using System;

DataTable table = new DataTable(); 

table.Columns.Add("Sex"); 

table.Columns.Add("Height", typeof(double)); 

table.Columns.Add("Weight", typeof(double)); 

table.Columns.Add("FootSize", typeof(double)); 

​

//training data. 

table.Rows.Add("male", 6, 180, 12); 

table.Rows.Add("male", 5.92, 190, 11); 

table.Rows.Add("male", 5.58, 170, 12); 

table.Rows.Add("male", 5.92, 165, 10); 

table.Rows.Add("female", 5, 100, 6); 

table.Rows.Add("female", 5.5, 150, 8); 

table.Rows.Add("female", 5.42, 130, 7); 

table.Rows.Add("female", 5.75, 150, 9); 

table.Rows.Add("transgender", 4, 200, 5); 

table.Rows.Add("transgender", 4.10, 150, 8); 

table.Rows.Add("transgender", 5.42, 190, 7); 

table.Rows.Add("transgender", 5.50, 150, 9);

​

Classifier classifier = new Classifier(); 

classifier.TrainClassifier(table);

//output would be transgender.

Console.WriteLine(classifier.Classify(new double[] { 4, 150, 12 }));

Console.Read();

​

public void TrainClassifier(DataTable table)

{

    dataSet.Tables.Add(table);

​

    //table

    DataTable GaussianDistribution = dataSet.Tables.Add("Gaussian");

    GaussianDistribution.Columns.Add(table.Columns[0].ColumnName);

​

    //columns

    for (int i = 1; i < table.Columns.Count; i++)

    {

        GaussianDistribution.Columns.Add(table.Columns[i].ColumnName + "Mean");

        GaussianDistribution.Columns.Add(table.Columns[i].ColumnName + "Variance");

    }

​

    //calc data

    var results = (from myRow in table.AsEnumerable()

                   group myRow by myRow.Field<string>(table.Columns[0].ColumnName) into g

                   select new { Name = g.Key, Count = g.Count() }).ToList();

​

    for (int j = 0; j < results.Count; j++)

    {

        DataRow row = GaussianDistribution.Rows.Add();

        row[0] = results[j].Name;

​

        int a = 1;

        for (int i = 1; i < table.Columns.Count; i++)

        {

            row[a] = Helper.Mean(SelectRows(table, i, string.Format("{0} = '{1}'", 

                                 table.Columns[0].ColumnName, results[j].Name)));

            row[++a] = Helper.Variance(SelectRows(table, i, 

                       string.Format("{0} = '{1}'", 

                       table.Columns[0].ColumnName, results[j].Name)));

            a++;

        }

    }

}

​

​

public string Classify(double[] obj)

{

    Dictionary<string,> score = new Dictionary<string,>();

​

    var results = (from myRow in dataSet.Tables[0].AsEnumerable()

                   group myRow by myRow.Field<string>(

                         dataSet.Tables[0].Columns[0].ColumnName) into g

                   select new { Name = g.Key, Count = g.Count() }).ToList();

​

    for (int i = 0; i < results.Count; i++)

    {

        List<double> subScoreList = new List<double>();

        int a = 1, b = 1;

        for (int k = 1; k < dataSet.Tables["Gaussian"].Columns.Count; k = k + 2)

        {

            double mean = Convert.ToDouble(dataSet.Tables["Gaussian"].Rows[i][a]);

            double variance = Convert.ToDouble(dataSet.Tables["Gaussian"].Rows[i][++a]);

            double result = Helper.NormalDist(obj[b - 1], mean, Helper.SquareRoot(variance));

            subScoreList.Add(result);

            a++; b++;

        }

​

        double finalScore = 0;

        for (int z = 0; z < subScoreList.Count; z++)

        {

            if (finalScore == 0)

            {

                finalScore = subScoreList[z];

                continue;

            }

​

            finalScore = finalScore * subScoreList[z];

        }

​

        score.Add(results[i].Name, finalScore * 0.5);

    }

​

    double maxOne = score.Max(c => c.Value);

    var name = (from c in score

                where c.Value == maxOne

                select c.Key).First();

​

    return name;

}