■ k-평균 클러스터링(k-means clustering)을 사용하는 방법을 보여준다.
▶ SourceModel.cs
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using Microsoft.ML.Data; namespace TestProject { /// <summary> /// 소스 모델 /// </summary> public class SourceModel { //////////////////////////////////////////////////////////////////////////////////////////////////// Property ////////////////////////////////////////////////////////////////////////////////////////// Public #region 알코올 - Alcohol /// <summary> /// 알코올 /// </summary> [LoadColumn(0)] public float Alcohol { get; set; } #endregion #region 사과산 - MalicAcid /// <summary> /// 사과산 /// </summary> [LoadColumn(1)] public float MalicAcid { get; set; } #endregion #region 애쉬 - Ash /// <summary> /// 애쉬 /// </summary> [LoadColumn(2)] public float Ash { get; set; } #endregion #region 애쉬 알카니티 - AshAlcanity /// <summary> /// 애쉬 알카니티 /// </summary> [LoadColumn(3)] public float AshAlcanity { get; set; } #endregion #region 마그네슘 - Magnesium /// <summary> /// 마그네슘 /// </summary> [LoadColumn(4)] public float Magnesium { get; set; } #endregion #region 전체 페놀 - TotalPhenols /// <summary> /// 전체 페놀 /// </summary> [LoadColumn(5)] public float TotalPhenols { get; set; } #endregion #region 플라보노이드 - Flavanoids /// <summary> /// 플라보노이드 /// </summary> [LoadColumn(6)] public float Flavanoids { get; set; } #endregion #region 비 플라보노이드 페놀 - NonflavanoidPhenols /// <summary> /// 비 플라보노이드 페놀 /// </summary> [LoadColumn(7)] public float NonflavanoidPhenols { get; set; } #endregion #region 프로안토시아닌 - Proanthocyanins /// <summary> /// 프로안토시아닌 /// </summary> [LoadColumn(8)] public float Proanthocyanins { get; set; } #endregion #region 색상 강도 - ColorIntensity /// <summary> /// 색상 강도 /// </summary> [LoadColumn(9)] public float ColorIntensity { get; set; } #endregion #region 색조 - Hue /// <summary> /// 색조 /// </summary> [LoadColumn(10)] public float Hue { get; set; } #endregion #region OD280 - OD280 /// <summary> /// OD280 /// </summary> [LoadColumn(11)] public float OD280 { get; set; } #endregion #region 프롤린 - Proline /// <summary> /// 프롤린 /// </summary> [LoadColumn(12)] public float Proline { get; set; } #endregion } } |
▶ TargetModel.cs
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using Microsoft.ML.Data; namespace TestProject { /// <summary> /// 타겟 모델 /// </summary> public class TargetModel { //////////////////////////////////////////////////////////////////////////////////////////////////// Property ////////////////////////////////////////////////////////////////////////////////////////// Public #region PredictedClusterID /// <summary> /// 예측 클러스터 ID /// </summary> [ColumnName("PredictedLabel")] public uint PredictedClusterID; #endregion #region DistanceArray /// <summary> /// 거리 배열 /// </summary> [ColumnName("Score")] public float[] DistanceArray; #endregion } } |
▶ Program.cs
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using System; using System.Linq; using Microsoft.ML; using Microsoft.ML.Data; namespace TestProject { /// <summary> /// 프로그램 /// </summary> class Program { //////////////////////////////////////////////////////////////////////////////////////////////////// Method ////////////////////////////////////////////////////////////////////////////////////////// Static //////////////////////////////////////////////////////////////////////////////// Private #region 프로그램 시작하기 - Main() /// <summary> /// 프로그램 시작하기 /// </summary> private static void Main() { string dataFilePath = "wine-clustering.csv"; MLContext mlContext = new MLContext(); IDataView sourceDataView = mlContext.Data.LoadFromTextFile<SourceModel>(dataFilePath, hasHeader : true, separatorChar : ','); string[] columnNameArray = sourceDataView.Schema.Select(column => column.Name).ToArray(); var data = mlContext.Data.TrainTestSplit(sourceDataView, testFraction : 0.2); var pipeline = mlContext.Transforms.Concatenate("Feature", columnNameArray).Append(mlContext.Clustering.Trainers.KMeans("Feature", numberOfClusters : 3)); var model = pipeline.Fit(data.TrainSet); IDataView testDataView = model.Transform(data.TestSet); ClusteringMetrics metrics = mlContext.Clustering.Evaluate(testDataView, scoreColumnName : "Score", featureColumnName : "Feature"); PredictionEngine<SourceModel, TargetModel> predictionEngine = mlContext.Model.CreatePredictionEngine<SourceModel, TargetModel>(model); TargetModel target = predictionEngine.Predict ( new SourceModel() { Alcohol = 11, Ash = 2.33f, AshAlcanity = 3.21f, ColorIntensity = 2.9f, Flavanoids = 1.54f, Hue = 1.55f, Magnesium = 120, MalicAcid = 1.44f, NonflavanoidPhenols = 0.3f, OD280 = 2.93f, Proanthocyanins = 1.72f, TotalPhenols = 3.86f, Proline = 1300 } ); Console.WriteLine($"예측 클러스터 ID : {target.PredictedClusterID}"); } #endregion } } |
