Exemplo n.º 1
0
// Score implements the graph.Viterbier interface.
// The argument x must be of type []float64.
func (nv nodeValue) Score(x interface{}) float64 {
	if nv.scorer == nil {
		return 0 // non-emitting node.
	}
	o := model.NewFloatObs(x.([]float64), model.SimpleLabel(""))
	return nv.scorer.LogProb(o)
}
Exemplo n.º 2
0
func (m *Net) logProb(s int, x []float64) float64 {
	o := model.NewFloatObs(x, model.SimpleLabel(""))
	return m.B[s].LogProb(o)
}
Exemplo n.º 3
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)
}
Exemplo n.º 4
0
// Sample returns a Gaussian sample.
func (g *Model) Sample(r *rand.Rand) model.Obs {
	obs := model.RandNormalVector(r, g.Mean, g.StdDev)
	return model.NewFloatObs(obs, model.SimpleLabel(""))
}