0.5的值代表什么？

Question

朴素贝叶斯分类器算法的

This is an implementation。

我无法理解第score.Add(results[i].Name, finalScore * 0.5);行。

此值 0.5 来自哪里？

为什么 0.5 ？为什么没有任何其他价值？

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;
}

Answer 1

我明白了。

0.5是先验概率。