Getting started

libgp is a C++ library for Gaussian process regression. A Gaussian process defines a distribution over functions and inference takes place directly in function space. It is fully specified by a mean function and a positive definite covariance function. This library uses two types of covariance functions, simple and composite. Composite functions can be composed of other composite functions, allowing flexible structures. Currently the following covariance functions are implemented:

Simple covariance functions

• Linear covariance function.
• Linear covariance function with automatic relevance detection.
• Matern covariance function with nu=1.5 and isotropic distance measure.
• Matern covariance function with nu=2.5 and isotropic distance measure.
• Independent covariance function (white noise).
• Radial basis covariance function with compact support.
• Isotropic rational quadratic covariance function.
• Squared exponential covariance function with automatic relevance detection.
• Squared exponential covariance function with isotropic distance measure.

Composite covariance functions

• Sums of covariance functions.

Mean function

• The mean function is fixed to zero.

Training a model

Initialize the model by specifying the input dimensionality and the covariance function.

GaussianProcess gp(2, "CovSum ( CovSEiso, CovNoise)");

Set log-hyperparameter of the covariance function.

gp.covf().set_loghyper(params);

Add data to the training set. Input vectors x must be provided as double[] and targets y as double.

gp.add_pattern(x, y);

Predict value or variance of an input vector x.

f = gp.f(x);
v = gp.var(x);

Read and write

Use write function to save a Gaussian process model and the complete training set to a file.

void write(const char * filename);

A new instance of the Gaussian process can be instantiated from this file using the following constructor.

GaussianProcess (const char * filename);

Advanced topics

• hyper-parameter optimization
• custom covariance functions
• the libgp file format

Get support

For support send an email to the developer: libgp@bluman.de

Requirements

• cmake: cross-platform, open-source build system
• Eigen3: template library for linear algebra
• googletest (optional)

Release Notes

• 2012/10/11 version 0.1.4
  log likelihood function and gradient computation
  hyper-parameter optimization using RProp
  online updates of the Cholesky decomposition
  

• 2011/09/28 version 0.1.3
  improved organization of training data
  improved interfaces

• 2011/06/03 version 0.1.2
  added Matern5 covariance function
  added isotropic rational quadratic covariance function
  added function to draw random data according to covariance function

• 2011/05/27 version 0.1.1
  google-tests added
  added Matern3 covariance function
  various bugfixes

• 2011/05/26 version 0.1.0 basic functionality for standard gp regression
  most important covariance functions implemented
  capability to read and write models to disk