def main():
hypos = numpy.linspace(0, 12, 201)
# start with a prior based on a pseudo observation
# chosen to yield the right prior mean
suite1 = Soccer(hypos, label='Germany')
suite1.Update(0.34)
suite2 = suite1.Copy(label='Argentina')
# update with the results of World Cup 2014 final
suite1.Update(1)
suite2.Update(0)
print('posterior mean Germany', suite1.Mean())
print('posterior mean Argentina', suite2.Mean())
# plot the posteriors
thinkplot.PrePlot(2)
thinkplot.Pdfs([suite1, suite2])
thinkplot.Show()
# TODO: compute posterior prob Germany is better than Argentina
# TODO: compute the Bayes factor of the evidence
# compute predictive distributions for goals scored in a rematch
pred1 = suite1.PredictiveDist(label='Germany')
pred2 = suite2.PredictiveDist(label='Argentina')
# plot the predictive distributions
thinkplot.PrePlot(2)
thinkplot.Pdfs([pred1, pred2])
thinkplot.Show()
def PlotBeliefs(self):
""" Plots prior and posterior beliefs to console
"""
thinkplot.Clf()
thinkplot.PrePlot(num=2)
thinkplot.Pdfs([self.prior, self.posterior])
thinkplot.decorate(xlabel='price ($)', ylabel='PMF')
def Specific_Character(House, Gender, Class, ksweep, lamsweep, Title=''):
"""Knits many function together to produce a prediction for a given house, gender and class
The house can be any key in hd, class can be 'Noble' or 'Small' or 'All' , and the gender can
be 'M' or 'F' or 'All'. This also needs to make a linspace for k and lambda, so ksweep and
lsweep are lists of the form [lower limit, upper limit, number of points]. You can also
choose what to title your graph."""
hd = PrepData() #Get the data
alive, dead = char_lists(hd, House, Gender,
Class) #Sort by alive/dead for given attributes
introductions, lifetimes = ages(alive, dead) #Get ages and lifespans
sf, haz = SurvivalHaz(introductions, lifetimes) #Use kaplan-meyer
lam = thinkbayes2.MakeUniformPmf(lamsweep[0], lamsweep[1],
lamsweep[2]) #Our uniform priors
k = thinkbayes2.MakeUniformPmf(ksweep[0], ksweep[1], ksweep[2])
k, lam = MakeDistr(introductions, lifetimes, k, lam) #Get our posterior
thinkplot.PrePlot(2)
thinkplot.Pdfs([k, lam])
plt.xlabel('Value')
plt.ylabel('Probability')
plt.title('Posterior Distributions')
print('If these distributions look chopped off, adjust kweep and lsweep')
thinkplot.Show()
mk = k.Mean()
ml = lam.Mean()
kl, kh = k.Percentile(5), k.Percentile(95)
ll, lh = lam.Percentile(5), lam.Percentile(95)
CredIntPlt(sf, kl, kh, ll, lh, House, mk, ml, Title)
plt.show()
def PlotBeliefs(self, root):
"""Plots prior and posterior beliefs.
root: string filename root for saved figure
"""
thinkplot.Clf()
thinkplot.PrePlot(num=2)
thinkplot.Pdfs([self.prior, self.posterior])
thinkplot.Save(root=root,
xlabel='price ($)',
ylabel='PMF',
formats=FORMATS)
def main():
exam = Exam()
alice = Sat(exam)
alice.label = 'alice'
alice.Update(780)
bob = Sat(exam)
bob.label = 'bob'
bob.Update(760)
print('Prob Alice is "smarter":', PmfProbGreater(alice, bob))
print('Prob Bob is "smarter":', PmfProbGreater(bob, alice))
thinkplot.PrePlot(2)
thinkplot.Pdfs([alice, bob])
thinkplot.Show(xlabel='x',
ylabel='Probability',
loc='upper left',
xlim=[0.7, 1.02])
def MakePlots(player1, player2):
"""Generates two plots.
price1 shows the priors for the two players
price2 shows the distribution of diff for the two players
"""
# plot the prior distribution of price for both players
thinkplot.Clf()
thinkplot.PrePlot(num=2)
pmf1 = player1.PmfPrice()
pmf1.label = 'showcase 1'
pmf2 = player2.PmfPrice()
pmf2.label = 'showcase 2'
thinkplot.Pdfs([pmf1, pmf2])
thinkplot.Save(root='price1',
xlabel='price ($)',
ylabel='PDF',
formats=FORMATS)
# plot the historical distribution of underness for both players
thinkplot.Clf()
thinkplot.PrePlot(num=2)
cdf1 = player1.CdfDiff()
cdf1.label = 'player 1'
cdf2 = player2.CdfDiff()
cdf2.label = 'player 2'
print('Player median', cdf1.Percentile(50))
print('Player median', cdf2.Percentile(50))
print('Player 1 overbids', player1.ProbOverbid())
print('Player 2 overbids', player2.ProbOverbid())
thinkplot.Cdfs([cdf1, cdf2])
thinkplot.Save(root='price2',
xlabel='diff ($)',
ylabel='CDF',
formats=FORMATS)
