def main():
#cdf, place = total_percentile_rank(results)
speeds = relay.GetSpeeds(results)
speed = relay.ConvertPaceToSpeed('6:53')
cdf = Cdf.MakeCdfFromList(speeds)
print cdf.Prob(speed),'speed'
print convert_speeds_to_time(speed),'time'
myplot.Cdf(cdf)
myplot.Show()
speeds_old = GetSpeeds_M4049(results)
cdf_old = Cdf.MakeCdfFromList(speeds_old)
rank = cdf_old.Prob(speed)
print rank,'rank', speed,'speed'
print convert_speeds_to_time(speed),'time'
myplot.Cdf(cdf_old)
myplot.Show()
speeds_5059 = GetSpeeds_M5059(results)
cdf_5059 = Cdf.MakeCdfFromList(speeds_5059)
future_speed = cdf_5059.Value(rank)
print future_speed,'speed'
print convert_speeds_to_time(future_speed),'time'
myplot.Cdf(cdf_5059)
myplot.Show()
fspeeds = GetSpeeds_F2039(results)
cdf_female = Cdf.MakeCdfFromList(fspeeds)
fspeed = cdf_female.Value(rank)
print fspeed,'speed'
print convert_speeds_to_time(fspeed),'time'
myplot.Cdf(cdf_female)
myplot.Show()
def observe_data(l, name=None, show=False):
cdf = pmf = None
if isinstance(l, list):
cdf = Cdf.MakeCdfFromList(l,name+' cdf')
pmf = Pmf.MakePmfFromList(l, name+' pmf')
elif isinstance(l, Pmf.Pmf):
pmf = l
cdf = Cdf.MakeCdfFromPmf(l)
if name is None: name = pmf.name
elif isinstance(l, Cdf.Cdf):
cdf = l
if name is None: name = cdf.name
else:
raise Exception('input parameter type is wrong')
v_25, median, v_75 = cdf.Percentile(25), cdf.Percentile(50), cdf.Percentile(75)
mean = cdf.Mean()
print('%s: 1/4:%4.2f(%4.2f), 1/2:%4.2f(mean-median:%4.2f), mean:%4.2f, 3/4:%4.2f(%4.2f)' % \
(name, v_25, median-v_25, median, mean-median, mean, v_75,v_75-median))
if show:
if pmf is not None:
myplot.Pmf(pmf)
myplot.Show()
myplot.Cdf(cdf)
myplot.Show()
def Main():
truth = ReadTruth()
truth_map = {}
for pcode, label in truth:
truth_map[pcode] = label
labels = ReadLabels()
photo_map, labeler_map = MakeObjects(labels)
RunUpdates(photo_map, labeler_map, labels)
yes = []
no = []
for pcode, photo in photo_map.iteritems():
if pcode in truth_map:
mean = photo.Mean()
if truth_map[pcode] == '1':
yes.append(mean)
else:
no.append(mean)
myplot.Clf()
cdf_yes = thinkbayes.MakeCdfFromList(yes, name='yes')
cdf_no = thinkbayes.MakeCdfFromList(no, name='no')
myplot.Cdfs([cdf_yes, cdf_no])
myplot.Show()
return
myplot.Clf()
PlotPosteriorMeans(photo_map, 'photos')
PlotPosteriorMeans(labeler_map, 'labelers')
myplot.Show()
def process(data):
# Hist 分布图
hist = Pmf.MakeHistFromList(data, name='hist')
myplot.Hist(hist, color='blue')
myplot.Show()
# Pmf 分布图
pmf = Pmf.MakePmfFromHist(hist, name='pmf')
myplot.Pmf(pmf, color='yellow')
myplot.Show()
myplot.Clf()
# 实际数据的CDF分布图
cdf = Cdf.MakeCdfFromList(data, name='loafs')
myplot.Cdf(cdf)
mu, var = thinkstats.MeanVar(data)
sigma = math.sqrt(var)
print("mu = %.3f, sigma = %.3f" % (mu, sigma))
# 正态分布
xs = normal_sample(len(data), mu, sigma) # xs = data
ys = [erf.NormalCdf(x, mu=mu, sigma=sigma) for x in xs]
myplot.Scatter(xs, ys, color='red', label='sample')
myplot.Show()
def main():
import myplot
pmf = Pmf.MakePmfFromList([1, 2, 3, 3, 4, 4, 5, 5, 5, 6, 6, 7])
remain = PmfRemainingLifeTime(pmf, age=4)
myplot.Hist(pmf)
myplot.Show()
myplot.Hist(remain)
myplot.Show()
def main():
list = [100 * random.random() for i in range(1000)]
pmf = Pmf.MakePmfFromList(list, name='pfm')
cdf = Cdf.MakeCdfFromList(list, name='cdf')
myplot.Pmf(pmf)
myplot.Show()
myplot.Clf()
myplot.Cdf(cdf)
myplot.Show()
def main():
resp = brfss.Respondents()
resp.ReadRecords(data_dir='res')
d = resp.SummarizeHeight()
man_d = d[1]
lady_d = d[2]
# 男性的mu, var, sigma, 变异系数CV
man_mu, man_var = thinkstats.TrimmedMeanVar(man_d)
man_sigma = math.sqrt(man_var)
man_cv = man_sigma/man_mu
print("man: mu = %.3f, var = %.3f, sigma = %.3f, cv = %.3f" % (man_mu, man_var, man_sigma, man_cv))
# 女性的mu, var, sigma, 变异系数CV
lady_mu, lady_var = thinkstats.TrimmedMeanVar(lady_d)
lady_sigma = math.sqrt(lady_var)
lady_cv = lady_sigma/lady_mu
print("lady: mu = %.3f, var = %.3f, sigma = %.3f, cv = %.3f" % (lady_mu, lady_var, lady_sigma, lady_cv))
# 男性, 女性Hist分布
man_hist = Pmf.MakeHistFromList(man_d, name='man hist')
myplot.Hist(man_hist)
myplot.Show()
myplot.Clf()
lady_hist = Pmf.MakeHistFromList(lady_d, name='lady hist')
myplot.Hist(lady_hist)
myplot.Show()
myplot.Clf()
# 男性, 女性Pmf分布
man_pmf = Pmf.MakePmfFromHist(man_hist, name='man pmf')
myplot.Pmf(man_pmf)
myplot.Show()
myplot.Clf()
lady_pmf = Pmf.MakePmfFromHist(lady_hist, name='lady pmf')
myplot.Pmf(lady_pmf)
myplot.Show()
myplot.Clf()
# 男性/女性Cdf累积分布
man_cdf = Cdf.MakeCdfFromPmf(man_pmf, name='man cdf')
lady_cdf = Cdf.MakeCdfFromPmf(lady_pmf, name='lady cdf')
myplot.Cdfs((man_cdf, lady_cdf), complement=False, transform=None)
myplot.Show()
def Main():
# make a redditor with some trustworthiness (mean_t = 0.67)
founder = Redditor(name='redditor')
beta = thinkbayes.Beta(2, 1)
for val, prob in beta.MakePmf().Items():
founder.Set(val * 100, prob)
# make a new item with unknown quality (mean_q = 0.5)
item = Item(range(0, 101), name='item')
# compute the means
mean_t = founder.Mean() / 100.0
mean_q = item.Mean() / 100.0
print mean_t
print mean_q
# perform simultaneous updates
founder.Update(('up', mean_q))
item.Update(('up', mean_t))
Summarize(item)
# display the posterior distributions
myplot.Pmf(founder)
myplot.Pmf(item)
myplot.Show()
def PlotAges(resp):
"""Plot the distribution of ages."""
ages = [r.age for r in resp.records]
cdf = Cdf.MakeCdfFromList(ages)
myplot.Clf()
myplot.Cdf(cdf)
myplot.Show()
def ExpoErlangDemo():
num = 10
lam1 = 1
lam2 = 2
t = MakeSeries(num, lam1, num, lam2)
series = Series(t)
n, s1, m, s2 = series.Split(num)
print n, s1, m, s2
low, high = 0.01, 5.01
lams = numpy.linspace(low, high, 101)
expo = Expo(lams)
expo.name = 'expo'
expo.Update((n, s1))
erlang = Erlang(lams)
erlang.name = 'erlang'
erlang.Update((n, s1))
myplot.Pmf(expo)
myplot.Pmf(erlang)
myplot.Show()
def ClassSizes():
# start with the actual distribution of class sizes from the book
d = {
7: 8,
12: 8,
17: 14,
22: 4,
27: 6,
32: 12,
37: 8,
42: 3,
47: 2,
}
# form the pmf
pmf = Pmf.MakePmfFromDict(d, 'actual')
print 'mean', pmf.Mean()
print 'var', pmf.Var()
# compute the biased pmf
biased_pmf = BiasPmf(pmf, 'observed')
print 'mean', biased_pmf.Mean()
print 'var', biased_pmf.Var()
# unbias the biased pmf
unbiased_pmf = UnbiasPmf(biased_pmf, 'unbiased')
print 'mean', unbiased_pmf.Mean()
print 'var', unbiased_pmf.Var()
# plot the Pmfs
myplot.Pmfs([pmf, biased_pmf, unbiased_pmf])
myplot.Show(xlabel='Class size', ylabel='PMF')
def main():
results = relay.ReadResults()
speeds = relay.GetSpeeds(results)
# plot the distribution of actual speeds
pmf = Pmf.MakePmfFromList(speeds, 'actual speeds')
# myplot.Clf()
# myplot.Hist(pmf)
# myplot.Save(root='observed_speeds',
# title='PMF of running speed',
# xlabel='speed (mph)',
# ylabel='probability')
# plot the biased distribution seen by the observer
biased = BiasPmf(pmf, 7.5, name='observed speeds')
myplot.Pmf(biased)
myplot.Show(title='soln. PMF of running speed',
xlabel='speed (mph)',
ylabel='probability')
myplot.Clf()
myplot.Hist(biased)
myplot.Save(root='observed_speeds',
title='PMF of running speed',
xlabel='speed (mph)',
ylabel='probability')
cdf = Cdf.MakeCdfFromPmf(biased)
myplot.Clf()
myplot.Cdf(cdf)
myplot.Save(root='observed_speeds_cdf',
title='CDF of running speed',
xlabel='speed (mph)',
ylabel='cumulative probability')
def main():
# Exercise 3.6
myBirthWeight = 163
table = survey.Pregnancies()
table.ReadRecords()
unfilteredLiveBirthWeights = [(p.birthwgt_lb, p.birthwgt_oz)
for p in table.records if p.outcome == 1]
liveBirthWeights = [
lbs * 16 + oz for lbs, oz in unfilteredLiveBirthWeights
if type(lbs) == int and type(oz) == int and lbs * 16 + oz <= 200
]
liveBirthWeightsCdf = Cdf.MakeCdfFromList(liveBirthWeights,
name="live birth weights")
print("My birth weight percentile rank (vs all births): %d" %
(100 * liveBirthWeightsCdf.Prob(myBirthWeight)))
unfilteredNotFirstLiveBirthWeights = [(p.birthwgt_lb, p.birthwgt_oz)
for p in table.records
if p.outcome == 1 and p.birthord != 1
]
notFirstLiveBirthWeights = [
lbs * 16 + oz for lbs, oz in unfilteredNotFirstLiveBirthWeights
if type(lbs) == int and type(oz) == int and lbs * 16 + oz <= 200
]
notFirstLiveBirthWeightsCdf = Cdf.MakeCdfFromList(
notFirstLiveBirthWeights, name="not first live birth weights")
print("My birth weight percentile rank (vs first births): %d" %
(100 * notFirstLiveBirthWeightsCdf.Prob(myBirthWeight)))
myplot.Cdf(notFirstLiveBirthWeightsCdf)
myplot.Show(title="not first live birth weight CDF",
xlabel="birth weight oz",
ylabel="probability")
def Summarize(data_dir):
"""Prints summary statistics for first babies and others.
Returns:
tuple of Tables
"""
table, firsts, others = MakeTables(data_dir)
ProcessTables(firsts, others)
print("Number of first babies", firsts.n)
print("Number of others", others.n)
mu1, mu2 = firsts.mu, others.mu
print("Mean gestation in weeks:")
print("First babies", mu1)
print("Others", mu2)
print("Difference in days", (mu1 - mu2) * 7.0)
var = thinkstats.Var(firsts.lengths)
sd = numpy.sqrt(var)
print("Firsts sd = ", sd)
var = thinkstats.Var(others.lengths)
sd = numpy.sqrt(var)
print("Others sd = ", sd)
histfirst = Pmf.MakeHistFromList(firsts.lengths)
histother = Pmf.MakeHistFromList(others.lengths)
myplot.Hists([histfirst, histother])
myplot.Show()
def main():
results = ReadResults()
speeds = GetSpeeds(results)
pmf = Pmf.MakePmfFromList(speeds, 'speeds')
myplot.Pmf(pmf)
myplot.Show(title='PMF of running speed',
xlabel='speed (mph)',
ylabel='probability')
import Cdf
cdf = Cdf.MakeCdfFromList(speeds, 'speeds')
myplot.Cdf(cdf)
myplot.Show()
myplot.Cdfs(cdf)
myplot.Show()
def ScatterPlot(self, root, heights, weights, alpha=1.0):
pyplot.scatter(heights, weights, alpha=alpha, edgecolors='none')
# myplot.Save(root=root,
myplot.Show(
xlabel='Height (cm)',
ylabel='Weight (kg)',
axis=[140, 210, 20, 200],
legend=False)
def main():
sz, alph, exem = 1000, 1.7, 100
lst = paretovariate(sz, alph, exem)
lst_cdf = Cdf.MakeCdfFromList(lst)
myplot.Clf()
myplot.Cdf(lst_cdf, complement=True, xscale='log', yscale='log')
myplot.Show(title='CCDF of {0} random paretovariates'.format(sz))
def main(): all_recs = cyb_records.Stats() all_recs.ReadRecords() print 'Number of total stats', len(all_recs.records) cdf = CdfPerDay(all_recs.records) myplot.Cdfs(cdf) myplot.Show(title="CDF: daily usage of machines at the YMCA", xlabel = 'Distance (in m / day)', ylabel = 'Percentile')
def main():
babies = BabyBoom.Babies()
babies.ReadRecords(data_dir='res', n=None)
lastmin = 0
interval = []
for item in babies.records:
interval.append(item.minutes - lastmin)
lastmin = item.minutes
cdf = Cdf.MakeCdfFromList(interval, name='baby interval')
myplot.Cdf(cdf, complement=False, transform=None)
myplot.Show()
# y轴取log(CCDF) : CCDF(X) = 1 - CDF(X)
myplot.Clf()
myplot.Cdf(cdf, complement=True, yscale='log')
myplot.Show()
def HexBin(self, root, heights, weights, cmap=matplotlib.cm.Blues):
pyplot.hexbin(heights, weights, cmap=cmap)
# myplot.Save(root=root,
myplot.Show(
xlabel='Height (cm)',
ylabel='Weight (kg)',
axis=[140, 210, 20, 200],
legend=False)
def main():
hypos = xrange(100, 1001)
suite = Train(hypos)
suite.Update(321)
print suite.Mean()
myplot.Pmf(suite)
myplot.Show()
def CheckCdf2():
"""Compare chi2 values from the simulation with a chi-squared dist."""
df = 3
t = [SimulateChi2() for i in range(1000)]
t2 = [scipy.stats.chi2.cdf(x, df) for x in t]
cdf = Cdf.MakeCdfFromList(t2)
myplot.Cdf(cdf)
myplot.Show()
def create_error_pmf():
all_events = cyb_records.Events()
all_events.ReadRecords()
all_records = all_events.records
my_pmfs = Pmf_errors(all_records)
myplot.Pmfs(my_pmfs)
myplot.Show(title="PDF of different types of errors Per Machine",
xlabel='Error Codes',
ylabel='Probability')
def main():
all_recs = cyb_records.Stats()
all_recs.ReadRecords()
print 'Number of total stats', len(all_recs.records)
cdf = CdfPerMachine(all_recs.records)
myplot.Cdfs(cdf)
myplot.Show(title="CDF of cardio machine average distances",
xlabel='Average Distances',
ylabel='Probability')
def CheckCdf():
"""
"""
xs, ys = Chi2Cdf(df=3, high=15)
pyplot.plot(xs, ys)
t = [SimulateChi2() for i in range(1000)]
cdf = Cdf.MakeCdfFromList(t)
myplot.Cdf(cdf)
myplot.Show()
def Q2(results):
results.sort()
# print(results)
cdf = Cdf.MakeCdfFromList(results, name='cdf')
myplot.Cdf(cdf)
myplot.Show()
p = [0.95, 0.99]
for i in p:
significant_value = cdf.Value(i)
print("p:%4.2f significant_value = %d" % (i, significant_value))
pass
def main():
results = relay.ReadResults()
speeds = relay.GetSpeeds(results)
# plot the distribution of actual speeds
cdf = Cdf.MakeCdfFromList(speeds, 'speeds')
myplot.Cdf(cdf)
myplot.Show(title='CDF of running speed',
xlabel='speed (mph)',
ylabel='cumulative probability')
def main():
results = relay.ReadResults()
speeds = relay.GetSpeeds(results)
pmf = Pmf.MakePmfFromList(speeds, 'actual speeds')
observed = BiasPmf(pmf, 7.5, 'observed speeds')
myplot.Clf()
myplot.Hist(observed)
myplot.Show(title='observed speeds',
xlabel='speed (mph)',
ylabel='probability')
def PlotPMF(records, machine_filter=[]):
pmfs = []
errors = GetErrorsPerWeek(records, machine_filter)
for key in errors.keys():
if SumErrors(errors.get(key)) > 10:
pmf = Pmf.MakeHistFromDict(errors.get(key), key)
pmfs.append(pmf)
myplot.Pmfs(pmfs)
myplot.Show(title="Histogram: Error Rate per Week",
xlabel='Date',
ylabel='Errors per week')
def main():
data_dir = '../chap1/'
preg = survey.Pregnancies()
preg.ReadRecords(data_dir)
cdf = weight_cdf(preg)
myplot.Cdf(cdf)
myplot.show()
sample = Sample(cdf, 1000)
cdf_sample = Cdf.MakeCdfFromList(sample)
myplot.Cdf(cdf_sample)
myplot.Show()
