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main.py
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main.py
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import DataPre as dp
import MultiMinded as MM
import SingleMinded as SM
import GreedyMech as GM
import WPG_infocom as wpg
import numpy as np
import copy
import matplotlib.pyplot as plt
import pylab as pl
import math
import time
import random
from random import sample
from scipy.stats import uniform
def computePoint(x_1, y_1, x_2, y_2, x):
y = (y_2 - y_1) * (x - x_1) / (x_2 - x_1) + y_1
return y
def getValuePaymentRelation(value_SM, payment_SM, maxValue, indexValue):
value = np.array([])
payment = np.array([])
index = 0
length = np.shape(value_SM)[0]
minIndex, maxIndex = -1, 0
while index < int(maxValue / indexValue):
tempValue = index * indexValue + indexValue
if maxIndex == length - 1:
minIndex = maxIndex - 1
flag = computePoint(value_SM[minIndex], payment_SM[minIndex], value_SM[maxIndex],
payment_SM[maxIndex], tempValue)
value = np.append(value, np.array([tempValue]))
payment = np.append(payment, np.array([flag]))
index = index + 1
else:
if value_SM[maxIndex] < tempValue:
minIndex = maxIndex
maxIndex = maxIndex + 1
else:
if minIndex == -1:
flag = computePoint(0, 0, value_SM[maxIndex], payment_SM[maxIndex], tempValue)
value = np.append(value, np.array([tempValue]))
payment = np.append(payment, np.array([flag]))
index = index + 1
else:
flag = computePoint(value_SM[minIndex], payment_SM[minIndex], value_SM[maxIndex],
payment_SM[maxIndex],
tempValue)
value = np.append(value, np.array([tempValue]))
payment = np.append(payment, np.array([flag]))
index = index + 1
print("value-payment", value, payment)
return value, payment
def controlUser(budget, taskSet, userCost, userTaskSet, totalTaskNum, indexUserNum, userTaskNumDis, userSetDict,
userSetSubsetDict):
initUser = 20
x_1 = np.array([])
y_1 = np.array([])
y_2 = np.array([])
y_3 = np.array([])
y_4 = np.array([])
y_5 = np.array([])
Y_1 = np.array([])
Y_2 = np.array([])
Y_3 = np.array([])
Y_4 = np.array([])
# 从50到800
for i in range(indexUserNum):
num = 100 + i * initUser
print("user 数量为：:", num)
userCostTemp_1 = userCost[:num]
userTaskSetTemp_1 = userTaskSet[:, :num]
userPayment_SM, finalValue_SM, S_w_SM, averageUtility_SM,value,payment = SM.SingleMindedAlg(budget / 2, taskSet,
userCostTemp_1,
userTaskSetTemp_1,
totalTaskNum, num,
userTaskNumDis)
print("SM总价值：", finalValue_SM, "SM平均收益：", averageUtility_SM)
userCostTemp_2 = userCost[:num]
userTaskSetTemp_2 = userTaskSet[:, :num]
userPayment_MM, finalValue_MM,finalValue_SPIM_MM, S_w_MM, averageUtility_MM,averageUtility_SPIM_MM,value1,payemnt1,value2,payment2 = MM.MultiMindedAlg(budget, taskSet,userTaskSetTemp_2,totalTaskNum, userCostTemp_2, num,
userSetDict, userSetSubsetDict)
print("MM总价值：", finalValue_MM, "MM平均收益：", averageUtility_MM)
print("SPIM_MM总价值：", finalValue_SPIM_MM, "SPIM_MM平均收益：", averageUtility_SPIM_MM)
userCostTemp_3 = userCost[:num]
userTaskSetTemp_3 = userTaskSet[:, :num]
userPayment_GM, finalValue_GM, S_w_GM, averageUtility_GM = GM.GreedyAlgSM(budget, taskSet, userCostTemp_3,
userTaskSetTemp_3, totalTaskNum,
num)
print("GM总价值：", finalValue_GM, "GM平均收益：", averageUtility_GM)
userCostTemp_4 = userCost[:num]
userTaskSetTemp_4 = userTaskSet[:, :num]
userPayment_SPIM_SM, finalValue_SPIM_SM, S_w_SPIM_SM, averageUtility_SPIM_SM, value_SPIM_SM, payment_SPIM_MM = wpg.SybilAlg(
budget, taskSet, userCostTemp_4, userTaskSetTemp_4,
totalTaskNum, num,
userTaskNumDis)
print("SPIM_SM总价值：", finalValue_SPIM_SM, "SPIM_SM平均收益：", averageUtility_SPIM_SM, "\n")
x_1 = np.append(x_1, np.array([num]))
y_1 = np.append(y_1, np.array([finalValue_SM]))
y_2 = np.append(y_2, np.array([finalValue_MM]))
y_3 = np.append(y_3, np.array([finalValue_GM]))
y_4 = np.append(y_4, np.array([finalValue_SPIM_SM]))
y_5 = np.append(y_5, np.array([finalValue_SPIM_MM]))
Y_1 = np.append(Y_1, np.array([averageUtility_SM]))
Y_2 = np.append(Y_2, np.array([averageUtility_MM]))
Y_3 = np.append(Y_3, np.array([averageUtility_SPIM_SM]))
Y_4 = np.append(Y_4, np.array([averageUtility_SPIM_MM]))
return x_1, y_1, y_2, y_3, y_4, y_5, Y_1, Y_2, Y_3, Y_4
def doControlUser(reNum, budget, totalTaskNum, taskValueDis, totalUserNum, userCosPerValueDis, userTaskNumDis):
# user 考虑的组数(user 考虑100-300，每次增加20)
indexUserNum = int(((totalUserNum - 100) / 20) + 1)
SM_platformUtility_1, SM_platformUtility_2, MM_platformUtility_2, GM_platformUtility_2, \
SM_averageUtility_2, MM_averageUtility_2, SPIM_SM_platformUtility_2, SPIM_MM_platformUtility_2, SPIM_SM_averageUtility_2, SPIM_MM_averageUtility_2 = np.zeros(
(indexUserNum,),
dtype=np.float), np.zeros(
(indexUserNum,), dtype=np.float), np.zeros((indexUserNum,), dtype=np.float), np.zeros((indexUserNum,),
dtype=np.float), np.zeros(
(indexUserNum,), dtype=np.float), np.zeros((indexUserNum,),
dtype=np.float), np.zeros((indexUserNum,), dtype=np.float), np.zeros(
(indexUserNum,), dtype=np.float), np.zeros((indexUserNum,), dtype=np.float), np.zeros((indexUserNum,),
dtype=np.float)
# 总共执行reNum组随机数据然后去平均值画图
for i in range(reNum):
print("重复组数：",i,"\n")
Data = dp.DataGenerate(budget, totalTaskNum, taskValueDis, totalUserNum, userCosPerValueDis, userTaskNumDis)
# taskSet = TaskSet(totalTaskNum, taskValueDis)
taskSet = Data.TaskSet()
# userTaskSet, userCost = UserSet(totalUserNum, userCosPerValueDis, userTaskNumDis, taskSet)
userTaskSet, userCost = Data.UserTaskSet()
# u_w, R, p, totalValue = SM(budget, taskSet, userTaskSet, userCost)
userSetDict = Data.userSetDictCompute(userTaskSet)
userSetSubsetDict = Data.userSetSubsetDictCompute(userSetDict)
# 控制user数量的图
SM_pu_1, SM_pu_2, MM_pu_2, GM_pu_2,SPIM_SM_pu_2,SPIM_MM_pu_2, SM_au_2, MM_au_2,SPIM_SM_au_2,SPIM_MM_au_2 = controlUser(budget,
taskSet,
userCost,
userTaskSet,
totalTaskNum,
indexUserNum,
userTaskNumDis, userSetDict,
userSetSubsetDict)
# print("GM_pu_1", GM_pu_1, "\n")
# print("GM_platformUtility_1", GM_platformUtility_1, "\n")
SM_platformUtility_1 = SM_platformUtility_1 + SM_pu_1
SM_platformUtility_2 = SM_platformUtility_2 + SM_pu_2
MM_platformUtility_2 = MM_platformUtility_2 + MM_pu_2
GM_platformUtility_2 = GM_platformUtility_2 + GM_pu_2
SPIM_SM_platformUtility_2 = SPIM_SM_platformUtility_2 + SPIM_SM_pu_2
SPIM_MM_platformUtility_2 = SPIM_MM_platformUtility_2 + SPIM_MM_pu_2
SM_averageUtility_2 = SM_averageUtility_2 + SM_au_2
MM_averageUtility_2 = MM_averageUtility_2 + MM_au_2
SPIM_SM_averageUtility_2 = SPIM_SM_averageUtility_2 + SPIM_SM_au_2
SPIM_MM_averageUtility_2 = SPIM_MM_averageUtility_2 + SPIM_MM_au_2
# print(SM_platformUtility_1,SM_platformUtility_2,MM_platformUtility_1,MM_platformUtility_2,SM_averageUtility_1,SM_averageUtility_2,MM_averageUtility_1,MM_averageUtility_2)
# for i in range(indexUserNum):
# SM_platformUtility_2[i]=math.log(SM_platformUtility_2[i]/reNum,10)
# MM_platformUtility_2[i] = math.log(MM_platformUtility_2[i] / reNum, 10)
# GM_platformUtility_2[i] = math.log(GM_platformUtility_2[i] / reNum, 10)
font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16, }
# 画图-----------------------------platformUtility 柱状图；
bar_width = 0.15 # 条形宽度
index_GM = np.arange(len(SM_platformUtility_1)) # 男生条形图的横坐标
index_SM = index_GM + bar_width # 女生条形图的横坐标
index_SPIM_SM = index_SM + bar_width
index_MM = index_SPIM_SM + bar_width # 女生条形图的横坐标
index_SPIM_MM = index_MM + bar_width
# 使用两次 bar 函数画出两组条形图
plt.bar(index_GM, height=GM_platformUtility_2 / reNum, width=bar_width, color='#FF6600', label='GM')
plt.bar(index_MM, height=MM_platformUtility_2 / reNum, width=bar_width, color='#339966', label='TBS-MM')
plt.bar(index_SM, height=SM_platformUtility_2 / reNum, width=bar_width, color='#3366FF', label='TBS-SM')
plt.bar(index_SPIM_SM, height=SPIM_SM_platformUtility_2 / reNum, width=bar_width, color='#55A868', label='SPIM-S')
plt.bar(index_SPIM_MM, height=SPIM_MM_platformUtility_2 / reNum, width=bar_width, color='#4C72B0', label='SPIM-M')
plt.legend() # 显示图例
plt.xticks(index_GM + 2*bar_width, SM_platformUtility_1/reNum)
plt.xlabel('Number of users',font2) # make axis labels
plt.ylabel('Platform Utility',font2) # 纵坐标轴标题
plt.title('Impact of users',font2) # 图形标题
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/platformUtility_users_bar" + string + ".pdf")
plt.show()
# ----------------画图-platformUtility
plt.figure()
plt.plot(SM_platformUtility_1 / reNum, SM_platformUtility_2 / reNum, 'r', marker='x',
label='TBS-SM') # use pylab to plot x and y : Give your plots names
plt.plot(SM_platformUtility_1 / reNum, MM_platformUtility_2 / reNum, 'g', marker='.', label='TBS-MM')
plt.plot(SM_platformUtility_1 / reNum, GM_platformUtility_2 / reNum, 'b', marker='*', label='GM')
plt.plot(SM_platformUtility_1 / reNum, SPIM_SM_platformUtility_2 / reNum, 'y', marker='o', label='SPIM-S')
plt.plot(SM_platformUtility_1 / reNum, SPIM_MM_platformUtility_2 / reNum, 'orange', marker=',', label='SPIM-M')
plt.title('Impact of users',font2) # give plot a title
plt.xlabel('Number of users',font2) # make axis labels
plt.ylabel('Platform Utility',font2)
plt.legend()
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/platformUtility_users" + string + ".pdf")
plt.show() # show the plot on the screen
#-------------------------画图average-utility -bar
bar_width = 0.15 # 条形宽度
index_ave_SM = np.arange(len(SM_platformUtility_1)) # 男生条形图的横坐标
index_ave_SPIM_SM = index_ave_SM + bar_width
index_ave_MM = index_ave_SPIM_SM + bar_width # 女生条形图的横坐标
index_ave_SPIM_MM = index_ave_MM + bar_width
# 使用两次 bar 函数画出两组条形图
plt.bar(index_ave_SM, height=SM_averageUtility_2 / reNum, width=bar_width, color='#3366FF', label='TBS-SM')
plt.bar(index_ave_MM, height=MM_averageUtility_2 / reNum, width=bar_width, color='#339966', label='TBS-MM')
plt.bar(index_ave_SPIM_SM, height=SPIM_SM_averageUtility_2 / reNum, width=bar_width, color='#FF6600', label='SPIM-S')
plt.bar(index_ave_SPIM_MM, height=SPIM_MM_averageUtility_2 / reNum, width=bar_width, color='#993366', label='SPIM-M')
plt.legend() # 显示图例
plt.xticks(index_ave_SM +1.5* bar_width, SM_platformUtility_1 / reNum)
plt.title('Impact of users',font2) # give plot a title
plt.xlabel('Number of users',font2) # make axis labels
plt.ylabel('Average Utility',font2)
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/avgUtility_users_bar" + string + ".pdf")
plt.show()
# 画图-averageUtility
plt.figure()
plt.plot(SM_platformUtility_1 / reNum, SM_averageUtility_2 / reNum, 'r', marker='x',
label='TBS-SM') # use pylab to plot x and y : Give your plots names
plt.plot(SM_platformUtility_1 / reNum, MM_averageUtility_2 / reNum, 'g', marker='.', label='TBS-MM')
plt.plot(SM_platformUtility_1 / reNum, SPIM_SM_averageUtility_2 / reNum, 'y', marker='o', label='SPIM-S')
plt.plot(SM_platformUtility_1 / reNum, SPIM_MM_averageUtility_2 / reNum, 'orange', marker=',', label='SPIM-M')
plt.title('Impact of users',font2) # give plot a title
plt.xlabel('Number of users',font2) # make axis labels
plt.ylabel('Average Utility',font2)
# pl.xlim(10.0, 35.0) # set axis limits
# pl.ylim(35.0, 50.0)
plt.legend()
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/avgUtility_users" + string + ".pdf")
plt.show() # show the plot on the screen
def controlTask(budget, indexUserNum, taskValueDis, totalUserNum, userCosPerValueDis, userTaskNumDis):
initTask = 20
x_1 = np.array([])
y_1 = np.array([])
y_2 = np.array([])
y_3 = np.array([])
y_4 = np.array([])
y_5 = np.array([])
Y_1 = np.array([])
Y_2 = np.array([])
Y_3 = np.array([])
Y_4 = np.array([])
# 从20到60
for i in range(indexUserNum):
totalTaskNum = 60 + i * initTask
print("task 数量为：:", totalTaskNum)
Data = dp.DataGenerate(budget, totalTaskNum, taskValueDis, totalUserNum, userCosPerValueDis, userTaskNumDis)
# taskSet = TaskSet(totalTaskNum, taskValueDis)
taskSet = Data.TaskSet()
# userTaskSet, userCost = UserSet(totalUserNum, userCosPerValueDis, userTaskNumDis, taskSet)
userTaskSet, userCost = Data.UserTaskSet()
# u_w, R, p, totalValue = SM(budget, taskSet, userTaskSet, userCost)
userSetDict = Data.userSetDictCompute(userTaskSet)
userSetSubsetDict = Data.userSetSubsetDictCompute(userSetDict)
userPayment_SM, finalValue_SM, S_w_SM, averageUtility_SM,value,payment = SM.SingleMindedAlg(budget / 2, taskSet,
userCost,
userTaskSet,
totalTaskNum, totalUserNum,
userTaskNumDis)
print("SM总价值：", finalValue_SM, "SM平均收益：", averageUtility_SM)
userPayment_MM, finalValue_MM,finalValue_SPIM_MM, S_w_MM, averageUtility_MM,averageUtility_SPIM_MM,value1,payemnt1,value2,payment2 = MM.MultiMindedAlg(budget, taskSet,userTaskSet,totalTaskNum, userCost,
totalUserNum,
userSetDict, userSetSubsetDict)
print("MM总价值：", finalValue_MM, "MM平均收益：", averageUtility_MM)
print("SPIM_MM总价值：", finalValue_SPIM_MM, "SPIM_MM平均收益：", averageUtility_SPIM_MM)
userPayment_GM, finalValue_GM, S_w_GM, averageUtility_GM = GM.GreedyAlgSM(budget, taskSet, userCost,
userTaskSet, totalTaskNum,
totalUserNum)
print("GM总价值：", finalValue_GM, "GM平均收益：", averageUtility_GM)
userPayment_SPIM_SM, finalValue_SPIM_SM, S_w_SPIM_SM, averageUtility_SPIM_SM, value_SPIM_SM, payment_SPIM_MM = wpg.SybilAlg(budget, taskSet, userCost, userTaskSet,
totalTaskNum, totalUserNum,
userTaskNumDis)
print("SPIM_SM总价值：", finalValue_SPIM_SM, "SPIM_SM平均收益：", averageUtility_SPIM_SM,"\n")
x_1 = np.append(x_1, np.array([totalTaskNum]))
y_1 = np.append(y_1, np.array([finalValue_SM]))
y_2 = np.append(y_2, np.array([finalValue_MM]))
y_3 = np.append(y_3, np.array([finalValue_GM]))
y_4 = np.append(y_4, np.array([finalValue_SPIM_SM]))
y_5 = np.append(y_5, np.array([finalValue_SPIM_MM]))
Y_1 = np.append(Y_1, np.array([averageUtility_SM]))
Y_2 = np.append(Y_2, np.array([averageUtility_MM]))
Y_3 = np.append(Y_3, np.array([averageUtility_SPIM_SM]))
Y_4 = np.append(Y_4, np.array([averageUtility_SPIM_MM]))
return x_1, y_1, y_2, y_3,y_4,y_5 ,Y_1,Y_2,Y_3,Y_4
def doControlTask(reNum, budget, maxTaskNum, taskValueDis, totalUserNum, userCosPerValueDis, userTaskNumDis):
# 初始设置参数
# task 考虑的组数(user 考虑40-100，每次增加10)
indexTaskNum = int(((maxTaskNum - 60) / 20) + 1)
SM_platformUtility_1, SM_platformUtility_2, MM_platformUtility_2, GM_platformUtility_2, \
SM_averageUtility_2, MM_averageUtility_2,SPIM_SM_platformUtility_2,SPIM_MM_platformUtility_2,SPIM_SM_averageUtility_2,SPIM_MM_averageUtility_2 = np.zeros((indexTaskNum,),
dtype=np.float), np.zeros(
(indexTaskNum,), dtype=np.float), np.zeros((indexTaskNum,), dtype=np.float), np.zeros((indexTaskNum,),
dtype=np.float), np.zeros(
(indexTaskNum,), dtype=np.float), np.zeros((indexTaskNum,),
dtype=np.float),np.zeros((indexTaskNum,), dtype=np.float),np.zeros((indexTaskNum,), dtype=np.float),np.zeros((indexTaskNum,), dtype=np.float),np.zeros((indexTaskNum,), dtype=np.float)
# 执行并画图
for i in range(reNum):
# 控制user数量的图
SM_pu_1, SM_pu_2, MM_pu_2, GM_pu_2,SPIM_SM_pu_2,SPIM_MM_pu_2, SM_au_2, MM_au_2,SPIM_SM_au_2,SPIM_MM_au_2 = controlTask(budget, indexTaskNum, taskValueDis,
totalUserNum, userCosPerValueDis,
userTaskNumDis)
# print("GM_pu_1", GM_pu_1, "\n")
# print("GM_platformUtility_1", GM_platformUtility_1, "\n")
SM_platformUtility_1 = SM_platformUtility_1 + SM_pu_1
SM_platformUtility_2 = SM_platformUtility_2 + SM_pu_2
MM_platformUtility_2 = MM_platformUtility_2 + MM_pu_2
GM_platformUtility_2 = GM_platformUtility_2 + GM_pu_2
SPIM_SM_platformUtility_2 = SPIM_SM_platformUtility_2 + SPIM_SM_pu_2
SPIM_MM_platformUtility_2 = SPIM_MM_platformUtility_2 + SPIM_MM_pu_2
SM_averageUtility_2 = SM_averageUtility_2 + SM_au_2
MM_averageUtility_2 = MM_averageUtility_2 + MM_au_2
SPIM_SM_averageUtility_2 = SPIM_SM_averageUtility_2 + SPIM_SM_au_2
SPIM_MM_averageUtility_2 = SPIM_MM_averageUtility_2 + SPIM_MM_au_2
# print(SM_platformUtility_1,SM_platformUtility_2,MM_platformUtility_1,MM_platformUtility_2,SM_averageUtility_1,SM_averageUtility_2,MM_averageUtility_1,MM_averageUtility_2)
# 画图-----------------------------platformUtility 柱状图；
bar_width = 0.15 # 条形宽度
index_GM = np.arange(len(SM_platformUtility_1)) # 男生条形图的横坐标
index_MM = index_GM + bar_width # 女生条形图的横坐标
index_SM = index_MM + bar_width # 女生条形图的横坐标
index_SPIM_MM=index_SM+bar_width
index_SPIM_SM = index_SPIM_MM + bar_width
# 使用两次 bar 函数画出两组条形图
plt.bar(index_GM, height=GM_platformUtility_2 / reNum, width=bar_width, color='#FF6600', label='GM')
plt.bar(index_MM, height=MM_platformUtility_2 / reNum, width=bar_width, color='#339966', label='TBS-MM')
plt.bar(index_SM, height=SM_platformUtility_2 / reNum, width=bar_width, color='#3366FF', label='TBS-SM')
plt.bar(index_SPIM_SM, height=SPIM_SM_platformUtility_2 / reNum, width=bar_width, color='#55A868', label='SPIM-S')
plt.bar(index_SPIM_MM, height=SPIM_MM_platformUtility_2 / reNum, width=bar_width, color='#4C72B0', label='SPIM-M')
plt.legend() # 显示图例
plt.xticks(index_GM + 2*bar_width,SM_platformUtility_1 / reNum)
font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16, }
plt.title('Impact of Tasks',font2) # give plot a title
plt.xlabel('Number of Tasks',font2) # make axis labels
plt.ylabel('Platform Utility',font2) # 图形标题
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/platformUtility_tasks_bar" + string + ".pdf")
plt.show()
# 画图-platformUtility
plt.figure()
plt.plot(SM_platformUtility_1 / reNum, SM_platformUtility_2 / reNum, 'r', marker='x',
label='TBS-SM') # use pylab to plot x and y : Give your plots names
plt.plot(SM_platformUtility_1 / reNum, MM_platformUtility_2 / reNum, 'g', marker='.', label='TBS-MM')
plt.plot(SM_platformUtility_1 / reNum, GM_platformUtility_2 / reNum, 'b', marker='*', label='GM')
plt.plot(SM_platformUtility_1 / reNum, SPIM_SM_platformUtility_2 / reNum, 'y', marker='o', label='SPIM-S')
plt.plot(SM_platformUtility_1 / reNum, SPIM_MM_platformUtility_2 / reNum, 'orange', marker=',', label='SPIM-M')
font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16, }
plt.title('Impact of Tasks',font2) # give plot a title
plt.xlabel('Number of Tasks',font2) # make axis labels
plt.ylabel('Platform Utility',font2)
# pl.xlim(10.0, 35.0) # set axis limits
# pl.ylim(35.0, 50.0)
plt.legend()
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/platformUtility_tasks" + string + ".pdf")
plt.show() # show the plot on the screen
# -------------------------画图average-utility -bar
bar_width = 0.15 # 条形宽度
index_ave_SM = np.arange(len(SM_platformUtility_1)) # 男生条形图的横坐标
index_ave_MM = index_ave_SM + bar_width # 女生条形图的横坐标
index_ave_SM = index_ave_MM + bar_width # 女生条形图的横坐标
index_ave_SPIM_MM = index_ave_SM + bar_width
index_ave_SPIM_SM = index_ave_SPIM_MM + bar_width
# 使用两次 bar 函数画出两组条形图
plt.bar(index_ave_SM, height=SM_averageUtility_2 / reNum, width=bar_width, color='#3366FF', label='TBS-SM')
plt.bar(index_ave_MM, height=MM_averageUtility_2 / reNum, width=bar_width, color='#339966', label='TBS-MM')
plt.bar(index_ave_SPIM_SM, height=SPIM_SM_averageUtility_2 / reNum, width=bar_width, color='#FF6600', label='SPIM-S')
plt.bar(index_ave_SPIM_MM, height=SPIM_MM_averageUtility_2 / reNum, width=bar_width, color='#993366', label='SPIM-M')
plt.legend() # 显示图例
plt.xticks(index_ave_SM + 1.5*bar_width, SM_platformUtility_1 / reNum)
font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16, }
plt.title('Impact of Tasks',font2) # give plot a title
plt.xlabel('Number of Tasks',font2) # make axis labels
plt.ylabel('Average Utility',font2)
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/avgUtility_tasks_bar" + string + ".pdf")
plt.show()
# 画图-averageUtility
plt.figure()
plt.plot(SM_platformUtility_1 / reNum, SM_averageUtility_2 / reNum, 'r', marker='x',
label='TBS-SM') # use pylab to plot x and y : Give your plots names
plt.plot(SM_platformUtility_1 / reNum, MM_averageUtility_2 / reNum, 'g', marker='.', label='TBS-MM')
plt.plot(SM_platformUtility_1 / reNum, SPIM_SM_averageUtility_2 / reNum, 'y', marker='o', label='SPIM-S')
plt.plot(SM_platformUtility_1 / reNum, SPIM_MM_averageUtility_2 / reNum, 'orange', marker=',', label='SPIM-M')
font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16, }
plt.title('Impact of Tasks',font2) # give plot a title
plt.xlabel('Number of Tasks',font2) # make axis labels
plt.ylabel('Average Utility',font2)
# pl.xlim(10.0, 35.0) # set axis limits
# pl.ylim(35.0, 50.0)
plt.legend()
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/avgUtility_tasks" + string + ".pdf")
plt.show() # show the plot on the screen
def controlBudget(indexBudget, taskSet, userCost, userTaskSet, totalTaskNum, totalUserNum, userTaskNumDis, userSetDict,
userSetSubsetDict):
initUser = 40
x_1 = np.array([])
y_1 = np.array([])
y_2 = np.array([])
y_3 = np.array([])
y_4 = np.array([])
y_5 = np.array([])
Y_1 = np.array([])
Y_2 = np.array([])
Y_3 = np.array([])
Y_4 = np.array([])
# 从50到800
for i in range(indexBudget):
budget = 120 + i * initUser
print("budget为:", budget)
userPayment_SM, finalValue_SM, S_w_SM, averageUtility_SM,value,payment = SM.SingleMindedAlg(budget / 2, taskSet,
userCost,
userTaskSet,
totalTaskNum, totalUserNum,
userTaskNumDis)
print("SM总价值：", finalValue_SM, "SM平均收益：", averageUtility_SM)
userPayment_MM, finalValue_MM,finalValue_SPIM_MM, S_w_MM, averageUtility_MM,averageUtility_SPIM_MM,value1,payemnt1,value2,payment2 = MM.MultiMindedAlg(budget, taskSet, userTaskSet,totalTaskNum,userCost,
totalUserNum,
userSetDict, userSetSubsetDict)
print("MM总价值：", finalValue_MM, "MM平均收益：", averageUtility_MM)
userPayment_GM, finalValue_GM, S_w_GM, averageUtility_GM = GM.GreedyAlgSM(budget, taskSet, userCost,
userTaskSet, totalTaskNum,
totalUserNum)
print("GM总价值：", finalValue_GM, "GM平均收益：", averageUtility_GM)
userPayment_SPIM_SM, finalValue_SPIM_SM, S_w_SPIM_SM, averageUtility_SPIM_SM, value_SPIM_SM, payment_SPIM_MM = wpg.SybilAlg(
budget, taskSet, userCost, userTaskSet,
totalTaskNum, totalUserNum,
userTaskNumDis)
print("SPIM_SM总价值：", finalValue_SPIM_SM, "SPIM_SM平均收益：", averageUtility_SPIM_SM, "\n")
x_1 = np.append(x_1, np.array([budget]))
y_1 = np.append(y_1, np.array([finalValue_SM]))
y_2 = np.append(y_2, np.array([finalValue_MM]))
y_3 = np.append(y_3, np.array([finalValue_GM]))
y_4 = np.append(y_4, np.array([finalValue_SPIM_SM]))
y_5 = np.append(y_5, np.array([finalValue_SPIM_MM]))
Y_1 = np.append(Y_1, np.array([averageUtility_SM]))
Y_2 = np.append(Y_2, np.array([averageUtility_MM]))
Y_3 = np.append(Y_3, np.array([averageUtility_SPIM_SM]))
Y_4 = np.append(Y_4, np.array([averageUtility_SPIM_MM]))
return x_1, y_1, y_2, y_3, y_4, y_5, Y_1, Y_2, Y_3, Y_4
def doControlBudget(reNum, maxBudget, totalTaskNum, taskValueDis, totalUserNum, userCosPerValueDis, userTaskNumDis):
# user 考虑的组数(user 考虑100-300，每次增加20)
indexBudget = int(((maxBudget - 120) / 40) + 1)
SM_platformUtility_1, SM_platformUtility_2, MM_platformUtility_2, GM_platformUtility_2, \
SM_averageUtility_2, MM_averageUtility_2, SPIM_SM_platformUtility_2, SPIM_MM_platformUtility_2, SPIM_SM_averageUtility_2, SPIM_MM_averageUtility_2 = np.zeros(
(indexBudget,),
dtype=np.float), np.zeros(
(indexBudget,), dtype=np.float), np.zeros((indexBudget,), dtype=np.float), np.zeros((indexBudget,),
dtype=np.float), np.zeros(
(indexBudget,), dtype=np.float), np.zeros((indexBudget,),
dtype=np.float), np.zeros((indexBudget,), dtype=np.float), np.zeros(
(indexBudget,), dtype=np.float), np.zeros((indexBudget,), dtype=np.float), np.zeros((indexBudget,),
dtype=np.float)
# 总共执行reNum组随机数据然后去平均值画图
for i in range(reNum):
print("------------------重复次数为-----------：", i, "\n")
Data = dp.DataGenerate(budget, totalTaskNum, taskValueDis, totalUserNum, userCosPerValueDis, userTaskNumDis)
# taskSet = TaskSet(totalTaskNum, taskValueDis)
taskSet = Data.TaskSet()
# userTaskSet, userCost = UserSet(totalUserNum, userCosPerValueDis, userTaskNumDis, taskSet)
userTaskSet, userCost = Data.UserTaskSet()
# u_w, R, p, totalValue = SM(budget, taskSet, userTaskSet, userCost)
userSetDict = Data.userSetDictCompute(userTaskSet)
userSetSubsetDict = Data.userSetSubsetDictCompute(userSetDict)
# 控制user数量的图
SM_pu_1, SM_pu_2, MM_pu_2, GM_pu_2,SPIM_SM_pu_2,SPIM_MM_pu_2, SM_au_2, MM_au_2,SPIM_SM_au_2,SPIM_MM_au_2 = controlBudget(indexBudget, taskSet, userCost,
userTaskSet, totalTaskNum, totalUserNum,
userTaskNumDis, userSetDict,
userSetSubsetDict)
# print("GM_pu_1", GM_pu_1, "\n")
# print("GM_platformUtility_1", GM_platformUtility_1, "\n")
SM_platformUtility_1 = SM_platformUtility_1 + SM_pu_1
SM_platformUtility_2 = SM_platformUtility_2 + SM_pu_2
MM_platformUtility_2 = MM_platformUtility_2 + MM_pu_2
GM_platformUtility_2 = GM_platformUtility_2 + GM_pu_2
SPIM_SM_platformUtility_2 = SPIM_SM_platformUtility_2 + SPIM_SM_pu_2
SPIM_MM_platformUtility_2 = SPIM_MM_platformUtility_2 + SPIM_MM_pu_2
SM_averageUtility_2 = SM_averageUtility_2 + SM_au_2
MM_averageUtility_2 = MM_averageUtility_2 + MM_au_2
SPIM_SM_averageUtility_2 = SPIM_SM_averageUtility_2 + SPIM_SM_au_2
SPIM_MM_averageUtility_2 = SPIM_MM_averageUtility_2 + SPIM_MM_au_2
# print(SM_platformUtility_1,SM_platformUtility_2,MM_platformUtility_1,MM_platformUtility_2,SM_averageUtility_1,SM_averageUtility_2,MM_averageUtility_1,MM_averageUtility_2)
font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16, }
# 画图-----------------------------platformUtility 柱状图；
bar_width = 0.15 # 条形宽度
index_GM = np.arange(len(SM_platformUtility_1)) # 男生条形图的横坐标
index_MM = index_GM + bar_width # 女生条形图的横坐标
index_SM = index_MM + bar_width # 女生条形图的横坐标
index_SPIM_MM = index_SM + bar_width
index_SPIM_SM = index_SPIM_MM + bar_width
# 使用两次 bar 函数画出两组条形图
plt.bar(index_GM, height=GM_platformUtility_2 / reNum, width=bar_width, color='#FF6600', label='GM')
plt.bar(index_MM, height=MM_platformUtility_2 / reNum, width=bar_width, color='#339966', label='TBS-MM')
plt.bar(index_SM, height=SM_platformUtility_2 / reNum, width=bar_width, color='#3366FF', label='TBS-SM')
plt.bar(index_SPIM_SM, height=SPIM_SM_platformUtility_2 / reNum, width=bar_width, color='#55A868', label='SPIM-S')
plt.bar(index_SPIM_MM, height=SPIM_MM_platformUtility_2 / reNum, width=bar_width, color='#4C72B0', label='SPIM-M')
plt.legend() # 显示图例
plt.xticks(index_GM + 2*bar_width, SM_platformUtility_1 / reNum)
plt.title('Impact of budget',font2) # give plot a title
plt.xlabel('Budget',font2) # make axis labels
plt.ylabel('Platform Utility',font2)
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/platformUtility_budget_bar" + string + ".pdf")
plt.show()
# 画图-platformUtility
plt.figure()
plt.plot(SM_platformUtility_1 / reNum, SM_platformUtility_2 / reNum, 'r', marker='x',
label='TBS-SM') # use pylab to plot x and y : Give your plots names
plt.plot(SM_platformUtility_1 / reNum, MM_platformUtility_2 / reNum, 'g', marker='.', label='TBS-MM')
plt.plot(SM_platformUtility_1 / reNum, GM_platformUtility_2 / reNum, 'b', marker='*', label='GM')
plt.plot(SM_platformUtility_1 / reNum, SPIM_SM_platformUtility_2 / reNum, 'y', marker='o', label='SPIM-S')
plt.plot(SM_platformUtility_1 / reNum, SPIM_MM_platformUtility_2 / reNum, 'orange', marker=',', label='SPIM-M')
font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16, }
plt.title('Impact of budget',font2) # give plot a title
plt.xlabel('Budget',font2) # make axis labels
plt.ylabel('Platform Utility',font2)
# pl.xlim(10.0, 35.0) # set axis limits
# pl.ylim(35.0, 50.0)
plt.legend()
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/platformUtility_budget"+string+".pdf")
plt.show() # show the plot on the screen
# -------------------------画图average-utility -bar
bar_width = 0.15 # 条形宽度
index_ave_SM = np.arange(len(SM_platformUtility_1)) # 男生条形图的横坐标
index_ave_MM = index_ave_SM + bar_width # 女生条形图的横坐标
index_ave_SPIM_MM = index_ave_MM + bar_width
index_ave_SPIM_SM = index_ave_SPIM_MM + bar_width
# 使用两次 bar 函数画出两组条形图
plt.bar(index_ave_SM, height=SM_averageUtility_2 / reNum, width=bar_width, color='#3366FF', label='TBS-SM')
plt.bar(index_ave_MM, height=MM_averageUtility_2 / reNum, width=bar_width, color='#339966', label='TBS-MM')
plt.bar(index_ave_SPIM_SM, height=SPIM_SM_averageUtility_2 / reNum, width=bar_width, color='#FF6600',
label='SPIM-S')
plt.bar(index_ave_SPIM_MM, height=SPIM_MM_averageUtility_2 / reNum, width=bar_width, color='#993366',
label='SPIM-M')
plt.legend() # 显示图例
plt.xticks(index_ave_SM +1.5* bar_width, SM_platformUtility_1 / reNum)
plt.title('Impact of budget',font2) # give plot a title
plt.xlabel('Budget',font2) # make axis labels
plt.ylabel('Average Utility',font2)
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/avgUtility_budget_bar" + string + ".pdf")
plt.show()
# 画图-averageUtility
plt.figure()
plt.plot(SM_platformUtility_1 / reNum, SM_averageUtility_2 / reNum, 'r', marker='x',
label='TBS-SM') # use pylab to plot x and y : Give your plots names
plt.plot(SM_platformUtility_1 / reNum, MM_averageUtility_2 / reNum, 'g', marker='.', label='TBS-MM')
plt.plot(SM_platformUtility_1 / reNum, SPIM_SM_averageUtility_2 / reNum, 'y', marker='o', label='SPIM-S')
plt.plot(SM_platformUtility_1 / reNum, SPIM_MM_averageUtility_2 / reNum, 'orange', marker=',', label='SPIM-M')
plt.title('Impact of budget',font2) # give plot a title
plt.xlabel('Budget',font2) # make axis labels
plt.ylabel('Average Utility',font2)
# pl.xlim(10.0, 35.0) # set axis limits
# pl.ylim(35.0, 50.0)
plt.legend()
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/avgUtility_budget"+string+".pdf")
plt.show() # show the plot on the screen
def compareBudget(indexBudget, taskSet, userCost, userTaskSet, totalTaskNum, totalUserNum, userTaskNumDis, userSetDict,
userSetSubsetDict):
initBudget = 20
x_1 = np.array([])
y_1 = np.array([])
y_2 = np.array([])
y_3 = np.array([])
# Y_1 = np.array([])
# Y_2 = np.array([])
# 从40到800
budget=0
for i in range(indexBudget):
budget = 40 + i * initBudget
print("budget为:", budget)
userPayment_SM, finalValue_SM, S_w_SM, averageUtility_SM, value_SM, payment_SM = SM.SingleMindedAlg(budget / 2,
taskSet,
userCost,
userTaskSet,
totalTaskNum,
totalUserNum,
userTaskNumDis)
print("SM总价值：", finalValue_SM, "SM平均收益：", averageUtility_SM)
# userCostTemp_2 = userCost[:num]
# userTaskSetTemp_2 = userTaskSet[:, :num]
userPayment_MM, finalValue_MM,finalValue_SPIM_MM, S_w_MM, averageUtility_MM,averageUtility_SPIM_MM,value_MM,payment_MM,value_SPIM_MM,payment_SPIM_MM = MM.MultiMindedAlg(budget, taskSet,userTaskSet,totalTaskNum, userCost,
totalUserNum,
userSetDict, userSetSubsetDict)
print("MM总价值：", finalValue_MM, "MM平均收益：", averageUtility_MM)
userPayment_GM, finalValue_GM, S_w_GM, averageUtility_GM = GM.GreedyAlgSM(budget, taskSet, userCost,
userTaskSet, totalTaskNum,
totalUserNum)
print("GM总价值：", finalValue_GM, "GM平均收益：", averageUtility_GM, "\n")
x_1 = np.append(x_1, np.array([budget]))
y_1 = np.append(y_1, np.array([finalValue_SM]))
y_2 = np.append(y_2, np.array([finalValue_MM]))
y_3 = np.append(y_3, np.array([finalValue_GM]))
# Y_1 = np.append(Y_1, np.array([averageUtility_SM]))
# Y_2 = np.append(Y_2, np.array([averageUtility_MM]))
# 计算infocom方法SPIM-S
userPayment_IN, finalValue_IN, S_w_IN,averageUtility_IN, value_IN, payment_IN = wpg.SybilAlg(budget,taskSet, userCost, userTaskSet,
totalTaskNum, totalUserNum,
userTaskNumDis)
return x_1, y_1, y_2, y_3, value_IN, payment_IN
def doCompareBudget(reNum, maxBudget, totalTaskNum, taskValueDis, totalUserNum, userCosPerValueDis, userTaskNumDis,
maxValue, indexValue):
# user 考虑的组数(user 考虑100-300，每次增加20)
indexBudget = int(((maxBudget*2/3 - 40) / 20) + 1)
budget, finalValue_SM, finalValue_MM, finalValue_GM, finalValue_SPIM_S = np.zeros((indexBudget,),
dtype=np.float), np.zeros(
(indexBudget,), dtype=np.float), np.zeros((indexBudget,),
dtype=np.float), np.zeros(
(indexBudget,), dtype=np.float), np.zeros((indexBudget,),
dtype=np.float)
valueControlIndex = int(maxValue / indexValue)
controlValue_Index, controlValue_SPIM_S, controlValue_SM_S ,controlValue_SPIM_M,controlValue_SM_M = np.zeros((valueControlIndex,),
dtype=np.float), np.zeros(
(valueControlIndex,), dtype=np.float), np.zeros((valueControlIndex,), dtype=np.float),np.zeros(
(valueControlIndex,), dtype=np.float),np.zeros(
(valueControlIndex,), dtype=np.float)
# 总共执行reNum组随机数据然后去平均值画图
for i in range(reNum):
print("------------------重复次数为-----------：", i, "\n")
Data = dp.DataGenerate(maxBudget, totalTaskNum, taskValueDis, totalUserNum, userCosPerValueDis, userTaskNumDis)
# taskSet = TaskSet(totalTaskNum, taskValueDis)
taskSet = Data.TaskSet()
# userTaskSet, userCost = UserSet(totalUserNum, userCosPerValueDis, userTaskNumDis, taskSet)
userTaskSet, userCost = Data.UserTaskSet()
# u_w, R, p, totalValue = SM(budget, taskSet, userTaskSet, userCost)
userSetDict = Data.userSetDictCompute(userTaskSet)
userSetSubsetDict = Data.userSetSubsetDictCompute(userSetDict)
# 控制user数量的图
currentBudget, y_1, y_2, y_3, value_IN, payment_IN = compareBudget(indexBudget, taskSet, userCost,
userTaskSet, totalTaskNum, totalUserNum,
userTaskNumDis, userSetDict,
userSetSubsetDict)
s_1, s_2, s_3, s_4, value_SM, payment_SM = SM.SingleMindedAlg(maxBudget / 2,
taskSet,
userCost,
userTaskSet,
totalTaskNum,
totalUserNum,
userTaskNumDis)
s_5, s_6, s_7, s_8,s_9,s_10, value_MM, payment_MM, value_SPIM_MM, payment_SPIM_MM = MM.MultiMindedAlg(
maxBudget, taskSet,userTaskSet,totalTaskNum, userCost,
totalUserNum,
userSetDict, userSetSubsetDict)
# print("GM_pu_1", GM_pu_1, "\n")
# print("GM_platformUtility_1", GM_platformUtility_1, "\n")
budget = budget + currentBudget
finalValue_SM = finalValue_SM + y_1
finalValue_MM = finalValue_MM + y_2
finalValue_GM = finalValue_GM + y_3
# 得到WPG在固定payment序列下的value（用的参数IN）
tempValue = np.zeros((indexBudget,), dtype=np.float)
for i in range(indexBudget):
for j in range(payment_IN.shape[0]):
if payment_IN[j] <= 40 * (i + 1):
tempValue[i] = value_IN[j]
finalValue_SPIM_S = finalValue_SPIM_S + tempValue
# 得到WPG在固定value序列下的payment
f_1, f_2 = getValuePaymentRelation(value_IN, payment_IN, maxValue, indexValue)
controlValue_SPIM_S = controlValue_SPIM_S + f_2
# 得到WPG_MM在固定value下的payment
f_1, f_2 = getValuePaymentRelation(value_SPIM_MM, payment_SPIM_MM, maxValue, indexValue)
controlValue_SPIM_M = controlValue_SPIM_M + f_2
# contral value 得到SM在固定value下的payment
f_1, f_2 = getValuePaymentRelation(value_SM, payment_SM, maxValue, indexValue)
controlValue_Index, controlValue_SM_S = f_1, controlValue_SM_S + f_2
# 得到本文MM在固定value下的payment
f_1, f_2 = getValuePaymentRelation(value_MM, payment_MM, maxValue, indexValue)
controlValue_SM_M = controlValue_SM_M + f_2
# print(SM_platformUtility_1,SM_platformUtility_2,MM_platformUtility_1,MM_platformUtility_2,SM_averageUtility_1,SM_averageUtility_2,MM_averageUtility_1,MM_averageUtility_2)
# 画图-platformUtility
plt.figure()
plt.plot(budget / reNum, finalValue_SM / reNum, 'r', marker='x',
label='TBS-SM') # use pylab to plot x and y : Give your plots names
plt.plot(budget / reNum, finalValue_MM / reNum, 'y', marker='.', label='TBS-MM')
plt.plot(budget / reNum, finalValue_GM / reNum, 'b', marker='*', label='GM')
plt.plot(budget / reNum, finalValue_SPIM_S / reNum, 'g', marker='o', label='SPIM-S')
# 设置输出的图片大小figsize = 11,9figure, ax = plt.subplots(figsize=figsize)
# #在同一幅图片上画两条折线A,=plt.plot(x1,y1,'-r',label='A',linewidth=5.0)B,=plt.plot(x2,y2,'b-.',label='B',linewidth=5.0)
# #设置图例并且设置图例的字体及大小font1 = {'family' : 'Times New Roman','weight' : 'normal','size' : 23,}
# legend = plt.legend(handles=[A,B],prop=font1)
# #设置坐标刻度值的大小以及刻度值的字体plt.tick_params(labelsize=23)labels = ax.get_xticklabels() + ax.get_yticklabels()
# [label.set_fontname('Times New Roman') for label in labels]
# #设置横纵坐标的名称以及对应字体格式
font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16, }
# plt.title('Impact of budgets', font2) # give plot a title
plt.xlabel('Budgets', font2) # make axis labels
plt.ylabel('Platform Utility', font2)
# pl.xlim(40, 2000) # set axis limits
# pl.ylim(35.0, 50.0)
plt.legend()
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/paymentCompare" + string + ".pdf")
# plt.savefig("paymentCompare.pdf")
plt.show() # show the plot on the screen
# 画图-control value
plt.figure()
plt.plot(controlValue_Index , controlValue_SM_S / reNum, 'r', marker='x',
label='TBS-SM') # use pylab to plot x and y : Give your plots names
plt.plot(controlValue_Index, controlValue_SM_M / reNum, 'y', marker='.', label='TBS-MM')
plt.plot(controlValue_Index , controlValue_SPIM_S / reNum, 'g', marker='*', label='SPIM-S')
plt.plot(controlValue_Index, controlValue_SPIM_M / reNum, 'b', marker='o', label='SPIM-M')
# 设置输出的图片大小figsize = 11,9figure, ax = plt.subplots(figsize=figsize)
# #在同一幅图片上画两条折线A,=plt.plot(x1,y1,'-r',label='A',linewidth=5.0)B,=plt.plot(x2,y2,'b-.',label='B',linewidth=5.0)
# #设置图例并且设置图例的字体及大小font1 = {'family' : 'Times New Roman','weight' : 'normal','size' : 23,}
# legend = plt.legend(handles=[A,B],prop=font1)
# #设置坐标刻度值的大小以及刻度值的字体plt.tick_params(labelsize=23)labels = ax.get_xticklabels() + ax.get_yticklabels()
# [label.set_fontname('Times New Roman') for label in labels]
# #设置横纵坐标的名称以及对应字体格式
font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16, }
# plt.title('Payment Compare', font2) # give plot a title
plt.xlabel('Value', font2) # make axis labels
plt.ylabel('Payment', font2)
# pl.xlim(40, 2000) # set axis limits
# pl.ylim(35.0, 50.0)
plt.legend()
string=time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/valuePaymentCompare"+string+".pdf")
plt.show() # show the plot on the screen
print(controlValue_Index,controlValue_SM_S / reNum, controlValue_SPIM_S / reNum,controlValue_SM_M/reNum,controlValue_SPIM_M/reNum)
def truthfulnessValidate(maxBudget,totalTaskNum,taskValueDis, totalUserNum, userCosPerValueDis, userTaskNumDis):
Data = dp.DataGenerate(maxBudget, totalTaskNum, taskValueDis, totalUserNum, userCosPerValueDis, userTaskNumDis)
# taskSet = TaskSet(totalTaskNum, taskValueDis)
taskSet = Data.TaskSet()
# userTaskSet, userCost = UserSet(totalUserNum, userCosPerValueDis, userTaskNumDis, taskSet)
userTaskSet, userCost = Data.UserTaskSet()
# u_w, R, p, totalValue = SM(budget, taskSet, userTaskSet, userCost)
userSetDict = Data.userSetDictCompute(userTaskSet)
userSetSubsetDict = Data.userSetSubsetDictCompute(userSetDict)
# 计算没有谎报情形下的最终结果；
userPayment_SM, finalValue_SM, S_w_SM, averageUtility_SM, value_SM, payment_SM = SM.SingleMindedAlg(budget / 2,
taskSet,
userCost,
userTaskSet,
totalTaskNum,
totalUserNum,
userTaskNumDis)
userSet=Data.getUserSet(totalUserNum)
#得到随机的一个loser
losers=userSet-S_w_SM
loser=random.sample(losers,1).pop()
#得到loser的真实utility
truthful_cost_loser = userCost[loser]
truthful_utility_loser = userPayment_SM[loser]
print("loser:",truthful_cost_loser,truthful_utility_loser,"\n")
selectSet = set(random.sample(S_w_SM, 2))
print(selectSet)
selectSetTemp=copy.deepcopy(selectSet)
#计算两个seller的真实收益
seller_1 = selectSetTemp.pop()
truthful_cost_1=userCost[seller_1]
truthful_utility_1= userPayment_SM[seller_1]-userCost[seller_1]*len(SM.getUserTaskSet(seller_1,userTaskSet,totalTaskNum))
print("loser:", truthful_cost_1, truthful_utility_1, "\n")
seller_2 = selectSetTemp.pop()
truthful_cost_2 = userCost[seller_2]
truthful_utility_2 = userPayment_SM[seller_2] - userCost[seller_2] * len(
SM.getUserTaskSet(seller_2, userTaskSet, totalTaskNum))
print("loser:", truthful_cost_2, truthful_utility_2, "\n")
#得到测试用的三个user集合
sellerList=list(copy.deepcopy(selectSet))
sellerList.append(loser)
print(sellerList)
#假定现在每个user在single-minded case可能谎报他的cost
selectSeller_bid = np.array([1,2,3,4,5,6,7,8,9,10])
selectSellerBidUtility=np.array([1,2,3,4,5,6,7,8,9,10])
for item in sellerList:
print(item)
selectSeller_utility = np.array([])
for bid in range(userCosPerValueDis):
userCost_temp=copy.deepcopy(userCost)
userCost_temp[item]=bid
x_0_payment, x_1, S_w, x_3, x_4, x_5 = SM.SingleMindedAlg(
budget / 2,
taskSet,
userCost_temp,
userTaskSet,
totalTaskNum,
totalUserNum,
userTaskNumDis)
if item in S_w:
utility=x_0_payment[item]-len(SM.getUserTaskSet(item,userTaskSet,totalTaskNum))*userCost[item]
else:
utility=0
selectSeller_utility = np.append(selectSeller_utility, np.array([utility]))
selectSellerBidUtility=np.row_stack((selectSellerBidUtility,selectSeller_utility))
print(selectSellerBidUtility)
#画图
plt.figure()
plt.plot(selectSellerBidUtility[0], selectSellerBidUtility[1], 'r', marker='x',markersize=8,
label='user:'+str(sellerList[0])+', cost='+str(round(userCost[sellerList[0]],2))) # use pylab to plot x and y : Give your plots names
plt.plot(truthful_cost_1,truthful_utility_1,'r',marker='x',markersize=16)
plt.plot(selectSellerBidUtility[0], selectSellerBidUtility[2], 'g', marker='.',markersize=8, label='user:'+str(sellerList[1])+', cost='+str(round(userCost[sellerList[1]],2)))
plt.plot(truthful_cost_2, truthful_utility_2, 'g', marker='.',markersize=16)
plt.plot(selectSellerBidUtility[0], selectSellerBidUtility[3], 'b', marker='*',markersize=8, label='user:'+str(sellerList[2])+', cost='+str(round(userCost[sellerList[2]],2)))
plt.plot(truthful_cost_loser,truthful_utility_loser, 'b', marker='*',markersize=16)
font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16, }
# plt.title('Impact of budget', font2) # give plot a title
plt.xlabel('Bids', font2) # make axis labels
plt.ylabel('User Utilities', font2)
# pl.xlim(40, 2000) # set axis limits
# pl.ylim(35.0, 50.0)
plt.legend()
string = time.strftime('%Y%m%d%H%M%S')
plt.savefig("F:/Results/BidUserUtility_SM" + string + ".pdf")
# plt.savefig("paymentCompare.pdf")
plt.show() # show the plot on the screen
def falseName(num,seller,budget,taskSet,userCost,userTaskSet,totalTaskNum,totalUserNum,userCosPerValueDis,userTaskNumDis):
#更新数据usercost
userCostTemp=copy.deepcopy(userCost)
totalUserNumTemp=totalUserNum+num-1
userCostTemp[seller] = userCost[seller]
for i in range(num-1):
userCostTemp = np.append(userCostTemp,np.array(userCost[seller]))
#更新taskset
userTaskSetTemp=copy.deepcopy(userTaskSet)
eachUserTask=SM.getUserTaskSet(seller,userTaskSet,totalTaskNum)
length=len(eachUserTask)
R=set()
randomSize=random.randint(1,length)
randomSet=set(random.sample(eachUserTask,randomSize))
for i in range(totalTaskNum):
if i in randomSet:
userTaskSetTemp[i][seller]=1
else:
userTaskSetTemp[i][seller]=0
R=R|randomSet
for i in range(num-1):
temp=np.array([])
if i!=num-2:
randomSize = random.randint(1, length)
randomSet = set(random.sample(eachUserTask, randomSize))
for j in range(totalTaskNum):
if j in randomSet:
temp=np.append(temp,np.array([1]))
else:
temp = np.append(temp, np.array([0]))
R = R | randomSet
else:
if R==eachUserTask:
randomSize = random.randint(1, length)
randomSet = set(random.sample(eachUserTask, randomSize))
for j in range(totalTaskNum):
if j in randomSet:
temp = np.append(temp, np.array([1]))
else:
temp = np.append(temp, np.array([0]))
else:
randomSet=eachUserTask-R
for j in range(totalTaskNum):
if j in randomSet:
temp = np.append(temp, np.array([1]))
else:
temp = np.append(temp, np.array([0]))
userTaskSetTemp=np.c_[userTaskSetTemp,temp]
# 计算没有谎报之后下的最终结果；
userPayment_SM, finalValue_SM, S_w_SM, averageUtility_SM, value_SM, payment_SM = SM.SingleMindedAlg(budget / 2,
taskSet,
userCostTemp,
userTaskSetTemp,
totalTaskNum,
totalUserNumTemp,