import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from copy import deepcopy

# 设置图形样式
plt.rcParams['figure.figsize'] = (16, 9)
plt.style.use('ggplot')

# 创建示例数据并保存为CSV文件
def create_sample_data():
    # 生成三个簇的示例数据
    np.random.seed(42)
    cluster1 = np.random.normal(loc=[0, 0], scale=1, size=(100, 2))
    cluster2 = np.random.normal(loc=[10, 5], scale=1.5, size=(100, 2))
    cluster3 = np.random.normal(loc=[5, 10], scale=1.2, size=(100, 2))
    data = np.vstack([cluster1, cluster2, cluster3])
    
    # 创建DataFrame并保存为CSV
    df = pd.DataFrame(data, columns=['V1', 'V2'])
    df.to_csv('xclara.csv', index=False)

# 创建示例CSV文件
create_sample_data()

# 从CSV读取数据
data = pd.read_csv('xclara.csv')
f1 = data['V1'].values
f2 = data['V2'].values
X = np.array(list(zip(f1, f2)))

# 距离计算函数
def dist(a, b, ax=1):
    return np.linalg.norm(a - b, axis=ax)

# 设置聚类数
k = 3

# 随机初始化质心（修正：使用数据范围）
C_x = np.random.uniform(np.min(f1), np.max(f1), size=k)
C_y = np.random.uniform(np.min(f2), np.max(f2), size=k)
C = np.array(list(zip(C_x, C_y)), dtype=np.float32)

# 绘制初始数据点和质心（修正颜色拼写错误）
plt.scatter(f1, f2, c='black', s=7)  # 修正：'balck' -> 'black'
plt.scatter(C_x, C_y, marker='*', s=200, c='red')
plt.title("Initial Data Points and Centroids")
plt.show()

# ---- 可选：添加完整的K-Means算法实现 ----
# 复制原始质心用于后续更新
C_old = np.zeros(C.shape)
clusters = np.zeros(len(X))
error = dist(C, C_old, None)

# K-Means迭代
while error != 0:
    # 分配点到最近质心
    for i in range(len(X)):
        distances = dist(X[i], C)
        cluster = np.argmin(distances)
        clusters[i] = cluster
    
    # 保存旧质心
    C_old = deepcopy(C)
    
    # 计算新质心
    for i in range(k):
        points = [X[j] for j in range(len(X)) if clusters[j] == i]
        if points:
            C[i] = np.mean(points, axis=0)
    
    # 计算质心移动距离
    error = dist(C, C_old, None)

# 绘制最终聚类结果
colors = ['r', 'g', 'b', 'c', 'm', 'y']
fig, ax = plt.subplots()
for i in range(k):
    points = np.array([X[j] for j in range(len(X)) if clusters[j] == i])
    ax.scatter(points[:, 0], points[:, 1], s=7, c=colors[i])
ax.scatter(C[:, 0], C[:, 1], marker='*', s=200, c='black')
plt.title("Final Clustering Result")
plt.show()