Exemplo n.º 1
0
def QUpdatePlot(pmf, data, thresh, label=None):
    if (type(data) is int or type(data) is float):
        pmf.Update(data)
        thinkplot.Pdf(pmf.generateQ(thresh), label=label)
    else:
        pmf.UpdateSet(data)
        thinkplot.Pdf(pmf.generateQ(thresh), label=label)
def main():
    coords = numpy.linspace(-100, 100, 101)
    joint = Gps(product(coords, coords))

    joint.Update((51, -15))
    joint.Update((48, 90))

    pairs = [(11.903060613102866, 19.79168669735705),
             (77.10743601503178, 39.87062906535289),
             (80.16596823095534, -12.797927542984425),
             (67.38157493119053, 83.52841028148538),
             (89.43965206875271, 20.52141889230797),
             (58.794021026248245, 30.23054016065644),
             (2.5844401241265302, 51.012041625783766),
             (45.58108994142448, 3.5718287379754585)]

    joint.UpdateSet(pairs)

    thinkplot.PrePlot(2)
    pdfx = joint.Marginal(0)
    pdfy = joint.Marginal(1)
    thinkplot.Pdf(pdfx, label='posterior x')
    thinkplot.Pdf(pdfy, label='posterior y')
    thinkplot.Show()

    print(pdfx.Mean(), pdfx.Std())
    print(pdfy.Mean(), pdfy.Std())
def main():
    hypos = range(0, 101)
    suite = Electorate(hypos)

    thinkplot.PrePlot(3)
    thinkplot.Pdf(suite, label='prior')

    data = 1.1, 3.7, 53
    suite.Update(data)
    thinkplot.Pdf(suite, label='posterior1')
    thinkplot.Save(root='electorate1',
                   xlabel='percentage of electorate',
                   ylabel='PMF',
                   formats=['png'],
                   clf=False)

    print(suite.Mean())
    print(suite.Std())
    print(suite.ProbLess(50))

    data = -2.3, 4.1, 49
    suite.Update(data)

    thinkplot.Pdf(suite, label='posterior2')
    thinkplot.Save(root='electorate2',
                   xlabel='percentage of electorate',
                   ylabel='PMF',
                   formats=['png'])

    print(suite.Mean())
    print(suite.Std())
    print(suite.ProbLess(50))
def main():
    hypos = numpy.linspace(0, 12, 201)
    suite = Soccer(hypos)

    # the mean number of goals per game was 2.67
    mean_rate = 2.67 / 2
    mean_interarrival = 90 / mean_rate

    # start with a prior based on the mean interarrival time
    suite.Update(mean_interarrival)
    thinkplot.Pdf(suite, label='prior')
    print('prior mean', suite.Mean())

    suite.Update(11)
    thinkplot.Pdf(suite, label='posterior 1')
    print('after one goal', suite.Mean())

    suite.Update(12)
    thinkplot.Pdf(suite, label='posterior 2')
    print('after two goals', suite.Mean())

    thinkplot.Show()

    # plot the predictive distribution
    suite.PredRemaining(90 - 23, 2)
Exemplo n.º 5
0
def main():
    """
    """

    user = User(label='user')
    beta = thinkbayes2.Beta(2, 1)
    for val, prob in beta.MakePmf().Items():
        user.Set(val * 100, prob)
    thinkplot.Pdf(user)
    thinkplot.Show()
    print(user.Mean(), user.CredibleInterval(90))
    mean_r = user.Mean() / 100.0

    link = Link(range(0, 101), label='link')
    thinkplot.Pdf(link)
    thinkplot.Show()
    print(link.Mean(), link.CredibleInterval(90))
    mean_q = link.Mean() / 100.0

    user.Update(('up', mean_q))
    thinkplot.Pdf(user)
    thinkplot.Show()
    print(user.Mean(), user.CredibleInterval(90))

    link.Update(('up', mean_r))
    thinkplot.Pdf(link)
    thinkplot.Show()
    print(link.Mean(), link.CredibleInterval(90))

    return 0
def main():
    hypos = numpy.linspace(0, 100, 101)
    suite = Electorate(hypos)

    thinkplot.Pdf(suite, label='prior')

    data = 1.1, 3.7, 53
    suite.Update(data)

    thinkplot.Pdf(suite, label='posterior')
    thinkplot.Show()
Exemplo n.º 7
0
def MakePdfs(greq, less):
    greqpdf = thinkstats2.EstimatedPdf(greq.totalwgt_lb.dropna())
    lesspdf = thinkstats2.EstimatedPdf(less.totalwgt_lb.dropna())
    thinkplot.PrePlot(rows=1, cols=2)
    thinkplot.SubPlot(1)
    thinkplot.Pdf(greqpdf, label='greater/equal to 30')
    thinkplot.Config(xlabel='Birth weight (lbs)', ylabel='PDF')
    thinkplot.SubPlot(2)
    thinkplot.Pdf(lesspdf, label='less than 30')
    thinkplot.Config(xlabel='Birth weight (lbs)', ylabel='PDF')
    thinkplot.Show()
Exemplo n.º 8
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def main():
    hypos = numpy.linspace(0, 12, 201)
    suite = Soccer(hypos)

    thinkplot.Pdf(suite, label='prior')
    print('prior mean', suite.Mean())

    suite.Update(11)
    thinkplot.Pdf(suite, label='posterior 1')
    print('after one goal', suite.Mean())

    thinkplot.Show()
Exemplo n.º 9
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def main():
    hypos = range(1, 1000)
    suite = Hyrax(hypos)
    suite2 = Hyrax2(hypos)

    data = 10, 10, 2
    suite.Update(data)
    suite2.Update(data)

    thinkplot.Pdf(suite, label='binomial')
    thinkplot.Pdf(suite, label='hypergeom')
    thinkplot.Show()
Exemplo n.º 10
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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()
Exemplo n.º 11
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def main():
    hypos = numpy.linspace(0, 100, 101)
    suite = Electorate(hypos)

    thinkplot.Pdf(suite, label='prior')

    data = 1.1, 3.7, 53
    suite.Update(data)

    thinkplot.Pdf(suite, label='posterior')
    thinkplot.Show()

    print(suite.Std())
    print(suit.Mean())
    print(suite.ProbLess(50))
Exemplo n.º 12
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def main():

    suite = Version3()
    print(suite.Mean())

    thinkplot.Pdf(suite)
    thinkplot.Show(legend=False)
Exemplo n.º 13
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def main():
    suite = Euro(range(0, 101))

    suite.Update('H')

    thinkplot.Pdf(suite)
    thinkplot.Show(xlabel='x', ylabel='Probability', legend=False)
Exemplo n.º 14
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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()
Exemplo n.º 15
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def main():
    hypos = range(1, 1000)
    suite = Hyrax(hypos)

    data = 10, 10, 2
    suite.Update(data)

    thinkplot.Pdf(suite, label='posterior')
    thinkplot.Show()
Exemplo n.º 16
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def main():
    hypos = numpy.linspace(0, 100, 101)
    suite = Electorate(hypos)

    thinkplot.Pdf(suite, label='prior')

    data = 1.1, 3.7, 53 #mean prior error, std, measurement
    suite.Update(data)
    PrintSuiteInfo(suite)

    thinkplot.Pdf(suite, label='before poll')

    newpolldata = -2.3, 4.1, 49
    suite.Update(newpolldata)
    PrintSuiteInfo(suite)

    thinkplot.Pdf(suite, label='after poll')
    thinkplot.Show()
Exemplo n.º 17
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def PriorPost(pmf, data):
    thinkplot.Pdf(pmf, label='Prior')
    pmf.UpdateSet(data)
    thinkplot.Pdf(pmf, label='Posterior')

    # thinkplot.Show(xlabel='Lambda',
    #                ylabel='Probability',
    #                legend=True,
    #                title='Arrival Rate Distribution')

    formats = ['png']
    root = 'PriorAndPosterior'
    thinkplot.Save(root=root,
                   xlabel='Lambda',
                   ylabel='Probability',
                   legend=True,
                   formats=formats,
                   title='Arrival Rate Distribution')
Exemplo n.º 18
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def generate_pmf(fb, hk):
    pmf_fb = Pmf(degrees(fb))
    pmf_hk = Pmf(degrees(hk))

    thinkplot.preplot(cols=2)

    thinkplot.plot([30, 2000], [5e-2, 2e-4], color='gray', linestyle='dashed')

    thinkplot.Pdf(pmf_fb, style='.', label='Facebook')
    thinkplot.config(xscale='log', yscale='log', xlabel='degree', ylabel='PMF')

    thinkplot.subplot(2)

    thinkplot.plot([55, 500], [5e-2, 2e-4], color='gray', linestyle='dashed')

    thinkplot.Pdf(pmf_hk, style='.', label='HK graph')
    thinkplot.config(xscale='log', yscale='log', xlabel='degree', ylabel='PMF')

    plt.savefig('PMFGraphs_Original.png')
Exemplo n.º 19
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def generate_pmf(fb, hk):
    pmf_fb = Pmf(degrees(fb))
    pmf_hk = Pmf(degrees(hk))

    thinkplot.plot([6, 150], [5e-1, 2e-4], color='gray', linestyle='dashed')

    thinkplot.Pdf(pmf_hk, style='.', label='RPA')
    thinkplot.config(xscale='log', yscale='log', xlabel='degree', ylabel='PMF')

    plt.savefig('PMFGraphs_Modified.png')
Exemplo n.º 20
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def MakePdfExample():
    # mean and var of women's heights in cm, from the BRFSS
    mean, var = 163, 52.8
    std = math.sqrt(var)

    # make a PDF and compute a density, FWIW
    pdf = thinkstats2.GaussianPdf(mean, std)
    print(pdf.Density(mean + std))

    # make a PMF and plot it
    thinkplot.PrePlot(2)
    thinkplot.Pdf(pdf, label='Gaussian')

    # make a sample, make an estimated PDF, and plot it
    sample = [random.gauss(mean, std) for i in range(100)]
    sample_pdf = thinkstats2.EstimatedPdf(sample)
    thinkplot.Pdf(sample_pdf, label='sample KDE')

    thinkplot.Save(root='pdf_example', xlabel='Height (cm)', ylabel='Density')
Exemplo n.º 21
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def main():
    suite = Euro(range(0, 101))

    obs = 'H' * 140 + 'T' * 110
    for o in obs:
        suite.Update(o)

    thinkplot.Pdf(suite)
    thinkplot.Show(xlabel='x', ylabel='Probability', legend=False)

    print(suite.Mean(), suite.MaximumLikelihood(), suite.CredibleInterval(90))
Exemplo n.º 22
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def ComputeSkewnesses():
    """Plots KDE of birthweight and adult weight.
    """
    def VertLine(x, y):
        thinkplot.Plot([x, x], [0, y], color='0.6', linewidth=1)

    live, firsts, others = first.MakeFrames()
    data = live.totalwgt_lb.dropna()
    print('Birth weight')
    mean, median = Summarize(data)

    y = 0.35
    VertLine(mean, y)
    thinkplot.Text(mean - 0.15, 0.1 * y, 'mean', horizontalalignment='right')
    VertLine(median, y)
    thinkplot.Text(median + 0.1, 0.1 * y, 'median', horizontalalignment='left')

    pdf = thinkstats2.EstimatedPdf(data)
    thinkplot.Pdf(pdf, label='birth weight')
    thinkplot.Save(root='density_totalwgt_kde', xlabel='lbs', ylabel='PDF')

    df = brfss.ReadBrfss(nrows=None)
    data = df.wtkg2.dropna()
    print('Adult weight')
    mean, median = Summarize(data)

    y = 0.02499
    VertLine(mean, y)
    thinkplot.Text(mean + 1, 0.1 * y, 'mean', horizontalalignment='left')
    VertLine(median, y)
    thinkplot.Text(median - 1.5,
                   0.1 * y,
                   'median',
                   horizontalalignment='right')

    pdf = thinkstats2.EstimatedPdf(data)
    thinkplot.Pdf(pdf, label='adult weight')
    thinkplot.Save(root='density_wtkg2_kde',
                   xlabel='kg',
                   ylabel='PDF',
                   xlim=[0, 200])
Exemplo n.º 23
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def main():

    data = 20, 15, 3
    probs = numpy.linspace(0, 1, 31)
    hypos = []
    for n in range(32, 350):
        for p1 in probs:
            for p2 in probs:
                hypos.append((n, p1, p2))

    suite = Lincoln(hypos)
    suite.Update(data)

    n_marginal = suite.Marginal(0)

    thinkplot.Pmf(n_marginal, label='n')
    thinkplot.Save(root='lincoln1',
                   xlabel='number of bugs',
                   ylabel='PMF',
                   formats=['pdf', 'png'])

    print('post mean n', n_marginal.Mean())
    print('MAP n', n_marginal.MaximumLikelihood())

    p1_marginal = suite.Marginal(1, label='p1')
    p2_marginal = suite.Marginal(2, label='p2')

    thinkplot.Pdf(p1_marginal)
    thinkplot.Pdf(p2_marginal)
    thinkplot.Show()

    print('post mean p1', p1_marginal.Mean())
    print('MAP p1', p1_marginal.MaximumLikelihood())

    print('post mean p2', p2_marginal.Mean())
    print('MAP p2', p2_marginal.MaximumLikelihood())

    print('p1 > p2', p1_marginal > p2_marginal)
    print('p1 < p2', p1_marginal < p2_marginal)
Exemplo n.º 24
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def MakePdfExample(n=500):
    """Plots a normal density function and a KDE estimate.

    n: sample size
    """
    # mean and var of women's heights in cm, from the BRFSS
    mean, var = 163, 52.8
    std = math.sqrt(var)

    # make a PDF and compute a density, FWIW
    pdf = thinkstats2.NormalPdf(mean, std)
    print(pdf.Density(mean + std))

    # make a PMF and plot it
    thinkplot.PrePlot(2)
    thinkplot.Pdf(pdf, label='normal')

    # make a sample, make an estimated PDF, and plot it
    sample = [random.gauss(mean, std) for _ in range(n)]
    sample_pdf = thinkstats2.EstimatedPdf(sample)
    thinkplot.Pdf(sample_pdf, label='sample KDE')

    thinkplot.Save(root='pdf_example', xlabel='Height (cm)', ylabel='Density')
Exemplo n.º 25
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def MakePmfPlot(alpha = 10):
    """Plots Pmf of location for a range of betas."""
    locations = range(0, 31)

    betas = [10, 20, 40]
    thinkplot.PrePlot(num=len(betas))

    for beta in betas:
        pmf = MakeLocationPmf(alpha, beta, locations)
        pmf.name = 'beta = %d' % beta
        thinkplot.Pdf(pmf)

    thinkplot.Save('paintball1',
                xlabel='Distance',
                ylabel='Prob',
                formats=FORMATS)
Exemplo n.º 26
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def main():
    ps = numpy.linspace(0, 1, 101)
    bill = Billiards(ps)
    bill.Update((5, 3))
    thinkplot.Pdf(bill)
    thinkplot.Save(root='billiards1',
                   xlabel='probability of win',
                   ylabel='PDF',
                   formats=['png'])

    bayes_result = ProbWinMatch(bill)
    print(thinkbayes.Odds(1-bayes_result))

    mle = 5 / 8
    freq_result = (1-mle)**3
    print(thinkbayes.Odds(1-freq_result))
Exemplo n.º 27
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def MakeConditionalPlot(suite):
    """Plots marginal CDFs for alpha conditioned on beta.

    suite: posterior joint distribution of location
    """    
    betas = [10, 20, 40]
    thinkplot.PrePlot(num=len(betas))

    for beta in betas:
        cond = suite.Conditional(0, 1, beta)
        cond.name = 'beta = %d' % beta
        thinkplot.Pdf(cond)

    thinkplot.Save('paintball3',
                xlabel='Distance',
                ylabel='Prob',
                formats=FORMATS)
Exemplo n.º 28
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def main():
    df = hinc.ReadData()
    log_sample = InterpolateSample(df, log_upper=6.0)

    log_cdf = thinkstats2.Cdf(log_sample)
    thinkplot.Cdf(log_cdf)
    thinkplot.Show(xlabel='household income', ylabel='CDF')

    sample = np.power(10, log_sample)
    mean, median = density.Summarize(sample)

    cdf = thinkstats2.Cdf(sample)
    print('cdf[mean]', cdf[mean])

    pdf = thinkstats2.EstimatedPdf(sample)
    thinkplot.Pdf(pdf)
    thinkplot.Show(xlabel='household income', ylabel='PDF')
Exemplo n.º 29
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def main():
    df = hinc.ReadData()
    log_sample = InterpolateSample(df, log_upper=6.0)

    log_cdf = thinkstats2.Cdf(log_sample)
    thinkplot.Cdf(log_cdf)
    thinkplot.Show(xlabel='household income', ylabel='CDF')

    sample = np.power(10, log_sample)
    mean = np.mean(sample)
    cdf = thinkstats2.Cdf(sample)
    print "Median:", np.median(sample)
    print "Mean:", mean
    print "Skewness:", thinkstats2.Skewness(sample)
    print "Pearson's Skewness:", thinkstats2.PearsonMedianSkewness(sample)
    print "Percent of people with incomes <= mean:", cdf[mean]
    pdf = thinkstats2.EstimatedPdf(sample)
    thinkplot.Pdf(pdf)
Exemplo n.º 30
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#
# - probability dencity function
#
# $PDF_{normal}(x) = \frac{1}{\sigma \sqrt{2\pi}}\exp[-\frac{1}{2}(\frac{x-\mu}{\sigma})^2]$

#%%
import thinkstats2
import math
mean, var = 163, 52.8
std = math.sqrt(var)
pdf = thinkstats2.NormalPdf(mean, std)
pdf.Density(mean + std)

#%%
import thinkplot
thinkplot.Pdf(pdf, label='normal')
thinkplot.Show(xlabel='height (cm)', ylabel='dencity')

#%%
pmf = pdf.MakePmf()

#%% [markdown]
# ## 6.2 KDE
#
# - Kernel density estimation

#%%
import random
sample = [random.gauss(mean, std) for _ in range(500)]
sample_pdf = thinkstats2.EstimatedPdf(sample)
thinkplot.Pdf(sample_pdf, label='sample KDE')