使用Python语言手写K均值算法。

下面是使用Python手写K均值算法的示例代码：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs


class KMeans:
    def __init__(self, k=3, max_iters=100):
        self.k = k
        self.max_iters = max_iters
        self.centroids = None

    def fit(self, X):
        self.centroids = X[np.random.choice(X.shape[0], self.k, replace=False)]
        for i in range(self.max_iters):
            # Assign each data point to the closest centroid
            distances = np.sqrt(((X - self.centroids[:, np.newaxis])**2).sum(axis=2))
            labels = np.argmin(distances, axis=0)

            # Update the centroids to be the mean of the assigned data points
            for j in range(self.k):
                self.centroids[j] = X[labels == j].mean(axis=0)

    def predict(self, X):
        distances = np.sqrt(((X - self.centroids[:, np.newaxis])**2).sum(axis=2))
        return np.argmin(distances, axis=0)


# Generate sample data
X, y = make_blobs(n_samples=300, centers=3, random_state=42)

# Fit KMeans algorithm
kmeans = KMeans(k=3, max_iters=100)
kmeans.fit(X)

# Plot the results
plt.scatter(X[:, 0], X[:, 1], c=kmeans.predict(X))
plt.scatter(kmeans.centroids[:, 0], kmeans.centroids[:, 1], marker='x', s=200, linewidths=3, color='r')
plt.show()

这段代码使用了numpy和matplotlib库，以及scikit-learn库中的make_blobs函数生成样本数据。KMeans类包含了fit和predict两个方法，其中fit方法用于训练K均值模型，predict方法用于预测新的数据点所属的簇。在示例代码中，我们使用KMeans类对样本数据进行聚类，并将聚类结果可视化展示。