示例#1
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 def __init__(self, dist_dir):
     Pmf.__init__(self)
     self.dist_dir = dist_dir
     self.pmf = None
     self.infs_data = {}
     self.json_data = {}
     self.dist_file = ''
示例#2
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    def __init__(self, hypos):
        """Initialize self.

        hypos: sequence of string bowl IDs
        """
        Pmf.__init__(self)
        for hypo in hypos:
            self.Set(hypo, 1)
        self.Normalize()
示例#3
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    def __init__(self, hypos):
        """Initialize the distribution.

        hypos: sequence of hypotheses
        """
        Pmf.__init__(self)
        for hypo in hypos:
            self.Set(hypo, 1)
        self.Normalize()
    def __init__(self, hypos):
        """Initialize self.

        hypos: sequence of string bowl IDs
        """
        Pmf.__init__(self)
        for hypo in hypos:
            self.Set(hypo, 1)
        self.Normalize()
示例#5
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 def __init__(self, hypos, alpha=1.0):
     Pmf.__init__(self)
     '''
     父类对prior的概率分布采用的是平均分布(uniform distribution)
     此处改写prior的概率分布为幂律(power law distribution)
     '''
     for hypo in hypos:
         self.Set(hypo, hypo**(-alpha))
     self.Normalize()
示例#6
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 def __init__(self, bowl1, bowl2):
     """Constructor"""
     self.bowl1 = bowl1
     self.bowl2 = bowl2
     Pmf.__init__(self)
     self.Set(bowl1.bowl_name, 1)
     self.Set(bowl2.bowl_name, 1)
     self.Normalize()
     self.set_mixes()
 def __init__(self, hypos):
     Pmf.__init__(self)
     for hypo in hypos:
         self.Set(hypo, 1)
     self.Normalize()
     self.mixes = {
                   'Bowl 1':dict(vanilla=0.75,    chocolate=0.25),
                   'Bowl 2':dict(vanilla=0.5,    chocolate=0.5)
                   }
示例#8
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    def __init__(self, hypos):
        """Initialize the distribution.

        hypos: sequence of hypotheses
        """
        Pmf.__init__(self)
        for hypo in hypos:
            self.Set(hypo, 1)
        self.Normalize()
示例#9
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  def __init__(self, hypos, bowls):
    """Initialize self.

    hypos: sequence of string bowl IDs
    """
    Pmf.__init__(self)
    for hypo in hypos:
      self.Set(hypo, 1)
    self.Normalize()
    self.bowls = dict()
    for bowl in bowls.keys():
      self.bowls[bowl] = Bowl(bowls[bowl])
示例#10
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    def __init__(self, hypos):
        """Initialize self.

        hypos: sequence of string bowl IDs
        """

        Pmf.__init__(self)
        for hypo in hypos:
            self.Set(hypo, 1)
        self.Normalize()

        bowl1 = Bowl(vanilla=30, chocolate=10)
        bowl2 = Bowl(vanilla=20, chocolate=20)

        self.bowls = {'Bowl 1': bowl1, 'Bowl 2': bowl2}
示例#11
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    def MakePmf(self, steps=101, label=None):
        """Returns a Pmf of this distribution.

        Note: Normally, we just evaluate the PDF at a sequence
        of points and treat the probability density as a probability
        mass.

        But if alpha or beta is less than one, we have to be
        more careful because the PDF goes to infinity at x=0
        and x=1.  In that case we evaluate the CDF and compute
        differences.

        The result is a little funny, because the values at 0 and 1
        are not symmetric.  Nevertheless, it is a reasonable discrete
        model of the continuous distribution, and behaves well as
        the number of values increases.
        """
        if label is None and self.label is not None:
            label = self.label

        if self.alpha < 1 or self.beta < 1:
            cdf = self.MakeCdf()
            pmf = cdf.MakePmf()
            return pmf

        xs = [i / (steps - 1) for i in range(steps)]
        probs = [self.EvalPdf(x) for x in xs]
        pmf = Pmf(dict(zip(xs, probs)), label=label)
        return pmf
示例#12
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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'))
示例#13
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# -*- coding: utf-8 -*-
"""
Created on Tue Apr 17 12:08:20 2018

@author: QJ
"""

###Think Bayes
from thinkbayes2 import Pmf
pmf = Pmf()
for x in [1, 2, 3, 4, 5, 6]:
    pmf.Set(x, 1 / 6.0)
pmf.Set('Bowl1', 0.5)
pmf.Set('Bowl2', 0.5)
pmf.Mult('Bowl1', 0.75)
pmf.Mult('Bowl2', 0.5)
print pmf.Prob('Bowl 1')
print(pmf)
示例#14
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# 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'))
示例#15
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 def __init__(self, hypos, priors):
     Pmf.__init__(self)
     '''Set the priors (for each H, the corrisponding p(H)'''
     for i in range(len(hypos)):
         self.Set(hypos[i],priors[i])
     self.Normalize()
示例#16
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 def __init__(self, hypos, alpha=None):
     Pmf.__init__(self, hypos)
     if alpha is not None:
         for hypo in hypos:
             self.Set(hypo, hypo**(-alpha))
         self.Normalize()
示例#17
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 def __init__(self, sides):
     Pmf.__init__(self)
     for x in range(1, sides + 1):
         self.Set(x, 1)
     self.Normalize()
示例#18
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#!/usr/env python
import sys
sys.path.append("../lib/ThinkBayes2/code/")
from thinkbayes2 import Pmf

pmf = Pmf()
#Prior Dist
pmf.Set('Bowl1', 0.5)
pmf.Set('Bowl2', 0.5)
print(pmf)

#Update based on new data
pmf.Mult('Bowl1', 0.75)
pmf.Mult('Bowl2', 0.5)
print(pmf)

#Hypotheses are mutally exclusive and collectively exhaustive, we can renormalize!
pmf.Normalize()
print(pmf) #posterior distribution

#book covers a more complicated implementation, but it is the same as Monty

print("Finished")
示例#19
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from __future__ import print_function, division
import sys
sys.path.append("../lib/ThinkBayes2/code/")
from thinkbayes2 import Suite, Pmf, SampleSum, MakeMixture
import thinkplot
from simulationDD02 import Die

pmf_dice = Pmf()
pmf_dice.Set(Die(4), 2)
pmf_dice.Set(Die(6), 3)
pmf_dice.Set(Die(8), 2)
pmf_dice.Set(Die(12), 1)
pmf_dice.Set(Die(20), 1)
pmf_dice.Normalize()
print(pmf_dice)

print("#################################################")
mix = Pmf()
for die, weight in pmf_dice.Items():
    for outcome, prob in die.Items():
        mix.Incr(outcome, weight * prob)

#Shorthand for above
#mix = MakeMixture(pmf_dice)
print(mix)

thinkplot.Hist(mix)
thinkplot.Save(root='bar',
               xlabel='Mixture over a set of dice',
               ylabel='Probability',
               formats=['pdf'])
示例#20
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from thinkbayes2 import Pmf

if __name__ == '__main__':
    txt = []
    with open('/Users/glenn/math/stats/ThinkBayes2/data/ch02_02_text_data.txt'
              ) as f:
        txt = f.read().split()

    print "txt = ", txt

    pmf = Pmf()
    for word in txt:
        pmf.Incr(word, 1)

    pmf.Normalize()

    pmf.Print()
示例#21
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from thinkbayes2 import Pmf
import thinkplot

d6 = Pmf()

for x in [1, 2, 3, 4, 5, 6]:
    d6[x] = 1

d6.Normalize()
print(d6)

twice = d6 + d6

# If the sum of two dice is greater than 3, then we update the dictionary
twice[2] = 0
twice[3] = 0
twice.Normalize()
print(twice)

thinkplot.Hist(twice)
thinkplot.show()
示例#22
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from thinkbayes2 import Pmf

if __name__ == '__main__':
    pmf = Pmf()
    for x in range(1, 7):
        pmf.Set(x, 1 / 6.0)

    pmf.Print()
示例#23
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#!/usr/env python
import sys
sys.path.append("../lib/ThinkBayes2/code/")
from thinkbayes2 import Pmf

pmf = Pmf()
for x in [1, 2, 3, 4, 5, 6]:
    pmf.Set(x, 1 / 6.0)
print(pmf)

words = Pmf()
f = open('alice.txt', 'r')
for line in f:
    for word in line.split():
        words.Incr(word.strip(), 1)
#print(words)
words.Normalize()
print(words)

print("Finished")
示例#24
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from thinkbayes2 import Pmf, Suite
import thinkplot

# Probability of each dice given that when rolled the output is 6

dice = Pmf(['4-sided', '6-sided', '8-sided', '12-sided'])

dice['4-sided'] *= 0
dice['6-sided'] *= 1 / 6
dice['8-sided'] *= 1 / 8
dice['12-sided'] *= 1 / 12
dice.Normalize()
print(dice)

suite = Suite([4, 6, 8, 12])
suite[4] *= 0
suite[6] *= 1 / 6
suite[8] *= 1 / 8
suite[12] *= 1 / 12
suite.Normalize()
print(suite)


class Dice(Suite):
    # hypo is the number if sides in the die
    # dat is the outcome

    def Likelihood(self, data, hypo):
        return 0 if data > hypo else 1 / hypo

示例#25
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class InfulsContribution(Pmf):
    def __init__(self, dist_dir):
        Pmf.__init__(self)
        self.dist_dir = dist_dir
        self.pmf = None
        self.infs_data = {}
        self.json_data = {}
        self.dist_file = ''

    def prior(self, json_data):
        self.pmf = Pmf()
        self.posterior(json_data)
        return self

    def posterior(self, json_data):
        self.set_json_data(json_data)
        self.update_infs_data()
        self.update()
        self.set_dist_file(json_data, self.dist_dir)
        self.output()
        return self

    def output(self):
        pmf = self.pmf
        pmf.Normalize()
        dist_data = self.make_result(pmf)
        self.save_file(dist_data)
        return self

    #  main nethods
    #  set jsondata as dict to  self.jsondata
    def set_json_data(self, json_data):
        with open(json_data, 'rb') as f:
            data = json.load(f)
        self.json_data = data

    #  update self.infs_data
    def update_infs_data(self):
        # print('sel', self.json_data)
        datas = self.json_data['analythics_data']
        click_sum = self.json_data['meta_data']['click_sum']
        # infs_data = 0
        # for d in datas:
        #     infs_data += int(datas[d]['prof_clicks'])
        for d in datas:
            if not self.infs_data.get(d):
                self.infs_data[d] = {'click_sum': 0, 'prof_clicks': 0}
            self.infs_data[d]['click_sum'] += click_sum
            self.infs_data[d]['prof_clicks'] += datas[d]['prof_clicks']

    #  set self dist_file
    def set_dist_file(self, src_file, dist_dir):
        dist_file = ''
        a = re.search(r'\d{4}-\d{2}-\d{2}', src_file)
        b = re.search(r'\d{8}', src_file)
        if a: dist_file = a.group()
        if b: dist_file = b.group()
        dist_file = os.path.join(dist_dir, f'result{dist_file}.json')
        self.dist_file = dist_file

    #  LiKelihood methods
    def liKelihood(self, infl):
        return (self.infs_data[infl]['prof_clicks'] /
                self.infs_data[infl]['click_sum'])

    #  update datas
    def update(self):
        data = self.json_data['analythics_data']
        click_sum = self.json_data['meta_data']['click_sum']
        print('d', data)
        # data = {'@yoshiki_ruby': data['@yoshiki_ruby']}
        [self.pmf.Incr(infl, self.liKelihood(infl)) for infl in data]

    #  create result json
    def make_result(self, pmf):
        d = pmf.Values()
        res_d = {k: pmf.Prob(k) for k in d}
        res_d = sorted(res_d.items(), key=lambda x: x[1], reverse=True)
        # print(res_d)
        return {
            v[0]: {
                "rating": v[1],
                "prof_clicks": self.infs_data[v[0]]['prof_clicks'],
                "click_sum": self.infs_data[v[0]]['click_sum'],
            }
            for v in res_d
        }

    #  output json
    def save_file(self, json_file):
        with open(self.dist_file, 'w') as f:
            json.dump(json_file, f, indent=2)
示例#26
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"""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'))
示例#27
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import sys
sys.path.insert(0, '/Users/carol/python/ThinkBayes2/thinkbayes2/')

import numpy as np
import matplotlib.pyplot as plt

from thinkbayes2 import Pmf, Suite, CredibleInterval, Beta

# PMF for 6-sided die
pmf = Pmf()
for x in [1, 2, 3, 4, 5, 6]:
    pmf.Set(x, 1 / 6)
print(pmf)

# How to build up a pmf from a list of strings
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)
示例#28
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 def prior(self, json_data):
     self.pmf = Pmf()
     self.posterior(json_data)
     return self
示例#29
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 def __init__(self, hypos):
     Pmf.__init__(self)
     for hypo in hypos:
         self.Set(hypo, 1)
     self.Normalize()
示例#30
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文件: cookie.py 项目: Roderich25/mac
from thinkbayes2 import Pmf

# Cookie Problem

# Prior 50%-50%
cookie = Pmf(['Bowl 1', 'Bowl 2'])

# p (Vanilla|Bowl1) = 30/40
cookie['Bowl 1'] *= 0.75
# p (Vanilla|Bowl2) = 20/40
cookie['Bowl 2'] *= 0.5

# Normalize, return values is p(D)
cookie.Normalize()

# Posteriors
print(cookie)

# Suppose we put the first cookie back, stir, choose again from the same bowl, and
# get a chocolate cookie

# p (Chocolate|Bowl1) = 10/40
cookie['Bowl 1'] *= 0.25
# p (Chocolate|Bowl2) = 20/40
cookie['Bowl 2'] *= 0.5

cookie.Normalize()
print(cookie)