func Evaluate(evalData chan *model.Datum, nb model.Interface) {
var correct, wrong uint
evalStartTime := time.Now()
for d := range evalData {
estimator, _ := nb.Classify(d.Features)
class, _ := distribution.ArgMax(estimator)
if class == d.Class {
correct += 1
} else {
fmt.Println("For:", d.Features, "Was:", d.Class, "Got:", class)
wrong += 1
}
}
elapsed := time.Since(evalStartTime).Seconds()
fmt.Println("Took", elapsed, "for", correct+wrong, "queries.", elapsed/float64(correct+wrong), "sec/query")
accuracy := float64(correct) / (float64(correct) + float64(wrong))
fmt.Println(accuracy*100., "Got", correct, "correct and", wrong, "wrong.")
}
// ServeHTTP classifies
func (h ClassifyHandler) ServeHTTP(w http.ResponseWriter, req *http.Request) {
req.ParseForm()
query := req.FormValue("q")
corpus := req.FormValue("corpus")
n, err := strconv.Atoi(req.FormValue("n"))
if err != nil {
n = 2
}
fmt.Println(req)
m := model.NewNGramModel(h.pool.Get(corpus), n)
features := strings.Split(query, " ")
estimator, explain := m.Classify(features)
prediction, _ := distribution.ArgMax(estimator)
output := map[string]interface{}{
"prediction": prediction,
"estimator": distribution.JSON(estimator),
"explain": explain,
}
jsonBytes, _ := json.MarshalIndent(output, "", "\t")
w.Write(jsonBytes)
return
}