def main(): maleRates = [0.52, 0.38, 0.39, 1.01, 2.63] femaleRates = [50, 20.5] pdfMale = thinkbayes2.EstimatedPdf(maleRates) pdfFemale = thinkbayes2.EstimatedPdf(femaleRates) low, high = 0, 100 n = 1001 xs = numpy.linspace(low, high, n) pmfMale = pdfMale.MakePmf(steps=xs) pmfFemale = pdfFemale.MakePmf(steps=xs) thinkplot.Pdf(pdfMale, label='Male Prior') thinkplot.Pdf(pdfFemale, label='Female Prior') thinkplot.show()
def RunSimpleProcess(gap_times, lam=0.0333, num_passengers=15, plot=True):
"""Runs the basic analysis and generates figures.
gap_times: sequence of float
lam: arrival rate in passengers per second
num_passengers: int number of passengers on the platform
plot: boolean, whether to generate plots
Returns: WaitTimeCalculator, ElapsedTimeEstimator
"""
global UPPER_BOUND
UPPER_BOUND = 1200
cdf_z = thinkbayes2.Cdf(gap_times).Scale(1.0 / 60)
print('CI z', cdf_z.CredibleInterval(90))
xs = MakeRange(low=10)
pdf_z = thinkbayes2.EstimatedPdf(gap_times)
pmf_z = pdf_z.MakePmf(xs=xs, label="z")
wtc = WaitTimeCalculator(pmf_z, inverse=False)
if plot:
wtc.PlotPmfs()
wtc.MakePlot()
ete = ElapsedTimeEstimator(wtc, lam, num_passengers)
if plot:
ete.MakePlot()
return wtc, ete
def TestGte():
"""Tests the GapTimeEstimator."""
random.seed(17)
xs = [60, 120, 240]
gap_times = [60, 60, 60, 60, 60, 120, 120, 120, 240, 240]
# distribution of gap time (z)
pdf_z = thinkbayes2.EstimatedPdf(gap_times)
pmf_z = pdf_z.MakePmf(xs=xs, label="z")
wtc = WaitTimeCalculator(pmf_z, inverse=False)
lam = 0.0333
n = 100
passenger_data = wtc.GenerateSamplePassengers(lam, n)
pcounts = [0, 0, 0]
ite = GapTimeEstimator(xs, pcounts, passenger_data)
thinkplot.Clf()
# thinkplot.Cdf(wtc.pmf_z.MakeCdf(label="actual z"))
thinkplot.Cdf(wtc.pmf_zb.MakeCdf(label="actual zb"))
ite.MakePlot()
def main(): maleRates = [0.52, 0.38, 0.39, 1.01, 2.63, 30] femaleRates = [50, 20.5, 40, 30, 45] pdfMale = thinkbayes2.EstimatedPdf(maleRates) pdfFemale = thinkbayes2.EstimatedPdf(femaleRates) low, high = 0, 100 n = 1001 xs = numpy.linspace(low, high, n) pmfMale = MakePmfTest(pdfMale,steps=xs) pmfFemale = MakePmfTest(pdfFemale,steps=xs) pmfMale.Normalize() pmfFemale.Normalize() thinkplot.Pdf(pmfMale, label='Male Prior') thinkplot.Pdf(pmfFemale, label='Female Prior') thinkplot.show()
def __init__(self, prices, bids, diffs):
"""Construct the Player.
prices: sequence of prices
bids: sequence of bids
diffs: sequence of underness (negative means over)
"""
self.pdf_price = thinkbayes2.EstimatedPdf(prices)
self.cdf_diff = thinkbayes2.MakeCdfFromList(diffs)
mu = 0
sigma = numpy.std(diffs)
self.pdf_error = thinkbayes2.NormalPdf(mu, sigma)
def RunLoop(gap_times, nums, lam=0.0333):
"""Runs the basic analysis for a range of num_passengers.
gap_times: sequence of float
nums: sequence of values for num_passengers
lam: arrival rate in passengers per second
Returns: WaitMixtureEstimator
"""
global UPPER_BOUND
UPPER_BOUND = 4000
thinkplot.Clf()
RandomSeed(18)
# resample gap_times
n = 220
cdf_z = thinkbayes2.Cdf(gap_times)
sample_z = cdf_z.Sample(n)
pmf_z = thinkbayes2.Pmf(sample_z)
# compute the biased pmf and add some long delays
cdf_zp = BiasPmf(pmf_z).MakeCdf()
sample_zb = numpy.append(cdf_zp.Sample(n), [1800, 2400, 3000])
# smooth the distribution of zb
pdf_zb = thinkbayes2.EstimatedPdf(sample_zb)
xs = MakeRange(low=60)
pmf_zb = pdf_zb.MakePmf(xs=xs)
# unbias the distribution of zb and make wtc
pmf_z = UnbiasPmf(pmf_zb)
wtc = WaitTimeCalculator(pmf_z)
probs = []
for num_passengers in nums:
ete = ElapsedTimeEstimator(wtc, lam, num_passengers)
# compute the posterior prob of waiting more than 15 minutes
cdf_y = ete.pmf_y.MakeCdf()
prob = 1 - cdf_y.Prob(900)
probs.append(prob)
# thinkplot.Cdf(ete.pmf_y.MakeCdf(label=str(num_passengers)))
thinkplot.Plot(nums, probs)
thinkplot.Save(
root='redline5',
xlabel='Num passengers',
ylabel='P(y > 15 min)',
formats=FORMATS,
)
def GenerateSampleData(gap_times, lam=0.0333, n=10):
"""Generates passenger data based on actual gap times.
gap_times: sequence of float
lam: arrival rate in passengers per second
n: number of simulated observations
"""
xs = MakeRange(low=10)
pdf_z = thinkbayes2.EstimatedPdf(gap_times)
pmf_z = pdf_z.MakePmf(xs=xs, label="z")
wtc = WaitTimeCalculator(pmf_z, inverse=False)
passenger_data = wtc.GenerateSamplePassengers(lam, n)
return wtc, passenger_data
