def PredictiveDist(self, label='pred'):
"""Computes the distribution of goals scored in a game.
returns: new Pmf (mixture of Poissons)
"""
# TODO: fill this in
lam = 1
pred = thinkbayes2.MakePoissonPmf(lam, 15)
return pred
def _test1(show=0):
# 已知r, 求n的分布 即泊松分布
r = 150
# MakePoissonPmf: 存在一个上限(无极限), 需要归一化
pmf = thinkbayes2.MakePoissonPmf(r, 2 * r, 1)
if show:
thinkplot.Clf()
thinkplot.Pmf(pmf)
thinkplot.Show(title="test1",
xlabel='Event Count',
ylabel='Probality')
print("Total: ", pmf.Total())
return pmf
def MakeGoalPmf(suite, high=10):
"""Makes the distribution of goals scored, given distribution of lam.
suite: distribution of goal-scoring rate
high: upper bound
returns: Pmf of goals per game
"""
metapmf = thinkbayes2.Pmf()
for lam, prob in suite.Items():
pmf = thinkbayes2.MakePoissonPmf(lam, high)
metapmf.Set(pmf, prob)
mix = thinkbayes2.MakeMixture(metapmf, label=suite.label)
return mix
def PredRemaining(self, rem_time, score):
"""Plots the predictive distribution for final number of goals.
rem_time: remaining time in the game in minutes
score: number of goals already scored
"""
metapmf = thinkbayes2.Pmf(
) #PMF about PMFS. probabilities of pmf values
for lam, prob in self.Items(): #loop through probabilities of lamdas
lt = lam * rem_time / 60
pmf = thinkbayes2.MakePoissonPmf(lt, 20)
metapmf[pmf] = prob
mix = thinkbayes2.MakeMixture(metapmf)
mix += score
return mix
def PredRemaining(self, rem_time, score):
"""Plots the predictive distribution for final number of goals.
rem_time: remaining time in the game in minutes
score: number of goals already scored
"""
metapmf = thinkbayes2.Pmf()
for lam, prob in self.Items():
lt = lam * rem_time / 90
pred = thinkbayes2.MakePoissonPmf(lt, 15)
metapmf[pred] = prob
#thinkplot.Pdf(pred, color='gray', alpha=0.1, linewidth=0.5)
mix = thinkbayes2.MakeMixture(metapmf)
mix += score
thinkplot.Hist(mix)
thinkplot.Show()
def PredRemaining(self, rem_time, score):
"""Plots the predictive distribution for final number of goals.
rem_time: remaining time in the game in minutes
score: number of goals already scored
"""
# TODO: fill this in
# lam = goals / game
lam_total = 0
for lam, prob in self.Items():
goals_in_remaining_time = lam * rem_time / 90 # convert to goals in remaining time
lam_total += lt * prob
pmf = thinkbayes2.MakePoissonPmf(goals_in_remaining_time, 12)
pmf += score
thinkplot.Pmf(pmf)
thinkplot.Show()
def PredRemaining(self, rem_time, score):
"""Plots the predictive distribution for final number of goals.
rem_time: remaining time in the game in minutes
score: number of goals already scored
"""
metapmf = thinkbayes2.Pmf(
) #PMF about PMFS. probabilities of pmf values
for lam, prob in self.Items(): #loop through probabilities of lamdas
#print(lam,prob)
lt = lam * rem_time / 60
pmf = thinkbayes2.MakePoissonPmf(lt, 20)
#thinkplot.Pdf(pmf,linewidth=1,alpha=0.2,color='purple')
metapmf[pmf] = prob
mix = thinkbayes2.MakeMixture(metapmf)
mix += score #shift by 2 because we've already seen 2
return mix
def __init__(self, r, label='Detecter'):
pmf = thinkbayes2.MakePoissonPmf(r, 500, 2)
thinkbayes2.Suite.__init__(self, pmf, r)
