// Sample generates rows(batch) samples using the Metropolis Hastings sample
// generation method. The initial location is NOT updated during the call to Sample.
//
// The number of columns in batch must equal len(m.Initial), otherwise Sample
// will panic.
func (m MetropolisHastingser) Sample(batch *mat64.Dense) {
burnIn := m.BurnIn
rate := m.Rate
if rate == 0 {
rate = 1
}
r, c := batch.Dims()
if len(m.Initial) != c {
panic("metropolishastings: length mismatch")
}
// Use the optimal size for the temporary memory to allow the fewest calls
// to MetropolisHastings. The case where tmp shadows samples must be
// aligned with the logic after burn-in so that tmp does not shadow samples
// during the rate portion.
tmp := batch
if rate > r {
tmp = mat64.NewDense(rate, c, nil)
}
rTmp, _ := tmp.Dims()
// Perform burn-in.
remaining := burnIn
initial := make([]float64, c)
copy(initial, m.Initial)
for remaining != 0 {
newSamp := min(rTmp, remaining)
MetropolisHastings(tmp.View(0, 0, newSamp, c).(*mat64.Dense), initial, m.Target, m.Proposal, m.Src)
copy(initial, tmp.RawRowView(newSamp-1))
remaining -= newSamp
}
if rate == 1 {
MetropolisHastings(batch, initial, m.Target, m.Proposal, m.Src)
return
}
if rTmp <= r {
tmp = mat64.NewDense(rate, c, nil)
}
// Take a single sample from the chain.
MetropolisHastings(batch.View(0, 0, 1, c).(*mat64.Dense), initial, m.Target, m.Proposal, m.Src)
copy(initial, batch.RawRowView(0))
// For all of the other samples, first generate Rate samples and then actually
// accept the last one.
for i := 1; i < r; i++ {
MetropolisHastings(tmp, initial, m.Target, m.Proposal, m.Src)
v := tmp.RawRowView(rate - 1)
batch.SetRow(i, v)
copy(initial, v)
}
}
