// AROWClassify classifies the input using the given model having stateName.
func AROWClassify(ctx *core.Context, stateName string, featureVector data.Map) (data.Map, error) {
s, err := lookupAROWState(ctx, stateName)
if err != nil {
return nil, err
}
scores, err := s.arow.Classify(FeatureVector(featureVector))
return data.Map(scores), err
}
func (v FeatureVector) toNNFV() (nearest.FeatureVector, error) {
ret := make(nearest.FeatureVector, 0, len(v))
err := nested.Flatten(data.Map(v), func(key string, value float32) {
ret = append(ret, nearest.FeatureElement{Dim: key, Value: value})
})
if err != nil {
return nil, err
}
return ret, nil
}
func (v FeatureVector) toInternal() (fVector, error) {
ret := make(fVector, 0, len(v))
err := nested.Flatten(data.Map(v), func(key string, value float32) {
ret = append(ret, fElement{dim: dim(key), value: value})
})
if err != nil {
return nil, err
}
return ret, nil
}
// toInternal converts a feature vector to internal format. It requires write lock for intern.
func (v FeatureVector) toInternal(intern *intern.Intern) (fVectorForScores, fVector, error) {
full := make(fVector, 0, len(v))
err := nested.Flatten(data.Map(v), func(key string, value float32) {
full = append(full, fElement{dim(intern.Get(key)), value})
})
if err != nil {
return nil, nil, err
}
return fVectorForScores(full), full, nil
}
// toInternalForScores converts a feature vector to internal format. It requires read lock for intern.
func (v FeatureVector) toInternalForScores(intern *intern.Intern) (fVectorForScores, error) {
ret := make(fVectorForScores, 0, len(v))
err := nested.Flatten(data.Map(v), func(key string, value float32) {
if d := intern.GetOrZero(key); d != 0 {
ret = append(ret, fElement{dim(d), value})
}
})
if err != nil {
return nil, err
}
return ret, nil
}