//Benchmark training
func BenchmarkTrain(t *testing.B) {
classifier := bayes.New()
t.ResetTimer()
//Yes, naive classifiers need a large corpus. Lets just go with this for now.
classifier.Train("That is the ugliest haircut", "negative")
classifier.Train("Totally, it looks like a mullet.", "negative")
classifier.Train("I kinda like it!", "positive")
classifier.Train("It looks niiiice", "positive")
}
func BenchmarkClassify(t *testing.B) {
classifier := bayes.New()
//Train first
classifier.Train("amazing, awesome movie!! Yeah!! Oh boy.", "positive")
classifier.Train("Sweet, this is incredibly, amazing, perfect, great!!", "positive")
classifier.Train("terrible, crappy thing. darn. Sucks!!", "negative")
t.ResetTimer()
classifier.Classify("awesome, cool, amazing!! Yay.")
}
func TestClassify(t *testing.T) {
classifier := bayes.New()
//Train first
classifier.Train("amazing, awesome movie!! Yeah!! Oh boy.", "positive")
classifier.Train("Sweet, this is incredibly, amazing, perfect, great!!", "positive")
classifier.Train("terrible, crappy thing. darn. Sucks!!", "negative")
result := classifier.Classify("awesome, cool, amazing!! Yay.")
if !strings.EqualFold(result, "positive") {
t.Error("Classifier is not classifying accurately (or has too few inputs)")
}
}
//Test constructor
func TestNew(t *testing.T) {
classifier := bayes.New()
if classifier == nil {
t.Errorf("bayes.New() returns nil pointer")
}
}
