 for n in range(N)]) # compute jacobian of f at x J = jacobian(1,x) # compute gradient of f at x if M==1: g = gradient(1,x)
THIS VERSION OF PYADOLC IS KNOWN TO WORK WITH:
- Ubuntu Linux, Python 2.7.3, NumPy 1.8.0
- Debian Stretch Linux, Python 2.7.13, NumPy 1.13.1
- OSX 10.9 (Mavericks), Python 2.7.6, NumPy 1.8.0
- OSX 10.11 (El Capitan), Python 2.7.11, NumPy 1.10.11
There are several branches available for different versions of ADOL-C/Colpack and Boost. In case you have issues installing the master branch, you can have a look there.
INSTALLATION UBUNTU / DEBIAN (Stretch):
- install boost-python via apt-get
- install autotools-dev libtool libboost-all-dev
./bootstrap.shto download ADOL-C and ColPack and compile them.
python setup.pyand follow the instructions
Run:brew install wget brew install automake brew install shtool brew install libtool brew install boost brew install boost-python brew link boost --force brew link boost-python --force
If you installed homebrew in the default location
/usr/local, you can skip this step. Otherwise, if you installed homebrew somewhere else on your system, you will need to edit
setup.py. First, in the ColPack build section of
bootstrap.sh, add the flags:--with-boost-libdir='<homebrew_libdir>' --with-boost-includedir='<homebrew_includedir>'
to the end of the
<homebrew_includedir>are the locations of homebrew's
includedirectories, respectively. Similarly, edit setup.py so that
BOOST_DIR = '<homewbrew_root>'where
<homebrew_root>is the base directory of your homebrew install (where
include, ... are located).
Run:./bootstrap.sh CC=clang CXX=clang++ python setup.py build python setup.py install
You may have to run
brew link automaketo generate symbolic links.
TEST YOUR INSTALLATION:
- install nose, matplotlib, e.g., via pip install nose matplotlib
- add pyadolc to your python path
python -c "import adolc; adolc.test()". All tests should pass.
- If anything goes wrong, please file a bug report.
If you run the test from the root folder of pyadolc you will get
ImportError: No module named _adolcsince it first looks in the local folder
./adolcbefore trying the other directories in your PYTHONPATH.
Follow the steps in
./bootstrap.shand adapt if necessary.