K-means algorithm for iris data clustering
作者：互联网
K-means 是一种经典的聚类的算法，简单好用，火的一塌糊涂，对于刚刚入坑的小白们有着重要的学习价值，好了不虾扯蛋了，上代码。
iris datasets row= 150 ，column 4, 3-type, each type has 4 features . ok baby , let us to code
调包，预处理数据集
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
import numpy as np
iris = load_iris()
# 特征向量，并且是按顺序排列的
X = iris.data
# 标签
lable = iris.target
# 数据集预处理，以花萼面积为横坐标，以花瓣面积做纵坐标
arr = np.array(X)
hua_e = arr[:, 0] * arr[:, 1]
hua_ban = arr[:, 2] * arr[:, 3]
定义 main 函数
# 这里聚3类，k取3
k = 3
b = init_data(k)
test_hua_e = [hua_e[i] for i in range(len(hua_e)) if (i in b)]
test_hua_ban = [hua_ban[i] for i in range(len(hua_ban)) if (i in b)]
test_lable = [lable[i] for i in range(len(lable)) if (i in b)]
x = hua_e
y = hua_ban
x0 = test_hua_e
y0 = test_hua_ban
# 第一次随机聚类
n = 0
ds = getDistance(x, y, x0, y0, k)
temp = cluster(ds, x)
temp1 = EDistance(x, y, x0, y0, k)
n = n + 1
center = cent(temp)
x0 = center[0]
y0 = center[1]
ds = getDistance(x, y, x0, y0, k)
temp = cluster(ds, x)
temp2 = EDistance(x, y, x0, y0, k)
n = n + 1
# 比较两次平方误差 判断是否相等，不相等继续迭代
while np.abs(temp2 - temp1) != 0:
temp1 = temp2
center = cent(temp)
x0 = center[0]
y0 = center[1]
ds = getDistance(x, y, x0, y0, k)
temp = cluster(ds, x)
temp2 = EDistance(x, y, x0, y0, k)
n = n + 1
print(n, temp2)
# 结果可视化
print("迭代次数: ", n) # 统计出迭代次数
print('质心位置：', x0, y0)
plt.scatter(x0, y0, color='r', s=50, marker='s')
plt.scatter(x, y, c=temp, s=25, marker='o')
plt.show()
初始化数据
# 在集合中随机放入了3条数据
def init_data(k):
b = set()
while (len(b) < k):
b.add(np.random.randint(0, 150))
return (b)
每个点到中心点距离
def getDistance(point_x, point_y, cent_x, cent_y, k):
x = point_x
y = point_y
x0 = cent_x
y0 = cent_y
i = 0
j = 0
ds = [[] for i in range(len(x))]
while i < len(x):
while j < k:
M = np.sqrt((x[i] - x0[j]) * (x[i] - x0[j]) + (y[i] - y0[j]) * (y[i] - y0[j]))
M = round(M, 1)
j = j + 1
ds[i].append(M)
j = 0
i = i + 1
return (ds)
计算每次迭代的距离误差
def EDistance(point_x, point_y, cent_x, cent_y, k):
x = point_x
y = point_y
x0 = cent_x
y0 = cent_y
i = 0
j = 0
sum = 0
while i < k:
while j < len(x):
M = (x[j] - x0[i]) * (x[j] - x0[i]) + (y[j] - y0[i]) * (y[j] - y0[i])
M = round(M, 1)
sum += M
j = j + 1
j = 0
i = i + 1
return (sum)
计算中心点和更新中心点
# 计算中心点
def cent(lable):
temp = lable
mean_x = []
mean_y = []
i = 0
j = 0
while i < 3:
cent_x = 0
cent_y = 0
count = 0
while j < len(x):
if i == temp[j]:
count = count + 1
cent_x = cent_x + x[j]
cent_y = cent_y + y[j]
j = j + 1
cent_x = cent_x / count
cent_y = cent_y / count
# 更新中心点
mean_x.append(cent_x)
mean_y.append(cent_y)
j = 0
i = i + 1
return [mean_x, mean_y]
按K值依次聚类
def cluster(ds, x):
x = x
x = len(x)
i = 0
temp = []
while i < x:
temp.append(ds[i].index(min(ds[i])))
i = i + 1
return (temp)
迭代结果：
可视化结果
总的代码：
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
import numpy as np
iris = load_iris()
# 特征向量，并且是按顺序排列的
X = iris.data
# 标签
lable = iris.target
# 数据集预处理，以花萼面积为横坐标，以花瓣面积做纵坐标
arr = np.array(X)
hua_e = arr[:, 0] * arr[:, 1]
hua_ban = arr[:, 2] * arr[:, 3]
# 在集合中随机放入了3条数据
def init_data(k):
b = set()
while (len(b) < k):
b.add(np.random.randint(0, 150))
return (b)
# 每个点到中心点距离距离
def getDistance(point_x, point_y, cent_x, cent_y, k):
x = point_x
y = point_y
x0 = cent_x
y0 = cent_y
i = 0
j = 0
ds = [[] for i in range(len(x))]
while i < len(x):
while j < k:
M = np.sqrt((x[i] - x0[j]) * (x[i] - x0[j]) + (y[i] - y0[j]) * (y[i] - y0[j]))
M = round(M, 1)
j = j + 1
ds[i].append(M)
j = 0
i = i + 1
return (ds)
# 计算距离误差
def EDistance(point_x, point_y, cent_x, cent_y, k):
x = point_x
y = point_y
x0 = cent_x
y0 = cent_y
i = 0
j = 0
sum = 0
while i < k:
while j < len(x):
M = (x[j] - x0[i]) * (x[j] - x0[i]) + (y[j] - y0[i]) * (y[j] - y0[i])
M = round(M, 1)
sum += M
j = j + 1
j = 0
i = i + 1
return (sum)
# 计算中心点
def cent(lable):
temp = lable
mean_x = []
mean_y = []
i = 0
j = 0
while i < 3:
cent_x = 0
cent_y = 0
count = 0
while j < len(x):
if i == temp[j]:
count = count + 1
cent_x = cent_x + x[j]
cent_y = cent_y + y[j]
j = j + 1
cent_x = cent_x / count
cent_y = cent_y / count
# 更新中心点
mean_x.append(cent_x)
mean_y.append(cent_y)
j = 0
i = i + 1
return [mean_x, mean_y]
# 按照k值聚类
def cluster(ds, x):
x = x
x = len(x)
i = 0
temp = []
while i < x:
temp.append(ds[i].index(min(ds[i])))
i = i + 1
return (temp)
# 主程序部分
# 这里聚3类，k取3
k = 3
b = init_data(k)
test_hua_e = [hua_e[i] for i in range(len(hua_e)) if (i in b)]
test_hua_ban = [hua_ban[i] for i in range(len(hua_ban)) if (i in b)]
test_lable = [lable[i] for i in range(len(lable)) if (i in b)]
x = hua_e
y = hua_ban
x0 = test_hua_e
y0 = test_hua_ban
# 第一次随机聚类
n = 0
ds = getDistance(x, y, x0, y0, k)
temp = cluster(ds, x)
temp1 = EDistance(x, y, x0, y0, k)
n = n + 1
center = cent(temp)
x0 = center[0]
y0 = center[1]
ds = getDistance(x, y, x0, y0, k)
temp = cluster(ds, x)
temp2 = EDistance(x, y, x0, y0, k)
n = n + 1
# 比较两次平方误差 判断是否相等，不相等继续迭代
while np.abs(temp2 - temp1) != 0:
temp1 = temp2
center = cent(temp)
x0 = center[0]
y0 = center[1]
ds = getDistance(x, y, x0, y0, k)
temp = cluster(ds, x)
temp2 = EDistance(x, y, x0, y0, k)
n = n + 1
print(n, temp2)
# 结果可视化
print("迭代次数: ", n) # 统计出迭代次数
print('质心位置：', x0, y0)
plt.scatter(x0, y0, color='r', s=50, marker='s')
plt.scatter(x, y, c=temp, s=25, marker='o')
plt.show()
标签：clustering,iris,hua,temp,cent,y0,x0,data,ds 来源： https://blog.csdn.net/weixin_40440610/article/details/111473091