tests/testthat/test_sampling.R

In volesti: Volume Approximation and Sampling of Convex Polytopes

context("Sampling test")

library(volesti)

runsample <- function(P, name_string, dist){
  if (dist == "uniform") {
    p = sample_points(P, n = 100)
  } else {
    p = sample_points(P, n = 100, distribution = list("density" = "gaussian"))
  }
  if (length(p[is.nan(p)])>0 | length(p[is.infinite(p)])>0) {
    res = 0
  } else {
    res = 1
  }
  return(res)
  
}

path = system.file('extdata', package = 'volesti')

for (i in 1:2) {
  
  if (i==1) {
    distribution = 'gaussian'
  } else {
    distribution = 'uniform'
  }
  
  test_that("Sampling test", {
    P= gen_cube(10, 'H')
    res = runsample(P, 'H-cube10', distribution)
    expect_equal(res, 1)
  })
  
  test_that("Sampling test", {
    P = gen_cross(10, 'H')
    res = runsample(P, 'H-cross10', distribution)
    expect_equal(res, 1)
  })
  
  test_that("Sampling test", {
    P = gen_prod_simplex(5)
    res = runsample(P, 'H-prod_simplex_5_5', distribution)
    expect_equal(res, 1)
  })
  
  test_that("Sampling test", {
    P = gen_prod_simplex(10)
    res = runsample(P, 'H-prod_simplex_10_10', distribution)
    expect_equal(res, 1)
  })
  
  test_that("Sampling test", {
    P = gen_simplex(10, 'H')
    res = runsample(P, 'H-prod_simplex10', distribution)
    expect_equal(res, 1)
  })
  
  test_that("Sampling test", {
    P = gen_skinny_cube(10)
    res = runsample(P, 'H-skinny_cube10', distribution)
    expect_equal(res, 1)
  })
  
  test_that("Sampling test", {
    P = gen_skinny_cube(20)
    res = runsample(P, 'H-skinny_cube20', distribution)
    expect_equal(res, 1)
  })
  
  test_that("Sampling test", {
    Z = gen_rand_zonotope(4, 8)
    res = runsample(Z, 'zonotope_4_8', distribution)
    expect_equal(res, 1)
  })
  
}