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Efficient Multiple Linear Regression in C# / .Net

Asked 2010-05-26T05:50:12.923
16

Does anyone know of an efficient way to do multiple linear regression in C#, where the number of simultaneous equations may be in the 1000's (with 3 or 4 different inputs). After reading this article on multiple linear regression I tried implementing it with a matrix equation:

Matrix y = new Matrix(
    new double[,]{{745},
                  {895},
                  {442},
                  {440},
                  {1598}});

Matrix x = new Matrix(
     new double[,]{{1, 36, 66},
                 {1, 37, 68},
                 {1, 47, 64},
                 {1, 32, 53},
                 {1, 1, 101}});

Matrix b = (x.Transpose() * x).Inverse() * x.Transpose() * y;

for (int i = 0; i < b.Rows; i++)
{
  Trace.WriteLine("INFO: " + b[i, 0].ToDouble());
}

However it does not scale well to the scale of 1000's of equations due to the matrix inversion operation. I can call the R language and use that, however I was hoping there would be a pure .Net solution which will scale to these large sets.

Any suggestions?

EDIT #1:

I have settled using R for the time being. By using statconn (downloaded here) I have found it to be both fast & relatively easy to use this method. I.e. here is a small code snippet, it really isn't much code at all to use the R statconn library (note: this is not all the code!).

_StatConn.EvaluateNoReturn(string.Format("output <- lm({0})", equation));
object intercept = _StatConn.Evaluate("coefficients(output)['(Intercept)']");
parameters[0] = (double)intercept;
for (int i = 0; i < xColCount; i++)
{
  object parameter = _StatConn.Evaluate(string.Format("coefficients(output)['x{0}']", i));
  parameters[i + 1] = (double)parameter;
}
c# .net linear-regression
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2 Answers

1

Try Meta.Numerics:

Meta.Numerics is a library for advanced scientific computation in the .NET Framework. It can be used from C#, Visual Basic, F#, or any other .NET programming language. The Meta.Numerics library is fully object-oriented and optimized for speed of implementation and execution.

To populate a matrix, see an example of the ColumnVector Constructor (IList<Double>). It can construct a ColumnVector from many ordered collections of reals, including double[] and List.

answered 2010-05-26T06:24:13.193
1

I can suggest to use FinMath. It is extremely-optimized .net numerical computation library. It uses Intel Math Kernel Library to do complex calculations such as linear regression or matrix inverse, but most classes have very simple approachable interfaces. And of course, it's scalable to a large sets of data. mrnye's example will be look like this:

using FinMath.LeastSquares;
using FinMath.LinearAlgebra;

Vector y = new Vector(new double[]{745,
    895,
    442,
    440,
    1598});

Matrix X = new Matrix(new double[,]{
    {1, 36, 66},
    {1, 37, 68},
    {1, 47, 64},
    {1, 32, 53},
    {1, 1, 101}});

Vector b = OrdinaryLS.FitOLS(X, y);

Console.WriteLine(b);
answered 2011-10-20T18:20:38.433

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