Gaussian process have both the ability to extract a lot of information from their training data and to return a prediction and an uncertainty on their prediction. Furthermore, they can handle non-linear phenomenons, take uncertainty on the inputs into account and encode a prior on the output.
All of those properties make them an algorithm of choice to perform regression when data is scarce or when having uncertainty bars on the ouput is a desirable property.
o(n^3) complexity of the algorithm makes the classical implementation unsuitable for large training datasets.
This implementation lets you :
- define a gaussian process with default parameters or using the builder pattern
- train it on multidimensional data
- fit the parameters (kernel, prior and noise) on the training data
- add additional samples efficiently (
O(n^2)) and refit the process
- predict the mean, variance and covariance matrix for given inputs
- sample the distribution at a given position
- save and load a trained model with serde
Most methods of this library can currently work with the following
input -> ouput pairs :
Vec<f64> -> f64a single, multidimensional, sample
Vec<Vec<f64>> -> Vec<f64>each inner vector is a training sample
DMatrix<f64> -> DVector<f64>using a nalgebra matrix with one row per sample
ArrayBase<f64, Ix1> -> f64a single sample stored in a ndarray array (using the
ArrayBase<f64, Ix2> -> Array1<f64>each row is a sample (using the
Input trait if you want to add you own input type.
Input -> Output type pairs