Lucky

To train a new classifier you should create two files and pass it to params. After train the classifier, two files are created to speed up the training fase, (model.bin and categories.bin)

Example files

Training file:

120#Pago Pesos TEF
26#COMISION MENSUAL POR MANTENCION
109#COMPRAS APL* ITUNES.COM/BILL
89#PAGO EN LINEA AUTOPISTA VESPUCIO SUR
89#Compra Internet COSTANERA NORTE
...

Labels file:

94#Ajuste por cierre de cuenta
12#Gimnasio
2#Bonos
86#Intereses
35#Jardín
13#Ropa Deportiva
...

Basic model

type Samples struct {
    Ngram    string
    Freq     float64
    Classes  map[uint]float64
    Probs    map[uint]float64
    Maximum  float64
    Minimum  float64
    Weighted bool
}

Setup structure

type Lucky struct {
    Model            map[string]*model.Samples
    CatNum           map[uint]float64
    CatStr           map[uint]string
    LabelsPath       string
    TrainingDataPath string
    URL              string
}

Public response

type BestCategory struct {
  ID    uint    // category id
  Name  string  // category name
  Score float64 // category probability
}

Public Methods

Fit() void
Predict(test string) (*model.BestCategory)

To see the classifier in action see lucky_test.go