func main() {
flag.Parse()
runtime.GOMAXPROCS(runtime.NumCPU())
// 载入训练集
set := contrib.LoadLibSVMDataset(*libsvm_file, false)
options := rbm.RBMOptions{
NumHiddenUnits: *hidden,
NumCD: *numCD,
Worker: runtime.NumCPU(),
LearningRate: *learning_rate,
MaxIter: *maxIter,
BatchSize: *batch_size,
Delta: *delta,
UseBinaryHiddenUnits: *useBinary,
}
// 创建训练器
machine := rbm.NewRBM(options)
machine.Train(set)
machine.Write(*model)
}
func main() {
flag.Parse()
runtime.GOMAXPROCS(runtime.NumCPU())
// 载入训练集
set := contrib.LoadLibSVMDataset(*libsvm_file, false)
// 创建训练器
machine := rbm.LoadRBM(*model)
visibleDim := set.GetOptions().FeatureDimension
hiddenDim := machine.GetOptions().NumHiddenUnits + 1
iter := set.CreateIterator()
iter.Start()
for !iter.End() {
instance := iter.GetInstance()
v := util.NewVector(visibleDim)
content := fmt.Sprintf("%s", instance.Output.LabelString)
for i := 0; i < visibleDim; i++ {
value := instance.Features.Get(i)
v.Set(i, value)
if value != 0.0 && *append {
content = fmt.Sprintf("%s %d:%d", content, i+1, int(value))
}
}
h := machine.SampleHidden(v, *numCD, *useBinary)
for i := 1; i < hiddenDim; i++ {
value := h.Get(i)
if value != 0.0 {
if *append {
if *useBinary {
content = fmt.Sprintf("%s %d:%d", content, visibleDim+i-1, int(value))
} else {
content = fmt.Sprintf("%s %d:%.3f", content, visibleDim+i-1, value)
}
} else {
if *useBinary {
content = fmt.Sprintf("%s %d:%d", content, i, int(value))
} else {
content = fmt.Sprintf("%s %d:%.3f", content, i, value)
}
}
}
}
fmt.Printf("%s\n", content)
iter.Next()
}
}
func main() {
flag.Parse()
set := contrib.LoadLibSVMDataset(*input, true)
iterator := set.CreateIterator()
client := &http.Client{}
for {
iterator.Start()
for !iterator.End() {
instance := iterator.GetInstance()
if *mode != "train" {
instance.Output = nil
}
httpBody, errMarshal := json.Marshal(instance)
if errMarshal != nil {
log.Print("无法JSON串行化样本")
}
req, _ := http.NewRequest("POST", "http://"+*server+"/train", bytes.NewReader(httpBody))
req.Header.Set("Content-Type", "application/json")
res, err := client.Do(req)
io.Copy(ioutil.Discard, res.Body)
if err != nil {
log.Print("http请求失败, err=", err)
} else {
res.Body.Close()
}
iterator.Next()
}
}
}
func main() {
flag.Parse()
runtime.GOMAXPROCS(runtime.NumCPU())
// 载入训练集
set := contrib.LoadLibSVMDataset(*libsvm_file, false)
// 设置训练器参数
trainerOptions := supervised.TrainerOptions{
Optimizer: optimizer.OptimizerOptions{
OptimizerName: *opt,
RegularizationScheme: *reg,
RegularizationFactor: *reg_factor,
LearningRate: *learning_rate,
CharacteristicTime: *characteristic_time,
ConvergingDeltaWeight: *delta,
ConvergingSteps: 3,
MaxIterations: *max_iter,
GDBatchSize: *batch_size,
}}
// 创建训练器
trainer := supervised.NewMaxEntClassifierTrainer(trainerOptions)
// 打开处理器profile文件
if *cpuprofile != "" {
f, err := os.Create(*cpuprofile)
if err != nil {
log.Fatal(err)
}
pprof.StartCPUProfile(f)
defer pprof.StopCPUProfile()
}
// 进行交叉评价
evaluators := eval.NewEvaluators([]eval.Evaluator{
&eval.PREvaluator{}, &eval.AccuracyEvaluator{}})
if *folds != 0 {
result := eval.CrossValidate(trainer, set, evaluators, *folds)
log.Print(*folds, "-folds 交叉评价：")
log.Printf("精度 = %.2f %%", result.Metrics["precision"]*100)
log.Printf("召回率 = %.2f %%", result.Metrics["recall"]*100)
log.Printf("F1 = %.2f %%", result.Metrics["fscore"]*100)
log.Printf("准确度 = %.2f %%", result.Metrics["accuracy"]*100)
return
}
// 在全部数据上训练模型
model := trainer.Train(set)
model.Write(*model_file)
// 测试模型
if *test_file != "" {
// 载入测试集
testSet := contrib.LoadLibSVMDataset(*test_file, false)
// 在测试集上评价模型并输出结果
result := evaluators.Evaluate(model, testSet)
log.Print("测试数据集评价：")
log.Printf("精度 = %.2f %%", result.Metrics["precision"]*100)
log.Printf("召回率 = %.2f %%", result.Metrics["recall"]*100)
log.Printf("F1 = %.2f %%", result.Metrics["fscore"]*100)
log.Printf("准确度 = %.2f %%", result.Metrics["accuracy"]*100)
}
}