private static void Main()
{
var mlContext = new MLContext(seed: 0);
var dataView = mlContext.Data.LoadFromTextFile <IrisData>(DataPath, hasHeader: false, separatorChar: ',');
const string featuresColumnName = "Features";
var pipeline = mlContext.Transforms
.Concatenate(featuresColumnName, "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
.Append(mlContext.Clustering.Trainers.KMeans(featuresColumnName, numberOfClusters: 3));
var model = pipeline.Fit(dataView);
using (var fileStream = new FileStream(ModelPath, FileMode.Create, FileAccess.Write, FileShare.Write))
{
mlContext.Model.Save(model, dataView.Schema, fileStream);
}
var predictor = mlContext.Model.CreatePredictionEngine <IrisData, ClusterPrediction>(model);
var setosa = new IrisData
{
SepalLength = 5.1f,
SepalWidth = 3.5f,
PetalLength = 1.4f,
PetalWidth = 0.2f
};
var prediction = predictor.Predict(setosa);
Console.WriteLine($"Cluster: {prediction.PredictedClusterId}");
Console.WriteLine($"Distances: {string.Join(" ", prediction.Distances)}");
Console.ReadKey();
}
static void Main(string[] args)
{
Helper.PrintLine("创建 MLContext...");
MLContext mlContext = new MLContext(seed: 0);
ITransformer model;
if (File.Exists(ModelPath))
{
Helper.PrintLine("加载神经网络模型...");
model = mlContext.Model.Load(ModelPath, out DataViewSchema inputScema);
}
else
{
// 训练数据集合
IDataView trainingDataView = mlContext.Data.LoadFromTextFile <IrisData>(TrainingDataPath, hasHeader: false, separatorChar: ',');
// 创建神经网络管道
Helper.PrintLine("创建神经网络管道...");
IEstimator <ITransformer> pipeline = mlContext.Transforms
.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
// 拆分为三个集群
.Append(mlContext.Clustering.Trainers.KMeans("Features", numberOfClusters: 3));
// 开始训练神经网络
Helper.PrintSplit();
Helper.PrintLine("开始训练神经网络...");
model = pipeline.Fit(trainingDataView);
Helper.PrintLine("训练神经网络完成");
Helper.PrintSplit();
Helper.PrintLine($"导出神经网络模型...");
mlContext.Model.Save(model, trainingDataView.Schema, ModelPath);
}
IrisData setosa = new IrisData
{
SepalLength = 5.1f,
SepalWidth = 3.5f,
PetalLength = 1.4f,
PetalWidth = 0.2f
};
// 预测
Helper.PrintLine("预测：");
var predictor = mlContext.Model.CreatePredictionEngine <IrisData, ClusterPrediction>(model);
var prediction = predictor.Predict(setosa);
Helper.PrintLine($"所属集群: {prediction.PredictedClusterId}");
Helper.PrintLine($"特征差距: {string.Join(" ", prediction.Distances)}");
Helper.Exit(0);
}