// Estimate computes model parameters using sufficient statistics.
func (g *Model) Estimate() error {
if g.NSamples > minNumSamples {
/* Estimate the mean. */
floatx.Apply(floatx.ScaleFunc(1.0/g.NSamples), g.Sumx, g.Mean)
/*
* Estimate the variance. sigma_sq = 1/n (sumxsq - 1/n sumx^2) or
* 1/n sumxsq - mean^2.
*/
tmp := g.variance // borrow as an intermediate array.
// floatx.Apply(sq, g.Mean, g.tmpArray)
floatx.Sq(g.tmpArray, g.Mean)
floatx.Apply(floatx.ScaleFunc(1.0/g.NSamples), g.Sumxsq, tmp)
floats.SubTo(g.variance, tmp, g.tmpArray)
floatx.Apply(floatx.Floorv(smallVar), g.variance, nil)
} else {
/* Not enough training sample. */
glog.Warningf("not enough training samples, name [%s], num samples [%e]", g.ModelName, g.NSamples)
floatx.Apply(floatx.SetValueFunc(smallVar), g.variance, nil)
floatx.Apply(floatx.SetValueFunc(0), g.Mean, nil)
}
g.setVariance(g.variance) // to update varInv and stddev.
/* Update log Gaussian constant. */
floatx.Log(g.tmpArray, g.variance)
g.const2 = g.const1 - floats.Sum(g.tmpArray)/2.0
glog.V(6).Infof("gaussian reest, name:%s, mean:%v, sd:%v", g.ModelName, g.Mean, g.StdDev)
return nil
}
// 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) Estimate() error {
// Estimate mixture weights.
floatx.Apply(floatx.ScaleFunc(1.0/gmm.NSamples), gmm.PosteriorSum, gmm.Weights)
floatx.Log(gmm.LogWeights, gmm.Weights)
// Estimate component density.
for _, c := range gmm.Components {
err := c.Estimate()
if err != nil {
return err
}
}
gmm.Iteration++
return nil
}