c# - 使用 Encog 框架的垃圾邮件过滤示例

Question

我正在寻找有关如何使用 Encog Framework 创建简单的垃圾邮件过滤/分类或集群应用程序的示例。我在谷歌上找不到任何东西。

我还购买了 Jeff Heaton 的书《Programming Neural Networks with Encog3 in C#》，但我找不到此类应用程序的任何示例。

任何人都可以提供有关如何根据主题和正文将电子邮件分类为垃圾邮件的简单应用程序的任何信息吗？

编辑:我已经看到了如何在 Python 中执行此操作的方法，但我想问，任何人都可以提供有关如何创建垃圾邮件过滤/分类应用程序的 Encog + C# 特定示例吗？

Answer 1

大多数垃圾邮件过滤器使用一种贝叶斯分类，这是最流行的朴素贝叶斯分类。这是一些无需任何额外框架即可使用的代码。

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;
}

编辑:这就是你如何使用它!

    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();