鸢尾花k-means聚类分析c语言代码示例

由于C语言本身的特性，直接提供完整的鸢尾花K-means聚类分析代码可能比较冗长且不易于理解。但这里可以给出一个简化的框架，你可以基于这个基础进行扩展：

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

// 定义鸢尾花数据结构 (简化版)
typedef struct {
    double sepal_length;
    double sepal_width;
    double petal_length;
    double petal_width;
} IrisData;

// 计算欧氏距离
double euclidean_distance(IrisData *data1, IrisData *data2) {
    // 省略具体计算细节...
}

int kmeans(IrisData *data, int n_samples, int n_clusters, IrisData *centroids, int max_iterations) {
    // 初始化随机质心
    for (int i = 0; i < n_clusters; i++) {
        centroids[i] = data[rand() % n_samples];
    }

    // 主循环
    for (int iteration = 0; iteration < max_iterations; iteration++) {
        int cluster assignments[n_samples]; // 分配给每个簇的索引
        for (int i = 0; i < n_samples; i++) {
            cluster assignments[i] = -1;
        }

        // 分配样本到最近的质心
        for (int i = 0; i < n_samples; i++) {
            int closest_cluster = 0;
            double min_distance = euclidean_distance(&data[i], &centroids[0]);
            for (int j = 1; j < n_clusters; j++) {
                double dist = euclidean_distance(&data[i], &centroids[j]);
                if (dist < min_distance) {
                    min_distance = dist;
                    closest_cluster = j;
                }
            }
            cluster_assignments[i] = closest_cluster;
        }

        // 更新质心
        for (int j = 0; j < n_clusters; j++) {
            IrisData new_centroid;
            int count = 0;
            for (int i = 0; i < n_samples; i++) {
                if (cluster_assignments[i] == j) {
                    new_centroid.sepal_length += data[i].sepal_length;
                    new_centroid.sepal_width += data[i].sepal_width;
                    new_centroid.petal_length += data[i].petal_length;
                    new_centroid.petal_width += data[i].petal_width;
                    count++;
                }
            }
            new_centroid.sepal_length /= count;
            new_centroid.sepal_width /= count;
            new_centroid.petal_length /= count;
            new_centroid.petal_width /= count;
            centroids[j] = new_centroid;
        }
    }

    return 1; // 或者返回最终的簇中心...
}

int main() {
    // 加载或生成鸢尾花数据
    IrisData dataset[] = { ... };
    int n_samples = sizeof(dataset) / sizeof(dataset[0]);

    // 调用K-means
    IrisData centroids[3]; // 假设3个簇
    int result = kmeans(dataset, n_samples, 3, centroids, 100); // 运行100次迭代

    // 打印结果
    for (int i = 0; i < 3; i++) {
        printf("Centroid %d: ", i);
        // 输出质心坐标...
    }

    return 0;
}

请注意，上述代码仅为基本框架，实际应用中你需要根据实际情况处理数据输入、文件读取、距离计算以及结果可视化等部分。此外，K-means算法的性能和收敛速度在C语言中可能不如专门用于机器学习的数据科学库（如RapidJSON、NumPy等）高效。