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| using System; | |
| using MathNet.Numerics.LinearAlgebra.Double; | |
| using MathNet.Numerics.LinearAlgebra.Double.Factorization; | |
| namespace PolyRegressionCoefficients | |
| { | |
| class Program | |
| { | |
| public static void DrawRegression(double[] coeffs, double[] xList) | |
| { | |
| double xValue1 = xList[0]; | |
| double yValue1 = Polyval(coeffs, xList[0]); | |
| double xValue2; | |
| double yValue2; | |
| for (int i = 1; i < xList.Length; i++) | |
| { | |
| xValue2 = xList[i]; | |
| yValue2 = Polyval(coeffs, xList[i]); | |
| MyDrawLibrary.drawLine(xValue1, yValue1, xValue2, yValue2); | |
| xValue1 = xValue2; | |
| yValue1 = yValue2; | |
| } | |
| } | |
| // From http://rosettacode.org/wiki/Polynomial_regression#C.23 | |
| public static double[] Polyfit(double[] x, double[] y, int degree) | |
| { | |
| // Vandermonde matrix | |
| var v = new DenseMatrix(x.Length, degree + 1); | |
| for (int i = 0; i < v.RowCount; i++) | |
| for (int j = 0; j <= degree; j++) v[i, j] = Math.Pow(x[i], j); | |
| var yv = new DenseVector(y).ToColumnMatrix(); | |
| QR qr = v.QR(); | |
| // Math.Net doesn't have an "economy" QR, so: | |
| // cut R short to square upper triangle, then recompute Q | |
| var r = qr.R.SubMatrix(0, degree + 1, 0, degree + 1); | |
| var q = v.Multiply(r.Inverse()); | |
| var p = r.Inverse().Multiply(q.TransposeThisAndMultiply(yv)); | |
| return p.Column(0).ToArray(); | |
| } | |
| public static double Polyval(double[] coeffs, double xValue) | |
| { | |
| double val = 0; | |
| for (int i = 0; i < coeffs.Length; i++) | |
| { | |
| val += coeffs[i] * Math.Pow(xValue, i); | |
| } | |
| return val; | |
| } | |
| static void Main(string[] args) | |
| { | |
| double[] x = new[] { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 }; | |
| double[] y = new[] { 1.0, 6.0, 17.0, 34.0, 57.0, 86.0, 121.0, 162.0, 209.0, 262.0, 321.0 }; | |
| double[] coefficients = Polyfit(x, y, 6); | |
| DrawRegression(coefficients, x); | |
| } | |
| } | |
| } |
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http://rosettacode.org/wiki/Polynomial_regression#C.23