def winProb(p1,p2):
norm = scipy_norm()
cdf = norm.cdf
deltaMu = p1.skill[0] - p2.skill[0]
rsss = sqrt(p1.skill[1]**2 + p2.skill[1]**2)
print cdf(deltaMu/rsss)
plt.plot([x[1] for x in p1.skillHistory])
plt.plot([x[1] for x in p2.skillHistory])
plt.show()
def ProbabilityPositive(self):
mu, sigma = self.MuSigma()
return 1.0 - scipy_norm(loc=mu, scale=sigma).cdf(0.0)
"TrueSkill(TM): A Bayesian Skill Rating System".
"""
from __future__ import print_function
__author__ = "Doug Zongker <*****@*****.**>"
import sys
if sys.hexversion < 0x02060000:
print("requires Python 2.6 or higher")
sys.exit(1)
from scipy.stats.distributions import norm as scipy_norm
from math import sqrt
norm = scipy_norm()
pdf = norm.pdf
cdf = norm.cdf
icdf = norm.ppf # inverse CDF
# Update rules for approximate marginals for the win and draw cases,
# respectively.
def Vwin(t, e):
return pdf(t - e) / cdf(t - e)
def Wwin(t, e):
return Vwin(t, e) * (Vwin(t, e) + t - e)
"""
import math
import sys
import csv
from scipy.stats.distributions import norm as scipy_norm
from collections import defaultdict
import numpy as np
beta = 25.0
gamma = 1.0 / 12
STDEVS = 3
MEAN = 25.0
epsilon = 0.5
norm = scipy_norm()
def pdf(x):
return norm.pdf(x)
def cdf(x):
return norm.cdf(x)
def Vwin(t, e):
return pdf(t - e) / cdf(t - e)
def Wwin(t, e):
def ProbabilityPositive(self): mu, sigma = self.MuSigma() return 1.0 - scipy_norm(loc=mu, scale=sigma).cdf(0.0)
