"""This file contains code used in "Think Bayes",
by Allen B. Downey, available from greenteapress.com
Copyright 2014 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function, division
from variability import *
import thinkplot
import thinkbayes2
def RunEstimate(update_func, num_points=31, median_flag=False):
"""Runs the whole analysis.
update_func: which of the update functions to use
num_points: number of points in the Suite (in each dimension)
"""
d = ReadHeights(nrows=None)
labels = {1:'male', 2:'female'}
suites = {}
for key, xs in d.items():
label = labels[key]
print(label, len(xs))
Summarize(xs)
xs = thinkbayes2.Jitter(xs, 1.3)
mus, sigmas = FindPriorRanges(xs, num_points, median_flag=median_flag)
suite = Height(mus, sigmas, label)
suites[label] = suite
update_func(suite, xs)
print('MAP', suite.MaximumLikelihood())
suite1 = suites['male']
suite2 = suites['female']
mu1 = suite1.Marginal(0)
sigma1 = suite1.Marginal(1)
mu2 = suite2.Marginal(0)
sigma2 = suite2.Marginal(1)
diff = mu1 - mu2
sigma = (sigma1 + sigma2) / 2
pmf_d = diff / sigma
thinkplot.Cdf(pmf_d.MakeCdf())
thinkplot.Show(xlabel='# stddev between means',
ylabel='PMF')
def main():
random.seed(17)
func = UpdateSuite5
median_flag = (func == UpdateSuite5)
RunEstimate(func, median_flag=median_flag)
if __name__ == '__main__':
main()
