48 lines
1.4 KiB
Plaintext
48 lines
1.4 KiB
Plaintext
from copy import deepcopy
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import numpy as np
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import pandas as pd
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from matplotlib import pyplot as plt
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plt.rcParams['figure.figsize'] = (16,9)
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plt.style.use('ggplot')
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data = pd.read_csv('xclara.csv')
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f1 = data['V1'].values
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f2 = data['V2'].values
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X = np.array(list(zip(f1, f2)))
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# 距离计算函数
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def dist(a, b, ax=1):
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return np.linalg.norm(a - b, axis=ax)
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# 设置聚类数
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k = 3
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# 随机初始化质心（修正：使用数据范围）
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C_x = np.random.randint(0,np.max(X)-20, size=k)
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C_y = np.random.randint(0,np.max(X)-20, size=k)
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C = np.array(list(zip(C_x, C_y)), dtype=np.float32)
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C_old = np.zeros(C.shape)
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print(C)
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clusters = np.zeros(len(X))
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iteration_flag = dist(C,C_old,1)
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tmp = 1
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while iteration_flag.any() != 0 and tmp<20:
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for i in range(len(X)):
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distances = dist(X[i],C,1)
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clusters[i] = clusters
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C_old = deepcopy(C)
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for i in range(C):
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points = [X[j] for j in range(len(X)) if clusters[j] == i]
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C[i] = np.mean(points,axis=0)
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print('%d'%tmp)
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tmp = tmp + 1
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iteraction_flag = dist(C,C_old,1)
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print('distance:',iteraction_flag)
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colors = ['r','g','b','y','c','m']
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fig,ax = plt.subplots()
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for i in range(k):
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points = np.array([X[j] for j in range(len(X) if clusters[j] == i)])
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ax.scatter(points[:,0],points[:,1],s=7,c=colors[i])
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ax.scatter(C[:,0],C[:,1],marker="*",s=200,c='black')
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plt.show() |