AbstractGPs.jl

AbstractGPs.jl is a library for fitting Gaussian Processes in Julia.

Basic example

Here is an example:

julia> using JuMP, MathOptAI, AbstractGPs
julia> x_data = 2π .* (0.0:0.1:1.0);
julia> y_data = sin.(x_data);
julia> fx = AbstractGPs.GP(AbstractGPs.Matern32Kernel())(x_data, 0.1);
julia> p_fx = AbstractGPs.posterior(fx, y_data);
julia> model = Model();
julia> @variable(model, 1 <= x[1:1] <= 6, start = 3);
julia> predictor = MathOptAI.Quantile(p_fx, [0.1, 0.9]);
julia> y, formulation = MathOptAI.add_predictor(model, predictor, x);
julia> y2-element Vector{JuMP.VariableRef}:
 moai_quantile[1]
 moai_quantile[2]
julia> formulationQuantile(_, [0.1, 0.9])
├ variables [0]
└ constraints [0]
julia> @objective(model, Max, y[2] - y[1])moai_quantile[2] - moai_quantile[1]