误差平⽅和⽤python_⼀个标准的k-means（误差平⽅和版contactus
本）
# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
import matplotlib.pyplot as plt
accusationimport numpy as np
import sys
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np.t_printoptions(suppress=True)
def quarterback_method(vector):softrock
'''
四分位法
:param vector:
:return:
'''
lower_q = np.quantile(vector, 0.25, interpolation='lower') # 下四分卫数
higher_q = np.quantile(vector, 0.75, interpolation='higher') # 上四分位数
middle_q = np.median(vector) # 中位数
int_r = higher_q - lower_q # 四分位距
numbers = [lower_q, middle_q, higher_q, int_r] # 分位数整理
data = [vector[vector < i] for i in numbers][:-1] # 前75%分位的数
new_data = np.tdiff1d(vector, np.hstack((data[1], data[2]))) # 后25分位的数
return numbers, data + [new_data] # 分位数和四分位后的数据
industrial engineeringdef K_point(number):
'''
随机选择K中⼼
:param number:
:return:
'''
K = lambda: data[np.random.randint(len(data))]
return [np.array(K())[0] for i in range(number)]
def random_point(number):
'''
willful随机选择K中⼼
:param number:
:return:
'''
K = lambda: data[np.random.randint(len(data))]
return [np.array(K())[0] for i in range(number)]
def euclidean_distance(pointA, pointB):
'''
欧式距离计算公式
:param pointA:
:
param pointB:
:return:
'''
return np.array(np.sqrt(np.dot(np.power((pointA - pointB), 2), np.ones(2))))[0] def mean_point(datat):
'''
求中⼼点
:param datat:
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:return:
'''
#x_mean = np.array(sum(datat[:, 0:1]) / len(datat))[0, 0]
#y_mean = np.array(sum(datat[:, -1:]) / len(datat))[0, 0]
x_mean = (np.squeeze(np.array(datat[:,0:1]))/len(datat)).sum()
y_mean = (np.squeeze(np.array(datat[:,-1:]))/len(datat)).sum()
return np.array([x_mean, y_mean])
def error_sum_of_squares(new_K, old_K):
'''
误差平⽅和
:param data:
:param K:
:return:
'''
return ((old_K - new_K) ** 2).sum()
def K_means(datat, K_point, restart=0):
'''标准计算循环完成K-means，误差平⽅和版本'''
print('restart', restart)
#print(mean_point(datat),datat)
#the_original_error = error_sum_of_squares(np.array(K_point), )
expr1 = [(np.array(i)[0], [euclidean_distance(j, np.array(i)) for j in K_point]) for i in datat]
NK = -1
classified_of_data = []
new_K_point = []
for kindex in range(len(K_point)):
NK += 1
kindex = []
for i in range(len(expr1)):
Name = expr1[i][-1].index(min(expr1[i][-1]))
data = list(np.array(datat[i])[0]) # 最近的点的下标
if Name == NK:
kindex.append(data)
el:
pass
new_K_point.append(mean_point(np.mat(kindex))) # 新的k点整理数据结构
classified_of_data.append(np.mat(kindex))
error_sum_of_squares = sum([((mean_point(i)-j)**2).sum() for i,j in zip(classified_of_data,K_point)]) #误差平⽅和if error_sum_of_squares == 0 : # 如果不满⾜分类条件，则递归继续
print('Ok , K-means 平⽅误差和版')
return np.array(new_K_point), classified_of_data
el:
restart += 1
return K_means(datat, new_K_point, restart)
def K_means_run(class_number, all_datas, drawing=Fal):
'''
误差平⽅和版本
:param class_number:
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:param all_datas:
:param drawing:
'''stacy
k_of_constant = K_point(class_number)
result = K_means(all_datas, k_of_constant, 0)
if drawing == True:
for data, k in zip(result[-1], result[0]):
x, y = np.squeeze(np.array(data[:, 0:1])), np.squeeze(np.array(data[:, -1:])) # ,
plt.scatter(x, y)
plt.scatter(k[0], k[-1], c='black', marker='x', alpha=1.0)
plt.show()
plt.clo()
el:
pass
return result
# %%
data = np.mat([[0.0, 7.8031236401009165], [0.1, 4.426411521373964], [0.2, 8.947706236035394], [0.3,
8.200555461071133], [0.4, 0.5142706050449086], [0.5, 3.1008492860590753], [0.6, 7.1785370142703], [0.7,
3.872126889453009], [0.8, 1.025577102380758], [0.9,
4.833507884197839], [1.0, 0.9345186488455648], [1.1,
4.812032669803522], [1.2, 3.4191665496674375], [1.3, 0.022961590431520573], [1.4, 0.8785497842851486], [1.5, 3.2381682303766004], [1.6, 6.663617230163021], [1.7, 0.9093960835056158], [1.8, 3.680521995791508],
[1.9, 4.900535080573809], [2.0, 0.5937324344804573], [2.1, 7.783916463844781], [2.2, 7.11179421378249], [2.3,
2.4446164389372083], [2.4, 0.31413070861756043], [2.5, 8.793728586574554], [2.6, 5.826793655403399], [2.7, 5.694595209349293], [2.8, 1.810999124588204], [2.9, 2.5891746519869896], [
3.0, 7.989527941362203], [3.1,
语言学习规律3.8888255411877237], [3.2, 6.980965568743198], [3.3, 6.183466511452052], [3.4, 8.994788345820844], [3.5,
7.713561865457374], [3.6, 5.373007398355321], [3.7, 5.857041801728861], [3.8, 1.2873991273003293], [3.9,
7.90280175406894], [4.0, 2.114965228889154], [4.1, 1.9122653164064485], [4.2, 1.430294828685612], [4.3,
6.447866312224862], [4.4, 1.6034730966952893], [4.5, 5.020523561692603], [4.6, 8.434230931666896], [4.7,
8.926232142246732], [4.8, 7.21575735052221], [4.9, 0.8242497089572909], [5.0, 3.2773727950676923], [5.1,
4.789459791385374], [
5.2, 5.7771008824695205], [5.3, 5.475006081618368], [5.4, 0.1095089272289862], [5.5, 3.708028945757401], [5.6, 5.868541457709577], [5.7, 3.82129152340557], [5.8, 4.672995214563882], [5.9,
7.139883221140032], [6.0, 1.9266318905278135], [6.1, 8.37752018393436], [6.2, 0.38698741212173093], [6.3, 6.809020238683873], [6.4, 6.7650503938175754], [6.5, 3.622205528236381], [6.6, 6.2275067570806755], [6.7, 9.272360909999943], [6.8, 6.937813473353321], [6.9, 6.719818328095723], [7.0, 4.1761213627699085], [7.1,
2.2186584343523554], [7.2, 4.325155968720139], [7.3, 6.346030576482492], [7.4, 6.681255604404174], [7.5,
0.9022441439624651], [7.6, 3.5585144004608313], [7.7, 3.0389210408048797], [7.8, 1.2906111157619915], [7.9, 5.5836998346201785], [8.0, 9.35888445552132], [8.1, 7.895786013311998], [8.2, 8.908751780051233], [8.3,
0.5462991244242466], [8.4, 5.430329386519083], [8.5, 5.632137779391064], [8.6, 8.919006991403911], [8.7,
1.881419639976668], [8.8, 0.8227664012604219], [8.9, 6.551076298674367], [9.0, 0.8128558657766949], [9.1,
3.8227215510033807], [9.2, 5.628006555285536], [9.3, 7.4386834204408645], [9.4, 3.7984910271263073], [9.5,
4.012712837404079], [9.6, 9.755417444517088], [9.7, 2.8443889672100973], [9.8, 4.143513529213513], [9.9,
4.093044136223663]])
# %%
plt.scatter(np.squeeze(np.array(data[:, 0:1])), np.squeeze(np.array(data[:, -1:])))
plt.clo()
# %%
K_means_run(5,data,True) K_means_run(5,data,True) # %%
