Пример #1
0
// 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
}
Пример #2
0
// 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
}
Пример #3
0
// 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
}