func main() {
flag.Parse()
if len(flag.Args()) != 2 {
fmt.Printf("Usage: %s lexicon modelname\n", os.Args[0])
os.Exit(1)
}
f, err := os.Open(flag.Arg(0))
if err != nil {
fmt.Printf("Could not open file: %s\n", flag.Arg(0))
os.Exit(1)
}
r := bufio.NewReader(f)
dict := word_classification.ReadDictionary(r)
word_classification.FilterDictionary(dict, 6)
problem, metadata := word_classification.ExtractFeatures(dict)
param := golinear.DefaultParameters()
model, err := golinear.TrainModel(param, problem)
if err != nil {
panic(err)
}
modelName := flag.Arg(1)
err = model.Save(fmt.Sprintf("%s.model", modelName))
if err != nil {
panic(err)
}
bMetadata, err := json.Marshal(metadata)
if err != nil {
panic(err)
}
metadataFile, err := os.OpenFile(fmt.Sprintf("%s.metadata", modelName),
os.O_WRONLY|os.O_CREATE|os.O_TRUNC, 0644)
if err != nil {
log.Fatal(err)
}
defer metadataFile.Close()
metadataFile.Write(bMetadata)
//testPrefix := prefixes("Microsoft", 3)
//features := stringFeatureToFeature(testPrefix, featureMapping, norm)
//class := model.Predict(features)
//numberTagMapping := reverseMapping(tagMapping)
//fmt.Printf("Predicted class: %s\n", numberTagMapping[int(class)])
}
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)
}