正向云模型（生成云）计算及绘图
import csv
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import style
import math
def forwardCloud(Ex,En,He,N): #正向云模型（生成云）
cloudpoint=[];
plt.rcParams['font.sans-serif'] = ['SimHei']
# 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False
# 用来正常显示负号
fig = plt.figure(0)
ax = fig.add_subplot(111)
for i in range(N):
En_1=np.random.normal(En,He)
x=np.random.normal(Ex,abs(En_1))
y=math.exp(-pow(x-Ex,2)/(2*pow(En_1,2)))
cloudpoint.append([x,y])
for w in range(len(cloudpoint)):
ax.scatter(cloudpoint[w][0],cloudpoint[w][1], s=10, alpha=0.5, c='r', marker='o')
plt.show()
return cloudpoint
def plot_cloud_model(Ex, En, He, n, title='', grid=False, moni=False):
Y = np.zeros((1, n))
X = np.random.normal(loc=En, scale=He, size=n)
Y = Y[0]
plt.rcParams['font.sans-serif'] = ['SimHei']
# 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False
# 用来正常显示负号
fig = plt.figure(0)
ax = fig.add_subplot(111)
for i in range(n):
Enn = X[i]
X[i] = np.random.normal(loc=Ex, scale=np.abs(Enn), size=1)
Y[i] = np.exp(-(X[i] - Ex) * (X[i] - Ex) / (2 * Enn * Enn))
# if X[i]>Ex:
# Y[i]=2-Y[i]
ax.scatter(X[i], Y[i], s=10, alpha=0.5, c='black', marker='o')
# ax.scatter(X[i], Y[i], s=10, alpha=0.5, c='black', marker='o')
# if title == '':
# title = '期望:%.2f,熵:%.2f,超熵:%.2f,云滴数:%d' % (Ex, En, He, n)
# ax.scatter(0.162169504,0.48860678,s=10, alpha=0.5, c='black', marker='o')
# ax.scatter(0.459859948, 1.156647128, s=10, alpha=0.5, c='black', marker='o')
# ax.scatter(0.464770087, 1.171339248, s=10, alpha=0.5, c='black', marker='o')
# ax.scatter(0.499928759, 1.51139322, s=10, alpha=0.5, c='black', marker='o')#社会影响力的三人数据
# ax.scatter(0.164790462, 0.919005218, s=10, alpha=0.5, c='black', marker='o')
# ax.scatter(0.08666426, 0.734359773, s=10, alpha=0.5, c='black', marker='o')
# ax.set_title(title)
# ax.arrow(0,0,0.162169504,0.48860678,length_includes_head = True,head_width = 0.05,head_length = 0.1,fc = 'black',ec = 'black')
# ax.arrow(0.5, 0, 0.459859948-0.5, 1.156647128, length_includes_head=True, head_width=0.05, head_length=0.1, fc='black',
# ec='black')
# ax.arrow(1, 1.5, 0.464770087-1, 1.171339248-1.5, length_includes_head=True, head_width=0.05, head_length=0.1, fc='black',
# ec='black')
# ax.text(0,0,'柯平',fontsize=12)
#
# ax.text(0.5,0, '章成志',fontsize=12)
# ax.text(1, 1.5, '马海群 ',fontsize=12)
ax.set_xlabel('评价值')
ax.set_ylabel(' ')
ax.grid(False)
plt.savefig('y11.svg', dpi=600, format='svg', bbox_inches='tight')
plt.show()
# def Cloud_compute(xl):
# '''计算云滴的数字特征'''
# xl = np.array(xl)
# # S2 = np.var(xl) #用的方差
# S2 = np.std(xl) #用的标准差
# Ex = np.mean(xl)
# En = np.sqrt(np.pi/2) * np.mean( np.abs(xl-Ex) )
# He = np.sqrt( np.abs(S2*S2 - En*En) )
# return (Ex, En, He)
def plot_2d_cloud_model(Ex, En, He, n, title='', grid=False):
Y = np.zeros((1, n))
X0 = np.random.normal(loc=En[0], scale=He[0], size=n)
Y = Y[0]
X1 = np.random.normal(loc=En[1], scale=He[1], size=n)
plt.rcParams['font.sans-serif'] = ['SimHei']
# 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False
# 用来正常显示负号
fig = plt.figure(0)
ax = fig.add_subplot(111, projection='3d')
for i in range(n):
Enn0 = X0[i]
X0[i] = np.random.normal(loc=Ex[0], scale=np.abs(Enn0), size=1)
# Y0[i] = np.exp(-(X0[i]-Ex[0])*(X0[i]-Ex[0])/(2*Enn*Enn))
Enn1 = X1[i]
X1[i] = np.random.normal(loc=Ex[1], scale=np.abs(Enn1), size=1)
Y[i] = np.exp(-(X0[i] - Ex[0]) * (X0[i] - Ex[0]) / (2 * Enn0 * Enn0) - (X1[i] - Ex[1]) * (X1[i] - Ex[1]) / (
2 * Enn1 * Enn1))
ax.scatter(X0[i], X1[i], Y[i], s=10, alpha=0.5, c='r', marker='o')
# if title == '':
# title = '期望:[%.2f,%.2f],熵:[%.2f,%.2f],超熵:[%.2f,%.2f],云滴数:%d' % (Ex[0], Ex[1], En[0], En[1], He[0], He[1], n)
# X0=X0.tolist()
# X1 = X1.tolist()
# Y = Y.tolist()
# a = [x for x in X0]
# b = [y for y in X1]
# c = [z for z in Y]
# with open('D:\Python_DATA\datatest1.csv', 'a', encoding='utf-8', newline='') as csvfile:
# writer = csv.writer(csvfile)
# writer.writerow(X0)
# with open('D:\Python_DATA\datatest2.csv', 'a', encoding='utf-8', newline='') as csvfile:
# writer = csv.writer(csvfile)
# writer.writerow(X1)
# with open('D:\Python_DATA\datatest3.csv', 'a', encoding='utf-8', newline='') as csvfile:
# writer = csv.writer(csvfile)
# writer.writerow(Y)
# dataframe = pd.DataFrame(a, b,c)
# dataframe.to_csv('D:\Python_DATA\datatest5.csv', sep=',')
ax.set_title(title)
ax.set_xlabel('学术影响力')
ax.set_ylabel('社会影响力')
ax.set_zlabel('隶属度')
ax.grid(True)
ax.text(0.45,1,0.5,'柯平',fontsize=12)
ax.text(-0.45,- 0.7, 0.45, '章成志',fontsize=12)
ax.text(0.75, 0, -0.1, '马海群 ',fontsize=12)
ax.quiver(0.45,1,0.5,0.009859948, 0.164790462-1 ,0.354144082,1,color='black')
ax.quiver(-0.4, 0, 0.4, 0.562169504, 0.499928759, 0.012413722, 1, color='black')
ax.quiver(0.75, 0, 0, 0.464770087-0.75, 0.08666426, 0.163588352, 1, color='black')
# plt.arrow(0.459859948,0.164790462,0.854144082,0.5,0.5,0.5,fc='blue',ec='blue',alpha=0.9, overhang=0.5)
# ax.annotate('柯平', xyz=(0.459859948,0.164790462,0.854144082), xyztext=(0.459859948,0.164790462,0.854144082),xyzcoords='data', arrowprops=dict(arrowstyle="->", connectionstyle="arc3"))
plt.savefig('yun7.svg', dpi=600, format='svg', bbox_inches='tight')
plt.show()
if __name__ == '__main__':
# 二维云图
# plot_cloud_model(0.362266513, 0.1671896068094979, 0.08904459487007008, 2000)
# plot_cloud_model(0.2504611603333334, 0.20844084547405517, 0.10636381322231545, 2000)
plot_cloud_model(0, 10, 1, 2000)
# forwardCloud(0.362266513, 0.1671896068094979, 0.08904459487007008, 2000)
# plot_2d_cloud_model([0.362266513, 0.2504611603333334], [0.00557298689364993, 0.006948028182468506], [0.14139436539913042, 0.17912579435447862], 2000)
# print Cloud_compute(x)
