func TestImportance(t *testing.T) {
// Test by finding the expected value of a multi-variate normal.
dim := 3
target, ok := randomNormal(dim)
if !ok {
t.Fatal("bad test, sigma not pos def")
}
muImp := make([]float64, dim)
sigmaImp := mat64.NewSymDense(dim, nil)
for i := 0; i < dim; i++ {
sigmaImp.SetSym(i, i, 3)
}
proposal, ok := distmv.NewNormal(muImp, sigmaImp, nil)
if !ok {
t.Fatal("bad test, sigma not pos def")
}
nSamples := 100000
batch := mat64.NewDense(nSamples, dim, nil)
weights := make([]float64, nSamples)
Importance(batch, weights, target, proposal)
compareNormal(t, target, batch, weights)
}
// NewProposalNormal constructs a new ProposalNormal for use as a proposal
// distribution for Metropolis-Hastings. ProposalNormal is a multivariate normal
// distribution (implemented by distmv.Normal) where the covariance matrix is fixed
// and the mean of the distribution changes.
//
// NewProposalNormal returns {nil, false} if the covariance matrix is not positive-definite.
func NewProposalNormal(sigma *mat64.SymDense, src *rand.Rand) (*ProposalNormal, bool) {
mu := make([]float64, sigma.Symmetric())
normal, ok := distmv.NewNormal(mu, sigma, src)
if !ok {
return nil, false
}
p := &ProposalNormal{
normal: normal,
}
return p, true
}
// randomNormal constructs a random Normal distribution.
func randomNormal(dim int) (*distmv.Normal, bool) {
data := make([]float64, dim*dim)
for i := range data {
data[i] = rand.Float64()
}
a := mat64.NewDense(dim, dim, data)
var sigma mat64.SymDense
sigma.SymOuterK(1, a)
mu := make([]float64, dim)
for i := range mu {
mu[i] = rand.NormFloat64()
}
return distmv.NewNormal(mu, &sigma, nil)
}
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)
}
}
}