def _make_figures(pmf, biased_pmf):
"""Makes figures showing the CDF of the biased and unbiased PMFs"""
cdf = _13_Cdf._make_cdf_from_pmf(pmf, 'unbiased')
print('unbiased median', cdf._percentile(50))
print('percent < 100', cdf._prob(100))
print('percent < 1000', cdf._prob(1000))
biased_cdf = _13_Cdf._make_cdf_from_pmf(biased_pmf, 'biased')
print('biased median', biased_cdf._percentile(50))
_05_myplot._clf()
_05_myplot._cdfs([cdf, biased_cdf])
_05_myplot._save(root='slashdot.logx',
xlabel='Number of friends/foes',
ylabel='CDF',
xscale='log')
def main():
results = _10_relay._read_results()
speeds = _10_relay._get_speeds(results)
# plot the distribution of actual speeds
pmf = _04_Pmf._make_pmf_from_list(speeds, 'actual speeds')
# myplot.Clf()
# myplot.Hist(pmf)
# myplot.Save(root='observed_speeds',
# title='PMF of running speed',
# xlabel='speed (mph)',
# ylabel='probability')
# plot the biased distribution seen by the observer
biased = _bias_pmf(pmf, 7.5, name='observed speeds')
_05_myplot._clf()
_05_myplot._hist(biased)
_05_myplot._save(root='observed_speeds',
title='PMF of running speed',
xlabel='speed (mph)',
ylabel='probability')
cdf = _13_Cdf._make_cdf_from_pmf(biased)
_05_myplot._clf()
_05_myplot._cdf(cdf)
_05_myplot._save(root='observed_speeds_cdf',
title='CDF of running speed',
xlabel='speed (mph)',
ylabel='cumulative probability')
def testMakePmfFromCdf(self):
t = [1, 2, 2, 3, 5]
pmf = _04_Pmf._make_pmf_from_list(t)
self.checkPmf(pmf)
cdf = _13_Cdf._make_cdf_from_pmf(pmf)
pmf2 = _04_Pmf._make_pmf_from_cdf(cdf)
self.checkPmf(pmf2)
def _plot_marginals(suite):
"""Plot the marginal distributions for a 2-D joint distribution."""
pmf_m, pmf_s = _compute_marginals(suite)
pyplot.clf()
pyplot.figure(1, figsize=(7, 4))
pyplot.subplot(1, 2, 1)
cdf_m = _13_Cdf._make_cdf_from_pmf(pmf_m, 'mu')
_05_myplot._cdf(cdf_m)
pyplot.xlabel('Mean height (cm)')
pyplot.ylabel('CDF')
pyplot.subplot(1, 2, 2)
cdf_s = _13_Cdf._make_cdf_from_pmf(pmf_s, 'sigma')
_05_myplot._cdf(cdf_s)
pyplot.xlabel('Std Dev height (cm)')
pyplot.ylabel('CDF')
_05_myplot._save(root='bayes_height_marginals_%s' % suite.name)
def _log_cdf_time_interval():
timeInterval = _calc_time_interval()
pmf = _04_Pmf._make_pmf_from_list(timeInterval, "baby birth interval")
cdf = _13_Cdf._make_cdf_from_pmf(pmf, "baby birth interval")
_05_myplot._clf()
_05_myplot._cdf(cdf, complement=True, xscale="linear", yscale="log")
_05_myplot._save(
root="baby_birth_interval_logccdf",
title="LogCCDF of baby birth interval",
xlabel="interval(minutes)",
ylabel="LogCCdf",
)
def _cdf_time_interval():
timeInterval = _calc_time_interval()
pmf = _04_Pmf._make_pmf_from_list(timeInterval, "baby birth interval")
_05_myplot._clf()
_05_myplot._hist(pmf)
_05_myplot._save(
root="baby_birth_interval_pmf",
title="PMF of baby birth interval",
xlabel="interval(minutes)",
ylabel="probability",
)
cdf = _13_Cdf._make_cdf_from_pmf(pmf)
_05_myplot._clf()
_05_myplot._cdf(cdf)
_05_myplot._save(
root="baby_birth_interval_cdf",
title="CDF of baby birth interval",
xlabel="interval(minutes)",
ylabel="cumulative probability",
)
def _plot_coef_variation(suites):
"""
Plot the posterior distributions for CV.
Args:
suites: map from label to Pmf of CVs.
"""
pyplot.clf()
pmfs = {}
for label, suite in suites.iteritems():
pmf = _compute_coef_variation(suite)
cdf = _13_Cdf._make_cdf_from_pmf(pmf, label)
_05_myplot._cdf(cdf)
pmfs[label] = pmf
_05_myplot._save(root='bayes_height_cv',
title='Coefficient of variation',
xlabel='cv',
ylabel='CDF')
print('female bigger', _prob_bigger(pmfs['female'], pmfs['male']))
print('male bigger', _prob_bigger(pmfs['male'], pmfs['female']))
