/
normal.go
65 lines (59 loc) · 1.48 KB
/
normal.go
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package inference
import (
"github.com/umbralcalc/stochadex/pkg/simulator"
"golang.org/x/exp/rand"
"gonum.org/v1/gonum/mat"
"gonum.org/v1/gonum/stat/distmv"
)
// NormalLikelihoodDistribution assumes the real data are well described
// by a normal distribution, given the input mean and covariance matrix.
type NormalLikelihoodDistribution struct {
Src rand.Source
defaultCov []float64
defaultCovOk bool
}
func (n *NormalLikelihoodDistribution) Configure(
partitionIndex int,
settings *simulator.Settings,
) {
n.Src = rand.NewSource(settings.Seeds[partitionIndex])
n.defaultCov, n.defaultCovOk = settings.
OtherParams[partitionIndex].FloatParams["default_covariance"]
}
func (n *NormalLikelihoodDistribution) getDist(
mean *mat.VecDense,
covariance mat.Symmetric,
) *distmv.Normal {
dist, ok := distmv.NewNormal(
mean.RawVector().Data,
covariance,
n.Src,
)
if !ok {
if n.defaultCovOk {
dist, _ = distmv.NewNormal(
mean.RawVector().Data,
mat.NewSymDense(mean.Len(), n.defaultCov),
n.Src,
)
} else {
panic("covariance matrix is not positive-definite")
}
}
return dist
}
func (n *NormalLikelihoodDistribution) EvaluateLogLike(
mean *mat.VecDense,
covariance mat.Symmetric,
data []float64,
) float64 {
dist := n.getDist(mean, covariance)
return dist.LogProb(data)
}
func (n *NormalLikelihoodDistribution) GenerateNewSamples(
mean *mat.VecDense,
covariance mat.Symmetric,
) []float64 {
dist := n.getDist(mean, covariance)
return dist.Rand(nil)
}