def ProcessScoresTeamwise(pairs):
"""Average number of goals for each team.
pairs: map from (team1, team2) to (score1, score2)
"""
# map from team to list of goals scored
goals_scored = {}
for key, entries in pairs.iteritems():
t1, t2 = key
for entry in entries:
g1, g2 = entry
goals_scored.setdefault(t1, []).append(g1)
goals_scored.setdefault(t2, []).append(g2)
# make a list of average goals scored
lams = []
for key, goals in goals_scored.iteritems():
lam = thinkbayes2.Mean(goals)
lams.append(lam)
# make the distribution of average goals scored
cdf = thinkbayes2.MakeCdfFromList(lams)
thinkplot.Cdf(cdf)
thinkplot.Show()
mu, var = thinkbayes2.MeanVar(lams)
print('mu, sig', mu, math.sqrt(var))
def __init__(self, prices, bids, diffs):
"""Construct the Player.
prices: sequence of prices
bids: sequence of bids
diffs: sequence of underness (negative means over)
"""
self.pdf_price = thinkbayes2.EstimatedPdf(prices)
self.cdf_diff = thinkbayes2.MakeCdfFromList(diffs)
mu = 0
sigma = numpy.std(diffs)
self.pdf_error = thinkbayes2.NormalPdf(mu, sigma)
def Summarize(xs):
"""Prints summary statistics from a sequence of values.
xs: sequence of values
"""
# print smallest and largest
xs.sort()
print('smallest', xs[:10])
print('largest', xs[-10:])
# print median and interquartile range
cdf = thinkbayes2.MakeCdfFromList(xs)
print(cdf.Percentile(25), cdf.Percentile(50), cdf.Percentile(75))
def GenerateCdf(n=1000, pc=0.35, lam1=0.79, lam2=5.0):
"""Generates a sample of RDTs and returns its CDF.
n: sample size
pc: probablity of negative growth
lam1: exponential parameter of positive growth
lam2: exponential parameter of negative growth
Returns: Cdf of generated sample
"""
xs = GenerateSample(n, pc, lam1, lam2)
cdf = thinkbayes2.MakeCdfFromList(xs)
return cdf
def MedianIPR(xs, p):
"""Computes the median and interpercentile range.
xs: sequence of values
p: range (0-1), 0.5 yields the interquartile range
returns: tuple of float (median, IPR)
"""
cdf = thinkbayes2.MakeCdfFromList(xs)
median = cdf.Percentile(50)
alpha = (1 - p) / 2
ipr = cdf.Value(1 - alpha) - cdf.Value(alpha)
return median, ipr
def PlotOutliers(samples):
"""Make CDFs showing the distribution of outliers."""
cdfs = []
for label, sample in samples.items():
outliers = [x for x in sample if x < 150]
cdf = thinkbayes2.MakeCdfFromList(outliers, label)
cdfs.append(cdf)
thinkplot.Clf()
thinkplot.Cdfs(cdfs)
thinkplot.Save(root='variability_cdfs',
title='CDF of height',
xlabel='Reported height (cm)',
ylabel='CDF')
def PlotCdfs(d, labels):
"""Plot CDFs for each sequence in a dictionary.
Jitters the data and subtracts away the mean.
d: map from key to sequence of values
labels: map from key to string label
"""
thinkplot.Clf()
for key, xs in d.items():
mu = thinkbayes2.Mean(xs)
xs = thinkbayes2.Jitter(xs, 1.3)
xs = [x - mu for x in xs]
cdf = thinkbayes2.MakeCdfFromList(xs)
thinkplot.Cdf(cdf, label=labels[key])
thinkplot.Show()
def TestCorrelation(cdf):
"""Tests the correlated generator.
Makes sure that the sequence has the right distribution and correlation.
"""
n = 10000
rho = 0.4
rdt_seq = CorrelatedGenerator(cdf, rho)
xs = [rdt_seq.next() for _ in range(n)]
rho2 = correlation.SerialCorr(xs)
print((rho, rho2))
cdf2 = thinkbayes2.MakeCdfFromList(xs)
thinkplot.Cdfs([cdf, cdf2])
thinkplot.Show()