def makePosteriorPlot(suite):
"""Plots the posterior marginal distributions for alpha and beta.
suite: posterior joint distribution of location
"""
marginal_alpha = suite.marginal(0)
marginal_alpha.name = 'PMF(alpha)'
marginal_beta = suite.marginal(1)
marginal_beta.name = 'PMF(beta)'
print("\n(ALPHA, BETA): Shooter locations: ")
print('alpha CI', marginal_alpha.credibleInterval(50))
print('beta CI', marginal_beta.credibleInterval(50))
thinkplot.prePlot(num=2)
#thinkplot.Pmf(marginal_alpha)
#thinkplot.Pmf(marginal_beta)
thinkplot.cdf(thinkbayes.makeCdfFromPmf(marginal_alpha))
thinkplot.cdf(thinkbayes.makeCdfFromPmf(marginal_beta))
thinkplot.save('paintball2_posteriorMarginalDist_for_alpha_and_beta',
xlabel='Distance',
ylabel='Prob',
loc=4,
formats=FORMATS)
def plotCoefVariation(suites):
"""Plot the posterior distributions for CV.
suites: map from label to Pmf of CVs.
"""
thinkplot.clf()
thinkplot.prePlot(num=2)
pmfs = {}
for label, suite in suites.iteritems():
pmf = coefVariation(suite)
print('CV posterior mean', pmf.mean())
cdf = thinkbayes.makeCdfFromPmf(pmf, label)
thinkplot.cdf(cdf)
pmfs[label] = pmf
thinkplot.save(root='variability_cv',
xlabel='Coefficient of variation',
ylabel='Probability')
print('female bigger',
thinkbayes.pmfProbGreater(pmfs['female'], pmfs['male']))
print('male bigger', thinkbayes.pmfProbGreater(pmfs['male'],
pmfs['female']))
def plotPosteriors(self, other):
"""Plots posterior distributions of efficacy.
self, other: Sat objects.
"""
thinkplot.clf()
thinkplot.prePlot(num=2)
cdf1 = thinkbayes.makeCdfFromPmf(self, 'posterior %d' % self.score)
cdf2 = thinkbayes.makeCdfFromPmf(other, 'posterior %d' % other.score)
thinkplot.cdfs([cdf1, cdf2])
thinkplot.save(xlabel='efficacy',
ylabel='CDF',
axis=[0, 4.6, 0.0, 1.0],
root='sat_5_posteriors_eff',
formats=['pdf'])
def plotMarginals(suite):
"""Plots marginal distributions from a joint distribution.
suite: joint distribution of mu and sigma.
"""
thinkplot.clf()
pyplot.subplot(1, 2, 1)
pmf_m = suite.marginal(0)
cdf_m = thinkbayes.makeCdfFromPmf(pmf_m)
thinkplot.cdf(cdf_m)
pyplot.subplot(1, 2, 2)
pmf_s = suite.marginal(1)
cdf_s = thinkbayes.makeCdfFromPmf(pmf_s)
thinkplot.cdf(cdf_s)
thinkplot.show()
def calibrateDifficulty(self):
"""Make a plot showing the model distribution of raw scores."""
thinkplot.clf()
thinkplot.prePlot(num=2)
cdf = thinkbayes.makeCdfFromPmf(self.raw, name='data')
thinkplot.cdf(cdf)
efficacies = thinkbayes.makeGaussianPmf(0, 1.5, 3)
pmf = self.makeRawScoreDist(
efficacies) # mixture model of raw score, prob = p1 * p2
cdf = thinkbayes.makeCdfFromPmf(pmf, name='model')
thinkplot.cdf(cdf)
thinkplot.save(root='sat_2_calibrate',
xlabel='raw score',
ylabel='CDF',
formats=['pdf'])
def generateSampleGaps(self, n):
"""Generates a random sample of gaps seen by passengers.
n: sample size
Returns: sequence of values
"""
cdf_zb = thinkbayes.makeCdfFromPmf(self.pmf_zb)
sample = cdf_zb.sample(n)
return sample
def generateSampleWaitTimes(self, n):
"""Generates a random sample of wait times.
n: sample size
Returns: sequence of values
"""
cdf_y = thinkbayes.makeCdfFromPmf(self.pmf_y)
sample = cdf_y.sample(n)
return sample
def plotPriorDist(pmf):
"""Plot the prior distribution of p_correct.
pmf: prior
"""
thinkplot.clf()
thinkplot.prePlot(num=1)
cdf1 = thinkbayes.makeCdfFromPmf(pmf, 'prior')
thinkplot.cdf(cdf1)
thinkplot.save(root='sat_1_prior',
xlabel='p_correct',
ylabel='CDF',
formats=['pdf']) # ['pdf', 'eps'])
def main():
comparePriors()
dataset = [30, 60, 90]
thinkplot.clf()
thinkplot.prePlot(num=3)
for high in [500, 1000, 2000]:
suite = makePosterior(high, dataset, Train2)
print(high, suite.mean())
# TODO: doesn't work:
thinkplot.save(root='train3',
xlabel='Number of trains',
ylabel='Probability')
interval = percentile(suite, 5), percentile(suite, 95)
print(interval)
cdf = thinkbayes.makeCdfFromPmf(suite)
interval = cdf.percentile(5), cdf.percentile(95)
print(interval)