func MultiClassRunOnDataSet(classifier algo.MultiClassClassifier, train_dataset, test_dataset *core.DataSet, pred_path string, params map[string]string) float64 {
if train_dataset != nil {
classifier.Train(train_dataset)
}
var pred_file *os.File
if pred_path != "" {
pred_file, _ = os.Create(pred_path)
}
accuracy := 0.0
total := 0.0
for _, sample := range test_dataset.Samples {
prediction := classifier.PredictMultiClass(sample)
label, _ := prediction.KeyWithMaxValue()
if int(label) == sample.Label {
accuracy += 1.0
}
total += 1.0
if pred_file != nil {
pred_file.WriteString(strconv.Itoa(label) + "\n")
}
}
if pred_path != "" {
defer pred_file.Close()
}
return accuracy / total
}
func MultiClassRun(classifier algo.MultiClassClassifier, train_path string, test_path string, pred_path string, params map[string]string) (float64, error) {
global, _ := strconv.ParseInt(params["global"], 10, 64)
train_dataset := core.NewDataSet()
err := train_dataset.Load(train_path, global)
if err != nil {
return 0.5, err
}
test_dataset := core.NewDataSet()
err = test_dataset.Load(test_path, global)
if err != nil {
return 0.5, err
}
classifier.Init(params)
accuracy := MultiClassRunOnDataSet(classifier, train_dataset, test_dataset, pred_path, params)
return accuracy, nil
}
func MultiClassTest(classifier algo.MultiClassClassifier, test_path string, pred_path string, params map[string]string) (float64, error) {
global, _ := strconv.ParseInt(params["global"], 10, 64)
model_path, _ := params["model"]
classifier.Init(params)
if model_path != "" {
classifier.LoadModel(model_path)
} else {
return 0.0, nil
}
test_dataset := core.NewDataSet()
err := test_dataset.Load(test_path, global)
if err != nil {
return 0.0, err
}
accuracy := MultiClassRunOnDataSet(classifier, nil, test_dataset, pred_path, params)
return accuracy, nil
}
func MultiClassTrain(classifier algo.MultiClassClassifier, train_path string, params map[string]string) error {
global, _ := strconv.ParseInt(params["global"], 10, 64)
train_dataset := core.NewDataSet()
err := train_dataset.Load(train_path, global)
if err != nil {
return err
}
classifier.Init(params)
classifier.Train(train_dataset)
model_path, _ := params["model"]
if model_path != "" {
classifier.SaveModel(model_path)
}
return nil
}