Example #1
0
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)
}
Example #2
0
// 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
}
Example #3
0
// 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)
}
Example #4
0
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)
		}
	}
}