def makeRawScoreDist(self, efficacies):
"""Makes the distribution of raw scores for given difficulty.
efficacies: Pmf of efficacy
"""
pmfs = thinkbayes.PMF()
for efficacy, prob in efficacies.items():
scores = self.pmfCorrect(efficacy)
pmfs.set(scores, prob)
mix = thinkbayes.makeMixture(pmfs)
return mix
def makeGoalTimePmf(suite):
"""Makes the distribution of time til first goal.
suite: distribution of goal-scoring rate
returns: Pmf of goals per game
"""
metaPmf = thinkbayes.PMF()
for lam, prob in suite.items():
pmf = thinkbayes.makeExponentialPmf(lam, high=2, n=2001)
metaPmf.set(pmf, prob)
mix = thinkbayes.makeMixture(metaPmf, name=suite.name)
return mix
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 = thinkbayes.PMF()
for lam, prob in suite.items():
pmf = thinkbayes.makePoissonPmf(lam, high)
metaPmf.set(pmf, prob)
mix = thinkbayes.makeMixture(metaPmf, name=suite.name)
return mix
def pmfOfWaitTime(pmf_zb):
"""Distribution of wait time.
pmf_zb: dist of gap time as seen by a random observer (the biased distribution)
Returns: dist of wait time (also dist of elapsed time)
"""
# NOTE: zb = Y + X, where Y = dist of wait time, and X = dist of elapsed time.
# NOTE: Y = wait times = time between arrival of passenger and arrival of train.
# NOTE: X = wait time = time between arrival of previous train and arrival of next passenger.
metaPmf = thinkbayes.PMF()
for gap, prob in pmf_zb.items():
uniform = makeUniformPmf(0, gap)
metaPmf.set(uniform, prob)
pmf_y = thinkbayes.makeMixture(metaPmf, name='y')
return pmf_y
def __init__(self, wtc, are, num_passengers=15):
"""Constructor.
wtc: WaitTimeCalculator
are: ArrivalTimeEstimator
num_passengers: number of passengers seen on the platform
"""
self.metaPmf = thinkbayes.PMF()
for lmbda, prob in sorted(are.posteriorLambda.items()):
ete = ElapsedTimeEstimator(wtc, lmbda, num_passengers)
self.metaPmf.set(ete.pmf_y, prob)
self.mixture = thinkbayes.makeMixture(self.metaPmf)
lmbda = are.posteriorLambda.mean()
ete = ElapsedTimeEstimator(wtc, lmbda, num_passengers)
self.point = ete.pmf_y
def main():
pmfDice = thinkbayes.PMF()
pmfDice.set(Die(4), 5)
pmfDice.set(Die(6), 4)
pmfDice.set(Die(8), 3)
pmfDice.set(Die(12), 2)
pmfDice.set(Die(20), 1)
pmfDice.normalize()
#@fix: was unhashable error here:
# http://stackoverflow.com/questions/10994229/how-to-make-an-object-properly-hashable
# http://stackoverflow.com/questions/2909106/python-whats-a-correct-and-good-way-to-implement-hash
mix = thinkbayes.PMF()
for die, weight in pmfDice.items():
for outcome, prob in die.items():
mix.incr(outcome, weight * prob)
mix = thinkbayes.makeMixture(pmfDice)
colors = thinkplot.Brewer.getColors()
thinkplot.hist(mix, width=0.9, color=colors[4])
thinkplot.save(root='dungeons3',
xlabel='Outcome',
ylabel='Probability',
formats=FORMATS)
random.seed(17)
d6 = Die(6, 'd6')
# finding distribution of rolled-dice sum by SIMULATION
dice = [d6] * 3
three = thinkbayes.sampleSum(dice, 1000)
three.name = 'sample'
print("\n\nSAMPLING: ")
three.printSuite()
# finding distribution of rolled-dice sum by ENUMERATION
threeExact = d6 + d6 + d6
threeExact.name = 'exact'
print("\n\nENUMERATION:")
threeExact.printSuite()
thinkplot.prePlot(num=2)
thinkplot.pmf(three)
thinkplot.pmf(threeExact, linestyle='dashed')
thinkplot.save(root='dungeons1',
xlabel='Sum of three d6',
ylabel='Probability',
axis=[2, 19, 0, 0.15],
formats=FORMATS)
thinkplot.clf()
thinkplot.prePlot(num=1)
# Note: pmf of max (best) attribute:
bestAttribute2 = pmfMax(threeExact, threeExact)
bestAttribute4 = pmfMax(bestAttribute2, bestAttribute2)
bestAttribute6 = pmfMax(bestAttribute4, bestAttribute2)
thinkplot.pmf(bestAttribute6)
# Note: finding pmf max using efficient Cdf method:
bestAttributeCdf = threeExact.max(6) #@ Max() in class Cdf
bestAttributeCdf.name = ''
bestAttributePmf = thinkbayes.makePmfFromCdf(bestAttributeCdf)
bestAttributePmf.printSuite()
thinkplot.pmf(bestAttributePmf)
thinkplot.save(root='dungeons2',
xlabel='Sum of three d6',
ylabel='Probability',
axis=[2, 19, 0, 0.23],
formats=FORMATS)
def distOfN(self, name=''):
"""Returns the PMF of n."""
return thinkbayes.makeMixture(self, name=name)