/
poisson.go
48 lines (43 loc) · 1.17 KB
/
poisson.go
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
package inference
import (
"github.com/umbralcalc/stochadex/pkg/simulator"
"golang.org/x/exp/rand"
"gonum.org/v1/gonum/mat"
"gonum.org/v1/gonum/stat/distuv"
)
// PoissonLikelihoodDistribution assumes the real data are well described
// by a Poisson distribution, given the input mean and covariance matrix.
type PoissonLikelihoodDistribution struct {
Src rand.Source
}
func (p *PoissonLikelihoodDistribution) Configure(
partitionIndex int,
settings *simulator.Settings,
) {
p.Src = rand.NewSource(settings.Seeds[partitionIndex])
}
func (p *PoissonLikelihoodDistribution) EvaluateLogLike(
mean *mat.VecDense,
covariance mat.Symmetric,
data []float64,
) float64 {
dist := &distuv.Poisson{Lambda: 1.0, Src: p.Src}
logLike := 0.0
for i := 0; i < mean.Len(); i++ {
dist.Lambda = mean.AtVec(i)
logLike += dist.LogProb(data[i])
}
return logLike
}
func (p *PoissonLikelihoodDistribution) GenerateNewSamples(
mean *mat.VecDense,
covariance mat.Symmetric,
) []float64 {
samples := make([]float64, 0)
dist := &distuv.Poisson{Lambda: 1.0, Src: p.Src}
for i := 0; i < mean.Len(); i++ {
dist.Lambda = mean.AtVec(i)
samples = append(samples, dist.Rand())
}
return samples
}