using System;

using Accord.Statistics.Analysis;

using Accord.Math;

​

public class Program

{

    public static void Main()

    {

        // Step 1. Get some data

        double[][] data =

        {

        new[] { 2.5, 2.4, 1, 2, 3, 4, 5, 6, 7, 8 },

        new[] { 0.5, 0.7, 1, 2, 3, 4, 5, 6, 7, 8  },

        new[] { 2.2, 2.9, 1, 2, 3, 4, 5, 6, 7, 8  },

        new[] { 1.9, 2.2, 1, 2, 3, 4, 5, 6, 7, 8  },

        new[] { 3.1, 3.0, 1, 2, 3, 4, 5, 6, 7, 8  },

        new[] { 2.3, 2.7, 1, 2, 3, 4, 5, 6, 7, 8  },

        new[] { 2.0, 1.6, 1, 2, 3, 4, 5, 6, 7, 8  },

        new[] { 1.0, 1.1, 1, 2, 3, 4, 5, 6, 7, 8  },

        new[] { 1.5, 1.6, 1, 2, 3, 4, 5, 6, 7, 8  },

        new[] { 1.1, 0.9, 1, 2, 3, 4, 5, 6, 7, 8 }

        };

​

        // Step 2. Create the Principal Component Analysis

        var pca = new PrincipalComponentAnalysis(PrincipalComponentMethod.Center);

​

        // Step 3. Compute the analysis

        pca.Learn(data);

​

        // Step 4. Transform your data

        pca.NumberOfOutputs = 3;

        double[][] finalData = pca.Transform(data);

        pr(finalData);

        Console.WriteLine("-------");

        var scaled_finalData = Scale(finalData);

        pr(scaled_finalData);

    }

​

    // WARNING - Mutates input matrix!

    public static double[][] Scale(double[][] m){

        var min_m = m.Min(); // set this explicitly if you want control over min value

        var max_m = m.Max(); // set this explicitly if you want control over max value

        for (int i = 0; i < m.GetLength(0); i++){

            for (int j = 0; j < m[i].GetLength(0); j++){

                m[i][j] = (m[i][j] - min_m)/(max_m - min_m);

            }

        }

        return m;

    }

    public static void pr(double[][] m){

        for (int i = 0; i < m.GetLength(0); i++){

            for (int j = 0; j < m[i].GetLength(0); j++){

                Console.Write(m[i][j] + ", ");

            }

            Console.Write("\n");

        }

    }

​

​

}