#import seaborn as sns
import numpy as np
import pandas as pd
#votesim.logconfig.setInfo()
#votesim.logconfig.setDebug()
votesim.logconfig.setWarning()
outputfile = 'election3way.pkl'
types = [
'irv', 'score', 'star', 'plurality', 'smith_minimax', 'approval50',
'approval75'
]
metric_name = 'stats.regret.vsp'
v = spatial.SimpleVoters(0)
v.add_random(500, ndim=1)
c = spatial.Candidates(v, 0)
e = spatial.Election(v, None, seed=0, scoremax=5)
### Develop parametric study variables
distances = np.linspace(0, 3, 20)[1:]
skew = np.linspace(0, 1, 10)[1:-1]
offset = np.linspace(0, 0.5, 10)
### Loop through parameters
loop_product = itertools.product(distances, skew, offset)
data = []
for ii, (d, s, o) in enumerate(loop_product):
print(ii, end=',')
c.reset()
import matplotlib.pyplot as plt
#import seaborn as sns
import numpy as np
import pandas as pd
#votesim.logconfig.setInfo()
#votesim.logconfig.setDebug()
votesim.logconfig.setWarning()
types = ['irv', 'score', 'star', 'plurality', 'smith_minimax']
metric_name = 'stats.regret.vsp'
v = spatial.SimpleVoters(0, strategy='voter')
v.add_random(500, ndim=1)
c = spatial.Candidates(v, 0)
e = spatial.Election(v, None, seed=0, scoremax=5)
### Develop parametric study variables
distances = np.linspace(0, 2, 10)[1:]
skew = np.linspace(0,1, 10)[1:-1]
offset = np.linspace(0, 0.5, 10 )
### Loop through parameters
loop_product = itertools.product(distances, skew, offset)
for ii, (d, s, o) in enumerate(loop_product):
print(ii, end=',')
c.reset()
a = np.array([0, s, 1]) - 0.5
# -*- coding: utf-8 -*- import numpy as np import sys import votesim from votesim.votesystems import tools from votesim.models import vcalcs, spatial import matplotlib.pyplot as plt ### Create two simulations using 2 strategies voternum = 1000 candidates = [-0.3, 0.1, 0.4] candidates = np.atleast_2d(candidates).T v1 = spatial.SimpleVoters(seed=0, strategy='candidate') v1.add_random(voternum) c1 = spatial.Candidates(voters=v1, seed=0) c1.add(candidates) v1.calc_ratings(c1.candidates) v2 = spatial.SimpleVoters(seed=0, strategy='voter') v2.add_random(voternum) c2 = spatial.Candidates(voters=v2, seed=0) c1.add(candidates) v2.calc_ratings(c1.candidates) hist, bins = np.histogram(v1.voters, bins=20, density=True) bins = (bins[0:-1] + bins[1:]) / 2. for i in range(3):
#methods = votesim.votesystems.all_methods.keys()
methods = [
'plurality', 'irv', 'score5', 'star5', 'star10', 'smith_minimax',
'approval50', 'approval75', 'ranked_pairs'
]
filename = 'electionNd_data.pkl.zip'
cnums = np.arange(2, 8)
iters = itertools.product(strategies, ndims, cnums)
e = spatial.Election(None, None, seed=seed)
itercount = 0
for (strategy, ndim, cnum) in iters:
v = spatial.SimpleVoters(seed=seed, strategy=strategy)
v.add_random(numvoters, ndim=ndim)
itercount += 1
print(itercount)
for trial in range(trialnum):
c = spatial.Candidates(v, seed=trial)
c.add_random(cnum, sdev=1.5)
c.add_random(cnum, sdev=3)
e.set_models(voters=v, candidates=c)
for method in methods:
e.run(etype=method)
print('building dataframe')
df = e.dataframe()