GP emulators

Info:	Gaussian process (GP) emulators for Python
Author:	J Gomez-Dans <[email protected]>
Date:	2015-03-17
Description:	README file

This repository contains an implementation of GPs for emulation in Python. Although many different implementations exist, this particular one deals with fast GP predictions for large number of input vectors, where the training data sets are typically modest (e.g. less than 300 samples). Access to the emulation's partial derivatives and Hessian matrix is calculated, and training is also taken care of.

Requirements:

• python ( 2.7 or later )
• numpy
• scipy

• GPU predict module:

  • cmake
  • CUDA 5.0 or later
  • CUnit

Install with GPU predict:

1. decide precision by modifying CMakeList.txt add_definition(-DDOUBLE_PRECISION) # double
2. install python setup.py install

Tests (for GPU predict):

test: python setup.py test

benchmark:python setup.py benchmark

1. Unit testing ([unit_tests.py](https://github.com/UCL/gp_emulator/blob/master/tests/unit_tests.py)): * random inputs generated by python predict * operate unit testings of GPU functions. * compare GPU and python outputs
2. benchmark [benchmark.py](https://github.com/UCL/gp_emulator/blob/master/tests/benchmark.py) * obtain speedup of GPU predict * random inputs
3. testing emulator ([testing_emulator.py](https://github.com/UCL/gp_emulator/blob/master/tests/test_perband_emulator.py)) * read real data from .zpn file * run emulator with and without GPU