// UpdateOne updates model using a single weighted sample.
func (m *Model) UpdateOne(o model.Obs, w float64) {
m.updateCount++
// We create a chain of hmms to update stats for an obs sequence.
// The chain is released once the update is done.
// The assigner has the logic for mapping labels to a chain of models.
chain, err := m.Set.chainFromAssigner(o, m.assigner)
if err != nil {
glog.Warningf("skipping, failed to update hmm model stats, oid:%s, error: %s", o.ID(), err)
return
}
// Use the forward-backward algorithm to compute counts.
if m.useAlignments {
err = chain.updateFromAlignments()
if err != nil {
m.updateFailCount++
if glog.V(6) {
glog.Fatal(err)
}
glog.Warning(err)
return
}
} else {
err = chain.update()
if err != nil {
m.updateFailCount++
if glog.V(6) {
glog.Fatal(err)
}
glog.Warning(err)
return
}
// Print log(prob(O/model))\
p := chain.beta.At(0, 0, 0)
m.logProb += p
glog.V(1).Infof("update hmm stats, oid:%s, logProb:%.2f total:%.2f", o.ID(), p, m.logProb)
}
}
// Use the label to create a chain of hmms.
// If model set only has one hmm, no need to use labels, simply assign the only hmm.
func (ms *Set) chainFromAssigner(obs model.Obs, assigner Assigner) (*chain, error) {
var hmms []*Net
var fos model.FloatObsSequence
switch o := obs.(type) {
case model.FloatObsSequence:
fos = o
case *model.FloatObsSequence:
fos = *o
default:
return nil, fmt.Errorf("obs must be of type model.FloatObsSequence, found type %s which is not supported",
reflect.TypeOf(obs))
}
if len(fos.ValueAsSlice()) == 0 {
return nil, fmt.Errorf("obs sequence has no data")
}
if assigner == nil && ms.size() == 1 {
hmms = append(hmms, ms.Nets[0])
if glog.V(3) {
glog.Warningf("assigner missing but model set has only one hmm network - assigning model [%s] to chain", ms.Nets[0].Name)
}
} else if assigner == nil {
return nil, fmt.Errorf("need assigner to create hmm chain if model set is greater than one")
} else {
// Get the labeler and check that it is of type SimpleLabeler
// otherwise return error.
labeler, ok := obs.Label().(model.SimpleLabel)
if !ok {
return nil, fmt.Errorf("labeler must be of type model.SimpleLabel, found type %s which is not supported",
reflect.TypeOf(obs.Label()))
}
// Now we need to assign hmms to the chain.
// We will use the sequence of labels to lookup the models by name.
labels := strings.Split(labeler.String(), ",")
if len(labels) == 1 && len(labels[0]) == 0 {
return nil, fmt.Errorf("no label found, can't assign models")
}
modelNames := assigner.Assign(labels)
glog.V(5).Infof("obsid:%s, labels:%v", obs.ID(), labels)
glog.V(5).Infof("obsid:%s, models:%v", obs.ID(), modelNames)
for _, name := range modelNames {
h, ok := ms.net(name)
if !ok {
return nil, fmt.Errorf("can't find model for name [%s] in set - assignment failed", name)
}
hmms = append(hmms, h)
}
}
nq := len(hmms)
if nq == 0 {
return nil, fmt.Errorf("the assigner returned no models")
}
ch := &chain{
hmms: hmms,
nq: nq,
ns: make([]int, nq, nq),
obs: obs,
vectors: fos.Value().([][]float64),
nobs: len(fos.Value().([][]float64)),
ms: ms,
}
for k, hmm := range hmms {
if hmm.A.Shape[0] > ch.maxNS {
ch.maxNS = hmm.A.Shape[0]
}
ch.ns[k] = hmm.A.Shape[0]
}
ch.alpha = narray.New(ch.nq, ch.maxNS, ch.nobs)
ch.beta = narray.New(ch.nq, ch.maxNS, ch.nobs)
ch.computeLikelihoods()
// Validate.
// Can't have models with transitions from entry to exit states
// at the beginning or end of a chain.
if isTeeModel(hmms[0]) {
return nil, fmt.Errorf("the first model in the chain can't have a transition from entry to exit states - model name is [%s] with id [%d]", hmms[0].Name, hmms[0].id)
}
if isTeeModel(hmms[nq-1]) {
return nil, fmt.Errorf("the last model in the chain can't have a transition from entry to exit states - model name is [%s] with id [%d]", hmms[nq-1].Name, hmms[nq-1].id)
}
return ch, nil
}
func (ms *Set) chainFromNets(obs model.Obs, m ...*Net) (*chain, error) {
fos, ok := obs.(model.FloatObsSequence)
if !ok {
return nil, fmt.Errorf("obs must be of type model.FloatObsSequence, found type %s which is not supported", reflect.TypeOf(obs.Value()))
}
if len(fos.ValueAsSlice()) == 0 {
return nil, fmt.Errorf("obs sequence has no data")
}
ch := &chain{
hmms: m,
nq: len(m),
ns: make([]int, len(m), len(m)),
obs: obs,
vectors: fos.Value().([][]float64),
nobs: len(fos.Value().([][]float64)),
ms: ms,
}
for k, v := range m {
if !ms.exist(v) {
return nil, fmt.Errorf("model [%s] with id [%d] is not in set", v.Name, v.id)
}
if v.A.Shape[0] > ch.maxNS {
ch.maxNS = v.A.Shape[0]
}
ch.ns[k] = v.A.Shape[0]
}
ch.alpha = narray.New(ch.nq, ch.maxNS, ch.nobs)
ch.beta = narray.New(ch.nq, ch.maxNS, ch.nobs)
if glog.V(6) {
glog.Info("lab: ", ch.obs.Label())
glog.Info("obs: ", ch.obs.Value())
}
ch.computeLikelihoods()
// Validate.
// Can't have models with transitions from entry to exit states
// at the beginning or end of a chain.
if isTeeModel(m[0]) {
return nil, fmt.Errorf("first model in chain can't have entry to exit transition - model name is [%s] with id [%d]", m[0].Name, m[0].id)
}
if isTeeModel(m[len(m)-1]) {
return nil, fmt.Errorf("last model in chain can't have entry to exit transition - model name is [%s] with id [%d]", m[0].Name, m[0].id)
}
return ch, nil
}
// LogProb returns log probability for observation.
func (gmm *Model) LogProb(obs model.Obs) float64 {
o := obs.Value().([]float64)
return gmm.logProbInternal(o, nil)
}
func (s scorer) LogProb(o model.Obs) float64 {
return s.op[int(o.Value().([]float64)[0])]
}
// LogProb returns log probability for observation.
func (g *Model) LogProb(obs model.Obs) float64 {
return g.logProb(obs.Value().([]float64))
}