c++实现KNN算法

以下是使用C++实现KNN算法的示例代码：

#include <iostream>
#include <vector>
#include <cmath>
#include <algorithm>

// 定义数据点结构体
struct DataPoint {
    std::vector<double> features;
    int label;
};

// 计算欧式距离
double calculateDistance(const std::vector<double>& point1, const std::vector<double>& point2) {
    double distance = 0.0;
    for (int i = 0; i < point1.size(); i++) {
        distance += pow(point1[i] - point2[i], 2);
    }
    return sqrt(distance);
}

// KNN算法
int knn(const std::vector<DataPoint>& trainingData, const std::vector<double>& testPoint, int k) {
    // 计算测试点与所有训练点的距离
    std::vector<std::pair<double, int>> distances;
    for (const auto& dataPoint : trainingData) {
        double distance = calculateDistance(dataPoint.features, testPoint);
        distances.push_back(std::make_pair(distance, dataPoint.label));
    }

    // 根据距离排序
    std::sort(distances.begin(), distances.end());

    // 统计前k个最近邻的标签
    std::vector<int> labels(10, 0);
    for (int i = 0; i < k; i++) {
        labels[distances[i].second]++;
    }

    // 找到出现次数最多的标签
    int maxCount = 0;
    int maxLabel = -1;
    for (int i = 0; i < labels.size(); i++) {
        if (labels[i] > maxCount) {
            maxCount = labels[i];
            maxLabel = i;
        }
    }

    return maxLabel;
}

int main() {
    // 训练数据
    std::vector<DataPoint> trainingData = {
        {{1.0, 2.0}, 0},
        {{2.0, 1.0}, 0},
        {{4.0, 5.0}, 1},
        {{5.0, 4.0}, 1}
    };

    // 测试数据
    std::vector<double> testPoint = {3.0, 3.0};

    // 设置k值
    int k = 3;

    // 进行KNN分类
    int predictedLabel = knn(trainingData, testPoint, k);

    // 输出预测结果
    std::cout << "Predicted label: " << predictedLabel << std::endl;

    return 0;
}