Exemple #1
0
func TestTrainGaussian(t *testing.T) {

	if testing.Short() {
		t.Skip("skipping test in short mode.")
	}
	dim := 8
	mean := []float64{0.1, 0.2, 0.3, 0.4, 1, 1, 1, 1}
	std := []float64{0.5, 0.5, 0.5, 0.5, 0.1, 0.2, 0.3, 0.4}
	g := NewModel(dim, Name("test training"))

	r := rand.New(rand.NewSource(33))
	for i := 0; i < 2000000; i++ {
		rv := model.RandNormalVector(r, mean, std)
		g.UpdateOne(model.F64ToObs(rv, ""), 1.0)
	}
	g.Estimate()
	t.Logf("Mean: \n%+v", g.Mean)
	t.Logf("STD: \n%+v", g.StdDev)

	for i, _ := range mean {
		if !gjoa.Comparef64(mean[i], g.Mean[i], tolerance) {
			t.Errorf("Wrong Mean[%d]. Expected: [%f], Got: [%f]",
				i, mean[i], g.Mean[i])
		}
		if !gjoa.Comparef64(std[i], g.StdDev[i], tolerance) {
			t.Errorf("Wrong STD[%d]. Expected: [%f], Got: [%f]",
				i, std[i], g.StdDev[i])
		}
	}
}
Exemple #2
0
func TestTrainGaussian2(t *testing.T) {

	if testing.Short() {
		t.Skip("skipping test in short mode.")
	}
	dim := 8
	numSamp := 2000000

	// Use a FloatObserver.
	values := make([][]float64, numSamp, numSamp)
	labels := make([]model.SimpleLabel, numSamp, numSamp)
	mean := []float64{0.1, 0.2, 0.3, 0.4, 1, 1, 1, 1}
	std := []float64{0.5, 0.5, 0.5, 0.5, 0.1, 0.2, 0.3, 0.4}
	g := NewModel(dim, Name("test training"))

	r := rand.New(rand.NewSource(33))
	for i := 0; i < numSamp; i++ {
		rv := model.RandNormalVector(r, mean, std)
		values[i] = rv
	}

	fo, err := model.NewFloatObserver(values, labels)
	if err != nil {
		t.Fatal(err)
	}

	g.Update(fo, model.NoWeight)
	g.Estimate()
	t.Logf("Mean: \n%+v", g.Mean)
	t.Logf("STD: \n%+v", g.StdDev)

	for i, _ := range mean {
		if !gjoa.Comparef64(mean[i], g.Mean[i], tolerance) {
			t.Errorf("Wrong Mean[%d]. Expected: [%f], Got: [%f]",
				i, mean[i], g.Mean[i])
		}
		if !gjoa.Comparef64(std[i], g.StdDev[i], tolerance) {
			t.Errorf("Wrong STD[%d]. Expected: [%f], Got: [%f]",
				i, std[i], g.StdDev[i])
		}
	}
}
Exemple #3
0
// Another version of previous test.
func TestTrainGMM2(t *testing.T) {
	dim := 2
	numComp := 2
	numIter := 10
	numObs := 2000000
	gmm0 := MakeGMM(t)
	gmm := NewModel(dim, numComp, Name("mygmm"))

	t.Logf("Initial Weights: \n%+v", gmm.Weights)
	mean01 := []float64{2.5, 3}
	sd01 := []float64{0.70710678118, 0.70710678118}
	gmm = RandomModel(mean01, sd01, numComp, "mygmm", 99)

	for iter := 0; iter < numIter; iter++ {
		t.Logf("Starting GMM training iteration %d.", iter)

		// Reset all counters..
		gmm.Clear()

		for i := 0; i < numObs; i++ {
			// random from gmm0
			//			rv := gmm0.Sample().(model.FloatObs)
			//			gmm.UpdateOne(rv.Value().([]float64), 1.0)
			rv := gmm0.Sample()
			gmm.UpdateOne(rv, 1.0)

		}
		gmm.Estimate()

		t.Logf("Iter: %d", iter)
		t.Logf("GMM: %+v", gmm)
		t.Logf("Weights: \n%+v", gmm.Weights)
		t.Logf("Likelihood: %f", gmm.Likelihood)
		t.Logf("Num Samples: %f", gmm.NSamples)
		for _, c := range gmm.Components {
			t.Logf("%s: Mean: \n%+v", c.Name(), c.Mean)
			t.Logf("%s: STD: \n%+v", c.Name(), c.StdDev)
		}

	}
	// Checking results
	// The components can be in different orders
	if gjoa.Comparef64(1.0, gmm.Components[0].Mean[0], epsilon) {
		CompareGaussians(t, gmm0.Components[0], gmm.Components[0], epsilon)
		CompareGaussians(t, gmm0.Components[1], gmm.Components[1], epsilon)
	} else {
		CompareGaussians(t, gmm0.Components[1], gmm.Components[0], epsilon)
		CompareGaussians(t, gmm0.Components[0], gmm.Components[1], epsilon)
	}
}
Exemple #4
0
func TestGaussian(t *testing.T) {

	mean := []float64{0.5, 1, 2}
	sd := []float64{1, 1, 1}

	g := NewModel(3, Name("testing"), Mean(mean), StdDev(sd))
	obs := []float64{1, 1, 1}

	p := g.logProb(obs)
	t.Logf("LogProb: %f", p)
	t.Logf("Prob: %f", g.prob(obs))

	expected := -3.3818
	if !gjoa.Comparef64(expected, p, tolerance) {
		t.Errorf("Wrong LogProb. Expected: [%f], Got: [%f]", expected, p)
	}
}
Exemple #5
0
// Trains a GMM as follows:
// 1 - Estimate a Gaussian model params for the training set.
// 2 - Use the mean and sd of the training set to generate
//     a random GMM to be used as seed.
// 3 - Run several iterations of the GMM max likelihood training algorithm
//     to estimate the GMM weights and the Gaussian component mean, and
//     variance vectors.
func TestTrainGMM(t *testing.T) {

	var seed int64 = 33
	numComp := 2
	numIter := 10
	numObs := 1000000

	mean0 := []float64{1, 2}
	std0 := []float64{0.3, 0.3}
	mean1 := []float64{4, 4}
	std1 := []float64{1, 1}
	dim := len(mean0)
	gmm := NewModel(dim, numComp, Name("mygmm"))
	t.Logf("Initial Weights: \n%+v", gmm.Weights)
	{
		// Estimate mean variance of the data.
		g := gaussian.NewModel(dim, gaussian.Name("test training"))

		r := rand.New(rand.NewSource(seed))
		for i := 0; i < numObs; i++ {
			rv, err := model.RandNormalVector(mean0, std0, r)
			if err != nil {
				t.Fatal(err)
			}
			g.UpdateOne(model.F64ToObs(rv), 1.0)
			rv, err = model.RandNormalVector(mean1, std1, r)
			if err != nil {
				t.Fatal(err)
			}
			g.UpdateOne(model.F64ToObs(rv), 1.0)
		}
		g.Estimate()
		t.Logf("Gaussian Model for training set:")
		t.Logf("Mean: \n%+v", g.Mean)
		t.Logf("SD: \n%+v", g.StdDev)

		// Use the estimated mean and sd to generate a seed GMM.
		gmm = RandomModel(g.Mean, g.StdDev, numComp,
			"mygmm", 99)
		t.Logf("Random GMM: %+v.", gmm)
		t.Logf("Component 0: %+v.", gmm.Components[0])
		t.Logf("Component 1: %+v.", gmm.Components[1])
	}

	for iter := 0; iter < numIter; iter++ {
		t.Logf("Starting GMM training iteration %d.", iter)

		// Reset the same random number generator to make sure we use the
		// same observations in each iterations.
		r := rand.New(rand.NewSource(seed))

		// Update GMM stats.
		for i := 0; i < numObs; i++ {
			rv, err := model.RandNormalVector(mean0, std0, r)
			if err != nil {
				t.Fatal(err)
			}
			gmm.UpdateOne(model.F64ToObs(rv), 1.0)
			rv, err = model.RandNormalVector(mean1, std1, r)
			if err != nil {
				t.Fatal(err)
			}
			gmm.UpdateOne(model.F64ToObs(rv), 1.0)
		}

		// Estimates GMM params.
		gmm.Estimate()

		t.Logf("Iter: %d", iter)
		t.Logf("GMM: %+v", gmm)
		t.Logf("Weights: \n%+v", gmm.Weights)
		t.Logf("Likelihood: %f", gmm.Likelihood)
		t.Logf("Num Samples: %f", gmm.NSamples)
		for _, c := range gmm.Components {
			t.Logf("%s: Mean: \n%+v", c.Name(), c.Mean)
			t.Logf("%s: STD: \n%+v", c.Name(), c.StdDev)
		}

		// Prepare for next iteration.
		gmm.Clear()
	}

	for i := 0; i < dim; i++ {
		g := gmm.Components[1]
		if !gjoa.Comparef64(mean0[i], g.Mean[i], epsilon) {
			t.Errorf("Wrong Mean[%d]. Expected: [%f], Got: [%f]",
				i, mean0[i], g.Mean[i])
		}
		if !gjoa.Comparef64(std0[i], g.StdDev[i], epsilon) {
			t.Errorf("Wrong STD[%d]. Expected: [%f], Got: [%f]",
				i, std0[i], g.StdDev[i])
		}
	}

	for i := 0; i < dim; i++ {
		g := gmm.Components[0]
		if !gjoa.Comparef64(mean1[i], g.Mean[i], epsilon) {
			t.Errorf("Wrong Mean[%d]. Expected: [%f], Got: [%f]",
				i, mean1[i], g.Mean[i])
		}
		if !gjoa.Comparef64(std1[i], g.StdDev[i], epsilon) {
			t.Errorf("Wrong STD[%d]. Expected: [%f], Got: [%f]",
				i, std1[i], g.StdDev[i])
		}
	}

	if !gjoa.Comparef64(0.5, gmm.Weights[0], epsilon) {
		t.Errorf("Wrong weights[0]. Expected: [%f], Got: [%f]",
			0.5, gmm.Weights[0])
	}

	if !gjoa.Comparef64(0.5, gmm.Weights[1], epsilon) {
		t.Errorf("Wrong weights[0]. Expected: [%f], Got: [%f]",
			0.5, gmm.Weights[1])
	}

}