# Let's set up a dictionary that associates integer representations of our candidates with their names. candMap = dict() candMap[1] = "john" candMap[2] = "jane" candMap[3] = "jill" # Now that we have this candidate mapping and a list of Preference objects, we can construct a # Profile object. profile = Profile(candMap, preferences) # Let's print the output of some of the Profile object's methods. print(profile.getRankMaps()) print(profile.getWmg()) print(profile.getElecType()) print(profile.getReverseRankMaps()) print(profile.getOrderVectors()) # Now let's see which candidate would win an election were we to use the Plurality rule. # First, we construct a Mechanism object mechanism = mechanism.MechanismPlurality() # Let's print the ouputs of some of the Mechanism object's methods. print(mechanism.getWinners(profile)) print(mechanism.getMov(profile)) # We can also call margin of victory functions directly without constructing a mechanism object. # Let's print the margin of victory using Borda rule. print(mov.movBorda(profile)) # Now we are going to use MCMC sampling to approximate the Bayesian loss of each candiate.
# Let's set up a dictionary that associates integer representations of our candidates with their names. candMap = dict() candMap[1] = "john" candMap[2] = "jane" candMap[3] = "jill" # Now that we have this candidate mapping and a list of Preference objects, we can construct a # Profile object. profile = Profile(candMap, preferences) # Let's print the output of some of the Profile object's methods. print(profile.getRankMaps()) print(profile.getWmg()) print(profile.getElecType()) print(profile.getReverseRankMaps()) print(profile.getOrderVectors()) # Now let's see which candidate would win an election were we to use the Plurality rule. # First, we construct a Mechanism object mechanism = mechanism.MechanismPlurality() # Let's print the ouputs of some of the Mechanism object's methods. print(mechanism.getWinners(profile)) print(mechanism.getMov(profile)) # We can also call margin of victory functions directly without constructing a mechanism object. # Let's print the margin of victory using Borda rule. print(mov.movBorda(profile)) # Now we are going to use MCMC sampling to approximate the Bayesian loss of each candiate.
if __name__ == '__main__':
# Grab and read a file.
# os.chdir('D:\Social Choice\data\soc-3-hardcase')
os.chdir('D:\\Social Choice\\data\\toc')
# inputfile = input("Input File: ")
inputfile = "ED-00006-00000038.toc"
# inputfile = "M30N30-233.csv"
inf = open(inputfile, 'r')
cmap, rmaps, rmapscounts, nvoters = prefpy_io.read_election_file(inf)
print("cmap=",cmap)
print("rmaps=",rmaps)
print("rmapscounts=",rmapscounts)
print("nvoters=",nvoters)
profile = Profile(cmap, preferences=[])
Profile.importPreflibFile(profile, inputfile)
print(profile.getOrderVectors())
print(profile.getPreferenceCounts())
ordering = profile.getOrderVectors()
rankmaps = profile.getRankMaps()
print("rankmaps=",rankmaps)
print(min(ordering[0]))
# if(min(ordering[0])==0):
# print("ok")
# else:
# print("not ok")
stvwinners = mechanism.MechanismSTV().STVwinners(profile)
print("stvwinners=", stvwinners)
baldwinners = mechanism.MechanismBaldwin().baldwin_winners(profile)
print("baldwinners=", baldwinners)
coombswinners = mechanism.MechanismCoombs().coombs_winners(profile)
