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])
}
}
}
func (ch *chain) update() error {
ch.fb() // Compute forward-backward probabilities.
logProb := ch.beta.At(0, 0, 0)
totalProb := math.Exp(logProb)
if logProb == math.Inf(-1) {
return fmt.Errorf("oid:%s, log prob is -Inf, skipping training sequence, num vectos:%d, chain len:%d, states per chain:%v", ch.obs.ID(), ch.nobs, ch.nq, ch.ns)
}
glog.V(2).Infof("oid:%s, log prob per observation:%e", ch.obs.ID(), logProb/float64(ch.nobs))
// Compute state transition counts.
glog.V(2).Infof("oid:%s, compute hmm state transition counts.", ch.obs.ID())
for q, h := range ch.hmms {
exit := ch.ns[q] - 1
for t, vec := range ch.vectors {
for i := 0; i < exit; i++ {
w := ch.doOccAcc(q, i, t, totalProb) / totalProb
ch.doTrAcc(q, i, t, totalProb)
if i > 0 {
o := model.F64ToObs(vec, "")
h.B[i].UpdateOne(o, w) // TODO prove!
}
}
}
}
return nil
}
func TestCloneGaussian(t *testing.T) {
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 cloning"))
r := rand.New(rand.NewSource(33))
for i := 0; i < 2000; i++ {
rv := model.RandNormalVector(r, mean, std)
g.UpdateOne(model.F64ToObs(rv, ""), 1.0)
}
g.Estimate()
ng := NewModel(g.ModelDim, Clone(g))
// compare g vs. ng
type table struct {
v1, v2 []float64
name string
}
tab := []table{
table{g.Sumx, ng.Sumx, "Sumx"},
table{g.Sumxsq, ng.Sumxsq, "Sumxsq"},
table{g.Mean, ng.Mean, "Mean"},
table{g.StdDev, ng.StdDev, "StdDev"},
table{g.variance, ng.variance, "variance"},
table{g.varianceInv, ng.varianceInv, "varianceInv"},
table{[]float64{g.const1}, []float64{ng.const1}, "const1"},
table{[]float64{g.const2}, []float64{ng.const2}, "const2"},
}
// compare slices
for _, v := range tab {
gjoa.CompareSliceFloat(t, v.v1, v.v2, "no match: "+v.name, 0.00001)
}
// if ng.BaseModel.Model == g.BaseModel.Model {
// t.Fatalf("Modeler is the same.")
// }
if ng.NSamples != g.NSamples {
t.Fatalf("NSamples doesn't match.")
}
}
func (ch *chain) updateFromAlignments() error {
// Get alignments.
aligner, ok := ch.obs.(model.Aligner)
if !ok {
return fmt.Errorf("oid:%s - obs object does not implement the aligner interface", ch.obs.ID())
}
al := aligner.Alignment()
glog.V(6).Infof("oid:%s, alignments: %v", ch.obs.ID(), al)
if len(al) == 0 {
return fmt.Errorf("oid:%s - alignment object has zero length", ch.obs.ID())
}
glog.V(2).Infof("oid:%s, estimating output PDF from alignment with %d nodes", ch.obs.ID(), len(al))
if al[len(al)-1].End != ch.nobs {
return fmt.Errorf("oid:%s - alignment length is [%d] - does not match num observations in sequence [%d]", ch.obs.ID(), al[len(al)-1].End, ch.nobs)
}
// Iterate over alignment nodes. Find the net by name and state number.
// TODO: hardcoded for state alignments. Need to impl. net-level alignments?
// Also, this is using a naming convention (xxx-N), can we use a better design?
// Include state index in alignment node?
for _, node := range al {
s := strings.Split(node.Name, "-") // format is xxx-N where xxx is the net name and N is the state index.
if len(s) != 2 {
return fmt.Errorf("oid:%s - there must be exactly one \"-\" in the alignment node name [%s]", ch.obs.ID(), node.Name)
}
glog.V(6).Infof("oid:%s - align node name: %s, state: %s", ch.obs.ID(), s[0], s[1])
h := ch.ms.byName[s[0]]
st, err := strconv.ParseInt(s[1], 10, 32)
if err != nil {
return err
}
for p := node.Start; p < node.End; p++ {
vec := ch.vectors[p]
o := model.F64ToObs(vec, "")
h.B[int(st)].UpdateOne(o, 1)
}
}
return nil
}
// Train without using sampler.
func BenchmarkTrain(b *testing.B) {
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))
buf := make([][]float64, 2000000, 2000000)
for i := 0; i < 2000000; i++ {
rv := model.RandNormalVector(r, mean, std)
buf[i] = rv
}
for i := 0; i < b.N; i++ {
for i := 0; i < 2000000; i++ {
g.UpdateOne(model.F64ToObs(buf[i], ""), 1.0)
}
g.Estimate()
g.Clear()
}
}
// Computes log prob for mixture.// SIDE EFFECT => returns logProb of
// Gaussian comp + logWeight in matrix pointed by func arg probs.
func (gmm *Model) logProbInternal(obs, probs []float64) float64 {
var max = -math.MaxFloat64
/* Compute log probabilities for this observation. */
for i, c := range gmm.Components {
o := model.F64ToObs(obs, "")
v1 := c.LogProb(o)
v2 := gmm.LogWeights[i]
v := v1 + v2
if probs != nil {
probs[i] = v
}
if v > max {
max = v
}
}
// To simplify computation, use the max prob in the denominator instead
// of the sum.
return max
}
// Returns the probability.
func (gmm *Model) prob(obs []float64) float64 {
return math.Exp(gmm.LogProb(model.F64ToObs(obs, "")))
}
// 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])
}
}