func init() {
myClassifier, err := bayesian.NewClassifierFromFile("classifier.gob")
if err != nil {
panic(err)
}
classifier = myClassifier
}
func (scanner *Scanner) LoadOrCreate() {
classifier, err := bayesian.NewClassifierFromFile(scanner.BayesianFile())
if err != nil {
if os.IsNotExist(err) {
log.Printf("%s does not exist. Creating db\n", scanner.BayesianFile())
classifier = bayesian.NewClassifier(BAYESIAN_CLASSES...)
}
}
cat, err := catalog.NewCatalogFromFile(scanner.CatalogFile())
if err != nil {
if os.IsNotExist(err) {
cat = &catalog.Catalog{Filename: scanner.CatalogFile(), Files: make([]uint32, 0)}
}
}
scanner.classifier = classifier
scanner.catalog = cat
}
// Sets up and trains a new analyzer to classify sentiment
func NewAnalyzer() Analyzer {
a := Analyzer{}
// Get the training data if not present
_, err := os.Stat(DATA_FILE)
if err != nil {
if os.IsNotExist(err) {
a.downloadDataSet()
}
}
c, err := bayesian.NewClassifierFromFile(DATA_FILE)
if err == nil {
a.classifier = c
} else {
// Note: Nothing will be trained at this point, but we'll still have a classifier that can be trained
a.classifier = bayesian.NewClassifier(Positive, Negative, Neutral)
}
return a
}
func Classifier() *bayesian.Classifier {
classifier, _ := bayesian.NewClassifierFromFile("classifier.serialized")
return classifier
}