■ Regression 클래스의 LinearRegression 정적 메소드를 사용해 데이터를 선형화 가능한 모델에 맞추는 방법을 보여준다.
▶ MainForm.cs
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using System; using System.Drawing; using System.Windows.Forms; using Steema.TeeChart.Functions; using Steema.TeeChart.Styles; namespace TestProject { /// <summary> /// 메인 폼 /// </summary> public partial class MainForm : Form { //////////////////////////////////////////////////////////////////////////////////////////////////// Field ////////////////////////////////////////////////////////////////////////////////////////// Private #region Field /// <summary> /// 포인트 시리즈 /// </summary> private Points points; /// <summary> /// Y 예측값 배열 /// </summary> private double[] yHatArray; /// <summary> /// 계수 배열 /// </summary> private double[] coefficientArray; /// <summary> /// 라인 시리즈 /// </summary> private Line line; #endregion //////////////////////////////////////////////////////////////////////////////////////////////////// Constructor ////////////////////////////////////////////////////////////////////////////////////////// Public #region 생성자 - MainForm() /// <summary> /// 생성자 /// </summary> public MainForm() { InitializeComponent(); #region 포인트 시리즈를 생성한다. this.points = new Points(); this.points.Title = "Source"; this.points.Pointer.Brush.Color = Color.FromArgb(115, 141, 192); this.points.Pointer.Pen.Color = Color.FromArgb(69, 85, 115); this.points.Pointer.Style = PointerStyles.Rectangle; #endregion #region 라인 시리즈를 생성한다. this.line = new Line(); this.line.Title = "Fitted"; this.line.Color = Color.Orange; #endregion #region 티차트를 설정한다. this.tChart.Series.Add(this.points); this.tChart.Series.Add(this.line); #endregion this.points.Add(1.1 , 1 ); this.points.Add(1.2 , 2.5); this.points.Add(1.25, 4 ); this.points.Add(1.3 , 3 ); this.points.Add(1.9 , 5.5); this.points.Add(2.1 , 6.2); this.points.Add(2.2 , 6.6); this.points.Add(2.4 , 7.2); this.points.Add(3 , 8 ); this.points.Add(3.5 , 12 ); Load += Form_Load; this.modelComboBox.SelectedIndexChanged += modelComboBox_SelectedIndexChanged; } #endregion //////////////////////////////////////////////////////////////////////////////////////////////////// Method ////////////////////////////////////////////////////////////////////////////////////////// Private //////////////////////////////////////////////////////////////////////////////// Event #region 폼 로드시 처리하기 - Form_Load(sender, e) /// <summary> /// 폼 로드시 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void Form_Load(object sender, EventArgs e) { this.modelComboBox.SelectedIndex = 0; } #endregion #region 모델 콤보 박스 선택 인덱스 변경시 처리하기 - modelComboBox_SelectedIndexChanged(sender, e) /// <summary> /// 모델 콤보 박스 선택 인덱스 변경시 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void modelComboBox_SelectedIndexChanged(object sender, EventArgs e) { this.yHatArray = new double[this.points.XValues.Count]; this.line.Clear(); if(FitModel(points, ref this.yHatArray, out this.coefficientArray, this.modelComboBox.SelectedIndex)) { this.line.XValues.Count = this.points.XValues.Count; this.line.XValues.Value = this.points.XValues.Value; this.line.YValues.Count = this.points.XValues.Count; this.line.YValues.Value = this.yHatArray; switch(this.modelComboBox.SelectedIndex) { case 0 : this.tChart.Header.Text = "y = a * Exp(b * x)\r\na = " + this.coefficientArray[0].ToString("0.00")+" b = "+this.coefficientArray[1].ToString("0.00"); break; case 1 : this.tChart.Header.Text = "y = a x ^ b\r\na = " + this.coefficientArray[0].ToString("0.00") + " b = " + this.coefficientArray[1].ToString("0.00"); break; case 2 : this.tChart.Header.Text = "y = b * ln(x) + a\r\na =" + this.coefficientArray[0].ToString("0.00") + " b = " + this.coefficientArray[1].ToString("0.00"); break; } } this.line.Repaint(); } #endregion //////////////////////////////////////////////////////////////////////////////// Function #region 모델 맞추기 - FitModel(source, yHatArray, coeffientArray, modelIndex) /// <summary> /// 모델 맞추기 /// </summary> /// <param name="source">소스 시리즈</param> /// <param name="yHatArray">Y 예측값 배열</param> /// <param name="coeffientArray">계수 배열</param> /// <param name="modelIndex">모델 인덱스</param> /// <returns>처리 결과</returns> private bool FitModel(Series source, ref double[] yHatArray, out double[] coeffientArray, int modelIndex) { double[] yArray = new double[source.Count]; double[] xArray = new double[source.Count]; double[] wArray = new double[source.Count]; bool validModel = true; switch(modelIndex) { case 0 : // y(x) = a * Exp(b * x) // linear model : ln(y) = b * x + ln(a) for(int i = 0; i < source.Count; i++) { xArray[i] = source.notMandatory[i]; yArray[i] = Math.Log(source.mandatory[i]); wArray[i] = source.mandatory[i] * source.mandatory[i]; } Regression.LinearRegression(source.Count, xArray, yArray, wArray, out coeffientArray); coeffientArray[0] = Math.Exp(coeffientArray[0]); for(int i = 0; i < source.Count; i++) { yHatArray[i] = coeffientArray[0] * Math.Exp(coeffientArray[1] * xArray[i]); } break; case 1 : // Power model y = a * x ^ b // linear model : ln(y) = b * ln(x) + ln(a) for(int i = 0; i < source.Count; i++) { xArray[i] = Math.Log(source.notMandatory[i]); yArray[i] = Math.Log(source.mandatory[i]); wArray[i] = source.mandatory[i] * source.mandatory[i]; } Regression.LinearRegression(source.Count, xArray, yArray, wArray, out coeffientArray); coeffientArray[0] = Math.Exp(coeffientArray[0]); for(int i = 0; i < source.Count; i++) { yHatArray[i] = coeffientArray[0] * Math.Pow(source.notMandatory[i], coeffientArray[1]); } break; case 2 : // Logarithmic model y = b * ln(x) + a for(int i = 0; i < source.Count; i++) { xArray[i] = Math.Log(source.notMandatory[i]); yArray[i] = source.mandatory[i]; wArray[i] = source.mandatory[i] * source.mandatory[i]; } Regression.LinearRegression(source.Count, xArray, yArray, wArray, out coeffientArray); for(int i = 0; i < source.Count; i++) { yHatArray[i] = coeffientArray[1] * Math.Log(source.notMandatory[i]) + coeffientArray[0]; } break; default : coeffientArray = new double[0]; break; } return validModel; } #endregion } } |
