Changeset 9706 for branches/StatisticalTesting/HeuristicLab.Analysis.Statistics/3.3/LinearLeastSquaresFitting.cs

Timestamp:

07/15/13 16:33:13 (11 years ago)

Author:

ascheibe

Message:

#2031

• added exponential fitting
• added logarithmic fitting
• refactored fitting code
• updated license headers

File:

• branches/StatisticalTesting/HeuristicLab.Analysis.Statistics/3.3/LinearLeastSquaresFitting.cs (modified) (4 diffs)

branches/StatisticalTesting/HeuristicLab.Analysis.Statistics/3.3/LinearLeastSquaresFitting.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -24 +24 @@
 
 namespace HeuristicLab.Analysis.Statistics {
-  public class LinearLeastSquaresFitting {
-    public static void Calculate(double[] dataPoints, out double a1, out double a0) {
+  public class LinearLeastSquaresFitting : IFitting {
+    public void Calculate(double[] dataPoints, out double p0, out double p1) {
+      var stdX = Enumerable.Range(0, dataPoints.Count()).Select(x => (double)x).ToArray();
+      Calculate(dataPoints, stdX, out p0, out p1);
+    }
+
+    public void Calculate(double[] y, double[] x, out double p0, out double p1) {
+      if (y.Count() != x.Count()) {
+        throw new ArgumentException("The lenght of x and y needs do be equal. ");
+      }
+
       double sxy = 0.0;
       double sxx = 0.0;
-      int n = dataPoints.Count();
-      double sy = dataPoints.Sum();
+      int n = y.Count();
+      double sy = y.Sum();
       double sx = ((n - 1) * n) / 2;
       double avgy = sy / n;
@@ -35 +44 @@
 
       for (int i = 0; i < n; i++) {
-        sxy += i * dataPoints[i];
-        sxx += i * i;
+        sxy += x[i] * y[i];
+        sxx += x[i] * x[i];
       }
 
-      a1 = (sxy - (n * avgx * avgy)) / (sxx - (n * avgx * avgx));
-      a0 = avgy - a1 * avgx;
+      p0 = (sxy - (n * avgx * avgy)) / (sxx - (n * avgx * avgx));
+      p1 = avgy - p0 * avgx;
     }
 
-    public static double CalculateError(double[] dataPoints, double a1, double a0) {
+    //TODO: adapt or remove
+    public double CalculateError(double[] dataPoints, double a1, double a0) {
       double r = 0.0;
       double avgy = dataPoints.Average();
@@ -58 +68 @@
       return r;
     }
+
+    public DataRow CalculateFittedLine(double[] y, double[] x, string rowName) {
+      double k, d;
+      Calculate(y, x, out k, out d);
+
+      DataRow newRow = new DataRow(rowName);
+      for (int i = 0; i < x.Count(); i++) {
+        newRow.Values.Add(k * x[i] + d);
+      }
+      return newRow;
+    }
+
+    public DataRow CalculateFittedLine(double[] dataPoints, string rowName) {
+      DataRow newRow = new DataRow(rowName);
+      double c0, c1;
+      Calculate(dataPoints, out c0, out c1);
+      var stdX = Enumerable.Range(0, dataPoints.Count()).Select(x => (double)x).ToArray();
+
+      for (int i = 0; i < stdX.Count(); i++) {
+        newRow.Values.Add(c0 * stdX[i] + c1);
+      }
+
+      return newRow;
+    }
+
+    public override string ToString() {
+      return "Linear Fitting";
+    }
   }
 }