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.

• 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.

• Sums of covariance functions.

• The mean function is fixed to zero.

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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);

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);

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

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

• 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