def _class_sizes():
# start with the actual distribution of class sizes from the book
d = {
7: 8,
12: 8,
17: 14,
22: 4,
27: 6,
32: 12,
37: 8,
42: 3,
47: 2,
}
# form the pmf
pmf = _04_Pmf._make_pmf_from_dict(d, 'actual')
print('mean', pmf._mean())
print('var', pmf._var())
# compute the biased pmf
biased_pmf = _bias_pmf(pmf, 'observed')
print('mean', biased_pmf._mean())
print('var', biased_pmf._var())
# unbias the biased pmf
unbiased_pmf = _unbias_pmf(biased_pmf, 'unbiased')
print('mean', unbiased_pmf._mean())
print('var', unbiased_pmf._var())
# plot the Pmfs
_05_myplot._pmfs([pmf, biased_pmf])
_05_myplot._show(xlabel='Class size', ylabel='PMF')
def _plot_ages(resp):
"""Plot the distribution of ages."""
ages = [r.age for r in resp.records]
cdf = _13_Cdf._make_cdf_from_list(ages)
_05_myplot._clf()
_05_myplot._cdf(cdf)
_05_myplot._show()
def main():
results = _read_results()
speeds = _get_speeds(results)
pmf = _04_Pmf._make_pmf_from_list(speeds, 'speeds')
_05_myplot._pmf(pmf)
_05_myplot._show(title='PMF of running speed',
xlabel='speed (mph)',
ylabel='probability')
def _check_cdf2():
"""Compare chi2 values from the simulation with a chi-squared dist."""
df = 3
t = [_simulate_chi2() for i in range(1000)]
t2 = [scipy.stats.chi2.cdf(x, df) for x in t]
cdf = _13_Cdf._make_cdf_from_list(t2)
_05_myplot._cdf(cdf)
_05_myplot._show()
def main():
suite = _make_uniform_suite(0.0, 1.0, 1001)
evidence = 140, 110
_update(suite, evidence)
suite.name = 'posterior'
# plot the posterior distributions
_05_myplot._pmf(suite)
_05_myplot._show(title='Biased coin',
xlabel='P(heads)',
ylabel='Posterior probability')