用c++编写程序实现一个简单的二值贝叶斯分类器

好的，以下是一个简单的二值贝叶斯分类器的C++代码：

#include <iostream>
#include <vector>
#include <unordered_map>
#include <cmath>

using namespace std;

class NaiveBayes {
public:
    NaiveBayes(int n_features) : n_features(n_features) {}

    void fit(vector<vector<int>>& X, vector<int>& y) {
        // 统计每个类别的样本数
        for (int i = 0; i < y.size(); ++i) {
            ++class_count[y[i]];
        }
        // 统计每个类别每个特征的取值次数
        for (int i = 0; i < X.size(); ++i) {
            for (int j = 0; j < n_features; ++j) {
                feature_count[{y[i], j, X[i][j]}]++;
            }
        }
        // 计算每个类别每个特征取值的概率
        for (auto& p : class_count) {
            int c = p.first;
            int n_samples = p.second;
            for (int j = 0; j < n_features; ++j) {
                for (int k = 0; k <= 1; ++k) {
                    int count = feature_count[{c, j, k}];
                    feature_prob[{c, j, k}] = (count + 1.0) / (n_samples + 2.0);
                }
            }
        }
    }

    int predict(vector<int>& x) {
        double max_prob = -INFINITY;
        int best_class = -1;
        for (auto& p : class_count) {
            int c = p.first;
            double prob = log(p.second);
            for (int j = 0; j < n_features; ++j) {
                prob += log(feature_prob[{c, j, x[j]}]);
            }
            if (prob > max_prob) {
                max_prob = prob;
                best_class = c;
            }
        }
        return best_class;
    }

private:
    int n_features;
    unordered_map<int, int> class_count;
    unordered_map<tuple<int, int, int>, int> feature_count;
    unordered_map<tuple<int, int, int>, double> feature_prob;
};

int main() {
    vector<vector<int>> X = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
    vector<int> y = {0, 0, 1, 1};
    NaiveBayes nb(2);
    nb.fit(X, y);
    vector<int> x = {0, 1};
    cout << nb.predict(x) << endl;  // 输出 0
    return 0;
}

这个二值贝叶斯分类器使用了朴素贝叶斯算法，假设每个特征之间相互独立，计算每个类别每个特征取值的概率，然后根据贝叶斯公式计算每个类别的后验概率，最后选择后验概率最大的类别作为预测结果。