c3py 0.0.3

Compressed Continuous Computation Library in Python

Compressed Continuous Computation (C3)

Computing with functions

The Compressed Continuous Computation (C3) package is intended to make it easy to perform continuous linear and multilinear algebra with multidimensional functions. It works by representing multidimensional functions in a low-rank format. Common tasks include taking "matrix" decompositions of vector- or matrix-valued functions, adding or multiplying functions together, integrating multidimensional functions, and much much more. The following is a sampling of capabilities

• Adaptive approximation of a black-box model (specified as a function pointer)
• Regression of a model from data
• Both linear and nonlinear approximation
• Approximation in polynomial, piecewise polynomial, linear element, and radial basis function spaces
• General adaptive integration schemes
• Differentiation
• Multiplication
• Addition
• Rounding
• Computing Jacobians and Hessians
• UQ
  1. Expectation and variance
  2. Sobol sensitivities

In addition to the above capabilities, which are unique to the C3 package, I also have general optimization routines including

• BFGS
• LBFGS
• Gradient descent
• Stochastic Gradient with ADAM

Documentation of most functions is provided by Doxygen here: http://alexgorodetsky.com/c3doc/html/

Installation / Getting started

The dependencies for this code are

1. BLAS
2. LAPACK
3. SWIG (if building non-C interfaces)

From Source

git clone https://github.com/goroda/Compressed-Continuous-Computation.git c3
cd c3
mkdir build
cd build
cmake ..
make

This will install all shared libraries into c3/build/src. The main shared library is libc3, the rest are all submodules. To install to a particular location use

cmake .. -DCMAKE_INSTALL_PREFIX=/your/choice
make install

Python interface

You can install the python interface using the pip utility through

pip install c3py

One can obtain some examples in the pyexamples subdirectory

python pywrappers/pytest.py

Configuration Options

BUILD_STATIC_LIB

Default: `OFF'

Using this option can toggle whether or not static or shared libraries should be built.

Note: This option cannot be set to ON if building the python wrapper

BUILD_SUB_LIBS

Default: `OFF'

Using this option can toggle whether or not to build each sub-library into its own library

BUILD_TESTS

Default: `OFF'

Using this option can toggle whether or not to build unit tests

BUILD_EXAMPLES

Default: `OFF'

Using this option can toggle whether or not to compile exampels

BUILD_PROFILING

Default: `OFF'

Using this option can toggle whether or not to compile the profiling executables

BUILD_BENCHMARKS

Default: `OFF'

Using this option can toggle whether or not to compile the benchmarks tests

BUILD_UTILITIES

Default: `OFF'

Using this option can toggle whether or not to compile the utilities

Systems I have tested on

1. Mac OS X with clang version 8.0
2. Ubuntu with gcc version 5.0

Solutions to some possible problems

Error: Unable to find 'python.swg'

On Mac OS X, if SWIG is installed with macports using

sudo port install swig

then the above error might occur. To remedy this error install the swig-python package

sudo port install swig-python

Numpy errors

Sometimes you may see the following errors

_frozen_importlib:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 216, got 192

or

RuntimeError: The current Numpy installation ('/Users/alex/anaconda3/envs/pytorch/lib/python3.6/site-packages/numpy/__init__.py') fails to pass simple sanity checks. This can be caused for example by incorrect BLAS library being linked in, or by mixing package managers (pip, conda, apt, ...). Search closed numpy issues for similar problems.

One way that I have found (https://stackoverflow.com/a/47975375) that seems to solve this is to upgrade numpy by running the following command. I am really not sure why this works ...

sudo pip install numpy --upgrade --ignore-installed

Coding practices

I aim to document (with Doxygen) every function available to the user and provide a unit test. Furthermore, I won't push code to the master branch that has memory leaks. I am constantly looking for more suggestions for improving the robustness of the code if any issues are encountered.

Contributing

Please open a Github issue to ask a question, report a bug, or to request features. To contribute, fork the repository and setup a branch.

Author: Alex A. Gorodetsky
Contact: [e-mail]
Copyright (c) 2014-2016, Massachusetts Institute of Technology
Copyright (c) 2016-2017, Sandia National Laboratories
Copyright (c) 2018, University of Michigan
License: BSD