mf2 2019.11.3

A collection of analytical benchmark functions in multiple fidelities

Multi-Fidelity-Functions

This package contains Python implementations for a variety of multi-fidelity benchmark functions. The only dependency is the numpy package.

Installation

The recommended way to install mf2 is with Python's Pip:

python3 -m pip install --user mf2

For the latest version, you can install directly from source:

python3 -m pip install --user https://github.com/sjvrijn/mf2/archive/master.zip

To work in your own version locally, it is best to clone the repository first:

git clone https://github.com/sjvrijn/mf2.git
cd mf2
python3 -m pip install --user -e .

Example Usage

import mf2
import numpy as np

forrester = mf2.Forrester(ndim=2)
np.random.seed(42)
X = np.random.random((5, 2))  # 5 samples in 2D

print(forrester.high(X))
# Out: array([ 6.20598519, -2.90702413, -0.96082789,  0.78490341, -2.56183228])

print(forrester.low(X))
# Out: array([6.47672047, 1.89322581, 7.95952025, 5.77115291, 2.17314591])

For more usage examples, please refer to the full documentation on readthedocs.

Contributing

Contributions to this project are more than welcome!

Bugs

If you've found a problem of some sort, please open an issue on GitHub.

Additions

To add new functions to this package, you can roughly follow the following steps:

1. Implement the function in a new file in the appropriate (sub)folder
2. Add it to the tests:
   • add the function in the tests/property_test.py and tests/regression_test.py files
   • run the tests/create_regression_data.py file to generate the new data files
   • run the tests
3. Make sure to commit all new and updated files to git (Travis-CI will complain otherwise ;)
4. Create a pull-request!

If you need any help with this process, please get in touch as outlined under Contact.

Contact

The Gitter channel is the preferred way to get in touch for any other questions, comments or discussions about this package.