Esempio n. 1
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// 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
}
Esempio n. 2
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// Clear resets sufficient statistics.
func (gmm *Model) Clear() {

	for _, c := range gmm.Components {
		c.Clear()
	}
	floatx.Apply(floatx.SetValueFunc(0), gmm.PosteriorSum, nil)
	gmm.NSamples = 0
	gmm.Likelihood = 0
}
Esempio n. 3
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// NewModel creates a new Gaussian mixture model.
func NewModel(dim, numComponents int, options ...Option) *Model {

	gmm := &Model{
		ModelName:   "GMM", // default name
		ModelDim:    dim,
		NComponents: numComponents,
		Diag:        true,
		tmpProbs:    make([]float64, numComponents),
	}
	gmm.Type = reflect.TypeOf(*gmm).String()

	// Set options.
	for _, option := range options {
		option(gmm)
	}
	if len(gmm.PosteriorSum) == 0 {
		gmm.PosteriorSum = make([]float64, gmm.NComponents)
	}

	// Create components if not provided.
	if len(gmm.Components) == 0 {
		gmm.Components = make([]*gaussian.Model, numComponents, numComponents)
		for i := range gmm.Components {
			cname := componentName(gmm.ModelName, i, gmm.NComponents)
			gmm.Components[i] = gaussian.NewModel(gmm.ModelDim, gaussian.Name(cname))
		}
	}

	// Initialize weights.
	// Caller may pass weight, log(weights), or no weights.
	switch {

	case len(gmm.LogWeights) > 0 && len(gmm.Weights) > 0:
		glog.Fatal("options not allowed: provide only one of LogWeights or Weights")

	case len(gmm.LogWeights) == 0 && len(gmm.Weights) == 0:
		gmm.LogWeights = make([]float64, numComponents)
		logw := -math.Log(float64(gmm.NComponents))
		floatx.Apply(floatx.SetValueFunc(logw), gmm.LogWeights, nil)
		gmm.Weights = make([]float64, gmm.NComponents)
		floatx.Exp(gmm.Weights, gmm.LogWeights)
		glog.Infof("init weights with equal values: %.6f", gmm.Weights[0])

	case len(gmm.LogWeights) > 0:
		gmm.Weights = make([]float64, gmm.NComponents)
		floatx.Exp(gmm.Weights, gmm.LogWeights)

	case len(gmm.Weights) > 0:
		gmm.LogWeights = make([]float64, numComponents)
		floatx.Log(gmm.LogWeights, gmm.Weights)
	}
	return gmm
}
Esempio n. 4
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// 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
}
Esempio n. 5
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// 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
}
Esempio n. 6
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// 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
}
Esempio n. 7
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// NewModel creates a new Gaussian model.
func NewModel(dim int, options ...Option) *Model {

	g := &Model{
		ModelName:   "Gaussian",
		ModelDim:    dim,
		Diag:        true,
		variance:    make([]float64, dim),
		varianceInv: make([]float64, dim),
		tmpArray:    make([]float64, dim),
	}
	g.Type = reflect.TypeOf(*g).String()

	// Set options.
	for _, option := range options {
		option(g)
	}
	if len(g.Sumx) == 0 {
		g.Sumx = make([]float64, dim)
	}
	if len(g.Sumxsq) == 0 {
		g.Sumxsq = make([]float64, dim)
	}
	if g.Mean == nil {
		g.Mean = make([]float64, dim)
	}
	if g.StdDev == nil {
		g.StdDev = make([]float64, dim)
		floatx.Apply(floatx.SetValueFunc(smallSD), g.StdDev, nil)
	}

	floatx.Sq(g.variance, g.StdDev)

	// Initializes variance, varianceInv, and StdDev.
	g.setVariance(g.variance)

	floatx.Log(g.tmpArray, g.variance)
	g.const1 = -float64(g.ModelDim) * math.Log(2.0*math.Pi) / 2.0
	g.const2 = g.const1 - floats.Sum(g.tmpArray)/2.0
	return g
}
Esempio n. 8
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func (g *Model) setVariance(variance []float64) {
	copy(g.variance, variance)
	floatx.Apply(floatx.Inv, g.variance, g.varianceInv)
	g.StdDev = g.standardDeviation()
}
Esempio n. 9
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// Clear resets sufficient statistics.
func (g *Model) Clear() {

	floatx.Apply(floatx.SetValueFunc(0), g.Sumx, nil)
	floatx.Apply(floatx.SetValueFunc(0), g.Sumxsq, nil)
	g.NSamples = 0
}