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testScriptAkandaMain.py
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testScriptAkandaMain.py
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from Transfer import WriteToFile
from MultipleNetworksSimulation import SalibPreprocessGetParamsForSobol, Simulate
import multiprocess.context as ctx
from sys import platform
import time
import sys
from multiprocessing import Pool
sys.setrecursionlimit(100000000)
from Networks import RandomSocialGraphAdvanced
#
# folderPath = 'D:\\sensitivityAnalaysisVirtualSoc\\'
# modelOutputFolder = 'D:\\outputTest\\'
# modelTypes = ['linear_model.LinearRegression()','linear_model.Ridge()','linear_model.LassoLarsIC(criterion=\'bic\')','linear_model.LassoLarsIC(criterion=\'aic\')',
# 'linear_model.ElasticNet(alpha=1.0, l1_ratio=0.5, fit_intercept=True, normalize=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, random_state=None, selection=\'cyclic\')',
# 'linear_model.Lars(fit_intercept=True, verbose=False, normalize=True, precompute=\'auto\', n_nonzero_coefs=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False)',
# 'linear_model.LassoLars(alpha=.1)',
# 'linear_model.BayesianRidge()',
# 'Pipeline([(\'poly\', PolynomialFeatures(degree=3)),(\'linear\', LinearRegression(fit_intercept=False))])',
# 'kernel_ridge.KernelRidge(alpha=1.0)',
# 'svm.SVR()',
# 'linear_model.SGDRegressor(max_iter=1000)',
# 'KNeighborsRegressor.KNeighborsRegressor(n_neighbors=3)',
# 'gaussian_process.GaussianProcessRegressor(kernel=sklearn.gaussian_process.kernels.DotProduct() + sklearn.gaussian_process.kernels.WhiteKernel(), random_state=0)',
# 'tree.DecisionTreeRegressor()',
# 'neural_network.MLPClassifier(alpha=0.01, random_state=1)']
# modelTypes = [ 'neighbors.KNeighborsRegressor(n_neighbors=3)',
# 'gaussian_process.GaussianProcessRegressor(kernel=gaussian_process.kernels.DotProduct() + gaussian_process.kernels.WhiteKernel(), random_state=0)',
# 'tree.DecisionTreeRegressor()',
# 'neural_network.MLPClassifier(alpha=0.01, random_state=1)']
#
#
# SocLearner.trainModelsIndividual(folderPath, modelOutputFolder, modelTypes)]]
#
# import threading
# from threading import Thread
# # #
G2 = RandomSocialGraphAdvanced(labelSplit=[20,40,60],connectionPercentageWithMatchedNodes=30,connectionPercentageWithMatchedNodesWithRandomness=0.5,explorationProbability=0.3,addTraidtionalFeatures=True,additionalFeatureLen=2, npDistFunc=['np.random.randint(10, high=20)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=1,mutualPreferenceIntensity=[3,2,1],genFeaturesFromSameDistforAllLabel=False)
G2.mutateDNA(mutationIntensity=0.9,mutatePreference=True)
G2.mutateDNAandSocialise(mutationIntensity=0.9)
G2.socialise()
#
#
# # G2.writeFileA('D:\\testVirtualSoc\\testAdjFile.txt')
# #
WriteToFile(G2).easySaveEverything('D:\\VirtualSocGCN\soc1')
# # #
# G2 = RandomSocialGraphAdvanced(labelSplit=[500,1000,1500],connectionPercentageWithMatchedNodes=30,connectionPercentageWithMatchedNodesWithRandomness=0.5,explorationProbability=0.3,addTraidtionalFeatures=True,additionalFeatureLen=2, npDistFunc=['np.random.randint(10, high=20)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=1,mutualPreferenceIntensity=[3,2,1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
#
#
# # G2.mutateDNA(mutationIntensity=0.9,mutatePreference=True)
# # G2.mutateDNAandSocialise(mutationIntensity=0.9)
# # G2.socialise()
# #
#
# # G2.writeFileA('D:\\testVirtualSoc\\testAdjFile.txt')
# #
# WriteToFile(G2).easySaveEverything('D:\\VirtualSocGCN\\ShufledDNATest2\\')
#
#
# G2 = RandomSocialGraphAdvanced(labelSplit=[5,10,15],connectionPercentageWithMatchedNodes=30,connectionPercentageWithMatchedNodesWithRandomness=0.5,explorationProbability=0.3,addTraidtionalFeatures=True,additionalFeatureLen=2, npDistFunc=['np.random.randint(10, high=20)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=1,mutualPreferenceIntensity=[3,2,1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
#
# WriteToFile(G2).easySaveEverything('D:\\VirtualSocGCN\\ShuffledDNATestSmall\\')
#################################
# G2 = RandomSocialGraphAdvanced(labelSplit=[10000,20000,30000],connectionPercentageWithMatchedNodes=30,connectionPercentageWithMatchedNodesWithRandomness=0.5,explorationProbability=0.3,addTraidtionalFeatures=True,additionalFeatureLen=2, npDistFunc=['np.random.randint(10, high=20)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=1,mutualPreferenceIntensity=[3,2,1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
#
# WriteToFile(G2).easySaveEverything('D:\\VirtualSocGCN\\ShufledDNATestGiant1\\')
#
# G2 = RandomSocialGraphAdvanced(labelSplit=[10000,20000,30000],connectionPercentageWithMatchedNodes=30,connectionPercentageWithMatchedNodesWithRandomness=0.5,explorationProbability=0.3,addTraidtionalFeatures=True,additionalFeatureLen=2, npDistFunc=['np.random.randint(10, high=20)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=12,mutualPreferenceIntensity=[0.9,0.6,0.3],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
#
# WriteToFile(G2).easySaveEverything('D:\\VirtualSocGCN\\ShufledDNATestGiant2\\')
#
# dir = 'D:\\VirtualSocGCN\\'
# os.makedirs(dir+'ShuffledDNAFeatureBased1')
# G2 = RandomSocialGraphAdvanced(labelSplit=[2000,4000,6000],connectionPercentageWithMatchedNodes=30,connectionPercentageWithMatchedNodesWithRandomness=0.5,explorationProbability=0.3,addTraidtionalFeatures=True,additionalFeatureLen=2, npDistFunc=['np.random.randint(10, high=20)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=0.3,mutualPreferenceIntensity=[0.3,0.3,0.3],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
#
# WriteToFile(G2).easySaveEverything('D:\\VirtualSocGCN\\ShuffledDNAFeatureBased1\\')
#
# dir = 'D:\\VirtualSocGCN\\'
# os.makedirs(dir+'ShuffledDNAFeatureBased2')
# G2 = RandomSocialGraphAdvanced(labelSplit=[2000,4000,6000],connectionPercentageWithMatchedNodes=15,connectionPercentageWithMatchedNodesWithRandomness=0.7,explorationProbability=0.7,addTraidtionalFeatures=True,additionalFeatureLen=2, npDistFunc=['np.random.randint(10, high=20)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=0.3,mutualPreferenceIntensity=[0.3,0.3,0.3],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
#
# WriteToFile(G2).easySaveEverything('D:\\VirtualSocGCN\\ShuffledDNAFeatureBased2\\')
# os.makedirs('D:\\VirtualSocGCN\\ShuffledDNAFeatureBased4')
# G2 = RandomSocialGraphAdvanced(labelSplit=[500,1000,1500,2000,2500],connectionPercentageWithMatchedNodes=50,connectionPercentageWithMatchedNodesWithRandomness=0.3,explorationProbability=0.7,addTraidtionalFeatures=True,additionalFeatureLen=20, npDistFunc=['np.random.randint(10, high=20)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=0.1,mutualPreferenceIntensity=[0.1,0.1,0.1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
#
# WriteToFile(G2).easySaveEverything('D:\\VirtualSocGCN\\ShuffledDNAFeatureBased4\\')
# file = 'D:\\VirtualSocGCN\\OptimisedNewNew\\'
# os.makedirs(file)
# G2 = RandomSocialGraphAdvanced(labelSplit=[1000,2000,3000],connectionPercentageWithMatchedNodes=15,connectionPercentageWithMatchedNodesWithRandomness=0.7,explorationProbability=0.7,addTraidtionalFeatures=False,additionalFeatureLen=100, npDistFunc=['np.random.randint(10, high=20)'],popularityPreferenceIntensity=0.1,mutualPreferenceIntensity=[0.9,0.1,0.1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False,useGPU = True)
#
# WriteToFile(G2).easySaveEverything(file)
# file = 'D:\\VirtualSocGCN\\OptimisedNewNew2\\'
# os.makedirs(file)
# G2 = RandomSocialGraphAdvanced(labelSplit=[1000,2000,3000],connectionPercentageWithMatchedNodes=15,connectionPercentageWithMatchedNodesWithRandomness=0.7,explorationProbability=0.7,addTraidtionalFeatures=False,additionalFeatureLen=500, npDistFunc=['np.random.randint(10, high=20)'],popularityPreferenceIntensity=0.1,mutualPreferenceIntensity=[0.9,0.1,0.1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False,useGPU = True)
#
# WriteToFile(G2).easySaveEverything(file)
if __name__ == '__main__':
# multiprocessing.set_start_method('forkserver')
#
# file = '/home/akanda/virtualSocNetworks/network10/'
# os.makedirs(file)
#
# G2 = RandomSocialGraphAdvanced(labelSplit=[1000,2000,3000],connectionPercentageWithMatchedNodes=30,connectionPercentageWithMatchedNodesWithRandomness=1,explorationProbability=0.5,addTraidtionalFeatures=False,additionalFeatureLen=50, npDistFunc=['np.random.randint(10, high=20)'],popularityPreferenceIntensity=0.1,mutualPreferenceIntensity=[0.9,0.1,0.1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False,useGPU = True,numberofProcesses=8,createInGPUMem=True)
#
# WriteToFile(G2).easySaveEverything(file)
file = 'H:/Networksim_test_multiprocess/'
# os.makedirs(file)
params = SalibPreprocessGetParamsForSobol(numberOfSamples=1000,folderPathToSaveParamsAndProblem=
'H:/Networksim_test_multiprocess/',labelSplit=[100, 200, 300], npDistFunc=['np.random.randint(18, high=80)',
'np.random.binomial(2, 0.5)'],bounds=[[0.1,1.0], [0.1, 10], [1, 80],[0.7, 0.9], [0.3, 0.6],[0.1, 0.2]] )
if __name__ == '__main__':
Simulate(processes=6,params=params,evalParam='graph.socialise()')
G2 = RandomSocialGraphAdvanced(labelSplit=[100,200,300,400],connectionPercentageWithMatchedNodes=5,connectionPercentageWithMatchedNodesWithRandomness=1,explorationProbability=0.3,addTraidtionalFeatures=False,additionalFeatureLen=50, npDistFunc=['np.random.randint(3, high=500)'],popularityPreferenceIntensity=0.5,mutualPreferenceIntensity=[0.9,0.3,0.1],genFeaturesFromSameDistforAllLabel=False,keepHistory=True,useGPU = True,numberofProcesses=None,createInGPUMem=True)
G2.mutateDNA(mutationIntensity=0.0001, mutatePreference=True,mutatePreferenceProbability=True)
G2.mutateDNA(mutationIntensity=0.01, mutatePreference=True,mutatePreferenceProbability=True)
#
# G2.socialise()
# G2.socialise()
# G2.socialise()
# WriteToFile(G2).easySaveEverything(file)
# file = 'H:/testEvolution2/'
# os.makedirs(file)
#
# G2 = RandomSocialGraphAdvanced(labelSplit=[250, 500, 750, 1000], connectionPercentageWithMatchedNodes=10,
# connectionPercentageWithMatchedNodesWithRandomness=1, explorationProbability=0.3,
# addTraidtionalFeatures=False, additionalFeatureLen=1000,
# npDistFunc=['np.random.randint(3, high=500)'], popularityPreferenceIntensity=0.5,
# mutualPreferenceIntensity=[0.9, 0.3, 0.1], genFeaturesFromSameDistforAllLabel=False,
# keepHistory=False, useGPU=False, numberofProcesses=None, createInGPUMem=False)
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# WriteToFile(G2).easySaveEverything(file)
#
# #
# file = '/home/akanda/virtualSocNetworks/networkNew6/'
# os.makedirs(file)
#
# G2 = RandomSocialGraphAdvanced(labelSplit=[500,1000,1500,2000],connectionPercentageWithMatchedNodes=30,connectionPercentageWithMatchedNodesWithRandomness=1,explorationProbability=0.3,addTraidtionalFeatures=False,additionalFeatureLen=1000, npDistFunc=['np.random.randint(3, high=500)'],popularityPreferenceIntensity=0.5,mutualPreferenceIntensity=[3,2,1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False,useGPU = True,numberofProcesses=None,createInGPUMem=True)
# G2.socialise()
# WriteToFile(G2).easySaveEverything(file)
#
# file = '/home/akanda/virtualSocNetworks/networkNew7/'
# os.makedirs(file)
#
# G2 = RandomSocialGraphAdvanced(labelSplit=[500,1000,1500,2000],connectionPercentageWithMatchedNodes=30,connectionPercentageWithMatchedNodesWithRandomness=1,explorationProbability=0.3,addTraidtionalFeatures=False,additionalFeatureLen=1000, npDistFunc=['np.random.randint(3, high=500)'],popularityPreferenceIntensity=1,mutualPreferenceIntensity=[3,2,1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False,useGPU = True,numberofProcesses=None,createInGPUMem=True)
# G2.socialise()
# WriteToFile(G2).easySaveEverything(file)
#
# file = '/home/akanda/virtualSocNetworks/networkNew8/'
# os.makedirs(file)
#
# G2 = RandomSocialGraphAdvanced(labelSplit=[500, 1000, 1500, 2000], connectionPercentageWithMatchedNodes=30,
# connectionPercentageWithMatchedNodesWithRandomness=1, explorationProbability=0.3,
# addTraidtionalFeatures=False, additionalFeatureLen=1000,
# npDistFunc=['np.random.randint(3, high=500)'], popularityPreferenceIntensity=2,
# mutualPreferenceIntensity=[3, 2, 1], genFeaturesFromSameDistforAllLabel=False,
# keepHistory=False, useGPU=True, numberofProcesses=None, createInGPUMem=True)
# G2.socialise()
# WriteToFile(G2).easySaveEverything(file)
#
# file = 'D:\\VirtualSocGCN\\test\\'
# os.makedirs(file)
# G2 = RandomSocialGraphAdvanced(labelSplit=[250,500],connectionPercentageWithMatchedNodes=10,connectionPercentageWithMatchedNodesWithRandomness=0.5,explorationProbability=0.1,addTraidtionalFeatures=True,additionalFeatureLen=3, npDistFunc=['np.random.randint(10, high=1500)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=2,mutualPreferenceIntensity=[3,2,1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
# WriteToFile(G2).easySaveEverything(file)
#
# file = 'D:\\VirtualSocGCN\\test11\\'
# # os.makedirs(file)
# G2 = RandomSocialGraphAdvanced(labelSplit=[5,10,15],dna='auto',connectionPercentageWithMatchedNodes=95,connectionPercentageWithMatchedNodesWithRandomness=0.15,explorationProbability=0.5,addTraidtionalFeatures=True,additionalFeatureLen=50, npDistFunc=['np.random.randint(10, high=20)','np.random.randint(1, high=10)'],popularityPreferenceIntensity=0.5,mutualPreferenceIntensity=[3,2,1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
# G2.socialise()
#
# WriteToFile(G2).easySaveEverything(file)
# file = 'D:\\VirtualSocGCN\\test15\\'
# os.makedirs(file)
# G2 = RandomSocialGraphAdvanced(labelSplit=[1000,2000,3000],dna='auto',connectionPercentageWithMatchedNodes=50,connectionPercentageWithMatchedNodesWithRandomness=0.15,explorationProbability=0.5,addTraidtionalFeatures=True,additionalFeatureLen=1500, npDistFunc=['np.random.randint(1, high=300)','np.random.randint(1, high=10)'],popularityPreferenceIntensity=0.5,mutualPreferenceIntensity=[0.9,0.6,0.3],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
# G2.socialise()
# WriteToFile(G2).easySaveEverything(file)
# file = 'D:\\VirtualSocGCN\\test13\\'
# os.makedirs(file)
# G2 = RandomSocialGraphAdvanced(labelSplit=[2000,4000,6000],dna='autoWeightless',connectionPercentageWithMatchedNodes=95,connectionPercentageWithMatchedNodesWithRandomness=0.15,explorationProbability=0.5,addTraidtionalFeatures=True,additionalFeatureLen=1500, npDistFunc=['np.random.randint(1, high=300)','np.random.randint(1, high=10)'],popularityPreferenceIntensity=2,mutualPreferenceIntensity=[3,2,1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
#
#
# WriteToFile(G2).easySaveEverything(file)
# file = 'D:\\VirtualSocGCN\\ShuffledDNAFeatureBased9\\'
# os.makedirs(file)
# G2 = RandomSocialGraphAdvanced(labelSplit=[500,1000,1500,2000,3000],connectionPercentageWithMatchedNodes=1,connectionPercentageWithMatchedNodesWithRandomness=0.3,explorationProbability=0.5,addTraidtionalFeatures=True,additionalFeatureLen=100, npDistFunc=['np.random.randint(10, high=1500)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=0.5,mutualPreferenceIntensity=[0.9,0.3,0.1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# WriteToFile(G2).easySaveEverything(file)
# file = 'D:\\VirtualSocGCN\\ShuffledDNAFeatureBased10\\'
# os.makedirs(file)
# G2 = RandomSocialGraphAdvanced(labelSplit=[500,1000,1500,2000,3000,4000,5000],connectionPercentageWithMatchedNodes=1,connectionPercentageWithMatchedNodesWithRandomness=0.3,explorationProbability=0.5,addTraidtionalFeatures=True,additionalFeatureLen=100, npDistFunc=['np.random.randint(10, high=1500)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=0.5,mutualPreferenceIntensity=[0.9,0.3,0.1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# G2.socialise()
# WriteToFile(G2).easySaveEverything(file)
# file = 'D:\\VirtualSocGCN\\ShuffledDNAFeatureBased11\\'
# os.makedirs(file)
# G2 = RandomSocialGraphAdvanced(labelSplit=[500,1000,1500,2000,3000,4000,5000],connectionPercentageWithMatchedNodes=90,connectionPercentageWithMatchedNodesWithRandomness=0.3,explorationProbability=0.1,addTraidtionalFeatures=True,additionalFeatureLen=100, npDistFunc=['np.random.randint(10, high=1500)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=0.5,mutualPreferenceIntensity=[0.9,0.3,0.1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
# G2.socialise()
# G2.socialise()
#
# WriteToFile(G2).easySaveEverything(file)
###########################################
#
# G2 = RandomSocialGraphAdvanced(labelSplit=[500,1000,1500],connectionPercentageWithMatchedNodes=30,connectionPercentageWithMatchedNodesWithRandomness=0.5,explorationProbability=0.3,addTraidtionalFeatures=True,additionalFeatureLen=2, npDistFunc=['np.random.randint(10, high=20)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=1,mutualPreferenceIntensity=[3,2,1],genFeaturesFromSameDistforAllLabel=False,keepHistory=False)
#
#
# G2.mutateDNA(mutationIntensity=0.9,mutatePreference=True)
# G2.mutateDNAandSocialise(mutationIntensity=0.9)
# G2.socialise()
# #
#
# # G2.writeFileA('D:\\testVirtualSoc\\testAdjFile.txt')
# #
# WriteToFile(G2).easySaveEverything('D:\\VirtualSocGCN\\soc4\\')
# #
#
#
#
# #
# G2 = RandomSocialGraphAdvanced(labelSplit=[500,1000,1500],connectionPercentageWithMatchedNodes=30,connectionPercentageWithMatchedNodesWithRandomness=0.5,explorationProbability=0.3,addTraidtionalFeatures=True,additionalFeatureLen=2, npDistFunc=['np.random.randint(10, high=20)','np.random.binomial(2, 0.5)'],popularityPreferenceIntensity=1,mutualPreferenceIntensity=[3,2,1],genFeaturesFromSameDistforAllLabel=False)
#
#
# G2.mutateDNA(mutationIntensity=0.9,mutatePreference=True)
# G2.mutateDNAandSocialise(mutationIntensity=0.9)
# G2.socialise()
#
#
# # G2.writeFileA('D:\\testVirtualSoc\\testAdjFile.txt')
# #
# WriteToFile(G2).easySaveEverything('D:\\VirtualSocGCN\soc2')
# # np.random.beta()
# test = ['np.random.beta(5,1)','binomial(10,0.5)']
#
#
# # popularityPreferenceIntensityV = np.arange(1, 10, 1)
# # popularityPreferenceIntensityV=popularityPreferenceIntensityV.tolist()
# if __name__ == '__main__':
# # pool = Pool(processes=20)
# # pool.map_async(simulateNetworksEasy2('D:\\newVirtualSim1\\'))
# # pool.map_async(simulateNetworksEasy3('D:\\newVirtualSim2\\'))
#
# t1 = Thread(target=simulateNetworksEasy2('D:\\newVirtualSim1\\'))
# t2 = Thread(target=simulateNetworksEasy3('D:\\newVirtualSim2\\'))
# t1.start()
# t2.start()
# simulateNetworksEasy2('D:\\VirtualSocEP\\')
# simulateNetworksEasyExplorationProbabilityTest('D:\\VirtualSocEP\\')
# simulateNetworksEasyConnectionPercentageWithMatchedNodesTest('D:\\VirtualSocPCMN\\')
# simulateNetworksEasypopularityPreferenceIntensityTest1('D:\\VirtualSocPP1\\')
# simulateNetworksEasypopularityPreferenceIntensityTest2('D:\\VirtualSocPP2\\')
# simulateNetworksEasymutualPreferenceIntensityTest1('D:\\VirtualSocMP1\\')
# simulateNetworksEasymutualPreferenceIntensityTest2('D:\\VirtualSocMP2\\')
# t = 0.0123
# print(str(" %6.5f " %t
# problem = {
# 'num_vars': 6,
# 'names': ['explorationProbabilityV', 'popularityPreferenceIntensityV', 'connectionPercentageWithMatchedNodesV', 'mutualPreferenceIntensityV2', 'mutualPreferenceIntensityV3', 'mutualPreferenceIntensityV4'],
# 'bounds': [[0.0,1.0],
# [0.1, 10],
# [1, 80],
# [0.7, 0.9],
# [0.3, 0.6],
# [0.1, 0.2]]
# }
#
# param_values = saltelli.sample(problem, 1)
#
#
# simulateNetworks(param_values=param_values,folderPath='D:\\sensitivityAnalaysisVirtualSoc\\')