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 makePlot(self, root='redline4'):
"""Makes a plot showing the mixture."""
thinkplot.clf()
# plot the MetaPmf
for pmf, prob in sorted(self.metaPmf.items()):
cdf = pmf.makeCdf().scale(1.0 / 60)
width = 2 / math.log(-math.log(prob))
thinkplot.plot(cdf.xs,
cdf.ps,
alpha=0.2,
linewidth=width,
color='blue',
label='')
# plot the mixture and the distribution based on a point estimate
thinkplot.prePlot(2)
#thinkplot.Cdf(self.point.MakeCdf(name='point').Scale(1.0/60))
thinkplot.cdf(self.mixture.makeCdf(name='mix').scale(1.0 / 60))
thinkplot.save(root=root,
xlabel='Wait time (min)',
ylabel='CDF',
formats=FORMATS,
axis=[0, 10, 0, 1])
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 makePlot(self):
"""Plot the CDFs."""
thinkplot.cdf(self.pmf_y.makeCdf())
thinkplot.cdf(self.prior_zb.makeCdf())
thinkplot.cdf(self.post_zb.makeCdf())
thinkplot.cdf(self.pmf_mean_zb.makeCdf())
thinkplot.show()
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 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.iteritems():
mu = thinkstats.mean(xs)
xs = thinkstats.jitter(xs, 1.3)
xs = [x - mu for x in xs]
cdf = thinkbayes.makeCdfFromList(xs)
thinkplot.cdf(cdf, label=labels[key])
thinkplot.show()
def MakeNormalModel(weights):
"""Plots a CDF with a Normal model.
weights: sequence
"""
cdf = thinkbayes2.Cdf(weights, label='weights')
mean, var = thinkbayes2.TrimmedMeanVar(weights)
std = math.sqrt(var)
print('n, mean, std', len(weights), mean, std)
xmin = mean - 4 * std
xmax = mean + 4 * std
xs, ps = thinkbayes2.RenderNormalCdf(mean, std, xmin, xmax)
thinkplot.plot(xs, ps, label='model', linewidth=4, color='0.8')
thinkplot.cdf(cdf)
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'])