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 = thinkbayes.EstimatedPdf(gap_times)
pmf_z = pdf_z.MakePmf(xs, name="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(name="actual z"))
thinkplot.Cdf(wtc.pmf_zb.MakeCdf(name="actual zb"))
ite.MakePlot()
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 = thinkbayes.MakeCdfFromList(gap_times).Scale(1.0 / 60)
print('CI z', cdf_z.CredibleInterval(90))
xs = MakeRange(low=10)
pdf_z = thinkbayes.EstimatedPdf(gap_times)
pmf_z = pdf_z.MakePmf(xs, name="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 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 = thinkbayes.MakeCdfFromList(gap_times)
sample_z = cdf_z.Sample(n)
pmf_z = thinkbayes.MakePmfFromList(sample_z)
# compute the biased pmf and add some long delays
cdf_zp = BiasPmf(pmf_z).MakeCdf()
sample_zb = cdf_zp.Sample(n) + [1800, 2400, 3000]
# smooth the distribution of zb
pdf_zb = thinkbayes.EstimatedPdf(sample_zb)
xs = MakeRange(low=60)
pmf_zb = pdf_zb.MakePmf(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(name=str(num_passengers)))
thinkplot.Plot(nums, probs)
thinkplot.Save(
root='redline5',
xlabel='Num passengers',
ylabel='P(y > 15 min)',
formats=FORMATS,
)
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 = thinkbayes.EstimatedPdf(prices)
self.cdf_diff = thinkbayes.MakeCdfFromList(diffs)
mu = 0
sigma = numpy.std(diffs)
self.pdf_error = thinkbayes.GaussianPdf(mu, sigma)
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 = thinkbayes.EstimatedPdf(gap_times)
pmf_z = pdf_z.MakePmf(xs, name="z")
wtc = WaitTimeCalculator(pmf_z, inverse=False)
passenger_data = wtc.GenerateSamplePassengers(lam, n)
return wtc, passenger_data
def CH6_2(price1, price2):
"""
两组展览品的价格分布
"""
thinkplot.Clf()
thinkplot.PrePlot(num=2)
# 因为price变量值没有重复的, 所以PMF绘图是看不出什么的.
# price1_pmf = thinkbayes.MakePmfFromList(price1, name='showcase1')
# price2_pmf = thinkbayes.MakePmfFromList(price2, name='showcase2')
price1_max = max(price1)
price2_max = max(price2)
price_max = max(price1_max, price2_max)
xs = numpy.linspace(0, price_max + 100, num=150)
price1_pdf = thinkbayes.EstimatedPdf(price1)
price2_pdf = thinkbayes.EstimatedPdf(price2)
price1_pmf = price1_pdf.MakePmf(xs, name='showcase1')
price2_pmf = price2_pdf.MakePmf(xs, name='showcase2')
thinkplot.Pmfs([price1_pmf, price2_pmf])
thinkplot.Show(xlabel='price $', ylabel='PMF')
def main():
data = ReadData()
cols = zip(*data)
price1, price2, bid1, bid2, diff1, diff2 = cols
pdf = thinkbayes.EstimatedPdf(price1)
# print(type(pdf))
low, high = 0, 75000
n = 101
xs = numpy.linspace(low, high, n)
# print(pdf.Density(25000))
pmf = pdf.MakePmf(xs)
thinkplot.PrePlot(1)
thinkplot.Pmf(pmf)
thinkplot.Save(root='price_self2',
xlabel='',
ylabel='Probability_density',
formats=['pdf'])
import numpy
import thinkbayes
def ReadData(filename='showcases.2011.csv'):
fp = open(filename)
reader = csv.reader(fp)
res = []
for t in reader:
_heading = t[0]
data = t[1:]
try:
data = [int(x) for x in data]
# print heading, data[0], len(data)
res.append(data)
except ValueError:
pass
fp.close()
return zip(*res)
prices = ReadData()
pdf = thinkbayes.EstimatedPdf(prices)
'''low,high = 0,75000
n=101
xs=numpy.linspace(low,high,n)
pmf=pdf.MakePmf(xs)
for x,y in pmf.Items():
print x,y'''
