scipy stats.gilbrat () | python

Michael Zippo

Parameter:
- > q: untere und obere Randwahrscheinlichkeit
- > x: Quantile
- > loc: [optional] Standortparameter. Standard = 0
- > scale: [optional] Skalierungsparameter. Standard = 1
- > Größe: [Tupel von Ganzzahlen, optional] Form oder zufällige Varianten.
- > Momente: [optional] zusammengesetzt aus Buchstaben [`mvsk`]; `m` = Mittelwert, `v` = Varianz, `s` = Fisher`s Schiefe und `k` = Fisher`s Kurtosis. (Standard = `mv`).
Ergebnisse: Gilbrat-kontinuierliche Zufallsvariable

Code Nr. 1: Erzeugen einer kontinuierlichen Zufallsvariable Variable Gilbrat

Ausgabe t:

Verteilung: [0. 0.06122449 0.12244898 0.18367347 0.24489796 0.30612245 0.36734694 0.42857143 0.48979592 0.55102041 0.6122449 0.67346939 0.73469388 0.79591837 0.85714286 0.91836735 0.97959184 1.04081633 1.10204082 1.16326531 1.2244898 1.28571429 1.34693878 1.40816327 1.46938776 1.53061224 1.59183673 1.65306122 1.71428571 1.7755102 1.83673469 1.89795918 1.95918367 2.02040816 2.08163265 2.14285714 2.20408163 2.26530612 2.32653061 2.3877551 2.44897959 2.51020408 2.57142857 2.63265306 2.69387755 2.75510204 2.81632653 2.87755102 2.93877551 3. ]

Code Nr. 4: Verschiedene Positionsargumente

import matplotlib. pyplot as plt

import numpy as np

x = np.linspace ( 0 , 5 , 100 )

# Verschiedene Positionsargumente

y1 = gilbrat.pdf (x, 1 , 3 )

y2 = gilbrat.pdf (x , 1 , 4 )

plt.plot (x, y1, " * " , x, y2, " r-- " )

Ausgabe:

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from scipy.stats import gilbrat

numargs = gilbrat .numargs

[] = [ 0.7 ,] * numargs

rv = gilbrat ( )

print ( " RV: " , rv)

Ausgabe:

RV: "scipy.stats._distn_infrastructure.rv_frozen Objekt bei 0x000001E39A3B4AC8 >

Code Nr. 2: Gilbrat-Zufallsvariablen und Wahrscheinlichkeitsverteilung

Import numpy als np

import numpy als np

Quantil = np.arange ( 0,01 , 1 , 0.1 )

# Random Variants

R = gilbrat.rvs (Maßstab = 2 , size = 10 )

print ( "Random Variates:" , R)

# PDF

R = gilbrat.pdf (quantile, loc = 0 , scale = 1 )

print ( "Wahrscheinlichkeitsverteilung: " , R)

Ausgabe:

 Zufallszahlen: [0.66090031 1.39027118 1.33876164 1.50366592 5.21419497 5.24225463 3.98547687 0.30586938 9.11346685 0.93014057] Wahrscheinlichkeitsverteilung: [0.00099024 0.31736749 0.5620854 0.64817773 0.65389139 0.6 2357239 0,57879516 0,52988354 0,48170703 0,43645277]

Code # 3: Grafische Darstellung

import numpy as np

import matplotlib.pyplot as plt

Verteilung = np .linspace ( 0 , np.minimum (rv.dist.b, 3 ))

print ( " Verteilung: " , Verteilung)

plot = plt.plot (distribution, rv.pdf (distribution))