我現(xiàn)在正在嘗試用python重新創(chuàng)建這個(gè)指數(shù)概率論文的圖表。為了做到這一點(diǎn)，我必須將 CDF 函數(shù)線性化為：x = a*g(Fx(x)) + b然后繪制xvs g(Fx(x))。該圖顯示了不同分布的參數(shù)但是我不知道如何繼續(xù)。顯然 x 軸的比例必須改變。我已經(jīng)嘗試過(guò)使用 probplot，但結(jié)果完全相同。有任何想法嗎？import matplotlib.pyplot as pltimport numpy as npfrom scipy.stats import exponfrom scipy.stats import probplot# Creating plotfig,ax =plt.subplots()newax=ax.twiny()ax.grid()# Lognormallambda_expon=0.04i=1/lambda_exponprobs=np.arange(0.01,0.99,0.01) # array with probabilitiesppf =expon.ppf(probs,i) cdf=expon.cdf(ppf,i) x=-np.log(1-cdf) # variabley=-np.log(1-cdf)*i # linearized form CDFax.plot(x,y)newax.set_xticks([0.01,0.5,0.8,0.9,0.96,0.99])newax.set_xticks([0.01,0.5,0.90,0.99])ax.plot()
1 回答
吃雞游戲
TA貢獻(xiàn)1829條經(jīng)驗(yàn) 獲得超7個(gè)贊
不確定，為什么你認(rèn)為這是 CDF 圖。如果在 X 軸上放置概率，在 Y 軸上繪制x，它看起來(lái)像分位數(shù)圖。
我沒(méi)有在 Python/Matplotlib 中看到內(nèi)置的此類(lèi)比例，logit看起來(lái)像是最佳近似值。
或者我誤解了你的問(wèn)題？
代碼，Python 3.8 x64 Win10
import numpy as np
import matplotlib.pyplot as plt
# exponential distribution
def PDF(x, λ):
return λ*np.exp(-λ*x)
def CDF(x, λ):
return 1.0 - np.exp(-λ*x)
def Q(p, λ): # quantile
x = -np.log(1.0 - p)/λ
return x
# plots
λ = 0.04
p = np.linspace(0.01, 0.99, 101)
x = Q(p, λ)
fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)
ax.set_xscale('logit')
ax.plot(p, x, 'r-')
plt.show()
你會(huì)得到類(lèi)似的東西
更新
如果您需要概率圖，這里是
代碼
from scipy.stats import expon
rve = expon(loc=0.0, scale=1.0/λ)
p = np.linspace(0.01, 0.99, 101)
x = rve.ppf(p) # Q(p, λ)
q = rve.rvs(size = 10000)
fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)
res = stats.probplot(q, dist=rve, plot=ax)
plt.show()
和圖表
好吧，當(dāng)你發(fā)送消息時(shí)我已經(jīng)寫(xiě)好了代碼。無(wú)論如何將其放置在這里，它看起來(lái)與您詢問(wèn)的圖表完全相同 - 與自定義標(biāo)簽呈線性。
import matplotlib.ticker as ticker
def linear(x0, xn, y0, yn, x):
q = (x-x0)/(xn-x0)
return q * yn + (1.0 - q) * y0
pmin = 0.009
pmax = 0.991
xmin = Q(pmin, λ)
xmax = Q(pmax, λ)
x = np.linspace(xmin, xmax, 2)
p = np.linspace(pmin, pmax, 2)
tick_values = [0.01, 0.50, 0.80, 0.90, 0.96, 0.99] # probabilies
tick_names = [str(v) for v in tick_values] # ticks to place on graph
tick_xvals = [Q(p, λ) for p in tick_values] # x values for each p
tick_places = [linear(xmin, xmax, pmin, pmax, x) for x in tick_xvals] # x from linear function
fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)
ax.axes.xaxis.set_major_locator(ticker.FixedLocator((tick_places)))
ax.xaxis.set_major_formatter(ticker.FixedFormatter((tick_names)))
ax.plot(p, x, 'r-')
plt.show()
和圖表本身
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