コード例 #1
0
def main():
    pmf = Pmf()

    pmf.Set('Bowl 1', 0.5)
    pmf.Set('Bowl 2', 0.5)

    pmf.Mult('Bowl 1', 0.75)
    pmf.Mult('Bowl 2', 0.5)

    pmf.Normalize()

    print(pmf.Prob('Bowl 1'))
    print(pmf.Prob('Bowl 2'))
コード例 #2
0
ファイル: cookie.py プロジェクト: junghh21/ThinkBayes2-1
"""This file contains code for use with "Think Bayes",
by Allen B. Downey, available from greenteapress.com

Copyright 2012 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""

from __future__ import print_function, division

from thinkbayes2 import Pmf

pmf = Pmf()
pmf.Set('Bowl 1', 0.5)
pmf.Set('Bowl 2', 0.5)

pmf.Mult('Bowl 1', 0.75)
pmf.Mult('Bowl 2', 0.5)

pmf.Normalize()

print(pmf.Prob('Bowl 1'))
コード例 #3
0
pmf2 = Pmf()
for word in ['a', 'in', 'or', 'to', 'a', 'me', 'in']:
    pmf2.Incr(word, 1)
pmf2.Normalize()
print(pmf2)
print("Probability of letter a:", pmf2.Prob(
    'a'))  # Typo p12 print pmf.Prob('the') should read print(pmf.Prob('the'))

# PMF for the Cookie problem
pmf = Pmf()
# Prior:
pmf.Set("Bowl 1", 0.5)
pmf.Set("Bowl 2", 0.5)
# Posterior:
# First multiply prior by likelihood
pmf.Mult("Bowl 1", 0.75)
pmf.Mult("Bowl 2", 0.5)
# Then normalise (we can do this because the hypotheses are mutually exclusive and collectively exhaustive,
# i.e. only one of the hypotheses can be true and there can be no other hypothesis)
pmf.Normalize()
print(pmf)


# Create a Cookie class that inherits from Pmf and represents the Cookie problem
class Cookie(Pmf):

    proportions = {
        'Bowl 1': dict(vanilla=0.75, chocolate=0.25),
        'Bowl 2': dict(vanilla=0.5, chocolate=0.5),
    }
コード例 #4
0
# http://www.greenteapress.com/thinkbayes/thinkbayes.pdf
# http://thinkbayes.com/thinkbayes.py
# python -m pip install scipy numpy matplotlib pandas

from thinkbayes2 import Pmf

pmf = Pmf()

pmf.Set('tazon1', 0.5)
pmf.Set('tazon2', 0.5)

pmf.Mult('tazon1', 0.75)
pmf.Mult('tazon2', 0.5)

pmf.Normalize()

print(pmf.Prob('tazon1'))