func main() {
problem := golinear.NewProblem()
problem.Add(golinear.TrainingInstance{0, golinear.FromDenseVector([]float64{1, 1, 1, 0, 0})})
problem.Add(golinear.TrainingInstance{1, golinear.FromDenseVector([]float64{1, 0, 1, 1, 1})})
param := golinear.DefaultParameters()
model, err := golinear.TrainModel(param, problem)
if err != nil {
log.Fatal("Could not train the model: " + err.Error())
}
label := model.Predict(golinear.FromDenseVector([]float64{1, 1, 0, 0, 0}))
fmt.Printf("Predicted label: %f\n", label)
}
func ExtractFeatures(dict Dictionary) (*golinear.Problem, ModelMetadata) {
problem := golinear.NewProblem()
//featureMap := make(map[string]int)
featureMapping := make(map[string]int)
tagMapping := make(map[string]int)
var norm uint64 = 0
for _, tags := range dict {
for _, count := range tags {
if count > norm {
norm = count
}
}
}
for word, tags := range dict {
featureVec := StringFeatureToFeature(ApplyTemplates(DefaultTemplates, word),
featureMapping, float64(norm))
for tag, count := range tags {
id, found := tagMapping[tag]
if !found {
tagMapping[tag] = len(tagMapping)
}
id, _ = tagMapping[tag]
for i := 0; i < int(count); i++ {
var inst golinear.TrainingInstance
inst.Features = featureVec
inst.Label = float64(id)
if err := problem.Add(inst); err != nil {
panic(err)
}
}
}
}
return problem, ModelMetadata{featureMapping, tagMapping, float64(norm)}
}