This package provides local function approximators that interpolates a scalar-valued function across a vector space. It does so based on the values of the function at "nearby" points, based on an appropriate locality metric, and not via any global regression or fitting function. Currently it supports multi-linear and simplex interpolations for multi-dimensional grids, and k-nearest-neighbor interpolation. Two important dependencies are GridInterpolations and NearestNeighbors.
Start Julia and run the following:
Pkg.add("LocalFunctionApproximation") using LocalFunctionApproximation
Create a rectangular grid for interpolation using
GridInterpolations and create the function approximator
that uses it:
using GridInterpolations # Make the grid interpolations module available grid = RectangleGrid([0., 0.5, 1.],[0., 0.5, 1.]) # rectangular grid grid_values = [8., 1., 6., 3., 5., 7., 4., 9., 2.] # corresponding values at each grid point gifa = LocalGIFunctionApproximator(grid, grid_values) # create the function approximator using the grid and values
Create a nearest neighbor tree using
NearestNeighbors and create the corresponding approximator:
using NearestNeighbors, StaticArrays points = [SVector(0.,0.), SVector(0.,1.), SVector(1.,1.), SVector(1.,0.)] # the 4 corners of the unit square nntree = KDTree(points) # create a KDTree using the points vals = [1., 1., -1., -1] # values corresponding to points k = 2 # the k parameter for knn approximator knnfa = LocalNNFunctionApproximator(nntree, points, k)
point = rand(2) # random 2D point compute_value(gifa, point) # obtain the value by interpolating the function at that point compute_value(knnfa, point) # do the same for the kNN approximator
A typical use case for this package is for Local Approximation Value Iteration, as shown here.
over 1 year ago