def randon_main(g, folder):
g = gen(g)
metrics = extractor(g.copy(), 1)
graphBuilder(g, metrics[1], metrics[0], 'GiantRandon', folder, 1)
metrics = extractor(g.copy(), 2)
graphBuilder(g, metrics[1], metrics[0], 'EficRandon', folder, 2)
def processDay(data, day):
print('\nStarting Processing Day: ' + str(day + 1) + '\n')
# problem builder
sp = graphBuilder(data, day)
str_ucs = '\nUCS Result for Day ' + str(day + 1) + ': \n'
str_ida = '\nIDA* Result for Day ' + str(day + 1) + ': \n'
# UCS Segment
t0 = time.time()
availActions = searchProblem.ucs(sp)
t1 = time.time()
ucstDiff = t1 - t0
actualCost = sp.GetPathCost(availActions, sflag=True)
ucsCost = actualCost
# day result
ucsResult = (sp.visited, ucstDiff, sp.GetPathCost(availActions),
actualCost, availActions)
print('UCS Result: \n')
print('\t' + str(ucsResult))
str_ucs += ('\t' + str(ucsResult) + '\n')
ucsPerf = (sp.visited, ucstDiff)
# End of UCS Segment
# IDA* Segment
t0 = time.time()
availActions, costEstimation = searchProblem.idaStarCallback(sp)
t1 = time.time()
idatDiff = t1 - t0
actualCost = sp.GetPathCost(availActions, sflag=True)
idaCost = actualCost
idaResult = (sp.visited, idatDiff, costEstimation, actualCost,
availActions)
print('\nIDA Result: \n')
print('\t' + str(idaResult))
str_ida += ('\t' + str(idaResult) + '\n')
idaPerf = (sp.visited, idatDiff)
# End of IDA* Segment
print('\nDone for day: ' + str(day + 1) + '\n\n')
# return is for processing
return (ucsCost, idaCost, str_ucs, str_ida, ucsPerf, idaPerf)
def processDay(data, day):
print('\nStarting Processing Day: ' + str(day+1) + '\n')
# problem builder
sp = graphBuilder(data, day)
str_ucs = '\nUCS Result for Day ' + str(day+1) + ': \n'
str_ida = '\nIDA* Result for Day ' + str(day+1) + ': \n'
# UCS Segment
t0 = time.time()
availActions = searchProblem.ucs(sp)
t1 = time.time()
ucstDiff = t1-t0
actualCost = sp.GetPathCost(availActions, sflag = True)
ucsCost = actualCost
# day result
ucsResult = (sp.visited, ucstDiff, sp.GetPathCost(availActions), actualCost, availActions)
print('UCS Result: \n')
print('\t' + str(ucsResult))
str_ucs += ('\t' + str(ucsResult) + '\n')
ucsPerf = (sp.visited, ucstDiff)
# End of UCS Segment
# IDA* Segment
t0 = time.time()
availActions, costEstimation = searchProblem.idaStarCallback(sp)
t1 = time.time()
idatDiff = t1-t0
actualCost = sp.GetPathCost(availActions, sflag = True)
idaCost = actualCost
idaResult = (sp.visited, idatDiff, costEstimation, actualCost, availActions)
print('\nIDA Result: \n')
print('\t' + str(idaResult))
str_ida += ('\t' + str(idaResult)+'\n')
idaPerf = (sp.visited, idatDiff)
# End of IDA* Segment
print('\nDone for day: ' + str(day+1) + '\n\n')
# return is for processing
return (ucsCost, idaCost, str_ucs, str_ida, ucsPerf, idaPerf)
def grapher(g):
csv_gen(g.copy(), "Vinicios", 'giant', 1)
metrics = extractor(g.copy(), 1)
graphBuilder(g, metrics[1], metrics[0], 'GiantComponent',
"Vinicios/Graphs/Giant", 1)
csv_gen(g.copy(), "Vinicios", 'eff', 2)
metrics = extractor(g.copy(), 2)
graphBuilder(g, metrics[1], metrics[0], 'Efficiency',
"Vinicios/Graphs/Effc", 2)
csv_gen(g.copy(), "Vinicios", 'flow', 3)
metrics = extractor(g.copy(), 3)
graphBuilder(g, metrics[1], metrics[0], 'TotalFlow',
"Vinicios/Graphs/Flow", 3)
def main(network):
for net in network:
g = _main(net)
# csv_gen(g.copy(), 'csvOutPut', net)
metrics = extractor(g.copy(), 1)
graphBuilder(g, metrics[1], metrics[0], net,
'GeneratedGraph/GiantComponent', 1)
# csv_gen(g.copy(),'csvOutPut', net, 2)
metrics = extractor(g.copy(), 2)
graphBuilder(g, metrics[1], metrics[0], net,
'GeneratedGraph/Efficiency', 2)
# csv_gen(g.copy(),'csvOutPut', net, 3)
metrics = extractor(g.copy(), 3)
graphBuilder(g, metrics[1], metrics[0], net,
'GeneratedGraph/TotalFlow', 3)
# -*- coding: utf-8 -*-
"""
socialGamers.py
This part is designed to support the data found in Section 4.A of the report.
@author: realhire
"""
from graphBuilder import graphBuilder
gamerTopic = {'140mafia': 2634, 'spymaster': 2304, 'vampirebite': 0}
i = 21
print "Gathering day ", i, "'s data..."
m = graphBuilder("tc21.txt")
print "apply topic filer and build the graph..."
m.topic_filter(gamerTopic)
print "outputing target file..."
m.outputToGexf()
print "socialGamer info analyzed complete. Results in", m.outfile
'''
# This part is a demonstration of topic collector and degree filter
print "find today's top ten topic in: ", m.infile, "..."
m.todayTopTenHashtag()
print "Today's top ten hashtags are: ", m.todayTopic
print "processing reply, retweet and hashtag: ", m.infile, "..."
m.processReplyRetweetHashtag(m.todayTopic)
degreeRange = [0, float("inf")]
print "filter out nodes not in degree range: [", degreeRange[0], ", ", degreeRange[1], "]..."
# -*- coding: utf-8 -*-
"""
mvp.py
Find Twitter MVP and check whether their topic overlap with their follower/sub
scribers; topic
@author: realhire
"""
from graphBuilder import graphBuilder
i = 21
print "Gathering day ", str(i) + "'s data"
m = graphBuilder("tc30.txt")
print "Build the graph, all nodes included..."
m.processReplyRetweetHashtag()
print "Find today's MVP:..."
m.todayTopTenMVP()
for mvp in m.MVP:
print mvp, ':\tTopic: ', m.G.node[mvp]
""" ''' Singer Trey '''
interestedUser = '******'
print "Now we are interested in", interestedUser
print "This account's topics are:", list(m.G.node[interestedUser])
print "Filter out all user not connected to", interestedUser
m.connection_filter(interestedUser, removeTargetUser=True)
print "outputing target file..."
m.outputToGexf("Day"+str(i)+"MVP"+interestedUser+".gexf")
print "MVP connection info analyzed complete. Results in", m.outfile
"""
