A package for selecting inducing points for sparse GPs
This package provide a collection of inducing point location selection algorithms, both offline and online.
Offline algorithms are meant to be run once over the data before training begins. Here is an example where we use the k-means algorithm
using InducingPoints
X = [rand(5) for _ in 1:100]
alg = KMeansAlg(10) # Create the kmeans algorithm
Z = inducingpoints(alg, X) # Returns a vector of vector of size 10 will return 10 inducing points selected as clusters by the k-means algorithm
Note that it is possible to pass data as a matrix as well following the convention of KernelFunctions.jl
X = rand(5 , 1000)
alg = KMeansAlg(10, Euclidean()) # We can also use different metrics
Z = inducingpoints(alg, X) # This still returns a vector of vector of size 10 Online algorithms needs two API, a first one to create the initial vector of inducing points and another one to update it with new data. For example following this work
alg = OIPS(200) # We expect 200 inducing points
kernel = SqExponential()
X = [rand(5) for _ in 1:100] # We have some initial data
Z = inducingpoints(alg, X; kernel=kernel) # We create an initial vector
X_new = [rand(5) for _ in 1:50] # We get some new data
updateZ!(Z, alg, X_new; kernel=kernel) # Points will be acordingly added (or removed!)Note that Z is directly changed in place.
Make sure to check each algorithm docs independently, they will give you more details on what arguments they need and what they do!