我在寻找如何使用Encog框架创建一个简单的垃圾邮件过滤/分类或聚类应用的示例。谷歌上我没能找到任何相关内容。

我还购买了Jeff Heaton的书，书名是《用C#编程的Encog3神经网络》，但我没能找到任何关于此类应用的示例。

有谁能提供一个简单的应用示例，展示如何根据邮件的主题和正文文本将邮件分类为垃圾邮件吗？

编辑：我已经看过如何在Python中实现的方法，但我问的是，有谁能提供使用Encog + C#的具体示例，展示如何创建一个垃圾邮件过滤/分类应用？

回答：

大多数垃圾邮件过滤器使用某种贝叶斯分类法，最常见的是朴素贝叶斯分类法。以下是一些无需额外框架即可使用的代码。

public void TrainClassifier(DataTable table){dataSet.Tables.Add(table);//tableDataTable GaussianDistribution = dataSet.Tables.Add("Gaussian");GaussianDistribution.Columns.Add(table.Columns[0].ColumnName);//columnsfor (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 datavar 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：这是如何使用它的方法！

    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));     //训练数据。     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);    //输出将是transgender。    Console.WriteLine(classifier.Classify(new double[] { 4, 150, 12 }));    Console.Read();