C# を使用してベイジアン分類アルゴリズムを作成する方法

// 统计每个类别的实例数量
int totalCount = trainingData.Count;
Dictionary<string, int> classCount = new Dictionary<string, int>();
foreach (var instance in trainingData)
{
    string label = instance.Label;
    if (!classCount.ContainsKey(label))
    {
        classCount[label] = 0;
    }
    classCount[label]++;
}

// 计算先验概率
Dictionary<string, double> priorProbability = new Dictionary<string, double>();
foreach (var label in classCount.Keys)
{
    int count = classCount[label];
    double probability = (double)count / totalCount;
    priorProbability[label] = probability;
}

// 统计每个类别下每个特征的出现次数
Dictionary<string, Dictionary<string, int>> featureCount = new Dictionary<string, Dictionary<string, int>>();
foreach (var instance in trainingData)
{
    string label = instance.Label;
    if (!featureCount.ContainsKey(label))
    {
        featureCount[label] = new Dictionary<string, int>();
    }
    foreach (var feature in instance.Features)
    {
        if (!featureCount[label].ContainsKey(feature))
        {
            featureCount[label][feature] = 0;
        }
        featureCount[label][feature]++;
    }
}

// 计算条件概率
Dictionary<string, Dictionary<string, double>> conditionalProbability = new Dictionary<string, Dictionary<string, double>>();
foreach (var label in featureCount.Keys)
{
    int totalCountForLabel = classCount[label];
    Dictionary<string, int> countForLabel = featureCount[label];
    Dictionary<string, double> probabilityForLabel = new Dictionary<string, double>();
    foreach (var feature in countForLabel.Keys)
    {
        int count = countForLabel[feature];
        double probability = (double)count / totalCountForLabel;
        probabilityForLabel[feature] = probability;
    }
    conditionalProbability[label] = probabilityForLabel;
}

// 预测分类
string Predict(List<string> features)
{
    Dictionary<string, double> probability = new Dictionary<string, double>();
    foreach (var label in priorProbability.Keys)
    {
        double prior = priorProbability[label];
        double likelihood = 1.0;
        foreach (var feature in features)
        {
            if (conditionalProbability[label].ContainsKey(feature))
            {
                double conditional = conditionalProbability[label][feature];
                likelihood *= conditional;
            }
        }
        probability[label] = prior * likelihood;
    }
    return probability.OrderByDescending(x => x.Value).First().Key;
}