Пример #1
0
func makeHMM(t *testing.T) *Model {

	// Gaussian 1.
	mean1 := []float64{1}
	sd1 := []float64{1}
	g1 := gm.NewModel(1, gm.Name("g1"), gm.Mean(mean1), gm.StdDev(sd1))

	// Gaussian 2.
	mean2 := []float64{4}
	sd2 := []float64{2}
	g2 := gm.NewModel(1, gm.Name("g2"), gm.Mean(mean2), gm.StdDev(sd2))

	var err error
	h0 := narray.New(4, 4)
	//	h0.Set(.8, 0, 1)
	h0.Set(1, 0, 1)
	//	h0.Set(.2, 0, 2)
	h0.Set(.5, 1, 1)
	h0.Set(.5, 1, 2)
	h0.Set(.7, 2, 2)
	h0.Set(.3, 2, 3)
	h0 = narray.Log(nil, h0.Copy())

	ms, _ = NewSet()
	_, err = ms.NewNet("hmm0", h0,
		[]model.Modeler{nil, g1, g2, nil})
	fatalIf(t, err)

	return NewModel(OSet(ms), UpdateTP(true), UpdateOP(true))
}
Пример #2
0
func MakeGMM(t *testing.T) *Model {

	mean0 := []float64{1, 2}
	sd0 := []float64{0.3, 0.3}
	mean1 := []float64{4, 4}
	sd1 := []float64{1, 1}
	weights := []float64{0.6, 0.4}
	dim := len(mean0)

	g0 := gaussian.NewModel(2, gaussian.Name("g0"), gaussian.Mean(mean0), gaussian.StdDev(sd0))
	g1 := gaussian.NewModel(2, gaussian.Name("g1"), gaussian.Mean(mean1), gaussian.StdDev(sd1))
	components := []*gaussian.Model{g0, g1}
	gmm := NewModel(dim, 2, Name("mygmm"), Components(components), Weights(weights))
	return gmm
}
Пример #3
0
// creates a random gaussian by adding a perturbation to an existing gaussian.
func initGaussian(r *rand.Rand, m model.Modeler) *gm.Model {

	g := m.(*gm.Model)
	var mean, sd []float64
	for i := 0; i < g.ModelDim; i++ {
		a := r.NormFloat64()*0.2 + 1.0 // pert 0.8 to 1.2
		mean = append(mean, g.Mean[i]*a)
		sd = append(sd, g.StdDev[i]*a)
	}
	return gm.NewModel(g.ModelDim, gm.Name(g.ModelName), gm.Mean(mean), gm.StdDev(sd))
}
Пример #4
0
func randomGaussian(r *rand.Rand, id string, dim int) *gm.Model {

	var mean, sd []float64
	startSD := 40.0
	for i := 0; i < dim; i++ {
		mean = append(mean, float64(r.Intn(10)*100.0))
		a := r.NormFloat64()*0.2 + 1.0 // pert 0.8 to 1.2
		sd = append(sd, startSD*a)
	}
	return gm.NewModel(dim, gm.Name(id), gm.Mean(mean), gm.StdDev(sd))
}
Пример #5
0
// RandomModel generates a random Gaussian mixture model using mean and variance vectors as seed.
// Use this function to initialize the GMM before training. The mean and sd
// vector can be estimated from the data set using a Gaussian model.
func RandomModel(mean, sd []float64, numComponents int,
	name string, seed int64) *Model {

	n := len(mean)
	if !floats.EqualLengths(mean, sd) {
		panic(floatx.ErrLength)
	}
	cs := make([]*gaussian.Model, n, n)
	r := rand.New(rand.NewSource(seed))
	for i := 0; i < n; i++ {
		rv := RandomVector(mean, sd, r)
		cs[i] = gaussian.NewModel(n, gaussian.Mean(rv), gaussian.StdDev(sd))
	}
	gmm := NewModel(n, numComponents, Name(name), Components(cs))
	return gmm
}
Пример #6
0
// should be equivalent to training a single gaussian, great for debugging.
func TestSingleState(t *testing.T) {

	// HMM to generate data.
	g01 := gm.NewModel(1, gm.Name("g01"), gm.Mean([]float64{0}), gm.StdDev([]float64{1}))

	h0 := narray.New(3, 3)
	h0.Set(1, 0, 1)
	h0.Set(.8, 1, 1)
	h0.Set(.2, 1, 2)
	h0 = narray.Log(nil, h0.Copy())

	ms0, _ := NewSet()
	net0, e0 := ms0.NewNet("hmm", h0,
		[]model.Modeler{nil, g01, nil})
	fatalIf(t, e0)
	hmm0 := NewModel(OSet(ms0))
	_ = hmm0

	// Create gaussian to estimate without using the HMM code.
	g := gm.NewModel(1, gm.Name("g1"), gm.Mean([]float64{-1}), gm.StdDev([]float64{2}))

	// Create initial HMM and estimate params from generated data.
	g1 := gm.NewModel(1, gm.Name("g1"), gm.Mean([]float64{-1}), gm.StdDev([]float64{2}))

	h := narray.New(3, 3)
	h.Set(1, 0, 1)
	h.Set(.5, 1, 1)
	h.Set(.5, 1, 2)
	h = narray.Log(nil, h.Copy())

	ms, _ = NewSet()
	net, e := ms.NewNet("hmm", h,
		[]model.Modeler{nil, g1, nil})
	fatalIf(t, e)
	hmm := NewModel(OSet(ms), UpdateTP(true), UpdateOP(true))

	iter := 5
	// number of sequences
	m := 1000
	numFrames := 0
	t0 := time.Now() // Start timer.
	for i := 0; i < iter; i++ {
		t.Logf("iter [%d]", i)

		// Make sure we generate the same data in each iteration.
		r := rand.New(rand.NewSource(33))
		gen := newGenerator(r, false, net0)

		// Reset all counters.
		hmm.Clear()
		g.Clear()

		// fix the seed to get the same sequence
		for j := 0; j < m; j++ {
			obs, states := gen.next("oid-" + fi(j))
			numFrames += len(states) - 2
			hmm.UpdateOne(obs, 1.0)

			// Update Gaussian
			for _, o := range obs.ValueAsSlice() {
				vec := o.([]float64)
				gobs := model.NewFloatObs(vec, model.SimpleLabel(""))
				g.UpdateOne(gobs, 1.0)
			}
		}
		hmm.Estimate()
		g.Estimate()
		t.Logf("iter:%d, hmm g1:   %+v", i, net.B[1])
		t.Logf("iter:%d, direct g1:%+v", i, g)
	}
	dur := time.Now().Sub(t0)
	tp0 := narray.Exp(nil, h0.Copy())
	tp := narray.Exp(nil, net.A.Copy())
	ns := tp.Shape[0]
	for i := 0; i < ns; i++ {
		for j := 0; j < ns; j++ {
			p0 := tp0.At(i, j)
			logp0 := h0.At(i, j)
			p := tp.At(i, j)
			logp := h.At(i, j)
			if p > smallNumber || p0 > smallNumber {
				t.Logf("TP: %d=>%d, p0:%5.2f, p:%5.2f, logp0:%8.5f, logp:%8.5f", i, j, p0, p, logp0, logp)
			}
		}
	}

	t.Log("")
	t.Logf("hmm0 g1:%+v", net0.B[1])
	t.Logf("hmm  g1: %+v", net.B[1])

	t.Log("")
	t.Logf("direct g1:%+v", g)

	// Print time stats.
	t.Log("")
	t.Logf("Total time: %v", dur)
	t.Logf("Time per iteration: %v", dur/time.Duration(iter))
	t.Logf("Time per frame: %v", dur/time.Duration(iter*numFrames*m))

	gjoa.CompareSliceFloat(t, tp0.Data, tp.Data,
		"error in Trans Probs [0]", .03)

	CompareGaussians(t, net0.B[1].(*gm.Model), net.B[1].(*gm.Model), 0.03)

	if t.Failed() {
		t.FailNow()
	}

	// Recognize.
	sg := ms.SearchGraph()

	dec, e := graph.NewDecoder(sg)
	if e != nil {
		t.Fatal(e)
	}

	r := rand.New(rand.NewSource(5151))
	gen := newGenerator(r, true, net0)
	//	testDecoder(t, gen, dec, 1000)
	testDecoder(t, gen, dec, 10)
}
Пример #7
0
func TestHMMGauss(t *testing.T) {

	// Create reference HMM to generate observations.

	g01 := gm.NewModel(1, gm.Name("g01"), gm.Mean([]float64{0}), gm.StdDev([]float64{1}))
	g02 := gm.NewModel(1, gm.Name("g02"), gm.Mean([]float64{16}), gm.StdDev([]float64{2}))

	h0 := narray.New(4, 4)
	h0.Set(.6, 0, 1)
	h0.Set(.4, 0, 2)
	h0.Set(.9, 1, 1)
	h0.Set(.1, 1, 2)
	h0.Set(.7, 2, 2)
	h0.Set(.3, 2, 3)
	h0 = narray.Log(nil, h0.Copy())

	ms0, _ := NewSet()
	net0, e0 := ms0.NewNet("hmm", h0,
		[]model.Modeler{nil, g01, g02, nil})
	fatalIf(t, e0)
	hmm0 := NewModel(OSet(ms0), UpdateTP(true), UpdateOP(true))
	_ = hmm0

	// Create random HMM and estimate params from obs.

	g1 := gm.NewModel(1, gm.Name("g1"), gm.Mean([]float64{-1}), gm.StdDev([]float64{2}))
	g2 := gm.NewModel(1, gm.Name("g2"), gm.Mean([]float64{18}), gm.StdDev([]float64{4}))

	h := narray.New(4, 4)
	h.Set(.5, 0, 1)
	h.Set(.5, 0, 2)
	h.Set(.5, 1, 1)
	h.Set(.5, 1, 2)
	h.Set(.5, 2, 2)
	h.Set(.5, 2, 3)
	h = narray.Log(nil, h.Copy())

	ms, _ = NewSet()
	net, e := ms.NewNet("hmm", h,
		[]model.Modeler{nil, g1, g2, nil})
	fatalIf(t, e)
	hmm := NewModel(OSet(ms), UpdateTP(true), UpdateOP(true))

	iter := 10
	// number of sequences
	m := 500
	numFrames := 0
	t0 := time.Now() // Start timer.
	for i := 0; i < iter; i++ {
		t.Logf("iter [%d]", i)

		// Make sure we generate the same data in each iteration.
		r := rand.New(rand.NewSource(33))
		gen := newGenerator(r, false, net0)

		// Reset all counters.
		hmm.Clear()

		// fix the seed to get the same sequence
		for j := 0; j < m; j++ {
			obs, states := gen.next("oid-" + fi(j))
			numFrames += len(states) - 2
			hmm.UpdateOne(obs, 1.0)
		}
		hmm.Estimate()
	}
	dur := time.Now().Sub(t0)
	tp0 := narray.Exp(nil, h0.Copy())
	tp := narray.Exp(nil, net.A.Copy())
	ns := tp.Shape[0]
	for i := 0; i < ns; i++ {
		for j := 0; j < ns; j++ {
			p0 := tp0.At(i, j)
			logp0 := h0.At(i, j)
			p := tp.At(i, j)
			logp := h.At(i, j)
			if p > smallNumber || p0 > smallNumber {
				t.Logf("TP: %d=>%d, p0:%5.2f, p:%5.2f, logp0:%8.5f, logp:%8.5f", i, j, p0, p, logp0, logp)
			}
		}
	}

	t.Log("")
	t.Logf("hmm0 g1:%+v, g2:%+v", net0.B[1], net0.B[2])
	t.Logf("hmm  g1: %+v, g2:%+v", net.B[1], net.B[2])

	// Print time stats.
	t.Log("")
	t.Logf("Total time: %v", dur)
	t.Logf("Time per iteration: %v", dur/time.Duration(iter))
	t.Logf("Time per frame: %v", dur/time.Duration(iter*numFrames*m))

	gjoa.CompareSliceFloat(t, tp0.Data, tp.Data,
		"error in Trans Probs [0]", .03)

	CompareGaussians(t, net0.B[1].(*gm.Model), net.B[1].(*gm.Model), 0.03)
	CompareGaussians(t, net0.B[2].(*gm.Model), net.B[2].(*gm.Model), 0.03)

	if t.Failed() {
		t.FailNow()
	}

	// Recognize.
	g := ms.SearchGraph()

	dec, e := graph.NewDecoder(g)
	if e != nil {
		t.Fatal(e)
	}

	r := rand.New(rand.NewSource(5151))
	gen := newGenerator(r, true, net0)
	testDecoder(t, gen, dec, 1000)
}