文件列表:

.travis.yml (128, 2021-05-11)
LICENSE.md (1200, 2021-05-11)
Project.toml (676, 2021-05-11)
bench (0, 2021-05-11)
bench\Project.toml (175, 2021-05-11)
bench\benchmark.jl (1784, 2021-05-11)
bench\interpBenchmarks.jl (3964, 2021-05-11)
sampleInterpolation.png (94544, 2021-05-11)
src (0, 2021-05-11)
src\GridInterpolations.jl (12028, 2021-05-11)
test (0, 2021-05-11)
test\REQUIRE (15, 2021-05-11)
test\rectangularMagicTest20.txt (4189, 2021-05-11)
test\runtests.jl (13703, 2021-05-11)
test\simplexMagicTest20.txt (4591, 2021-05-11)

# GridInterpolations [](https://travis-ci.org/sisl/GridInterpolations.jl) [](https://coveralls.io/r/sisl/GridInterpolations.jl) This package performs multivariate interpolation on a rectilinear grid. At the moment, it provides implementations of multilinear and simplex interpolation. As of benchmarks in December 2016, multilinear interpolation performs fastest and with the most accuracy. The following image visualizes grid-based interpolation in two dimensions, with shape of interpolater for (0.3,0.8) inscribed. The small dots reflect the interpolation's estimate for sin(x)+2cos(y)+sin(5xy), which is the underlying reward function approximated by the large dot lattice.  For a description of multilinear and simplex interpolation see: Scott Davies, _Multidimensional Triangulation and Interpolation for Reinforcement Learning_, Advances in Neural Information Processing Systems, Cambridge, MA: MIT Press, 1997. [pdf](http://papers.nips.cc/paper/1229-multidimensional-triangulation-and-interpolation-for-reinforcement-learning.pdf) There are some related packages, such as [Grid.jl](https://github.com/timholy/Grid.jl) and [Interpolations.jl](https://github.com/tlycken/Interpolations.jl). ## Installation Start Julia and run the following command: ```julia Pkg.add("GridInterpolations") ``` ## Usage To use the GridInterpolations module, begin your code with ```julia using GridInterpolations ``` ## Interpolation Create two-dimensional interpolation grids, a data array, and a point of interest: ```julia grid = RectangleGrid([0., 0.5, 1.],[0., 0.5, 1.]) # rectangular grid sGrid = SimplexGrid([0., 0.5, 1.],[0., 0.5, 1.]) # simplex grid gridData = [8., 1., 6., 3., 5., 7., 4., 9., 2.] # vector of value data at each cut x = [0.25, 0.75] # point at which to perform interpolation ``` Perform interpolation on the rectangular grid: ```julia julia> interpolate(grid,gridData,x) 5.25 ``` Or interpolate on the simplex grid: ```julia julia> interpolate(sGrid,gridData,x) 6.0 ``` Compute interpolants for the grids: ```julia julia> sGrid = SimplexGrid([0., 0.5, 1.],[0., 0.5, 1.]) [[0.0,0.5,1.0],[0.0,0.5,1.0]] julia> interpolants(sGrid, x) ([4,5,8],[0.5,0.0,0.5]) ``` Convert an index to a Grid coordinate: ```julia julia> ind2x(grid, 3) 2-element Array{Float***,1}: 1.0 0.0 ``` Number of vertices in the grid: ```julia julia> length(grid) 9 ``` Number of dimensions: ```julia julia> dimensions(grid) 2 ``` Multi-dimensional indexing using Cartesian coordinates: ```julia julia> [grid[c] for c in CartesianIndices((3,3))] 3×3 Array{Array{Float***,1},2}: [0.0, 0.0] [0.0, 0.5] [0.0, 1.0] [0.5, 0.0] [0.5, 0.5] [0.5, 1.0] [1.0, 0.0] [1.0, 0.5] [1.0, 1.0] ``` or multi-dimensional indices ```julia julia> grid[2,2] 2-element Array{Float***,1}: 0.5 0.5 ``` Sequential iteration over grid points: ```julia julia> for x in grid # do stuff end ``` ## Credits Contributors to this package include Maxim Egorov, Eric Mueller, and Mykel Kochenderfer.

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：