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
}
func (s scorer) LogProb(o model.Obs) float64 {
return s.op[int(o.Value().([]float64)[0])]
}
// LogProb returns log probability for observation.
func (gmm *Model) LogProb(obs model.Obs) float64 {
o := obs.Value().([]float64)
return gmm.logProbInternal(o, nil)
}
// LogProb returns log probability for observation.
func (g *Model) LogProb(obs model.Obs) float64 {
return g.logProb(obs.Value().([]float64))
}
