Как выглядят все дистрибутивы, доступные в scipy.stats?

Визуализация `scipy.stats` распределения

Гистограмма может быть сделана scipy.stats нормальная случайная величина, чтобы увидеть, как выглядит распределение.

% matplotlib inline
import pandas as pd
import scipy.stats as stats
d = stats.norm()
rv = d.rvs(100000)
pd.Series(rv).hist(bins=32, normed=True)

Как выглядят другие дистрибутивы?

Ответ 1

Визуализация всех `scipy.stats` дистрибутивов

Основываясь на списке scipy.stats дистрибутивов, ниже представлены histogram и PDF каждой непрерывная случайная величина. Код, используемый для генерации каждого дистрибутива, внизу. Примечание. Константы формы были взяты из примеров на страницах документации по распространению scipy.stats.

`alpha(a=3.57, loc=0.00, scale=1.00)`

`anglit(loc=0.00, scale=1.00)`

`arcsine(loc=0.00, scale=1.00)`

`beta(a=2.31, loc=0.00, scale=1.00, b=0.63)`

`betaprime(a=5.00, loc=0.00, scale=1.00, b=6.00)`

`bradford(loc=0.00, c=0.30, scale=1.00)`

`burr(loc=0.00, c=10.50, scale=1.00, d=4.30)`

`cauchy(loc=0.00, scale=1.00)`

`chi(df=78.00, loc=0.00, scale=1.00)`

`chi2(df=55.00, loc=0.00, scale=1.00)`

`cosine(loc=0.00, scale=1.00)`

`dgamma(a=1.10, loc=0.00, scale=1.00)`

<Т411 >

`dweibull(loc=0.00, c=2.07, scale=1.00)`

`erlang(a=2.00, loc=0.00, scale=1.00)`

`expon(loc=0.00, scale=1.00)`

`exponnorm(loc=0.00, K=1.50, scale=1.00)`

`exponpow(loc=0.00, scale=1.00, b=2.70)`

`exponweib(a=2.89, loc=0.00, c=1.95, scale=1.00)`

`f(loc=0.00, dfn=29.00, scale=1.00, dfd=18.00)`

`fatiguelife(loc=0.00, c=29.00, scale=1.00)`

`fisk(loc=0.00, c=3.09, scale=1.00)`

`foldcauchy(loc=0.00, c=4.72, scale=1.00)`

`foldnorm(loc=0.00, c=1.95, scale=1.00)`

`frechet_l(loc=0.00, c=3.63, scale=1.00)`

`frechet_r(loc=0.00, c=1.89, scale=1.00)`

`gamma(a=1.99, loc=0.00, scale=1.00)`

`gausshyper(a=13.80, loc=0.00, c=2.51, scale=1.00, b=3.12, z=5.18)`

`genexpon(a=9.13, loc=0.00, c=3.28, scale=1.00, b=16.20)`

`genextreme(loc=0.00, c=-0.10, scale=1.00)`

`gengamma(a=4.42, loc=0.00, c=-3.12, scale=1.00)`

`genhalflogistic(loc=0.00, c=0.77, scale=1.00)`

`genlogistic(loc=0.00, c=0.41, scale=1.00)`

`gennorm(loc=0.00, beta=1.30, scale=1.00)`

`genpareto(loc=0.00, c=0.10, scale=1.00)`

`gilbrat(loc=0.00, scale=1.00)`

`gompertz(loc=0.00, c=0.95, scale=1.00)`

`gumbel_l(loc=0.00, scale=1.00)`

`gumbel_r(loc=0.00, scale=1.00)`

`halfcauchy(loc=0.00, scale=1.00)`

`halfgennorm(loc=0.00, beta=0.68, scale=1.00)`

`halflogistic(loc=0.00, scale=1.00)`

`halfnorm(loc=0.00, scale=1.00)`

`hypsecant(loc=0.00, scale=1.00)`

`invgamma(a=4.07, loc=0.00, scale=1.00)`

`invgauss(mu=0.14, loc=0.00, scale=1.00)`

`invweibull(loc=0.00, c=10.60, scale=1.00)`

`johnsonsb(a=4.32, loc=0.00, scale=1.00, b=3.18)`

`johnsonsu(a=2.55, loc=0.00, scale=1.00, b=2.25)`

`ksone(loc=0.00, scale=1.00, n=1000.00)`

`kstwobign(loc=0.00, scale=1.00)`

`laplace(loc=0.00, scale=1.00)`

`levy(loc=0.00, scale=1.00)`

`levy_l(loc=0.00, scale=1.00)`

`loggamma(loc=0.00, c=0.41, scale=1.00)`

`logistic(loc=0.00, scale=1.00)`

`loglaplace(loc=0.00, c=3.25, scale=1.00)`

`lognorm(loc=0.00, s=0.95, scale=1.00)`

`lomax(loc=0.00, c=1.88, scale=1.00)`

`maxwell(loc=0.00, scale=1.00)`

`mielke(loc=0.00, s=3.60, scale=1.00, k=10.40)`

`nakagami(loc=0.00, scale=1.00, nu=4.97)`

`ncf(loc=0.00, dfn=27.00, nc=0.42, dfd=27.00, scale=1.00)`

`nct(df=14.00, loc=0.00, scale=1.00, nc=0.24)`

`ncx2(df=21.00, loc=0.00, scale=1.00, nc=1.06)`

`norm(loc=0.00, scale=1.00)`

`pareto(loc=0.00, scale=1.00, b=2.62)`

`pearson3(loc=0.00, skew=0.10, scale=1.00)`

`powerlaw(a=1.66, loc=0.00, scale=1.00)`

`powerlognorm(loc=0.00, s=0.45, scale=1.00, c=2.14)`

`powernorm(loc=0.00, c=4.45, scale=1.00)`

`rayleigh(loc=0.00, scale=1.00)`

`rdist(loc=0.00, c=0.90, scale=1.00)`

`recipinvgauss(mu=0.63, loc=0.00, scale=1.00)`

`reciprocal(a=0.01, loc=0.00, scale=1.00, b=1.01)`

`rice(loc=0.00, scale=1.00, b=0.78)`

`semicircular(loc=0.00, scale=1.00)`

`t(df=2.74, loc=0.00, scale=1.00)`

`triang(loc=0.00, c=0.16, scale=1.00)`

`truncexpon(loc=0.00, scale=1.00, b=4.69)`

`truncnorm(a=0.10, loc=0.00, scale=1.00, b=2.00)`

`tukeylambda(loc=0.00, scale=1.00, lam=3.13)`

`uniform(loc=0.00, scale=1.00)`

`vonmises(loc=0.00, scale=1.00, kappa=3.99)`

`vonmises_line(loc=0.00, scale=1.00, kappa=3.99)`

`wald(loc=0.00, scale=1.00)`

`weibull_max(loc=0.00, c=2.87, scale=1.00)`

`weibull_min(loc=0.00, c=1.79, scale=1.00)`

`wrapcauchy(loc=0.00, c=0.03, scale=1.00)`

Код генерации

Здесь Jupyter Notebook используется для создания графиков.

%matplotlib inline

import io
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib
import matplotlib.pyplot as plt

matplotlib.rcParams['figure.figsize'] = (16.0, 14.0)
matplotlib.style.use('ggplot')

# Distributions to check, shape constants were taken from the examples on the scipy.stats distribution documentation pages.
DISTRIBUTIONS = [
    stats.alpha(a=3.57, loc=0.0, scale=1.0), stats.anglit(loc=0.0, scale=1.0), 
    stats.arcsine(loc=0.0, scale=1.0), stats.beta(a=2.31, b=0.627, loc=0.0, scale=1.0), 
    stats.betaprime(a=5, b=6, loc=0.0, scale=1.0), stats.bradford(c=0.299, loc=0.0, scale=1.0),
    stats.burr(c=10.5, d=4.3, loc=0.0, scale=1.0), stats.cauchy(loc=0.0, scale=1.0), 
    stats.chi(df=78, loc=0.0, scale=1.0), stats.chi2(df=55, loc=0.0, scale=1.0),
    stats.cosine(loc=0.0, scale=1.0), stats.dgamma(a=1.1, loc=0.0, scale=1.0), 
    stats.dweibull(c=2.07, loc=0.0, scale=1.0), stats.erlang(a=2, loc=0.0, scale=1.0), 
    stats.expon(loc=0.0, scale=1.0), stats.exponnorm(K=1.5, loc=0.0, scale=1.0),
    stats.exponweib(a=2.89, c=1.95, loc=0.0, scale=1.0), stats.exponpow(b=2.7, loc=0.0, scale=1.0),
    stats.f(dfn=29, dfd=18, loc=0.0, scale=1.0), stats.fatiguelife(c=29, loc=0.0, scale=1.0), 
    stats.fisk(c=3.09, loc=0.0, scale=1.0), stats.foldcauchy(c=4.72, loc=0.0, scale=1.0),
    stats.foldnorm(c=1.95, loc=0.0, scale=1.0), stats.frechet_r(c=1.89, loc=0.0, scale=1.0),
    stats.frechet_l(c=3.63, loc=0.0, scale=1.0), stats.genlogistic(c=0.412, loc=0.0, scale=1.0),
    stats.genpareto(c=0.1, loc=0.0, scale=1.0), stats.gennorm(beta=1.3, loc=0.0, scale=1.0), 
    stats.genexpon(a=9.13, b=16.2, c=3.28, loc=0.0, scale=1.0), stats.genextreme(c=-0.1, loc=0.0, scale=1.0),
    stats.gausshyper(a=13.8, b=3.12, c=2.51, z=5.18, loc=0.0, scale=1.0), stats.gamma(a=1.99, loc=0.0, scale=1.0),
    stats.gengamma(a=4.42, c=-3.12, loc=0.0, scale=1.0), stats.genhalflogistic(c=0.773, loc=0.0, scale=1.0),
    stats.gilbrat(loc=0.0, scale=1.0), stats.gompertz(c=0.947, loc=0.0, scale=1.0),
    stats.gumbel_r(loc=0.0, scale=1.0), stats.gumbel_l(loc=0.0, scale=1.0),
    stats.halfcauchy(loc=0.0, scale=1.0), stats.halflogistic(loc=0.0, scale=1.0),
    stats.halfnorm(loc=0.0, scale=1.0), stats.halfgennorm(beta=0.675, loc=0.0, scale=1.0),
    stats.hypsecant(loc=0.0, scale=1.0), stats.invgamma(a=4.07, loc=0.0, scale=1.0),
    stats.invgauss(mu=0.145, loc=0.0, scale=1.0), stats.invweibull(c=10.6, loc=0.0, scale=1.0),
    stats.johnsonsb(a=4.32, b=3.18, loc=0.0, scale=1.0), stats.johnsonsu(a=2.55, b=2.25, loc=0.0, scale=1.0),
    stats.ksone(n=1e+03, loc=0.0, scale=1.0), stats.kstwobign(loc=0.0, scale=1.0),
    stats.laplace(loc=0.0, scale=1.0), stats.levy(loc=0.0, scale=1.0),
    stats.levy_l(loc=0.0, scale=1.0), stats.levy_stable(alpha=0.357, beta=-0.675, loc=0.0, scale=1.0),
    stats.logistic(loc=0.0, scale=1.0), stats.loggamma(c=0.414, loc=0.0, scale=1.0),
    stats.loglaplace(c=3.25, loc=0.0, scale=1.0), stats.lognorm(s=0.954, loc=0.0, scale=1.0),
    stats.lomax(c=1.88, loc=0.0, scale=1.0), stats.maxwell(loc=0.0, scale=1.0),
    stats.mielke(k=10.4, s=3.6, loc=0.0, scale=1.0), stats.nakagami(nu=4.97, loc=0.0, scale=1.0),
    stats.ncx2(df=21, nc=1.06, loc=0.0, scale=1.0), stats.ncf(dfn=27, dfd=27, nc=0.416, loc=0.0, scale=1.0),
    stats.nct(df=14, nc=0.24, loc=0.0, scale=1.0), stats.norm(loc=0.0, scale=1.0),
    stats.pareto(b=2.62, loc=0.0, scale=1.0), stats.pearson3(skew=0.1, loc=0.0, scale=1.0),
    stats.powerlaw(a=1.66, loc=0.0, scale=1.0), stats.powerlognorm(c=2.14, s=0.446, loc=0.0, scale=1.0),
    stats.powernorm(c=4.45, loc=0.0, scale=1.0), stats.rdist(c=0.9, loc=0.0, scale=1.0),
    stats.reciprocal(a=0.00623, b=1.01, loc=0.0, scale=1.0), stats.rayleigh(loc=0.0, scale=1.0),
    stats.rice(b=0.775, loc=0.0, scale=1.0), stats.recipinvgauss(mu=0.63, loc=0.0, scale=1.0),
    stats.semicircular(loc=0.0, scale=1.0), stats.t(df=2.74, loc=0.0, scale=1.0),
    stats.triang(c=0.158, loc=0.0, scale=1.0), stats.truncexpon(b=4.69, loc=0.0, scale=1.0),
    stats.truncnorm(a=0.1, b=2, loc=0.0, scale=1.0), stats.tukeylambda(lam=3.13, loc=0.0, scale=1.0),
    stats.uniform(loc=0.0, scale=1.0), stats.vonmises(kappa=3.99, loc=0.0, scale=1.0),
    stats.vonmises_line(kappa=3.99, loc=0.0, scale=1.0), stats.wald(loc=0.0, scale=1.0),
    stats.weibull_min(c=1.79, loc=0.0, scale=1.0), stats.weibull_max(c=2.87, loc=0.0, scale=1.0),
    stats.wrapcauchy(c=0.0311, loc=0.0, scale=1.0)
]

bins = 32
size = 16384
plotData = []
for distribution in DISTRIBUTIONS:
    try:  
        # Create random data
        rv = pd.Series(distribution.rvs(size=size))
        # Get sane start and end points of distribution
        start = distribution.ppf(0.01)
        end = distribution.ppf(0.99)

        # Build PDF and turn into pandas Series
        x = np.linspace(start, end, size)
        y = distribution.pdf(x)
        pdf = pd.Series(y, x)

        # Get histogram of random data
        b = np.linspace(start, end, bins+1)
        y, x = np.histogram(rv, bins=b, normed=True)
        x = [(a+x[i+1])/2.0 for i,a in enumerate(x[0:-1])]
        hist = pd.Series(y, x)

        # Create distribution name and parameter string
        title = '{}({})'.format(distribution.dist.name, ', '.join(['{}={:0.2f}'.format(k,v) for k,v in distribution.kwds.items()]))

        # Store data for later
        plotData.append({
            'pdf': pdf,
            'hist': hist,
            'title': title
        })

    except Exception:
        print 'could not create data', distribution.dist.name

plotMax = len(plotData)

for i, data in enumerate(plotData):
    w = abs(abs(data['hist'].index[0]) - abs(data['hist'].index[1]))

    # Display
    plt.figure(figsize=(10, 6))
    ax = data['pdf'].plot(kind='line', label='Model PDF', legend=True, lw=2)
    ax.bar(data['hist'].index, data['hist'].values, label='Random Sample', width=w, align='center', alpha=0.5)
    ax.set_title(data['title'])

    # Grab figure
    fig = matplotlib.pyplot.gcf()
    # Output 'file'
    fig.savefig('~/Desktop/dist/'+data['title']+'.png', format='png', bbox_inches='tight')
    matplotlib.pyplot.close()