// UpdateOne updates sufficient statistics using one observation.
func (g *Model) UpdateOne(o model.Obs, w float64) {
glog.V(6).Infof("gaussian update, name:%s, obs:%v, weight:%e", g.ModelName, o, w)
/* Update sufficient statistics. */
obs, _, _ := model.ObsToF64(o)
floatx.Apply(floatx.ScaleFunc(w), obs, g.tmpArray)
floats.Add(g.Sumx, g.tmpArray)
floatx.Sq(g.tmpArray, obs)
floats.Scale(w, g.tmpArray)
floats.Add(g.Sumxsq, g.tmpArray)
g.NSamples += w
}
// Estimate computes model parameters using sufficient statistics.
func (gmm *Model) UpdateOne(o model.Obs, w float64) {
obs, _, _ := model.ObsToF64(o)
maxProb := gmm.logProbInternal(obs, gmm.tmpProbs)
gmm.Likelihood += maxProb
floatx.Apply(floatx.AddScalarFunc(-maxProb+math.Log(w)), gmm.tmpProbs, nil)
// Compute posterior probabilities.
floatx.Exp(gmm.tmpProbs, gmm.tmpProbs)
// Update posterior sum, needed to compute mixture weights.
floats.Add(gmm.PosteriorSum, gmm.tmpProbs)
// Update Gaussian components.
for i, c := range gmm.Components {
c.UpdateOne(o, gmm.tmpProbs[i])
}
// Count number of observations.
gmm.NSamples += w
}