实现K-Means算法并将算法应用于我给定的鸢尾花数据集iris，给出完整可运行的Python代码。

好的，以下是实现K-Means算法并应用于iris数据集的Python代码：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# 加载数据
data = pd.read_csv("iris.csv")
X = data.iloc[:, [0, 1, 2, 3]].values

# 定义K-Means类
class KMeans:
    def __init__(self, n_clusters=3, max_iter=300):
        self.n_clusters = n_clusters  # 聚类数
        self.max_iter = max_iter  # 最大迭代次数

    def fit(self, X):
        self.centroids = {}  # 质心
        for i in range(self.n_clusters):
            self.centroids[i] = X[i]

        for i in range(self.max_iter):
            self.classes = {}  # 聚类结果
            for j in range(self.n_clusters):
                self.classes[j] = []

            # 计算距离并分配数据点到聚类
            for x in X:
                distances = [np.linalg.norm(x - self.centroids[c]) for c in self.centroids]
                classification = distances.index(min(distances))
                self.classes[classification].append(x)

            # 计算新的质心
            prev_centroids = dict(self.centroids)
            for c in self.classes:
                self.centroids[c] = np.average(self.classes[c], axis=0)

            # 判断是否收敛
            is_converged = True
            for c in self.centroids:
                if not np.array_equal(self.centroids[c], prev_centroids[c]):
                    is_converged = False
                    break
            if is_converged:
                break

    def predict(self, X):
        distances = [np.linalg.norm(X - self.centroids[c]) for c in self.centroids]
        classification = distances.index(min(distances))
        return classification

# 运行K-Means算法
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)

# 绘制聚类结果
colors = ['r', 'g', 'b']
for classification in kmeans.classes:
    color = colors[classification]
    for features in kmeans.classes[classification]:
        plt.scatter(features[0], features[1], color=color, s=30)
for centroid in kmeans.centroids:
    plt.scatter(kmeans.centroids[centroid][0], kmeans.centroids[centroid][1], marker='*', color='k', s=100)
plt.show()

在这个例子中，我们使用了iris数据集，并把数据分为了3个聚类。在运行K-Means算法后，我们绘制了聚类结果。