func TestLatinHypercube(t *testing.T) {
for _, nSamples := range []int{1, 2, 5, 10, 20} {
for _, dist := range []lhDist{
distmv.NewUniform([]distmv.Bound{{0, 3}}, nil),
distmv.NewUniform([]distmv.Bound{{0, 3}, {-1, 5}, {-4, -1}}, nil),
} {
dim := dist.Dim()
batch := mat64.NewDense(nSamples, dim, nil)
LatinHypercube(batch, dist, nil)
// Latin hypercube should have one entry per hyperrow.
present := make([][]bool, nSamples)
for i := range present {
present[i] = make([]bool, dim)
}
cdf := make([]float64, dim)
for i := 0; i < nSamples; i++ {
dist.CDF(cdf, batch.RawRowView(i))
for j := 0; j < dim; j++ {
p := cdf[j]
quadrant := int(math.Floor(p * float64(nSamples)))
present[quadrant][j] = true
}
}
allPresent := true
for i := 0; i < nSamples; i++ {
for j := 0; j < dim; j++ {
if present[i][j] == false {
allPresent = false
}
}
}
if !allPresent {
t.Errorf("All quadrants not present")
}
}
}
}
func TestRejection(t *testing.T) {
// Test by finding the expected value of a uniform.
dim := 3
bounds := make([]distmv.Bound, dim)
for i := 0; i < dim; i++ {
min := rand.NormFloat64()
max := rand.NormFloat64()
if min > max {
min, max = max, min
}
bounds[i].Min = min
bounds[i].Max = max
}
target := distmv.NewUniform(bounds, nil)
mu := target.Mean(nil)
muImp := make([]float64, dim)
sigmaImp := mat64.NewSymDense(dim, nil)
for i := 0; i < dim; i++ {
sigmaImp.SetSym(i, i, 6)
}
proposal, ok := distmv.NewNormal(muImp, sigmaImp, nil)
if !ok {
t.Fatal("bad test, sigma not pos def")
}
nSamples := 1000
batch := mat64.NewDense(nSamples, dim, nil)
weights := make([]float64, nSamples)
_, ok = Rejection(batch, target, proposal, 1000, nil)
if !ok {
t.Error("Bad test, nan samples")
}
for i := 0; i < dim; i++ {
col := mat64.Col(nil, i, batch)
ev := stat.Mean(col, weights)
if math.Abs(ev-mu[i]) > 1e-2 {
t.Errorf("Mean mismatch: Want %v, got %v", mu[i], ev)
}
}
}